The present work demonstrates the new nanofiber mats prepared through co-electrospinning of two different polymers i.e. corn protein namely Zein and Nylon-6. The composite nanofiber membrane was used as an effective adsorbent material for the removal of toxic reactive dye i.e. Reactive Blue 19 (RB 19) from water solution. These co-electrospun nanofibers had good mechanical strength compared to zein nanofibers alone. Experimental results suggested that zein/nylon nanofibers have greater potential for total removal of RB19 at room temperature within 10 min of contact time from aqueous solution. The maximum capacity was found to be 70 mg/g of nanofibers. The mechanism of RB19 removal on proposed nanofibers is mainly through hydrogen bond and electrostatic means.

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

Algaecides are chemicals that cause serious health problems. Conventional paints contain algaecides to improve the algae resistance on the paint film. Present research has suggested an environmental friendly paint formulation that focuses on developing algae resistance without having algaecides. In this research, Algae growth on newly developed paint is modeled by incorporating dirt resistance of paint and natural phenomena including humidity, temperature and time respectively. The fitted Model revealed explained variation of 59.65% in the average algae growth, of which, Dirt Resistance, Humidity and temperature and some of their interactions play significant role in this variation. Model suggests that the proposed newly developed paint without algaecides is more resilient to algae growth and significantly decreased the average algae growth rate by 0.53% as compared to conventional paints. Keeping the effect of all other factors constant, if dirt resistance of paint (D_c value) increases by one percent, average algae growth decreases by 12.98%; when temperature increases by 1^oC, average algae growth decreases by 22.4%; a positive unit change in the joint linear effect dirt resistance, temperature and humidity caused a decrease in average algae growth by 0.0031%. It was also observed that the individual effect of humidity variable was inversely related with average algae growth. However the combination of humidity and temperature, humidity and dirt resistance, humidity and time, and the quadratic effect of humidity were found to increase the average algae growth rate. The cubic effect of temperature variable by one degree centigrade resulted in decrease of average algae growth by 0.000907%.

Spontaneous parametric downconversion At the heart of all quantum optics experiments is called as Spontaneous parametric downconversion. This is a downconversion process when the output frequency is smaller than the input frequency. The whole process is Spontaneous. Experimentally we have to downconvert the photons and convert them into pairs of a photon. We use the Beta-barium borate(BBO) crystals to downconvert the coming violet-blue laser. We find the maximum number of counts on detectors and their coincidence. When the BBO crystals is well aligned, we find no difference in detecting vertically or horizontally polarized photons because they are orthogonal to each other. Proof of existence of photons We want to investigate the properties and verification of quantization of light. Experimently we want to establish the existence of single photon which shows the grainy-ness of individual photon quantum mechanically and shows wave like behaviour classically. We get the coincidences counts on detectors with addition of detector B’ by using PBS in the path of signal beam. Experimentally we prove the Quantum as well as classical nature of light by the measurement of second-order degree coherence in light g²(0). For classically the g²(0)≥1 and quantum mechanically g²(0)=0.When we add the delay time interval of 1ns after reflected and transmitted photons from PBS signal beam. The delay generator did not create a significant difference in the coincidence counts detection in the value of g²(τ ). Single photon interference and quantum eraser we want to observe the interference by making the single photons pass through the interferometer, and we will see the interference of the single photons. If the light passes through the interferometer, the visibility of the interference pattern to which extent these fringes are observed is dependent on the extent to which the ’which-way’ or ’which-path’ information is available to the experimenter. If the observer knows the information about the ’which-way’ of the light path, then we cannot observe the inference pattern. Because we only can make an interference pattern if we erase the which-way information of the light path. If you erase the information about the’ which-way,’ we can observe a very high interference pattern. We can erase the information about the light path’s ’which-way’ or ’which-path’ by using a polarizer and orienting at 45^o. In this way, we can observe a very high pattern. If we introduce the angle of the polarizer at 0^o or 90^o, we see very low interference.

Efficient resource allocation is crucial in clusters with frugal Single-Board Computers (SBCs) possessing limited computational resources. These clusters are increasingly being deployed in edge computing environments in resource-constrained settings where energy efficiency and cost-effectiveness are paramount. A major challenge in Hadoop YARN scheduling is load-balancing, as frugal nodes within the cluster can become overwhelmed, resulting in degraded performance and frequent occurrences of out-of-memory errors, ultimately leading to job failures. In this study, we introduce an Adaptive Multi-criteria Selection for Efficient Resource Allocation (AMS-ERA) in Frugal Heterogeneous Hadoop Clusters. Our criterion considers CPU, memory and disk requirements for jobs and aligns the requirements with available resources in the cluster for optimal resource allocation. To validate our approach, we deploy a heterogeneous SBC-based cluster consisting of 11 SBC nodes and conduct several experiments to evaluate the performance using Hadoop wordcount and terasort benchmark for various workload settings. The results are compared to the Hadoop-Fair, FOG and IDaPS scheduling strategies. Our results demonstrate a significant improvement in performance with the proposed AMS-ERA, reducing execution time by 27.2%, 17.4% and 7.6% respectively using terasort and wordcount benchmarks.

Two-dimensional (2D) semiconductors like Transitional Metal Dichalcogenides (TMDCs) have attracted strong research interest in the last decade. Unlike the more celebrated 2D material graphene, TMDCs like molybdenum disulfide(MoS₂) possess bandgap. The number of layers critically depends on the band structure of MoS₂. For example, it is indirect for bulk MoS₂. However, it becomes direct bandgap material for a monolayer MoS₂. Monolayer MoS₂, therefore, becomes an important material for photoelectronic devices. An essential aspect of such devices is the metal-semiconductor junction. Metal-TMDC junction is, therefore, a well-studied interface due to its importance for 2D materials-based photoelectronic devices. Most importantly, it can be flexible, stretchable to it’s length, and bent to a large angle. We used metals like Au and Al to interface with TMDs like MoS₂. We used silicon and PDMS as substrates. So, we have to focus on investigating the relative properties of various TMDCs. We see change in band energy and vibrational modes via Photoluminiscence and Raman spectroscopy as shown in the article. However, certain aspects are yet to be explored, for example, how this interface behaves in the presence of strain.

Pine nuts are non-wood forest products (NWFP) with constantly growing market notwithstanding a series of phytosanitary issues and related trade problems. The aim of paper is to review the literature on the relationship between phytosanitary problems and trade development. Production and trade of pine nuts in Mediterranean Europe have been negatively affected by the spreading of Sphaeropsis sapinea (a fungus) associated to an adventive insect Leptoglossus occidentalis (fungal vector), with impacts on forest management activities, production and profitability and thus in value chain organization. Reduced availability of domestic production in markets with growing demand has stimulated the import of pine nuts. China has become a leading exporter of pine nuts, but its export is affected by a symptom associated to the nuts of some pine species: the ‘pine nut syndrome’ (PNS). Most of the studies embraced during the review are associated to PNS occurrence associated to the nuts of Pinus armandii. In the literature review we highlight the need for a comprehensive and interdisciplinary approach to the analysis of the pine nuts value chain organisation, where research on food properties and clinical toxicology be connected to breeding and forest management, forest pathology and entomology and trade development studies.

Hydrothermal /super critical processes are important process to synthesize materials which are otherwise difficult to form under normal conditions. A mathematical model is developed using standard transport equations to calculate the time for heating of hydrothermal reactor and computer simulation of the model was carried out in SOLIDWORKS® to validate it. The materials used to form reactor vessel were stainless steel (outer body) and TEFLON (PFA) (inner vessel). It was shown that composite wall, its geometry, construction & properties greatly affect the time and pattern of heat transfer. The time calculated and pattern generated were found to be in good agreement with experimental values.

Abstract:The digital landscape is rapidly reshaping under the influence of AI-powered cloud-based e-commerce platforms (PCEPs). These platforms leverage the transformative power of AI to personalize customer experiences, optimize pricing strategies, enhance security, and streamline operations, offering businesses a significant competitive edge. However, challenges surrounding data privacy, algorithmic bias, and potential job displacement raise crucial ethical questions regarding widespread AI adoption in e-commerce.This study delves into the untapped potential and ethical considerations of PCEPs, distinguishing itself by proposing practical strategies for responsible development and ethical implementation.Objectives: Investigate the transformative potential of PCEPs: Personalized product recommendations:Analyze effectiveness and potential bias. Dynamic pricing strategies:Explore impact on consumer fairness and market stability. Enhanced fraud detection:Evaluate accuracy and privacy implications. Optimized supply chain management:Assess efficiency gains and job displacement risks. Critically analyze the ethical considerations of AI in e-commerce: Data privacy:Assess current practices and develop mitigation strategies. Algorithmic bias:Identify potential sources and propose solutions for fairer decision-making. Job displacement:Evaluate impacts and propose reskilling initiatives. Propose comprehensive and practical strategies for responsible PCEP development and implementation: Transparency:Enhance user awareness and control over data usage. Fairness:Design AI systems to mitigate bias and uphold consumer rights. Accountability:Establish clear guidelines and oversight mechanisms for ethical AI development.Conclusion: While PCEPs offer a plethora of transformative benefits, their ethical implementation is paramount. By adhering to principles of transparency, fairness, and accountability, businesses can unlock the full potential of PCEPs while minimizing potential risks and shaping the future of online commerce in a sustainable and equitable manner

Monitoring of groundwater quality in today's scenario is very much important. Due to urbanization and population pressure regular monitoring of groundwater for drinking as well as irrigation purposes need a major concern. With this aim, a study has been carried out consisting 26 groundwater samples in May 2017, to access the physiochemical characteristic, water quality index (WQI) of groundwater by using GIS software and to find out the groundwater suitableness for drinking as well as for irrigation purpose. The pH is slightly alkaline and the TDS is much more than prescribed limits of BIS. The trend of cations in groundwater are Ca2+>Na+>Mg2+>K+ while anions trend is HCO3->SO42->Cl->NO3->CO32->F-. The Ca-Mg-HCO3 and Na-K-Cl-SO4 types of groundwater facies were dominant. Generally, the chemical changes in groundwater are administered by the evaporation process with ion exchange, and mixing of particles is the significant source of the solute acquisition process. WQI of the study area suggested that the 15% sample is unsuitable, 69% is poor and remaining is good for drinking uses. The potential salinity of the groundwater sample is nearly high although the majority of the sample is suited for irrigation activities.

AbstractThe emergence of AI-powered deepfakes, manipulatively realistic synthetic media, raises unprecedented challenges for human information processing and decision-making. This paper explores the cognitive and emotional consequences of exposure to deepfakes, delving into how these digitally fabricated experiences can reshape perception, trust, and social interactions. By reviewing existing research and outlining potential methodological approaches, this paper aims to establish a framework for investigating the multifaceted impact of deepfakes on the human brain.BackgroundThe digital age has ushered in an era of unprecedented information abundance and accessibility. Yet, this abundance breeds vulnerability, particularly with the emergence of AI-powered deepfakes. These hyper-realistic synthetic media creations present a potent threat to the fundamental trust and integrity of information sources. Deepfakes have infiltrated various domains, from entertainment and social media to politics and journalism, blurring the lines between truth and fabrication. Consequently, understanding the cognitive and emotional consequences of encountering deepfakes becomes crucial for navigating this increasingly deceptive landscape.ObjectivesThis research aims to: Investigate how exposure to deepfakes impacts various cognitive functions, including perception, attention, memory, and decision-making. Elucidate the emotional responses elicited by deepfakes and their underlying neural mechanisms. Evaluate the role of individual differences, such as susceptibility to deception and emotional sensitivity, in mediating the impact of deepfakes. Develop a comprehensive framework for understanding the multifaceted influence of deepfakes on the human brain. Results (anticipated)This research is expected to yield insights into: The specific cognitive and emotional processes altered by deepfakes, providing evidence for potential mechanisms of deception and manipulation. The neural circuitry involved in detecting and responding to deepfakes, leading to the development of more effective detection methods. The influence of individual differences (e.g., age, cognitive style) on susceptibility to deepfakes, informing targeted interventions and educational efforts.ConclusionBy deciphering the impact of deepfakes on the human brain, this research will offer valuable tools for mitigating the harmful consequences of misinformation and promoting responsible use of this powerful technology. Ultimately, understanding how deepfakes manipulate our cognitive and emotional landscape is not merely a scientific pursuit, but a critical step towards ensuring a future where truth prevails in the digital world.

AbstractThe escalating impact of artificial intelligence (AI) has birthed the fascinating technology of deepfakes, capable of meticulously crafting hyper-realistic audio-visual manipulations. Within the realm of healthcare, this technology presents a double-edged sword, brimming with potential for revolutionizing various facets of care delivery while simultaneously harboring significant ethical and practical challenges. This research delves into the complex narrative surrounding AI deepfakes in healthcare, meticulously analyzing both the exciting opportunities they offer, and the inherent risks associated with their implementation.Background:Traditional healthcare practices face limitations in patient engagement, accessibility, and training efficacy. Deepfakes, with their ability to simulate real-world scenarios and interactions, emerge as a potential solution, offering personalized patient education, immersive mental health interventions, remote consultations with virtual avatars, and realistic training simulations for medical professionals.Objective:This research aims to comprehensively understand the multifaceted impact of AI deepfakes in healthcare systems. It seeks to: Identify and analyze the potential benefits of deepfakes in specific healthcare applications. Assess the ethical and practical challenges associated with deepfake implementation in healthcare settings. Propose recommendations for safe, responsible, and ethical integration of deepfakes within the healthcare system.Results/Conclusion:The research findings illuminate a landscape replete with opportunities and challenges. Deepfakes offer the potential to enhance patient education, personalize mental health interventions, facilitate remote consultations, and revolutionize medical training. However, concerns regarding misinformation, ethical breaches, data privacy, and technical limitations pose significant challenges.The conclusion emphasizes the need for a balanced approach that capitalizes on the innovative potential of deepfakes while mitigating potential harm. Collaborative efforts involving healthcare professionals, technologists, and policymakers are crucial for establishing ethical frameworks, ensuring responsible development, and harnessing the positive power of deepfakes to build a future where healthcare benefits from cutting-edge technology while prioritizing patient well-being and ethical principles.

Background: This research examines how market knowledge and artificial intelligence (AI) interact in different market designs such as business-to-business (B2B) settings while taking emerging technologies and the changing digitalization landscape into account. Objective: The main goal is to understand how AI affects market knowledge in different market designs such as business-to-business (B2B) contexts, taking into account language barriers, practical difficulties, and the revolutionary effects on decision-making and customer interactions. Result: They underscore the transformative potential of artificial intelligence (AI) by highlighting how it shapes market knowledge, encourages customized approaches, and improves marketing efficacy in the business-to-business (B2B) space. Conclusion: In order to create a path for responsible AI integration in B2B marketing, the study concludes with recommendations for standardized terminology related to AI, practical insights into implementation challenges, and ethical issues.

The practical application of a pH-responsive Nanoparticle Drug Delivery System (NDDS) in cancer treatment is often hampered by several issues such as the protection of therapeutic molecules from external stresses, inefficient targeted delivery, sustained drug release, and poor efficacy. This study presents an effective design strategy for the synthesis of a pH-sensitive controlled hydrophobic drug delivery method based on the formulation of chitosan (CS)-coated mesoporous silica nanoparticles (MSNs) through the sol-gel method, where hydrolysis takes place in the acidic medium followed by polycondensation of the hydrolyzed products. For this purpose, NH2 modified-MSNs were prepared by using tetraethyl orthosilicate (TEOS) as precursor and cetyltrimethylammonium bromide (CTAB) as a template, and 3-aminopropyltriethoxysilane (APTES) for amine modification, followed by hydrophobic drug loading and CS coating of various concentrations. Camptothecin (CPT) was used as a model drug. Fabricated monodispersed functionalized nanoparticles had sizes ranging from 200nm to 245nm with an encapsulation efficiency as high as 90%. The highest encapsulation efficiency was found for 1% CS coating, which released 50% drug in 120h at pH 6.4 and 20% at pH 7.4 respectively. These nanoformulations exhibited pH-responsive release patterns of CPT under two different pH values (pH=7.4 and pH=6.4). These results contribute to the optimization of NDDS, with potential implications for nanoformulations designed for controlled and sustained drug release particularly to tumors without affecting healthy cells owing to differences in the pH of the tumor microenvironment and the normal physiological environment of cells.

Purpose: To evaluate the efficacy and survival rates of same session ab interno trabeculectomy with the trabectome and Ahmed glaucoma valve implant (AT) in comparison to the Ahmed glaucoma valve alone (A). Method: A total of 107 eyes undergoing primary glaucoma surgery were enrolled in this retrospective comparative case series, including 48 eyes which underwent AT and 59 eyes which received A alone. Participants were identified using the procedural terminology codes, and their medical records were reviewed. The primary outcome measure was surgical success, defined as intraocular pressure (IOP) > 5 mmHg, ≤ 21 mmHg, and IOP reduction ≥ 20% from baseline at two consecutive visits after three months, no reoperation for glaucoma. Secondary outcome measures were IOP, the number of glaucoma medications, incidence of a hypertensive phase, and best corrected visual acuity (BCVA). Results: The cumulative probability of success at one year was 70% in AT, and 65% in A (p=0.85). IOP decreased significantly from 26.6 ± 10.1 mmHg at baseline to 14.7 ± 3.3 mmHg at the final follow-up in AT (p= 0.001). The corresponding numbers for A were 28.8 ± 10.2 and 16.7 ± 4.9, respectively (p= 0.001). The final IOP was significantly lower in AT (p= 0.022). The number of medications at baseline was comparable in both groups (2.6 ± 1.2 in AT and 2.5 ± 1.3 in A, p=0.851). Corresponding number at 1 year visit was 1.2±2 in AT and 2.8±1.8 in A (p=0.001). The incidence of a hypertensive phase was 18.7% in AT and 35.5% in A (p=0.05). HP resolved in only 30% of eyes. The criteria for HP resolution were fulfilled in 9 eyes (30%). There was no difference in the rate of resolution of the hypertensive phase between AT and A (33.3% and 28.5%, respectively, p=0.67). Conclusion: Ahmed glaucoma valve implant with same session trabectome surgery significantly decreased the rate of the hypertensive phase and postoperative IOP as well as the number of glaucoma medications.

Plasma being the fourth and most abundant form of matter extensively exists in the universe in the inter-galactic regions. It provides an electrically neutral medium of unbound negative and positive charged particles, which has been produced by subjecting air and various other gaseous mixtures to strengthen the electromagnetic field and by heating compressed air or inert gasses for creating negative and positive charged particles known as ions. Nowadays, many researchers are paying attention to the formation of artificial Plasma and its potential benefits for mankind. The literature is sparsely populated with the applications of Plasma. This paper presents specific methods of generation and applications of Plasma, which benefits humankind in various fields, such as in electrical, mechanical, chemical and medical fields. These applications include hydrogen production from alcohol, copper bonding, semiconductor processing, surface treatment, Plasma polymerization, coating, Plasma display panels, antenna beam forming, nanotechnology, Plasma Torch, Plasma pencils, low-current non-thermal Plasmatron, treatment of prostate cancer, Plasma source ion implantation, cutting by Plasma, Plasma etching, pollution control, neutralization of liquid radioactive waste, etc. Resultantly, worth of Plasma technology in the medical industry is increasing exponentially that is closing the gap between its benefits and cost of equipment used for generating and controlling it.

The graphene based highly conductive and flexible devices has received momentous attraction and growth in recent years for potential multifunctional uses. The development of flexible and washable textile sensors, actuators, energy storage and energy harvesting systems. Textile fabrics coated with graphene and conductive polymers have been used for wearable self-powered e-textiles, such as tribo-electric, piezoelectric, and Thermoelectric. Herein this research, 100% woven cotton fabric is Fictionalized with graphene oxide (GO) using industrial-scale cold pad batch dyeing technique for the development of low-grade body heat into self-powered wearable e-textiles. The conductive polymer PEDOT: PSS was coated using layer by layer approach. The study demonstrates that the resultant textile electrodes showed reduced electrical sheet resistance from 2.5 MΩ to 15KΩ, with a reduced graphene oxide and further reduced to (2.5 -12.5 Ω) for conductive polymer. The sheet resistance was decreased with the number of dyeing cycles (10-15) and increased with 20 washing cycles vice versa. The study demonstrates that the sensor response and performance were improved with increasing the content percent of rGO and PEDOT: PSS. The tensile strength of as coated fabric electrodes was improved from (25.0-85.5 mPa) and fabric thickness was increased to (26-38µm) without compromising or influencing the air permeability. The resultant textile electrodes may potentially be used in smart textiles for self-powered flexible wearable generators, sensors, and energy storage. The study demonstrates that the cold pad batch dyeing method is a suitable and effective method for the fabrication of graphene-based textiles.

Automotive aerodynamics comprises of the study of aerodynamics of road vehicles. Its main goals are reducing drag, minimizing noise emission, improving fuel economy, preventing undesired lift forces and minimizing other causes of aerodynamic instability at high speeds. The Ahmed body has the form of a highly simplified car, consisting of a blunt nose with rounded edges fixed onto a box-like middle section and a rear end that has an upper slanted surface, the angle of which can be varied. It retains vital features of real vehicles in order to study the flow fields around it and the related turbulence models which characterizes the actual flow at elevated Reynolds number. In the present study, the aerodynamic behavior of this body is investigated numerically by the aid of commercial CFD tool: Ansys Fluent. The results of the simulation are validated with available experimental data and results of the simulations from other literatures. The numerical data were obtained for a fixed free stream velocity of 25 m/s at the inlet. The simulations were performed at a fixed slant angle of 25 degree and zero yaw angle. The present study focuses on how local refinement of mesh inside the concerned body and the outside, helps affect the results and for which grid dependency test is the primary objective of this paper. The present study also helps demonstrate how the drag of the body behaves, which is mainly the effect of pressure drag force generated at the rear portion of the body. The study also focuses on important properties like the velocity magnitude at different locations for different meshing cases, and to capture the flow pattern in the front or near the wake region. The study can be further helpful to future researchers in determining resistance, fuel efficiency etc. helping designers to optimize in specialized areas for better efficiency.

Since the release of ChatGPT, numerous studies have highlighted the remarkable performance of ChatGPT, which often rivals or even surpasses human capabilities in various tasks and domains. However, this paper presents a contrasting perspective by demonstrating an instance where human performance excels in typical tasks suited for ChatGPT, specifically in the domain of computer programming. We utilize the IEEExtreme Challenge competition as a benchmark — a prestigious, annual international programming contest encompassing a wide range of problems with different complexities. To conduct a thorough evaluation, we selected and executed a diverse set of 102 challenges, drawn from five distinct IEEExtreme editions, using three major programming languages: Python, Java, and C++. Our empirical analysis provides evidence that contrary to popular belief, human programmers maintain a competitive edge over ChatGPT in certain aspects of problem-solving within the programming context. In fact, we found that the average score obtained by ChatGPT on the set of IEEExtreme programming problems is 3.9 to 5.8 times lower than the average human score, depending on the programming language. This paper elaborates on these findings, offering critical insights into the limitations and potential areas of improvement for AI-based language models like ChatGPT.

Radiologists refer to any area of cerebral parenchymal loss with or without surrounding gliosis as encephalomalacia. This archaic phrase, which literally translates to "softening of the brain" due to liquefactive necrosis, was coined by pathologists to describe the macroscopic appearance of the brain following a variety of traumas, such as cerebral infarction. The final outcome of brain parenchymal liquefactive necrosis after insult, which typically happens after cerebral ischemia, cerebral infection, hemorrhage, traumatic brain damage, surgery, or other insults. Gliosis, or the growth of glial cells in reaction to injury, is frequently seen around it. The location, size, and number of the lesions as well as the existence of other issues like seizures, hydrocephalus, or infection affect the symptoms and prognosis of encephalomalacia. While some people might not have any symptoms, others might have neurological abnormalities such hemiparesis, aphasia, cognitive decline, or behavioral changes. Depending on the underlying reason and the severity of the problem, treatment options may include medication, surgery, or rehabilitation. This is a case of a young male who reported for rehabilitation his left upper limb weakness, upon investigations Encephalomalacia was diagnosed.

Smart city infrastructure has a significant impact on improving the quality of humans life. However, a substantial increase in the urban population from the last few years is posing challenges related to resource management, safety, and security. In order to ensure safe mobility and security in the smart city environment, this paper proposes a novel Artificial Intelligence (AI) based approach empowering the authorities to better visualize the threats and to help them identify the highly-reported crime zones yielding greater predictability of crime hot-spots in a smart city. To this end, it first investigates the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) to detect the hot-spots that have a higher risk of crimes to be committed. Second, for crime prediction, Seasonal Auto-Regressive Integrated Moving Average (SARIMA) exploited in each dense crime region to predict the number of crimes in the future with spatial and temporal information. The proposed HDBSCAN and SARIMA based crime prediction model is evaluated on ten years of crime data (2008-2017) for New York City (NYC). The accuracy of the model is measured by considering different time period scenarios i.e. (a) year-wise, i.e., for each year and (b) for the whole period of ten years, using an 80:20 ratio where 80\% data was used for training and 20\% data was used for testing. The proposed approach outperforms with an average Mean Absolute Error (MAE) of 11.47.

Background: The research is conducted against the backdrop of the rising incidence of diabetes and the demand for novel approaches to improve patient outcomes. With the goal of revolutionizing conventional methods and addressing the changing healthcare landscape, the study concentrated on the application of AI technology in diabetes treatment. Objective: Examining the various ways AI can be used in diabetes care and assessing how it can enhance automated screenings, tailored treatment recommendations, predictive modeling, and patient self-management were the main goals of the study. Result: The findings demonstrated how well AI works to anticipate a patient's unique reaction to a therapy, assist medical personnel in making decisions, and give patients more control over their own care. Artificial intelligence (AI)-powered solutions showed promise in improving real-time monitoring and encouraging patient adherence, supporting a comprehensive strategy for managing diabetes. Conclusion: The research indicates that artificial intelligence (AI) has great potential to change the way diabetes is treated. However, it also highlights the need for more research, ethical issues, and validation of AI. The results advocate for joint efforts to ensure responsible and patient-centered technology innovation in diabetes care and encourage the integration of AI with new technologies.

Advancements in technology have revolutionized communication on social media platforms, where reviews and comments greatly influence product selection. Current opinion mining methods predominantly focus on textual content, overlooking the rich information within customer-posted images, termed here as Multus-Medium. This research introduces an innovative deep learning approach, Multus Medium Opinion Mining (MMOM), capitalizing on both text and image data for comprehensive product review analysis. MMOM employs an integrated model of Bidirectional Long Short-Term Memory (BiLSTM) and embedded Convolutional Neural Network (CNN), incorporating architectures of GoogleNet and VGGNet, thus enabling efficient extraction and fusion of textual and visual features. This synergistic approach enables data collection, preprocessing, feature extraction, fusion strategy-based feature vector generation, and subsequent product recommendation. Performance evaluation on two diverse real-world datasets name as “flicker8k” and “t4sa”. These datasets show substantial improvement over existing methods. MMOM outperforms standard benchmark models, achieving an accuracy, F1 score and ROC over fliker8k are 90.38%, 88.75% and 93.08%, whereas for twitter dataset are 88.54%, 86.34%, and 92.26% respectively, the accuracy of purposed model is 7.34% and 9.54% higher than the other two mentioned techniques. These statistics highlight the robustness and applicability of MMOM across various domains. The compelling results underscore the potential of MMOM, providing a more holistic and precise approach to opinion mining in the era of social media product reviews. This research, product recommendation helps the customer to make purchasing decision. Last but not the least, the purposed scheme can further be expanded in any other sentiment task like hospital recommendation system, crop farming recommendation and medical diagnostic system etc.

This study examined the prevalence of illegal drug use in UK students and motivators behind such behaviour. Additionally, we explored possible relationships between substance use, psychosocial motivators, and psychiatric distress. N=543 students completed online measures of substance use, anxiety, depression, perceived stress, insomnia. A series of reasons behind their use were ranked based on importance. Reported cannabis, cocaine, nitrous oxide, ketamine and MDMA use were most prevalent based on lifetime, past year, and month assessments. The experience of anxiety, depression, perceived stress, and insomnia were related to increased reports of substance use. Poor self-confidence and self-medication were key motivators of illicit drug use in those presenting greater psychiatric distress. These outcomes add to the sparse body of literature concerning illicit substance use in relation to psychiatric distress amongst UK students. Furthermore, we provided novel insight into the psychosocial motivators of such use.

Inflammation and insulin resistance are recognized as central features in the pathology of psoriasis, profoundly affecting the lives of those affected by this condition. The relationship between lifestyle choices and psoriasis is well-documented, highlighting the potential benefits of dietary modifications and physical activity in managing the condition during exacerbations. Furthermore, growing evidence emphasizes the significant impact of nutritional interventions on various skin disorders, particularly those characterized by inflammation and insulin dysregulation. Notably, the role of dietary fats is of particular interest, with their influence on inflammation and insulin resistance in psoriasis garnering increasing attention. Moreover, the association between excessive adiposity and heightened psoriatic severity underscores the importance of weight management strategies in disease management. This review explores the intricate relationship between specific dietary fats and inflammation in psoriasis, offering a comprehensive analysis of their implications for disease progression and management.

pH is considered one of the paramount factors in bodily functions, because most of the cellular tasks exclusively rely on precise pH values. The regulation of pH is a necessary feature of the intracellular atmosphere and can be established as a strong indicator to judge a physiological abnormality in most of the cases. In this context, the current techniques for pH sensing provide us with the futuristic insight to further design therapeutic and diagnostic tools. Thus, pH-sensing (electrochemically and optically) is rapidly evolving toward exciting new applications and expanding researchers’ interests in many chemical contexts, especially in biomedical applications. The adaptation of cutting-edge technology is subsequently producing the modest form of these biosensors as wearable devices, which are providing us the opportunity to target the real-time collection of vital parameters, including pH for improved healthcare systems. The motif of this review is to provide an insight of trending tech-based systems employed in real time or in-vivo pH responsive monitoring. Herein, we briefly go through the pH regulation in the human body to help the beginners and scientific community with quick background knowledge, recent advances in the field, and pH detection in cancerous environments. In the end, we summarize our review by providing an outlook; challenges that need to be addressed and prospective integration of various pH in vivo platforms with modern electronics that can open new avenues of cutting-edge techniques for disease diagnostics and prevention.

We investigate the exact travelling wave solutions of the Tzitzéica-Dodd–Bullough–Mikhailov (TDBM) and Tzitzéica-type (TT) equations, appear in nonlinear optics, using simple ansatz approach. We use the Painlevé and traveling wave transformations to convert the mentioned nonlinear equations into nonlinear ordinary differential equations. Then, the simple ansatz approach has been employed to procure wave solutions, in terms of bright, singular soliton and trigonometric functions, for the Tzitzéica-Dodd–Bullough–Mikhailov (TDBM) and Tzitzéica-type (TT) equations. The study shows that the simple ansatz approach is straightforward and easy to implement for solving nonlinear problems.

A highly dirt resistant paint for building facades without chemicals harmful to nature and environment, is developed which resolves the unattractive disfigurement of building walls caused by dirt. The experimentation is scientifically and statistically planned with the aid of computer programming. It consists of a sequence of phases which include the selection of appropriate raw materials, adopting of Basic Language computer programming to generate a target population of paint formulations. The average PVC percentage is computed using theory and found to be 54.98% for the target population of 543143 paint formulations hence verifies the literature results. Experimentation and statistical analysis are performed to compare the classical conventional agitator with latest lab equipment like Nano mill and it is concluded that Nano mill performs better on the average than conventional agitator in preparation of paint formulations. Hence the sample of paint formulations is prepared on Nano mill and tested in laboratory using advanced available technology for the analysis and comparison of paint properties to determine the best paint formulation. The results are analyzed using Analysis Of Variance Technique (ANOVA) and it is concluded that the paint formulation named “O3” has the highest dirt resistance on the average. The final selected formula O3 is compared with three other competitor paints in market under natural environment for a period of almost one year. A regression model is also constructed to study the effect of environmental factors like time, temperature and humidity on dirt resistance of paints. It is found that O3 formulation is the best environment friendly which performs equally well with one competitor paint and has higher dirt resistance than two other competitor paint formulations containing harmful chemicals. The regression model of dirt resistance on variables including time, temperature and humidity shows that these factors are significantly affecting the dirt resistance of a given paint at 5% level of significance. 95.34% variation in the dirt resistance of a given paint is due to and explained by the given factors. The regression model is useful to predict the average dirt resistance of a given paint with a certain level of confidence. The project exemplifies the work of an applied research from conceptualization to successful commercialization for the paint industry.

The fact is quite transparent that almost everybody around the world is using android apps. Half of the population of this planet is associated with messaging, social media, gaming, and browsers. This online marketplace provides free and paid access to users. On the Google Play store, users are encouraged to download countless of applications belonging to predefined categories. In this research paper, we have scrapped thousands of users reviews and app ratings. We have scrapped 148 apps’ reviews from 14 categories. We have collected 506259 reviews from Google play store and subsequently checked the semantics of reviews about some applications form users to determine whether reviews are positive, negative, or neutral. We have evaluated the results by using different machine learning algorithms like Naïve Bayes, Random Forest, and Logistic Regression algorithm. we have calculated Term Frequency (TF) and Inverse Document Frequency (IDF) with different parameters like accuracy, precision, recall, and F1 and compared the statistical result of these algorithms. We have visualized these statistical results in the form of a bar chart. In this paper, the analysis of each algorithm is performed one by one, and the results have been compared. Eventually, We've discovered that Logistic Regression is the best algorithm for a review-analysis of all Google play store. We have proved that Logistic Regression gets the speed of precision, accuracy, recall, and F1 in both after preprocessing and data collection of this dataset.

This paper presents an Application Based Active Learning (ABAL) methodology on Power Electronics (PE) and Electric Machines (EM) as a hybrid laboratory course for the undergraduate students to design and implement the real-world engineering problems. The ABAL is a type of active learning which is a branch of Learner-centered teaching (LCT). The DC/DC converter along with the speed control of DC separately excites the motor. In addition, a DC/AC converter is designed to control the speed of an induction motor. The results are then investigated on a hardware platform under the ABAL experimental methodology. This paper also discusses the problem identification selection of the equipment, circuit design, hardware mounting and critical analysis of the results acquired from the hybrid laboratory. The ABAL methodology was evaluated based on student satisfaction, feedback, grades and interest to solve the real-world problem rather than cramming the engineering concepts and fulfill so-called lab routine and tasks

The current study aims to apply and compare the performance of six machine learning algorithms, including three basic classifiers: random forest (RF), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGB), as well as their hybrid classifiers, using the logistic regression (LR) method (RF+LR, GBDT+LR, and XGB+LR), in order to map the landslide susceptibility of Zhangjiajie City, Hunan Province, China. First, a landslide inventory map was created with 206 historical landslide points and 412 non-landslide points, which was randomly divided into two datasets for model training (80%) and model testing (20%). Second, 15 landslide conditioning factors (i.e., altitude, slope, aspect, plane curvature, profile curvature, relief, roughness, rainfall, topographic wetness index (TWI), normalized difference vegetative index (NDVI), distance to roads, distance to rivers, land use/land cover (LULC), soil texture, and lithology) were initially selected to establish a landslide factor database. Thereafter, the multicollinearity test and information gain ratio (IGR) technique were applied to rank the importance of the factors. Subsequently, we used a series of metrics (e.g., accuracy, precision, recall, f-measure, area under the ROC (receiver operating characteristic) curve (AUC), kappa index, mean absolute error (MAE), and root mean square error (RMSE)) to evaluate the accuracy and performance of the six models. Based on the AUC values derived from the models, the GBDT+LR model with the highest AUC value (0.8168) was identified as the most efficient model for mapping landslide susceptibility, followed by the XGB+LR, XGB, RF+LR, GBDT, and RF models, which achieved AUC values of 0.8124, 0.8118, 0.8060, 0.7927, and 0.7883, respectively. The results from this study suggest that the stacking ensemble machine learning method is promising for use in landslide susceptibility mapping in the Zhangjiajie area and is capable of targeting the areas prone to landslides.

Abstract: The Tropical Rainfall Measuring Mission (TRMM) and then Global Precipitation Mission (GPM) are the most important and widely used data sources in the forecasting of drought, flood, and water resources management. However, since this sensor’s data is primarily used for tropical regions, it is necessary to evaluate the accuracy for optimal use of the data across varying climatic and physiographic conditions. In this study, the accuracy of the satellite data for a span of 17 years (2000-2017) for three climatic zones has been explored using synoptic ground station data. The climates include a) arid and low rainfall, b) semi-arid and low rainfall, and c) humid and high rainfall. We evaluated satellite data accuracy in drought and wet conditions based on the Standard Precipitation Index (S.P.I.) and different seasons. For available ground control stations, 13 stations were used in the humid, seven stations in a semi-arid climate, and 12 stations in the dry climate. The results show that the monthly precipitation product of GPM (IMERG product) and TRMM (TMPA/3B43 product) overestimate the rainfall. In the arid climate, the precipitation is estimated 43%, in the semi-arid environment 50%, and in the humid weather 11% more than the ground-based data on average. Therefore, to use satellite data in different climates, it is necessary to make corrections to obtain precise results. Based on 32 ground stations, the correction coefficient has a positive relationship with average precipitation and altitude and an inverse relationship with the latitude. Further in-depth investigations showed that the accuracy of satellite data in wet conditions is higher than the accuracy of normal circumstances, and the accuracy of normal conditions is more accurate than drought conditions. Besides, the accuracy of satellite data in wet or dry conditions increases with increasing time scales. The highest accuracy was obtained for a 12-month time scale and the lowest accuracy for the 3-month time scale of drought conditions in the arid climate.

In the Agriculture sector, control of plant leaf diseases is crucial as it influences the quality and production of plant species with an impact on the economy of any country. Therefore, automated identification and classification of plant leaf disease at an early stage is essential to reduce economic loss and to conserve the specific species. Previously, to detect and classify plant leaf disease, various Machine Learning models have been proposed; however, they lack usability due to hardware incompatibility, limited scalability and inefficiency in practical usage. Our proposed DeepLens Classification and Detection Model (DCDM) approach deal with such limitations by introducing automated detection and classification of the leaf diseases in fruits (apple, grapes, peach and strawberry) and vegetables (potato and tomato) via scalable transfer learning on A.W.S. SageMaker and importing it on AWS DeepLens for real-time practical usability. Cloud integration provides scalability and ubiquitous access to our approach. Our experiments on extensive image data set of healthy and unhealthy leaves of fruits and vegetables showed an accuracy of 98.78% with a real-time diagnosis of plant leaves diseases. We used forty thousand images for the training of deep learning model and then evaluated it on ten thousand images. The process of testing an image for disease diagnosis and classification using AWS DeepLens on average took 0.349s, providing disease information to the user in less than a second.

In the presented paper, a comprehensive study will be done on shape factor analysis of MoS2-GO in H2O-C2H6O2 based hybrid nanoliquids associated with effect and influence of transverse magnetic field and thermal radiation. The effect of variation in different parameters and nanoliquids shapes under temperature and velocity distribution is explored and also non-linear thermal radiation will be analyzed. Algorithms are introduced in proportion to mathematical modeling based on their numerical results and comparative curves for further explanation. In addition, it will be done research for influence and effect of new significant parameters emerged to the model to do sensitivity analysis and also their output results are demonstrated, examined and compared together by presenting graphs and tables. Based on detailed discussions, authentication of attained results designates the high accuracy of applied methods deployed to solve presented model in the paper. Our results satisfy that our used approach is accurate, highly reliable and also effective. All mentioned steps will be described throughout the literature.

The emergence of drug-resistant parasitic nematodes of both humans and livestock calls for development of alternative and cost-effective control strategies. For the economically important ruminant strongylid Haemonchus contortus, Barbervax® remains the only registered vaccine available. Here we compared the microbiome, genome-wide diversity and transcriptome of H. contortus adult male populations that survived vaccination with an experimental vaccine after inoculation in sheep. Our genome-wide SNP analysis revealed 16 putative candidate vaccine evasion genes. However, we did not identify any evidence for changes in microbial community profiling based of 16S rRNA gene sequencing results of vaccine surviving parasite populations. A total of 58 genes were identified as significantly differentially expressed with six being long non-coding (lnc) RNAs and no putative candidate SNP associated genes. The genes highly upregulated in surviving parasites from vaccinated animals were associated with GO terms belonging to predominantly molecular function and a few biological processes that may have facilitated evasion or potentially lessened the effect of the vaccine. These included five targets: astacin (ASTL), carbonate dehydratase (CA2), phospholipase A2 (PLA2), glutamine synthetase (GLUL) and fatty acid-binding protein (FABP3). We searched all five DEG targets against the proteomes of selected Nematoda (Clades III, V, IV, C, I) and Platyhelminthes (Clades Monogenea, Trematoda, Cestoda, Rhabditophora) to determine homologs within the H. contortus NZ_HCO_NP v1.0 genome and identified single-copy orthologous groups (OGs) in selected proteomes. All but one (FABP3) demonstrated high levels of duplication and wide-spread occurrence in closely related Caenorhabditis elegans and Pristionchus pacificus, with complete absence of all five gene targets among other Clade III (Toxocara canis) and V (Ascaris suum, Ascaris lumbricoides and Parascaris univalens) nematodes, further supporting their vital biological functions in nematodes. Phylogenetic analyses inferred the presence of only ASTL and CA2 in almost all Nematoda, platyhelminthes and metazoans examined, with loss of GLULs observed among all outgroup vertebrate species and the presence of FABP3 in only three other species (Schmidtea mediterranea, Fasciola gigantica and F. hepatica). Our tertiary structure predictions and modelling analyses were used to perform in silico searches of all published and commercially available inhibitor molecules or substrate analogues with potential broad-spectrum efficacy against nematodes of human and veterinary importance.

Maternal and child nutrition has been a critical component of health, sustainable development, and progress in low- and middle-income countries (LMIC). While a decrement in maternal mortality is an important indicator, simply surviving pregnancy and childbirth does not imply better maternal health. One of the fundamental obligations of nations under international human rights law is to enable mothers and teenage girls to endure pregnancy and delivery as an aspect of their enjoyment of reproductive and sexual health and rights and live a dignified life. The aim of this study was to discover the correlation between the Maternal Observation and Motivation (MOM) program and m-Health support for maternal and newborn health. A Comparative study was done among 109 pregnant mothers (study group-94; control group-102 mothers) with not less than 20 weeks of gestation. Maternal outcomes such as Hb, weight gain and newborn results like birth weight and crown- heel length was obtained on the baseline, 28 and 36 weeks of gestation. Other secondary data collected were abortion, stillbirth, low birth weight, major congenital malformations, twin or triplet pregnancies, physical activity and maternal wellbeing. The MOM intervention included initial face to face education, three in-person visits and eight virtual health coaching by WhatsApp. The baseline data on Hb of the mothers show that 31(32.98%) vs 27(28.72%) of the study and control group had anaemia, which improved to 27.66% and 14.98% among study group mothers at 28 and 36 weeks of gestation (p<0.001). The weight gain (p< 0.001), level of physical activity (p< 0.001), and maternal wellbeing (p< 0.01) also had significant differences after the Intervention. Even after controlling for potentially confounding variables, the maternal food practices regression model revealed that birth weight was directly correlated with consumption of milk (p 0.001), fruits (p 0.01), and green vegetables (p 0.05).As per the physical activity and maternal wellbeing regression model, the birth weight and crown heel length were strongly related with the physical activity and maternal wellbeing of mothers at 36 weeks of gestation (p <0.05). Combining the MOM intervention with standard antenatal care is a safe and effective way to improve maternal welfare while upholding pregnant mothers' human rights.

The wettability of the oil-brine-rock systems is very challenging mainly for the carbonate reservoirs and it plays a significant role to enhance the oil recovery (EOR). Carbonate rocks are mad-up of complex lithology, hence these requires careful treatments in controlling their wetting behavior for enhanced oil recovery. Therefore, it is extremely important to modify their wettability from an oil-wet rock behavior to a water-wet so that the residual oil can be produced efficiently. Thus, the present study has examined the effectiveness of alumina nanofluid as well as a new chemical methyl blue to alter the wettability. The methyl blue is released on massive scale from various industries i.e. pharma, textile and food industry which is a key environmental concern which contaminates the water table. Hence the study, explores the effects of MB and alumina nanofluid on the wettability. The effect of nanofluids formulated via dispersing the alumina nanoparticles in aqueous solutions at various concentration (0. 0.05, 0.3, 0.50, 0.75 and 1.0 wt. %) were tested for wettability modification under different physio-thermal conditions. Subsequently, the wettability change was examined for the samples treated with different concentrations of MB (10, 15, 30, 50 and 100 mg/L) for 7-days at two different temperatures of (25 and 50 0C). The results showed that hydrophobicity of the SML carbonate rock has significantly reversed while treating with alumina nanofluids and MB. Thus, the wettability modification/reversal via treatment of MB and alumina nanofluid can be an effective mechanism to hydrogen injection and for EOR processes.

There are evidences of shared biological mechanisms between obesity and hypertension during childhood in the adulthood, and loads of research literatures have proven that, it will cost the economies and health of nations profoundly if neglected. The Prevention and early diagnosis of cardiovascular risk factors such as overweight and hypertension is an essential strategy for control, effective treatment and prevention of its’ complications. The aim of the study is to assess the effect of school based Exercise and Life style Motivation Intervention (SEAL-MI) on adolescent's cardiovascular risk factors and academic performance. An experimental study was conducted among 1005 adolescents - 520 and 485 adolescents were randomly selected for control and study group, respectively. Demographic details and the data related to dietary habits, physical activity and sleep quality were collected by a structured interview questionnaire. The study group adolescents were given the SEAL-MI for six months which includes a school based rope exercise for 45 minutes per day for 5 days a week and a motivation intervention related to dietary habits, physical activity and sleep. Post-test 1 and 2 were done after 3 and 6 months of intervention. The prevalence of overweight among adolescents was 28.73% and prehypertension was 9.26%. Among overweight adolescents, the prevalence of prehypertension was found to be very high (32.25%). In post intervention, there was a significant reduction in weight, BP (p=0.000) and improvement in dietary habits, physical activity, sleep (p=0.000) and academic performance. A significant positive correlation was found between BMI and SBP (p=0.000) and BMI and academic performance (p=0.003). The linear regression analyses revealed that the gender (ß: 0.47, 95% CI: 0.39, 0.81), age (ß: 0.39, 95% CI: 0.17, 0.46), family income (ß: 0.2, 95% CI: 0.41, 0.5), residence (ß: 0.19, 95% CI: 0.01, 0.27) and type of family (ß: 0.25, 95% CI: 0.39, 0.02) had the strongest correlate with the BMI of the adolescents. Also, Mother’s education Mother’s education (ß: 0.35, 95% CI: 0.18, 0.59) had the strongest correlate with the SBP of the adolescents. In contrast, the DBP was negatively persuaded by age (ß: -0.36, 95% CI: 1.54, 0.29) and gender (ß: -0.26, 95% CI: 1.34, 0.12) of the adolescents. Regular practice of rope exercise and lifestyle modification such as diet, physical activity and sleep quality among adolescents prevent and control childhood CVD risk factors like overweight, hypertension. The SEAL-MI may lead to age appropriate development of adolescents and improves their academic performance and quality of life. Giving importance to the adolescents from urban habitat, from affluent nuclear family and catching them young will bring significant change in the disease burden.

Solid-phase joining is a technique used to fuse two materials by applying heat and pressure, causing plastic deformation without reaching the melting point. This method is applicable for similar and dissimilar materials, including metals, polymers, and ceramics, without the need for filler material. Among the various methods, Friction Stir Welding (FSW) is preferred for joining dissimilar materials due to its solid-state process, which enhances mechanical properties, metallurgical compatibility, and reduces distortion. FSW is particularly effective for materials with significant differences in melting points. The current project focuses on assessing the strength and mechanical properties of FSW joints between aluminum and polymer-based composites. FSW was selected for its ability to produce fine-grained, strong welds free from fusion-related defects such as porosity and cracks. Applications in sectors like automotive, aerospace, and shipbuilding are explored, with impact, hardness, and tensile tests conducted to evaluate joint performance. This comprehensive study includes literature review, fabrication, testing, and tool design to ensure a thorough understanding of dissimilar joint properties.

Machine Learning (ML) and Artificial Intelligence(AI) have revolutionized almost all fields that are linked to the acquisition of intelligent behavior in the real world. It is an attractive alternative for a researcher of artificial intelligence. Contrary to rule-based programming, ML is an algorithmic approach in which learning comes from existing data. The more data we have these computer systems look at, we say we’re ‘training’ the computer system, and as the computers begin to identify patterns in the data, identify abnormalities in the data from these abnormalities we improve the system architect according to the requirement. This article introduces the use of comprehensive concepts of machine learning, in general, particular, and their potential applications in communications. Furthermore, the current state and futuristic potentials of enabling universal communication with implications of machine learning methods have been explained. In this review paper, we offer a comprehensive talk on distinctive methods/techniques of information analytics, artificial intelligence (AI), and machine learning (ML) moved forward the contact aware communication system.

In this paper we analyze the relationship between bankruptcy risk and the corporate life cycle in Pakistan from 2005 to 2014. For this purpose, we run a Hierarchical Linear Mixed Model (HLM) for a sample of 301 non-financial listed firms in 12 different sectors. The empirical outcomes reveal that firms during introduction, growth and, decline stages (mature stage) of life-cycle experience higher (lower) bankruptcy risk. Moreover, in juxtaposition with growth stage, bankruptcy risk is higher at the introduction stage of life-cycle. These findings suggest that financial managers should be cautious about the financial fragility of the firm at each stage of corporate life-cycle. The results also entail that Pakistani firms do not follow a sequential pattern in their life-cycle rather they have the tendency to revert to a previous stage or jump to the next stage of life-cycle. This is the first study that empirically examines the association between firm life-cycle stage and corresponding bankruptcy risk and asserts that managers must incorporate the life-cycle effects into their financial planning and decision making for sustainable working of an enterprise.

Beta thalassemia is an inherited blood disorder that results in inefficient erythropoiesis due to genetic mutation that leads to reduction or absence of the hemoglobin beta-globulin protein. Approximately 8.5% of UAE residents suffer from β-thalassemia, a significant health and financial problem. The treatment options available for β-Thalassemia major are limited and associated with a wide range of complications. β-thalassemia gene therapy is emerging as a potential novel treatment option that eliminates the complications caused by the current long-term treatment modalities and the associated economic burden. This paper reviews the scientific literature related to emerging gene-therapy for β-Thalassemia by analyzing all the articles published from January 2010 to December 2023 in the English language on Databases like PubMed, Scopus, ProQuest, and CINAHL. The use of gene therapy has demonstrated promising outcomes for a permanent cure of β-Thalassemia. ZYNTEGLO (Betibeglogene autotemcel) is the only cell-based gene therapy which was approved by the FDA on 19th August, 2018. To conclude, Gene therapy is an innovative solution. It demonstrates a promising future, but does come with its own set-backs and is something that must be tackled in-order to revolutionize it in the medical world. FDA approved ZYNTEGLO is a potentially one-time curative treatment for β-Thalassemia. Although cutting-edge, its use is limited because of the high cost; a price of $2.8million per patient.

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

The 2-Acetyl-1-pyrroline (2-AP) is the most important volatile in determining the aroma of fragrant rice, however, the transcriptional regulation mechanism of 2-AP biosynthesis in fragrant rice is still unclear. In this study, the Osp5cs1 knockout mutant lines and OsP5CS1 over-expression lines were constructed by genetic transformation of indica rice cultivar i.e., ‘Zhonghua11’, which was knocked out OsBADH2 to produce fragrance in aromatic rice. The OsP5CS1 gene was also identified as a key gene in the 2-AP biosynthesis pathway of aromatic rice. The OsP5CS1 promoter was used as bait, and the transcription factor OsbZIP60-like was screened by yeast one-hybrid assays. The transcription factor OsbZIP60-like specifically bound to the OsP5CS1 gene. The dual luciferase reporting system found that OsbZIP60-like transcription factor promoted the transcriptional activation of OsP5CS1. Compared with the wild type, the OsP5CS1 gene expression was significantly down-regulated in Osbzip60-like mutant, and resulted in substantial reduction in 2-AP biosynthesis. Moreover, the OsP5CS1 gene expression was significantly up-regulated in OsbZIP60-like over-expressed plants, and the 2-AP concentrations were also increased whereas the Osbzip60-like mutants were found to be sensitive to Zn deficiency. Overall, the OsbZIP60-like transcription factor promoted the 2-AP accumulation. This study provides a theoretical basis for the transcriptional regulation mechanism of 2-AP biosynthesis and explores the function of OsbZIP transcription factor in fragrant rice.

Precision medicine (PM), also termed stratified, individualised, targeted or personalised medicine, embraces a rapidly expanding area of research, knowledge, and practice. It brings together two emerging health technologies to deliver better individualised care: the many “-omics” arising from increased capacity to understand the human genome; and “big data” and data analytics including Artificial Intelligence (AI). PM has the potential to transform an individual’s health, moving from population-based disease prevention to more personalised management. There is however a tension between the two, with a real risk that this will exacerbate health inequalities and divert funds and attention from basic healthcare requirements leading to worse health outcomes for many. All areas of medicine should consider how this will affect their practice, with PM now strongly encouraged and supported by government initiatives and research funding. In this review we discuss examples of PM in current practice and its emerging applications in primary care, such as clinical prediction tools that incorporate genomic markers and pharmacogenomic testing. We look towards potential future applications and consider some key questions for PM including evidence of real-world impact, its affordability, the risk of exacerbating health inequalities, and the computational and storage challenges of applying PM technologies at scale.

The demand of high data rate and ubiquitous coverage in heterogeneous cellular (HetNets) is increasing progressively. In order to meet this demand, sophisticated model having applied interference reduction scheme and cell association technique is needed. The small base station (sBS)are deployed inside the broadcasting area of macro base station (mBS), in heterogeneous cellular networks (HetNets). Since mBS has high transmission power therefore a large number of users get connected with mBS. This causes disproportion of load distribution across the HetNets. For load balancing users from high power mBS are migrated to low power sBS to increase network capacity and to decrease the load from mBS. This results in interference in the communication signal because of strong mBS Interference. Hence, we need interference management technique to mitigate interference and user association and to efficiently use sBSs’ resources. Inter-cell interference (ICI) limit the HetNets’ performance. Additionally, there exist deliberate jamming interference which depends on jammers transmission power and its proximity with the target, which notably degrades the network performance. In this paper, we employ reverse frequency allocation scheme (RFA) to reduce inter cell interference, deliberate jamming interference and to accomplish load balancing. The proposed setup is analyzed inquisitively and with the help of simulations. The result shows reduction in interferences as well as balance of load distribution in the network achieved by employing RFA scheme together with cell association.

Retinitis pigmentosa (RP) clinically and genetically heterogeneous group of inherited retinal disorders (IRD) that result in retinal degeneration. This study aimed to identify the genetic findings of patients with autosomal recessive retinitis pigmentosa (arRP). Whole exome sequencing (WES) was performed in two unrelated Pakistani families underlying arRP. Data analysis and mutation screening was performed for all the known RP genes following bi-directional Sanger sequencing to determine whether any of the candidate variants co-segregated with the disease phenotype in the families. WES data analysis revealed a novel homozygous missense variant (c.1274T>C) in the in Tubby like Protein 1 (TULP1 NM_003322.6) gene in family 1 and a novel homozygous frameshift variant (c.351delC) in the retinoid isomerohydrolase 65 (RPE65 NM_000329.3) gene in family 2. The identified variants perfectly co-segregated with the disease phenotype within the families. Our results strongly suggest that mutations in TULP1 and RPE65 are responsible for the retinal phenotype in the affected individuals. These mutations will increase the mutation spectrum of these genes; furthermore, it will enhance our knowledge and understanding of the underlying molecular mechanisms of retinitis pigmentosa.

Atmospheric pressure Microplasma is a simple, cost-effective, efficient, and eco-friendly procedure, which is superior to the traditional nanomaterials synthesis techniques. It generates high yields and allows for a controlled growth rate and morphology of nanomaterials. The silver (Ag) nanomaterials, with their unique physical and chemical properties, exhibit outstanding antibacterial and anti-fungal properties. Similarly, zinc oxide (ZnO) nanomaterials, known for their low toxicity and relatively lower cost, find wide applications in wound repair, bone healing, and antibacterial and anticancer applications. The use of core-shell nanomaterials in certain situations where some nanoparticles can cause serious harm to host tissues or organs is a testament to their potential. A benign material is coated over the core to reduce toxicity in these cases. This review compares the numerous configurations of microplasma systems used for synthesizing nanomaterials and their use in producing Ag, ZnO, and their core-shell (Ag-ZnO) nanomaterials for biomedical applications. The summary also includes the effect of control parameters, including cathode diameter, gas flow rate, precursor concentration, voltage, and current, on the nanomaterial's characteristics and applications. In addition, it provides a research gap in the synthesis of Ag, ZnO, and core shall nanomaterials by this technique, as well as the development and limitations of this technique and the use of these nanoparticles for biomedical applications.

Covid-19 pandemic has greatly affected the whole world since the beginning and it continues to affect it. The main aim of this study is to show how Covid-19 affect other mortal diseases; cancer, heart diseases, and diabetes in near future. With this purpose two mathematical models are proposed via Ordinary Differential Equations (ODEs); one for the relationship between Covid-19 and cancer and one for the relationship between Covid-19, diabetes and heart diseases. Afterwards, stability analyses of these models are demonstrated. In order to see the effect of parameters on the disease compartments, sensitivity analysis is applied. Results of sensitivity analysis revealed that huge percentage of people are still scared of visiting doctors and this may lead a massive increase in the diagnosis of other diseases for upcoming years. Moreover, figures displayed that there exists a relationship between diabetes and heart diseases. Especially, diabetes patients should be careful about their health situations and take care of their heart. In order to provide these, awareness of people should be developed.

The spatial distribution of elements can be regarded as a numerical field of concentration values with a continuous spatial coverage. An active area of research is to discover geologically meaningful relationships among elements from their spatial distribution. To solve this problem, we propose an association rule mining method based on clustered events of spatial auto-correlation and applied it to the polymetallic deposits of the Chahanwusu River area, Qinghai Province, China. The elemental data for stream sediments were first clustered into HH (high-high), LL (low-low), HL (high-low), and LH (low-high) groups by using local Moran’s I clustering map (LMIC). Then the Apriori algorithm was used to mine the association rules among different elements in these clusters. More than 86% of the mined rule points are located within 1000 m of faults and near known ore occurrences, and occur in the upper reaches of the stream and catchment areas. In addition, we found that the Indosinian granodiorite is enriched in sulfophile elements, e.g., Zn, Ag and Cd, and the Variscan granite quartz diorite (P1γδο) coexists with Cu and associated elements. Therefore, the proposed algorithm is an effective method for mining co-existence patterns of elements and provides an insight into their enrichment mechanisms.

In the realm of digital transformation, effective leadership and motivation are pivotal for organisations navigating the complexities of today’s systems. This study explores the intersection of intrinsic motivation and mental well-being among mature students—an analogy that sheds light on strategies applicable to organisational contexts. In developed nations like the UK, mental health for mature students is increasingly recognised as a crucial component of their educational journey. Mature students, who typically enrol in higher education after an educational gap and upon turning 21, often face specific challenges that can impact their mental well-being while pursuing academic goals. The primary objective of our study was to assess the relationship between intrinsic motivation and the mental well-being of mature students. Our research revealed a significant positive correlation between these two variables. Confirmatory factor analysis (CFA) results supported a one-factor solution for the sample data from mature students. Additionally, we found that intrinsic motivation is a significant predictor of the mental well-being of mature students. Recognising and addressing the unique difficulties individuals encounter and providing appropriate support can enhance their well-being and contribute to the overall success of the higher education community.

With the increase of the Artificial Neural Network (ANN), machine learning has taken a forceful twist in recent times. One of the most spectacular kinds of ANN design is the Convolutional Neural Network (CNN). The Convolutional Neural Network (CNN) is a technology that mixes artificial neural networks and up to date deep learning strategies. In deep learning, Convolutional Neural Network is at the center of spectacular advances. This artificial neural network has been applied to several image recognition tasks for decades and attracted the eye of the researchers of the many countries in recent years as the CNN has shown promising performances in several computer vision and machine learning tasks. This paper describes the underlying architecture and various applications of Convolutional Neural Network.

The architecture, Engineering, and Construction (AEC) industry is dealing with serious challenges on a global scale, i.e., project delays, budget overruns, and insufficient stakeholder collaboration. In this context, Value Engineering (VE) and Building Information Modeling (BIM) have been used recently to address these challenges and cope with large-scale and complex construction projects. An extensive body of literature highlights significant advantages of integrated VE within BIM technology. Despite this, it is full practical potential has not yet been realized. For this reason, this paper aims to investigate the integration of Value Engineering (VE) and Building Information Modeling (BIM) within a Common Data Environment (CDE) for the Architecture, Engineering, and Construction (AEC) industry. In doing this research, a comprehensive framework was developed and systematically implemented into practice through the use of four interconnected modules: (M. 1); Creating CDE, (M. 2); Design BIM Model Development, (M. 3); Value Analysis / Engineering Model, and (M. 4); Value Engineering Study. Introducing a new term the ""VEIDEA"" databank based on the OmniClass classification system provides a structured database utilized to store, manage, and accumulate VE ideas. Moreover, the study incorporates the Analytical Hierarchy Process (AHP) to automate the evaluation phase. Such a framework seeks to assist designers and VE team members make informed decisions when choosing appropriate design alternatives. Lastly, a case study method was adopted to examine the proposed framework for an office building. Quantitative data showed a significant link between cost savings and VR integration with BIM technology. For instance, cost of the RC slabs dropped by 20%, while flooring materials cost dropped by a 39%. Empirically, evidence from the case study supports literature regarding t the potential for significant enhancements in both cost-effectiveness and overall project performance through this innovative integration. In the context of knowledge contribution, this study provides an innovative way to assist designers and VE team members in making well-informed decisions when choosing appropriate design alternatives.

In this digital era, online learning and teaching is an important platform for college and university learners which required many technical facilities and in depth knowledge to have learning process effectively. Students and teachers in Higher Education Institutions (HEIs) were forced to grapple with the inevitability of shifting to online classes due to the advantages of effective distance learning or for large learning group. In the case of Oman, there have been some efforts to integrate alternative learning mechanisms such as online and mobile education in the past but not followed the scale in the post pandemic world. This study seeks to utilize the principles of Total Quality Management (TQM) through the implementation of Quality Function Deployment (QFD) as a way to better understand the experiences of Omani teachers and students from HEIs. A voice of customer from teachers and students have been collected and recorded. In this regard, 165 staff and 355 students from HEIs of Engineering participated in the survey. After the survey, a house of quality analysis was done to analyze the requirements based on how strongly they are related to the standard learning outcomes expected from HEIs in Oman. The results show that more attention is required in online education in the Sultanate of Oman before it is implemented as a major part in learning and teaching.

Purpose: To compare the efficacy and safety of graft-free short tunnel small flap (STSF) technique with that of scleral patch graft (SPG) in Ahmed glaucoma valve (AGV) implantation. Design: Randomized clinical trial. Participants: Eighty-eyes of eighty patients with medically uncontrolled glaucoma including 41 in STSF and 39 eyes in SPG. Methods: Patients were enrolled and assigned randomly to STSF or SPG. Main Outcome Measures: tube exposure, Intraocular pressure (IOP), number of glaucoma medications, best corrected visual acuity (BCVA), surgical complications, and success rate ( defined as intraocular pressure (IOP) >5 mmHg, ≤21 mmHg, and IOP reduction ≥20% from baseline at two consecutive visits after three months, no reoperation for glaucoma). Results: only one case in SPG developed tube exposure at 1-year follow-up. The cumulative probability of success during the first year of follow-up was 70% in the STSF and 65% in SPG (P = 0.36). IOP decreased significantly from 29.6 ± 8.6 mmHg at baseline to 16.4 ± 3.6 mmHg at the final follow-up in STSF (p = 0.001). The corresponding numbers for SPG were 30.9 ± 11.2 and 15.8 ± 4.7, respectively (p = 0.001). The final IOP was comparable between both groups (p = 0.65). Mean ± standard deviation of the number of glaucoma medications was 1.8 ± 0.9 in STSF and 1.6 ± 0.9 in SPG at final follow-up (P = 0.32). Postoperative complications developed in 8 patients (19%) in STSF and 9 patients (23%) in SPG (P = 0.81). Conclusions: STSF and SPG techniques had comparable complication rate at one-year follow-up. Both techniques were comparable in terms of success rate, postoperative IOP, and glaucoma medications.

Embracing Fourth Industrial Revolution(4IR) techniques is a need for a long-term sustainability and to do automation in the processes of Industrial and education sector. Rapid advancement in the information technology and other engineering technologies demand more proactive steps in the education institutes. In the post pandemic world, there is an increasing demand of changes in teaching and learning methodologies with the support of modern digital technologies. So, all of the teaching institutes need to have a significant step change in the areas of learning and teaching. The impact of 4 IR is more on the education sectors compared to previous generation’s industrial revolution. Keeping track of key technologies can be quite a challenge as they keep evolving at a very fast pace. Fourth Industrial revolution is the current technological digital revolution in which major focus is on cyber physical and biological systems such as Artificial Intelligence, Robotics, Internet on Things, Virtual Reality etc., The educators and institutions should establish the pedagogies and teaching with the aid of Industry 4.0 techniques to evolve education 4.0. So, it is very important to understand the challenges and competencies required about the implementation of 4IR by the faculty members and students who are currently studying in Higher Education Institutes (HEIs). This paper aim is to do a systematic review about industry 4.0 techniques and its application in relate to the online learning and teaching, change in pedagogies, enhancement of data management in HEIs, digital and virtual laboratories etc.

Traditional metals such as stainless steel, titanium and cobalt chrome are used in biomedical applications (implants, scaffolds etc.) but suffer from issues such as osseointegration and compatibility with existing bone. One way to improve traditional biomaterials is to incorporate ceramics with these metals so that their mechanical properties can be similar to cortical bones. Tricalcium phosphate is such a ceramic with properties so that it can be used in human body. This research explores the use of binder jetting based additive manufacturing process to create a novel biocomposite made of cobalt chrome and tricalcium phosphate. Experiments were conducted and processing parameters were varied to study their effect on the printing of this biocomposite. Layer thickness, binder saturation and drying time affected the dimensional tolerance and the density of the green samples. This effect is important to understand so that the material can be optimized for use in specific applications.

One of the most prevalent illnesses, diabetes does not directly result in patient mortality. But, it increases the risk of death. Any disease that may be predicted in its early stages can lessen its fatal effects while also enhancing the quality of the healthcare system. For the early-stage prediction of diabetes or such types of non-communicable diseases, we need the proper set of influential features. This research has developed a machine learning-based disease prediction model to identify the influential features for diabetes prediction and give a near-perfect classification accuracy. This model includes Min-Max normalization for data normalization, Isolation Forest (iForest) for outlier removal, Synthetic Minority Oversampling Technique (SMOTE) for oversampling, Random Forest based Recursive Feature Elimination (RFE-RF) test, Chi-Square test, and Minimum Redundancy Maximum Relevancy (mRMR) test based feature selection methods for identifying the influential features, and Support Vector Machine (SVM), K Nearest Neighbor (KNN), and Naive Bayes (NB) for the classification. The results clarify that the proposed model outperforms the previous models and studies. The SVM has attained an accuracy of 99.58% in classification using the five features chosen from the Chi-Square test. Lastly, SHAP, an explainable AI model, has been used to assess the classifier model’s performance. These selected features and the classifier model can be used for early-stage diabetes prediction.

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

Dataspaces are decentralized and open ecosystems that guarantee trustworthy and secure sharing of data among their participants. In recent years, dataspaces have gained popularity due to their design for managing and sharing heterogeneous data from various sources and domains; and their capability to incrementally solve data integration issues. Leveraging dataspace and advanced technologies plays a vital role in solving many real-world applications effectively and efficiently in real-time. Drone technology is one type of technology that can be deployed to gather data from different resources in harsh or smart environments. Beyond fifth-generation (B5G) communication networks significantly contribute to drones’ development and widespread use by providing low latency and high throughput. Therefore, data sharing among drones in B5G networks offer significant potential to enhance commercial and civilian applications. However, several security issues for collaboration and data sharing, such as data privacy leakage, because of sensitive data and the lack of trustworthy centralized monitoring. Furthermore, sharing data is one of the essential requirements for drone collaboration to achieve their tasks effectively and efficiently in real-time. This conceptual framework presents a novel dataspace in the sky, focusing on securing drone data sharing in B5G for Industry 4.0 toward Industry 5.0. We present how Federated Learning (FL) assists drones in collaboration effectively and efficiently, sharing models instead of raw data. However, because of the fragility of the central curator, the reliability of contribution recording, and the poor quality of shared local models, there are still significant security and privacy issues for drone-assisted smart environments in B5G. Therefore, we present the conceptual framework for leveraging blockchain and FL to secure and manage data sharing of collaborative drones’ dataspace in space in a decentralized fashion. The decentralisation of dataspaces would significantly expand the drive and market for the development of citizen-friendly mobility services.

High-grade gliomas are extremely fatal tumors, marked by severe hypoxia and therapeutic resistance. Autophagy is a cellular degradative process that can be activated by hypoxia, ultimately resulting in tumor advancement and chemo-resistance. Our study aimed to examine the link between autophagy markers expression in low (LGG) and high-grade gliomas (HGG). In 39 glioma cases, we assessed protein expression of autophagy markers LC3B, p62, and DRAM by immunohistochemistry (IHC) and mRNA expression of autophagy genes; PI3K, AKT, m-TOR, PTEN, ULK1, ULK2, UVRAG, Beclin 1 and VPS34 using RT-qPCR. Livak’s method was used to analyze the relative changes in gene expression and multi-variate Anova test and Spearman's correlation tests were performed for statistical significance. LC3B, p62, and DRAM expression were positive in 64.1%, 51.3%, and 28.2% of glioma cases respectively. The expression of LC3B and p62 was notably higher in HGG compared to LGG. VPS34 exhibited a significant differential expression and increased fold change and demonstrated strong positive correlations with Beclin1 (rs=0.768), UVRAG (rs=0.802), and ULK2 (rs=0.786) in HGG compared to LGG, thus highlighting the potential link between autophagy and glioma progression. We Provide preliminary data for functional analysis of autophagy using cell culture model and to identify potential targets for therapeutic interventions

Google play store allow the user to download a mobile application (app) and user get inspired by the rating and reviews of the mobile app. A recent study analyzes that user preferences, user opinion for improvement, user sentiment about particular feature and detail with descriptions of experiences are very useful for an application developer. However, many application reviews are very large and difficult to process manually. Star rating is given of the whole application and the developer cannot analyze the single feature. In this research, we have scrapped 282,231 user reviews through different data scraping techniques. We have applied the text classification on these user reviews. We have applied different algorithms and find the precision, accuracy, F1 score and recall. In evaluated results, we have to also find the best algorithm.

From the hospital wastewater, a novel bacteriophage was isolated and characterized. According to characterization properties, this bacteriophage belongs to the Siphoviridae family, the maximum bacteriophage titer was recorded at 37°C and a pH of 7.2, had a 44,789 bp linear double-strand DNA genome, and within the genome sequence, there are 61 genes, all of which are encoded into proteins. Although this bacteriophage does not have any virulence factors or antimicrobial resistance genes and had specific lytic activity against some antimicrobial resistance S. aureus clinical isolates.

Saudi Arabia is an oil-reliant nation as a large percentage of its GDP comes from oil resources. Oil dependency leaves a county at the mercy of the international crude market, and a decrease in the price of crude can seriously destabilize the economy of such nations. An example is the case of Venezuela whose dependence on oil caused a national disaster (McCarthy, 2017). As such, the nation’s exports, GDP, and government revenue are primarily dependent on oil revenue, and the recent decrease in the oil prices has decreased Venezuela’s national revenue resulting in economic collapse as well as inflation. A shift from a resource based economy to a knowledge based economy will help Saudi Arabia become less reliant on its oil revenues for its economic stability and growth (Nurunnabi, 2017).

Annually, more than 1.5 million preventable new hepatitis B (HBV) infections continue to occur, with an estimated global burden of 296 million individuals living with chronic hepatitis B infection. This substantial health challenge results in over 820,000 annual deaths attributed to complications such as liver cirrhosis and hepatocellular carcinoma (HCC). HBV vaccination remains the cornerstone of public health policy to prevent chronic hepatitis B and its related complications. It serves as a crucial element in the global effort to eliminate HBV, as established by the World Health Organization (WHO), with an ambitious 90% vaccination target by 2030. However, reports on global birth dose coverage reveal substantial variability, with an overall coverage rate of only 46%. This comprehensive review thoroughly examines global trends in HBV vaccination coverage, investigating the profound impact of vaccination on HBV prevalence and its consequences across diverse populations, including both high-risk and general demographics. Additionally, the review addresses the formidable challenges and facilitating factors essential for achieving WHO's HBV vaccination coverage objectives and elimination strategies in the coming decade and beyond.

Alzheimer’s is a degenerative brain condition brought on by intricate brain changes after cell injury. The prevalence of Alzheimer’s disease is highest in those over 65. Presently, AD is detected by manual examination of a patient’s Magnetic Resonance Imaging (MRI) scan and neuropsychological assessments. Due to its subpar accuracy and performance, solutions based on artificial intelligence (AI) have not been made more widely available. This paper proposes a deep Convolutional Neural Network (CNN) with the pre-trained MobileNet-V2 architecture as the model’s base for feature extraction and reducing the parameter size and training time. The proposed model accurately classifies various early stages of AD. The proposed approach visualizes the class activation maps in the form of a heat map on brain MRI scans via the Grad-CAM algorithm that lifts the black-box nature of the deep model. To handle the imbalance problem of the AD data set acquired from Kaggle, the oversampling algorithm SMOTE interpolates new images to even the class samples. The proposed multi-class classification deep model is named Bio-AD and evaluated using different quality metrics. The Bio-AD model has the following values for evaluation metrics: 97.92%, 99.87%, 97.87%, 98%, 98%, and 0.0646 for accuracy, Area Under the Curve (AUC), F1-score, Precision, Recall, and loss respectively. The proposed Bio-AD model is compared with DenseNet-169, VGG-19, and InceptionResNet-V2 using different quality metrics and outperformed in comparison with all other models using the AD data set.

This paper presents a concise mathematical framework for investigating both feed-forward and backward process, during the training to learn model weights, of an artificial neural network (ANN). Inspired from the idea of the two-step rule for backpropagation, we define a notion of F_adjoint which is aimed at a better description of the backpropagation algorithm. In particular, by introducing the notions of F-propagation and F-adjoint through a deep neural network architecture, the backpropagation associated to a cost/loss function is proven to be completely characterized by the F-adjoint of the corresponding F-propagation relatively to the partial derivative, with respect to the inputs, of the cost function.

The objective of the study was to investigate the dynamics of collaborative learning in TBL through students’ reflections and feedback. The study utilized a mixed-methods approach. Quantitative data collection was done via feedback structured questionnaire. Qualitative data were collected from reflections submitted by 107 first-year MBBS students at the end of TBL session. We found five main themes related to students’ engagement with the session i.e. ( 1) conceptual clarity as the foundation, (2) collaborative learning culture, (3) communication skills, (4) informative and useful team discussions and (5) interactive learning for team development. The majority of the respondents (93%) listened to classmates and coordinated with group members in discussion, (77%) were willing to learn from other group members and contributed ideas to them, (79%) came prepared for session, (75%) found learning material assigned to them as appropriate. Respondents had positive perceptions regarding their participation in group work with a strong inclination towards agreement with the statements provided. The study highlights the positive effects of TBL in promoting peer to peer engagement, active engagement, teamwork skills, and knowledge sharing among undergraduate medical students, ultimately contributing to an effective learning experience.

This study examines the relationship between entrepreneurship and sustainable devel-opment in Saudi Arabia from 2006 to 2022, focusing on the economic, social, and en-vironmental dimensions. Using the Autoregressive Distributed Lag (ARDL) model, the research investigates both short-run and long-run dynamics to understand how entre-preneurial activities influence sustainable development within the framework of Saudi Arabia's Vision 2030 initiative. The findings indicate that entrepreneurship significantly contributes to economic growth through job creation and innovation, thereby aiding in the diversification of the economy away from oil dependency. Socially, entrepreneurial ini-tiatives have a positive impact on gender equality and social inclusion by empowering women and integrating youth into the labour market. Environmentally, entrepreneurship helps reduce carbon emissions and promotes sustainable business practices. Despite these positive outcomes, challenges such as regulatory barriers, limited access to finance, and the need for a supportive entrepreneurial ecosystem remain. Recommendations include enhancing entrepreneurial education, providing financial incentives, and streamlining regulatory processes to better support start-ups and SMEs. The study highlights the ne-cessity of addressing these challenges to fully realize the potential of entrepreneurship for sustainable development in Saudi Arabia. By providing empirical evidence on the sig-nificant role of entrepreneurship in driving sustainable development, this research offers actionable insights for policymakers aiming to foster a more resilient and diversified economy in line with Vision 2030. .

This study investigates the impact of intellectual capital on the sustainable performance of banks in Saudi Arabia, focusing on the period from 2012 to 2022. Utilizing data from ten major Saudi banks, the research examines how different components of intellectual capital—Human Capital Efficiency (HCE), Structural Capital Efficiency (SCE), and Capital Employed Efficiency (CEE)—influence key financial performance indicators such as Return on Equity (ROE) and Net Profit Margin (NPM). The study is grounded in the resource-based view of the firm, which suggests that a firm’s internal resources can provide a sustainable competitive advantage. The analysis employs panel data techniques, including fixed and random effects models, to assess the relationships between intellectual capital components and bank performance while controlling for variables like bank size, financial leverage, GDP growth, inflation, and the impact of the COVID-19 pandemic. The findings reveal that intellectual capital significantly enhances bank performance in Saudi Arabia, with human capital efficiency showing the most decisive influence on ROE and NPM. Structural capital and capital employed efficiency also contribute positively but vary in their impact on different performance measures. This research contributes to the theoretical and practical understanding of the role of intellectual capital in promoting sustainable banking practices. It underscores the importance of strategic management of intellectual capital in enhancing financial performance and achieving sustainability goals, particularly in the context of Saudi Arabia's Vision 2030. The study's insights are relevant for bank managers and policymakers who leverage intellectual capital for economic diversification and sustainable development.

Aiming at the limitations of single-functionality, limited applicability, and complex designs prevalent in current metasurfaces, we propose a terahertz multifunctional and multiband tunable metasurface utilizing a VO2-metal hybrid structure. This metasurface structure comprises a top VO2-metal resonance layer, a middle polyimide dielectric layer, and a gold film reflective layer at the bottom. This metasurface exhibits multifunctionality, operating independently of polarization and incident angle. The varying conductivity states of the VO2 layers, enable the metasurface to achieve various terahertz functionalities, including single-band absorption, broadband THz absorption, and multiband perfect polarization conversion for linear (LP) and circularly polarized (CP) incident waves. Finally, we believe that the functional adaptability of the proposed metasurface expands the repertoire of options available for future terahertz device designs.

This paper examines how housing prices are determined by macroeconomic factors in Saudi Arabia, namely, Gross Domestic Product Per capita (GDPP), Consumer Prices Index (CPI), and Unemployment Rate (UNEMP). Quarterly data for a period (2014q1 – 2019q4) were collected from publications of Saudi Arabian Monetary Authority (SAMA). Vector Autoregression Analysis (VAR) is employed to capture the dynamic effect of the variables on housing prices. Granger Causality, Variance Decomposition and Impulse response function are also used. The results show that housing prices are insignificantly and positively related to GDPP, whereas it is negatively related to both (CPI & UNEMP). Only CPI has a significant relationship. The three variables, jointly, have Granger causality on HPI. Variance decompositions show that CPI is the variable with the highest explanatory power over the variation of housing prices, followed by GDPP and the UNEMP respectively indicating that CPI is the most influential determinants for housing prices.

The paper draws attention and opens the debate on major societal issues that concern not only Muslims but human societies, at large, because of their profound implications, afflictions, and their present and future transitive dimensions. The paper discusses two major issues that have attracted a great deal of attention in the recent past: wealth and income disparities, and global indebtedness. Four remedies have stressed upon and/or proposed to eradicate and/or lessen some of the burdens of these problems have been thoroughly examined. These are: - Stripping commercial banks of their power to create money out of thin air (i.e., Ex Nihilo). Thus, reviving financial reform in line with the spirit of the call for the “Establishment of a National Bank [in England]” in the 1820s by the renowned British economist David Ricardo (1772-1823). The Bank of England was a private commercial entity at that time. - Prohibiting of ‘harmful’ speculation activities in financial and economic transactions and/or imposing hefty taxes on transactions relating to such activities. The implementation of such a policy requires and clear-cut distinction between the ‘harmful’ and ‘non-harmful’ speculative activities in financial and non-financial markets. - Levying adequate and fair taxes on the wealthiest upper class at the global scene. Calls for such a policy is on rise in the past few years. For instance, “Speakers at the European Development Days summit in Brussels, [in 2017], said that a just tax system should be “at the heart” of the development debate, and that more transparency is needed to ensure that companies pay their fair share. Tax avoidance schemes used by multinational companies, such as moving profits to tax havens, result in global losses of around $650 billion a year in tax receipts — with developing countries losing $200 billion, according to a study from the International Monetary Fund”, (Edwards, 2017; Crivello et al., 2015, p. 21) - Establishing a neutral legislative and tax regimes for different modes of financing. Thus, providing ‘a level playing field’ for equity and debt modes of financing due to the current biasness of the tax regimes to debt-based financing instruments as it has been documented in various studies carried out by the IMF, the European Commission, and other international prominent bodies.

The research article provides an in-depth analysis of the European Medicines Agency (EMA) and the Directorate General of Drug Administration (DGDA), focusing on their historical development, organizational structures, and functional roles in ensuring the quality and safety of pharmaceuticals. The study highlights the origins of drug regulation, tracing back to the early 19th century in the United States with the establishment of the Food and Drug Administration (FDA), and discusses the evolution and significance of drug regulatory bodies worldwide. The EMA and DGDA are scrutinized for their efforts in maintaining high standards for drug approval, monitoring clinical trials, and managing post-marketing surveillance to prevent adverse drug reactions (ADRs). The EMA’s role in coordinating with EU member states on pharmacovigilance and the DGDA’s initiatives in Bangladesh, including the implementation of a nationwide drug database and pharmacovigilance system, are explored in detail. The article concludes by emphasizing the essential role of these regulatory bodies in safeguarding public health through stringent regulation and continuous monitoring of pharmaceuticals, thereby ensuring that only safe and effective medicines are available to consumers . It also provides an in-depth analysis of the roles, structures, and historical contexts of the European Medicines Agency (EMA) and the Directorate General of Drug Administration (DGDA). It outlines how these regulatory bodies ensure the safety, efficacy, and quality of medicinal products. The EMA, serving the European Union, and the DGDA, operating in Bangladesh, play critical roles in drug approval processes, pharmacovigilance, and maintaining public health standards. By setting rigorous guidelines and monitoring compliance, these agencies help safeguard the health of millions, ensuring that only safe and effective medications are available in the market. The paper underscores the importance of these regulatory bodies in promoting public health and supporting the pharmaceutical industry's development and innovation.

The climate crisis necessitates a global shift to achieve a secure, sustainable, and affordable energy system toward a green energy transition reaching climate neutrality by 2050. Because of this, renewable energy sources have come to the forefront and the research interest in microgrids which rely on distributed generation and storage systems has exploded. Furthermore, many new markets for energy trading, ancillary services, and frequency reserve markets have provided attractive investment opportunities in exchange for balancing the supply and demand of electricity. Artificial intelligence can be utilized to locally optimize energy consumption, trade energy with the main grid, and participate in these markets. Reinforcement learning (RL) is one of the most promising approaches to achieve this goal because it enables an agent to learn optimal behavior in a microgrid by executing specific actions that maximize the long-term reward signal/function. The study focuses on testing two optimization algorithms: logic-based optimization and reinforcement learning. This paper proposes an optimization algorithm based on reinforcement learning in an industrial microgrid that is capable of trading energy with the main grid, providing significant cost savings. The RL-based approach is implemented in Python based on real data from the site and in combination with MATLAB-Simulink to validate its results. The application of the RL algorithm achieved an average monthly cost saving of 20% compared to logic-based optimization and 86% savings compared to not using any optimization. These findings contribute to digitalization and decarbonization of energy technology and support fundamental goals and policies of the European Green Deal.

This study delves into the dynamic field of medical education research, emphasizing the integration of research training within medical curricula. It seeks to understand the impact of such integration on the competencies of future medical professionals. The primary aim is to systematically categorize and analyze the current trends and future directions in medical education research. This involves assessing the influence of research training on medical students' skills and the methodologies used in such research. Methods: The research employs an extensive bibliographic literature review across multiple databases. It classifies studies into categories like experiential or case studies, editorials, and original research articles. This classification is based on criteria such as geographical location, research objectives, theoretical frameworks, and methodologies. Results: Findings reveal a diverse landscape in medical education research, with a significant emphasis on research training. The research showcases varying methodologies and approaches used globally, highlighting the thematic focus and geographical distribution of these studies. Conclusion: Medical education research is a globally expansive and evolving field. It underscores the importance of continuous investigation, particularly focusing on integrating research elements at curricular levels and exploring innovative educational strategies. The study also points out potential gaps in research, especially in underrepresented regions, indicating directions for future research efforts.

As prominent oil producers, the Gulf Cooperation Council (GCC) countries have played a significant role in the global energy market. However, as the world's attention increasingly shifts towards environmental sustainability, understanding the implications of the GCC's economic activities on CO2 emissions becomes indispensable. This research paper investigates the relationship between specific economic indicators and their impact on CO2 emissions in the GCC from 2001 to 2021. The study employs quantile regression, a powerful statistical method that estimates the conditional quantiles of a response variable given a set of predictor variables. The findings reveal several important insights: Financial institution efficiency is significant and negative at a 1% level at the lower (10th) -83537.3 and the higher quantile (90th) -549002.3. The relationship between GDP per capita and CO2 emissions varies across quantiles, highlighting the complexity of the growth-environment nexus. Total patents exhibit a positive and significant relationship with emissions, underscoring the importance of directing innovation towards environmentally sustainable solutions. Renewable energy consumption displays a nuanced relationship with CO2 emissions, with a stronger negative impact observed at higher consumption levels. This underscores the potential of renewable energy to mitigate emissions when integrated at scale. The study's outcomes hold crucial policy implications for the GCC countries as they seek to align economic growth with environmental sustainability. The findings emphasize the importance of fostering financial institution efficiency, promoting green innovation, and expanding renewable energy sources to reduce emissions

COVID-19 has substantially influenced healthcare systems, requiring early prognosis for innovative therapies and optimal results, especially in individuals with comorbidities. Healthcare practitioners have used AI systems for investigating, anticipating, and predicting diseases, through means including medication development, clinical trial analysis, and pandemic forecasting. This study proposes the use of AI to predict disease severity in terms of hospital mortality among COVID-19 patients. Methodology: A cross-sectional study was conducted the approval from the Research Ethics Committee of King Abdulaziz University (KAU), Saudi Arabia. The study used sequential sampling approaches to include 50 Real-Time Polymerase Chain Reaction (RT-PCR) positive COVID-19 patients from KAU's coronavirus isolation wards. A pre-designed form was used to collect each patient's demographic information, including age and gender, signs and symptoms, illness severity (mild, moderate, severe), and laboratory findings. Furthermore, the length of the hospital stay and the result, whether the patient recovered or died, were reported. Results: The study involved 50 patients with varying degrees of disease severity, most of whom suffered from fever, fatigue, cough, sore throat, and diarrhoea. Laboratory analysis of COVID-19 patients showed increased white blood cell and platelet counts, with C-reactive protein levels above normal. Elevated LDH levels indicated possible tissue damage, while ferritin levels were elevated. Other enzymes were in the normal range, but bilirubin levels were slightly elevated. Overall, the patient's lab results indicated inflammation and possible blood clot formation. The study evaluated the predictive accuracy for outcomes and mortality of COVID-19 patients based on factors such as CRP, LDH, Ferritin, ALP, Bilirubin, D-dimers, and hospital stay (p-value ≤0.05). The predictive accuracy mortality of patients with COVID-19 using AI showed Hospital stay, D-Dimers ALP, Bilirubin, LDH, CRP, and Ferritin significantly affected hospital mortality. (p ≤ 0.0001). Conclusion: Artificial Intelligence is crucial for identifying early coronavirus infections and monitoring patient conditions. It improves treatment consistency and decision-making via the development of algorithms. AI can track the crisis at various scales, facilitate research, and aid in developing treatment regimens, prevention strategies, and drugs and vaccines. It also aids in monitoring health and facilitating research on the virus.

Mesentery was discovered as a new organ in 2017. It is a continuous membranous tissue from the duodenojejunal flexure to the anorectal junction. It has distinct anatomy, physiology, and disease states. Primary mesenteropathies include vascular and non-vascular diseases. Some of them are common, and some of them are rarely seen in clinical practice. Secondary mesenteropathies occur when infection or malignancy in another organ spreads to the mesentery. Each entity has specific diagnostic and treatment protocols.

In this article, we show theoretically and experimentally the formation of spin-waves band gaps in a magnonic crystal that was implemented by the deposition of periodic micro-structured strips of magnetite nanoparticles. A theoretical model describing the spectra of the transmitted spin-waves bandgaps is proposed. This is achieved using a simple model based on microwave transmission line theory and considering the presence of micro-structured strips of magnetite nanoparticles on the surface. Such magnonic crystal of equally spaced micro-structured strips of magnetite nanoparticles on the surface of an yttrium iron garnet thin film has been implemented and measured. The periodic micro-structured nanoparticles are deposited on the surface of such yttrium iron garnet single-crystal film grown on a gallium-gadolinium garnet substrate via dip-coating technique. Propagation of magnetostatic surface spin-waves is studied and it is shown that the presence of such periodic structure leads to the formation of spin-wave band gaps in the transmission characteristics. The spin-wave detection has been carried out using a pair of microwave antennas and a vector network analyzer. The results show that the periodic structure formed by the magnetite strips modifies the spectra of the transmitted spin waves producing band gaps.

Waste management is a critical issue globally, and Bangladesh is no exception, facing significant challenges due to rapid urbanization, population growth, and inadequate infrastructure. This article explores comprehensive waste management strategies, including waste segregation, recycling, composting, and waste-to-energy technologies, emphasizing their environmental and health benefits. It examines the past and present waste management conditions in Bangladesh, highlighting the progress made and the persistent challenges. The article also compares waste management practices in neighboring countries like India, Bhutan, Sri Lanka, and Nepal, offering insights into potential improvements for Bangladesh. Emphasizing the need for public awareness, community involvement, and modern infrastructure, it calls for collaborative efforts to achieve sustainable waste management and environmental preservation in Bangladesh. This article examines the critical issue of waste management, emphasizing its importance in mitigating environmental pollution, conserving resources, and protecting public health. It provides a detailed analysis of various waste management strategies, including waste segregation, recycling, composting, and waste-to-energy technologies, highlighting their benefits and implementation challenges. Focusing on Bangladesh, the article traces the evolution of the country's waste management system, from past inadequacies to recent improvements, while acknowledging persistent issues such as rapid urbanization and insufficient infrastructure. Comparisons with waste management practices in neighboring countries like India, Bhutan, Sri Lanka, and Nepal offer valuable insights and potential strategies for Bangladesh to adopt. The article underscores the necessity of public awareness, community involvement, and robust infrastructure to achieve effective waste management. It calls for a collaborative approach involving government, private sector, NGOs, and local communities to develop sustainable solutions. By investing in modern waste management technologies and fostering responsible consumption and disposal habits, Bangladesh can address its waste management challenges, ensuring environmental preservation, public health, and overall national well-being.

Magneto-rheological dampers (MR-dampers) are increasingly used in construction applications to reduce the dynamic response of structures to seismic activities or severe wind loading. Sensors attached to the structure will signal the computer to supply the dampers with electric charge that transfers the MR fluid to a near-solid material with different physical and mechanical properties (viscoelastic behavior). Control algorithms govern the fluid to near-solid conversion, which controls the behavior of the damper, and the performance of the structure under the seismic or wind loading event. The successful optimization of control parameters minimize the overall structural response to dynamic forces. The main objective of this research is to change the output behavior of specific floors within a building subjected to seismic excitation by optimizing the MR-dampers control parameters to impact the behavior of a specific floor or number of floors within the building. The adjustment of control parameters to attain this objective was validated in multiple case studies throughout this research. The successful implementation of the research outcome will result in optimized MR-damper design to meet the performance-based criteria of building projects.

Inertial navigation system (INS) is a basic component for obtaining a continuous navigation solution in various applications. The INS navigation solution suffers from a growing error over time. In particular, its navigation solution depends mainly on the quality and grade of the inertial measuring unit (IMU) that provides the INS with both accelerations and angular rates. However, low-cost small micro-electro-mechanical systems (MEMS) suffer from tremendous error sources such as bias, scale factor, scale factor instability, and highly non-linear noise. Therefore, MEMS-IMU measurements lead to drifted solutions when used as a control input to the INS. Accordingly, several approaches have been introduced to model and mitigate the errors associated with IMU. In this paper, a machine learning-based adaptive neuro-fuzzy inference system (ML-ANFIS) is proposed to leverage the performance of low-grade IMUs in two phases. The first phase is training 50% of the low-grade IMU measurements with a high-end IMU to generate a suitable error model. The second phase involved testing the developed model on the remaining low-grade IMU measurements. A real road trajectory was conducted to evaluate the performance of the proposed algorithm. The results show the effectiveness of utilizing the proposed ML-ANFIS algorithm in removing errors and improving the INS solution compared to the traditional one. An improvement of 70% in the 2D-positioning and 92% in the 2D-velocity INS solution is attained when the proposed algorithm was applied compared to the traditional INS solution.

This study tries to focusing on applying information communication technology for economic importance of Mid Oil Company (MDOC), which is among contemporary oil companies in Iraq. Since ICT forms a significant aspect of achieving organizational economic objectives, most of public and private bodies seek to utilize ICT tool and techniques to improve work efficiencies. The emerging growth in ICT has made it process and service automation possible in managing organizational activities. To determine this effect of ICT on organizations, this study formulated statistical research model at MDOC through using fifteen questionnaires, which were distributed on three axes. The outcome of the questionnaire analysis revealed that there is inadequate comprehensive infrastructure to suitably support ICT within the selected organization management. In addition, it revealed that there are no ideal standards to maintain and sustain internet networks. Based on these revelations, the study presents recommendation for the adoption of ICT with the aim of minimizing cost, enhancing communication and managing infrastructure. Also recommends the need to support IT department within an organization, with essential equipment, in terms of man and machine, for better efficiency to employ the key of ICT Impact in Iraqi governmental oil companies.

The branding of cities through their historical and cultural assets has become a pivotal strategy in urban development and tourism. This study focuses on Erbil Citadel, a UNESCO World Heritage Site, and its potential as a brand for Erbil, a city with a rich historical backdrop and flourishing urban landscape. The study aimed to explore the perceptions of Erbil's residents regarding the cultural significance of the Citadel and its potential as a city brand. A mixed-method survey questionnaire was conducted, collecting data from around 440 residents through both manual distribution around the Citadel and an online platform. The findings were analysed using SPSS to provide both descriptive and inferential statistical insights.

High-Performance Liquid Chromatography (HPLC) is an advanced analytical technique employed to separate, identify, and quantify components in complex mixtures. The method operates based on the differential affinities of sample molecules for the mobile and stationary phases within a chromatographic column. HPLC is recognized for its high resolution, accuracy, and efficiency, making it indispensable across various fields including pharmaceuticals, biotechnology, environmental analysis, and forensic science. The technique's adaptability to different detectors such as UV-visible, fluorescence, and mass spectrometry enhances its versatility and precision in analyzing a wide array of substances. Despite its advantages, HPLC presents challenges such as high operational costs, the need for specialized expertise, and susceptibility to contamination.Nonetheless, its role in modern analytical chemistry is pivotal, providing crucial insights and supporting developments in diverse scientific and industrial domains. This abstract encapsulates the core aspects of the article, highlighting the significance and applications of HPLC while also acknowledging its limitations.

This paper deals with the techno-economic study of the hybrid renewable energy system based on energy storage aspect under the form of hydrogen and methane. Indeed, with the intermittency of the renewable energy sources such as photovoltaic and wind energy, several problems of produced energy injection to the power system network can be encountered due to the shortage or the excess of these sources. This situation appeals the use of systems that ensure the stability of network based on the storage of energy surplus into gas using electrolyzer systems, which will be used afterward to cover the eventual shortage. In the present paper, the study of performance of each pathway of methane and hydrogen storage has been performed by the treatment of multiple scenarios via different architecture case studies in an Algerian location. Whereas, the energy produced by the photovoltaic system, the wind energy and the gas micro turbine sources are considered similar in each case. The modeling and simulation of the studied system operation under optimization criteria has been performed in this work, where the main aim is to define the appropriate configuration taking into account the different with low costs of investment, maintenance operation and immediate reactivity with a big storage capacity.

In this paper, we investigate the behaviour of a relativistic quantum oscillator under the effects of Lorentz symmetry violation determined by a tensor (KF)µναβ out of the Standard Model Extension. We analyze the quantum system under a Coulomb-type radial electric field and a uniform magnetic induced by Lorentz symmetry breaking effects under a Cornell-type potential, and obtain the bound states solution by solving the Klein-Gordon oscillator. We see a quantum effect due to the dependence of the angular frequency of the oscillator on the quantum numbers of the system, and the energy eigenvalues and the wave-function of the oscillator field get modified by the Lorentz symmetry breaking parameters as well as due to the presence of Cornell-type potential.

Tamarix nilotica (Ehrenb.) Bunge (Tamaricaceae), an indigenous plant to the Middle East region, is well-known as a medicinal plant for treating many human ailments. The current study aimed at exploring the polyphenols profile of T.nilotica alcohol soluble fraction of aqueous extract, assessing its in-vivo antifibrotic activity and the possible underlying mechanism, to unravel the impact of quantitative difference of sulphated polyphenols content on the antifibrotic activity of T.nilotca grown in two different habitats. Polyphenols profiling of T. nilotica extracts was performed using HPLC-HRESI-QTOF-MS-MS. The major polyphenol components included sulphated flavonoids, phenolic acids and free aglycones. The antifibrotic activity was evaluated through carbon tetrachloride-induced liver fibrosis in rats. Biochemical evaluations revealed that both fractions ameliorated the increased levels of hepatic aminotransferases, lipid peroxidation, hydroxyproline, α-smooth muscle actin (α-SMA), tumor necrosis factor-α (TNF-α), cyclooxygenase-2 (COX-2) and nuclear factor kappa B (NF-κB). Moreover, both fractions reduced catalase activity (CAT) and enhanced hepatic glutathione (GSH) content. Histopathological imaging undoubtedly confirmed such results. In conclusion, T. nilotica polyphenols rich fraction exhibited potential antifibrotic activity in rats. Significant alterations in GSH levels were recorded based on sulphated polyphenol metabolites content.

Systematics plays the most crucial role in biodiversity conservation which is at stake due to anthropogenic activities and environmental degradations. The ever-increasing decline of classical taxonomic expertise drives the need to develop molecular marker-based tools for quick, efficient, and reliable detection of organisms, to assess their ecological impacts for deepening our understanding of systematic and evolutionary relationships between organisms which is central to biology. The pace of alpha taxonomy has quickened by its integration with an increasingly fashionable and novel concept called DNA barcoding which utilizes a short genetic marker or barcode to categorize species for enhanced biodiversity assessment. As a supplementary but not complete alternative of systematics research, DNA barcoding, however, not error-free, brings precision in identification by solving existing problems of classical taxonomy and phylogenetics, irrespective of the growth stage of organisms, particularly for known taxa rather than unknown ones. Mitochondrial gene Cytochrome C Oxidase 1 (COI) serves as a universal animal barcode but there is no such universal barcode for plants and developing a suitable one is more challenging. With the recent advancement of Next Generation Sequencing (NGS), DNA metabarcoding technology is advancing rapidly. Still, ambiguity and error prevail with the correct identification of species due to some problems. After extensive analysis of the existing DNA barcoding papers, this review discusses commonly used DNA barcodes in plants and animals, their roles, advantages, limitations to solve existing problems of conservation biology and add the author’s views and recommendations.

The general objective of the study is to analyze food insecurity determinants mainly related to food price at macro level. The study used a time series data from the year 1988 quarter one to 2018 quarter four. Our empirical finding on the macroeconomic model show the short term consequence of food price change is positive with food security (negative with food insecurity). The long term consequence of food price change is, however, negative with food security (positive with food insecurity) at macro level. From the variance decomposition, it can be learned that the speed by which the undernourished people decrease in number in the short term because of the inflation happened is higher than the speed by which the number of undernourished people increase in the long run. At macro level, concern should be made to the supply side for food security improvement.

In this paper, a relativistic scalar particle under Lorentz symmetry breaking effects in the presence of a scalar potential is investigated. We introduce the scalar potential by modifying the mass via transformation M → M+S(r) in the wave equation and analyze the behaviour of a scalar particle. We see that the analytical solution to the KleinGordon equation can be achieved, and the energy eigenvalues and the wave function depends on the Lorentz symmetry breaking parameters as well as potential

Background and Objectives: In Libya, prescription medicines can easily be dispensed without a prescription, as self-medication with the subsequent of potential misuse and unnecessary risk for patients. This systematic review and meta-analysis explored the prevalence of self-medication among citizens in Libya. Methods: We systematically searched PubMed, ISI Web of Knowledge, EMBASE, Medline, Scopus, google scholar and ResearchGate databases for studies published in Libya reporting the prevalence of non-prescription use of medicines in community pharmacies, targeting studies published from Jan 2008 until Dec 2019. A random meta-analysis was performed to analyze pooled estimates of non-prescription usage of medicines. Results: Out of 63 articles identified, a total of 13 papers from 7 cities were met the inclusion criteria and involved a total of 4741 subjects. The overall pooled proportion of self-medication of drugs was 53.6% (95% CI: 0.93% - 1.08%), with low heterogeneity and the P-value of the whole population tasted was found to be = 1.000 (df = 12). Seven out of the 13 studies reported data on self-medication for antibiotics without prescription. The records ranged from 15.3% (95% CI 0.61–1.65) in Misurata to 76.6% (95% CI 0.80–1.25) in Tripoli. Conclusion: Self-medication use of medicines among Libyan population is a common phenomenon involving a high proportion use of antibiotics. This misuse of medications could enhance the development and spread of antibiotic resistance.

Polymorphism of vitamin D3 receptor (VDR), has been associated with low bone mineral density and other immune and metabolic disorders; however, its impact on mortality of female dialysis patients is not well studied. This study aimed to identify bone mass-related factors, VDR gene polymorphism, and their interaction with morbid conditions that could influence all-cause mortality. In 246 female dialysis patients, age 43±11 years on continuous ambulatory dialysis peritoneal; 48%, haemodialysis; 23% and automated peritoneal dialysis; 29%. Tscore, Ca, PO4, albumin, hs-CRP, osteoprotegerin, fetuin, osteocalcin, iPTH, PINP and β-CTx were measured. PCR products were digested with Bsml to analyze VDR polymorphisms. Patients n=229; were followed for a median of 17 (15-31) months; 42 patients died. Bsml polymorphism, bb=64% and BB+Bb=36%. Hs-CRP was the risk of death in multivariate Cox Analysis. Patients with bb and inflammation had a higher risk of death (HR 2.48, 95% CI 1.08-5.68) persisted after adjustment for age, diabetes and iPTH (HR 2.33; 95% CI, 1.01-8.33) and after further adjustment for vintage, albumin, osteoprotegerin and vitamin D therapy (HR 3.49; 95% CI, 1.20-10.9). We may conclude that the presences of the bb genotype of VDR and inflammation have additive effects on all cause-mortality in females in CAPD, APD, and HD patients.

The purpose of this study was to develop an in-vitro digestion protocol to evaluate the antioxidant potential of the peptides found in processed cheddar cheese using digestion enzymes. We studied first antioxidant and angiotensin converting enzyme (ACE) inhibition and antioxidant activities of processed cheddar cheese with the addition of spices e.g. cumin, clove and black pepper made from buffalo milk and ripened for 9 months. Then we conducted an in vitro digestion of processed cheddar cheese by gastric and duodenal enzymes. Freeze dried water (WSE) and ethanol soluble fractions (ESE) of processed cheddar cheese were also monitored for their ACE inhibition activity and antioxidant activities. In our preliminary experiments, different levels of spices (cumin, clove and black pepper) were tested into cheese matrix and only one level 0.2g/100g (0.2%) on the basis of cheese weight was considered good concerning sensory evaluation. Significant increase in ACE-inhibition (%) of processed Cheddar cheese as well as its WSE and ESE was obtained. Lower IC50 values were found after duodenal phase digestion compared to oral phase digestion.

Background Apolipoprotein E (APOE) gene polymorphism has been implicated in the pathogenesis of various metabolic disorders, including type 2 diabetes mellitus (T2DM). Type 2 diabetes mellitus (T2DM) is a major public health concern worldwide, including in Pakistan. Cardiovascular problems linked with T2DM have a significant impact on people and society. The study's goal is to investigate the relationship between Apolipoprotein E (ApoE) genotypes, lipid profiles, and cardiovascular complications such as ischemic heart disease (IHD) and stroke. Methods: This study was carried out on 260 subjects, divided into control D1: Diabetics without cardiovascular issues. D2: Diabetics with heart disease. D3: Diabetics with stroke. D4: Diabetics with both heart disease and stroke. Anthropometric parameters (age, BMI) and risk factors (smoking, diabetes duration, hypertension) were assessed in all groups. Serum levels of TC, TG, LDL, HDL, VLDL, creatinine, BSF, and HbA1c were measured. Apolipoprotein E gene polymorphism was determined using PCR-RFLP. Results: Hypertension, BMI, and dyslipidemia, defined as elevated levels of total cholesterol, triglycerides, LDL, HDL, and VLDL, as well as uncontrolled hyperglycemia (elevated fasting blood sugar and glycated hemoglobin) in T2DM, were linked to vascular complications such as IHD and stroke. Hypertension was prevalent in 75.4% of the population, with greater rates among the elderly. Stage 2 hypertension was more prevalent in all age groups. Apolipoprotein E gene polymorphism was discovered, with genotypes 3/3, 4/4, 2/3, and 3/4 being the most prevalent. Genotype 3/4 was more common in diabetics who had IHD/stroke or both problems, whereas genotype 2/3 was linked to DM and stroke. Conclusion: It is concluded that BMI, hypertension, hyperglycemia, atherosclerosis, and dyslipidemia are linked with cardiovascular complications of type 2 diabetes. Apolipoprotein E gene polymorphism is associated with cardiovascular disease in diabetic patients by affecting the lipid profile.

The advent of the digital age has introduced unprecedented opportunities for communication and connectivity, but it has also given rise to new forms of harassment, collectively termed cyber harassment. This research article provides an in-depth exploration of cyber harassment, examining its prevalence, the profound impact it has on victims, and potential solutions to mitigate its effects.The study is structured around three primary research objectives. Firstly, it investigates the psychological impact of cyber harassment on victims, highlighting the severe emotional and mental health consequences that can arise from such abuse. Secondly, the article scrutinizes the role of social media platforms in both facilitating and combating cyber harassment, analyzing how these platforms can act as both enablers of harassment and as venues for intervention. Thirdly, the research evaluates the current legal frameworks and responses to cyber harassment, considering their effectiveness and suggesting possible improvements. Recent research findings and diverse methodologies are integrated to provide a comprehensive understanding of the multi-faceted nature of cyber harassment. By addressing these key areas, the article aims to contribute to the development of more effective strategies to prevent and respond to cyber harassment, ultimately fostering a safer online environment.

We study a localization of gravity through the matching point between non-inertial frames and local inertial frames. This localization of gravity is done through defining relative gravitational red-shift. This lead to an emergence of a timeless state in a mathematically consistent way. In this timeless state of gravity, we find a geometric interpretation of the speed of light and mass. The experimental support of the timeless state is the quantum entanglement and internal symmetries that are independent of time. Therefore gravity would be responsible for measurements independent of time including quantum entanglement. Timeless state in that sense explain why do we have gravity/gauge correspondence. We conjecture that the universe emerged from a black hole with a global $SU(3)\times SU(2)\times U(1)$ symmetry on its event horizon that produces gauge fields Electromagnetism, weak and strong nuclear force through localization of this global symmetry. Through the localization in the gravity field, the timeless measurements such as spin will be correlated with the varying of timeless measurement which is relative gravitational red-shift. We present a gravitational or geometric interpretation of spin-0, spin-1, and spin-1/2 states. We present an interpretation of why do we measure matter rather than anti-matter. We found that the Higgs scalar field is represented by the gravitational red-shift at every point in the space around the black hole. We derive the numerical value of the cosmological constant that agrees with experimental observations.

Perilla frutescens (L.) Britt. (PF) is an annual herbal medicinal, aromatic, functional food and ornamental plant that belongs to the mint family, Lamiaceae. The origin of perilla traces back to East Asian countries (China, Japan, Korea, Taiwan, Vietnam and India), where it has been used as a valuable source of culinary and traditional medicinal uses. Leaves, seeds and stems of P. frutescens are used for various therapeutic applications in folk medicine. In the absence of comprehensive review regarding all aspects of perilla, thus this review aims to present an overview pertaining to the botanical drug, ethnobotany, phytochemistry and biological activity. It was found that the taxonomic classification of perilla species is quite confused, and the number of species is vague. Perilla has traditionally been prescribed to treat depression-related disease, anxiety, asthma, chest stuffiness, vomiting, cough, cold, flus, phlegm, tumour, allergy, intoxication, fever, headache, stuffy nose, constipation, abdominal pain, indigestion, analgesic, anti-abortive agent, and sedative. Until now, 271 natural molecules have been identified in perilla organs including; polyphenols, flavonoids, essential oils, triterpenes, carotenoids, phytosterols, fatty acids, tocopherols and policosanols. In addition to solvent extracts, these individual compounds (rosmarinic acid, perillaldehyde, luteolin, apigenin, tormentic acid, isoegomaketone) have attracted researchers' interest for pharmacological properties. Its bioactivity showed antioxidant, antimicrobial, anti-allergic, antidepressant, anti-inflammatory, anticancer, neuroprotection activity. Although the results are promising in preclinical studies (in vitro and in vivo) as well, clinical studies are insufficient, therefore further study needs to be done to validate its therapeutic effects and to ensure its safety and efficacy.

Few prior works study self-driving cars by deep learning with IoT collaboration. SDC-Net, which is an end-to-end multitask self-driving car camera cocoon IoT-based system, is one of the research that tackles this direction. However, by design SDC-Net is not able to identify the accident locations, it only classifies if it is a crash scene or not. In this work, we introduce an enhanced design for the SDC-Net system by 1) replacing the classification network with a detection one, 2) adapting our benchmark dataset labels built on the CARLA simulator to include the vehicles bounding boxes while keeping the same training, validation, and testing samples, 3) modifying the shared information via IoT to include the accident location. We keep the same path planning and automatic emergency braking network, the digital automation platform, and the input representations to formulate the comparative study. SDC-Net++ system is proposed to 1) output the relevant control actions, especially in case of accidents: accelerate, decelerate, maneuver, and brake, and 2) share the most critical information to the connected vehicles via IoT, especially the accident locations. A comparative study is also conducted between SDC-Net and SDC-Net++ with the same input representations: front camera only, panorama and bird-eye views, and with single-task networks: crash avoidance only, and multitask networks. Multitask network with a BEV input representation outperforms the nearest representation in precision, recall, f1-score, and accuracy by more than 15.134%, 12.046%, 13.593%, and 5%, respectively. SDC-Net++ multitask network with BEV outperforms SDC-Net multitask with BEV in precision, recall, f1-score, accuracy, and average MSE by more than 2.201%, 2.8%, 2.505%, 2%, and 18.677% respectively.