Abstract: The empirical soil loss model, RUSLE, was used with a conjunction of remotely sensed data and geographic information system technology to delineate the soil erosion and watershed priorities in terms of conservation practices at seven boundary sub-watersheds (labeled as SW-00, SW-01, …, SW-06) between Iraq and Iran at Ali Al-Gharbi area, southern Iraq. The six factors of the RUSLE model, ie. the rainfall erosivity, the soil erodibility, the slope steepness length, the crop management, and management practice, were calculated or estimated using information from different data sources such as remotely sensed data and previous studies. The finding showed that the annual soil erosion loss ranges from 0 - 1890 (tons h-1 y-1) with an average of 0.66 (tons h-1 y-1). Values of soil erosion were classified into five classes: very low, low, moderate, high, and very high. The potential soil loss in the high and very high classes ranges from 14.84 to 1890 (tons h-1 y-1), and these classes occupy only 27 km2 of the study area, indicating that the soil loss is very low in the area being examined. In terms of the spatial distribution of soil loss, the northern and northeastern parts (mountains and hills) of the sub-watersheds where the slope is steeper are more likely to erode than the plain area in the southern and southeastern portions, indicating that slope, in addition to rainfall erosivity, has a dominant effect on the soil erosion rate. The study of soil erosion in the watersheds under consideration reveals that only the northern portions of the SW-00, SW-02, and SW-04 watersheds require high priority conservation plans; however, these portions are primarily located in mountain regions, making conservation plans implementation in these areas impractical. Due to low soil loss, other sub-watersheds, particularly SW-01, SW-03, SW-05, and SW-06, are given low priority.

We describe a 90-year-old male presenting with disequilibrium, loss of balance and difficulty walking for three days prior to initial presentation. Interestingly, he denied cough, fever or dyspnea prior to arrival. Over the course of 48 hours, the patient developed acute respiratory distress syndrome (ARDS) requiring intubation, diagnosed with COVID-19 infection and was treated in the intensive care unit where he died. Since the initial cases in Wuhan China in Dec 2019, the medical and epidemiological communities have learned much about the presenting features, symptomatology, epidemiology, transmission and common physical, laboratory and radiological findings of this disease. Although common symptoms are already established, it is very important to learn and record atypical symptoms or presentations of this highly contagious disease. By doing so, we will be able to recognize earlier atypical symptoms and prevent the environmental exposure to Health care workers and future patients as well. We report that Central disequilibrium may be such as initial presenting sign and symptom of impending respiratory failure from SARS-CoV-2 virus. These atypical findings such as presyncope may precede common respiratory complications of SARS-CoV-2.

Synthetic materials such as Carbon, Kevlar, and Glass fibers are extensively utilized in various industries due to their superior characteristics including high stiffness, great strength, low den-sity, and high wear resistance, these advantages contributed to making them widespread in ap-plications like; aerospace, automobile, civil and electronics. This research is carried out to enhance the creep compliance of epoxy composite material by considering two types of fibers as reinforcement. A composite material is produced by combining two fibers, Kevlar fiber mixed with sika epoxy resin, along with Carbon fiber also mixed with sika epoxy resin using a Hand-Layup process. Experiments and numerical modeling are examined to offer a comprehensive understanding creep behavior of epoxy composite material in different weight ratios. Burger and Prony models are used to represent stress relaxation and creep strain of Visco-elastic material. According to the results, sika epoxy resin reinforced with Kevlar and Carbon fibers exhibits enhancement in the mechanical properties compared with pure epoxy. The use of Kev-lar/Sika epoxy composite results in higher creep compliance and modulus of elasticity compared with Carbon/Sika epoxy composite.

The implementation of systems condition monitoring is of utmost importance in ensuring effective maintenance, optimal efficiency, and adherence to established design criteria. This research presents a novel and economically efficient Digital Twin Model (DTM) designed for the purpose of Live Condition Monitoring (LCM) and Predictive Lifecycles. This study discusses the enhancement of DTs modelling by decreasing the number of dimensions from five to three: Physical, Digital, and Connection entities. The resulting model exhibits a notable improvement in accuracy and efficiency. The suggested Dynamic Time Model (DTM) enhances the empirical pre-set average load for both simulation and experimentation by 35.7%, which is equivalent to a 1.6-fold increase. The DTM demonstrates a significant enhancement in the empirical predefined average lifecycles of the system, as shown in the real-life case study. Specifically, the DTM achieves a 12-fold improvement compared to the simulated results and a nine-fold improvement compared to the experimental findings. The suggested Dynamic Time Management (DTM) demonstrates a significant enhancement in the average lifecycles of the system, with a 19.7% improvement (equivalent to 1.2 times greater) compared to the wireless DTM results obtained through the application of the real system load. The suggested Dynamic Time Warping (DTW) method's accuracy and efficiency are shown via a practical case study using the suspension system of a Peugeot 3008 vehicle.

The current study aims to fermentation parameter for producing microbial products. Microwave parameters consist on 245 MHz, and 600-watt for 60 seconds. Ethyl acetate was the best solvent used for extraction purposes. Antioxidant properties were differentiated by blocking the oxidation of the linoleic acid with an inhibition rate of 73.13% at a concentration of 200 mg/mL, in addition to increasing its effectiveness for free radical extraction and reduction strength by increasing concentrations gradually. The bond ability to irons was lower compared to the EDTA-2Na, in addition to the obtained total content corresponding to phenolic compounds in the ethyl acetate extract of fermented rice (Koji) by A. flavus was 232.11 mg, on the basis of galic acid/mg. The stability of the antioxidant compounds of the ethyl acetate extract of fermented rice (Koji) by A. flavus was also studied; showing stability under neutral conditions, as well as at high temperatures (185 °C during two hours). However, no stability was obtained under acidic and alkaline conditions.

Artificial roughness applied to the Solar Air Heater (SAH)’s absorber plate is a popular technique to increase its total-thermal efficiency (η_(t-th)). In this paper, the influence of geometrical parameters of V-down ribs attached below the corrugated absorbing plate of SAH on the η_(t-th) has been examined. The impacts of key roughness parameters, including relative pitch p⁄e (6-12), relative height e⁄D (0.019-0.043), angle of attack α (30°-75°), and Re (1000-20000), are examined under real weather conditions. The SAH η_(t-th) roughened by V-down ribs is predicted using an in-house developed conjugate heat transfer numerical model. It has been shown that the maximum SAH η_(t-th) is 78.8%, predicted under steady-state conditions of Re = 20000, solar irradiance G =1000 W/m2, p/e = 8, e/D = 0.043, and angle =60. That is 15.7% more efficient than that of the default smooth surface. Under real weather condition, the η_(t-th) of the roughened SAH with single and double glass covers are 17.7% and 20.1%, respectively, higher than those of smooth SAH.

The Ali-Cesaro Stolz theorem, introduced in this paper, extends the classical Cesaro-Stolz theorem by addressing cases where the latter does not yield results or provides a "Does Not Existe (DNE) form. This novel theorem leverages the properties of limits and Z-transforms, offering a robust framework for analyzing sequences and series. The theorem is particularly effective in scenarios where traditional approaches fail, providing new insights and tools for mathematical analysis. This paper presents a detailed formulation of the Ali-Cesaro Stolz theorem, including proofs, applications, and a discussion on its implications and advantages over existing methods.

Underwater Wireless Sensor Networks (UWSN) enables various oceanic applications which require effective packet transmission. In this case, sparse node distribution, dynamic network topology and inappropriate selection of relay nodes cause void holes. Addressing this problem, we present a Relay based Void Hole Prevention and Repair protocol (ReVOHPR) by multiple Autonomous Underwater Vehicles (AUV) for UWSN. ReVOHPR efficiently identifies and avoids void holes and trap relay nodes to avoid it. ReVOHPR adopts the following operations as Ocean Depth (levels) based Equal Cluster Formation, Dynamic Sleep Scheduling, Virtual Graph based Routing, and Relay Assisted Void Hole Repair. For energy efficient cluster forming, Entropy based Eligibility Ranking (E2R) is presented which elects stable cluster heads (CHs). Then, dynamic sleep scheduling is implemented Dynamic Kernel Kalman Filter (DK2F) algorithm in which Sleep and Active modes based on the nodes current status. Inter Cluster Routing is performed by maximum matching nodes which selects by Dual criteria and also data transmitted to AUV. Finally, void holes are detected and repair by Bi-Criteria Mayfly Optimization (BiCMO) algorithm. The BiCMO focuses on reducing the number of holes, data packet loss and maximizes Quality of Service (QoS) and energy efficiency of the networks. This protocol is timely deal with node failures in packet transmission via multi-hop routing. Simulation is implemented by NS3 (AquaSim module) simulator that evaluates the performance in network simulation for following metrics as average energy consumption, delay, packet delivery rate and throughput.

This paper introduces a novel algorithm for solving the point-to-point shortest path problem in a static regular 8-neighbor connectivity (G8) grid. This algorithm can be seen as a generalization of Hadlock algorithm to G8 grids, and is shown to be theoretically equivalent to the relaxed A* (RA*) algorithm in terms of the provided solution's path length, but with substantial time and memory savings, due to a completely different computation strategy, based on defining a set of lookup matrices. Through an experimental study on grid maps of various types and sizes (1290 runs on 43 maps), it is proven to be 2.25 times faster than RA* and 17 times faster than the original A*, in average. Moreover, it is more memory-efficient, since it does not need to store a G score matrix.

In videos, the human's actions are of three-dimensional (3D) signals. These videos investigate the spatiotemporal knowledge of human behavior. The promising ability is investigated using 3D convolution neural networks (CNNs). The 3D CNNs have not yet achieved high output for their well-established two-dimensional (2D) equivalents in still photographs. Board 3D Convolutional Memory and Spatiotemporal fusion face training difficulty preventing 3D CNN from accomplishing remarkable evaluation. In this paper, we implement Hybrid Deep Learning Architecture that combines STIP and 3D CNN features to enhance the performance of 3D videos effectively. After implementation, the more detailed and deeper charting for training in each circle of space-time fusion. The training model further enhances the results after handling complicated evaluations of models. The video classification model is used in this implemented model. Intelligent 3D Network Protocol for Multimedia Data Classification using Deep Learning is introduced to further understand space-time association in human endeavors. In the implementation of the result, the well-known dataset, i.e., UCF101 to, evaluates the performance of the proposed hybrid technique. The results beat the proposed hybrid technique that substantially beats the initial 3D CNNs. The results are compared with state-of-the-art frameworks from literature for action recognition on UCF101 with an accuracy of 95%.

Supplier selection/replacement strategies and optimized purchasing policies play a key role in efficient supply chain management in today’s dynamic market. Here we study supplier replacement in a one-level assembly system (OLAS) producing one type of finished product. To assemble the product, we need to provide multi-type components, but assembly will be interrupted if any single component is missing, and incoming units will get hoarded until the missing component arrives. The assembly process can be interrupted by various sources of uncertainty, including delays in component deliveries. There is consequently a non-negligible risk that the assembly process may get stopped any moment. This brings inventory-related costs, which should be minimized. Here we consider discrete lead-time distributions to mimic industry-world reality. We present a model that takes into account not only optimal assignment of component order release dates but also replacement of a critical supplier. For a given unit, we model several alternative suppliers with alternative pricing and lead-time uncertainties, and we evaluate the impact on the total assembly system. For a more general case where several suppliers may be replaced, we propose a genetic algorithm.

The current revolution in communication and information technology is facilitating the Internet of Things (IoT) infrastructure. Wireless Sensor Networks (WSN) are a broad category of IoT applications. However, power management in WSN poses a significant challenge when the WSN is required to operate for a long duration without the presence of a consistent power source. In this paper, we develop a batteryless, ultra-low-power Wireless Sensor Transmission Unit (WSTx) depending on the solar-energy harvester and LoRa technology. We investigate the feasibility of harvesting ambient indoor light using polycrystalline photovoltaic (PV) cells with a maximum power of 1.4mW. The study provides comprehensive power management design details and a description of the anticipated challenges. The power consumption of the developed WSTx was 21.09µW during the sleep mode and 11.1mW during the operation mode. The harvesting system can harvest energy up to 1.2mW per second, where the harvested energy can power the WSTx for six hours with a maximum power efficiency of 85.714%.

This study presents the design and comprehensive 3D multiphysics simulation of a novel microfluidic immunosensor for real-time, non-invasive monitoring of pro-inflammatory biomarkers in human sweat. The patch-like device, designed using COMSOL Multiphysics, integrates magnetofluidic manipulation with direct-field capacitive sensing. The sensor comprises two distinct units: an immunocomplex enhancement unit, employing a series of microcoils to optimize the binding efficiency, reaction kinetics, and homogeneity of biomarker-magnetic nanoparticle (MNP) interactions, thereby enhancing the sensor's specificity; and a layered capacitive sensing unit, designed to concentrate and detect biomarker-tagged MNPs, thus amplifying sensitivity. Simulations of the capacitive sensing unit revealed a substantial sensitivity increase of up to 42.48\% at an 85\% MNP concentration within the detection zone. These findings highlight the potential of the proposed immunosensor for efficient and precise real-time biomarker monitoring, which may facilitate early disease diagnosis and enable personalized healthcare interventions.

The present study was designed to investigate the bioactive compound in Rumextunetanus extracts (polygonaceae), a plant growing in GarâaSejnane region (NW-Tunisia). Hydro-methanol extracts of flowers and stems of Rumextunetanus were analyzed by RP-UHPLC-ESI-QTOF-MS in the negative mode to identify the maximum of bioactive compounds. Applied the aforementioned method, a total of 60 bioactive compounds were characterized for the first time in Rumextunetanus between them, 18 photochemical were firstly identified in the Polygonaceae family in negative ionization mode. Quantification of the identified compounds revealed that quercetin-3-O-glucuronide and (-)-epicatechingallate were the most abundant phenolic compounds in flowers and stems, respectively. Moreover, positive correlations were found between the antioxidant activity measured by DPPH and FRAP assays with the total phenolic compounds (r = 0.98; r = 0.99, respectively) and the abundance of some phenolic subfamilies such as hydroxycinnamic acids, hydroxybenzoic acids, flavonols and flavones with r > 0.86. The compounds displaying significant (P < 0.01) and good correlations with the antioxidant activity (r > 0.93) were hydroxybenzoic acid, rutin, quercetin-3-O-glucuronide, quercetin-3-O-glucoside, quercetin and luteolin-7-O-rutinoside. In addition, the flowers and stems of Rumex tunetanus showed different bioactive compound profiles and significant antioxidant properties of extracts. These results highlight the potential of the RP-UHPLC-ESI-QTOF-MS and MS/MS system to identify untargeted metabolic profiling of Rumex tunetanus. Overall, these results contribute to the clear explanation of the past and current usage of genus Rumex in folk medicine. Future investigations are necessary to develop purified antioxidant extracts, with the application of more selective extraction techniques.

Herein, we have fabricated graphitic carbon nitride (g-C3N4) nanosheets with embedded ZnCdS nanoparticles to form a type II heterojunction using a facile synthesis approach and used for photocatalytic H2 production. The morphologies, chemical structure and optical properties of the obtained g-C3N4‒ZnCdS samples were characterized by a battery of techniques such as, TEM, XRD, XPS and UV-Vis DRS. The as-synthesized g-C3N4‒ZnCdS, photocatalyst exhibits the highest hydrogen production rate of 108.9 μmol·g-1·h-1 compared to individual components (g-C3N4: 13.5 μmol·g-1·h-1, ZnCdS: 45.3 μmol·g-1·h-1). The improvement of its photocatalytic activity is mainly attributed to the heterojunction formation and resulting synergistic effect, which provide more channels for charge carrier migration and reduce the recombination of photogenerated electrons and holes. Meanwhile, the g-C3N4‒ZnCdS heterojunction catalyst also showed higher stability over a number of repeated cycles. Our work sheds insight of using g-C3N4 and metal sulfide combination to develop low-cost, efficient, visible-light active hydrogen production photocatalysts.

The emergence of blockchain know-how currently presents the opportunity for the health sector to adopt such technologies in electronic health records. Blockchain assists in maintaining and sharing the relevant medical records of the patient with the relevant group of healthcare providers and the hospital. Numerous specific applications include traceability of drug and patient monitoring or Electronic Health Records (EHR). While Blockchain assists in maintaining and sharing the relevant medical records of the patient with the relevant group of healthcare providers and the hospital, it is important to note that the moral consciousness of the healthcare professionals is the main guide of the moral consciousness is ethics. This paper presents an overview of the application of blockchain in the healthcare and medical sector, highlighting the specific challenges and concerns. The study adopted a systematic review of secondary literature in answering the research question.

Effective sports training should be attuned to the athlete's specific conditionings and characteristics. In motor learning research, two often neglected factors that influence this resonance are the learner's athletic background and the structural diversity of exercises (e.g., relative similarity). This study examines the effects of three learning approaches (i.e., contextual interference (CI), differential learning (DL), and free-play control condition (CO)) on the parallel learning of Handball (HB), Volleyball (VB), and Basketball (BB) skills, considering participants' prior sport backgrounds. Forty-five males (15 HB, 15 VB, and 15 BB players) with a mean age of 22±1.4 years and at least 6 years of experience in the mastered discipline voluntarily participated in this study. A pre-post-retention test design including a 6-week-intervention program was employed. In terms of short-term (pre-post) gain, only the DL- group significantly improved their performance in both non-mastered disciplines (p=0.03 for the BB free-throw and p=0.05 for the HB shooting tests), with a trend (ES=0.53) towards an improvement in the performance of the mastered VB underarm-pass skill. In terms of relatively permanent gains, the CI group significantly improved their performances from pre- to retention-test only in the non-mastered BB free-throw skill (p=0.018). In contrast, the DL group significantly improved their performance at retention compared to the pre-test in both non-mastered BB (p=0.004) and HB (p=0.003) skills, with a trend (ES=0.4) towards improvement in the mastered VB test. In both short, and relatively permanent terms, higher composite score gains were observed in DL compared to CI (p=0.001 and 0.002) and CO (p=0.001 and <0.0001). In conclusion, the present findings provide additional support for the potential advantages of the DL model over those of CI. These findings can serve as the basis for tailored training and intervention strategies and provide a new perspective for addressing various issues related to individual and situational learning.

In this paper, we address the problem of car detection from aerial images using Convolutional Neural Networks (CNN). This problem presents additional challenges as compared to car (or any object) detection from ground images because features of vehicles from aerial images are more difficult to discern. To investigate this issue, we assess the performance of two state-of-the-art CNN algorithms, namely Faster R-CNN, which is the most popular region-based algorithm, and YOLOv3, which is known to be the fastest detection algorithm. We analyze two datasets with different characteristics to check the impact of various factors, such as UAV’s altitude, camera resolution, and object size. The objective of this work is to conduct a robust comparison between these two cutting-edge algorithms. By using a variety of metrics, we show that none of the two algorithms outperforms the other in all cases.

Spinal and bulbar muscular atrophy (SBMA), also known as Kennedy’s disease, is a debilitating neuromuscular disease characterized by progressive muscular weakness and neuronal degeneration, affecting 1-2 individuals per 100,000 globally. While SBMA is relatively rare, recent studies have shown a significantly higher prevalence of the disease within the indigenous population of Western Canada compared to the general population. The disease is caused by a pathogenic expansion of polyglutamine residues in the androgen receptor protein, which acts as a key transcriptional regulator for numerous genes. SBMA has no cure, and current treatments are primarily supportive and focused on symptom management. Recently, a form of precision medicine known as antisense therapy has gained traction as a promising therapeutic option for numerous neuromuscular diseases. Antisense therapy uses small synthetic oligonucleotides to confer therapeutic benefit by acting on pathogenic mRNA molecules, serving to either degrade pathogenic mRNA transcripts or helping to modulate splicing. Recent studies have explored the suitability of antisense therapy for the treatment of SBMA, primarily focused on antisense-mediated mRNA knockdown approaches. Advancements in understanding the pathogenesis of SBMA and the development of targeted therapies offer hope for improved quality of life for individuals affected by this debilitating condition. Continued research is essential to optimize these genetic approaches, ensuring their safety and efficacy.

Introduction:In this study we evaluated the connivance of oxidative and antioxidative parameters in the pathogenesis of spinal cord injury (SCI). Although the etiology and pathogenesis of SCI remain to be fully understood, it has been suggested that reactive oxygen species (ROS) and oxidative stress may play a significant role in the pathophysiology of SCI. Furthermore, there is little information available in scientific literature about oxidative and antioxidative parameters in SCI patients. Methods:Oxidative stress was determined by measuring the levels of Lipid Peroxides (LPO) and Protein carbonyl in plasma and antioxidative parameters like Glutathione Reductase (GR), catalase and Glutathione peroxidase (GPx) in lysate in 40 SCI patients and 40 healthy subjects without SCI. However, pain was measured by McGill pain questionnaire. Results: Concentrations of catalase (p<0.01), GR (p<0.01) and GPx (p<0.01) were significantly lower in patients with SCI than in controls, and levels of oxidative stress parameters, LPO (p<0.01), Protein carbonyl (p<0.01) were significantly higher in patients than in controls. A significant positive correlation was found between LPO and pain score among SCI patients group. Furthermore, a significant positive correlation was also found between Protein carbonyl and pain score among SCI patients group than in control group. Conclusion: The present results indicate that SCI patients are exposed to oxidative stress and this escalated oxidative stress may play a role in the etiopathogenesis of the disease. Moreover, our results also show that increased oxidative stress parameters are more strongly amalgamated with pain in SCI patients.

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

In this paper, we address the problem of car detection from aerial images using Convolutional Neural Networks (CNN). This problem presents additional challenges as compared to car (or any object) detection from ground images because features of vehicles from aerial images are more difficult to discern. To investigate this issue, we assess the performance of two state-of-the-art CNN algorithms, namely Faster R-CNN, which is the most popular region-based algorithm, and YOLOv3, which is known to be the fastest detection algorithm. We analyze two datasets with different characteristics to check the impact of various factors, such as UAV's altitude, camera resolution, and object size. The objective of this work is to conduct a robust comparison between these two cutting-edge algorithms. By using a variety of metrics, we show that YOLOv3 yields better performance in most configurations, except that it exhibits a lower recall and less confident detections when object sizes and scales in the testing dataset differ largely from those in the training dataset.

Portable Document Format (PDF) files are one of the most universally used file types. This has fascinated hackers to develop methods to use these normally innocent PDF files to create security threats via infection vectors PDF files. This is usually realized by hiding embedded malicious code in the victims’ PDF documents to infect their machines. This, of course, results in PDF Malware and requires techniques to identify benign files from malicious files. Research studies indicated that machine-learning methods provide efficient detection techniques against such malware. In this paper, we present a new detection system that can analyze PDF documents in order to identify benign PFD files from malware PFD files. The proposed system makes use of the AdaBoost decision tree with optimal hyperparameters, which is trained and evaluated on a modern-inclusive dataset, viz. Evasive-PDFMal2022. The investigational assessment demonstrates a lightweight-accurate PDF detection system, achieving a 98.84% prediction accuracy with a short prediction interval of 2.174 μSec. To this end, the proposed model outperforms other state-of-the-art models in the same study area. Hence, the proposed system can be effectively utilized to uncover PDF malware at high detection performance and low detection overhead.

Wireless sensor networks (WSNs) are a critical research area with numerous practical applications. WSNs are utilized in real-life scenarios, including environmental monitoring, healthcare, industrial automation, smart homes, and agriculture. As WSNs advance and become more sophisticated, they offer limitless opportunities for innovative solutions in various fields. However, due to their unattended nature, it is essential to develop strategies to improve their performance without draining the battery power of the sensor nodes, which is their most valuable resource. This paper proposes a novel sink mobility model based on constructing a bipartite graph from a deployed wireless sensor network. Using the bipartite graph’s properties, the mobile sink node can visit stationary sensor nodes in an optimal way to collect data and transmit it to the base station. We evaluated the proposed approach through simulations using the NS-2 simulator to investigate the performance of wireless sensor networks when adopting this mobility model. Our results show that using the proposed approach can significantly enhance the performance of wireless sensor networks while conserving the energy of the sensor nodes.

The development of sustainability, the improvement of the quality of life, and the protection of future generations have become of prime importance in light of climate changes and carbon emissions. Universities are at the top of institutions that should participate in creating an environmentally friendly infrastructure. This study thus aimed to explore the extent to which Saudi universities achieve sustainability in the light of the Saudi Green Initiative from the faculty members’ perspective. Surely, this investigation would produce a proposal to promote sustainability in Saudi universities. A questionnaire consisting of five dimensions, namely academic sustainability, community partnership, resource management, planning and administration, and innovation and leadership was developed and administered to 120 faculty members from five Saudi universities. The participants’ ratings of Saudi universities’ achievement of sustainability dimensions ranged from high to moderate. Based on the results, a proposal was offered to take sustainability in Saudi universities to a higher level.

Hepatitis-C is one of the most common viral diseases caused by hepatitis C virus (HCV). It is responsible for millions of deaths each year in the developing world. The common dissemination paths of HCV include the use of contaminated water and transfusion of infected blood. Control of this virus has become a challenge for scientists and health professionals due to its versatility and adaptability in different host environments. Along with other problems, lack of efficient diagnosis, quantification and genotyping of viral strains are the major hindrances in a management of this notorious epidemic. The knowledge of HCV genotype and an amount of virus in patient’s blood are pre-requisites to determine the duration and method of treatment. In this review, we discuss the implications of HCV molecular diagnostic methods and their clinical applications. We conclude that while, several commercial and home-brewed methods are available for this purpose, and there is a visible vacuum for cost effective, robust, sensitive assays that can detect multiple viral genotypes in a single reaction. We are of the view that the level of sensitivity offered by Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) technique is unequivocal as compared to other techniques. Therefore, researchers may explore further possibilities using this technique in the management of HCV.

Finding sustainable renewable energy sources for energy conversion and management is still an obscure topic. This paper presents a model-based simulation for an energy conversion system using piezoelectric energy harvester (PEH) technology. A dynamic mathematical model of a PEH device consists of a piezoelectric transducer, a controlled PWM rectifier, and a closed-loop buck boost converter is developed. A piezoelectric transducer converts mechanical vibrations from a wind force into an oscillating train of electrical charges. The sinewave peak-to-peak level relationship describes the changeable values of these charges, which depend on the severity of the wind vibration. The controlled PWM rectifier, in conjunction with a closed-loop buck-boost converter, converts this train of sinewave cycles into DC voltages. The control blocks of the closed-loop buck-boost converter use the perturbation and observation (P&amp;O) algorithm based on maximum power point tracking (MPPT), which adapts the operational voltage of the piezoelectric source to deliver the maximum power to load and improves the PEHS output voltage stability. A simulation program is employed to perform mathematical analysis on various wind vibration scenarios, piezoelectric sources without PWM converters, and piezoelectric vibration sources connected to a closed-loop P&amp;O converter. The crucial results of this paper demonstrate that the proposed dynamic PEHS model effectively feeds low-power electronic loads by directly adjusting the output voltage level to the set voltage, even under different vibration severity levels. As a result, the proposed PEHS dynamic model serves as a guideline for researchers in the development of self-powered sensors, which contributes to understanding sustainable energy alternatives.

Salmonella is a foodborne pathogen associated with localized outbreaks that potentially pose a huge risk to the public health in some countries such as Africa and India, especially in the regions lacking health care measures. The focus of the present study is the molecular detection method of salmonella. The study was conducted to identify Salmonella strains by PCR targeting flagellin specific gene from suspected patient’s clinical blood samples. Serologically, Widal test results showed that all serum samples except one patient (ID 7) were positive for O antigen. The serum of three patients (ID 4, 5 and 10) were found to be positive for H and O antigens while one patient (ID 9) showed agglutination for antigen H. The PCR approach used in this study was successful for fast and precise detection of clinically related Salmonella Typhi. Seven samples out of 10 were positive for Salmonella Typhi as fragments of 458-497bp were observed on the gel-agarose 0.8% corresponds to flagellin gene of S. Typhi. Relatively, smaller amplicons of 366-343bp were observed utilizing nested PCR as well, which seems more sensitive than the conventional PCR. Therefore, we recommend the PCR approach performed in this study to be used as a fast, cost-effective, and time-consuming tool for diagnostic purposes of Salmonella.

Existing implants used with Total Knee Arthroplasty (TKA), Total Hip Arthroplasty (THA), and other joint reconstruction treatments, have displayed premature failures and frequent needs for revision surgery in recent years, particularly with young active patients who represent more than 55% of all joint reconstruction patients. Bone cement and stress shielding have been identified as the major reasons for premature joint failures. A breakdown of the cement may happen, and revision surgery may be needed because of the aseptic loosening. The significant mismatch of stiffness properties of patient trabecular bones and metallic implant materials in joint reconstruction surgery results in the stress shielding phenomenon. This could lead to significant bone resorption and increased risk of bone fracture and the aseptic loosening of implants. The present project introduces an approach for development of customized cellular structures to match the mechanical properties and architecture of human trabecular bone. It consists of four modules:1- characterization of geometry, microarchitecture, and stiffness properties of patient’s bones; 2- developing customized lattice structures tailored to mimic properties established in module 1; 3 - integration of developed lattice structure into full scale implant design and manufacturing; 4- implementation of the developed technology for use in implants that would overcome most of the issues related to aseptic loosening phenomenon. The present work aims at fulfilling the objectives of module 2 by exploring new designs of customized lattice structures and texture tailored to mimic closely patients’ bone anisotropic properties and that can incorporate an engineered biological press-fit fixation technique. The effects of various lattice design variables on mechanical performance of the structure are examined through systematic experimental plan using statistical design of experiments technique and analysis of variance method. All tested lattice designs were explored under realistic geometrical, biological, and manufacturing constraints. Of the four design factors examined in this study, strut thickness was found to have the highest percent contribution (41%) regarding the structure stiffness, followed by unit cell type, and cell size. Srut shape was found to have the lowest effect with only 11% contribution. The introduced solution offers lattice structure designs that can be adjusted to match bone stiffness distribution and promote bone ingrowth and hence eliminating the phenomenon of stress shielding while incorporating biological press-fit fixation technique.

Recent years have witnessed significant advances in reinforcement learning (RL), which has registered remarkable success in solving various sequential decision-making problems in machine learning. Most of the successful RL applications, e.g., the games of Go and Poker, robotics, and autonomous driving, involve the participation of more than one single agent, which naturally fall into the realm of multi-agent RL (MARL), a domain with a relatively long history, and has recently re-emerged due to advances in single-agent RL techniques. Although, typically, the communication protocol between agents is manually specified and not altered during training, recently, some papers have shown signs of trying to emerge a communication between agents on the one hand and, on the other hand, to understand what is exchanged between agents. So, there is a growing body of literature on this topic which includes qualitative and quantitative studies and the ones that apply mixed methods. This study presents the scoping review of the methodological strategies undertaken in a total of 16 research articles. The results present the critical appraisal of quantitative methods in terms of validity and reliability and for qualitative methods considering four trustworthiness factors. In the end, relevant insights are further explored with implications and reflections on how they can benefit one's research in the field.

Because the application of antibiotic growth promoters (AGP) causes accelerated adverse effects on the animal diet, the scientific community has taken progressive steps to enhance sustainable animal productivity without using AGP in animal nutrition. Organic acids (OAs) are non-antibiotic feed additives and a promising feeding strategy in the swine and broiler industry. Mechanistically, OAs improve productivity through multiple and diverse pathways: (a) reduction of pathogenic bacteria in the gastrointestinal tract (GIT) by reducing the gut pH; (b) boosting the digestibility of nutrients by facilitating digestive enzyme secretion and increasing feed retention time in the gut system; (c) having a positive impact and preventing meat quality deterioration without leaving any chemical residues. Recent studies have reported the effectiveness of using encapsulated OAs and synergistic mechanisms of OAs combinations in swine and broiler productivity. On the other hand, the synergistic mechanisms of OAs and the optimal combination of OAs in the animal diet are not completely understood, and further intensive scientific explorations are needed. Moreover, the ultimate production parameters are not similar owing to the type of OAs, concentration level, growth phase, health status of animals, hygienic standards, and environmental factors. Thus, those factors need to be considered before implementing OAs in feeding practices. In conclusion, the current review evaluated the basics of OAs, mode of action, novel strategies to enhance utilization, influence on growth performances, nutrient digestibility, quality traits, and meat preservation of swine and broilers and their potential concerns regarding utilization.

Background: We aimed to study the frequency, management, and outcomes of patients with blunt pancreatic trauma. Methods: We reviewed the medical records for all patients admitted with pancreatic injuries between 2011 and 2017 at the only level 1 trauma center in the country. Results: There were 71 patients admitted with pancreatic trauma (0.6% of trauma admissions and 3.4% of abdominal injury admissions) with a mean age of 31 years. Sixty-two patients had pancreatic injury grade I-II and 9 had grade III-IV. Thirty-eight percent had GCS <9 and 73% had ISS >16. The level of pancreatic enzymes was significantly proportional to the grade of injury. Over half of patients required a laparotomy, of them 12 patients had an intervention on the pancreas. Eight patients developed complications related to pancreatic injuries ranging from pancreatitis to pancreatico-cutaneous fistula while 35% developed hemorrhagic shock. Mortality was 31% and regardless of the grade of injury, the mortality was associated with high ISS, low GCS and presence of hemorrhagic shock. Conclusion: Pancreatic injuries following blunt trauma are rare and the injured subjects are usually young male. However, most injuries are of low-grade severity. This study shows that shock, higher ISS and lower GCS are associated with worse in-hospital out-comes. Non-operative management may suffice in patients with lower grade injuries, which may not be the case in patients with higher grade injuries unless carefully selected

: Based on fairness perspective, this paper combines the social vulnerability assessment of natural hazards with multi-objective site selection model to propose a new site selection planning model for central refuges. To begin with, the social vulnerability assessment system of regional natural disasters is founded through literature analysis, and the social vulnerability index of natural disasters is constructed; and based on the exponential differentiation of index to depict regional refuge demand, design vulnerability coverage function quantitive site selection fairness; then taking the shortest weighted evacuation distance, maximum vulnerability coverage and construction cost, the coverage population and refuge quantity as constraint conditions to construct multi-objective optimization model; finally, taking Zigong City, Sichuan Province as the object of empirical research, adapting NSGA-II algorithm to get the Pareto optimal solution set, and using TOPSIS method to get the optimal location scheme. The research results can provide a new method for the location layout of central refuge.

The tumor necrosis factor receptor associated factor 1 (TRAF1) plays a key role in promoting lymphocyte survival, proliferation, and cytokine production. Recent evidence showed that TRAF1 plays opposing roles in monocytes and macrophages where it controls NF-B activation and limits pro-inflammatory cytokine production as well as inflammasome-dependent IL-1 secretion. Importantly, TRAF1 polymorphisms have been strongly linked to an increased risk of rheumatoid arthritis (RA). However, whether and how TRAF1 contributes to RA pathogenesis is not fully understood. Moreover, investigating the role of TRAF1 in driving RA pathogenesis is complicated by its multifaceted and opposing roles in various immune cells. In this study, we show that intraarticular injection of TRAF1 deficient macrophages into knee joints of wildtype (WT) mice subjected to the collagen antibody induced arthritis (CAIA) model of RA significantly exacerbated joint inflammation, immune cell infiltration and tissue damage compared to mice injected with WT macrophages. This study may lay the groundwork for novel therapies for RA that target TRAF1 in macrophages.

Hyperlipidemia is an identified risk factor for metabolic syndrome, obesity and cardiovascular complications characterized by elevated levels of lipids in blood. The present study aimed to inhibit HMG-CoA reductase with 15-oxoursolic acid through in-vivo and in-silico assessments. HFD-induced hyperlipidemia in rats was developed with a marked increase in total cholesterol (58.34 ± 0.21 mg/dl), triglycerides (45.45 ± 0.39 mg/dl), LDL-c (66.12 ± 0.10 mg/dl), VLDL-c (19.34 ± 0.16 mg/dl), and a decrease in HDL-c (10.34 ± 0.18 mg/dl). The oral administration of 15-oxoursolic acid at a concentration of 30 mg/Kg b.w significantly reduced the serum concentration of total cholesterol (42.65 ± 0.54 mg/dl), triglycerides (32.33 ± 0.16 mg/dl), LDL-c (50.34 ± 0.15 mg/dl) and VLDL-c (13.15 ± 0.45 mg/dl) respectively and an increased in the serum HDL-c (18.56 ± 0.34 mg/dl). 15-oxo ursolic acid also caused a decrease in the coronary risk index and atherogenic risk index. In-silico studies of 15-oxoursolic acid with HMG-CoA reductase showed significant interaction capability with binding energy (-9.26805 Kcal/mol). It is concluded that 15-oxoursolic acid could significantly reduce the total cholesterol, triglycerides and LDL levels in HFD-induced hyperlipidemia rats which might lead to new medication with significant hypolipidemic potential.

In a bandsaw machine the blade guides provide additional stiffness and help to align the blade near the cutting region. Typically these are either in form of blocks made of carbide or ceramics or as sealed bearings. Abrasive particles, generated while cutting hard and brittle materials like natural stones, settle between the contact surfaces of the guides and the blade causing wear and premature failure. The hydrostatic guide system as presented in this work, is a contactless blade guiding method that uses force of several pressurized water jets to align the blade to the direction of the cut. For this investigation, cutting tests were performed on a marble block using a galvanic diamond coated bandsaw blade with the upper roller guides replaced by hydrostatic guides. The results show that the hydrostatic guides help to reduce the passive force while cutting to a constant near zero in contrast to the traditional guides. This also resulted in reduced surface roughness of the stone plates that were cut indicating a reduction in lateral vibration of the band. Additionally, it has also been shown that using hydrostatic guides the bandsaw blade can be tilted to counter the bandsaw drift opening opportunities for further research in active alignment control. This original research work has shown that the hydrostatic guide systems are capable of replacing and in fact perform better than state of the art bearing or block guides particularly for stone cutting applications.

Background: Hereditary hematologic disorders (HHDs) originate from genetic factors that cause disruptions of blood homeostasis. The Thalassemia Unit, Najaf province, Iraq, keeps all medical records of all patients with HHD from the Najaf province. Objectives: We aimed to conduct an epidemiological study to obtain a comprehensive epidemiological picture with regard to sex distribution, and effects of blood groups and consanguinity. Patients and Methods: All registered HHD patients (until May 2019) in the Thalassemia Unit (1122 patients, aged 0.5-65 years old) were included in the study. Results: The prevalence of HHD in Najaf city is 75.6 per 100,000 population. The number of patients registered as new patients is increasing over the years. There are significantly more male than female HHD patients (Male=54.37%, Female=45.63%, p=0.023). The top 3 prevalent HHD are Major thalassemia (33.15%), intermediate thalassemia (31.91%), and Thalassemia+Sickle (12.65%). The highest percentage of patients were in the center of Najaf (56.42%) followed by Kufa, Mishkhab, Haidariah, Manathera, and Abbasiya. The distribution of blood groups and Rh factors of HHD patients in the Najaf population was not significantly different from that in the normal population. The consanguinity rate observed in our patients' parents (78.24%) was significantly higher (p<0.0001) as compared with the consanguinity rate in the population (45.8%). Conclusions: During the last few years, there was an increase in HHD cases in Najaf city and, therefore, there is an urgent need to increase awareness about the effects of consanguinity marriages on HHD in order to limit the incidence of HHD.

Automatic and blind parameter estimation based on the inherent features of wireless signals is a major research area due to the fact that these techniques lead to the simplification of receivers, especially in terms of coarse synchronization, more importantly reduce down the signaling load at the control channels. Thus, in the literature many techniques are proposed to estimate a vast set of parameters including modulation types and orders, data and chip rates, phase and frequency offsets and so on. In this paper, a cyclostationary feature detection based method is proposed to estimate the carrier numbers and carrier spacing of carrier-aggregated direct sequence spread spectrum cellular signals blindly. The particular chip rate of the signal is also estimated through the process jointly. In the paper, after the proposed method is formulated, the measurement setup which is developed to collect the data for the validation of the proposed method is introduced. The measurement results are post-processed for performance analysis purposes. To that end, the method is investigated in terms of SNR values, different channel conditions, and measurement durations. Furthermore, the performance of the proposed method is compared with that of energy detection. Measurement results indicate superior performance of the proposed method under significant wireless channel impairments and in low-SNR regions.

Energy storage systems play a crucial role in the overall performance of hybrid electric vehicles. Therefore, the state of the art in energy storage systems for hybrid electric vehicles has been discussed in this paper along with appropriate background information for facilitating future research in this domain. Specifically, we have compared the key parameters such as cost, power density, energy density, cycle life, and response time for various energy storage systems. For the energy storage systems employing ultra capacitors, we have presented the characteristics such as cell voltage, cycle life, power density and energy density. Furthermore, we have discussed and evaluated the interconnection topologies for the existing energy storage systems. We have also discussed the hybrid battery-flywheel energy storage system as well as the mathematical modeling of the battery-ultracapacitor energy storage system. Toward the end, we have discussed energy efficient powertrain for hybrid electric vehicles.

The governance of public sector infrastructure projects became an important topic of interest in the project, programme and portfolio management literature during the last decade. Today, it is becoming a central focus for policymakers seeking to ensure success in selecting, designing and implementing government-sponsored programme of multi-projects. Due to the multiple underlying risks and complexities, the governance of infrastructure programme constitutes a critical element in strategic planning in developing countries. This paper has analyzed infrastructure development programme and revealed shortcomings in the areas of appraisal, decision-making, quality assurance and stakeholder management. Approaches to remedy these shortcomings have been proposed.

This scoping review comprehensively explores the existing literature on fenugreek (Trigonella foenum-graecum), a versatile herb known for its culinary and traditional medicinal uses. The review focuses on two key aspects: the hypoglycemic mechanism of fenugreek and its impact on milk production in breastfeeding women. By systematically analyzing and synthesizing the available evidence, this review aims to enhance our understanding of fenugreek's potential applications and identify research gaps for future systematic reviews. The search strategy targeted English-language animal studies published within the last five years that specifically investigated fenugreek's hypoglycemic mechanism and its effects on milk production. A total of 15,236 articles were initially identified from databases such as PubMed, Sci finder and ScienceDirect. After a rigorous selection process, including thorough evaluation of titles, abstracts, and full-texts, a subset of articles (including those titled "Impact of Fenugreek on Milk Production in Rodent Models of Lactation Challenge" and "Fenugreek Stimulates the Expression of Genes Involved in Milk Synthesis and Milk Flow through Modulation of Insulin/GH/IGF-1 Axis and Oxytocin Secretion") were included for detailed analysis. The findings 18 articles from the included studies demonstrate promising results regarding fenugreek's potential to stimulate milk production and regulate blood glucose levels. However, further research is necessary to unravel the underlying molecular pathways, establish optimal dosage guidelines, and assess long-term side effects. Overall, this scoping review offers valuable insights for lactating women seeking to enhance milk production and individuals managing blood sugar levels, providing potential alternative approaches for supporting lactation and glycemic control.

This study aims to conduct a content analysis of the postgraduate theses on the education of the concept of atom in Turkey from 2000 to 2023. The study utilized the document analysis method, a qualitative research design approach. The theses were obtained by conducting a search in the "Thesis Center" of the Council of Higher Education (YÖK). "Thesis Classification Form" and "Thesis Screening Form" were used as data collection tools. Content analysis was performed on 51 theses that were conducted between 2000 and 2023 and whose full versions were accessed. It was observed that the theses were conducted in 25 different universities, using more than one data collection tool and analysis method. It was determined that most of the theses were supervised by academicians having the title of Prof.Dr. It was determined that almost all of the theses were conducted in the form of research-studies, qualitative research approach was mostly used as the research method, quasi-experimental model was mostly used as the research model, and students were generally used as the sample group. The most widely used data collection tools in the theses were interviews and achievement tests, while t-tests and content analysis were used as analysis techniques. In the selection of thesis topics, the studies in which teaching methods were tried the most were conducted. It was determined that students had difficulty in understanding the concept of atom and had misconceptions.

Pakistan relies mostly on fossil fuel for fulfilling its energy demand which is expensive as 11 well as not friendly for the environment. The need of the hour is to shift from fossil fuels to renew-12 able energy resources like geothermal, wind, solar etc. to cater for global warming issues. Pakistan 13 has a lot of potential geothermal sites because of several fault lines and hot springs. A sound 14 knowledge of ground temperature is essential to use geothermal energy, which is obtained by drill-15 ing boreholes and putting sensors making it an expensive procedure. Thus, to avoid huge cost for 16 drilling bore holes, particularly for ground temperature analysis, a numerical approach has been 17 considered for determining ground temperature. Furthermore, correlation charts between air and 18 ground temperatures have been developed as there were no proper studies on the ground temper-19 ature of Pakistan. Then, with the help of a boreholes drilled in National University of Sciences and 20 Technology Islamabad Pakistan, the actual ground and numerically calculated temperatures have 21 been compared which shows an error margin in the range between 0.27% for higher depths to 7.3% 22 near surface. Thus, it is shown that the proposed method is easy to implement and better than large 23 scale testing methods for the depths at which geothermal energy is extracted.

As V2X technology develops, acute problems related to reliable and secure information exchange between network objects in real time appear. The article aims to solve the scientific problem of building a network architecture for reliable delivery of correct and uncompromised data within the V2X concept to improve the safety of road users, using blockchain technology and mobile edge computing (MEC). The authors present a formalized mathematical model of the system, taking into account the interconnection of objects and V2X information channels, and an energy-efficient algorithm of traffic offloading to the MEC server. The paper presents the results of application of blockchain technologies and mobile edge computing in the developed system, their description, evaluation of advantages and disadvantages of the implementation.

VANET networks are a class of peer-to-peer wireless networks that are used to organize communication between cars (V2V), cars and infrastructure (V2I) and between cars and other types of nodes (V2X). These networks are based on the DSRC, 802.11 standards and are mainly intended for organizing the exchange of various types of messages, mainly emergency ones, to prevent road accidents or alert when road accident occur, or control the priority of the driveway. Initially it was assumed that cars would only interact with each other, but later, with the advent of the concept of Internet of things (IoT). Researchers began to analyze connectivity with other devices, which in general will allow to combine various road users and other devices that can used in the creation of intelligent transport infrastructure in a single smart city management system. Infrastructure is necessary for the provision of services, monitoring and management of the VANET network. As infrastructure objects it is proposed to use stationary objects of Roadside unit (RSU). The aim of this paper is to analyze the use of mobile edge computing to decrease the load to the base station and latency between RSU clouds and provide a real experiment using software defined networking and mobile edge computing for RSU.

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.

Neuro-rehabilitation services are essential in reducing post-stroke impairments, enhancing independence, and improving recovery in hospital and post-discharge. However these services are therapist-dependent and resource intensive. Patients’ disengagement and boredom in stroke units are common which adversely affect functional and psychological outcomes. Novel techniques such as use of iPads™ are increasingly researched to overcome such challenges. The aim of this review is to determine the feasibility, effectiveness, acceptability, and barriers to the use of iPads™ in stroke neuro-rehabilitation. Four databases and manual literature search were used to identify published studies using the terms “iPad”, “Stroke”, and “neuro-rehabilitation”. Studies were included in accordance with the review selection criteria. A total of 16 articles were included in the review. The majority of the studies focused on iPads use in speech and language therapy. Although of small scale, the studies highlighted that iPads are feasible, have the potential to improve rehabilitation outcomes, and can improve patient’s social isolation. Patients’ stroke severity and financial limitations are some of the barriers highlighted in this review. This review presents preliminary data supportive for the use of iPad technology in stroke neuro-rehabilitation. However, further research is needed to determine impact on rehabilitation goals acquisition, clinical efficacy, and cost-efficiency.

This paper focuses on studying the mapping properties of singular integral operators on product spaces. The boundedness of such operators is established on Triebel-Lizorkin spaces whenever their kernel functions belong to Grafakos and Stefanov class. Our results generalize and improve some previously known results.

Microencapsulation of polyherbal formulation (PHF) extract was carried out by freeze drying method, by employing gum arabic (GA), gelatin (GE), and maltodextrin (MD) with their designated different combinations as encapsulating wall materials. Antioxidant components (i.e total phenolic contents (TPC),Total flavonoids contents (TFC), and total condensed contents (TCT)), antioxidant activity (i.e. DPPH, β-carotene & ABTS⁺ assays), moisture contents, water activity (aw), solubility, hygroscopicity, glass transition temperature (T_g), particle size, morphology, in vitro alpha-amylase and alpha-glucosidase inhibition and bioavailability ratios of the powders were investigated. Amongst all encapsulated products, T_B (5% GA and 5% MD) and T_C (10% GA) has proven to be the best treatments with respect to the highest preservation of antioxidant components and their antioxidant potential by DPPH and β-carotene assays and noteworthy for an ABTS⁺ assays, in addition, the aforesaid treatments also demonstrated lower moisture content, aw, particle size and higher solubility, hygroscopicity and glass transition temperature (T_g). All freeze dried samples showed irregular (asymmetrical) microcrystalline structures. Furthermore, T_B and T_C also illustrated the highest in vitro anti-diabetic potential due to great potency for inhibiting alpha-amylase and alpha-glucosidase activities. In the perspective of bioavailability, T_A, T_B and T_C demonstrated the excellent bioavailability ratios (%).Futhermore, the phytochemo-profiling of ethanolic extract of PHF was also revealed to find out the bioactive compounds.

Herbal Coffee is getting more popular due to its unique flavor and higher concentration of bioactive compounds, mainly polyphenols. Rosemary is a unique herb with potent antioxidant properties. A green technique called ultrasound aids in releasing bioactive substances from the plant cell wall. In this context, ultrasound-assisted herbal coffee prepared and their anti-diabetic, anti-Alzheimer, and antioxidant activities were measured while the LC/MS was used to determine the bioactive components from the herbal coffee infusion. A total of 95 secondary metabolites were putatively reported, including alkaloids, phenolic acids, and flavonoids, which we identified in the herbal coffee infusion. The total phenolic content (61.5 ± 5.1 mg GAE/g), total flavonoid content (19.3 ± 0.9 mg QE/g), and total condensed tannin (7.6 ± 0.7 mg CE/g) of herbal coffee infusion were observed to be highest of the two remaining samples. The herbal coffee extracts were used to determine all samples' antioxidant, anti-diabetic, and anti-Alzheimer activities. Using LC-MS/MS, we semi-quantified 19 phenolic metabolites. The results show numerous bioactive components in herbal coffee infusion with more potent antioxidant, antidiabetic, and anti-Alzheimer potential. Moreover, more studies must be conducted to confirm how the metabolomic profile of herbal coffee affects its flavor profile.

In this paper, several classes of rough Marcinkiewicz integral operators along surfaces of revolution on product spaces are investigated. We prove the Lp boundedness of these operators when their kernels functions belong to a class of functions related to a class of functions introduced by Grafakos-Stefanov. The results in this paper extend and improve several known results on Marcinkiewicz integrals.

This study examines geothermal energy extraction potential from the basement rock within the Denver-Julesburg (DJ) Basin, focusing on flow performance and heat extraction efficiency of different geothermal well configurations. It specifically compares U-Shaped, V-Shaped, Inclined V-Shaped, and Pipe-in-Pipe configurations against Enhanced Geothermal Systems (EGS) setups. Through numerical modeling, we evaluated the thermal behavior of these systems under various operational scenarios and fracture conditions. Results suggest that while closed-loop systems offer moderate temperature increases, EGS configurations show substantial potential for high-temperature extraction. This underscores the importance of evaluating well configurations in complex geological settings like the DJ Basin. The insights from this study aid in strategic geothermal energy planning and development, marking significant advancements in geothermal technology and setting a foundation for future explorations and optimizations.

Emotional agility and social intelligence at small to medium-sized enterprises (SMEs) can be an invigorating experience, filled with opportunities for growth, innovation, and collaboration. This research aims to investigate the Omani entrepreneurs’ emotions and intelligence at workplace through self-experiences in SMEs. A sample of 222 Omani entrepreneurs participated in online survey that was designed to attain the objectives of this research. Data was collected using google drive and analyzed using multiple regression path analysis. The results revealed that emotional agility and social intelligence have positive and significant impact on employee thriving at work in Omani SMEs. Further, openness to experience mediates the relationships between entrepreneurs’ emotions and intelligence and sustainable workplace. These results confirm the effective role of openness to experience among Omani entrepreneurs where they should adopt new strategies and techniques to improve their skills and support their SMEs. This research highlighted the role of interpersonal skills including agility and intelligence of entrepreneurs in Arab context.

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

The aim of our study was to evaluate the impact of T. gondii status on eosinophils count (PNE), eosinophil to lymphocyte ratio (ELR) and eosinophil to neutrophil to lymphocytes ratio (ENLR) before and after can-nabis cessation in patients with psychiatric disorders. One hundred and eighty-eight patients were includ-ed in the study. T. gondii, PNE, ELR, ENLR, and urinary cannabis were measured at baseline and after 4 weeks of cannabis cessation. Highest PNE (p = .02), ELR (p = .03) and ENLR levels (p = .031) and highest increase of PNE (p = .044), and ENLR (p = .036) levels were found in patients after cannabis cessation only in the Toxo+ group. At four weeks, significant interactions between cannabis and T. gondii status for PNE (p = .038), and for ENLR (p = .043) levels were found, as well as for the evolution between baseline and 4 weeks for ENLR level (p = .049). After cannabis cessation, we found a positive correlation between negative symptoms and PNE levels at 4 weeks in the Toxo+ group. This study shows that the increase of inflammation after cannabis cessation might be modulated by T. gondii seropositivity status in patient after cannabis cessation.

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.

One of the most important objectives of the studies worldwide is to improve the performance of automotive engines to reduce fuel consumption and environmental pollution. Accordingly, the principal motivation of this research study is improving the tribological behavior of the piston ring/cylinder liner interfaces as a promising and straightforward approach in automotive fuel economy and increasing engine durability using Al₂O₃ and TiO₂ nanomaterials as smart nano-lubricant additives that adapted to different operating conditions by replenishing mechanisms anti-friction and anti-wear in automotive engines.

Competition between cotton, winter, and summer crops is the greatest obstacle for plant breeders in Egypt. Breeders are thus seeking early maturing, high-yielding cultivars that are suited for planting following winter crops. This study was conducted during the 2016 and 2018 growing seasons at the Sakha agriculture research station, Egypt's agricultural research facility. This research used breeding material from the F2, F3, and F4 generations that resulted from crossing Giza 94 and Uzbekistan. During the 2016 growing season, the F2 population was cultivated, and the top 5% of the F2 population (75 plants) were chosen based on the selection criteria. In the summer of 2017, the F3 families were cultivated, and 5% of the superior F3 families were chosen to become the F4 fam-ilies, which were planted in the summer of 2018 at the single plant and bulk studies in a randomised full block design with three replications. The average performance of the characteristics boll weight, seed cotton yield, lint percentage, and lint index increased as generations passed from F2 to F4. In the meanwhile, the characteristic value of the first flower to open decreased, showing the accu-mulation of advantageous alleles. For all examined characteristics, the phenotypic coefficient of variance was greater than the genotypic coefficient of variation. In addition, the data demonstrated that heritability in a broad sense was greater in F4 populations than in F3 and F2 populations. With a few exceptions, the phenotypic and genotypic associations for all investigated characteristics were positive and very significant. According to the data, initial opening flowering and boll weight had a substantial impact on seed cotton output per feddan. In general, five superior families No. 9, 13, 18, 19, and 20 were chosen from the F4 population, which were more significant for yield and yield component than the commercial variety, and these genotypes may be promising for the breeding programme to develop new genotypes with superior yield.

COVID-19 infection is extremely dangerous for persons with comorbidities such as kidney disease and diabetes. Although many people of all ages and socioeconomic backgrounds have suffered due to this pandemic, those with underlying medical conditions like kidney disease and diabetes are more vulnerable to becoming infected with COVID-19 related to weak immunity. However, the role of vaccination in the co-infection of COVID-19 with diabetics is not available in the literature. Therefore, we examine the unique challenges presented by the co-infection of COVID-19 in individuals with diabetes, focusing on its disease transmission dynamics. A mathematical modelling technique is to be employed in this work to analyse the dynamics of COVID-19 outbreaks with seven compartments, including vaccination with comorbidities such as diabetes. We also investigated analytically by displaying the solutions' existence, boundedness, positivity and sensitivity. After calculating the basic reproduction numbers, the stability analysis of model equilibrium points is also covered. If the reproduction numbers are less than unity, the disease-free equilibrium points of the model appear to be both locally and globally stable. The findings of this study are that as the vaccination rate rises, the incidence of COVID-19 and its co-infections with diabetes decreases. Effective disease treatment strategies may be based partly on the possible impact of vaccination on the co-infection of COVID-19 related to diabetic disease insights.

Increased exposure to artificial light at night can affect human health including disruption of melatonin production and circadian rhythms and extend to increased risks of hormonal cancers and other serious diseases. In addition, multiple negative impacts on fauna and flora are well documented, and it is a matter of fact that artificial light at night is a nuisance for ground-based astronomy. These impacts are frequently linked to the colour of the light or more specifically to its spectral content. Artificial light at night is often mapped by using space borne sensors, but most of them are panchromatic and thus insensitive to the colour. In this paper, we suggest a method that allows high resolution mapping of the Artificial light at night by using ground-based measurements with the LANcube system. The device separates the light detected in four bands (Red, Green, Blue, and Clear) and provides this information for six faces of a cube. We found relationships between the LANcube’s colour ratios and 1- the Melatonin Suppression Index, 2- the StarLight Index and 3- the Induced Photosynthesis Index. We show how such relationships combined with data acquisition from a LANcube positioned on the top of a car can be used to produce spectral indices maps of a whole city in a few hours.

During the 1970s, Tunisian durum wheat landraces were replaced progressively by modern cultivars. These landraces are nowadays maintained by smallholder farmers in some ecological niches and are threatened gradually by extinction resulting on the narrowing of the genetic diversity. This study aims to investigate patterns of phenotypic variability using twelve quantitative traits in a panel of 189 durum wheat landraces and seven checks, based on farmer’s population name attribution and genetic structure. Our results showed high phenotypic variability among and within landraces and checks for the majority of the studied traits. The principal components analysis showed similar grouping using farmers name attribution and genetic structure using K = 6, which confirmed the identification of a new gene pool in the oases of Tunisia, represented by the sub-population Jenah Zarzoura and the robustness and high relationships between phenotypic and genome-wide genetic structure using DArTseq method. These findings will enhance the conservation efforts of these landraces and their use in breeding efforts at national and international levels to adapt to dry conditions.

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

The application of metal-matrix composite coatings for protecting and improving the service life of sliding components has demonstrated to have the potentials of meeting the requirements of a diverse range of engineering industries. Recently, a significant body of research has been devoted to study the mechanical and tribological performance of dispersion-strengthened MCrAlY coatings. These coatings belong to a class of emerging wear-resistant materials, offering improved properties and being considered as promising candidates for the protection of engineering structural materials exposed to tribological damage, especially at elevated temperature regimes. This paper attempts to comprehensively review the different reinforcements used in the processing of MCrAlY-based alloys and how they influence the mechanical and tribological properties of the corresponding coatings. Further, the major fabrication techniques together with their benefits and challenges are also reviewed. Discussion on the failure mechanisms of these coatings as well as the main determining factors are also included. In addition, a comprehensive survey of studies and investigations in recent times are summarized and elaborated to further substantiate the review.

Currently, life cannot be imagined without the use of bank cards for purchases or money transfers; however, their use provides new opportunities for money launders and terrorist organizations. This paper proposes a Blockchain-enabled transaction scanning (BTS) method for the detection of anomalous actions. The BTS method specifies the rules for outlier detection and rapid movements of funds, which restrict anomalous actions in transactions. The specified rules determine the specific patterns of malicious activities in the transactions. Furthermore, the rules of the BTS method scan the transaction history and provide a list of entities that receive money suspiciously. Finally, the Blockchain-enabled process is used to restrict the money laundering. To validate the performance of the proposed BTS, the Spring Boot application is built based on the Java programming language. Based on experimental results, the proposed BTS method automates the process of investigating transactions and restricts the money laundering incident.

This review paper explores auditory tactile (AT) synesthesia, a rare neurological condition where sounds evoke tactile sensations. The paper provides a historical overview of the condition and discusses its epidemiology, with a prevalence of less than 1% of the general population. The neurological basis of AT synesthesia is explored, including the role of cross-modal processing and hyperconnectivity within the brain. The paper also describes the phenomenology of the condition, including the range of tactile sensations that can be experienced in response to different sounds. The occurrence of AT synesthesia in the present-day world is discussed, including its relationship to music and art. Various hypotheses surrounding the development and maintenance of AT synesthesia are reviewed, focusing on genetic and environmental factors. The implications for clinical practice are explored, including potential benefits for individuals with sensory processing disorders. Finally, the paper concludes with a discussion of future directions for research in this field, including the need to explore further the underlying neural mechanisms of AT synesthesia and potential therapeutic interventions.

In this article, we calculate the deflection angle of tidal charged black hole (TCBH) in weak field limits. First we obtain the Gaussian optical curvature and then apply the Gauss-Bonnet theorem on it. With the help of Gibbons-Werner method, we are able to calculate the light's deflection angle by TCBH in weak field limits. After calculating the deflection angle of light, we check the graphical behavior of TCBH. Moreover, we further find the light's deflection angle in the presence of plasma medium and also check the graphical behavior in the presence of plasma medium. Moreover, we investigate the shadow of TCBH.For calculating the shadow, we first find the null geodesics around the TCBH and then find its shadow radius. We also obtain TCBH's shadow in the plasma medium. Hence, we discuss the shadow of the TCBH using the $M87^{*}$ parameters announced by the Event Horizon Telescope.

We propose a pricing formula for a defaultable zero-coupon bond with imperfect information under a regime-switching model using a structural form of credit risk modelling. While the value of the firm’s equity is observed continuously, we assume that the total value of the firm is only observed at discrete times, such as the dates of the release of the firm’s annual reports, or quarterly reports. This uncertainty about the true value of the firm results in credit spreads that do not approach zero as the debt approaches maturity, which is a problem with many structural models. The firm’s value is typically defined as its equity and debt; however, we consider the asset-to-equity ratio, an accounting ratio used to examine a firm’s financial well-being. A dependent regime-switching process models the components of this ratio, and the Markov chain represents the states of the economy. The main contribution is to study this problem when the dynamics of a firm’s assets have different parameters with regime changes.

In this paper, we calculate the deflection angle of photon by Casimir wormhole in the weak field limit approximation. First we calculate Gaussian optical curvature with the help of optical spacetime geometry and so we use the Gauss-Bonnet theorem on the Gaussian optical metric. Then we find the deflection angle of photon by Casimir wormhole. Moreover, we calculate the photon's deflection angle in the presence of plasma medium and we also see the graphical nature of deflection angle in both cases. Second, we move towards the shadow of Casimir wormhole. After the observations of Event Horizon Telescope, the study of shadow become very important so that we plot the shapes of shadow of Casimir wormhole, and we calculate the photon geodesic around the Casimir wormhole.

In this study, an alternative method to conventional production methods involving thin section copper permanent mould casting (rapid solidification), hot rolling and heat treatment processes to manufacture AISI DC 53 cold work tool steel, has been investigated. In this new method, solidification of the AISI DC 53 was done in the thin section copper mould instead of traditional ingot casting. After solidification, thin slabs obtained was homogenized to 1150 ^oC and hold at this temperature for one hour and then hot rolling was be applied. During hot rolling process, about % 30 reduction in thickness of slab was obtained. After hot rolling, heat treatment (austenitizing, quenching and tempering) have been applied to thin slab. The samples have been taken as cast, as rolled and as heat treated (austenitized and tempered) and the microstructural analysis, hardness tests, retained austenite quantity measurement by XRD method have been performed. The heat treatment and tests have been performed for a commercially available AISI DC 53 which is manufactured by thick section ingot casting and rolling. Hardness values of the AISI DC 53 steels produced by traditional route is in the range of 52-54 HRC after austenitization at 1025 ^oC for one hour and quenched in compressed air and tempered twice at 525 ^oC for 120 mins. On contrary the permanent mould cast AISI DC 53 after homogenization at 1150 ^oC and immediately hot rolling then air cooling and austenitized at 1025 ^oC for one hour then quenched by compressed air and tempered twice at 525 ^oC for 120 mins have hardness valeus in the range of 55-57 HRC. Experiments have revealed that the properties of new rapid solidified steel in permanent copper mould are better than the commercially available steel and have a more refined microstructure than commercially produces alternative. Therefore, the new proposed method can be used as an alternative manufacturing method for cold-work tool steels.

This paper explores the capability of various machine learning algorithms, including Random Forests and advanced gradient boosting techniques such as XGBoost, LightGBM, and CatBoost, to predict customer churn in the telecommunications sector. For this analysis, a dataset available to the public was employed. The performance of these algorithms was assessed using recognized metrics, including Accuracy, Precision, Recall, F1-score, and the Receiver Operating Characteristic Area Under Curve (ROC AUC). These metrics were evaluated at different phases: subsequent to data preprocessing and feature selection; following the application of SMOTE and ADASYN sampling methods; and after conducting hyperparameter tuning on the data that had been adjusted by SMOTE and ADASYN. The outcomes underscore the notable efficiency of upsampling techniques such as SMOTE and ADASYN in addressing the imbalance inherent in customer churn prediction. Notably, the application of random grid search for hyperparameter optimization did not significantly alter the results, which remained comparatively unchanged. The algorithms' performance post- ADASYN application marginally surpassed that observed after SMOTE application. Remarkably, LightGBM achieved an exceptional F1-score of 89% and an ROC AUC of 95% subsequent to the ADASYN sampling technique. This underlines the effectiveness of advanced boosting algorithms and upsampling methods like SMOTE and ADASYN in navigating the complexities of imbalanced datasets and intricate feature interdependencies.

This article presents new PEM fuel cell models with hexagonal and pentagonal designs. After observing cell performance improvement in these models, we optimized them. Inlet pressure and temperature were used as input parameters, and consumption and output power were the target parameters of the multi-objective optimization algorithm. Then we used artificial intelligence techniques, including deep neural networks and polynomial regression, to model the data. Next, we employed the RSM (Response Surface Method) method to derive the target functions. Furthermore, we applied the NSGA-II multi-objective genetic algorithm to optimize the targets. Compared to the base model (Cubic), the optimized Pentagonal and Hexagonal models averagely increase the output current density by 21.819\% and 39.931\%, respectively.

The world is progressing towards a more advanced society where end-consumers have access to local renewable-based generation and advanced forms of information and technology. Hence, it is in a current state of transition between the traditional approach to power generation and distribution, where end-consumers of electricity have typically been inactive in their involvement with energy markets and a new approach that integrates their active participation. This new approach includes the use of distributed energy resources (DER) such as renewable-based generations and demand response (DR), which are being rapidly adopted by end-consumers, where incentives are strong. This paper presents the role of DR aggregator to effectively integrate DER technologies as a new source of energy capacity, into the electricity networks using information communication technology and industry knowledge. This framework based on DR aggregators will facilitate renewable energy integration and customer engagement in electricity market efficiently. To this aim, advantages and disadvantages of DR aggregators are discussed in this paper from political, economic, social and technological (PEST) point of views. Based on this analysis, a strengths, weaknesses, opportunities, and threats (SWOT) analysis for a typical DR aggregator is presented.

The leaf essential oils from five Eucalyptus spp.: E. astringens, E. camaldulensis, E. lehmanii, E. leucoxylon and E. sideroxylon growing in the same plantation area and conditions (plants with the same age and subjected to the same agronomic practices) were analyzed and evaluated for their antimicrobial activity. The essential oil yields ranged from 0.14 to 0.96% (w/w), and their chromatographic profiles were resolved into 48 compounds; among them, 11 were common to all essential oils. Terpenoids (oxygenated mono, and sesquiterpenes) dominated (55.66-76.67%) the oil profiles. Although 1,8-cineole (21.97-50.93%) was found as the main oxygenated monoterpene in all examined essential oils, principal component analysis observed and confirmed an interspecific chemical variability. Additionally, a set of distinctive chemical markers that could serve as a reliable tool for discrimination of Eucalyptus species and for authentication and quality control purposes (for commercial samples) has been defined for each essential oil. The antimicrobial disc-diffusion assay results revealed that all essential oils were endowed with a strong antimicrobial activity, with those derived from E. camaldulensis, E. lehmanii and E. leucoxylon being the most active.

Technology is in the way to reach in its climax but the basic understanding of science in many phenomena is still awaited even though the nature witnesses it. Scientific research reveals strong analogy between photon and electron. When an atom deals neutral state, it levitates electron of outer ring from the back surface while placing the bit-energy at front surface. Gravitation behavior of that electron starts at the centre of relaxation point by including the force of side pole where the pulling force of nearby unfilled state of that atom from the front surface results into depict forcing energy shape like Gaussian distribution symbol with both ends turned called unit photon. The inertia is being involved at each stage of changing direction of that electron by introducing the disappearances of forces of two poles against the appearances of forces of two opposite poles during rest to motion and motion to rest in the first half-cycle. The same is the case in the second half-cycle of that electron during rest to motion and motion to rest but it is under different introduction of the disappearances of forces against appearances of forces. However, at stage of levitating or gravitating period of electron, only one force is being involved at one time where the opposite force is disappeared. The uninterrupted confined inter-state electron-dynamics of atom under the availability of several bits of bit-energy results into generate forcing energy shape like a wave. Two bits of bit-energy where shape of bit for first half-cycle is like integral symbol and second half-cycle is like opposite integral symbol which are being placed along the configuring trajectory of inter-state electron-dynamics during forward-direction cycle and two bits of bit-energy shape in opposite order are being placed along the trajectory of inter-state electron-dynamics during back-direction cycle. Generating forcing energy of unit photon in each cycle is pushing to the rear side by remaining connected till the electron is not restoring the state of rest. Silicon atom is considered as a model system under neutral state. Uninterrupted confined inter-state electron-dynamics result into generate forcing energy that can travel immeasurable length and unavailability of necessitating bit-energy at any interrupted stage result into generate an overt photon. Inter-state electron-dynamics for at least two cycles generate an overt photon –a photon length twice to unit photon. Under certain interaction of unit photon, it divides equally into two bits of bit-energy instead of dividing into tits and bits of heat. The mechanism of generating photon characteristic current by the electron of neutral state atom validates that atoms are four-dimensional discs at centre of dealing no force. An isolated electron is being grounded under directed forcing energy to impinge a neutral state atom where the gained instantaneous velocity under merged energy resulting into distort atom at that point. Matter changes the role of energy and force under various sorts of interactions. Here, heat energy and photon energy explore matter at atomic and electron levels, thus, devise basis of science to describe.

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.

IoT is a new communication paradigm that gains a very high importance in the past few years. This communication paradigm supports various heterogeneous applications in many fields and with the dramatic increase of the number of sensor devices, it becomes a demand. Designing IoT networks faces many challenges that include security, massive traffic, high availability, high reliability and energy constraints. Thus, new communication technologies and paradigms should be deployed for IoT networks to overcome these challenges and achieve high system performance. Distributed computing techniques (e.g. fog and MEC), software defined networking (SDN), network virtualization and blockchain are common recent paradigms that should be deployed for IoT networks, either combined or individually, to achieve the main requirements of the IoT networks at a high system performance. Fog computing is a form of edge computing that has been developed to provide the computing capabilities (e.g. storage and processing) at the edge of the access network. Employing Fog computing in IoT networks, as an intermediate layer between IoT devices and the remote cloud, becomes a demand to make use of the edge computing benefits. In this work, we provide a framework for the IoT system structure that employs an edge computing layer of Fog nodes controlled and managed by SDN network with the blockchain technology to achieve a high level of security for latency sensitive IoT applications. The proposed system employs SDN network with distributed controllers and distributed OpenFlow switches; these switches are enabled with limited computing and processing capabilities. Furthermore, a data offloading algorithm is developed to allocate different processing and computing tasks to the distributed OpenFlow switches with available resources. Moreover, a traffic model is proposed to model and analyze the traffic among different parts of the network. The proposed work achieves various benefits to the IoT network, such as the latency reduction, security improvement and high efficiency of resources utilization. The proposed algorithm is simulated and also the proposed system is experimentally tested over a developed testbed to validate the proposed structure. Experimental results show that the proposed system achieves higher efficiency in terms of latency, security and resource utilization.

There is an urgent demand worldwide for the development of highly selective adsorbents and sensors of heavy metal ions and other organic pollutants. Within these environmental and public health frameworks, we are combining the salient features of clays and chelatant polymers to design selective metal ion adsorbents. Towards this end, the ion imprinting approach has been used to develop a novel nanohybrid material for the selective separation of Cu2+ ions in aqueous solution. The Cu2+-imprinted polymer/ montmorillonite nanocomposite (IIP/Mt) and non-imprinted polymer/montmorillonite nanocomposite (NIP/Mt) were prepared by radical photopolymerization process in the visible light. Ion imprinting was indeed important as the recognition of copper ions by IIP/Mt was significantly superior to that of NIP/Mt that is the nanocomposite synthesized in the same way but in the absence of Cu2+ ions. The adsorption process as batch study was investigated under the experimental condition affecting same parameters such as contact time, concentration of ions metals and pH. The adsorption capacity of Cu2+ ions is maximized at pH 5. Removal of Cu2+ ion achieved equilibrium within 15 minutes; the results obtained were found to be fitted by the pseudo-second order kinetics model. The equilibrium process was well described by the Langmuir isothermal model and the maximum adsorption capacity was found to be 23.6 mg/g.

ADHD, a neurodevelopmental condition, is distinguished by a triad of symptoms including inattention, hyperactivity, and impulsivity. It is frequently accompanied by comorbidities such as anxiety, depression, and learning disabilities. As a result, clinicians often face challenges in accurately diagnosing ADHD and differentiating it from other conditions. As a prospective remedy for ADHD, scientists have investigated Transcranial Direct Current Stimulation (tDCS), a non-invasive technique of stimulating the brain. It involves applying low-intensity electrical currents to specific regions of the brain to modulate neural activity. This review paper aims to provide an overview of the comorbidities associated with ADHD and the differential diagnosis of the condition. It also explores the potential of tDCS as a treatment option for ADHD, including its mechanisms of action and efficacy in improving ADHD symptoms. The comorbidities discussed in this review include anxiety disorders, mood disorders, and substance use disorders. These comorbidities are frequently seen in individuals with ADHD and can complicate the diagnosis and treatment of the condition. The paper also highlights the importance of considering comorbidities when assessing ADHD, as well as the potential impact of these conditions on treatment outcomes. The differential diagnosis section of the paper explores conditions that can present with symptoms similar to ADHD, such as anxiety disorders, mood disorders, and learning disabilities. It emphasizes the need for a thorough assessment and differential diagnosis to identify ADHD and differentiate it from other conditions accurately. The final section of the paper discusses the potential of tDCS as a treatment option for ADHD. It examines the mechanisms of action of tDCS and its efficacy in improving ADHD symptoms, including attention, hyperactivity, and impulsivity. It also explores the potential for tDCS to improve comorbid conditions associated with ADHD. Overall, this review provides a comprehensive overview of comorbidities and differential diagnoses in ADHD and the potential of tDCS as a treatment option. The paper highlights the importance of a thorough assessment and personalized treatment plan for individuals with ADHD, particularly those with comorbidities.

In this paper, we study quantum gravity effect on the symmergent black hole which is derived from quadratic-curvature gravity. To do so, we use the Klein-Gordon equation which is modified by generalized uncertainty principle (GUP). After solving the field equations, we examine the symmergent black hole's tunneling and Hawking temperature. We explore the graphs of the temperature through the outer horizon to check the GUP influenced conditions of symmergent black hole stability. We also explain how symmergent black holes behave physically when influenced by quantum gravity. The impacts of thermal fluctuations on the thermodynamics of a symmergent black holes spacetime are examined. We first evaluate the model under consideration's thermodynamic properties, such as its Hawking temperature, angular velocity, entropy, and electric potential. We evaluate the logarithmic correction terms for entropy around the equilibrium state in order to examine the impacts of thermal fluctuations. In the presence of these correction terms, we also examine the viability of the first law of thermodynamics. Finally, we evaluate the system's stability using the Hessian matrix and heat capacity. It is determined that a stable model is generated by logarithmic corrections arising from thermal fluctuations.

The present review aims to investigate the effect of alveolar bone grafting in canine impacted unilateral cleft lip and palate patients. The goal of this review is to identify and highlight the factors that may cause maxillary canine impaction, the role of primary and secondary bone graft and dental anomalies that may affect canine impaction in patients with cleft lip and palate.

The poultry industry is affected by many epidemics and Newcastle Disease (ND) is a constant threat, known as a devastating disease for poultry farmers around the world. According to the average death time of chicken embryos, virus strains can be classified as lentogenic, mesogenic, or velogenic. The current research will clarify the vulnerable host range as well as the epidemiology and geographic distribution of ND in Pakistan. The introduction of the virus into poultry can have serious economic consequences, including the loss of production of sick and dying poultry, the cost of control measures (such as population reduction and disinfection measures), and possible trade restrictions in the event of an outbreak. The virus is transmitted by direct contact with sick poultry or carriers. Infected birds can also spread the virus in their feces. It can also be spread through respiratory secretions, contaminated feed, equipment, water, or feces. We will also discuss vaccines that which vaccines are available for NDV in Pakistan and vaccines can fight against this disease or not? In this study, a qualitative risk analysis was carried out to assess Pakistan's vulnerability to the introduction of virulent NDV strains

We demonstrate a new quantitative method to the sieve of Eratosthenes, which is an alternative to the sieve of Legendre. In this method, every element of a given set is sifted out once only, and therefore, this method is free of the Mobius function and of the parity barrier. Using this method, we prove that every sufficiently large even number is the sum of two primes, and that every even number is the difference of two primes in infinitely many ways.

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.

Neuroscience is a vast discipline that deals with the anatomy, biochemistry, molecular biology, physiology and pathophysiology of central and peripheral nerves. Advances made through basic, translational, and clinical research in the field of neuroscience have great potential for long-lasting and beneficial impacts on human health. The emerging field of biological therapy is intersecting with the disciplines of neuroscience and rheumatology, creating new horizons for interdisciplinary and applied research. Biological drugs, growth factors, neuropeptides and monoclonal antibodies are being developed and tested for the treatment of painful arthritic and rheumatic diseases. This concise communication focuses on the solutions provided by the fields of neuroscience and neuroimmunology for real-world clinical problems in the field of rheumatology, focusing on synovial joint pain and the emerging biological treatments that specifically target pathways implicated in osteoarthritis pain in peripheral nerves.

Many studies discuss carbon-based materials because of the versatility of its element. They include different opinions for scientific problems and discuss fairly at convincing and compelling levels within the scope and application. A gas-state carbon atom converts into various states depending on its conditions of processing. The electron transfer mechanism in the gas-state carbon atom is responsible to convert it into various states, namely, graphite, nanotube, fullerene, diamond, lonsdaleite and graphene. The shape of ‘energy trajectory’ enables transferring electrons from the left- and right-sides of an atom is like a parabola. That ‘energy trajectory’ is linked to states (filled state and suitable nearby unfilled state) where force-exertion along the poles of transferring electrons is remained balance. So, the mechanism of originating different states of a gas-state carbon atom is under the involvement of energy first. This is not the case for atoms executing confined inter-state electron-dynamics as the force is involved first. Graphite-, nanotube- and fullerene-state atoms ‘partially evolve partially develop’ (form) their structures. These possess one-dimensional, two-dimensional and four-dimensional ordering of atoms, respectively. Their structural formation also comprises ‘energy curve’ having a shape-like parabola. Transferring suitable filled state electron to suitable nearby unfilled state is under a balance force exerting along the poles. The graphite structure under only attained-dynamics of atoms can also be formed but in two-dimension. Here, binding energy between graphite-state carbon atoms is for a small difference of exerting forces along their opposite poles. Structural formation in diamond, lonsdaleite and graphene atoms involve energy to gain required infinitesimal displacements of electrons through which they maintain orientationally-controlled exerting forces along dedicated poles. In this study, the growth of diamond is found to be south to east-west (ground) where atoms bound ground to south. Thus, diamond atoms merge for a tetra-electron ground to south topological structure. Lonsdaleite atoms merge for a bi-electron ground to just-south topological structure. The growth of graphene is found to be north to ground where atoms bound ground to north. Thus, graphene atoms merge for a tetra-electron ground to north topological structure. Glassy carbon exhibits layered-topological structure where, tri-layers of gas-, graphite- and lonsdaleite-state atoms successively bind in repetitive order. Nanoscale hardness is also sketched based on different force-energy behaviors of different state carbon atoms. Here, structure evolution in each carbon state atom explores its own science.

Sources are classified into two depending upon the factor of reviving. These sources, which cannot be revived into their original shape once they are consumed, are considered as nonrenewable energy resources, i.e., (coal, fuel) Moreover, those energy resources which are revivable to the original condition even after being consumed are known as renewable energy resources, i.e., (wind, solar, hydel) Renewable energy is a cost-effective way to generate clean and green electrical energy Now a day’s majority of the countries are paying heed to energy generation from RES Pakistan is mostly relying on conventional energy resources which are mostly nonrenewable in nature coal, fuel is one of the major resources, and with the advent of time their prices are increasing on the other hand RES have great potential in the country with the deployment of RES greater reliability and an effective power system can be obtained In this thesis, a similar concept is being used and a hybrid power system is proposed which is composed of intermixing of renewable and nonrenewable sources The Source side is composed of solar, wind, fuel cells which will be used in an optimal manner to serve load The goal is to provide an economical, reliable, uninterruptable power supply. This is achieved by optimal controller (PI, PD, PID, FOPID) Optimization techniques are applied to the controllers to achieve the desired results. Advanced algorithms (Particle swarm optimization, Flower Pollination Algorithm) will be used to extract the desired output from the controller Detailed comparison in the form of tables and results will be provided, which will highlight the efficiency of the proposed system.

Background & Aims: Gut microbial imbalances are linked to colorectal cancer (CRC), but archae-‎a's role remains underexplored. Here, we performed bioinformatic and statistical analysis looking ‎for archaeal taxonomic and functional signatures related to CRC. Methods: We analyzed pub-‎lished fecal metagenomic data from 390 subjects, comparing the archaeomes of CRC and healthy ‎individuals. We conducted a biostatistical analysis to investigate the relationship between Candi-‎datus Mancarchaeum acidiphilum (DPANN superphylum) and other archaeal species associated ‎with CRC. Using the Prokka tool, we annotated the data focusing on archaeal genes, subsequently ‎linking them to CRC and mapping them against UniprotKB and GO databases for specific ar-‎chaeal gene functions. Results: Our analysis identified enrichment of methanogenic archaea in ‎healthy subjects, with an exception for Methanobrevibacter smithii, which correlated with CRC. ‎Notably, CRC showed a strong association with archaeal species, particularly Natrinema sp. J7-2, ‎Ferroglobus placidus, and Candidatus Mancarchaeum acidiphilum. Furthermore, the DPANN ar-‎chaeon exhibited a significant correlation with other CRC-associated archaea (p < 0.001). Func-‎tionally, we found a marked association between MvhB-type polyferredoxin and colorectal can-‎cer. We also highlighted the association of archaeal proteins involved in the biosynthesis of leucine ‎and the galactose metabolism process with the healthy phenotype. Conclusion: The archaeomes ‎of CRC patients show identifiable alterations, including a decline in methanogens and an increase ‎in Halobacteria species. MvhB-type polyferredoxin, linked with CRC and species like Candidatus ‎Mancarchaeum acidiphilum, Natrinema sp. J7-2 and Ferroglobus placidus emerge as potential archaeal ‎biomarkers. Archaeal proteins may also offer gut protection, underscoring archaea’s role in CRC ‎dynamics.

The predominance of English and Latin-based large language models (LLMs) has led to a notable deficit in native Arabic LLMs. This discrepancy is accentuated by the prevalent inclusion of English tokens in existing Arabic models, detracting from their efficacy in processing native Arabic's intricate morphology and syntax. Consequently, there is a theoretical and practical imperative for developing LLMs predominantly focused on Arabic linguistic elements. To address this gap, this paper proposes ArabianGPT, a series of transformer-based models within the ArabianLLM suite designed explicitly for Arabic. These models, including ArabianGPT-0.1B and ArabianGPT-0.3B, vary in size and complexity, aligning with the nuanced linguistic characteristics of Arabic. The AraNizer tokenizer, integral to these models, addresses the unique morphological aspects of Arabic script, ensuring more accurate text processing. Empirical results from fine-tuning the models on tasks like sentiment analysis and summarization demonstrate significant improvements. For sentiment analysis, the fine-tuned ArabianGPT-0.1B model achieved a remarkable accuracy of 95%, a substantial increase from the base model's 56%. Similarly, in summarization tasks, fine-tuned models showed enhanced F1 scores, indicating improved precision and recall in generating concise summaries. Comparative analysis of fine-tuned ArabianGPT models against their base versions across various benchmarks reveals nuanced differences in performance, with fine-tuning positively impacting specific tasks like question answering and summarization. These findings underscore the efficacy of fine-tuning in aligning ArabianGPT models more closely with specific NLP tasks, highlighting the potential of tailored transformer architectures in advancing Arabic NLP.

The detection and recognition of tables in image-based documents is a challenging and essential task for automated data extraction and anal- ysis. This paper introduces a methodology that utilizes Convolutional Neural Networks (CNNs) to address this problem. By harnessing the ro- bust visual recognition abilities of CNNs, the proposed method effectively detects and extracts tables from image-based documents. Additionally, an Encoder-Dual Decoder (EDD) architecture is employed to extract the textual content from table cells, enabling the transformation of visual data into structured, machine-readable formats. This structured data is then further processed using a Vector Space Model (VSM)-based language modeling technique for query-based table retrieval. The methodology’s accuracy is assessed by analyzing the spatial relationships between the extracted cells and comparing them against a pre-defined table structure. The results provide an evidence to the effectiveness of the proposed ap- proach, with an impressive 85% accuracy rate and a corresponding error rate of 15%. This accurate extraction and recognition of tables from image-based documents can significantly enhance applications in data analysis, information retrieval, and knowledge extraction.

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

Rare earths are classified as most important and critical material for US economy and defense by Congress and a mandate has been set to increase their in-house production, domestic resource utilization and decrease reliance on foreign resources and reserves. They are widely available in earth crust as ore (bastnaesite (La, Ce)FCO₃, monazite, (Ce, La, Y, Th)PO₄, and xenotime, YPO₄), but their so-called economic reserves are sparsely located geographically. They may be produced by various means such as beneficiation (physical, chemical, mechanical, or electrical), reduction (direct or indirect), electrolysis (of aqueous or molten / fused single or mixed salt systems) at high temperature or hydrometallurgy. Out of these, direct reduction also known as metallothermic reduction (La and Ca reduction) is mostly utilized. Its variant, high temperature electrowinning of fused salts is also practiced widely. These processes are material and application specific. In this study, author will employ thermodynamics (Ellingham diagrams, free energy of formation, reduction potential, Nernst equation, Pourbaix (E_h-pH) diagrams, E-pO^-2 diagrams), kinetics and energetic of a chemical reaction (chemical metallurgy) to reduce rare earth oxide / salt to rare earth metals (REO/RES – REM). It is shown that materials and energy requirement vary greatly depending on type of mineral ore, production facility, and beneficiation / mineral processing method selected. Aim is to reduce dependence on coal deposits. It is anticipated this route will be able to produce rare earths with > 35% yield and > 98% purity which be described in subsequent studies and patents.

This paper presents Mathematical Formulation for Neutrino-Fission of a heavy nuclei in Neutrino-environments. It has been proposed that neutrino fission is a dynamic two-process reaction, where more than one neutrino can be captured by target nucleus. A quantity named Neutrino-Zeta with units of eV^-1 has been defined to represent Neutrino-environments. Neutrino-zeta with assumed excited energy states for heavy nuclei A∼230-270 is estimated to be 0.0212 MeV^-1, with the probability of fission Barrier energy state P(E_x,f)= 0.169064014 with E_x,f =6MeV. P(E_x,f)=0.222664 is highest at zeta=0.1MeV^-1 . It has been shown that probabilities of certain excitation energy states and their corresponding decay reactions at those excitation energies can be equal for two neutrino-zetas. Neutron induced fission for different neutrinos hypothesize that probability of symmetric fission channels is higher at low neutrino-zetas, which can be tested experimentally.

It is important to develop new antibacterial drugs since the emergence of bacterial resistance to available antibiotics. Metal nanoparticles have the potential to combat antibiotic resistance and are effective antibacterial agents. Here, we demonstrate the biogenic production of zinc nanoparticles using Rhododendron arboreum stem bark. The biosynthesized ZnNPs were characterized through different analytical techniques such as UV-vis, FTIR, EDX, SEM, and XRD. Clinical isolates such as E. coli, S. aureus, and K. pneumonia were used to check the antibacterial efficacy of the biosynthesized zinc nanoparticles. The MIC and MBC values against K. pneumonia, S. aureu, and E. coli were found to be 34±0.21 and 11.71±0.47, 47±0.11 and 23.86±0.84 and 94±0.18 and 40.43±0.16 µg/mL, respectively. It dramatically decreased the integrity of the cell membrane and markedly increased the permeability of both inner and outer membranes. The green synthesized ZnNPs have good catalytic activity. It degraded 65% of methyl orange dye within 6h when the mixture was kept in direct sunlight, thus demonstrating good photodegradation potential and can be employed as a good photocatalyst. According to these results, biosynthesized zinc nanoparticles could be a potential therapy option for controlling bacterial pathogenesis and an effective photocatalyst for the elimination of hazardous water contaminants.

In the dynamic and complex environment of industrial control rooms, operators are often inundated with a multitude of tasks and alerts, which can lead to an overwhelming situation known as task overload. This state can precipitate decision fatigue and a heavier reliance on cognitive biases, potentially compromising the decision-making process. To mitigate such risks, the implementation of decision support systems becomes crucial. These systems are designed to assist operators in making swift and well-informed decisions, particularly when they sense their own judgment may be faltering. Our research introduces an AI-based framework that leverages dynamic influence diagrams and reinforcement learning to construct an effective decision support system. The cornerstone of this framework is the creation of a robust, interpretable, and efficient tool that supports control room operators during critical process disturbances. By integrating expert knowledge, the dynamic influence diagram forms a comprehensive model that captures the uncertainties inherent in complex industrial processes. It is adept at anomaly detection and recommending optimal actions. Moreover, this model is enhanced through a strategic partnership with reinforcement learning algorithms, which fine-tune the recommendations to be more context-specific and precise. The ultimate goal of our framework is to provide operators with a live, reliable decision support system that can significantly improve their response during process upsets. This paper outlines the development of our AI framework and its application within a simulated control room environment. Our findings indicate that the use of decision support systems can lead to improved operator performance and reduced cognitive workload. However, it also reveals a trade-off with situation awareness, which tends to diminish as operators may become overly reliant on the system’s guidance. Trust emerges as a critical factor in the adoption and effectiveness of decision support systems. Our research underscores the importance of balancing the benefits of decision support with the need for maintaining operator engagement and comprehension during process operations.

Classical methods for inverse problems are mainly based on regularization theory. In particular those which are based on optimization of a criterion with two parts: a data-model matching and a regularization term. Different choices for these two terms and great number of optimization algorithms have been proposed. When these two terms are distance or divergence measures, they can have a Bayesian Maximum A Posteriori (MAP) interpretation where these two terms correspond, respectively, to the likelihood and prior probability models.

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.

Integrating nanomaterials like carbon nanotubes (CNTs) and iron oxide (Fe₃O₄) into epoxy composites has significant interest due to their potential to enhance mechanical properties. This study evaluates the impact of dispersion quality on the mechanical performance of CNT/Fe₃O₄/Epoxy composites, comparing stirring and sonication methods at three different loadings: 0.1, 0.3, and 0.5 wt.%. Tensile testing revealed that sonicated composites consistently outperformed stirred composites, with a significant increase in elastic modulus and ultimate tensile strength (UTS). However, fracture strain decreased in both composite types compared to pure epoxy, with sonicated composites experiencing a more significant reduction than stirred composites. These results underscore the importance of high-quality dispersion for optimizing mechanical properties.

Olfactory ensheathing cells (OECs) are the glial cells that accompany axons from the olfactory epithelium to their targets in the olfactory bulb. They possess unique features that have made them the subject of much study as being potentially useful in cell-based therapeutic approaches to CNS repair. Investigation of OECs has demonstrated antigenic and morphological heterogeneity in their population. No marker specific to and selective for OECs have yet been identified, and many of the markers used are variably expressed by other glial cells. Even among OECs, these markers appear to vary in vivo (depending on their anatomical location, contact with other cells, and developmental timing) and in vitro. The variation across the population of OECs has compromised their isolation and characterization. It has also made the task of identifying meaningful subpopulations - with greater or lesser therapeutic utility - dependent on identifying the source of their variability. Such information would aid in both the harvest and experimental manipulation of OECs to optimize their therapeutic effect. One way to understand the nature of this variability is to seek its potential causes in vivo. This must begin with an examination of the structure of the olfactory nerve. Here, the structure and development of the primary olfactory projection are thoroughly reviewed with an emphasis on OECs and the cells with which they make contact. The relevant experimental results are also discussed. The weight of anatomical evidence indicates that the structural variations described in different locations and across species are mostly the result of spatiotemporal developmental factors. As such, the formation of the olfactory projection is mediated primarily by the source tissue, the olfactory epithelium. Cell-autonomous development is common elsewhere, and suits the evolutionary age and importance of olfaction, as well as its continued regenerative capacity. Our findings provide a more systematic anatomical understanding of this nerve. That understanding indicates that variation in axon ensheathment by OECs is an inherent feature arising from the flexibility of the ensheathing program. This perspective, along with the anatomical data reviewed here, can inform more carefully controlled laboratory investigations designed to uncover the detailed mechanisms governing OEC biology.

This letter aims to show the connection between the sinc approximation for high-energy absorption cross section and the shadow radius of the spherically symmetric black hole. This connection can give a physical interpretation of the absorption cross section in the eikonal limit parameters. Moreover, the use of this alternative way, one can extract its shadow radius from the absorption cross section in high energy limits to gain more information about the black hole spacetime. Our results indicate that the increasing the value of the shadow radius of the black hole, exponentially increase the the absorption cross section of the black hole in high-energy limits which can be captured by the Event Horizon Telescope (EHT) collaboration.

The excess use of unsafe pesticides and mineral fertilizers in agriculture has led to serious health problems and environmental pollution. Nanotechnology has been solving these problems by providing nanoparticles (NPs) with excellent performance. By green synthesis of nanoparticles from plants, animals, and microbes, the use of hazardous and toxic chemicals has become limited. Nanoparticles have excellent performance in many fields such as electronics, cosmetics, automobiles, catalysis, biosensors, bioengineering, etc. NPs also showed excellent performance in agriculture by improving crop production and food quality. Various nano-based agroparticles that have conducted many smart and efficient agricultural systems involving nanopesticides, nanofertilizers, nanoherbicides etc. Apart from enhancing the food production, these materials operate some other functions like as identifying disease in plants, control release of nutrients, delivery of nutrients at target sites, etc. various nanofertilizers such as Fe, Mn, N, K, Mo, P, CNTs and P showed excellent targeted delivery performance. Nanopesticides and many nanoformulations have showed excellent pest protection performance. Here we reviewed the sources of nanomaterials and their excellent performance in agriculture.