Statistical modeling of lifetime data plays an essential role in a wide range of practical fields, such as health and engineering. There have been a lot of studies done to develop statistical models that can better describe health data than traditional models. For the first time, we pioneer a novel family of continuous probability distributions called the generalized odd beta prime generalized (GOBP-G) family of distributions. The cumulative distribution and probability density functions of the new family are presented. A new generalization of the Weibull distribution called "generalized odd beta prime-Weibull" (GOBPW) is proposed using the pioneered GOBP-G family. The mixture representations of the new distribution are defined and derived. Some formal statistical properties of the GOBPW distribution, such as the moments, moment generating function, incomplete moments, information generating function, entropies, stress-strength function, quantile function, and order statistics, are derived. The estimation of the parameters of the proposed distribution is evaluated using the maximum likelihood estimation approach. Different cancer disease data sets, such as the bladder, head and neck, acute bone, and blood cancers, are used to illustrate the applicability and usefulness of the new model and were compared using several statistical accuracy measures with that of well-established extended Weibull distributions, which are the beta modified Weibull distribution, Kumaraswamy modified Weibull distribution, gamma generalized modified Weibull distribution, gamma log-logistic Weibull distribution, and beta log-logistic Weibull distribution. The results show that the proposed model gives better results than the competitive models. This study could guide the relevant stakeholders in choosing a suitable statistical model for the health data instead of relying on traditional models to enhance decision-making.

Background: This study examines the relationship between energy usage, health issues, and pro-environmental behavior (PEB) in Kyrgyzstan, amidst the country's commitment to transition from coal-based energy to renewable sources in line with the Paris Agreement. The purpose is to investigate citizens' attitudes towards PEB and their intentions to engage in environmentally friendly actions, focusing on gas, electricity, and coal. Methods: Drawing upon the Theory of Planned Behavior (TPB) framework, a survey was conducted among 1,455 respondents to explore attitudes towards PEB and energy sources' impact on health issues. Results: Decarbonization efforts in Kyrgyzstan and Central Asia are in their early stages, with coal remaining a primary energy source. The study emphasizes the importance of governmental policies and citizen action in achieving decarbonization goals. Rising electricity costs outweigh the increase in indirect energy costs for food, posing challenges for households in adapting to changing energy dynamics. Conclusions: Targeted interventions and communication strategies are crucial to promote pro-environmental behavior and facilitate the transition to sustainable energy sources. Understanding the relationships between health concerns, air pollution awareness, PEB, and energy source choices can inform policymakers and organizations in their efforts to ensure a sustainable and healthy future for Kyrgyzstan and other Central Asian countries.

The last few years have witnessed an explosion of research on Sustainable development. Most of this research is concentrated on the developed countries related to the issues not compatible with developing countries. This paper fills the gap and reviews the literature related to developing and emerging economies and their environmental and social constraints under Renewable energy and sustainable development (RESD). It also investigates how RESD can be implemented in the presence of serious issues pertaining to population increase, shortage of energy supply, lack of transportation, shortage of clean water, less food production and bad environmental systems and these are coupled with war, and hunger and political instability. The main contribution of this paper is to present extensive discussion in the context of hypotheses of economic growth and its association with energy consumption, and renewable energy options for sustainable development.

The goal of the next generation World Wide Web is machine readability through linked databases. To improve web search, integration, and mining in local languages like Urdu, there is a growing need to develop ontologies and vocabulary in these languages. The majority of people use the web in local languages for agriculture, social media interaction, news, etc. How to create agents for the integration of web data. In our country, the literacy ratio is very low and IT literacy is negligible. More comprehensive information for its target audience is only possible through the World Wide Web. Our first target is to improve and enhance the use of social media and the web in local languages. That will encourage its constructive use in Urdu for society and the economy. The Web in natural languages is the source of income for small and medium enterprises. The semantic web is concerned with linked databases and structured data. In this work, we are focused on some selected ontologies to be translated into natural languages. Expertise in Ontology Engineering helps us in job production. Ontology Engineering has extensive freelancing opportunities. Only if the web is correctly interpreted in regional languages is an economic boost achievable. A standardized foundation for data sharing and reuse on the internet is provided by the Semantic Web. In other words, a group of standards and technology that enables computers to comprehend the semantics (meaning) of material on the Web.

Although vitamin D deficiency has been correlated with an increased risk of cardiovascular disease (CVD), there are few data on the association between 25-hydroxyvitamin D (25(OH) D) and atherogenic indices predictive of CVD. This study aimed to investigate the relationship of vitamin D status with lipid profile and atherogenic indices in adult women in Morocco. Three hundred healthy women, aged 18 to 50 years, from Meknes were included. Fasting 25(OH) D and lipid concentrations were determined using a one-step electrochemiluminescence-based immunoassay and an enzymatic method, respectively. Atherogenic indices (Atherogenic index of plasma (AIP), non-HDL cholesterol (Non-HDL-C), Atherogenic coefficient (AC), Castelli risk indices I and II (CRI-I and II), and CHOLIndex (CI)) were calculated using conventional lipid parameters. Logistic regression models and operating characteristic curve (ROC) analysis were used to assess the relationship of the variables and estimate the threshold of 25(OH) D levels associated with high atherogenic indices. 25(OH) D below 20 ng/ml was significantly associated with an enhanced risk of hypertriglyceridemia and elevated values of AIP, AC, Non-HDL-C, and CRI-I with an OR (95% CI) of 4.904 (1.856-12.959); 3.637(2.149-6.158); 3.589(1.673-7.700); 2.074(1.215-3.540), and 2.481(1.481-4.123), respectively. According to ROC analysis, the likelihood of hypertriglyceridemia and high values of AIP, AC, Non-HDL-C, and CRI-I were associated with 25(OH)D thresholds ≤15.15 ng/ml, ≤ 17.5 ng/ml, ≤ 19.8 ng/ml, ≤ 20.1 ng/ml and ≤ 19.5 ng/ml, respectively, all p < 0.01. Based on atherogenic indices, this study indicates that vitamin D below 20 ng/ml may increase the risk of cardiovascular disease in adult women. Additional health efforts are needed to raise awareness among women and healthcare providers in preventing and controlling cardiovascular risk factors.

Worldwide, the demand for natural and synthetic sweeteners in the food industry as an alternative to refined sugar is increasing. This prompted more research to estimate its safety and effect on health. The gut microbiome is critical in metabolizing selected sweeteners, which might affect overall health. Recently, more studies evaluated the relationship between sweeteners and the gut microbiome. This review summarizes the current knowledge addressing the role played by the gut microbiome in metabolizing selected sweeteners. It also addresses the influence of the five selected sweeteners or their metabolites on GI cancer-related pathways. Overall, the observed positive effect of sweetener consumption on GI cancer pathways, such as apoptosis and cell cycle arrest, requires further investigation to understand the underlying mechanism. Furthermore, more research is needed to address the main challenges of studying the field, thus advancing it further.

Smart farming, as a branch of the Internet of Things (IoT), combines the recognition of agricultural economic competencies, the progress of data and information collected from connected devices with statistical analysis to characterize the essentials of the assimilated information, allowing farmers to make intelligent conclusions that will maximize the harvest benefit. However, the integration of advanced technologies requires the adoption of high-tech security approaches. In this paper, we present a framework that promises to enhance the security and privacy of smart farms by leveraging the decentralized nature of blockchain technology. The framework stores and manages data acquired from IoT devices installed in smart farms using a distributed ledger architecture, which provides secure and tamper-proof data storage and ensures the integrity and validity of the data. The study uses the AWS cloud, ESP32, the smart farm security monitoring framework, and the Ethereum Rinkeby smart contract mechanism, which enables automated execution of pre-defined rules and regulations. As a result of a proof-of-concept implementation, the system can detect and respond to security threats in real time, and the results illustrate its usefulness in improving the security of smart farms.

Nature-inspired algorithms are very popular tools for solving optimization problems inspired by nature. However, there is no guarantee that optimal solution can be obtained using a randomly selected algorithm. As such, the problem can be addressed using trial and error via the use of different optimization algorithms. Therefore, the proposed study in this paper analyzes the time-complexity and efficacy of some nature-inspired algorithms which includes Artificial Bee Colony, Bat Algorithm and Particle Swarm Optimization. For each algorithm used, experiments were conducted several times with iterations and comparative analysis was made. The result obtained shows that Artificial Bee Colony outperformed other algorithms in terms of the quality of the solution, Particle Swarm Optimization is time efficient while Artificial Bee Colony yield a worst case scenario in terms of time complexity.

Nature-inspired algorithms are very popular tools for solving optimization problems inspired by nature. However, there is no guarantee that optimal solution can be obtained using a randomly selected algorithm. As such, the problem can be addressed using trial and error via the use of different optimization algorithms. Therefore, the proposed study in this paper analyzes the time-complexity and efficacy of some nature-inspired algorithms which includes Artificial Bee Colony, Bat Algorithm and Particle Swarm Optimization. For each algorithm used, experiments were conducted several times with iterations and comparative analysis was made. The result obtained shows that Artificial Bee Colony outperformed other algorithms in terms of the quality of the solution, Particle Swarm Optimization is time efficient while Artificial Bee Colony yield a worst case scenario in terms of time complexity.

Background: Convectional methods for drug delivery often faces setbacks due to systemic distribution, short half-life and degradation of therapeutics and therefore reduce concentrations of drug available to target tissue. Nanotheranostic provide a novel method for treating and diagnosing diseases Methodology: collection and review of relevant literatureResult: while nanotheranostic offer advantage of personalized medicine and often combines diagnosis and therapy using single molecular approach, nuclear medicine relies on radioactive isotopes to diagnosed and destroys cancer cells. In both cases, nanocarriers such as lipid-based, polymer-based, drug-conjugate, inorganic nanoparticles are used to deliver drugs/probes/isotopes to target site, generating images and thereafter chemotherapy/radiotherapy begins.Conclusion: Nanotheranostic plays important role in diseases diagnostic, therapy, imaging, monitoring of disease progression / response through the use of nanocarriers. This is made possible through nanoparticles/nanocarriers that delivers drug to the target tissues/cells.

As organizations are vulnerable to cyber attacks, their protection becomes a significant issue. Capability Maturity Models can enable organizations to benchmark current maturity levels against best practices. Although many maturity models have been already proposed in the literature, a need for models that integrate several regulations exists. This article presents a light web-based model that can be used as a cybersecurity assessment tool for Higher Education Institutes (HEIs) of the UK. The novel Holistic Cybersecurity Maturity Assessment Framework incorporates all security and privacy regulations and best practices that HEIs must be compliant to and can be used as a self-assessment or a cybersecurity audit tool.

Cash holding is important for Chinese manufacturing firms coping with the increasing cost of financing and stiff market conditions. This study examines the impact of cash holding on the firm value of Chinese manufacturing firms. We find evidence that a non-linear relationship exists between cash holding and firm value in manufacturing firms of China. The study reveals that financially constrained firms having a higher level of cash holding negatively affects the firm value, while the unconstrained firms having a lower level of cash holding positively influences the firm value. Finally, this research is enriched by adopting the novel measure of managerial op-timism and reveals the interactive role of cash holding and optimism on firm value. The study concludes that managerial optimism influences the firm’s cash holding decisions and this is more costly for unconstrained firms.

Dwindling Economy is otherwise known as depression economy or economy depression interchangeably and/or recess economy. It is an occurrence wherein an economy is in a state of financial turmoil, often the result of a period of negative activity based on the country’s Gross Domestic Product (GDP) rate. However, this has become a global phenomenon; a good example of a necessitating factor is the global oil crash market and pandemics virus (Covid-19) ravaging the human race. That has conjointly led to the decline in the GDP growth per capital of a country; which forces degradation in the performances of economic sectors, retrenchment of staff and wrapping-up of industries. It is a lot worse than a recession, with GDP falling significantly, and lasts for periods of time. Pen ultimately, Nigeria has been in deteriorating financial state for years; her economy in the last few years has been going through some turbulence. A country that had recorded an average GDP growth of 6.5 per cent, one of the highest in the world less than a decade ago, is now projected to grow at about 2.3 per cent in 2016. It is no longer news that Nigeria's economy is experiencing total collapse and if nothing is done to put the peg in the right spot something worse than what we are witnessing may soon be on sight. Based on some of all these issues and other, Nigeria was said to be technically recess.In this paper, efforts were made to explore the state of the Nigeria economy in the last 36 years (1981-2017) and correlate it with the recent phenomena that conjointly constitute to its dwindling economy. Our comprehensive and elusive literary survey and extemporariness suggested way forwards to rescue the raveling situation of Nigeria dwindling economics, if not providing lasting solution but temporarys’ one that could stand test of time.

Computational fluid dynamics (CFD) has, in the last decade, being an essential problem solving tool in industries such as pharmaceutical, pulp, petrochemical as well as Oil and Gas processing. The use of CFD for mixer design is unpopular in many countries in Africa. Therefore, this study investigates the characteristics of Brine-Surfactant mixing in a horizontal pipeline using CFD. The CFD is conducted by AnsysFluent software (licensed). A T-junction pipe is created and meshed with unstructured tetrahedral elements using design modeler. Discretization is done by Finite Volume Method (FVM), cell-centered scheme with Second-Order Upwind Scheme. The pressure term is introduced into the continuity equation by SIMPLE (Semi-Implicit Method for Pressure Linked Equations) algorithm. The kinetic epsilon model, also known as k-e model is administered to define the properties of the fluid and geometry, such as velocity, species mole fraction, pipe diameter. The boundary conditions is selected based on filed data. The rate of fluid flow in the primary region is 650 bbl/day at 400psi in 4in diameter pipeline, which is 100m long. The numerical simulation was based upon the governing equations such s continuity, Navier’s stoke, energy as well as specie transport equations. The findings shows that higher concentration results in increased mixing time, while 2% conc. of surfactant reaches homogeneity in 20 minutes at 72 meters of the pipe length. The result validated with field detail and the empirical result from literature, and is consistent. This study provide insight on industrial mixer design, chemical injection system, as well as gas pipeline design and optimization, especially in multiphase scale transport.

Carbon Capture and Storage (CCS) stands as a pivotal technological stride toward a sustainable future, with the practice of injecting supercritical CO2 into subsurface formations having already been an established practice for enhanced oil recovery operations. The overarching objective of CCS is to protract the operational viability and sustainability of platforms and oilfields, thereby facilitating a seamless transition towards sustainable practices. This study introduces a comprehensive framework for optimizing well placements in CCS operations, employing a derivative-free method known as Bayesian optimization. The development plan is tailored for scenarios featuring aquifers devoid of flow boundaries, incorporating production wells tasked with controlling pressure buildup and injection wells dedicated to CO2 sequestration. Notably, the wells operate under group control, signifying predefined injection and production targets and constraints that must be adhered to throughout the project’s lifespan. As a result, the objective function remains invariant under specific permutations of the well locations. Our investigation delves into the efficacy of Bayesian optimization under the introduced permutation invariance. The results reveal that it demonstrates critical efficiency in handling the optimization task extremely fast. In essence, this study advocates for the efficacy of Bayesian optimization in the context of optimizing well placements for CCS operations, emphasizing its potential as a preferred methodology for enhancing sustainability in the energy sector.

While visual assessment is the standard technique for burn evaluation, computer-aided diagnosis is increasingly sought due to high number of incidences globally. Patients are increasingly facing challenges which are not limited to shortage of experienced clinicians, lack of accessibility to healthcare facilities, and high diagnostic cost. Certain number of studies were proposed in discriminating burn and healthy skin using machine learning leaving a huge and important gap unaddressed; whether burns and related skin injuries can be effectively discriminated using machine learning techniques. Therefore, we specifically use pre-trained deep learning models due to deficient dataset to train a new model from scratch. Experiments were extensively conducted using three state-of-the-art pre-trained deep learning models that includes ResNet50, ResNet101 and ResNet152 for image patterns extraction via two transfer learning strategies: fine-tuning approach where dense and classification layers were modified and trained with features extracted by base layers, and in the second approach support vector machine (SVM) was used to replace top-layers of the pre-trained models, trained using off-the-shelf features from the base layers. Our proposed approach records near perfect classification accuracy of approximately 99.9%.

Selection of appropriate friction factors is paramount for accurate prediction of key flow characteristics in gas–liquid two-phase flows. In this work, experimental investigation of vertical air and oil (with viscosities up to 200 mPa s) flow in a 0.060-m ID pipe is reported. Superficial air and oil velocity ranges utilized are from 22.37 to 59.06 m/s and 0.05 to 0.16 m/s respectively. The influence of estimation of interfacial friction factor on accurate determination of film thickness, void fraction and pressure gradient was investigated using a two-fluid model. The results indicated that the two-fluid model is capable of accurately predicting flow characteristics. Further, it reveals that the best performing correlations are the Belt et al. and Ambrosini et al. correlations.

This paper analyses and evaluate the precision and the accuracy the capability of low-cost terrestrial photogrammetry by using many digital cameras to construct a 3D model of an object. To obtain the goal, a building façade has imaged by two inexpensive digital cameras such as Canon and Pentax camera. Bundle adjustment and image processing calculated by using Agisoft PhotScan software. Several factors will be included during this study, different cameras, and control points. Many photogrammetric point clouds will be generated. Their accuracy will be compared with some natural control points which collected by the laser total station of the same building. The cloud to cloud distance will be computed for different comparison 3D models to investigate different variables. The practical field experiment showed a spatial positioning reported by the investigated technique was between 2-4cm in the 3D coordinates of a façade. This accuracy is optimistic since the captured images were processed without any control points.

This paper provides an overview of modelling and simulation methods for complex systems, including queuing and life support systems. To realize and capture every aspect of a system or situation, a model is used in place of the actual one. Using a model and simulation, we may create scenarios and make decisions. Computer experiments with the model can save costs, time, and effort once its validity has been shown. A model of the thing under study is created by computer simulation, which then runs the model on a digital computer and examines the results. It's interesting to note that a knowledge base of data includes information about predicted behaviour of the model as well as coordinated determination of external variables and parameters. Selecting the precise model creation technique is the first and most difficult task in simulation. A review of techniques used in simulation model execution (serial and parallel discrete-event simulation), simulation model analysis (calibration, validation, verification, goal-seeking), and conceptual, declarative, functional, constraint, and multimodal design are presented in this paper.

Problem:Palestinian demographic landscape is undergoing an increase in the elderly population like many regions across the world. The elderly Palestinians face a unique challenges and problems of limited access to traditional care due to restricted movement, checkpoints, and also for limited healthcare resources. Therefore, the need arose to develop a sensitive and adaptable smart health monitoring system for Palestinian elderly to provide accurate and timely health data to improve well-being and prevent complications. Method: The proposed system (SHMS) leverages the Internet of Things (IoT) with appropriate sensors to track vital signs like heart rate, blood oximeter, body temperature and signal from a patient heart. Advanced algorithms analyze the data, generating alerts for potential health risks, triggering timely interventions, and enabling remote consultations. Results: This system has the potential to contributes to SDG 3 to significantly improve the health and well-being of elderly Palestinians by providing an accurate, low-power, and low-cost system for remote health monitoring. The system, being wearable, reads vital signs in real-time, overcoming geographical barriers and facilitating remote monitoring, especially in areas with limited healthcare centers and suffered from barriers along the way. The Doctor can suggest the patient’s prescription needed based on the momentary condition of the elderly and on previous readings of the sensors, if necessary. Which will also be notified via messages. Moreover, the doctor can take immediate intervention in case of an emergency. By implementing the system, it will be unnecessary for more than 50\% of the elderly to visit health care, thus reducing the cost to 50\%.}

Based on the Flashing Ratchet Potential (FRP) and Electro-Spectroscopy detection techniques, a new detection method was experimentally examined. This method allows the detection of sub-micro particles in the human exhaled air such as viral particles and other biological agents. The detection signal created by this method is carried out by electron current buildup forming an Electric Current-Spectrum (ECS) distinguishing the contents of the exhaled air samples. The detection method was developed based on FRP and a beam of free charges passing through the electrodes of the FRP. The ECS is characterized by low-energy free charges interacting with the medium through which they pass, where numerous free charges are scattered (delayed) as a result of these interactions. The resulting ECS is characterized by FRP's capacity to drift back the delayed free charges to their original starting positions. By comparing the ECS of two exhale air samples, this new technology can be utilized to identify the presence of viral and microorganisms’ particles. The time-escalating ECS difference quickly indicates the presence of such particles. Moreover, the FRP's optimal operating settings was experimentally defined, in addition to the experimental evaluation of its functionality to confirm the FRP's promising sensing capabilities, which were statistically examined several times to ensure the reproducibility of the results.

In the study of QSAR/QSPR, due to high degree of predictability of pharmaceutical properties, the eccentric-connectivity index has very important place among the other topological descriptors, In this paper, we compute the exact formulas of eccentric-connectivity index and its corresponding polynomial, total eccentric-connectivity index and its corresponding polynomial, first Zagreb eccentricity index, augmented eccentric-connectivity index, modified eccentric-connectivity index and its corresponding polynomial for a class of phosphorus containing dendrimers.

The current study characterizes the transient M/M/1 queue manifold info-geometrically, through devising Fisher Information matrix(FIM) and its inverse(IFIM). Additionally, the impact of stability on the existence of IFIM and explore the geodesic equations of motion has been revealed. More potentially, the paper discusses the relationship between stability and the Gaussian curvature, as well as the connections between queueing theory, information geometry, , Riemannian geometry, and the Theory of Relativity.

Starvation of photoreceptors induced by reduced dysregulated ocular blood flow is proposed as a common pathway for the pathogenesis of retinitis pigmentosa (RP). The current study evaluated the safety and efficacy of ophthalmic nerve stimulation (ONS) as an ocular blood flow neuromodulator, combined with ascorbic acid (AA) as a potent anti-oxidant in the treatment of RP. Additionally, the initial characteristics of rod responders were identified. Forty participants with simple, non-syndromic RP presented with a characteristic triad of RP (the bone spicule pigmentation, attenuation of retinal blood vessels, and waxy optic disc pallor), were en-rolled in a prospective open-label single-armed interventional study. The severity of the disease was clinically graded into six stages. Patients with an established diagnosis of RP; aged ≥ 4 years, with best corrected visual acuity (BCVA) ≥ 20/400 were included. All participants were daily treated with bilateral ONS sessions combined with intravenous administration of AA for two weeks. The primary efficiency endpoint was 6 months’ changes in scotopic vision as measured by a 10-item, 100-point, Low Luminance Questionnaire-10 (LLQ-10). The secondary efficiency points included best corrected visual acuity (BCVA) and contrast sensitivity. Rod responders were defined by ≥ 25 points increment of LLQ-10 score at 6 months after treatment. The results showed that ONS combined with AA treatment significantly improved low luminance vision, BCVA, and contrast sensitivity in patients with RP (p≤ 0.05). At 6-month visit, twenty-four (60%) patients were identified as rod responders and 16 (40%) patients were rod-non-responders. The mean change in LLQ-10 score was (46.35 ±16.81) in rod responders versus (4.9 ± 7.6) in non-responders (p < 0.0001). A clinically significant improvement of BCVA (≥0.2 logMAR) and contrast sensitivity (≥0.3 log unit) were demonstrated in 50% of the right eyes of rod responders. No serious adverse effects were reported and headache in 5 patients (12.5%) was the only en-countered side effect in this study. In conclusion, ocular neuromodulation is a safe therapeutic strategy for RP. It significantly improved night vision, BCVA, and contrast sensitivity. Determinants of rod responders include a shorter duration of night blindness, an earlier stage of the disease, and a relatively thicker ganglion cell layer at baseline. Additionally, two therapeutic scenarios were recognized; an early disease-modifying intervention that restores night vision and reverses the disease process and a late cone rescue intervention that improves/maintains central vision.

The Rényi entropy functional was optimised for the novel findings of this investigation under various conditions. Their research's findings suggest that the complete spectrum of Lévy stable distributions is covered by generalised t-distributions. Additionally, an exposition was undertaken to prove that the Lévy distribution generalizes Tsallisian distribution, rather than the reverse. The current study is a strong generalization of an existing research work in the literature.

Viral Encephalitis is a significant public health concern globally, especially west Africa. There are more than 500 known arboviruses with over 100 of them identified to cause encephalitic diseases in humans and animals, giving rise to a tremendous burden of the diseases, and socioeconomic strains in tropical and subtropical regions worldwide. Despite their importance, few effective preventive and control measures in form of vaccines and therapeutics are available and where they are, usage is limited. These limitations are largely hinged on the paucity of information about the molecular epidemiology and transmission patterns of VE in west Africa. Here, we reviewed the transmission dynamics, ecological drivers, and molecular epidemiology of VE in the region. Collectively, timely and accurate interventions are essential for encephalitic viral disease control. Moreover, the integrated health system approach, combining surveillance, vaccination, vector control, and community engagement could be effective in preventing viral encephalitis globally.

Information geometry (IG) is a fascinating combination of differential geometry and statistics where a Riemannian manifold's structure is applied to a statistical model. It may find numerous fascinating uses in the domains of theoretical neurology, machine learning, complexity, and (quantum) information theory, among others. (IG) aims to provide a differential-geometric perspective on statistical geodesic models' structure. In this case, IG, KD, and J-divergence (JD) are used to define the manifold of the Generalised Brownian Motion (GBM). Consequently, the geodesic equations (GEs) are devised, and GB information matrix exponential (IME) is presented. Moreover, for first time ever, the necessary and sufficient mathematical requirement that characterizes the developability of Generalized Brownian Motion(GBM) manifold is devised. Also, a novel sufficient and necessary conditions which characterizes the regions where the surface describing GBM is minimal is determined. Also, it is shown that GBM has a non-zero 0-Gaussian and Ricci curvatures. Consequently, this advances the establishment of a unified GBM- Relativistic Info-Geometric based analysis.

This paper discusses how mathematical models are used to predict environmental conditions. While these models are successful for larger scale phenomena, it becomes more complex for local weather or wave processes. Several approaches have been proposed to address this problem, including neural networks and statistical and mathematical models. The novelty in this approach is the use of statistical manifolds, which provide a more accurate way to estimate distances and cost functions. This work focuses on advancements in differential and information geometry for creating different types of models that can be used for environmental simulation and forecasting. These fields are vital for many institutes and centres worldwide, as they help solve challenges in areas such as meteorology, renewable energy, and aviation safety. Some potential IG applications to advance energy were given. Closing remarks with new open problems combined with the next research phase are provided.

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.

The paper explores the Kullback-Leibler divergence formalism (KLDF) applied to the stable MG1 queue manifold. It explores the analytic forms of state probabilities and their maximization based on entropy functionals, subject to normalization and mean value constraints. The credibility of KLDF is justified through consistency axioms, and the application of Rényi's and Tsallis's formalisms on a stable M/G/1 queue is examined, resulting in novel state probabilities and insights into information theory of Queue Learning.

The current work reports the upper bound of the state variable of the of the Pointwise Steady Fluid Flow Approximation (PSFFA) model of non-stationary M/Ek/1 queueing system.

Information about molecular mechanism of pesticide resistance in the rust-red flour beetle, a major pest destroying grains and flour across Nigeria is grossly lacking, hindering evidence-based con-trol. Here, we identified to species level three populations of the red-flour beetle from Kano, Ni-geria, as Tribolium castaneum, and investigated the mechanism driving their insecticide resistance. IRAC susceptibility bioassays established high cypermethrin resistance, with LC50s of 4.35-5.46mg/ml in the three populations, NNFM, R/Zaki and Yankaba. High DDT and malathion resistance was observed in NNFM, with LC50s of 15.32- and 3.71mg/ml, respectively. High sus-ceptibility was observed towards dichlorvos in all three populations with LC50s of 0.17-0.35mg/ml, respectively. Synergist bioassay with piperonylbutoxide significantly restored cypermethrin sus-ceptibility, with mortality increasing almost threefold, from 24.8% obtained from 1.25mg/ml of cypermethrin, to 63.3% in synergised group (p=0.013), suggesting a preeminent role of P450s. The two major knockdown resistance (kdr) mutations, T929I and L1014F in the IIS4 and IIS6 fragments of voltage-gated sodium channel were not detected in both cypermethrin-alive and cyperme-thrin-dead beetles, suggesting lesser role of target-site insensitivity. These findings highlight the need to explore alternative control tools for this pest and/or explore incorporation of synergists, like piperonylbutoxide as additional chemistries into pesticides formulations to improve their ef-ficacy.

Numerous scientific fields employ entropy, the Shannonian measure of disorder and inaccessible information. Current paper examines educational uses of entropy. The major objectives of this survey are to create a coherent perspective of the applicability of classical entropies and to provide a concise detailed explanation that blends technical pertinence with conceptual extraction from the most recent literature. What's more intriguing are the closing thoughts mixed with a few unsolved issues and the creation of a future work schedule in these areas.

Over the last decade the blockchain technology has emerged to provide solutions to the complexity and privacy challenges of using distributed databases. It reduces the cost for customers by eliminating intermediaries and builds the trust in peer-to-peer communications. Over this time, the concept of blockchain has shifted greatly due to its potential in business growth for enterprises and the rapidly evolving applications in a collaborative smart cities' ecosystem, healthcare, and governance. Many platforms, with different architectures and consensus protocols, have been introduced. Consequently, it becomes challenging for an application developer to choose the right platform. Furthermore, blockchain has misaligned with the goals for efficient green collaborative digital ecosystem. Therefore, it becomes critical to address this gap and to build new frameworks to align blockchain with those goals. In this paper, we discuss the evolution of blockchain architecture and consensus protocols, bringing a retrospective analysis and discussing the rationale of the evolution of the various architectures and protocols, as well as capturing the assumptions conducting to their development and contributions to building collaborative applications. We introduce a classification of those architectures helping developers to choose a suitable platform for applications and providing insights for future research directions in the field to build new frameworks.

The theory of Ismail's non-extensive maximum entropy solution (NME) is described in detail. It is used as an inductive inference technique for heavy-tailed queues with a non-robust mean queue length and a non-extensive "long-range" interaction. In our novel method, we substitute the non-robust mean queue length for the conventional Pollaczeck-Khinchin mean queue length. In other words, the new non-extensivity parameter q will be included in the resulting state probability function. Numerical portraits are provided to capture the influential effect of the derived formalism, , on the stable M//1 queue with heavy tails. More potentially, some applications of Hurst Exponent to social computing and connected health are provided. Conclusion with some challenging open problems and possible future research pathways are given.

An exposition of entropy applications to highlight some potential entropic applications to artificial intelligence and knowledge transfer. Entropy is a basic idea in artificial intelligence that is applied to many different tasks, such as reinforcement learning, data compression, and decision-making. By quantifying uncertainty and information content, it helps artificial intelligence models provide predictions and decisions that are well-informed. Therefore, this paper shines on spotlighting the significance of entropy to attract the attention of artificial intelligence research community to entropy as a powerful tool to advance artificial intelligence.The significance of knowledge transfer (KT), particularly intergenerational knowledge transfer (IGT), in knowledge management is also covered in this paper. To quantify the complexity of knowledge distribution inside an organisation and assess the efficacy of knowledge transfer (KT) initiatives, the notion of knowledge entropy (KE) is presented. Additionally, the KT model—which is based on the conceptions of tacitness and information content—is introduced. It combines personalisation and codification methodologies. Future research directions are offered alongside a few difficult open problems.

The current paper provides a cutting-edge information data length- theoretic approach to human emotions. This is a revolutionary breakthrough that unifies information data length and the dynamics of emotions. The significant impact of both time and its mean value associate on both the upper and lower bounds of the obtained information data length is observed and noted. The paper concludes with some challenging open problems, combined with concluding remarks and future research pathways.

This study focuses on examining various approaches for Tensor Decompositions (TDs) and their potential applications in satellite imaging (SI) and deep learning (DL). The research highlights how these decompositions can contribute to the advancement of SI and DL, providing valuable insights for future utilization of TDs in research. It explores how these techniques contribute to the advancement of SI and DL methods, providing insights into their potential applications and suggesting future research directions. The study aims to enhance the use of TDs techniques to further advance research efforts in these fields. More importantly, the current investigation offers a thorough analysis of the potential advantages and reasons for employing TDs techniques in different domains, including satellite imaging and deep learning. The aim is to enhance research outcomes by utilizing TDs. The review also identifies unresolved issues and proposes future directions for further investigation in this field. Fundamentally, these proposed open problems will open new grounds to the research community to articulate, innovate, and provide more real-life applications to improve the current state of the art by delving into a wider vision for a higher-level performance of both satellite imaging (SI) and deep learning (DL). Looking at the bigger scenario, this also suggests that TDs could be potentially employed to revolutionize existing machine learning technologies as well as the current space AI industry.

Entropy as a mathematical concept is the first of its own kind, and second to none. Barely one or two papers in literature have investigated fractal dimensions of very basic entropies, such as Shannon, Tsallis, and Rényi entropic expressions. This was a thoughtful inspiration to delve into a step forward towards a unified information- theoretic fractal theory. The storytelling of this current paper starts by introducing my third entropy formula, namely Ismail’s entropy, , as a novel generalization to Shannonian entropy with a visionary link to both long- and short-range interactions, (LRIs), (SRIs) respectively. Clearly, this would generate a fourfold unification with statistical mechanics as well as contemporary physics. Hence, this adds more credibility and weight for this study. The fractal dimension of , is identified in this paper. As an essential part of this work, it was influential to spotlight the importance of fractal geometry to space industry as well as education, which is crucial to develop our societies. Thus, some potential fractal applications to CubeSat Technologies and Education are highlighted. So, will the deep search for more ground-breaking research has come to an end. Certainly, not. This was another inspiration to propose new open problems to widen research gates to the scientific community for more research pathways. Consequently, the paper ends with closing remarks combined with some challenging open problems and the next phase of research.

Covid-19 pandemic lockdown has slow down the world economic system. The pandemic has cleared the roads, close factories and grounded planes causing severe economic challenges. The damaging impact of the pandemic amid lockdown has been a blessing in guise for the environment because of significant drop in pollution level as transport and industrial sectors shutdown. Transport and industrial sectors are major contributors to environmental degradation through various emissions as a result of fossil fuel consumption. Energy consumed by transport and industrial sectors will have to shift to viable, readily available, economically and environmentally friendly with no carbon build up post Covid-19 pandemic. Hydrogen energy remains the best alternative option technologies containing green house gas emission and pollutions of several forms. Hydrogen holds the potential to provide a clean, reliable, renewable and economical source of energy for meeting the growing and unending global energy needs post pandemic. The present paper explores the economic feasibility and potential of hydrogen to serve as a competitive fuel option post pandemic. In this paper, the role of hydrogen as an energy carrier hydrogen economy structure, potential of hydrogen economy, hydrogen production methods, hydrogen application and the economic and environmental importance of hydrogen as a viable fuel option post covid-19 pandemic were discussed. There will be a surge in demand and investment for hydrogen economy post Covid-19.

The novel Coronavirus (COVID-19) is spreading and has caused a large-scale infection in China since December 2019. Thefi rst infected person was declared on March 18, 2020 in Djibouti. This has led to a signi cant impact on the lives and economy in Djibouti and other countries. In this study, we propose a double compartment stochastic model which describes the evolution of the infection rate and the evolution of the number of infected in the period from May 20 to June 23, 2020. We will also propose the evolution of people infected in two states, recovered and deceased.

In this paper, it is introduced the notion of r-fuzzy β-T_i, i = 0, 1, 2 separation axioms related to a fuzzy operator β on the initial set X which is a generalization of previous fuzzy separation axioms. An r-fuzzy α-connectedness related to a fuzzy operator α on the set X is introduced which is a generalization of many types of r-fuzzy connectedness. An r-fuzzy α-compactness related to a fuzzy operator α on the set X is introduced which is a generalization of many types of fuzzy compactness.

The current work reveals the fine tuning between stability zones and randomness of GI/M/1 Pointwise Stationary Fluid Flow Approximation (PSFFA) model of the non-stationary D/M/1 queueing system. More specifically, this clearly provides more insights into developing a contemporary PSFFA theory that unifies non-stationary queueing theory with chaos theory and fields in both theoretical physics and chaotic systems. This opens new grounds for stability analysis of non-stationary queueing systems. A notable application of GI/M/1 queueing model to achieve ultra-low latency of autonomous driving service is highlighted. Concluding remarks associated with future avenues of research are given.

The current work reveals the fine tuning between stability zones and randomness of Pointwise Stationary Fluid Flow Approximation (PSFFA) model of the non-stationary queueing D/M/1 system. More specifically, this clearly provides more insights into developing a contemporary PSFFA theory that unifies non-stationary queueing theory with chaos theory and fields in both theoretical physics and chaotic systems. This opens new grounds for stability analysis of non-stationary queueing systems.

This work is the first in literature to tackle the difficult open problem of determining the upper bound and threshold theorem for the TDCDP (time-dependent controller parameter) of the (Fokker Planck Kolmogorov) probability density function. This revolutionary exposition will put control theory and other related inter-disciplinary fields to a higher level towards contemporary control theory. Notably, based on the influential role of control theory in both engineering and industry, this paper will be of great value to all engineering and industry professionals who seek to know more about advanced trends within control theory settings. On the other remit of the spectrum, Fokker Planck Kolmogorov(FPK) equations are of high importance to physicists as well as mathematicians, based on their multiple applicability to information theory, graph theory, data science, finance, economics, and beyond. So, this by default adds more taste and credibility to this study. This leads by nature to introducing a different flavor to this ground-breaking research by highlighting the impact of Fokker Planck Kolmogorov(FPK) to revolutionize crypocurrency,which have received its name because it uses encryption to verify transactions, a new debatable digital payment system that doesn't rely on banks to verify transactions. It’s a peer-to-peer system that can enable anyone anywhere to send and receive payments. The paper ends with closing remarks combined with some challenging open problems and the next phase of research.

Objective: The private healthcare providers (PHCP) are believed to improve access to healthcare services in Public-Private Mix (PPM) projects, as they are considered first point of contact for healthcare. The purpose of this study was to know the satisfaction level of TB patients. Design: A questionnaire-based, cross-sectional study was carried out during November and December 2017 for 572 under-treatment patients registered with PHCPs in the PPM project. Lot quality assurance sampling technique was used to randomly select 19 districts from sample frame of 75 districts. In each selected district, data collector retrieved TB register of 8 months (Jan – Aug 2017) and systematically selected patients by fixed periodic interval. SPSS (version 24.0) was used to analyze the data. Results: Study included 53% (n=301) of males and 47% (n=271) of females, with mean age of 38 years (SD, ±18). Almost half of the participants were illiterate (51%, n=289) and 64% (n=365) were non-earning members of the family. In practice, most of the participants visit private providers (71%, n=407), including private hospital/clinic (44%) and traditional practitioners (27%; n=153). 55% of participants visited the current doctor because of clinic’s proximity to residence. 82% of the participants (n=469) were satisfied with the TB care services and 85% (n=488) said that they would recommend this clinic to others. Conclusion: PHCPs are preferred providers for individuals, which is consistent with findings of other studies. Though they are satisfied with TB care and services, interventions should be introduced to reduce the financial burden on the patient. Partnering PHCP is a way forward to ensure universal health coverage, health system strengthening and better health outcomes of the population.

This paper provides a first-time ever unification of information theory, semi-group theory with the theory of uncertain reasoning, through functional perspective. Fundamentally, the threshold theorems for the inference functional were devised. Furthermore, numerical experiments are illustrated. The complex proofs in our paper are original results which emphasizes the credibility of the class of Rényi Generalized Entropies as measures of information, and that the field is open to extend an enhanced methodology regarding queuing networks with heavy tails.

Information geometry (IG) seeks to characterize the structure of statistical geodesic models from a differential geometric point of view. By considering families of probability distributions as manifolds with coordinate charts determined by the parameters of each individual model, the tools of differential geometry, such as divergences and metric tensors, provide effective means of studying their characteristics. The research undertaken in this paper presents a novel approach to the modelling study of information geometrics of a queueing system. In this context, the manifold of a stable M/G/1queue is characterised from the viewpoint of IG. The Fisher Information matrix (FIM) as well as the inverse of (FIM), (IFIM) of stable M/G/1 queue manifold are devised. In addition to that, new results that uncovered the significant impact of stability of M/G/1 queue manifold on the existence of (IFIM) are obtained. The Kullback’s divergence (KD), and J-divergence (JD).New result has been devised on the significant impact of both server utilization and squared coefficient of variation of the underlying M/G/1 queue manifold on both (KD) and (KD) are devised. Also, it is revealed that stable M/G/1 QM is developable (i.e., has a zero Gaussian curvature) and has a non-zero Ricci Curvature Tensor (RCT). Novel stability dynamics of M/G/1 queue manifold is revealed by discovering the mutual dual impact between the behaviour of (RCT) and the stability and the instability phases of the underlying M/G/1 queue manifold. Furthermore, a new discovery that presents the significant impact of stability of M/G/1 queue manifold and the continuity of the unique representation between M/G/1 queue manifold and Ricci Curvature Tensor (RCT). The information matrix exponential (IME) is devised. It is also shown that the obtained (IME) is unstable. Also, it is shown that stability of the devised (IME) enforces the instability of M/G/1 queue manifold. Unifying IG with Queueing Theory enables the study of dynamics of queueing system from a novel Riemannian Geometric (RG) point of view, leading to the analysis of the stable M/G/1 queue, based on the Theory of Relativity (TR).Extending the study over two new additional divergence measures, namely and together with a complete illustrative numerical results for all these measures including KD, JD. This links Queueing theory, IG with deep machine learning and metric learning. Furthermore, this reveals the revolutionary approach of queue learning. Full analytic study of Gaussian curvatures subject to both Angular and Monge techniques together with the overall stability dynamics impact on these curvatures. Full analytic study of Einestein Tensor and Stress Energy Tensor together with the overall stability dynamics impact on these curvatures. The inclusion of the definitions of Gaussian and Ricci, Ricci scalar curvatures and Einstein Tensor together with their physical interpretations; The proposed novel approach for the pioneer visualization of queueing systems via computational information geometry. The determination of new important links between classical queueing theory and other mathematical disciplines, such as IG, matrix theory Riemannian geometry and the THEORY OF RELATIVITY by providing for first time i) The full detailed derivations of the Gaussian curvature ii) The Ricci curvature tensor and iii) The full physical as well as the geometric interpretation of these new results. The provision of a novel link between Ricci Curvature (RCT) and the stability analysis of the stable M/G/1 QM. The full investigation of the newly introduced QT-IG unifiers together with the impact of stability/ instability of the underlying M/G/1 QM on them. The full investigation of the newly introduced (QIGU) unifiers together with the impact of stability/ instability of the underlying M/G/1 QM on them.

Using the potent tools of information geometry (IG), the current research analyses the information geometrics of the five-dimensional HTBFC Manifold. By enabling a unique revolutionary analysis for the performance of Human-Trust Based Feedback Control (HTBFC), the integration of information geometry into HTBFC facilitates the relativistic analysis of the five-dimensional HTBFC manifold. Both the two- and three-dimensional manifold subcases are investigated. This work provides a ground-breaking approach to IG unification of Human- Machine Interactions (HMIs). Some potential applications of HTBFC to robotics are presented.Conclusions are provided, along with some emerging open problems and recommendations for future research.

The current study characterizes the transient M/M/1 queue manifold info-geometrically, through devising Fisher Information matrix(FIM) and its inverse(IFIM). Additionally, the impact of stability on the existence of IFIM and explore the geodesic equations of motion has been revealed. More potentially, the paper discusses some info-geometric applications to Machine Learning are highlighted. Closing remarks with some challenging open problems combined with the next phase of research are given.

One of the most extensively studied topics in dozens of studies is the alteration process of ilmenite, the formation of leucoxene and the presence of some impurity oxides; SiO2 and Al2O3. The altered Egyptian black sand ilmenite grains of relatively lower magnetic characters are studied using the binocular microscope and the Cameca SX-100 microprobe instrument. Both of individual brown and black altered grains separated at 0.5 and 1 ampere values are investigated. The detection of the Various alteration phases, the mechanism of formation, their molecular formulas &amp; limits, and the role of SiO2&amp;Al2O3 in alteration mechanisms are detected. The alteration phases includes: pseudorutile (psr) and leached pseudorutile (lpsr) of different phases in addition also to rutile. Few analyzed spots are detected to be leached ilmenite (lilm). Several Excel softwares are adopted to calculate the chemical formulas of each alteration phase. The contents of TiO2 and Fe2O3 of all the investigated psr-lpsr in the study range between 59.16-86.56%, and 37.3-6.68%, respectively. The Ti/(Ti+Fe) ratio for these formulas ranges between 0.60 and 0.88. The psr-lpsr chemical formulas of all the investigated grains range as follows: Fe2.01-0.50Ti3O8.97-4.50(OH)0.03-4.50 . The concluded lowest cationic iron content of the well defined accepted lpsr phase is 0.5 with a corresponding molecular formula of Fe0.50Ti3O4.5(OH)4.5. It was concluded that in the region of 68-70 TiO2%, the mechanism of ilmenite alteration may be changed where neither all the analyzed TiO2 of the spot nor all the calculated structural water are contained within the molecular formula of lpsr. There are other associated mineral phases containing for some TiO2 and also some structural water which most probably are removed from the lpsr phase. As the analyzed spots are located at highly fissured locations, as the alteration process is relatively faster and the lpsr phase can be broken into rutile and hematite. Also, as the analyzed TiO2 and structural and/or molecular water contents increase, as the darkness of the BSE image areas of the grain increases; this may reflect the existence of an individual TiO2 phase; most probably rutile, mixed in homogeneity with the existed lpsr component. As the content of TiO2 increases; within a definite TiO2 range (80-85%), as the associated contents of Al2O3&amp;SiO2 increase. When the contents of the structural and/or molecular water contained within the lpsr phases decreases; the sum total oxides is more than 98%, the contents of Al2O3 are highly depleted. In the late alteration stages, the lpsr structure does not suddenly collapse but gradually producuing other associated mineral phases. The relatively enriched contents of SiO2 and/or Al2O3 in some secondary rutile grains can be explained as most of the SiO2% is associated with mol water or bearing for mol and/or str water necessary for the leachability of Fe3+ from the psr structure. The XRD patterns of the investigated grains before and after roasting at 1100 oC for one hour are detected and interpreted.

This paper relies on accounting-based measures of country risk to investigate U.S. global banks' exposure to foreign country risk over the 2017 fiscal year as measured by the sum of cross-border risk, foreign office risk, and derivative risk claims. We achieve this using panel linear modeling methods with country level heterogeneity and time fixed effects, along with a constructed panel data of 284 observations on 71 countries distributed across 6 world regional blocks, and observed over 4 consecutive quarters starting from 4th quarter 2016 and ending with 3rd quarter 2017. The results show that on average, over the four quarters, a 1% increase in foreign banking sector's claims significantly increases U.S. global banks cross border risk exposure by 0.34%, while reducing derivative risk exposure by 0.22%, but have no significant impact on foreign office risk exposure. Similar results are observed with public sector claims which significantly increase banks' exposure to cross border risk by 0.21%, while reducing derivative risk exposure by 0.19%. Conversely however, non-bank financial sector claims are found to have no significant affect on cross-border risk exposure, but significantly reduce foreign office risk exposure by 0.09%, while increasing derivative risk exposure by 0.06%. These results indicate the presence of sectoral heterogeneities in U.S. banks' exposure to foreign counterparties' risk, and also that overall, over the course of 2017 the level of U.S. global banks' cross-border risk exposure increased, while their level of derivative risk exposure decreased, and the level of foreign office risk exposure remained relatively unchanged.

Modern day civilization is dependent on energy generation by fossil fuels. But the major drawback of using fossil fuels is environmental pollution. Microalgae are potential candidate for production of various products of interest, such as proteins, mini food, pigments and triglycerides that can be converted into biofuels. Lignocellulosic feedstocks are the most abundantly available raw material of plants that can serve as a promising feedstock for cultivating bacteria, fungi, yeasts and microalgae to produce biofuels and other value-added products. Owing to the abundant availability of these low/no cost substrates, can be utilized as feedstocks for cultivating microalgae to generate biogas/biodiesel. Likewise, there is much room to exploit defatted algal biomass to be used as animal/fish feed and oil producing/accumulating genes knowledge in future to produce high and good quality biodiesel and biogas.

The current letter complements my info-geometric discoveries, especially those provided from two papers of mine. Fundamentally, the statistical relativization of the Generalized Brownian Motion Manifold. Following this innovative and unprecedented track of research will open a plethora of numerous info-geometric investigations to many unexplored related phenomena in the hope to uncover more new interpretations for the Generalized Brownian Motion Manifold from an info-geometric perspective.

In the current work, the stable queueuing system's Generalised Z-Entropy (GZE) of Rényian formalism is examined in its fractal dimension. Notably, fractal dimension’s resulting behaviour that corresponds to the GZE parameters is examined through numerical tests. This research makes a substantial generalization in the literature by fusing fractal geometry and information theory to shed light on how entropy and complexity interact. More fundamentally, the significant role of fractal dimension to advance Big Data Analytics(BDAs) is highlighted. Closing remarks combined with open problems and the next phase of research are provided.

The current paper provides a cutting-edge information data length- theoretic approach to the transient M/M/Infinity queueing system. This is a first time ever investigation that unifies information data length and queueing theories. The significant impact of both time and the number of states for the transient M/M/ Infinity queue on both the upper and lower bounds of the obtained information data length is observed and noted. The paper concludes with some challenging open problems, combined with concluding remarks and future research pathways.

From a differential geometric perspective, information geometry (IG) aims to characterise the structure of statistical geodesic models. The research done for this paper offers a novel method for modelling the information geometry of a queuing system. From the perspective of IG, the manifold of the transient M/M/infinity queue is described in this context. The Fisher Information matrix (FIM) as well as the inverse of (FIM), (IFIM) of transient M/M/infinity QM are devised. In addition to that, new results that uncovered the significant impact of stability of QM on the existence of (IFIM) are obtained. Moreover, the Geodesic equations of motion of the coordinates of the underlying transient are obtained. Also, it is revealed that stable QM is developable (i.e., has a zero Gaussian curvature) and has a non-zero Ricci Curvature Tensor (RCT). Novel stability dynamics of transient M/M/iInfinity queue manifold is revealed by discovering the mutual dual impact between the behaviour of (RCT) and the stability(instability) phases of M/M/Infinity QM. Furthermore, a new discovery that presents the significant impact of stability of queue manifold and the continuity of the unique representation between M/G/1 queue manifold and Ricci Curvature Tensor (RCT). The information matrix exponential (IME) is devised. Also, it is shown that stability of the devised (IME) enforces the instability of M/M/ Infinity QM. More interestingly, novel relativistic info-geometric queueing theoretic links are revealed .

My innovative entropy measure, Ismail entropy, namely, (IE) is an ultimate generalization to numerous generalized entropies within the literature. The fractal dimension of IE is identified in this paper. On another applicative note, this paper offers some potential applications of fractal dimension to smart cities. The paper ends with closing remarks combined with some challenging open problems and the next phase of research.

An exposition is undertaken to reveal the significant impact of the time-dependent root parameter, on the stability of Pointwise Stationary Fluid Flow Approximation (PSFFA) model of the non-stationary queueing system. This opens new grounds to stability analysis of non-stationary queueing systems. Potential PSFFA applications of IoT are highlighted.

Information geometry is a mathematical framework that analyses the structure of statistical models using concepts from differential geometry. It treats families of probability distributions as manifolds, where the parameters of each model determine the coordinate charts. By applying info-geometric tools, we can gain insights into the characteristics of these models. The approach involves characterizing the queueing system's manifold using information geometry and presenting the exponential of the information matrix. This integration of information geometry with queueing theory provides a novel perspective for analyzing the dynamics of queueing systems, incorporating relativistic and Riemannian concepts. Some applications to E-health are highlighted. Finally, closing remarks and the next phase of research.

Additive manufacturing (AM) is an emerged layer-by-layer manufacturing process. However, its broad adoption is still hindered by limited material options, different fabrication defects, and inconsistent part quality. Material extrusion (ME) is one of the most widely used AM technologies, and, hence, is adopted in this research. Low-cost metal ME is a new and AM technology used to fabricate metal composite parts using sintered metal infused filament material. Since the involved materials and process are relatively new, there is a need to investigate the dimensional accuracy of ME fabricated metal parts for real-world applications. Each step of the manufacturing process, from the material extrusion to sintering, might significantly affect the dimensional accuracy. This research provides a comprehensive analysis of dimensional changes of metal samples fabricated by the ME and sintering process, using statistical and machine learning algorithms. Machine learning (ML) methods can be used to assist researchers in sophisticated pre-manufacturing planning and product quality assessment and control. This study compares linear regression to neural networks in assessing and predicting the dimensional changes of ME made components after 3D printing and sintering process. The prediction outcomes using a neural network performed the best with the highest accuracy as compared to regression. The findings of this study can help researchers and engineers to predict the dimensional variations and optimize the printing and sintering process parameters to obtain high quality metal parts fabricated by the low-cost ME process.

The objective of this study was to compare the effect of different botanical powders and synthetic chemical in controlling Sitophilus zeamais in stored maize. A laboratory experiment was done to assess three botanicals, Zingiber officinale, Moringa oleifera, Xylopia aethiopica, in controlling the S. zeamais. The results of this study demonstrated the active potentials of these plant products as plant-derived insecticides against maize weevil. The synthetic chemical used showed both higher weevil mortality and higher grain loss than the botanicals (P< 0.05). The Botanicals also showed significance difference in the mortality rate (P < 0.05) as Z. officinale recorded the highest mortality rate (5.66%) while X. aethiopica recorded the lowest mortality rate (3.66%). The control attained the highest grain damage (7.33%) while amongst the botanicals, the highest was found in Z. officinale (7.33%) and lowest was on M. oliefera (5.33%). The exit holes made by the weevils at the end of the experiment were highest (2.66) in M. oliefera whereas lowest was recorded on X. aethiopica (1.00). Z. officinale, X. aethiopica and M. oleifera were efficacious against S. zeamais instead of synthetic chemical insecticides that have environmental health hazards and they can be used in integrated pest management by farmers and foods merchants. Therefore, since these botanicals have no any adverse effects on the seeds and safe to the environment, they are recommended for future usage in storage grains to control of S. zeamais.

In an attempt to improve the quality of the agricultural drain in Egypt for its reuse again in the irrigation, low cost solution such as sand filter along with/ without other filtration media have been used in this research, As a result of that, pilot plant of sand filter mixed with other filtration media was tested for its ability to improve the sand performance in removing the suspended solids and organic matters from agricultural drain water of the Belbeis drain (in Sharkia governorate in Egypt). Only sand compared with sand with sponge and sand with Liyan Nanfang activated carbon (L.N.A.C) have been tested to find the optimum mixing sand/ medium ratio &optimum infiltration rate. The work has been done on four runs. It was found that sand mixed with sponge gave the best removal efficiency compared to that of the sand only and the sand mixed with L.N.A.C. The results presented that the concentration of CODt, CODsol and TSS was reduced from 125, 47 and 162 mg/l to 44, 34 and 28 mg/l respectively at optimum infiltration rate of 2 m³/m²/d for sand mixed with sponge.

Tuberculosis (TB) associated stigma is well-documented phenomenon that may contribute to sub-optimal TB care in Pakistan. The objective of study was to assess TB related knowledge and perceived stigma among community members. This was cross-sectional survey using convenience sample of 183 individuals recruited between October and December 2017. A validated stigma measurement tool developed by Van Rie et al. was adapted. Data was analyzed using SPSS version 20.0. 183 individuals (73% males; n = 134) participated in survey. Eighty-seven percent were aware that TB is curable disease (n = 159) and 91% thought that it could be transmitted by coughing (n = 167). However, respondents also thought that TB was spread through contaminated food (73%; n = 134), sharing meals (55%; n = 100), sharing utensils (53%; n = 96) and by having sexual intercourse with a TB patient (51%; n = 93). Fifty-seven percent (n = 104) associated TB with high levels of stigma. Persons who had less than six years of education (crude OR = 1.2; 95% CI: 0.89, 1.72) and lacked knowledge that TB is curable (crude OR = 3.42; 95% CI: 1.20, 9.70) were more likely to associate TB with stigma. In addition, females (crude OR = 1.33; 95% CI: 0.87, 2.04) and those who were unemployed (crude OR = 1.06; 95% CI: 0.65, 1.74) were also more likely to associate TB with stigma. We found an association between lack of knowledge about TB and perceived stigma. This highlights need for improved education and awareness about TB.

The hard open problem of finding the lower bound for the Fokker Planck Kolmogorov’s time-dependent controller parameter probability density function is addressed for the first time in literature in this work. This revolutionary exposition will put control theory and other related inter-disciplinary fields to a higher level towards contemporary control theory. Notably, based on the influential role of control theory in both engineering and industry, this paper will be of great value to all engineering and industry professionals who seek to know more about advanced trends within control theory settings. On the other remit of the spectrum, Fokker Planck Kolmogorov(FPK) equations are of high importance to physicists as well as mathematicians, based on their multiple applicability to multi-interdisciplinary fields of human knowledge, and beyond. So, this by default adds more taste and credibility to this study. Concluding thoughts are included at the end of the work along with a difficult outstanding problem and the direction of future work.

This study aims to reveal the revolutionary Gamma Distribution Manifold (GDM) info-geometric analysis combined with potential Gamma distribution (GD) applications to advance Satellite Earth Observations (SEOs). An exposition of the GDM Fisher Information Matrix (FIM) was undertaken, together with the analytic investigation of the existence of FIM’s inverse. The derivation of GDM’s Geodesic Equations (GEs) is undertaken. The solution of the obtained Geodesic Equations is optimally complicated, and it provides more extensions to numerous open problems. Notably, the current paper provides a first-time unification between three multi-interdisciplinary fields, namely, information geometry theory, probability theory, and Satellite earth observations. Looking at the other side of the coin, the provided revolutionary approach in this current work reports the significant impact of info-geometric analysis on the probabilistic dynamics as well as the influential role of information geometry on satellite and space industry. This poses numerous open issues that initiates a new form of contemporary unification between these inter-disciplinaries to discover another visionary thinking out of traditional statistical frames. In other words, refraining ourselves from using classical statistical frames of thinking and delving into a new info-geometric vision of statistical distributions as well as examining real life applications of information geometry to advance satellite earth observations. The curtain is drawn on this paper by providing several challenging open problems in combination with a concluding remark and the next phase of research.

This paper provides a new discovery that adds to the contemporary theory of modelling of non-stationary queues, by capturing analytically the behavior of the root parameter, for the G1/M/1 PSFFA model of non-stationary Ek/M/1 queue. Additionally, it has been also revealed that the resulting root parameter reduces to the PSFFA model of non-stationary queue. Potential applications of PSFFA to Computer engineering are provided. Some challenging open problems are addressed combined with closing remarks and future research directions.

This paper investigates the search for an exact analytic solution to a temporal first-order differential equation that represents the number of customers in a non-stationary or time-varying

. Identifying students who might have difficulty in their course of studies ahead of time is crucial. There can be many reasons for performance issues, such as personality, family, social, and/or economic. We advocate that educational systems should use machine learning to predict students’ performance based on performance factors. This would allow educational professionals and institutions to put in place a preventive plan to help students towards achievements of their educational goals and success. In this chapter, we propose a student performance prediction method and evaluate its performance. We provide a taxonomy of performance factors that help to gauge students performance from different perspectives and give insights on the categories and features that have more significant impact on students’ performance. The results of this work can be used by education institutions to put in place a student-centric approach to tackle performance issues before they create long-term effects on student’s life. In addition, it will help education policymakers to introduce a tailored approach for the population in specific areas.

In the trend of transforming existing systems and services into non-face-to-face models, the concert industry is also showing movements toward transitioning to virtual formats. Physical concerts in the real world require venues that can accommodate hundreds to tens of thousands of spectators, but non-face-to-face methods that can accommodate large audiences face various limitations. Moreover, to elevate the satisfaction level of virtual concert attendees to that of real-world concerts, it's important to implement interaction between performers and audiences. Modern metaverse platforms apply cutting-edge network technologies to accommodate numerous users within a single channel. Many researchers are adopting network technologies such as SDN (Software Defined Networking) and CDN (Content Delivery Network) to set up a virtual concert that can accommodate large audiences. In this paper, we propose a network framework to be designed for the composition of virtual concerts. In particular, we separate a channel dedicated to interaction in order to provide an immersive experience of exchanging interactions between performers and audiences. As massive audiences transmitting interaction data to the performer in a 1:N format can lead to problems with acceptance and latency, this study introduces a concept of a channel form called 'Zone' and proposes an interaction data channel network framework that does not compromise immersion. The proposed framework supports tasks for effectively transmitting interaction data using network technologies for metaverse platforms like XDN and clustering algorithms like fuzzy c-means. We also suggest a CDN-based architecture that can ensure low latency for performers to transmit interaction data to the audience.

Detecting, tracking, and classifying unmanned aerial vehicles (UAVs) in a swarm presents significant challenges due to their small and diverse radar cross-sections, multiple flight altitudes, velocities, and close trajectories. To overcome these challenges, adjustments of the radar parameters and/or position of the radar (for airborne platforms) are often required during runtime. The runtime adjustments help to overcome the anomalies in the detection, tracking, and classification of UAVs. The runtime adjustments are performed either manually or through fixed algorithms, each of which can have its limitations for complex and dynamic scenarios. In this work, we propose the use of multi-agent reinforcement learning (RL) to carry out the runtime adjustment of the radar parameters and position of the radar platform. The radar used in our work is a multibeam multifunction phased array radar (MMPAR) placed onboard UAVs. The simulations show the cognitive adjustment of the MMPAR parameters and position of the airborne platform using RL helps to overcome anomalies in the detection, tracking, and classification of UAVs in a swarm. A comparison with other artificial intelligence (AI) algorithms shows that RL performs better due to runtime learning of the environment through rewards.

Undoubtedly, there is a potential impact of large language models like ChatGPT in revolutionizing information retrieval and knowledge discovery, particularly in the context of the vast amount of electronic material available. On another strong note, the current work offers a first time ever mathematical approach to accurately fine tune perplexity AI, which sets a giant step ahead towards accurate next generations AI. Having started this world leading discovery, the upper and lower bounds of perplexity AI, namely , , respectively, are obtained for the first-time ever. Concluding remarks combined with emerging open problems and next research phase are provided.

A difficult open problem that has never been fully resolved in the literature to date is the behaviour of the (TDCDP) third approximation. As a giant step towards modern control theory, this cutting-edge paper will advance control theory and other relevant inter-disciplinary subjects. Because control theory is important in industry and engineering, this study will be especially beneficial to anyone working in these disciplines who wish to understand more about recent advances in control theory settings. In contrast, the wealth of applications of Fokker Planck Kolmogorov (FPK) equations numerous disciplines As a result, the study instantly gains more taste and credibility. At the end of the paper, there are several challenging open problems, some concluding remarks, and recommendations for further investigation.

This paper provides the first-time ever analytic modelling for Pointwise Stationary Fluid Flow Approximation(PSFFA) model of the non-stationary

The paper explores the Kullback-Leibler divergence formalism (KLDF) applied to the stable MG1 queue manifold. More potentially, both service time probability and cumulative functions which make KLDF exact are obtained. The credibility of KLDF is justified through consistency axioms. Some potential applications of Kullback-Leibler divergence to Biometry are presented. The paper concludes with closing remarks combined with some challenging open problems and the next phase of research.

In recent years, genetic research has been developing rapidly and taking on a larger role in research and medicine. The synthesis of any particle that can be used in this research can pave the way for new developments in this field. This research aims to develop a chemical method to produce nitrogen-doped carbon quantum dots - economical and bio-sourced substances that bind to genomic DNA. Carbon quantum dots are produced by the green synthesis hydrothermal method from radish, which is abundant in nature, and economically affordable. Nitrogen is added to increase the fluorescence properties of the produced carbon quantum dots and to improve their binding to DNA. The synthesized nitrogen-doped carbon quantum dots (N-CQDs) were tested through chromosome imaging subsequently. It was shown that N-CQDs can bind to the chromosomes, and N-CQDs reflected green light better than DAPI under the FITC filter, a potential advantage of using N-CQDs over DAPI in chromosome imaging.

Abstract: Increasing population and urbanization are affecting human health and comfort. In order to get rid of these affects, mankind is changing its enviroment and looking for new life areas. This study investigates the influence of Land Cover Change (LCC) and Normalisied Densly Vegetation Index (NDVI) on Land Surface Temperature (LST) of Fethiye-Göcek Specially Protected Area (SEPA) in easterm mediterranean basin. In the study LCC, NDVI and LST were drived from landsat 5, 7 and 8 satellite image of resolation at 30x30 m acquired between 1995-2020. LST were computed based on Land Use/Land Cover (LULC) types. The Corine Index were used for determination of land uses. The results indicated that water, forest and maquia lands decreasing while urban fabric and bare lands are increasing depend upon the urbanization and forest fires in the basin. These changes in LULC widened the temperature differences between the urban and rural areas. The change in LST is associated with changes in constructional materials in urban land and in vegetation abundance both in the urban and rural areas. Vegetation has an important factor in the temperature of different land covers. That produces warming trend in temperetaure in built-up areas it causes to keep other lands warmer in cold weather. Another important result is affective Urban Heat Island (UHI) on climate change based on the impact of urbanization and land cover changes. Significantly possitive correlation were found between the urbanization rate, population and built-up area and warming rate of average air temperature and so the LST.

The prevalence of autism in children in the world is estimated as one per 62 children, higher levels reported in some countries. These children experience significant problems with the development of social, behavioural and verbal and non-verbal communication skills. The skills impairment levels varies from an individual to another and that made teaching autistics a challenge for caregivers such as teachers and relatives. Hence, there are quite a number of frameworks of a software learning systems which focus on gaining the children’s attention using representational visual illustration as a learning method instead of the textual form. However, majority of these tools are lacking the personalisation ability to suite everyone in the spectrum. Assistive technology offers an alternative way to attract children with autism. Therefore, this research is proposing an Adaptive Content Management Learning System (ACMLS) model to assist caregivers to produce, design and fine-tune or customise the learning materials appropriately so that the system interface and the materials are suitable for every individual in the spectrum according to each child personal profile aiming to make learning attractive and to contribute in improving their social, communication and behavioural skills and nonetheless, their attention level to the delivered educational topics. The ACMLS model design adopts four main components which are: (1) Design component: which covers the visual design, design principles and the mental model of the children with autism. (2) Technology component: which covers the assistive technology tools and the architecture of the ACMLS system. (3) Education component: Which covers the learning objectives, styles, strategies, methods and the cognitive model. (4) Participants component: which covers the main participants who’re playing a role in the ACMLS model such as: caregivers and children with autism.

Background: Immunosuppressant blood levels should be measured at regular periods in order to keep them within the therapeutic index. Although LC-MS/MS is preferred as a reliable method, some molecules like radiopaque agents in blood matrix may lead to false results. The aim of this study is to investigate the effect of seven different radiopaque agents on immunosuppressant drugs. Methods: Seven different radiopaque agents were added into control materials containing tacrolimus, everolimus, sirolimus and cyclosporine A drugs. Measurements were performed by LC-MS/MS instrument. The amount of deviations from target values were calculated. Results: Immunosuppressant blood levels significantly changed after the administration of radiopaque agents. Seven different radiopaque products led to false negative results in tacrolimus and cyclosporine A levels at a rate of 19.77% to 44.45%. The smallest deviations were seen in everolimus levels with administration of RM6 (gadodiamide) and in sirolimus levels with RM1 (gadobutrol) at rates of 4.04% and 2.11%, respectively. The highest deviations were observed with RM3 (iohexol) administration in everolimus and sirolimus levels at rates of 153.72% and 171.41%, respectively. Conclusions: False immunosuppressant results associated with radiopaque agents may result in organ rejection. Preferring radiopaque agents that cause the least interference risk is important to reduce the organ rejection risk. However, the least risky method is to obtain samples for drug levels before contrast-enhanced imaging.

The prevalence of autism in children in the world is estimated as one per 62 children, higher levels reported in some countries. These children experience significant problems with the development of social, behavioural and verbal and non-verbal communication skills. The skills impairment levels varies from an individual to another and that made teaching autistics a challenge for caregivers such as teachers and relatives. Hence, there are quite a number of frameworks of a software learning systems which focus on gaining the children’s attention using representational visual illustration as a learning method instead of the textual form. However, majority of these tools are lacking the personalisation ability to suite everyone in the spectrum. Assistive technology offers an alternative way to attract children with autism to use. Therefore, this research is proposing Autism Content Management Learning System (ACMLS) Framework to assist caregivers to produce, design and fine-tune or customise the learning materials appropriately so that the system interface and the materials are suitable for every individual in the spectrum according to each child personal profile aiming to make learning attractive and to contribute in improving their social, communication and behavioural skills and nonetheless, their attention level to the delivered educational topics. The ACMLS framework design adopts four main components which are: (1) Design component: which covers the visual design, design principles and the mental model of the children with autism. (2) Technology component: which covers the assistive technology tools and the architecture of the ACMLS system. (3) Education component: Which covers the learning objectives, styles, strategies, methods and the cognitive model. (4) Participants component: which covers the main participants who’re playing a role in the ACMLS framework such as: caregivers and children with autism.

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.

In this study, we aim to evaluate how cyclodextrin molecules affect the hydrophilicity and selec-tivity of PVDF membrane. Herein, we prepared PVDF-PVP(1-x) β-CDx membranes, with x rang-ing from 0 to 1 wt%. The membranes were deeply characterized by means of X-ray diffraction (XRD), attenuated total reflectance infrared (ATR-IR), Raman spectroscopies, Thermal gravimet-ric analysis (TGA), Contact angle measurement (CA), scanning electron microscopy (SEM) and atomic force microscopy (AFM). The study unequivocally demonstrates the effectiveness of the addition of β-CD molecules on membranes' structural, morphological, and dielectric properties. Adding 0.25wt% β-CD enhanced hydrophilicity, with CA decreasing from 72.6° to 56° and roughness decreasing from 81 to 29 nm. The PVDF-PVP(1-x) β-CDx membranes were then fully characterized using electrochemical impedance spectroscopy (EIS) to determine their dielectric properties, which clearly showed that the presence of β-CD induced polarization of the mem-brane. The separation of various dyes with different sizes and charges was utilized. These dyes included methylene blue (MB, positive, 0.504 nm), methyl red (MR, neutral, 0.487 nm), rose Bengal (RB, negative, 0.588 nm), and brilliant blue (BB, negative, 0.798 nm). Based on our re-search, we found that the membrane's selectivity is determined by both exclusion factors (such as size and dielectric properties) and inclusion inside the β-CD cage. We found that using alpha-CD instead of beta-CD led to the disappearance of dye inclusion. These findings strongly sug-gest that β-CD molecules can be a viable solution for dye removal from wastewater.

Bacteria’s ability to withstand the detrimental effects of antimicrobials may take the form of resistance or tolerance with the minimum inhibitory concentration (MIC), the mutant prevention concentration (MPC), and the mutant selection window as salient concepts. Isolates exposed for 30 days to different concentrations of selected antibiotics, biocides, and heavy metals, singly and in combination, were additionally exposed to very high (15 × MIC) concentrations of ampicillin., and their tolerance was calculated the time to kill 99.9% of the isolate population (MDK99.9). These values were increased by 30 to 50% in test isolates compared to the untreated control. BAC 10-exposed isolates had the highest tolerance increase, and the tolerance appeared to originate from two sources, i.e., stochastic persistence and genetic-induced persistence, involving multiple genes using diverse mechanisms. The mutant selection window of the isolates to ampicillin, amoxicillin, and oxytetracycline also slightly increased compared to the control, indicating the amplification of persister cells during the 30-day exposure. These findings indicate that bacterial exposure to sub-inhibitory concentrations of environmental chemical stressors may not always result in the development of antimicrobial resistance but could initiate this process by selecting for persisters that could evolve into resistant isolates.

Climate change has become a major agenda item in international relations and in national energy policy-making circles around the world. This article studies the surprising evolution of the energy policy, and more particularly the energy transition, currently happening in the Arabian Gulf region, which features some of the world’s largest exporters of oil and gas. Qatar, Saudi Arabia and other neighboring energy exporters, plan to export blue and green hydrogen across Asia as well as towards Europe in the years and decades to come. Although poorly known and understood abroad, this recent strategy doesn’t threaten the current exports of oil and gas (still needed for a few decades) but prepares the evolution of their national energy industries towards the future decarbonized energy demand of their main customers in East and South Asia, and beyond. The world’s largest exporters of Liquefied Natural Gas, Qatar, has established industrial policies and projects to upscale CCUS, which can enable blue hydrogen production, as well as natural carbon sinks domestically via afforestation projects.

Sombor index was recently defined in 2021 as a new vertex degree based topological index by Gutman and has received great attention of mathematicians and chemists. In this work, we determine how much the Sombor index is effected when we delete a vertex or an edge from a graph. Using the formulae obtained here successively, one can calculate the Sombor indices of large graphs by means of Sombor indices of smaller graphs which are obtained by deletion of vertices or edges.

This study investigates five-year-old learners’ vocabulary retention through case studies, on a one week, one month, three months, and five months basis. It concerns the vocabulary learning of kindergarten children learning English as a second language (L2). This study attempts to seek answers to the questions of to what extent the presentation of the three different sets of words more effective than the other two is, both on recall and recognition and whether the recall and recognition levels showed differences in the three sets of words in course of time. In the literature, some studies have favoured semantic mapping in which new words are presented and organized in terms of associated lexical meanings. Some other studies have disfavoured semantic mapping. This study has administered three different sets of vocabulary: semantically related, cognates and semantically unrelated words. This study indicated that all participants were more successful with semantically related words on word recall and with cognates on word recognition. The results of this study do not support those which claim that making semantic associations may cause interference and hinder vocabulary learning. This study further suggests that semantically related words should be taught thematically to facilitate vocabulary teaching, particularly on word recall.

The understanding of various health-oriented vital sign data generated from body sensor networks (BSN) and discovery of the association between the generated parameters is an important task that may assist and promote important decision making in healthcare. For example, in a smart home scenario where the occupants’ health status is continuously monitored remotely, it is essential to provide required assistance when an unusual or critical situation is detected in their vital sign data. In this paper, we present an efficient approach to mine the incomplete (partial) periodic patterns obtained from BSN data. In addition, we employ a correlation test on the generated patterns and introduce the productive-associated partial periodic frequent patterns as the set of correlated partial periodical frequent items. The combination of these measures has the advantage of empowering healthcare providers and patients for quality of diagnosis, and also for better treatment and smart care, especially for the elderly people at smart home. We developed an efficient algorithm named PPFP-Growth (Productive Periodic Frequent Pattern growth) to discover all productive associated partial periodic patterns using these measures. PPFP-Growth is efficient, and the productiveness measure removes uncorrelated periodic items. An experimental evaluation on synthetic and real datasets shows the efficiency of the proposed PPFP-Growth algorithm, and can filter a huge number of partial periodic patterns to reveal only the correlated ones.

The modern supply chain (SC) is growing in terms of data, devices, users, and stakeholders, which introduced new security challenges and threats, especially with the reliance on centralized servers or cloud platforms. In addition, increased trust among system participants exposes the SC to a higher risk of vulnerabilities which require strong security measures. This article proposes a hybrid security framework for SC systems, BC-DRLzSC, that integrates Blockchain (BC) and Deep Reinforcement Learning (DRL) designed to operate in a zero trust (ZT) environment. In particular, we propose a decentralized BC-based approach integrated with smart contracts to manage system participant registration and authentication and to control access to system resources. BC-DRLzSC adopts a ZT architecture to reinforce SC security, which can be achieved with an advocate to verify each entity’s trustworthiness before granting or retaining access to system resources. Incorporating the ZT architecture, with BC and DRL, can potentially and significantly bolster SC system security. DRL is employed to develop a proactive attack detection model that continuously monitors the incoming traffic from authenticated nodes within the network and predicts any malicious actions. Finally, we evaluate the performance of our proposed DRL solution using the NSL-KDD dataset.

Measuring reference evapotranspiration (ETo) is a difficult and costly process. Several parameters are needed to calculate it from meteorological parameters with the aid of some developed equations. There may be variable microclimates in some regions and these are often quite far from the climate stations. Therefore, for ETo, it is better to prefer local measurements rather than relying on regional values. ETo is of great importance for all water-related sectors. It is the primary factor designating the amount of irrigation water to be applied to the plant rootzone in agricultural activities. The amount of irrigation water to be applied is determined by correcting ETo value with the plant coefficient (kc) and irrigation efficiency. There have been many developments in measurement/calculation of ETo in recent years and it is expected that there will be more in the future. Within the scope of this study, ETo values measured with ETgage equipment were compared with the monthly ETo values calculated with the use of Thornthwaite, Blaney-Criddle, Cropwat (FAO Penman Monteith), Jensen-Haise and ASCE standardized Penman Monteith (ASCE SZ PM) methods. In terms of total ETo value, Thornthwaite (17%) and Jensen-Haise (14%) methods yielded lower values than ETgage readings. On the other hand, Blaney-Criddle (15%), Cropwat FAO PM (10%) and ASCE SZ PM (30%) methods yielded greater values than ETgage readings. Among the calculation methods, in Cropwat software, FAO PM equation yielded the closest value to ETgage readings. It was concluded based on present findings that ETgage equipment could reliably be used in determination of ETo.

Two important types of metal oxide nanoparticle catalysts Copper (II) oxide (CuO) and Cerium oxide (CeO₂) are prepared by a suitable method which was traditional precipitation (PT) method at calcination temperature of 400^oC for 5h and used for the synthesis of glycerol carbonate GC (C₄H₆O₄) from the direct reaction by the carbonylation of Glycerol GL (C₃H₈O₃) with Carbone Dioxide. The precipitation (PT) was an important route for the preparation of nanoparticles catalyst. The effects of performance of (CuO and CeO₂) nanoparticle catalysts on the conversion of glycerol GL, yield of glycerol carbonate GC, selectivity of glycerol carbonate are researched. XRD, XPS, BET, FT-IR, CO₂-TPD, H₂-TPR are used for the characterization of the prepared catalysts. Comparing the optimal performance between them under reaction conditions were 150 ^oC, 4MPa (40 bar.), 5h, and both CuO and CeO₂ catalyst amount 37.6 % (based on ratio of glycerol weight) by using 2-pyridinecarbonitrate (C₆H₄N₂) as dehydrating agent and dimethylformamide (DMF), (C₃H₇NO) as solvent. The glycerol conversion (X_GL), glycerol carbonate yield (Y_GC) and glycerol carbonate selectivity (S_GC) over 0.7g CuO are 57.151%, 47.524%, and 83.156%, respectively, and glycerol carbonate yield over 0.7 CeO₂ is 36.2185% or 35.076%, and the yield of GC could reach as high as 78.234% over 1.73g CeO₂, the both catalysts could be easily regenerated by washing with methanol and water after a reaction and then dried at 60 ^oC overnight after that calcination at 400 ^oC for 5h without loss of activity after five recycling times, In addition to, the (ICP- MS) results confirmed that the leaching of CuO and CeO₂ was below the detection limit.

An increasing number of studies have shown that safety culture factors have a substantial influence on safety performance in a variety of industrial sectors. These factors' impact on safety performance is unclear, especially at the public service and statutory authorities. On the other hand, the understanding of indicators for safety performance in every working sector in Malaysia is on the continuing progress. Hence, this study's contribution is to explore the influence of safety culture factors (i.e., management commitment and supervision in safety, safety system) and safety competence on safety performance in government paramedic training institutes. IPMA (importance-performance map analysis) is a technique used in Smart PLS to determine the significance and performance of each of these factors. The study was conducted via an online survey and involved 258 safety and health committee members in Ministry of Health paramedic training institute. As a matter of relevance, the IPMA's empirical data study revealed that management commitment and supervision in safety were the predominant factors in determining safety performance. Meanwhile, for performance, the findings showed that worker involvement, safety system, and safety competence perform well in determining the safety performance.

Low soil nitrogen status of savanna soils in Nigeria contributes to the persistent Striga hermonthica (Del.) Benth. infestation that limits maize production. The application of nitrogen fertilizer to Striga-resistant hybrids may reduce Striga infection and increase grain yields. This study assessed the performance of maize hybrids at low (30 kg ha-1) and high (120 kg ha-1) nitrogen application under natural infestation with Striga at Kafin Madaki and Tudun Wada in 2014 and 2015. Results showed that the application of nitrogen at 120 kg ha-1 reduced number of Striga plants by 59% compared to application at 30 kg N ha-1 in Kafin Madaki and by 21% in Tudun Wada. Compared to 30 kg N ha-1, the 120 kg N ha-1 rate also reduced Striga damage rating by 22% in Kafin Madaki and by 33% in Tudun Wada across the hybrids. Hybrids 8338-1 (5.3) and OBASUPER 1 (4.3) were the only entries with Striga damage rating greater than 4.5 (SDR > 4.5) when averaged across the nitrogen levels at both locations. Grain yield was 86 and 98% higher in Kafin Madaki and Tudun Wada, respectively when N was applied at 120 kg N ha-1 than at 30 kg N ha-1. The hybrids M1124-3 and M1227-14 produced grain yields that were significantly higher than those of the other hybrids in all locations. The hybrid 8338-1 produced the lowest grain yield across locations. Our results showed that, the application of 120 kg N ha-1 to Striga resistant maize hybrids will reduce Striga infection and increase grain yield.

Background and objectives: Lipoid proteinosis (LP) is an autosomal recessive transfer lysosomal storage disease, characterised by the accumulation of hyalin substance in the mucous membranes, skin, internal organs and brain, for which there is no biochemical diagnostic method. The aim of this study was to determine the carnitine and acylcarnitine metabolic profile with LC-MS/MS in LP patients and thereby examine the potential of this as a new biochemical method in the determination of biochemical markers in LP patients. Materials and Methods: In this study, 27 carnitine and acylcarnitine esters were measured with LC-MS/MS in serum samples taken from 14 healthy control subjects and 14 patients who presented at the Skin and Venereal Diseases Polyclinic and were diagnosed with LP as a result of clinical, radiological and histopathological examinations. Results: The results of the study showed that C0 (free carnitine) C3, C4, C4:DC, C5DC, C6, C8, C14:1, C14:2, C16 and C18 acylcarnitines were statistically significantly reduced in the LP patients (p<0.05, p<0.01). Conclusions: It was concluded that the application of carnitine profile screening, which is an inexpensive, rapid and reliable method, could make a contribution to the differential diagnosis as aa supporting laboratory test in individuals with suspected LP.

Bioconvective flows have attracted attention in recent years due to actual and potential applications. In this paper, we consider a steady and laminar convective MHD flow of a nanofluid with heat, mass and microorganism transfer with a heat source/sink present. In addition, we assume there exists a first order chemical reaction. The governing partial differential equations (PDEs) are reduced to ordinary differential equations (ODEs) using the scaling group transformation and the associated boundary value problem is then solved. The influences of selected governing parameters on the dimensionless velocity, temperature, nanoparticle concentration, density of motile microorganisms, skin friction, heat transfer, mass transfer, and motile microorganism density rates are computed and discussed

The present grievous situation of the tuberculosis disease can be improved by efficient case management and timely follow-up evaluations. With the advent of digital technology this can be achieved by quick summarization of the patient-centric data. The aim of our study was to assess the effectiveness of the ODK Scan paper-to-digital system during testing period of three months. A sequential, explanatory mixed-method research approach was employed to elucidate technology use. Training, smartphones, application and 3G enabled SIMs were provided to the four field workers. At the beginning, baseline measures of the data management aspects were recorded and compared with endline measures to see the impact of ODK Scan. Additionally, at the end, users’ feedback was collected regarding app usability, user interface design and workflow changes. 122 patients’ records were retrieved from the server and analysed for quality. It was found that ODK Scan recognized 99.2% of multiple choice bubble responses and 79.4% of numerical digit responses correctly. However, the overall quality of the digital data was decreased in comparison to manually entered data. Using ODK Scan, a significant time reduction is observed in data aggregation and data transfer activities, however, data verification and form filling activities took more time. Interviews revealed that field workers saw value in using ODK Scan, however, they were more concerned about the time consuming aspects of the use of ODK Scan. Therefore, it is concluded that minimal disturbance in the existing workflow, continuous feedback and value additions are the important considerations for the implementing organization to ensure technology adoption and workflow improvements.

Pesticides have a vital contribution to the agricultural industry in Australia. However, pesticide applications may not always selective to its target organism. Exposure to pesticide has been associated with negative impacts to human health. One of the ways to reduce pesticide risk to the population and the environment is through government regulations. To put Australia pesticide regulations into perspective, comparisons were made between Australia and other nation’s pesticides regulations. The EU pesticide regulatory system was chosen for comparison with the Australian’s in the aspect of protecting the population from pesticide exposure. The comparison showed that the assessment to authorize a pesticide in Australia is based on the risk while in the EU, the assessment is based on the hazard of the pesticide. A registered active substance in Australia can be authorized for use indefinitely unless it is nominated for reconsideration while the period of registration of an active substance in the EU lasts for 10 years. In addition, no routine systematic chemical residue surveys are conducted in Australia for food commodities. The results highlight areas for improvement in the Australian pesticide regulatory system from the perspective of controlling undue exposure among the general population.

In this study, we examined the effects of the organizational power resources used by school principals on teachers' work alienation and trait anxiety levels. Then, we examined the mediating role of trait anxiety in the relationship between organizational power resources and work alienation. We used the correlational scanning model and mediation model in the research. The research sample consists of 400 teachers working in Turkish public schools. In our research, "Organizational Power Scale in Schools", "Work Alienation Scale" and "Trait Anxiety Scale" were used. As a result of the research, we found significant relationships between the variables. Accordingly, there is a positive relationship between trait anxiety and work alienation, a positive relationship between trait anxiety and coercive power and legal power, a negative relationship between trait anxiety and expert, charismatic and reward power, a positive relationship between work alienation and coercive power, and a positive relationship between work alienation and reward power. Negative relationships were observed between expertise and charismatic power. Finally, it was determined that trait anxiety had a mediating role in the relationship between the power resources used by school principals and the level of work alienation of teachers.

In this paper, we give some upper and lower bounds for the multiplicative Randic index, reduced reciprocal Randic index, Narumi-Katayama index and symmetric division index a graph using solely the vertex degrees. Then we obtain upper and lower bounds for these indices for the complete graphs, path graphs and Fibonacci-sum graphs. Finally, we compared the bounds of these indices for a general graph and some special graphs.

Assessment of the trends of land cover and vegetation dynamics (VD) using remote sensing (RS) and indicators such as anthropogenic activities and the socio-demographic information is essential in order to make proper planning for sustainable management. This paper attempts to evaluate land cover change (LCC) and VD in Kebbi State, Nigeria using historical Landsat data from 1986-2016 by means of remote sensing. The Driver-Pressure-State-Impact-Response (DPSIR) framework was later employed using both primary and secondary data for a better understanding of the drivers, the state of the environmental condition, the causes as well as the impact of the change. The images were classified into five thematic land cover classes as Dense Vegetation, shrubs/built area, farmland, bare/grassland and water body by means of Maximum likelihood supervised classification technique in accordance with Anderson classification scheme level 1, with acceptable accuracy. Pre-classification and post-classification change detection (CD) methodologies were executed using Normalized difference vegetation index (NDVI) and Image differencing respectively. The study illustrates a steady decline in dense vegetation and shrubs/build areas while farmland and bare/grassland increases, however, water bodies remain unchanged. The DPSIR pin-point that the major drivers of change in the study area have been the pressing need for farming land as the population grows and socioeconomic demands including fuelwood consumption and endemic poverty. Expansion of Farming land, fuelwood consumption and the need for construction materials are identified as the main key elements exerting pressure for the change. The state of the condition indicates a steady decline in dense vegetation and shrubs areas while farmland and bare/grassland are increasing significantly. The impacts include land degradation, the decline in the provision ecosystem goods and services, biodiversity loss through loss of habitats. The study, however, noted that many international and national policies in response to land degradation are channelled toward land restoration and remediating of the environment, through afforestation programs and improving the livelihood of the rural people through providing alternative income sources since they depend heavily on land for sustenance. However, the state governments, communities and individual commonly organized annual tree planting campaign with the main purpose of environmental protection.

Course-based Undergraduate Research Experiences (CUREs) provide the same benefits as individual, mentored faculty research while expanding the availability of research opportunities. One important aspect of CUREs is student’s engagement in collaboration. We developed a partnership with the Caenorhabditis elegans (C. elegans) database, WormBase, in which students submitted annotations of published manuscripts to the website. This activity provided students with a collaborative research activity that benefitted the greater scientific community and enhanced students’ understanding of molecular genetics during the COVID-19 pandemic. WormBase relies on community annotators to read published articles and input phenotypic data. Students submitted a total of nine annotations directly to WormBase, which were curated by WormBase to ensure correctness and to reduce overlap from redundant annotations. Due to the stress on students during this time of crisis, qualitative data were collected in lieu of quantitative pre-post analyses. Students described their learning experiences in terms of interactions with the scientific community and the “real world”, content knowledge and competencies, and changes in perspectives and use of resources. Students also reported that this activity was helpful in their understanding of critical molecular genetics concepts. Most students reported on cognitive processes that represent mid-level Bloom’s categories. The shift to online learning during the COVID-19 pandemic created an immediate need for meaningful, collaborative experiences in CUREs. By partnering with WormBase, students gained insight into the scientific community and contributed as community members. We describe possible modifications for future courses, potential expansion of the WormBase collaboration, and future directions for quantitative analysis.