A reliable and practical renal-lipid quantification and imaging method is needed. Here, the feasibility of an accelerated MRSI method to map renal fat fractions (FF) at 3T and its repeatability were investigated. A 2D density-weighted concentric-ring-trajectory MRSI was used to accelerate acquiring 48×48 voxels (each of 0.25 ml spatial-resolution) without respiratory navigation implementations. The data was collected over 512 complex-FID timepoints with a 1250 Hz spectral bandwidth. The MRSI sequence was designed with a metabolite-cycling technique for lipid-water separation. The in vivo repeatability performance of the sequence was assessed by conducting a test-reposition-retest study within healthy subjects. The coefficient of variation (CV) in the estimated FF from the test-retest measurements showed a high degree of repeatability of the MRSI-FF (CV= 4.3 ±2.5%). Additionally, the matching level of spectral signature within the same anatomical region was also investigated, and their intrasubject repeatability was also high, with a small standard deviation (8.1 ±6.4%). The MRSI acquisition duration was ~3 minutes only. The proposed MRSI technique can be a reliable technique to quantify and map renal metabolites within a clinically acceptable scan time at 3T that supports the future application of this technique for the non-invasive characterization of heterogeneous renal diseases and tumors.

Single-molecule magnets (SMMs) have garnered significant interest in the field of molecular magnetism due to their unique magnetic properties at the nanoscale. These molecular systems exhibit magnetisation behaviour reminiscent of conventional bulk magnets but with distinct advantages such as size-dependent properties and potential applications in quantum computing and high-density data storage. However, challenges remain in harnessing their properties for practical applications, including their susceptibility to degradation and limited control over their assembly and organisation.

The concerning rise in emergence and prevalence of resistant strains to drugs of M. tuberculosis has ,the prompted researchers to look for new and effective treatments. With this motive, biscoumarins were identified as the lead molecules on a whole-cell-based screening of several less explored low molecular weight bioactive compounds against M. tuberculosis strains. Among the screened biscoumarins, the highest dock score derivatives were synthesized (a-h) using a programmable microwave synthesizer for better yields and reaction control. The synthesized derivatives were evaluated against H37Rv, H37Ra, M. smegmatis, an MDR surrogate model, and other bacterial strains for the structure-activity response. Assessment of the synthesized library against mycobacterial strains led to the identification of compounds (f and d) as lead anti-tuberculosis agents. Compounds (f and d) exhibited less toxicity against human cell lines. At the same time, it displayed enjoyable activity wherein MIC concentrations were observed to be 16- and 32 µg/mL against the susceptible H37Rv, and H37Ra strains of M. tuberculosis and MIC value of 128 µg/mL for M. smegmatis, respectively. For mechanistic insights and identification of drug binding targets, molecular docking and dynamic simulations were employed for a panel of 16 mycobacterial enzymes essential for mycobacterial growth and survival. These in silico studies revealed the DprE1 enzyme as a druggable target for the anti-tuberculosis activity of the selected biscoumarins derivatives. Further investigation is underway in our laboratory, leading to its development as an anti-tuberculosis drug (animal model studies).

Big Data classification has recently received a great deal of attention due to the main properties of Big Data, which are volume, variety, and velocity. The furthest-pair-based binary search tree (FPBST) shows a great potential for Big Data classification. This work attempts to improve the performance the FPBST in terms of computation time, space consumed and accuracy. The major enhancement of the FPBST includes converting the resultant BST to a decision tree, in order to remove the need for the slow K-nearest neighbors (KNN), and to obtain a smaller tree, which is useful for memory usage, speeding both training and testing phases and increasing the classification accuracy. The proposed decision trees are based on calculating the probabilities of each class at each node using various methods; these probabilities are then used by the testing phase to classify an unseen example. The experimental results on some (small, intermediate and big) machine learning datasets show the efficiency of the proposed methods, in terms of space, speed and accuracy compared to the FPBST, which shows great potential for further enhancements of the proposed methods to be used in practice.

Online identity-based theft is known to be one of the most serious and growing threats to victims, such as individuals and organizations, over the last 10 years due to the enormous economic damage these crimes have caused. The availability of personal information on the Internet has increased the chances of this cyber-crime. Online identity theft crime is the result of a combination of cyber-crimes on the one hand and lack of awareness and training of users on the other hand to protect personal data on the other. Education and awareness, which also contributes to early detection, is the strongest tool for consumers to safeguard themselves from online identity fraud. This paper provides a comprehensive explanation of online identity theft, the various approaches that thieves use to attack individuals and organizations and the types of fraud involved in this cyber-crime. The aim of this research is to evaluate the need for a reformulation of the concept of identity theft in order to be compatible with the evolution of behaviors and fraud.

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.

Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly termed nonalcoholic fatty liver disease (NAFLD), is a widespread global health concern that affects around 25% of the global population. Its influence is expanding, and it is anticipated to overtake alcohol as the leading cause of liver failure and liver-related death worldwide. Unfortunately, there are no approved therapies for MASLD; as such, national and international regulatory health agencies undertook strategies and action plans designed to expedite the development of drugs for treatment of MASLD. A sedentary lifestyle and an unhealthy diet intake are important risk factors. Western countries have a greater estimated prevalence of MASLD partly due to lifestyle habits. Mitochondrial dysfunction is strongly linked to the development of MASLD. Further, it has been speculated that mitophagy, a type of mitochondrial quality control, may be impaired in MASLD. Thyroid hormone (TH) coordinates signals from the nuclear and mitochondrial genomes to control mitochondrial biogenesis and function in hepatocytes. Mitochondria are known TH targets and preclinical and clinical studies suggest that TH, thyroid receptor β (TR-β) analogs, and synthetic analogs specific to liver could be of therapeutic benefit in treating MASLD. In this review, we highlight how mitochondrial dysfunction contributes to development of MASLD, and how understanding the role of TH in improving mitochondrial function paved the way for innovative drug development programs of TH-based therapies targeting MASLD.

We report the synthesis and characterization of Poly Methyl-Meth-Acrylate (PMMA)/Poly vinylalcohol (PVA) polymeric blend doped with different concentrations of Copper oxide (CuO) nanoparticles (NPs). The (PMMA-PVA)/CuO nanocomposite hybrid thin films (wt. % = 0%, 2%, 4%, 8%, and 16%) of CuO NPs are deposited on glass substrates via dip-coating technique. The transmittance (T%), reflectance (R%), the absorption coefficient (α), the optical constants [refractive index (n), extinction coefficient (k)], optical dielectric functions [ɛ',ɛ''] are investigated and interpreted. Tauc, Urbach, Spitzer-Fan, and Drude models are employed to calculate the optical bandgap energy (Eg) and the optoelectronic parameters of the nanocomposite thin films. The refractive index and optical bandgap energy of of (PMMA-PVA) polymeric thin film are found to be (1.5 to 1.85) and 4.101 eV, respectively. Incorporation of specific concentrations of CuO-NPs in (PMMA-PVA) polymeric thin films leads to a noticeable decrease in the optical bandgap energy and to an increase of the refractive index. Moreover, Fourier Transform Infrared Spectroscopy (FTIR) transmittance spectra are measured and analyzed for undoped and doped polymeric thin films to pinpoint the major vibrational modes in the spectral range (500 and 4000 cm-1), as well as, the nature of network bonding in both systems. Thermal stability of thin films is investigated by performing thermogravimetric analysis (TGA). The TGA thermograms confirm that both doped polymeric thin films are thermally stable at temperatures below 110°C which enables them to be attractive for a wide range of optical and optoelectronic applications. Our results indicate that optical, vibrational and thermal properties of both polymeric thin films can be tuned for specific applications by the appropriate corporation of particular concentrations of CuO-NPs.

This research aims to explore the complex interplay between supply chain resilience (SCR), digital supply chain (DSC), and sustainability, focusing on the moderating influence of supply chain dynamism. The goal is to understand how these elements interact within the framework of contemporary supply chain management and how they collectively contribute to enhancing sustainability outcomes. The sample size is 300 CEOs and managers. The study approach integrates quantitative research methods. Structural equation modeling (SEM) is utilized to quantitatively analyze the direct and indirect effects of SCR and DSC on sustainability. The numerous surveys we conduct among supply chain ecosystem stakeholders provide a rich picture of practical implications and contextual nuances. In sum, our early findings generally support a positive relationship between SCR and sustainability in and of itself decrying the need for more resilient supply networks for sustainability. We further find the beneficial impact of digital technologies in promoting sustainability via enhancing environmental control and controlling for efficiency in supply chains. We also offer evidence to show that supply chain dynamism compounds the positive logic between SCR and DSC and sustainability. As a final word, it must be noted that our work speaks to the burgeoning Supply Chain Dynamism as moderator literature by examining the direct and contingent effects of SCR and DSC on not only performance but sustainability. By shedding light on the moderating role of dynamism, the study provides fresh insights into the multifaceted nature of supply chain management and sustainability practices. The study's findings enhance theoretical understanding by elucidating the synergistic effects of SCR, DSC, and sustainability in dynamic supply chain settings. It augments existing theoretical frameworks by integrating concepts of resilience, digitalization, and sustainability into a comprehensive model. Practical and economical, the research offers actionable guidance for organizations aiming to improve sustainability performance through resilient and digitally advanced supply chains. By acknowledging the role of supply chain dynamism, managers can tailor strategies to manage disruptions effectively and leverage digital innovations. Economically, adopting sustainable practices can result in cost savings and competitive advantages. The research emphasizes the importance of aligning supply chain strategies with sustainability goals to drive long-term value and societal impact.

Due to its built-up chemoresistance after prolonged usage, the demand for replacing platinum in metal-based drugs (MBD) is rising. The first MBD approved by the FDA for cancer therapy was cisplatin in 1978. Even after nearly four and a half decades of trials, there has been no significant improvement in osteosarcoma (OS) therapy. In fact, many MBD have been developed, but the chemoresistance problem raised by platinum remains unresolved. This motivates us to elucidate the possibilities of the copper and zinc (CuZn) combination to replace platinum in MBD. Thus, the anti-chemoresistance properties of CuZn and their physiological functions for OS therapy are highlighted. Herein, we summarise their chelators, main organic solvents, and ligand functions in their structures that are involved in anti-chemoresistance properties. Through this review, it is rational to discuss their ligands’ roles as biosensors in drug delivery systems. Hereafter, an in-depth understanding of their redox and photoactive function relationships is provided. The disadvantage is that the other functions of biosensors cannot be elaborated on here. As a result, this review is being developed, which is expected to intensify OS drugs with higher cure rates. Nonetheless, this advancement intends to solve the major chemoresistance obstacle towards clinical efficacy.

Innovation and technology are important tools of minerals sector efficiency and productivity im-provement. The uptake of technologies has proven important levelrs to improve the productivity of mining sector. This paper provides a comprehensive analysis of mine-level productivity by using a global data of copper, gold, and platinum from 1991-2020. Various drivers of proioductivity have been analysed to draw policy insights. Empirical findings reveal significant disparities in technical efficiency and productivity across mines and regions. A further decomposition of TFP into its different components suggest that the adoption of innovative practices and investment in technology adoption could improve the overall productivity of these commodities. The findings also suggest that an appropriate input mix and optimal scale of production could boost platinum mining productivity. Regional disparities in productivity in different commodities (e.g., South Africa vs. Zimbabwe) give policymakers with insights into how to support production scale and productivity through appropriate input mixtures.

Abstract Background: Metabolic syndrome (MetS) is a complex disorder characterized by insulin resistance (IR), central obesity, atherogenic dyslipidemia and higher glucose levels. It increases the risk of cardiovascular disease (CVD) and type 2 diabetes mellitus (T2DM), imposing an economic burden on the healthcare system. However, the historical origins of MetS as well as the development and evolution of its definitions have not been conclusively documented in the literature. Aim: This study seeks to enhance the understanding of the developmental trends of MetS during the preceding two decades, placing particular emphasis on the definition, diagnosis and prevalence. Methods: An extensive search was performed from 1920 to 2023 across prominent scientific research engines, including Scopus, PubMed, MDPI, and others. Results: Despite advancements, many aspects of MetS remain inadequately understood. As the understanding of the nature and pathophysiology of MetS progresses, the development and refinement of its diagnostic criteria, and assessment and treatment guidelines will continue. Additionally, there exists significant variation in the global prevalence of metabolic syndrome, ranging from 14 to 39%. This prevalence is projected to increase due to the adoption of less healthy dietary patterns and sedentary lifestyles. The observed disparities in metabolic syndrome prevalence can be attributed to multiple factors, including demographic characteristics. Furthermore, the lack of a standardized definition across studies also contributes to the variation in reported prevalence rates. Conclusion: Further studies focusing on the standardization of the MetS definition across different research are crucial. The establishment of consistent criteria would enhance the reliability and validity of research findings, enabling more meaningful comparisons and interpretations.

The outbreak of coronavirus 2 (SARS-CoV-2) has already taken on pandemic proportions and has affected more than 100 countries in recent weeks. It is imperative that global healthcare systems are prepared. Containment initiatives in Jordan, on the other hand, have decreased new cases by over 63%. The Jordanian national health system has been very pleased with its ability to adequately meet the needs of the patients diagnosed with SARS-CoV-2 pneumonia, and includes intensive care. From 2 March to 14 April 2020, between 4% and 8% of active infected patients in Jordan registered daily in intensive care. The percentage is very low.

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

Purpose: To identify the impact of dealing with COVID-19 patients in clinical areas on nurses' professional self-concept and self-confidence. Background: Professional self-concept is considered a critical factor in the recruitment/retention process in nursing, nursing shortage, career satisfaction, and academic achievements. Professional self-confidence is also a crucial determinant in staff satisfaction, reducing turnover, and increasing work engagement. Design: Descriptive, comparative study. Methods: The study was conducted between February to May 2021 by utilizing a convenience sampling technique. A total of 170 nurses from two facilities were recruited from two COVID-19 and non-COVID-19 designated facilities. The level of professional self-concept and self-confidence was assessed by utilizing the Nurses' Self-Concept Instrument and Self-Confidence Scale. Results: The professional self-concept level among the exposed group to COVID-19 patients was lower than the comparison group, while the professional self-confidence level among the exposed group to COVID-19 patients was similar to the comparison group. On the other hand, the satisfied staff and those who received professional training in dealing with COVID-19 patients reported a higher level of professional self-concept. Conclusions: Dealing with COVID-19 patients has an impact on professional self-concept; the exposure group was lower than those who did not deal with COVID-19 patients, while the professional self-confidence level among the exposed group was similar to the comparison group. Getting professional training in dealing with COVID-19 patients and being satisfied at work were significant factors in improving the professional self-concept. Policymakers should create strategies that target the improvement of professional training in dealing with COVID-19 patients.

This study explored the living situations, financial conditions, religious obligations, and social distancing of Muslims during the covid 19 pandemic. In total, 20 Muslim community members living in the Kanto region were recruited, 15 of them were included in the in-depth qualitative and five in the focus group interviews. The Snowball method was used, and the questionnaires were designed into four themes. The audio/video interviews were conducted via Zoom and NAVIO was used to analyse the data thematically. The major Muslim events were cancelled, and the recommended physical distancing was maintained during the prayers at home and in the mosques. The Japanese government's financial support to each person was a beneficial step towards social protection, which was highlighted and praised by every single participant. Regardless of religious obligations, the closer of all major mosques in Tokyo demonstrates to the Japanese community how serious they are about adhering to the public health guidelines during the pandemic. This study highlighted that the pandemic has affected the religious patterns and behaviour of Muslims from inclusive to exclusive in a community and narrated the significance of religious commitments.

Molecular classifications of colorectal cancer (CRC) are benefitting cancer research by providing insights into subtype-specific disease prognosis and better therapeutic intervention. So far different conventional DNA markers such as microsatellite instability (MSI), CpG island methylator phenotype (CIMP), chromosomal instability (CIN), and BRAF and KRAS mutations have been used to classify CRC patients but have not shown promising prognostic values. Here, for the first time, we show classification of CRC tumors from Saudi Arabian patients based on gene expression profile (GEP). An existing method of CRC subtyping has been applied to the GEP of tumors from Saudi CRC patients. Survival analysis was carried out on predicted CRC subtypes. In-silico functional analyses were conducted on the gene signature used for subtype prediction. The predicted subtypes showed distinct but statistically insignificant overall survival distribution (log-rank test, p = 0.069). Comparison of predicted subtypes in Saudi CRC patients with that of the French one showed significant dissimilarity in the two populations (Chi-square test, p = 0.0091). Functional analyses of the gene signature used for subtyping suggest their association with “cancer” and “gastrointestinal diseases”. Most of the signature genes were found differentially expressed in CRC tumors compared to adjacent normal tissues. Such a classification framework might help improve the treatment of colorectal cancer patients.

Artificial intelligence is dramatically transforming medical imaging. We assessed the levels of artificial intelligence use among radiology trainees and explored their perceived impact of artifi-cial intelligence on the radiology workflow and radiology profession, in correlation with the perceived ease of use and behavioral intention to use artificial intelligence. This cross-sectional study enrolled radiology trainees from Saudi Arabia, and an online 5-part-structured question-naire was disseminated via online networks to trainee in July 2021. We included 98 participants (51 male; age 27.59±2.02 years). Level of use was low; few used it in routine practice (7%). The impact of artificial intelligence on the radiology workflow was positively perceived in all radi-ology workflow steps (3.64–3.97 out of 5). A positive impact on the radiology profession was more frequently perceived for technical and performance aspects (81%–85%) compared with prestige and legal aspects (64%–71%). Perceived ease of use and behavioral intention to use arti-ficial intelligence was associated with the current professional activity, level of use artificial in-telligence use, and perceived impact on the profession as well as on radiology workflow (p<0.05). Levels of artificial intelligence use in radiology are very low. The perceived positive impact of ar-tificial intelligence on radiology workflow and profession is correlated with an increase in be-havioral intention to use artificial intelligence. Thus, increasing awareness about the favorable impact can improve the behavioral use.

The epigenetic mechanism of DNA methylation is essential for regulating gene expression and adaptation in plants, especially when exposed to various environmental stressors such as drought, heat, and nutrient availability. The impact of these stressors can lead to alterations in DNA methylation patterns and gene expression, which this study aims to investigate. Specifically, we are interested in understanding how DNA methylation plays a role in controlling the expression of stress-response genes, as well as genes involved in plant growth and development. To accomplish this, we are analyzing the changes in DNA methylation patterns and gene expression in response to different stressors like drought and heat. Our research findings suggest that DNA methylation is a vital regulator of plant adaptation to environmental stress, and we hope to gain further insight into these mechanisms by identifying genes that exhibit differential expression in response to stress and analyzing their DNA methylation status. This study offers valuable insights into the development of crops that can thrive under changing climate conditions.

Current research in the field of antimicrobials is focused on the development of novel antibiotics and antimicrobial agents to counteract the huge dilemma that the human population is mainly facing in regards to the rise of bacterial resistance and biofilm infections. Host Defense peptides (HDPs) are a promising group of molecules for antimicrobial development as they share unique characteristics suitable for antimicrobial activity including their broad spectrum of activity and potency against bacteria. AamAP1 is a novel HDP that was identified through molecular cloning from the venom of the North African scorpion Androctonus amoeruxi. In vitro antimicrobial assays revealed that the peptide displays moderate activity against different strains of Gram-positive and Gram-negative bacteria. Additionally, the peptide proved to be highly hemolytic and displaying significantly high toxicity against mammalian cells. In our study, a novel synthetic peptide analogue named A3 was designed from the naturally occurring scorpion venom host defense peptide. The design strategy depended on modifying the amino acid sequence of the parent peptide in order to increase its net positive charge, percentage helicity and optimize other physico-chemical parameters involved theoretically in HDPs activity. Accordingly, A3 was evaluated for its in vitro antimicrobial and anti-biofilm activity individually and in combination with four different types of conventional antibiotics against clinical isolates of multi-drug resistant (MDR) Gram-positive bacteria. A3 was also evaluated for its cytotoxicity against mammalian cells. A3 displayed potent and selective in vitro antimicrobial activities against a wide range of MDR Gram-positive bacteria. Our results also showed that combining A3 with conventional antibiotics displayed a synergistic mode of action which resulted in decreasing the MIC value for A3 peptide as low as 0.125 µM. These effective concentrations were associated with negligible toxicities on mammalian cells. In conclusion, A3 exhibits enhanced activity and selectivity when compared with the parent natural scorpion venom peptide. The combination of A3 with conventional antibiotics may be pursued as a potential novel treatment strategy against MDR and biofilm forming bacteria.

The Coronavirus disease due to SARS-CoV-2 emerged in Wuhan city, China in December 2019 and rapidly spread more than 200 countries as a global health pandemic. There are more 3 million confirmed cases and around 207,000 fatalities. The primary manifestation is respiratory and cardiac but neurological manifestations are being reported in the literature as case reports and case series. The most common reported symptoms to include headache and dizziness followed by encephalopathy and delirium. Among the complications noted are Cerebrovascular accident, Guillian barre syndrome, acute transverse myelitis, and acute encephalitis. The most common peripheral manifestation was hyposmia. It is further noted that sometimes the neurological manifestations can precede the typical features like fever and cough and later on typical manifestations develop in these patients. Hence a high index of suspicion is required for timely diagnosis and isolation of cases to prevent the spread in neurology wards. We present a narrative review of the neurological manifestations and complications of COVID-19. Our aim is to update the neurologists and physicians working with suspected cases of COVID-19 about the possible neurological presentations and the probable neurological complications resulting from this novel virus infection.

Rebar cages form the skeletal framework of reinforced concrete structural members. They comprise longitudinal and transverse reinforcing bars, usually connected by tie wires. Past studies have identified tie-wire connections as the "weak links" in large, prefabricated rebar cages, leading to their failure during site handling and construction stages. Recent research has explored the use of mechanical U-bolt connectors to bolster stability and safety of rebar cages while reducing their vulnerability and risk of failure. This paper presents a comprehensive experimental and numerical investigation of a new type of mechanical connector, referred to as "Cage Clamp", to enhance rebar cage stability and safety. Component-level experimental tests are conducted to determine the force-displacement response of Cage Clamp connectors for crossbar connections in different degrees of freedom. The results highlight the remarkable strength and stiffness of Cage Clamp connectors, significantly outperforming traditional tie-wire connections. Furthermore, full-scale experimental tests are performed on a circular underground pile-shaft rebar cage with Cage Clamp connectors to assess its behavior under various site handling conditions. The data obtained from these experiments are used to construct and refine detailed 3D finite element models of the rebar cages, capable of capturing the complex behavior of rebar cages with Cage Clamp connectors. Using the numerical model, a sensitivity analysis is performed to assess the effect of the number of Cage Clamps on the deflection and stability of the rebar cage. The findings show that using cage clamps to connect pick-up rebar with lateral reinforcement at every 8 to 10 feet is sufficient to ensure the stability of the cage during typical site handling conditions. The results of this study serve as a basis for establishing analysis, design, fabrication, and handling guidelines for rebar cages, contributing to safe and efficient construction practices.

This research work focuses on the potential application of an organic compound, santalol obtained from santalum album in the inhibition of the enzyme tyrosinase which is actively involved in the biosynthesis of the melanin pigment. Over-production of melanin causes undesirable pigmentation in humans as well as other organisms that significantly downgrade their aesthetic value. The study is designed to explain the purification of tyrosinase from the mushroom Agaricus bisporus, followed by activity assay and enzyme kinetic to give insight into the santalol modulated tyrosinase inhibition in a dose dependent manner. The multi-spectroscopic techniques like UV-vis, fluorescence, and isothermal calorimetry are employed to deduce the efficiency of santalol as potential candidate against the tyrosinase enzyme activity. Experimental results are further verified by molecular docking. Santalol derived from the essential oils of santalum album, is widely used as remedy for skin disorders and potion for fair complexion since ancient times. Based on enzyme kinetics and biophysical characterization, this is the first scientific evidence where santalol inhibits tyrosinase, which may be employed in agriculture, food, and cosmetic industries by prevent excess melanin formation or browning.

This study examines the moderating effect of trained human capital on the associations of accounting information systems, generalized auditing software, and technology adoption with the performance of external auditors among Saudi listed companies. This study used a survey-based methodology. The final sample consisted of 136 responses with an 8% response rate. Using Smart-PL4 analysis, the results showed that there is a positive correlation between accounting information systems and external auditors. Conversely, a negative relationship between generalized auditing software and external auditors has been documented. In addition, this study reported a negative association between technology adoption and external auditors. However, the results highlight the positive relationship between trained human capital and external auditors. Overall, this study provides valuable insights for accounting and auditing policymakers on how external auditors’ performance is influenced by several factors in the Saudi audit market.

This study explores the potential of Eisenia fetida (Savigny) in vermicompost production within vermipits during winter months in the Rampur region of Uttar Pradesh, India. The vermipits were inoculated with 0.33 kg of E. fetida per pit, against the recommended rate of inoculation for assessment of real-time potential of earthworm on seasonal and regional grounds. The results revealed a significant increase in earthworm population, with three times production (4.1 kg) of the inoculated number (1.0 kg). The productivity dynamics of compost vs E. feida revealed a notable threefold and fourfold effectiveness, respectively. This dual advantage signifies the success of composting process, with the increased worm population indicating enhanced decomposition. The study attributes the combination of regional and edaphic factors viz., sandy loam soil with high humus content and good water passage, facilitating earthworm survival, reproduction and decomposition of agricultural waste. The loose and perforated soil structure allowed easy penetration of earthworms, transforming the soil into fertile compost. The research emphasizes the importance of soil moisture of semi-arid ecosystems thus highlighting the seasonal significance governing earthworm activity. The study provides insights into the mechanical ability of earthworms, its sensitivity to soil compaction and the influence of high hydration of the region. Inoculation during the winter period indicates increased earthworm activity setting the stage for enhanced compost production in the upcoming hot periods of summer months. The findings suggest that optimizing vermicomposting practices with altered feeding stocks, could further improvise compost and earthworm production and productivity in subsequent seasons. The study contributes for sustainable utilization of E. fetida in composting, its regional and seasonal efficacy turns trash to treasure for enhanced agricultural efficacy

In this paper, the boiling flow inside a nanochannel with 700000 argon particle has been simulated by molecular dynamics (MD) simulation. This approach has been employed to analysis the superheated flow and its heat transfer pattern as well. For all simulations an external thrust force varying from 1 PN to 12 PN is exerted on inlet nanoparticles along the channel to have the forced annular boiling flow. Computations reveal that saturation condition and superheat degree have significant impacts on the liquid-vapor interface. Furthermore, because of the major influence of surface tension throughout a nanochannel, the x-velocity of liquid film and vapor core has not considerable fluctuations and stay smooth. All provided results show the behaviors completely similar to the available outcomes in the literature.

Various studies on the physical characteristics of the canopy gaps in tropical and subtropical regions but that in the mangrove swamp forest in Malaysia is little known. The objectives of this study are to assess the canopy gaps physical characteristics (size, form, direction), to assess the changes in soil physical characteristics and microenvionment in and around the canopy gaps. Five canopy gaps were selected formed by lightning strikes, gaps size and shapes were determined using the portable Global Positioning System and photographic images were analysed using ForestCrown. Result shows that canopy gap opening has increased the total light reaching the forest floor, changed the canopy gap microenvironment. Strong correlation between canopy opening percentage and temperature of gaps explains the changes of canopy gaps characteristics. These changes have contributed to new seedling germination, regeneration and maintaining the canopy gap dynamics.

Coronavirus Disease 2019 (COVID-19) exhibits a significant variation in presentation of symptoms ranging from mild to severe life-threatening symptoms, including death. COVID-19 infection susceptibility has been linked with various covariates, but studies in Qatar are limited. Data from Qatar Biobank (QBB) (n = 10,000; 18- to 79-year-old adults) were analyzed for associations between sociodemographic and lifestyle factors and susceptibility to COVID-19. Logistic regression was used to assess the associations between COVID-19 and age, gender, body mass index (BMI), smoking status, education level, dietary patterns, supplement use, physical activity, history of bariatric surgery, diabetes, and hypertension. In total, 10.5% (n = 1045) of the participants had COVID-19. Compared to non-smokers, current and ex-smokers had lower odds of having COVID-19 (odds ratio [OR]= 0.55; 95% CI: 0.44-0.68 and OR= 0.70; 95% CI: 0.57-0.86, respectively). Vitamin D supplement use was associated with an 18% reduction of the likelihood of contracting COVID-19 (OR = 0.82; 95% CI: 0.69-0.97). Obesity (BMI ≥30 kg/m2), history of bariatric surgery and higher adherence to the modern dietary pattern– characterized by consumption of foods high in saturated fat and refined carbohydrates– were positively associated with COVID-19. Our findings indicate that adopting a healthy lifestyle may be helpful in the prevention of COVID-19 infection.

Despite the 12-lead electrocardiogram (ECG) being the gold standard in evaluating clinical presentations of arrythmias of various kinds, several notable limitations, namely in accuracy and localization, have recently garnered concern with respect to administering appropriate therapeutic measures. The following text puts these limitations under the spotlight, highlighting the difficulty and challenging nature of standard ECG analysis and therapeutic measures, while simultaneously showing a novel body surface electrocardiographic mapping system that utilizes 3D spatial imaging and pinpoint localization algorithms in order to compensate for the shortcomings of the standard ECG, thereby providing higher precision data collection. We provide a brief overview of the novel device in a selection of arrhythmic demographics, along with its validity and reliability, while illustrating the benefits and positive outcomes of its utility.

The new emergence and re-emergence of arbovirus infections vectored by Aedes mosquitoes have been spread across Southeast Asia, Central Africa, United States, tropical Oceania and become a major of public health concerns. These arbovirus diseases found to have a similar vector, symptoms of the diseases and environments. The situation has become complicated without any specific vaccine or treatment for the diseases. As far as we concern, vector control is the best defense with many challenges, a scattered breeding site and biological behavioral sometimes, more difficult to control. Herein, we present a review of studies on current techniques proposed to combat arbovirus infections vectored by Aedes mosquitoes. A crucial gap in a vector control program is the inability to eliminate and destroy the cryptic breeding site by using conventional control methods. The idea and proper concept of using mosquitoes to bring insecticide to their oviposition site had been getting more interest in people to explore. Autodissemination is a self-delivery technique by manipulating the behavior of mosquitoes, carrying the insecticide and disseminate to cryptic breeding sites. This technique has shown a promising result in some countries and can be considered as additional tools in a vector control program

The second-order factor effect of burner optical ports and edge inter-matrices (EIM) and the first-order factor of pressure on the soot formation process and behavior of premixed sooting flames in a high-pressure burner are numerically investigated here. Three-dimensional computational fluid dynamics (CFD) simulations of a premixed flame C₂H₄/air at p = 1.01 and 10 bar using a one-step chemistry approach are first performed to justify the satisfied predictability of the prospective axisymmetric two-dimensional (2D) and one-dimensional (1D) simulations. The justified 2D simulation approach shows the generation of an axial vorticity around the EIM and axial multi-vorticities due to the high expansion rate of burnt gases at the high pressure of 10 bar. This leads to the development of axial multi-sooting zones, which are manifested experimentally by visible luminous soot streaks, and to the boosting of soot formation conditions of a relatively low-temperature field, < 1800 K, and a high mixing rate of gases in combustion around and above the EIM location. Nevertheless, a tolerable effect on the centerline soot volume fraction (f_V) profile, f_V < 3%, is manifested only at high heights above the burner of the atmospheric sooting flame C₂H₄/air ϕ = 2.1, and early at the high pressure of 10 bar of this flame, f_V < 10%. Enhancing the combustion process reactivity by decreasing the rich equivalence ratio of the fuel/air mixture and/or rising the pressure results in the prior formation of soot precursors, which shifts the sooting zone upstream.

Background: Delivery of quality family planning services depends on availability of skilled health care providers, which is often a challenge in Low- and middle-income countries. This study evaluates the effects of a targeted training program on family planning services delivered by the Lady Health Workers (LHWs) in six districts of Punjab, Pakistan. The primary goal was to enhance the quality and accessibility of family planning services, recognizing the pivotal role of LHWs in community health. Methods: Using a robust panel data difference-in-differences (DiD) approach, we analyzed the electronic medical records (EMR) of 1,410 LHWs over a nine-month period. This timeframe in-cluded both pre-training (July to December 2022) and post-training (April to June 2023) phases. Our assessment focused on the impact of training on five outcome variables: the number of follow-up clients, the number of Intrauterine Contraceptive Device (IUCD) clients, the number of condom clients, the number of modern method clients, and the number of injection clients. We also con-sidered control variables such as LHW age, LHW education, the number of eligible couples in each LHW's catchment area, and a categorical variable for the districts. Results: The positive and statistically significant (p<0.05) coefficients across various service cat-egories – Follow Up Clients [Coeff: 1.00, CI: (0.30 - 1.70)], IUCD Clients [Coeff: 0.23, CI: (-0.01 - 0.46)], Condom Clients [Coeff: 1.41, CI: (0.97 – 1.86)], Modern Method Clients [Coeff: 1.82, CI: (0.96 – 2.68)], and Injection Clients [Coeff: 0.32, CI: (0.14 – 0.50)]. These findings indicate enhanced productivity among LHWs who have received training, with results significant at the 95% con-fidence interval (CI). Conclusion: The study highlights the effectiveness of targeted training programs in boosting family planning services at the community level and offers insightful implications for policy-makers and health practitioners. While focused on six districts in Punjab, these promising results emphasize the need for ongoing training and support for frontline health workers to bolster family planning initiatives.

The study titled "Molecular Choreography of DNA: Insights and Innovations" explores the intricate dance of DNA, shedding light on its structure, function, and regulation. This comprehensive review delves into the historical significance of DNA research, from Watson and Crick's double helix discovery to recent innovations in genomics and molecular biology. The review highlights DNA's roles in replication, repair, gene regulation, and chromatin dynamics, emphasizing the precision and complexity of its molecular choreography. Innovations like CRISPR-Cas9 gene editing and advanced sequencing technologies have revolutionized our understanding of DNA, opening new avenues for scientific exploration. This study aims to provide readers with a deeper appreciation of DNA's central role in life's choreographed processes.

Background: A person living with HIV / AIDS bargains with stressors such as discrimination, stigma depression, and several psychological impacts. The stressors experienced by people with HIV/ AIDS will certainly have an impact on daily activities, welfare, and management of medications which in general will have an impact on the quality of life. To deal with these stressors, it is necessary to have good and proper coping from within the PLWHA. Coping strategies need to be owned and carried out by PLWHA in order to respond adaptively to the stressor conditions experienced. Purpose: The aim of this review is to describe the stress experienced and the coping strategies used among PLWHA. Methods: This literature review used keywords in the search for international references are coping strategy, stressor, HIV-AIDS. Inclusion criteria: selection of titles that are relevant to the formulation of the problem and objectives, full-text articles in English, articles published from 2019 to 2021. The exclusion criteria used were coping strategy articles that did not involve HIV patients. Search references from electronic database sources namely ProQuest, CINAHL, and ScienceDirect.Six articles that are deemed worthy of analysis are then discussed or analyzed. Results: Age, gender and sexual orientation have contributed to the emerging stressor among PLWHA. The internal and external coping strategies focusing on the problem are important for PLWHA in handling the stressor. Conclusion: People with HIV-AIDS have many stressors in their lives, but they also have proper coping strategies depending on their internal and external conditions.

The water quality of the river is becoming deteriorated due to human interference. It is essential to understand the relationship between human activities and land-use types to assess the water quality of a region. GIS has the latest tool for analyzing the spatial correlation. Land use land cover and change detection is the best illustration to show the human interactions on land features. The study assessed water quality index of upper Ganga River near Haridwar, Uttarakhand and spatially correlated them with changing land use to reach a logical conclusion. At the upper course of Ganga along 78 Km long from Kaudiyala to Bhogpur, water samples were collected from five stations. For water quality index the physicochemical parameters like pH, EC, DO, TDS, CaCO3-, CaCO3, Cl¯, Ca++, Mg++, Na+, K+, F-, Fe2+ were considered. The result of the spatial analysis was evaluated through error estimation and spatial correlation. The root mean square error between spatial land use and water quality index of selected sampling sites was estimated as 0.1443. The spatial correlation between land-use change and site-wise differences in water quality index has also shown a high positive correlation with R² = 0.8455. The degree of positive correlation and root mean square error has strongly indicated that the water quality of the river at the upper course of Ganga is highly impacted through human activities.

Soil and Water Assessment Tool (SWAT) was used to simulate five glacierized river basins that are global in coverage and vary in climate. The river basins included the Narayani (Nepal), Vakhsh (Central Asia), Rhone (Switzerland), Mendoza (Central Andes, Argentina), and Central Dry Andes (Chile) with a total area of 85,000 km2. A modified SWAT snow algorithm was applied in order to consider spatial variation of associated snow melt /accumulation by elevation band across each subbasin. In the previous studies, melt rates varied as a function of elevation resulting from an air temperature gradient while the snow parameters were constant throughout the entire basin. A major improvement of the new snow algorithm is separating the glaciers from seasonal snow based on their characteristics. Two SWAT snow algorithms were evaluated in simulation of monthly runoff from glaciered watershed: 1) the snow parameters are lumped (i.e. constant throughout the entire basin) and 2) the snow parameters are spatially variable based on elevation band-subbasin (i.e. modified snow algorithm). Applying the distributed SWAT snow algorithm improved the model performance in simulation of monthly runoff with snow-glacial regime, so that mean RSR decreased to 0.49 from 0.55 and NSE increased to 0.75 from 0.69. Improvement of model performance was negligible in simulation of monthly runoff from the basins with monsoon runoff regime.

Background: This study was conducted to investigate medical students' lifestyle habits, including sleep quality, eating and drinking pattern, physical activity, and social status. Method: This research project is part two of a multi-institutional cross-sectional observational study conducted among medical students from six medical colleges in Saudi Arabia between September and December 2019. Results: 675 medical students were enrolled electively into the lifestyle study. About half of this number were male students, and the majority aged 18-24 years. Most students (87.6%) slept between 4-8 hours a day, and over 44% were dissatisfied with their sleep. Only 28.1% had three meals a day; about 40% of them usually or always skipped breakfast. While 44% usually or always eat fast food, 44.7% drink 2 liters of water per day. Moreover, male students were significantly consuming fast foods than females, p<0.001. The majority (63.3%) revealed they usually or always drink black coffee daily. Females were significantly more inclined to regular coffee consumption than males, p<0.001. Only 4.3% exercising for 30 minutes or more daily. The majority (65%) of the students were introverted; they had few close friends. Yet, 81% were somewhat satisfied or satisfied with their social life. Male students were predicted to be significantly more satisfied with their social life than females, p=0.001. Only 4.6% smoked cigarettes daily. Whereas 7.1% smoke e-cigarette daily. In contrast, only 0.3% use shisha (hookah) daily. Male medical students were substantially more predicted to be inclined to e-cigarette use than females (p <0.001. The top five leisure activities of a medical student are surfing social media (75.9%), watching movies (61.3%), hanging out with friends (58.1%), spent time with their family (55.4%), and browsing the internet (53.6%). Female medical students were significantly more inclined to surfing social media than male medical students, p=0.022; also, watching movies was preferred for females compared to males, p=0.006. Conclusion: This study revealed that the majority of medical students in Saudi Arabia exhibited healthy lifestyles to some extent, and these health-promoting behaviors differed based on gender, especially concerning physical activity and eating patterns. The findings of this study provide relevant information for future actions that will be geared towards effectively decreasing the occurrence of chronic illnesses and improving future doctor’s well-being.

There has been no significant efficacy in treatment for osteosarcoma (OS) metastasis after nearly four decades of trials. This motivates us to elucidate OS therapies according to their four bidirectional mutation stages. To refresh the OS therapy status quo, the historical developments and clinical advancements are briefly described. However, the main issue of metastasis remains unresolved, accounting for 90% of pulmonary metastasis deaths. Thus, this metastasis problem is related to immune evasion and chemoresistance that are being induced after long-term treatment by the use of immunotherapy for tumorigenesis. Therefore, it is rationale to discuss the relationship cycles of mutation stages including tumorigenesis, metastasis, immune evasion, and chemoresistance. Even though many combinational and targeted therapies have been developed to intensify these mutation treatments, successful clinical translations with higher cure rates are still rare. Through this review, an in-depth understanding of the bidirectional relationship between the four OS mutation stages and their respective therapies is provided. Herein, we summarise the medicines used to treat tumorigenesis, including COLGALT2 inhibitors, Tra2B, and AGAP1, miR-148a and miR-21-5p EVs, and the lncRNA LIFR-AS1. Following the medicines used to treat metastasis are AXL, miR-135a-5p, mRNA BCL6, TGFβ1, Tim-3, SOCS5, CASC15, KLF3-AS1, PDCD4, ATG5, and Rab22a-NeoF1. Then the medicines used to treat immune evasion are N-cadherin, anti-IL-9, USP12 inhibitor, IgG-4+ B-cells, LAP inhibitor, anti-Wnt2 mAb, anti-αvβ8 integrin, HK2-mediated IκBα, IDO inhibitor with NO, and TGF-βRII with anti-IgG1. Finally, the medicines used to treat chemoresistance are DHFR, FPGS, HSP-90AA1, XCT-790, ATKI, and IGF1. As a result, this contribution is expected to serve as a reference and guide for scientists and clinicians.

Jaundice is a common condition for newborns, and its complications can be severe and cause permanent damage to the patient’s brain if no action is taken at its early stages. Current methods for jaundice detection are invasive, which include collecting blood samples from the patient, which can be painful and stressful and may cause some complications. Alternatively, a non-invasive approach can be used to diagnose jaundice through image-processing and artificial intelligence (AI) techniques, requiring a database of infant images to achieve a high-accuracy diagnosis. This data article provides a collection of newborn images, called NJN, with various birthweight and skin tones, with ages ranging from 2 to 8 days, and an excel sheet file in CSV format for the values of RGB and YCrCb channels and the status for each raw which is freely accessible at (https://sites.google.com/view/neonataljaundice). It also provides Python code for data testing using different AI techniques. Thus, this article offers a unique resource for all AI researchers to train their AI system and develop algorithms to help neonatal intensive care unit (NICU) healthcare specialists monitor neonates and provide fast, real-time, non-invasive, and accurate jaundice diagnosis.

Extensive genome structure variation, such as copy number variations (CNVs) and presence/absence variations, is the basis for the remarkable maize genetic diversity; however, the effect of CNVs on maize herbivory defense remains largely underexplored. Here, we report that the naturally occurring duplication of the maize 9-lipoxygenase gene, ZmLOX5, leads to increased resistance of maize to herbivory by fall armyworms (FAW). Previously, we showed that ZmLOX5-derived oxylipins are required for defense against chewing insect herbivores and identified several inbred lines, including Yu796, that contained duplicated CNVs of ZmLOX5, referred to as Yu796-2×LOX5. To test whether introgression of the Yu796-2×LOX5 locus into the herbivore-susceptible B73 background that contains a single ZmLOX5 gene is a feasible approach to increase resistance, we generated a series of near-isogenic lines that contained either 2, 1 or 0 copies of the Yu796-2×LOX5 locus in the B73 background by six back crosses (BC6). Droplet digital PCR (ddPCR) confirmed the successful introgression of the Yu796-2×LOX5 locus in B73. The resulting B73-2×LOX5 inbred line displayed increased resistance against FAW associated with increased expression of ZmLOX5, increased wound-induced production of its primary oxylipin product, the α-ketol, 9-hydroxy-10-oxo-12(Z),15(Z)-octadecadienoic acid (9,10-KODA), and the downstream defense hormones regulated by this molecule, 12-oxo-phytodienoic acid (12-OPDA) and abscisic acid (ABA). Surprisingly, wound-induced JA-Ile production was not increased in B73-2×LOX5, resulting from the increased JA catabolism. Furthermore, B73-2×LOX5 displayed enhanced drought tolerance likely due to increased ABA and 12-OPDA content. Taken together, this study revealed that the duplicated CNV of ZmLOX5 quantitively contributes to maize antiherbivore defense and presents proof-of-concept evidence that the introgression of naturally occurring duplicated CNVs of a defensive gene into the productive, but susceptible, crop varieties is a feasible breeding approach for enhancing plant resistance to herbivory and tolerance to abiotic stress.

We present the design and overall development of an eight degrees of freedom (DOF) based Bioinspired Quadruped Robot (BiQR). The robot is designed with a skeleton made of cedar wood. The wooden skeleton is based on exploring the potential of cedar wood to be a choice for legged robots’ design. With a total weight of 1.19 kg, the robot uses eight servo motors that run the position control. Relying on the inverse kinematics, the control design enables the robot to perform the walk gait-based locomotion in a controlled environment. The robot has two main aspects: 1) the initial wooden skeleton development of the robot showing it to be an acceptable choice for robot design, 2) the robot’s applicability as a low-cost legged platform to test the locomotion in a laboratory or a classroom setting.

This study discusses the experimental assessment of the in-plane mechanical behavior of a multi-leaf stone masonry wall built from cut stone and reinforced with metal connectors (cramps and dowels). The wall, inspired by conventional multi-leaf stone walls, is meant for use in modern stone masonry buildings. The wall is constructed from two parallel load-bearing walls with a cavity between them, which aims to conceal the installation and insulation needed in modern buildings. The load-bearing walls are connected with cramps and dowels at certain intervals so that the wall works as a single section against horizontal and vertical loads. To characterize the in-plane behavior of the proposed wall, compressive, triplet, and diagonal compression tests were conducted to investigate the compressive strength, shear strength, modulus of elasticity, stiffness, ductility, and energy absorption of the wall. Compared with dry and mortar joint walls, the use of dowels increased the initial shear capacity of the wall by 11 and 19 times, respectively. The application of cramps without curving channels inside the individual stone elements decreased the compressive strength by 18%. The energy absorption of the designed walls with metal connectors was substantially increased to that of the specimens representing conventional stone walls. The results obtained show the applicability of the wall due to its higher shear strength and minimal drop in compressive strength, which is within acceptable limits.

This article will briefly review the progress of h-BN based solid-state metal semiconductor metal (MSM) neutron detectors. In the last decade, several groups have been working on hexagonal boron nitride (h-BN) based solid-state neutron detectors. Recently, the detection efficiency of 59% has been reported. Efficient, low-cost neutron detectors made from readily available materials are essential for various applications. Neutron detectors are widely used to detect fissile materials and nuclear power plants for security applications. The most common and widely used neutron detectors are 3He based, which are sometimes bulky, difficult to transport, have high absorption length, need relatively high bias voltage (>1000 V), and have low Q-value (0.764 MeV). Also, 3He is not readily available material. Thus, there is a strong need to find an alternative detection material. 10B isotope has a high neutron absorption cross-section, and it has been tested as a coating on the semiconducting materials. Due to the two-step process, neutron capture through 10B, and then electron-hole pair generation in a typical semiconducting material, the efficiency of these devices is not up to the mark. The progress in h-BN based detectors requires a review to envision the further improvement in this technology.

Abstract: The Internet of Things (IoT) has witnessed rapid and widespread adoption across various domains, including transportation, healthcare, education, agriculture, urban planning, smart homes, and more. Despite its transformative potential, this pervasive deployment of IoT devices has introduced new challenges, particularly concerning security and privacy threats such as unauthorized data access and device breaches. The excessive usage of these technological devices, coupled with the absence of robust security and privacy systems for user data, calls for a comprehensive approach to address these issues. In this study, we propose a novel framework designed to analyze, audit, test, and detect potential vulnerabilities within IoT environments and applications. The central components of the proposed framework include a machine learning algorithm for data classification and attack detection, along with the integration of Blockchain technology to enhance security measures. Specifically, the framework performs an in-depth analysis of user data to identify potential security or privacy vulnerabilities. Additionally, it conducts rigorous testing of smart services and automated data-collecting devices. To evaluate the effectiveness of our classification algorithm, we conducted a comprehensive implementation on a real-world IoT dataset. The results showcased the efficiency and accuracy of our approach in detecting and mitigating potential threats. Furthermore, based on our research findings, we provide valuable recommendations for enhancing security and privacy in IoT ecosystems. We also highlight emerging trends in the security and privacy domains, which can serve as valuable insights for researchers and practitioners. In conclusion, our proposed framework offers a robust and proactive approach to address the security and privacy challenges, such as unauthorized data access and device breaches posed by the widespread adoption of IoT devices. By combining machine learning algorithms and Blockchain technology, we contribute to safeguarding user data and fostering a secure environment for IoT applications. This study lays the groundwork for further advancements in the realm of IoT security and privacy, ensuring a safer and more resilient IoT landscape for the future.

Soil-borne Trichoderma spp. have been extensively studied for their biocontrol activities against pathogens and growth promotion ability with plants. However, the beneficial effect of Trichoderma on inducing resistance against insect herbivores has been considered only recently. Among diverse Trichoderma species, consistent with previous reports, we showed that root colonization by T. virens triggered induced systemic resistance (ISR) to leaf-infecting fungal pathogens. Whether T. virens induces ISR to insect pests has not been tested before. In this study, we investigated whether T. virens affects jasmonic acid (JA) biosynthesis and defense against fall armyworm (FAW) and western corn rootworm (WCR). Unexpectedly, the results showed that T. virens colonization of maize seedlings grown in autoclaved soil suppressed wound-induced production of JA, resulting in reduced resistance to FAW. Similarly, the bacterial endophyte Pseudomonas chlororaphis 30-84 suppressed systemic resistance to FAW due to reduced JA. Further comparative analyses of the systemic effects of these endophytes when applied into sterile or non-sterile field soil showed that both T. virens and P. chlororaphis 30-84 triggered ISR against hemibiotrophic fungal pathogen Colletotrichum graminicola in both soil conditions, but only suppressed resistance to FAW in sterile soil. In contrast to the effect on FAW defense, T. virens colonization of maize roots suppressed WCR larvae survival and weight gain. This is the first report suggesting the potential role of T. virens as a biocontrol agent against WCR.

This paper evaluates the global sustainable competitiveness of three blocks of power (US, China and EU) based on OGT (Organic Growth Theory) structure. In addition, a sensitivity analysis of COVID-19 on three measures of global sustainable competitiveness, economy, environment and social is also presented. OGT is a well-thought-through and standard framework for evaluation of any type of organiza-tion in their pathway to experience truly sustainable growth. GDP (Gross Domestic Product) is used as an economic measure; total carbon emissions equivalent is used as an environmental measure and HMI (Human Development Index) as a social measure. It is concluded that the pandemic has paused the evolution of countries to be truly world centric countries. However, the recovery plans seem to be highly aligned with the OGT scores and it is expected that the scores to improve dramatically.

The research aims to improve some of the physical and hydro-physical properties of some Vertisols in the southern region (part of the eastern Houran Plateau) in Sweda Governorate at the south of Syria. Using different quantities of volcanic ash, soil samples were collected from the Al-Thahallah village from a depth of (0-30) cm, The experiment was designed according to the complete random design with one factor that represents the ash quantity (1.25, 2.5, 5) %, with three replicates for each treatment in addition to the control treatment a0. The experiment was carried out within the plastic pots during agricultural season 2018/2019, in which the wheat of the Sham variety 3 were cultivated as a cover plant. The results showed that the addition of volcanic ash at the quantity of 5% led to a significant increase in the infiltration rate by (328.60) %,%, where the filtration rate increased from 0.42 cm/hr -1 to 1.80 cm.h-1, as well as for each of the air porosity by (89) % and the volume of infiltrate water by (40) %, compared with the control. The above-mentioned addition also resulted in a decrease in both dry bulk density, total soil porosity and volumetric swelling coefficient by (18.60, 5.80, 314) % Respectively, compared to the control. The addition also contributed to the reduce in the weighted moisture content when saturation and the field capacity, at the level of significance of 5%. The research recommends adding volcanic ash to the soil at a quantity of 5%, and adding enhancements with volcanic ash at various levels such as organic waste.

In this paper we designed system for smart university building based on artificial intelligence (AI) and internet of things (IOT). Our idea can be summarized in smart security system that has different sensors to detect the surrounding environment of the class room in campus which keep everyone and everything on campus safer. By using (IOT), (AI) technologies and applications and by using microcontroller programming we can make the university building safer, secure and more energy saves.

Speckle noise corrupt the major part of ultrasound image, because of which the quality deteriorate and loss of valuable information leads to false diagnosis. A large community of images like synthetic aperture radar (SAR) image, Synthetic image, and simulated ultrasound image, require despeckling at pre-processing stage for better processing. Cleaning the speckle from image and preserving the edge details is a vital task. Nowadays not only despeckling is considered as an important process but also preserving information at boundary and edges of image is also important. As most of the algorithms able to remove speckle noise but fails to preserve the details of edges. This paper covers several recent methods for removal of speckle noise along with various metrics opted for comparisons. The distinctive part of this paper is, a mathematical and parametric review has been done. Also a table is also included which summarizes the entire paper.

Machine Learning (ML) has brought about a transformative era in reactor operations, reshaping monitoring, control, and optimization strategies. This survey comprehensively explores the diverse spectrum of ML applications in industrial reactors. From real-time sensor analysis ensuring reactor functionality to adaptive control algorithms ensuring stability, ML's impact is profound and multifaceted. The benefits of ML are equally evident in optimization, encompassing performance trend prediction, proactive maintenance, and fine-tuning of operations for enhanced efficiency. This review identifies dominant ML techniques, operations stages receptive to ML integration, core data sources, and critical input-output parameters. Aimed at both academics and practitioners, this exploration enriches reactor technologies, unlocking their full potential through insights driven by ML.

Background: Although the link between environmental lead exposure and several patterns of cardiovascular disease (CVD) has been reported, its association with silent myocardial infarc-tion (SMI) has not been explored. We aimed to assess the association between blood lead levels (BLL) and risk of SMI. Methods: This analysis included 7283 (mean age 56.1±2.52 years, 52.5% women) participants from the Third National Health and Nutrition Examination Survey who were free of clinical CVD. Ascertainment of BLL done using graphite furnace atomic absorption spectrophotometry. SMI defined as ECG evidence of MI without history of MI. Multivariable lo-gistic regression analysis was used to examine the association between SMI and BLL. Results: SMI was detected in 120 participants corresponding to an unweighted prevalence of 1.65%. Par-ticipants with higher levels of BLL had higher prevalence of SMI (0.4%, 0.9%, and 2.4% across BLL tertiles; p-value

Detection of partial discharge (PD) activities in high-voltage equipment can be done according to the several mechanisms of signal detection, which are electromagnetic wave signal detection, acoustic signal detection, chemical reactions, electrical signal detection, or optical emission detection. Recently, multiple methods of detection and localization of partial discharge activities occurred in power transformers and gas-insulated switchgear (GIS) have been proposed to monitor the health condition of high-voltage equipment, especially when the awareness regarding preventive maintenance has been emphasized at the industrial level and electrical providers. In aligning the needs of industrial and the improvement of PD detection methods, this manuscript focused on reviewing the current practice methods for the detection and localization of PD signals in high-voltage equipment and comparing their efficacy, in summarizing the future direction of research work-related methods of PD detection. The comparative reviews are discussed in terms of the mechanism of PD signal detection, indication parameters, calibration technique, advantages, and limitations of each method of PD measurement in detail.

The Soil Conservation Service - Curve Number (SCS-CN) method is extensively used to calculate the runoff from rainfall over a large catchment over the world. Slope is an important criterion for runoff but a very few attempts have been made to evaluate the effect of slope on the CN with runoff potential. The objective of this paper is to summarise the historical review on the effects of slope on CN and runoff potential in various regions by the hydrologists. This paper also depicts that how the various researchers proved the importance of consideration of slope for CN and runoff estimation. In addition, paper highlights the key features of research in future like to classify the watersheds on slope based CN, accurate Antecedent Moisture Condition (AMC) and proper initial abstraction in the various regions etc. Considering these parameters an accurate runoff estimation can be predicted and managed properly in the urban watersheds.

The synchronous generator, as the main component of power systems, plays a key role in these system’s stability. Therefore, utilizing the most effective control strategy for modeling and control the synchronous generator results in the best outcomes in power systems’ performances. The advantage of using a powerful controller is to have the synchronous generator modeled and controlled as well as its main task i.e. stabilizing power systems. Since the synchronous generator is known as a complicated nonlinear system, modeling and control of it is a difficult task. This paper presents a sum of squares (SOS) approach to modeling and control the synchronous generator using polynomial fuzzy systems. This method as an efficacious control strategy has numerous superiorities to the well-known T–S fuzzy controller, due to the control framework is a polynomial fuzzy model, which is more general and effectual than the well-known T–S fuzzy model. In this case, a polynomial Lyapunov function is used for analyzing the stability of the polynomial fuzzy system. Then, the number of rules in a polynomial fuzzy model is less than in a T-S fuzzy model. Besides, derived stability conditions are represented in terms of the SOS approach, which can be numerically solved via the recently developed SOSTOOLS. This approach avoids the difficulty of solving LMI (Linear Matrix Inequality). The Effectiveness of the proposed control strategy is verified by using the third-part Matlab toolbox, SOSTOOLS.

The current study was formulated with the aim of assessing the impact of the Extent of Drug addiction on the Family environment and interventions for drug-addicted youth in rural areas of Dir Lower Khyber Pakhtunkhwa, Pakistan. The study comprised a dependent variable (Extent of Drug addiction) and an independent variable (Family Environment). A sample size of 266 was randomly selected for primary data collection through structured interviews. The collected information was evaluated through descriptive analysis (frequency and percentage distribution) and inferential analysis, specifically utilizing the Chi-square Statistical Test. The study concluded that drug addiction among youth has an adverse impact on the family environment, leading to instability and violent behavior. Further, families of drug-addicted youth experience a high level of dissatisfaction in relationships, often culminating in divorce. Similarly, the families of drug-addicted are characterized by low cohesion, role conflict, and tolerance. Thus, the government should formulate polices and strategies for the prevention of drug addiction, with speedy implementation across all institutional dynamics, including family. Moreover, It is also crucial to rehabilitate the lifeblood of a nation (youth) in terms of socio-economic, emotional and psychological growth. These recommendations were put forward in the light of the current study.

A three-phase multilevel inverter (MLI) synthesized with 31-levels in the output voltage is used to provide ac supply to a three-phase, squirrel-cage induction motor. The gating angles required for thirty power switches in MLI are optimized by both Genetic Algorithm (GA) and Gray Wolf Op-timization (GWO). In the optimization technique, the optimal angles are determined by solving the trigonometric equations from the Fourier analysis for targeting minimum total harmonic distortion (THD) at the MLI output. Simulation model and experimental prototype are developed for performance analysis and validation. The results obtained demonstrate that the MLI is effec-tively able to produce 31-levels of three-phase ac output voltages with THD not exceeding 5% when loaded with a resistive load and a three-phase induction motor. The voltages and currents are measured and recorded for different loads and operating conditions, including the amount of energy consumed by the loads, as well as frequency analysis, which shows the absence of most of the triple harmonics that have a negative impact on the efficiency of the inverter.

A hallmark of Alzheimer’s disease (AD) are the proteinaceous aggregates formed by the amyloid-beta peptide (Aβ) that is deposited inside the brain as amyloid plaques. The accumulation of aggregated Aβ may initiate or enhance pathologic processes in AD. According to the amyloid hypothesis any agent that has the capability to inhibit Aβ aggregation and/or destroy amyloid plaques represents a potential disease modifying drug. In 2023, a humanized IgG1 monoclonal antibody (lecanemab) against the Aβ soluble protofibrils was approved by US FDA for AD therapy thus providing compelling support to the amyloid hypothesis. To acquire a deeper insight on the in vivo Aβ aggregation, various animal models, including aged herbivores and carnivores, non-human primates, transgenic rodents, fish and worms are widely exploited. This review is based on the recent data obtained using transgenic animal AD models and presents experimental verification of the critical role in Aβ aggregation seeding of the interactions between zinc ions, Aβ with the isomerized Asp7 (isoD7- Aβ) and the α4β2 nicotinic acetylcholine receptor.

We present a very computationally light and fast approximation algorithm and then verify it with genetic algorithm and simulated annealing. We show that our algorithm is on par with GA and SA in terms of output produced while having a tightly bounded time complexity. Our algorithm works best when there is a strong positive correlation between the reliability of a component and its cost. We present two algorithms with the same essence. One of them is system cost bounded and the other is target reliability bounded. Our proposed algorithm works on a subsystem level redundancy instead of component level redundancy.

Bacterial infections are world-wide problem and resistant bacteria is increasing globally that lead to treatments failure. Thus, it is important to find new techniques that are effective for the treatment antibiotic-resistant microorganisms. Green medicine used for the medication of different bacterial infection. Therefore , the study aimed to evaluate the immunomodulatory activity of essential fatty acid semi carbazide (EFASC) compounds extracted from flaxseed to eradicate resistance pathogens . Crud extracts of Linum usitatissimum was extracted by hexane for extraction of EFASC. The results of LD₅₀ appear that a live suspension of E.coli was 0.49×10⁷ ,while HK E.coli was 10⁸ , 125 mg /ml were the optimum dose in stimulate immune response of EFASC which gave maximum dose of total WBC , Neutrophil and Monocyte count. The immunomodulatory activity appear that E.coli + EFASC released moderate levels of IL-4 . HK E.coli release higher IL-4 lead to increase the hypersensitivity and hyper responses of immune system. HK + EFASC immunogen revealed moderate increased in IL-12 mean level 0.99 pg /ml, HK bacteria recorded 1.49 pg/ml, while the mean of EFASC revealed no significant different compare with control. An effective protection was observed in immunized groups with HK E.coli and EFASC challenged with 100 of LD50 of a live E.coli.

Nanofibres have gained increasing attention due to their advantages of having a high porous structure, narrow pore size and distribution, and a high specific surface area. One of the most efficient techniques for producing nanofibres is via electrospinning. Nanofibres are currently being used in various fields, including water filtration. Although nanofibres possess the ability to filter various components, the fibres generally have a low mechanical strength that can mitigate the performance over time. Therefore, in overcoming this issue, several studies have focused on improving the strength of the nanofibre membranes for a water filtration application. This review discusses the advantages and challenges of nanofibres for water filtration membranes and summarises the methods to increase the strength of the membranes, including solvent vapour, thermal treatment, and chemical crosslinking.

Pancreatic cancer is a fatal disease with mortality rate of 5% and a with a limited survival rate of 5 years. Despite of the extensive efforts that has been made to cure the disease it still has been considered as ‘undruggable’. It is characterized by epithelial to dense stromal tumor formation however due to lack of diagnosis options and treatment test available to detect the disease to the point at which resection of the tumor is possible, it makes it the fourth leading cause of cancer related death. The unavailable information regarding the early detection of biological markers along with the increased invasive tumor, the inherit chemoresistance against medication radiation and chemotherapy stubbornly fail the therapeutic options available. These associated problems made the scientists to reevaluate the approaches that are currently in practice and take a step back to fully understand the basic biological pathways that are involved in the pancreatic cancer, the heterogeneity of the tumor itself along with expression and a number of mutations that are observed at different locations. Clinical trial along with new approaches are nowadays focus of research to treat this tumor. The review paper describes the basic cellular pathways involved in pancreatic cancer, the gene mutations and their expression having effect on the pathology of the diseases along with treatment options that are available to treat the tumor. These efforts will help with the expansion of our knowledge to undergo the clinical trial and the synthesis of novel medicines for the prognosis of the disease.

In this paper, we demonstrate a useful interaction between the theory of clique partitions, edge clique covers of a graph, and the spectra of graphs. Using a clique partition and an edge clique cover of a graph we introduce the notion of a vertex-clique incidence matrix for a graph and produce new lower bounds for the negative eigenvalues and negative inertia of a graph. Moreover, utilizing these vertex-clique incidence matrices, we generalize several notions such as the signless Laplacian matrix, and develop bounds on the incidence energy and the signless Laplacian energy of the graph. %The tight upper bounds for the energies of a graph and its line graph are given. More generally, we also consider the set $S(G)$ of all real-valued symmetric matrices whose off-diagonal entries are nonzero precisely when the corresponding vertices of the graph are adjacent. An important parameter in this setting is $q(G)$, and is defined to be the minimum number of distinct eigenvalues over all matrices in $S(G)$. For a given graph $G$ the concept of a vertex-clique incidence matrix associated with an edge clique cover is applied to establish several classes of graphs with $q(G)=2$.

Cold atmospheric plasma (CAP) has emerged as a promising therapeutic technology, distinguished by its safety, efficiency, and minimal side effects. With increasing applications in dermatology, CAP demonstrates antimicrobial efficacy against bacteria, viruses, and fungi, supports tissue proliferation and wound healing, and hinders the growth and migration of tumor cells. This review encompasses the concept, physical characteristics, and current research applications of CAP in dermatology. CAP, a gas cloud produced by ionizing gases, operates at room temperature, ensuring safety and efficacy in dermatological use. Its effects are mediated through active components like reactive oxygen and nitrogen species (RONS) and ultraviolet radiation. The extensive research covered in this paper explores CAP's applications in skin infections, immune-related diseases, and tumor diseases. As a biologically suitable technology, cold atmospheric pressure plasma finds widespread use in medicine, including medical device sterilization, dentistry, oncology, and dermatology. The direct and indirect forms of CAP applications, including plasma-activated liquids, complement each other. CAP holds promise as a dermatological treatment technique, but further exploration and research are needed. This article briefly explores CAP's diverse applications and potential utility within dermatology. The objective is to enhance the progress of plasma medicine by furnishing a concise overview of the applications of this groundbreaking technology in dermatological contexts.

The aims of this study are to measure the technical efficiency (TE) of Cambodian household’s rice production and trying to determine its main influencing factors using the stochastic frontier production function. The study utilized primary data collected from 301 rice farmers in three selected districts of Battambang by structured questionnaires. The empirical results indicated the level of household rice output varied according to differences in the efficiency of production processes. The mean TE is 0.34 which means that famers produce 34% of rice at best practice at the current level of production inputs and technology, indicates that rice output has the potential of being increased further by 66% at the same level of inputs if farmers had been technically efficient. Furthermore, between 2013-2015 TE of household’s rice production recorded -14.3% decline rate due to highly affected of drought during dry season of 2015. Moreover, evidence reveals that land, fertilizer, and pesticide are the major influencing input factors of household’s rice production, while disaster, education of household head, family size and other crops’ cultivated area are core influencing factors decreasing TE. Conversely, the main influencing factors increasing TE are irrigated area, number of plot area and sex of household head.

The aims of this study are to measure the technical efficiency (TE) of Cambodian household’s rice production and trying to determine its main influencing factors using the stochastic frontier production function. The study utilized primary data collected from 301 rice farmers in three selected districts of Battambang by structured questionnaires. The empirical results indicated the level of household rice output varied according to differences in the efficiency of the production processes. The mean TE is 0.34 which means that famers produce 34% of rice at best practice at the current level of production inputs and technology, indicates that rice output has the potential of being increased further by 66% at the same level of inputs if farmers had been technically efficient. Furthermore, between 2013-2015, TE of household’s rice production recorded -14.3% decline rate due to highly affected of drought during dry season of 2015. Moreover, evidence reveals that land, fertilizer, and pesticide are the major influencing input factors of household’s rice production, while disaster, education of household head, family size and other crops’ cultivated area are core influencing factors decreasing TE. Conversely, the main influencing factors increasing TE are irrigated area, number of plot area and sex of household head.

The present study aims to measure the technical efficiency and establish core factors effecting rice production in Cambodia. Four-years dataset generated from the central government document “Profile on Economics and Social” of entire 25 provinces between 2012-2015 and the stochastic production frontier model (SFA) was applied. The results indicated that the level of output (quantity) of Cambodian rice production varied according to the different level of capital investment in agricultural machineries, total rice actual harvested area, and technically fertilizers application within provinces. Furthermore, evidence revealed the overall mean efficiency of rice production is 78.4% implies that there is still room to further improve technical efficiency by given the same level of inputs and technology. More importantly, the findings revealed that irrigation, production technique and amount of agricultural supporting staffs are being as the most important influencing factors of rice production’s technical efficiency in Cambodia. In conclusion, present study strongly recommends the development of irrigation systems and good water management practices to be considered and bring into more effective actions by the central government as well as related agencies for improving rice production in Cambodia in addition to capital investment and improving technical skills of supporting staffs and rural farmers.

The response of building elements during earthquakes has been an area of concern for different researchers around the world. The NSEs are the first elements to attain failure, the buildings after damages in safety priority are classified as in “Life Safety” mode, but in reality, they are in “Collapse Safety” mode and the occupants inside the buildings are in a state of “Non-Life Safety” as a result of falling of the hazardous NSE’s like cladding's, partitions, hanging objects. In addition to the Life Cycle Cost Assessment (LCCA), the Social Life Cycle Assessment (SLCA) is overlooked by the majority of the researchers, the community must be prepared to respond to an event more efficiently without any major casualties or loss of life. It is expected that by the end of 2050, almost double the construction will take place, the embodied energy during the pre-construction, post-construction and demolition generates a lot of CO2s into the environment, hence sustainability should be given due importance, helping in the achievement of Sustainable Developmental Goals (SDGs).

Cotton is the most ubiquitous and profitable fiber with diverse industrial and domestic applications. Grown in over 100 countries, it has a global market value of about $40 billion and employs over 350 million people from fields to textile mills, contributing about 7% of total labor-force recruitment in developing economies. Cotton has an indeterminate growth habit and an extensive tap root system affected by soil physicochemical and environmental conditions. There is a consensus among experts that conventional cotton cultivation still has a long way to attain sustainability, which is essential if cotton will maintain its competitive edge over other natural and synthetic fibers like hemp, polyesters, and rayon. Despite several efforts already committed to growing cotton sustainably, sustainability has eluded cotton cultivation globally because of the intense farm inputs (freshwater, pesticides, and heavy-duty equipment) needs, especially in developing economies. Some of the technological advancements towards achieving the goal of sustainable cotton cultivation include the development of new varieties, improved irrigation and mulching and precision agriculture techniques, application of remote sensing and Unmanned Aerial Systems (UAS) combined with image processing, and the introduction of autonomous and multi-purpose robotic platforms for growing and harvesting cotton. This review attempts to evaluate the successes already achieved by stakeholders in moving cotton towards sustainable production and identify areas where efforts are still needed to reach sustainable production and improved profitability goals for cotton with projections for future research directions.

Digital Elevation Models (DEMs) are essential in watershed delineation, but the sensitivity of simulated runoff to DEM resolution is poorly understood. This study investigates the impact of DEM resolution on topological attributes and simulated runoff in the Mahabad Dam watershed, Iran. To delineate the watershed, DEMs with 12.5 m, 30 m, and 90 m resolutions were acquired from the ALOS PALSAR, Space Shuttle Radar Topography Mission (SRTM), and ASTER global DEM data source, respectively. Watershed and streamlines were delineated in ArcGIS, with hydrologic analyses performed using the Soil and Water Assessment Tool (SWAT). Sensitivity analysis on parameters contributing to runoff was done using Sequential Uncertainties Fitting Ver-2 (SUFI-2) Algorithm, in SWAT Calibration and Uncertainty Procedures (SWAT-CUP) software. Results showed the watershed area, reach lengths, and elevations in the watershed varied due to DEM resolutions. Higher amounts of runoff were generated when DEMs with finer resolutions were implemented. The 12.5 m DEM generated 3.48% and 0.42% more runoff compared with 90 m and 30 m DEMs, respectively. SWAT-CUP results showed the sensitivity of parameters contributing to runoff changes under different DEM resolutions. Regardless of DEM resolution, surface properties, available water capacity, and moisture levels in the soil are the most sensitive parameters. As the distribution of slope changes in different DEM resolutions, surface parameters are most affected. The findings indicate to reduce computation time and speed up computation procedures, researchers may use DEMs with coarser resolutions at the expense of minor decreases in accuracy.

This paper presents designing an attitude motion control strategy for a half-car model using an anti-jerk predictive controller. Anti-jerk predictive controller based on active aerodynamics surfaces is employed to deal with the anticipated future road maneuvers and improve vehicle ride performance by canceling out external jerks acting on the vehicle body. The body jerks are produced during vehicle traversing on a double lane-change maneuver, acceleration, or braking. The control strategy helps the vehicle to achieve a realistic operation of the active aerodynamic surface to improve the vehicle’s ride performance, i.e., ride comfort and road holding during cornering, acceleration, or braking. The anti-jerk predictive controller is developed based on the predictive control strategy, which predicts future road inputs and uses them to compensate for the vehicle’s attitude motion. The simulation results show that the proposed control strategy effectively reduces the effects of vehicle body jerks transmitted to the passengers, improving ride comfort without degrading vehicle handling. The anti-jerk predictive controller successfully tracks the desired attitude position by canceling the external body jerks.

Keywords: olfactory rehabilitation; chronic pain; fMRI;

Objective: To employ Latent Class Analysis (LCA) to investigate dietary intake patterns among Sudanese children aged 0 to 2 years and to examine the association of these patterns with sociodemographic factors. Methods: This study leveraged the Sudan Multiple Indicator Cluster Survey (MICS) 2014 data to uncover dietary intake patterns among 7,362 children using latent class analysis (LCA). We investigated class memberships concerning demographic and socioeconomic factors. The model's adequacy was determined using several fit indices, including BIC, AIC, entropy, CAIC, and SABIC, providing a holistic evaluation of the model's accuracy in capturing dietary behaviors. Results: Three latent classes were identified: Class 1 (55%) with an average nutrition composition, Class 2 (28%) with limited nutrition composition, and Class 3 (17%) with good nutrition composition. Significant associations were found between latent class membership and sociodemographic factors, particularly mother's education level and household wealth. The three-class solution provided the best balance between model fit and class distinction. Conclusions: The LCA revealed distinct dietary intake patterns and underscored the influence of sociodemographic factors on child nutrition. The findings suggest that targeted nutritional interventions should be developed according to the specific needs of different latent classes. The study also highlights the utility of LCA as a robust statistical and machine learning tool in public health research, capable of informing tailored interventions and policies for improving child nutrition. Implications: The study emphasizes the importance of maternal education and socio-economic status in shaping dietary behaviors of children in Sudan. It implies the need for policies that address educational disparities, food security, and economic development as part of comprehensive nutritional interventions.

There is a relatively high risk of virus transmission in dental procedures and oropharyngeal examinations. We investigated the effects of mouthwashes on covid-19 viral load reduction during dental practices and oro-pharyngeal assessments. We performed a systematic search in PubMed, EMBASE, Scopus, Web of Science, Cochrane library for relevant studies up to February 2021. Papers evaluating patients with covid-19 infection (patients) who rinse mouthwashes (intervention) compared to patients who don’t rinse them (comparison) for reducing covid-19 viral load or reducing cross-infection of covid-19 (outcome) in the randomized and non-randomized clinical trial and quasi-experimental studies (study) were included due to PICOS question. Three independent authors conducted literature screening and data extraction. We extracted the most relevant data and we evaluated the risk of bias from the included studies. Out of 344 potentially eligible articles, six studies were included in this systematic review. Regarding viral load and negative cycle threshold (ct) values, 1% PVP_I and Listerine mouthwash were effective. 0.12% CHX mouthwash was effective 0-2 hours post rinsing, but it was not effective after 2 hours. A mixture solution of 0.2% Chlorhexidine gluconate and 6% Hydrogen peroxide was effective on day 5 of intervention. Gargling 1% hydrogen peroxide, 0.075% Cetylpiridinum Chloride (CPC), 0.5%PVP-I and 0.2% CHX mouthwashes was not effective on SARS-COV-2. It cannot be guaranteed that rinsing a specific kind of mouthwash prevents cross-infection of covid-19; however, the viral load of SARS-COV-2 in saliva will be decreased after rinsing mouthwashes containing 1%PVP-I and Listerine.

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.

Revealing the spatial distribution mechanism of the coupling effect between soil water and salt is essential for managing saline-alkali farmland. This study utilized the multifractal method to analyze soil water and salt under varying water content levels in arid saline-alkali farmland. Soil samples were collected on the second (S1), fifth (S2), eighth (S3), eleventh (S4), and fourteenth (S5) days after a rainfall event. The findings showed that a significant decrease in soil water content, contrasting with an increase in soil salt content throughout the soil layers post-rainfall. As water content decreased, the spatial variability of soil water initially increased from S1-3 and then decreased, while the spatial variability of soil salt decreased. The spatial distribution between soil water and salt exhibited a high correlation at S3-4 (with relative water content of soil ranging from 0.52 to 0.75) due to their coupling effect. However, soil salt was unevenly leached by rainfall at high water content levels (S1-2) and precipitated at low water content levels (S5), resulting in low spatial variability correlations between soil water and salt. This study elucidated the coupling process of soil water and salt, identifying their spatiotemporal distribution mechanism in dryland agricultural areas.

Although automated Acute Lymphoblastic Leukemia (ALL) detection is essential, it is challenging due to the morphological correlation between malignant and normal cells. The traditional ALL classification strategy is arduous, time-consuming, often suffers inter-observer variations, and necessitates experienced pathologists. This article has automated the ALL detection task, employing deep Convolutional Neural Networks (CNNs). We explore the weighted ensemble of deep CNNs to recommend a better ALL cell classifier. The weights are estimated from ensemble candidates' corresponding metrics, such as accuracy, F1-score, AUC, and kappa values. Various data augmentations and pre-processing are incorporated for achieving a better generalization of the network. We train and evaluate the proposed model utilizing the publicly available C-NMC-2019 ALL dataset. Our proposed weighted ensemble model has outputted a weighted F1-score of 88.6%, a balanced accuracy of 86.2%, and an AUC of 0.941 in the preliminary test set. The qualitative results displaying the gradient class activation maps confirm that the introduced model has a concentrated learned region. In contrast, the ensemble candidate models, such as Xception, VGG-16, DenseNet-121, MobileNet, and InceptionResNet-V2, separately produce coarse and scatter learned areas for most example cases. Since the proposed ensemble yields a better result for the aimed task, it can experiment in other domains of medical diagnostic applications.

Background: The primary objective worldwide during the COVID-19 pandemic has been controlling disease transmission. However, lockdown measures used to mitigate transmission have affected human behavior and altered lifestyles, with a likely impact on chronic non-communicable diseases. More than a year into the pandemic, substantial peer-reviewed literature has emerged on altered lifestyles following the varying lockdown measures imposed globally to control the virus spread. Objective: To explore the impact of lockdown measures on the six lifestyle factors, namely diet, physical activity, sleep, stress, social connectedness, and the use of tobacco, alcohol, or other harmful substances.Methods: We comprehensively searched PubMed and the World Health Organization’s global literature database on COVID-19 and retrieved 649 relevant articles for the narrative review. A critical interpretative synthesis of the articles was performed.Results: Most of the articles included in the review identified the negative effect of lockdown measures on each of the lifestyle factors in many parts of the world. Interestingly, encouraging lifestyle trends were also highlighted in a few articles. Such trends can positively influence the outcome of lifestyle-related chronic diseases, such as obesity and diabetes.Conclusions: The COVID-19 pandemic provides an opportunity to better understand the negative impact of strict lockdown measures on lifestyles. At the same time, it helps us identify and initiate positive behavioral changes, which, if consolidated, could improve chronic disease outcomes. It is up to governments, communities, and healthcare/academic entities to derive benefit from lessons learned from the pandemic, with the ultimate objective of better educating and promoting healthy lifestyles among communities.

This paper investigates an innovative photovoltaic cell configuration, denoted as “Au/CuSCN/RbGeI3/CdTe/ZnO/TiO2/FTO", using SCAPS-1D simulation. The research focuses on optimizing device performance by analyzing key components, including ZnO as the electron transport layer (ETL), RbGeI3 as the perovskite absorber layer, CuSCN as the hole transport layer (HTL), and CdTe as the active buffer layer. A comparison is made with the standard “Au/CuSCN/CH3NH3SnI3/CdTe/ZnO/TiO2/FTO" configuration under dark and illuminated conditions. Through an in-depth investigation involving temperature effects, generation rate analysis, RbGeI3 absorber layer thickness, Nd level, and interface defect levels, an optimized design for a unit area is achieved. The proposed solar cell demonstrates outstanding performance metrics, including an open circuit voltage (Voc) of 0.94V, a short circuit current density (Jsc) of 34.52mA/cm2, a fill factor (FF) of 77.77%, and an energy conversion efficiency (η) of 25.40%. These values significantly outperform the reference models with energy conversion efficiencies of 10.11% and 21.09%. Real-world applicability is validated by implementing these optimized characteristics in a photovoltaic system for Pakistan’s geographical location using PVSyst software. The suggested solar cell achieves a remarkable performance ratio (PR) of 85.6%, surpassing the PR of the reference basic solar cell model at 83%. The findings of this research offer a promising and superior solar cell design, incorporating innovative materials and optimization techniques to achieve exceptional energy conversion efficiency and overall performance. This study’s outcomes have the potential to advance photovoltaic technology and contribute to sustainable energy solutions worldwide.

More than 20 years after the discovery of the first anelloviruses (AVs), they still remain a mysterious group of viruses. To date, there is no clear understanding of their impact on the host organism, although information is emerging about their participation in various pathologies. However, it is well known that AVs are an important component of the human virome. In the current study we assessed the abundance of AVs in viromes from four nasal and pharyngeal samples from children with respiratory symptoms using a metagenomic approach. It is worth noting that three patients had known respiratory viruses and one did not have any. Regarding AVs, they were detected in all samples, namely Torque teno mini virus (TTMV). The greatest abundance and diversity of AVs (Torque teno virus (TTV), TTMV and Torque teno midi virus (TTMDV)) were shown in a diseased patient with an absence of respiratory viruses, which may indicate the potential involvement of AVs in the maintenance of respiratory symptoms. An activated immune system to combat the main pathogens, against the background of which the number of AVs decreases, is discussed as a possible reason for the reduced diversity of anellome in other patients. We also compared the results of the metagenomic approach and qPCR for major respiratory viruses, which were in agreement. In addition, metagenomic sequencing made it possible to obtain additional data on viral genomes in order to establish their taxonomic identification and characterize individual viral profiles in patients from the Siberian part of Russia.

Insects quickly develop resistance to conventional chemical insecticides. The use of silica nanoparticles (SiO₂NPs) is a promising new approach for managing insect pests. The insects that have been studied are, in most cases, external feeders and internal leaf feeders have not been well studied. Here, we investigate the efficiency of SiO₂NPs in controlling the American serpentine leafminer Liriomyza trifolii (Diptera: Agromyzidae), a devastating insect pest of a wide range of crops. SiO₂NPs at concentrations of 50, 100, 200 and 400 mg/L compared to a control of distilled water were applied to intact Phaseolus vulgaris leaves by spraying to evaluate their effects, via the plant, on the number, survival, developmental/feeding speed, body mass/size and sex ratio of L. trifolii released after spraying. A qRT-PCR analysis was conducted to assess oxidative stress in L. trifolii based on the gene expression level of the two major antioxidant enzymes; catalase and superoxide dismutase. The number of mines, survival rate and puparia weight were lower than the control at the highest concentration of SiO₂NPs, while pupariation was delayed at most of the concentrations studied and puparium weight/size decreased at low concentrations. Sex ratio was not affected by SiO₂NPs. Gene expression levels of the antioxidant enzymes at the pupal stage were not significantly affected by SiO₂NPs at any concentration but some individuals showed up-regulation at low SiO₂NP concentrations, inferring development of resistance. This study demonstrated that a priori application of the highest concentration of SiO₂NPs to plants effectively reduces a leafminer population but that application of even the lowest concentration can decrease the life history trait values of surviving leafminers.

Among the most popular learning management systems available worldwide is Moodle [1]. This current study examines how learners' attitudes toward English as a foreign language (EFL) and their language proficiency are affected by Moodle's interactive language learning activities. Thirty-three undergraduate students participated in this study. We investigated the effects of engaging language learning exercises that were practiced on Moodle using an experimental re-search design. To find out if the experimental and control groups differed significantly from one another on pre- and post-measures regarding the development of language skills and attitudes toward language classrooms, a number of statistical tests were employed. We conducted data analysis using SPSS software. Results demonstrated that there were differences favoring the ex-perimental group in the language skills developments and attitudes of learners toward the lan-guage classroom. There were no apparent differences in the forming structures, speaking, or listening of the control group's learners. On the other hand, there were notable differences in the overall score, writing and reading skills, and lexical item mastery. Additionally, significant dif-ferences in the language acquisition growth of the experimental and control groups were found using an Independent Samples T-test in the post-test, with the experimental group benefiting.

Electrospun ultrathin fibers based on binary compositions of polylactide (PLA) and poly(ε-caprolactone) (PCL) with different ratios of polymer components from 0/100 to 100/0 wt. % were studied. Using a combination of thermal (DSC) and relaxation (EPR) measurements, the effect of biopolymers content on the characteristics of the crystal structure of PLA and PCL and the diffusive rotational mobility of the stable TEMPO radical in the amorphous regions of PLA/PCL compositions was shown. It was revealed that after PLA and PCL blending, significant changes in the degree of crystallinity of PLA, PCL segmental mobility, sorption capacity of the radical as well as activation energy of rotational diffusion in the amorphous regions of PLA/PCL composition are observed. The characteristic region of biopolymers’ composition from 50/50 to 30/70 % PLA/PCL blends ratio was found, where the inversion transition of PLA from dispersive medium to dispersive phase where an inversion transition is assumed when the continuous medium of the PLA transforms into a discrete phase. The performed studies made it possible for the first time to carry out a comprehensive analysis of the effect of the system component ratio on the structural and dynamic characteristics of the PLA/PCL film material at the molecular level.

In recent years, Nigeria is witnessing increasing frequency of flood occurrence with devastating impact translating into significant loss of lives (in Nigeria, over 300 people died in September 2022) and properties. Addressing flood disaster requires holistic approach from policy and governance perspectives, integration of policies and programs and synergies between institutions. Using synergies and eliminating trade-offs, flood governance and policy coherence integrate all relevant policy fields and institutions to achieve common policy outcomes. The objective of this study is to examine and understand how flood governance and policy coherence are approached, as well as institutional design and implementation for coherence in Nigeria. The findings revealed that there is no single flood policy in Nigeria. Due to this, there is no focus and no defined objectives for flood governance, prevention, control, and management, and no imperative for the government to seek both short-term and long-term flood solutions. There is no synergy and coordination among institutions for flood governance in the country. Since the country established the federal ministry of environment in 1999, the environment, floods, and climate-related hazards were given less priority. State and local governments handle most flood disasters and emergencies. Federal assistance is provided, however, when flood disasters exceed the capabilities of local and state governments. This study recommends that across the country, flood policy needs to be designed, formulated, and implemented while assigning governance responsibility and decentralizing policy to state and local governments.

Novel severe acute respiratory syndrome coronavirus 2 infection (SARS-Cov-2) is an acute respiratory and infectious disease. This perspective aims to provide the basic understanding of the inflammation caused by SARS-Cov-2 and relation to trigeminal ganglion (TG). Virus enters through the mucous membranes of orofacial region and reach the TG where it resides and take control of its peptides including Substance P (SP).SP is the main neuropeptide, neuromodulator and neuro-hormone of TG, associated with nociception and inflammation under noxious stimulus. SP release is triggered and consequently, it affects the immune cells, blood vessels to release the mediators for inflammation. Cytokine storming is initiated and cause respiratory distress, bronchoconstriction and death in complicated cases. Neurokinin-1 Receptor (NK-1R) antagonist and glucocorticoids may be used to alleviate the symptoms and treat this infection. SP is the main culprit seem to be involved in the triggering of inflammatory pathways in SARS-Cov-2 infection. It has direct association with cardiorespiratory rhythm, sleep-wake cycle, nociception, ventilator responses and regulates many important physiological and pathological roles. Its over-secretion should be blocked by NK-1R antagonist. However, experimental work leading to clinical trials are mandatory for further confirmation.

Lifestyle physical activity (PA) and nutrition are known to be effective interventions in preventing and managing obesity related comorbidities is adult populations, but less so in children and adolescents. We examined the effectiveness of lifestyle interventions in children from minority ethnic populations in western high-income countries. Our systematic review included 53 studies involving 26045 children from minority ethnic populations within western countries, who followed lifestyle intervention programs lasting between 8 weeks and 5 years with an aim to prevent and/or manage childhood obesity and associated comorbidities, including adiposity and cardiometabolic risks. Studies were heterogenous in terms of lifestyle intervention components (nutrition, PA, behavioural counselling) and settings (community vs. schools and after schools). Our meta-analysis included 31 eligible studies and showed no significant effects of lifestyle interventions when they focused on body weight and body mass index (BMI) outcomes [pooled BMI mean change = -0.09 (95% CI -0.19, 0.01), p=0.09]. This was irrespective of the intervention program duration (< 6 months vs. ≥ 6 months), type (PA vs nutrition/combined intervention) and weight status (overweight or obese vs no normal weight), all showed no significant effects in the sensitivity analysis. Nonetheless, 19 of the 53 studies reported reduction in BMI, BMI z scores and body fat percentage. However, majority of lifestyle interventions adopting a quasi-design with a combined primary and secondary obesity measures (11 out of 15 studies), were effective in reducing obesity comorbidities of cardiometabolic risks including metabolic syndrome, insulin sensitivity, and blood pressure in children with overweight and obesity. Preventing childhood obesity and associated comorbidities in children from high-risk ethnic minority groups is best achieved using a combined PA and nutrition intervention approach, which jointly target preventing obesity and cardiometabolic disease primary and secondary outcomes, especially measures of diabetes, hypertension, and cardiovascular disease.

Background: Low-level (LLLT) and high-intensity laser therapy (HILT) can be beneficial additions to knee osteoarthritis (KOA) rehabilitation exercises; however, it is still being determined which modality is more effective. Aim: To compare the effects of LLLT and HILT as adjuncts to rehabilitation exercise (LL+EX and HL+EX) on clinical outcomes in KOA. Methods: Thirty-four adults with mild to moderate KOA were randomly allocated to either LL+EX (n = 17) or HL+EX (n = 17) groups. All participants underwent their designated laser treatment combined with rehabilitation exercises weekly for twelve consecutive weeks. The Knee Injury and Osteoarthritis Outcome Score (KOOS), Numerical Pain Rating Scale (NPRS), active knee flexion, and Timed Up-and-Go test (TUG) were assessed at baseline and immediately post-intervention. Results: Post-intervention, both groups significantly improved their KOOS, NPRS, active knee flexion, and TUG scores compared to baseline (p &lt; 0.01). The mean difference of change in KOOS, NPRS, and active knee flexion scores for the HL+EX group surpassed the minimal clinically important difference threshold. In contrast, the LL+EX group only demonstrated clinical significance in NPRS scores. Conclusions: Incorporating HILT as an adjunct to usual KOA rehabilitation led to significantly higher improvements in pain, physical function, and knee-related disability compared to LLLT.

Because of hyper complexity, difficulty to define, multiple stakeholders with conflicting perspectives, and lack of clear-cut solutions, wicked problems necessitate innovative and adaptive strategies. Operations Research has been a valuable tool for managers to make informed decisions for years. However, as we face increasingly complex and messy problems, it has become apparent that relying solely on either hard or soft OR approaches is no longer sufficient. We need to explore more innovative methodologies to address these wicked problems effectively. This study has bridged the research gap by proposing a structured process encompassing a subdivision-based problem structuring method for defining the wicked problem, a multi-attribute decision-making (MADM) for prioritizing sub-problems, and a hard OR technique, data envelopment analysis (DEA) for tackling one of the most critical sub-divisions. The proposed methodology, implemented in a case study, focuses on a higher education institution experiencing a decline in student admissions and involves five steps. First, a diverse group of stakeholders is formed to ensure comprehensive consideration of perspectives. Second, the wicked problem is defined, considering long-term consequences, multiple stakeholders, and qualitative stakeholder opinions. Third, a hierarchical structure is created to break down the wicked problem into manageable sub-problems. Fourth, a multi-criteria decision-making (MCDM) method prioritizes sub-problems. Finally, the sub-problems are addressed one by one using a combination of soft and hard OR tools. The findings highlight the benefits of integrating hard and soft OR approaches. The article concludes with reflections on the implications of using a combined OR approach to tackle wicked problems in higher education and beyond.

The ability of S. aureus to infect humans and cause a wide range of infections so efficiently is a consequence of the many variant virulence factors that it can produce. Superantigens (SAgs) make up a group of them and include staphylococcal enterotoxins (SE). The main objective of this study was to detect enterotoxin gene cluster (EGC) in S. aureus isolates that caused infections in Paraguayan children in the year 2017, through an observational and cross-sectional study. The detection of the EGC including the seg, sei, sem, sen, seo, and seu genes, was carried out by end-point PCR. 181 S. aureus isolates were collected, most of them caused skin and soft tissue infections (SSTI, n=140, 77%) and came from children under 6 years 50% (n=90). At least one SE gene was detected in 96% (174/181) of isolates and 24% (44/181) carried the entire EGC. An association between methicillin resistance and seg, sei, seo, and seu gene carriage (p ≤0.05) was observed. A high prevalence of EGC was detected in S. aureus isolates collected from infected children attending the main health care centers in Paraguay, its importance is due to the potential of these toxins to aggravate staphylococcal infections.

We investigated the radioclinical outcomes of naviculectomy and limited/tailored soft-tissue releases in a short series of ambulatory children with complex/intractable congenital vertical talus subsets namely neglected, multiple operated and recurrent patients. We postulated that this technique will yield satisfactory radioclinical outcomes and minimal complications because it avoids extensive surgical release/trauma that is otherwise classically recommended for complex congenital vertical talus (CVT). The cohort consisted of five -four boys, one girl- complex CVT children with neglected, multiple operated and/or recurrent subsets. Patients were included if manipulative casting techniques were deemed unlikely to produce a plantigrade foot. Patients underwent naviculectomy with variable on-demand limited soft tissue releases. Two patients had bilateral affection and two had a non-idiopathic aetiology. The mean age was 5.2 years (4 to 6.25) and mean follow-up was 2.3 years (1 to 3). We reported satisfactory outcomes as per foot posture, function, overall parent satisfaction including pain and radiography per lateral views of talar-axis-first metatarsal base angle on the short/intermediate term. Whereas manipulative casting is unlikely to yield lasting outcomes in ambulatory children with complex subsets of CVT, extensive surgical soft-tissue releases have unfavourable long-term complications. As a substitute, naviculectomy as a form of resection arthroplasty created a practical and affordable third way between manipulative casting with minimally invasive surgery and the extensive surgical soft tissue releases on the short/intermediate term.

The increasing enrollments in the English Departments at Palestinian universities highlight the need to understand the attitudes and expectations of students, as these factors can significantly impact their motivation and proficiency in learning English as a foreign language (EFL). This study aimed to explore the attitudes and expectations of Palestinian first-year English majors in relation to EFL learning. A total of 244 students participated in the study, completing a questionnaire and participating in interviews to provide a comprehensive understanding of their perspectives. The results revealed a high level of enthusiasm among the students towards EFL, as evidenced by their strong attitudes and exceptionally high expectations. Moreover, the study identified specific student characteristics, including gender and the required hours dedicated to English learning, as well as the experience of studying abroad, which served as predictive factors for their attitudes and expectations. The findings emphasize the importance of regularly assessing the attitudes and expectations of learners in English departments to effectively cater to their needs and attract more students. By understanding the motivating factors and aligning instructional strategies accordingly, English departments can create a more engaging and tailored learning environment for students pursuing English as a foreign language.

Nowadays, brain signal processing is performed rapidly in various brain-computer interface (BCI) applications. Most researchers focus on developing new methods for the future or improving the basic implemented models to identify the optimum standalone feature set. Our research focuses on four ideas. One of them introduces future communication models, and the others are for improving old models or methods. These are: 1) new communication imagery model instead of speech imager using the mental task: Due to speech imagery is very difficult, and it is impossible to imagine sound for all of the characters in all of the languages. Our research introduces a new mental task model for all languages that call Lip-sync imagery. This model can use for all characters in all languages. This paper implemented two lip-sync for two sounds, characters or letters. 2) New combination Signals: Selecting an inopportune frequency domain can lead to inefficient feature extraction. Therefore, domain selection is so important for processing. This combination of limited frequency ranges proposes a preliminary for creating Fragmentary Continuous frequency. For the first model, two s intervals of 4 Hz as filter banks were examined and tested. The primary purpose is to identify the combination of filter banks with 4Hz (scale of each filter bank) from the 4Hz to 40Hz frequency domain as new combination signals (8Hz) to obtain well and efficient features using increasing distinctive patterns and decreasing similar patterns of brain activities.3) new supplement bond graph classifier for SVM classifier: When SVM linear uses in very noisy, the performance is decreased. But we introduce a new bond graph linear classifier to supplement SVM linear in noisy data. 4) a deep formula recognition model: it converts the data of the first layer into a formula model (formula extraction model). The main goal is to reduce the noise in the subsequent layers for the coefficients of the formulas. The output of the last layer is the coefficients selected by different functions in different layers. Finally, the classifier extracts the root interval of the formulas, and the diagnosis does based on the root interval. For all of the ideas achieved the results of implementing methods. The results are between 55% to 98%. Less result is 55% for the deep detection formula, and the highest result is 98% for new combination signals.

This paper analyses the application of deep learning techniques for predicting wave overtopping events in port environments using sea state and weather forecasts as inputs. The study was conducted in the outer port of Punta Langosteira, A Coruña, Spain. A video recording infrastructure was installed to monitor overtopping events from 2015 to 2022, identifying 3709 overtopping events. The data collected was merged with actual and predicted data for the sea state and weather conditions during the overtopping events, creating three datasets. We used these datasets to create several machine learning models to predict whether an overtopping event would occur based on sea state and weather conditions. The final models achieved a high accuracy level during the training and testing stages: 0.81, 0.73, and 0.84 average accuracy during training and 0.67, 0.48, and 0.86 average accuracy during testing, respectively. The results of this study have significant implications for port safety and efficiency, as wave overtopping events can cause disruptions and potential damage. Using deep learning techniques for overtopping prediction can help port managers take preventative measures and optimize operations, ultimately improving safety and helping to minimize the economic impact that overtopping events have on the port's activities.

“De-[Constructing] Growth” is offered as a deeper and more useful conceptualization that avoids the negative connotations of, and resistance to, “degrowth” by decoupling profit from unsustainable consumption, production, and inequality.

Despite advancements in antiretroviral therapy (ART) that reduces the viral load to undetectable levels, viral eradication has not been achieved due to HIV-1 persistence in resting CD4+ T-cells. We, therefore, characterized the tat gene, which is essential for HIV-1 replication, from 20 viro-logically controlled HIV-infected (HIV+) older patients on long-term ART with improved CD4+ T-cell counts. PBMC genomic DNA from HIV+ patients were used to amplify tat gene by PCR fol-lowed by nucleotide sequencing and analysis. Phylogenetic analysis showed that each patient’s tat sequences were confined to their own subtrees and well discriminated from other patients’ sequences. Moreover, there was a low degree of viral heterogeneity and lower estimates of genetic diversity within these patients’ tat sequences. Most patients’ Tat deduced amino acid sequences showed intact open reading frames and maintained the important functional domains for Tat functions, including transactivation, TAR binding and nuclear localization. Furthermore, Tat deduced amino acid sequences showed variation in previously characterized cytotoxic T lym-phocytes (CTL) epitopes, suggesting escape mutants. In conclusion, a low degree of genetic varia-bility and conservation of functional domains and variations in CTL epitopes were the features of tat sequences from 20 HIV+ older patients with undetectable viral load on long-term ART.

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

Brushless Synchronous Machines (BSM) are replacing conventional synchronous machines with static excitation in generation facilities due to the absence of sparking and lower maintenance. However, this excitation system makes challenging measuring electric parameters in the rotor. This fact highly difficult the detection of ground faults, which are the most common type of electrical fault in electric machines. In this paper, a ground fault detection method for BSM is proposed. It is based on an inductive AC/DC rotating current sensor installed in the shaft. In case of a ground fault in the rotating parts of the BSM, a fault current will flow through the rotor’s sensor, inducing voltage in its stator. Analyzing the frequency components of the induced voltage the detection of a ground fault in the rotating elements is possible. The ground faults detection method proposed covers the whole rotor and discern between DC and AC sides. To corroborate the detection method, experimental tests have been performed using a prototype of this sensor connected to laboratory synchronous machines achieving satisfactory results.

Image registration (IR) is a process that deforms images to align them with respect to a reference space, making it easier for medical practitioners to examine various medical images in a standardized reference frame, such as having the same rotation and scale. This document introduces image registration using a simple numeric example. It provides a definition of image registration along with a space-oriented symbolic representation. This review covers various aspects of image transformations, including affine, deformable, invertible, and bidirectional transformations, as well as medical image registration algorithms such as Voxelmorph, Demons, SyN, Iterative Closest Point, and SynthMorph. It also explores atlas-based registration and multistage image registration techniques, including coarse-fine and pyramid approaches. Furthermore, this survey paper discusses medical image registration taxonomies, datasets, evaluation measures, such as correlation-based metrics, segmentation-based metrics, processing time, and model size. It also explores applications in image-guided surgery, motion tracking, and tumor diagnosis. Finally, the document addresses future research directions, including the further development of transformers.

Nowadays, speed up development and use of digital devices such as smartphones have put people at risk of internet crimes. The evidence of present crimes in a computer file can be easily unreachable by changing the prefix of a file or other algorithms. In more complex cases, either file divided into different parts or the parts of a file that has information about the file type are deleted, where the file fragment recognition issue is discussed. The known files are divided into different fragments, and different classification algorithms to solve the problems of file fragment recognition. A confusion matrix measures the accuracy of type recognition. In the present study, first, the file is divided into different fragments. Then, the file fragment features, which are obtained from Binary Frequency Distribution (BFD), are reduced by 2 feature reduction algorithms; Sequential Forward Selection algorithm (SFS) as well as Sequential Floating Forward Selection algorithm (SFFS) to delete sparse features that result in increased accuracy and speed. Finally, the reduced features are given to 3 classifier algorithms, Multilayer Perceptron (MLP), Support Vector Machines (SVM), and K-Nearest Neighbor (KNN) for classification and comparison of the results. In this paper, we proposed the algorithm of file type recognition that can recognize 6 types of useful files ( pdf, txt, jpg, doc, html, exe), which may distinguish a type of file fragments with higher accuracy than the similar works done.

Use of rhizosphere microorganisms provides an alternative or supplement to conventional plant breeding to improve water deficit tolerance of tomato plants. Experiment was carried out to explore the effect of two microbial species, AMF (Rhizophagus irregularis) and Bacillus subtilis, in single and co-application, on growth, colonization, and molecular aspects of tomato plants under drought stress. Co-inoculated plants showed less reduction in growth traits, photosynthetic pigments, colonization rate, and increased compatible solutes like proline which help in sustaining relative water content than non-inoculated plants. Inoculation considerably enhanced proline dehydrogenase activity, and significantly reduced both Δ1-pyrroline-5-carboxylate reductase Δ1-pyrroline-5-carboxylate synthetase activity causing lower proline accumulation in inoculated plants under drought stress. Co-inoculated plants showed obvious upregulation of antioxidant system, thus facilitating amelioration of oxidative stress through exclusion of reactive oxygen species. No inoculation under drought stress upregulated abscisic acid related genes expression but have no effect in plants inoculated either sole or mixed inoculation. Expression of aquaporin genes was upregulated in plants co-inoculated and with AMF alone under normal condition. However the expression of aquaporin genes were decreased or unaffected in plants inoculated with Bacillus subtilis but increased in non-inoculated plants. Co-applied AMF and bacillus subtilis substantially increase drought tolerance by upregulating proline metabolism, antioxidant enzymes and aquaporin genes. Therefore our results suggest that co-inoculation mediated drought tolerance is linked with increased proline accumulation, enhanced antioxidant enzyme activities and differential regulation of ABA biosynthetic and aquaporin genes, which is vital for osmotic adjustment of host plant.

The modulation of Nrf2 activity has been reported to be implicated in the pathology of various neurological disorders, including epilepsy. Previous studies have demonstrated that Nrf2 is activated in the post-status epilepticus rat model, however, the spatio-temporal, as well as cell type-specific expression of Nrf2 following brief epileptic seizures remains unclear. Here, we evaluated how an acute epileptic seizure affected the expression of Nrf2 and its downstream genes in the cortex and the hippocampus up to 1-week following the induced seizure. We found that after a pentylenetetrazol-induced seizure, Nrf2 significantly increased at 24 h at the mRNA level and 3 to 6 h at the protein level in the cortex. In the hippocampus, the Nrf2 mRNA level peaked at 3 h after the seizure, and no significant changes were observed in the protein level. Interestingly, the mRNA level of Nrf2 downstream genes peaked at 3-6 h after seizure in both the cortex and the hippocampus. A significant increase in the expression of Nrf2 was observed in the neuronal population of CA1 and CA3 regions of the hippocampus, as well as in the cortex. Moreover, we observed no change in the co-localization of Nrf2 with astrocytes neither in the cortex nor in CA1 and CA3. Our results revealed that following a brief acute epileptic seizure, the expression of Nrf2 and its downstream genes is transiently increased and peaked at early timepoints after seizure predominantly in the hippocampus, and this expression is restricted to the neuronal population.