Managers play an important role in driving the progress and progression of businesses, specifi-cally in the perspective of risky and long-term innovation. While organizational theories have acknowledged the influence of managers on strategic change, there is still a need to expand our interpretation of the global managerial abilities that drive innovation in the emerging fintech based economy. This study aims to address this research gap by leveraging the GDMC theory. It posits that GDMC (i.e., Global dynamic Managerial capabilities), encompassing managerial social capital, human capital, and cognition, directly contributes to the innovativeness of fintech firms. To investigate this relationship, the study utilized survey data obtained from Jordon manufac-turing firms operating in the context of Industry 4.0. Through regression analysis, the data were analyzed to gain insights and draw meaningful conclusions. The findings of the study affirm the significant role played by GDMC in facilitating innovation. Notably, the results deviate from previous research conducted in non-digital industries, suggesting that it is the combined set of global innovation mic capabilities that drives innovativeness in fintech firms. This study provides compelling indication for the crucial role of GDMC in the fintech based economy, challenging previous findings in non-fintech industries regarding the individual impact of managers' human capital, social capital, and cognition on innovation. In addition, this study contributes significantly to the existing literature by conducting a comprehensive examination of the effects of GDMC on innovation in fintech firms. It stands out as the first study to holistically explore this relationship, providing a nuanced understanding of the impact of GDMC. Consequently, the applicability of the GDMC theory is extended to the fintech economy.

In this research, the primary goal was to investigate the relationship between strategic planning and organizational performance in Iraq's manufacturing context. The study's primary data sources were 360 manager respondents. The structured questionnaire was used to collect primary data from manufacturing firms located throughout Iraq. To analyze the result, the researcher used descriptive statistics, correlation, and multiple regression analysis. SPSS version 16 software was used to conduct data analysis. The result reveals that the process of strategic planning has a beneficial effect on financial performance. Environmental scanning has a statistically significant positive effect on a company's non-financial performance. Management participation and planning formality positively and statistically significantly affect a business's non-financial performance at the 10 percent level. The domain of strategy and technique has not impacted the company's non-financial performance.

Recently, environmental degradation has become an international issue due to the extensive economic activities around the globe. This aspect demands the policymakers' and new researchers’ emphasis. Hence, the present article examines the impact of corporate social responsibilities (CSR) to community and environment on the environmental performance (ENP) of the manufacturing industry in China. The article also investigates the mediating role of green innovation and environmental strategy among CSR to community, CSR to environment and ENP of manufacturing industry in China. The article gets the primary data from the selected employees of the manufacturing industry using questionnaires. The article also tests the data's validity and reliability, and association among variables using smart-PLS. The outcomes indicated that CSR to environment has a positive association with ENP. The outcomes also revealed that green innovation and environmental strategy significantly mediate among CSR to community, CSR to environment and ENP. The article guides the policymakers in making policies related to improve ENP using CSR to environment and green innovation.

Businesses now formally enter the phase of "think power differentiation and gap" in response to the new economic climate. While AI, big data, empowerment via the internet, and other aspects of the digital economy help to improve technical capabilities and save operating costs, they also push businesses to innovate their strategies at a faster rate. Enterprises are strategically choosing to de-enterprise, moving from focusing on a single business functioning benefit to building an ecosystem of platform services that will give them a competitive edge. Additionally, the organization's structure is evolving continually from a vertical model to a curved working style, in order to achieve this, businesses must recognize the importance of data analysis in day-to-day management and operations, encourage the development of an organizational atmosphere that is driven by data, fully utilize digital technology, modularize areas or production links that perform related tasks, and encourage the reform of organizational structures. By streamlining the output of the product or service to the external community while also enhancing the company's financial performance model, switching from process-to result-oriented evaluation, and putting in place a customized authority and accountability framework. Replace outcome-focused evaluation with process-focused evaluation, and create a customized incentive scheme that balances power, accountability, and rewards.

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

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

The musculoskeletal system consisting of bones and muscles have been recognized as endocrine organs secreting hormones that are involved in regulating metabolic and inflammatory pathways. Obesity and type 2 diabetes (T2D) are associated with several musculoskeletal system complications. We hypothesized that an interaction exists between adipomyokines namely, irisin and METRNL, and various bone markers in individuals with obesity and T2D. A total of 228 individuals were enrolled in this study, including 124 non-diabetic and 104 T2D. A Multiplex assay was used to assess the level of various bone markers namely Osteoactivin, Syndecan, osteoprotegerin (OPG) and osteonectin/SPARC. Our data shows elevated levels of Osteoactivin, Syndecan, OPG and SPARC in T2D as compared to non-T2D individuals (p ≤ 0.05). Using Spearman’s correlation, irisin was positively correlated only with Osteoactivin and OPG (p < 0.05). Similarly, a positive association was observed between METRNL and Osteoactivin (p < 0.05). The strong positive association shown in our study between irisin, METRNL and various bone markers emphasises the strong interaction between these organs. This suggests that a dysregulation in the functional interaction between these molecules could play a possible role in the development of bone and muscle related complications that are associated with obesity and T2D.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

COVID-19 pandemic will continue to pose a major public health threat until vaccination-mediated herd immunity is achieved. Most projections predict vaccine will reach a large subset of the population late in 2021 or early 2022. In the meantime, countries are exploring options to remove strict lockdown measures and allow for society and the economy to return to normal function. One possibility is to expand on existing COVID-19 testing strategies by including large-scale rapid point of care diagnostic tests (POCTs). Currently, there is significant variability in performance and features of available POCTs, making selection and procurement of appropriate test for specific use case difficult. In this review, we have used the World Health Organization’s (WHO) recently published Target Product Profiles (TPPs) for specific use cases of COVID-19 diagnostic tests to screen for top-performing POCTs on the market. Several top-performing POCTs, based on clinical sensitivity/specificity, the limit of detection, and time to results, that meet WHO TPP criteria for direct detection of SARS-CoV-2 (acute infection), or indirect diagnosis of past infection (host antibodies) are highlighted here.

In only a few months after initial discovery in Wuhan, China, SARS-CoV-2 and the associated COVID-19 disease has become a global pandemic causing significant mortality and morbidity. In the absence of vaccines and effective therapeutics, reliable serological testing can be a key element of public health policy to control further spread of the disease and gradually ease quarantine measures. However, prior to launch of large-scale seroprevalence studies to assess herd immunity, it is critical to understand the limits and potential of current SARS-CoV-2 serological tests on the market. In this study, we provide an overview of serological testing and conduct a systematic review of independent evaluations of SARS-CoV-2 serological tests performance. Our findings show significant variability in the accuracy of marketed tests and highlight several lab-based and point-of-care rapid diagnostic tests with high performance level in detecting SRAS-CoV-2 specific antibodies. The findings of this review highlight the need for ongoing independent evaluations of commercialized COVID-19 diagnostic tests.

In this work, we investigate the weak deflection angle of light from exact black hole within the non-linear electrodynamics. First we calculate the Gaussian optical curvature using the optical spacetime geometry. With the help of modern geometrical way popularized by Gibbons and Werner, we examine the deflection angle of light from exact black hole. For this desire, we determine the optical Gaussian curvature and execute the Gauss-Bonnet theorem on optical metric and calculate the leading terms of deflection angle in the weak limit approximation. Furthermore, we likewise study the plasma medium's effect on weak gravitational lensing by exact black hole. Hence we expose the effect of the non-linear electrodynamics on the deflection angle in the weak gravitational field.

In this paper, we study a three step iterative scheme to approximate fixed points of Suzuki's generalized non-expansive mappings. We establish some weak and strong convergence results for such mappings in uniformly convex Banach spaces. Further, we show numerically that iterative scheme (1.8) converges faster than some other known iterations for Suzuki's generalized non-expansive mappings. To support our claim, we give an illustrative example and approximate fixed points of such mappings using Matlab program. Our results are new and generalize several relevant results in the literature.

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

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

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

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

Urinary tract infections (UTIs) caused by Uropathogenic Escherichia coli (UPEC) are a serious healthcare concern that must be treated with appropriate antibiotic therapy. However, antibiotic resistance among UPEC strains is increasing, particularly among individuals with recurrent illness. The proliferation of Extended-Spectrum Beta-Lactamase (ESBL) generating UPEC, as well as the increase of UPEC strains that display resistance to several antimicrobial drugs, adds to the difficulty of managing UTIs. UPEC strain genetic variants have a significant influence in their capacity to resist antimicrobial agents and adapt to changing environments. Understanding the genomic landscape of developing UPEC strains is critical for understanding the genetic basis of their resistance. Furthermore, tracking these genetic strains is critical for identifying patterns of resistance spread, directing infection control efforts, and educating.

We define a spinor-Minkowski metric for SL(4,C). It is not a trivial generalization of the SL(2,C) metric and it involves the Minkowskian one. We define 4x4 version of the Pauli matrices and eight 4-component associated generalized eigenvectors that can be regarded as undotted covariant spinors. The 4-component spinors can be grouped into four categories. Each category transforms in its own way. The outer products of pairwise combinations of 4-component spinors can be associated with 4-vectors. Including the dotted covariant, undotted and dotted contravariant forms totally we have sixteen pairs of spinors. Eight of them live in the conjugate space which has no countepart in SL(2,C).

In this paper, we investigate how Rindler observer measures the universe in the ADM formalism. We compute his measurements in each slice of the space-time in terms of gravitational red-shift which is a property of general covariance. In this way, we found special relativity preferred frames to match with the general relativity Rindler frame in ADM formalism. This may resolve the widely known incompatibility between special relativity and general relativity on how each theory sees the red-shift. We found a geometric interpretation of the speed of light and mass.

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

A disaster wrecks those affected. It spares many in the affected areas, yet those spared may be indirectly impacted. Specific risks are often inherent within a social system or physical location, but they can also be created due to unavoidable natural or technological hazards. The consequences, however, can be similar in that they wreak havoc in communities and destroy economic systems. The analytical framework of deterrence and coping has ascertained beneficial in many circumstances, but a cost benefit calculation is a must to infer the feasibility of planning strategy and resource allocation. This study points to the Cost-Benefit Analysis (CBA) of flood management by District Disaster Management Kulgam. The assessment is established on secondary pooled data collected from administration offices, NGOs, published Journals, and local and national newspapers. It also characterised the strategy, the technique adopted, and the sources of flood damage cost information. The totalled benefits report for 78686.18 lakh of rupees, and that of total costs account for 2218.75lakh of rupees. The Benefit-Cost ratio greater than one (>1) indicates that Flood Management in District Kulgam was economically feasible and successfully governed. The State of Jammu and Kashmir put up with necessary prevention and administration measures to break the spell of devastation due to floods to significant status.

Flood routing in flood forecasting issue, calculation the height of flood bands, determining the river boundaries, and estimation of protective facilities for flood –exposed building is applicable. In many cases, due to the lack of measuring stations, the status of the upstream flood generating hydrograph is not known. The purpose of this study is to present an integrated method comprising of an optimization model and a hydrodynamic numerical model for flood modeling to determine the upstream hydrograph using the provided hydrograph at the downstream measuring station of a river. The routing procedure consists of three steps: (1) generating a hypothetical upstream hydrograph using genetic algorithm method; (2) hydrodynamic modeling using a numerical simulation model for flood routing according to the hypothetical hydrograph which is generated in the first step; (3) compare the calculated and observed hydrograph in downstream by using a fitness function. This recommended procedure was named as Reverse Flood Routing Method (RFRM) and was then applied to Karun River, the largest river in Iran. Comparing the generated upstream hydrograph by the RFRM model with the corresponding measured hydrograph at Ahvaz hydrometric station, as an ungauged river location, shows the high accuracy of the recommended model in this study.

The experimental verification for the computational method sometimes varies due to numerous factors such as the manufacturing process and the materials' property change due to environmental aspects. In this work, we performed verification of experimental and computational evaluation of a hybrid composite moderate thick plate. The experiment was performed with simplistic approaches and without the advanced tools of preparing composite materials. This is due to the fact that most of the students in many developing countries around the world cannot have access to such equipment. As such, in this research, we are presenting cheap and easy preparation methods, with some details, for even equipment calibration and some tricks to attain a reliable composite structure for educational purposes. Moreover, the software and solvers used in this study are freely provided by the supplier for educational purposes. This study examined two methods for producing carbon and glass/polyester composite plates and discussed which one was best based on mechanical properties for different volume fractions, random stacking sequences, and ply angles (using OCTAVE's random estimation program). It also determined the three natural frequencies experimentally and with the aid of ANSYS. Less than 6% separated the experimentally determined natural frequencies from the calculated results.

Despite being well established phenomena, solidification principles are experiencing tough challenges in their application to explain microscale transport phenomena in additive manufacturing melt pool. The problem becomes even more complicated when applied to multicomponent bulk metallic glass matrix composites (BMGMC) whose behavior is dubious and still not fully understood. The aim of the present study is to highlight pathways to overcome these challenges. A comprehensive nucleation and growth model based on original KGT theory and Rappaz modification is proposed encompassing actual transient thermophysical BMGMC data to predict evolving microstructure during additive manufacturing. The model is aimed at pictorial representation of in-situ ductile phase dendrite evolution from melt pool during solidification using two-dimensional cellular automaton (CA) methods. It is proposed to be coded in MATLAB® using commercial simulation code ABAQUS® at back end for macroscopic heat transfer model. The results will be compared with experimental values for validation.

Space shuttle has been a hall mark of American space program since its inception. Despite its temporary shutdown few years ago, with the recent interest in space exploration which includes revitalizing human outpost in microgravity and transportation required to build it, realize other experiments (e.g. in space telescopes, in space manufacturing) and interplanetary voyages, it has regained attention. Its superior design, manufacturing, materials, performance, durability, and efficiency place it among the best, in fact, the only effort by mankind to build a reusable craft horizontally, launch vertically like a rocket and fly back like a plane. Various requirements emerge during its design (thermal, fluid, acoustics, vibration and structural) and design of its main engine (Aerojet Rocketdyne RS 25) which requires considerable attention, heat transfer being most important. This facilitated and necessitated use of various types of heat exchangers such as single coil, heat pipe, built in internal heat exchanger (IHEX), external heat exchanger (EHEX), condensing heat exchanger (CHEX), Interface heat exchangers (InHEX), regenerative heat exchanger (RHEX) and compact heat exchangers (CoHEX), change, manipulate and optimize their configurations in piping and instrumentation diagrams (PIDs). In this short narrative, an effort has been made to summarize them, and their developments over time with a focus on the application, design, manufacturing, materials, and performance (in service and final operation).

NASA/ESA/CSA joint venture James Webb Space Telescope is about to be launched. It is hypothesized to operate in near-infrared range. It is also hypothesized to unveil early star formation, galaxies, and universe due to its orbit, point in orbit and orbital motion. It has been under manufacturing for over 20 years at a staggering cost of 10 billion US dollars (most expensive scientific experiment in history). Beryllium (Be) is chosen to be element for construction of its main mirrors due to its high stiffness, low density, low linear coefficient of thermal expansion (α) in cryogenics and high thermal conductivity. It is followed by gold (Au) layer deposition on its (Be) surface to enhance its sensitivity towards infrared radiation as later is hypothesized to bear superior properties. However, serious mistakes have been made in selecting this material for this application. Owing to its crystal structure (hexagonal close packed (hcp)), slip planes (basal, prismatic and pyramidal) and mechanisms of their activation, Be necessitates easy fracture at cryogenic temperature. It has anisotropic properties and prone to transverse fracture under tensile loading. Furthermore, its ductile to brittle transition temperature is very low making it entirely unsuitable for such an application. It is one of most expensive metals on planet. This study constitutes revisiting these fundamental properties and mechanisms which were entirely ignored during materials selection thus rendering whole project useless.

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

Computer-aided image diagnostics (CAD) have been used in many fields of diagnostic medicine. It relies heavily on classical computer vision and artificial intelligence. Quantum neural network (QNN) has been introduced by many researchers around the world and presented recently by research corporations such as Microsoft, Google, and IBM. In this paper, the investigation of the validity of using the QNN algorithm for machine-based breast cancer detection was performed. To validate the learnability of the QNN, a series of learnability tests were performed alongside with classical convolutional neural network (CCNN). QNN is built using the Cirq library to perform the assimilation of quantum computation on classical computers. Series of investigations were performed to study the learnability characteristics of QNN and CCNN under the same computational conditions. The comparison was performed for real Mammogram data sets. The investigations showed success in terms of recognizing the data and training. Our work shows better performance of QNN in terms of successfully training and producing a valid model for smaller data set compared to CCNN.

Brain tumor being one of the major health hazards always needs a prompt diagnosis for the early treatment options to improve the chances of the survival of the patients. Traditional manual assessment of MRI (magnetic resonance imaging) to identify these conditions is a common practice. Hence, automation of the diagnosis practices can considerably improve the quality of the treatment procedures. In recent years almost every method for the automated diagnosis of brain tumor detection uses a deep learning technique. Despite decent works current techniques need a significant improvement to produce better results. Along with failure to produce decent results, the current deep learning models present very complex architectures which sometimes require huge computational resources. Therefore, in this paper we present a novel method that uses a lightweight dual-stream model with dual-input to detect brain tumors on MRI images. Both the inputs use different pre-processing mechanisms based on Contrast Limited Adaptive Histogram Equalization (CLAHE) and White Patch Retinex algorithm in order to enhance the feature learning capabilities of the proposed model. In addition, the proposed method uses a two-fold margin loss to boost training process for better feature learning. The proposed method produces state-of-the-art results for the detection of Glioma, Meningioma and Pituitary tumors. The model presents accuracy of 99%, 98% and 99% respectively for Glioma, Meningioma and Pituitary tumor detection.

The integration of artificial intelligence (AI) into academic settings in Sabah, Malaysia, is transforming education and research landscapes. While AI technologies promise to enhance teaching, learning, and scholarly activities, they also pose challenges such as ethical concerns and skill gaps among academic staff. This study addresses the multifaceted impacts of AI adoption on academicians in Sabah, focusing on two key research questions: the impact of AI-based intelligent tutoring systems on student engagement, learning outcomes, and educational equity, and the challenges and opportunities in building academicians' capacity to integrate AI technologies effectively. Through a multidisciplinary approach, incorporating insights from education, computer science, social sciences, and ethics, this research aims to evaluate the effectiveness of AI tools in educational settings and identify strategies for skill development and infrastructure support. The study recommends empirical assessments within Sabah's educational institutions, considering contextual factors and fostering collaborative partnerships with local communities. Professional development programs, collaboration with industry partners, and improved access to AI infrastructure are proposed strategies. Longitudinal studies and interdisciplinary research initiatives are suggested for future research, along with qualitative methods to capture diverse perspectives. Ultimately, by embracing a collaborative and multidisciplinary approach, Sabah's academic community can harness the transformative potential of AI to advance education, research, and scholarship while promoting ethical and inclusive practices.

Vehicle-to-Vehicle (V2V) communication plays a pivotal role in modern intelligent transportation systems, enabling seamless information exchange among vehicles to enhance road safety, traffic efficiency, and overall driving experience. However, the secure transmission of sensitive data between vehicles remains a critical concern due to potential security threats and vulnerabilities. This research paper focuses on investigating the security protocols employed in Vehicle-to-Vehicle communication systems. A comprehensive review and analysis of relevant literature and research papers is conducted to gather information on existing V2V communication security protocols and techniques. The analysis encompasses key areas, including authentication mechanisms, encryption algorithms, key management protocols, and intrusion detection systems specifically applicable to V2V communication networks. Within the context of real-world V2V environments, this study delves into the challenges and limitations associated with implementing these protocols. Moreover, to foster a deeper understanding, the paper investigates present communication protocols in the field of Internet of Things (IoT) that are tailored for V2V. Various parameters, such as band-width consumption, energy consumption, latency, and message size, are considered during the evaluation of these protocols to gauge their effectiveness. The research outcomes aim to provide a comprehensive understanding of the strengths and weaknesses of the current V2V communication security protocols. Furthermore, based on the findings, this paper will propose improvements and recommendations to enhance the security measures of the V2V communication protocol. Ultimately, this research will contribute to the development of more secure and reliable V2V communication systems, propelling the advancement of intelligent transportation technology.

Bubble (point defect) – a precursor of fuzz or under dense nanostructure formation is crystal lattice defect. Suitable selection of crystal lattice which inhibit Frenkel pair generation and intrinsically promotes self- interstitial solid solution strengthening contributes effectively towards making plasma facing material. For this, interstitial sites, their size, amount / fraction, positions, tendency of occupation and diffusion parameters (e.g. activation energies (Q), activation volumes) are determined. Fcc iron carbon alloys (austenitic stainless steels AISI / SAE 321, fcc structure, Pearson code cF4, space group Fm3̅m) are proposed as suitable candidates. Along with their room temperature fcc structure having 12 interstitial positions (4 octahedral, 6 coordination sites and 8 tetrahedral, 4 coordination sites / unit cell) to allow insertion of self (iron) atoms, they have excellent corrosion resistance, thermal conductivity, and non- magnetic properties. After their melting, casting, and machining to required dimensions and geometry, stabilizing heat treatment is applied to precipitate all carbon as TiC and prevent formation of Cr23C6 (sensitization). This resist heat and surface degradation and yield excellent architecture which not onlyinhibit Frankel pair generation but will also allow bulk assimilation or surface annihilation (loop punching) of this lattice point defect. A superior thermal, fluid, and structural design augment above. A second choice is presented as Co base superalloys owing to same fcc crystal structure and excellent properties (such as strength, dimensional stability, oxidation resistance) at high temperature.

High quantities of wastewater produced from producing natural gas and oil from the aquifer, which called produced water. The produced water was comprised of dissolved solids, suspended solids, emulsified oil, and organic and inorganic compounds. That should be treated it's before disposal because it causes harm to the environment. This study takes the produced water from the southern Iraqi oilfield drilling company to adsorption by the Cane papyrus as natural and low-cost adsorbent. The analysis completed by using Fourier transforms infrared spectroscopy, EDX spectra and Scanning Electron Microscopic (SEM) for Cane papyrus. Investigating the effect of many parameters such as adsorbent dosage, temperature, solution pH, mixer speed and contact time. The Langmuir, Freundlich, Temkin and Harkins-Henderson isotherm models were tested, the results were 0.998,0.966, 0.931 and 0.966 respectively. The Langmuir model was more suitable described the adsorption process than the other models. The kinetics results were, 0.984 for Pseudo-first-order, 0.938 for Pseudo-second order is, 0.979 for Intra particle diffusion study and 0.912 for the Elovich model, the Pseudo-first-order kinetic equation best described the kinetics of the reaction. The thermodynamics study effect temperature changes on the thermodynamic parameters such as standard free energy change (∆G°), standard enthalpy change (∆H°) and standard entropy change (∆S°). The experimental data obtained demonstrated that Cane papyrus is a suitable adsorbent for removing oil from produced water.

Electrical Resistivity Tomography (ERT) was conducted to delineate groundwater potential zones in villages located in the Galhareri district of the Galgaduud region, central Somalia. A total of four ERT profiles were examined using the gradient configuration, chosen for its practical advantages over other configurations. The study revealed that all profiles were situated within similar geological environments, characterized by comparable rock types. However, notable disparities were observed in lithological variations, particularly in the texture of rocks encountered at different locations and in the thicknesses of the encountered geo-electric layers. The two-dimensional inversion results derived from the electrical resistivity data unveiled the presence of four geo-electrical layers. The first layer was interpreted as sand dunes. The second layer exhibited relatively higher resistivity values, indicating the presence of compact limestone and sandstone. The resistivity of the third layer suggested the existence of a lower resistivity layer, interpreted as weathered limestone, while the fourth layer demonstrated very low apparent resistivity, interpreted as sandy clay with sandstone. The ERT models constructed for the survey area effectively delineated the aquifer zone, represented by layer 3, which likely consists of weathered limestone, sandy clay, and sandstone. The resistivity values obtained for the aquifer zone, specifically at depths ranging from 200 to 300 m, were relatively low, suggesting that the groundwater quality is brackish in nature

Development of rovers and development of infrastructure which enables them to probe other planets (such as Mars) have sparked a lot of interest recently specially with increasing public attention in Moon and Mars program by National Aeronautics and Space Administration. This is designed to be achieved by various means such as advanced spectroscopy and artificial intelligent techniques such as deep learning and transfer learning to enable the rover to not only map the surface of planet but to get a detailed information about its chemical makeup in layers beneath (deep learning) and in areas around point of observation (transfer learning). In this work, which is part of a proposal, later approach is explored. A systematic strategy is presented which make use of aforementioned techniques developed for metallic glass matrix composites as benchmark and helps develop algorithms for chemistry mapping of actual Martian surface on Perseverance Rover launching shortly.

Metallic glass matrix composites are unique new materials which have superior mechanical properties as compared to existing conventional materials. Owing to this, they are potential candidates for various future structural applications. However, they suffer from disadvantages of brittleness, poor ductility and little or no toughness and they manifest catastrophic failure on application of force. Their behavior is dubious and require extensive experimentation to draw conclusive results. In present study, which is continuation of previous study by author, an effort has been made to overcome this pitfall by simulation. A quantitative mathematical model based on KGT theory has been developed to describe nucleation and growth of crystalline phase dendrites in glassy matrix during solidification. It yields information about numerical parameters to understand the behavior of each individual element in a phase in multicomponent sluggish slurry and their effect on final microstructural evolution. Model is programmed and simulated in MATLAB®. Its validation is done by comparison with identical curves reported in literature previously for similar alloys. Results indicate that effect of incorporating all heat transfer coefficients at macroscopic level and diffusion coefficients at microscopic level play a vital role in refining the model and bringing it closer to actual experimental observations. Two types of hypoeutectic and eutectic systems namely Zr₆₅Cu₁₅Al₁₀Ni₁₀ and Zr_47.5Cu_45.5Al₅Co₂ respectively were studied. Simulation results were found to be in good agreement with prior simulated and experimental values

Gestational diabetes mellitus (GDM) is a common complication of pregnancy that adversely affects maternal and offspring health. A variety of risk factors, such as BMI and age, have been associated with increased risks of gestational diabetes. However, in many cases gestational diabetes occurs in healthy nulliparous women with no obvious risk factors. Emerging data suggest that the tendency to develop gestational diabetes has genetic and environmental components. Here we develop a polygenic risk score for gestational diabetes. We further investigate relationships between the genetic architecture of GDM and genetically constructed risk factors and biomarkers. Our results show that genetics can be used as an early screening tool that identifies at-risk at higher risk of GDM before its onset to propose comprehensive monitoring and preventative programs to mitigate the risks.

The deployment of Artificial Intelligence (AI) systems in decentralized environments is on the rise, yet representation of shared conceptualization in such scenarios remains a challenging issue. The absence of a shared understanding can lead to suboptimal performance of AI systems and hinders the ability to comprehend the knowledge and beliefs of agents in the domain. This paper proposes a formal model for modeling conceptualization in AI systems that integrates ontology, epistemology, and epistemic logic. The model aims to address the gap in representing shared conceptualization in decentralized environments and enhance the performance of AI systems operating in such environments. The proposed model is a hybrid structure that blends extensional and intensional structures and leverages logic-based languages for modeling purposes. A case study in the healthcare sector is presented to illustrate the application of the proposed model. The study contributes to the existing literature by providing a formal model for representing shared conceptualization in decentralized environments, which can be utilized to optimize the performance of AI systems in these environments.

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

In light of growing concerns over water contamination, particularly in regions like Sindh, Pakistan, this research aimed to evaluate and map the groundwater quality in Taluka Larkana for both drinking and irrigation applications, addressing the expressed worries of the local community. A total of forty groundwater samples were collected from various sources such as hand pumps, taps, and tube wells. These samples underwent comprehensive analysis encompassing parameters including color, odor, taste, turbidity, pH, total hardness (TH), electrical conductivity (EC), and total dissolved solids (TDS), with comparisons drawn against WHO guidelines.Results indicated that taste, color, odor, and turbidity of the groundwater samples generally fell within acceptable ranges. However, EC levels varied from moderately elevated to excessively high. Notably, a significant proportion of samples (84% EC, 21% Cl, 30% Ca, 37% Mg, 22% TH, and 84% TDS) exceeded WHO-recommended thresholds.Assessment via the SPI model classified approximately 16% of samples as moderately contaminated, while 65% were deemed highly contaminated, and 19% were found unsuitable for drinking purposes. These findings underscore the pressing need for concerted efforts to address water quality issues in the region.

The precipitation, flocculation, and deposition of asphaltene cause severe formation damage within a reservoir and shorten a well’s productive life. Pressure depletion is one factor that contributes to asphaltene precipitation during production; therefore, the first step in managing asphaltene is to determine the onset pressure of the precipitation. While there are numerous equation of state models that can be used to predict the onset pressure, these models are complex and heavily reliant on tuning parameters. Using multivariate linear regression, this work attempts to develop a simple and accurate thermodynamic model for predicting the upper precipitation onset pressure under pressure depletion above the bubble point pressure (Pb) at various temperatures. A total of 94 experimental data points from 37 published crude oil data sets were compiled from the literature. To develop the model, 59 experimental data points were used as training data and 35 experimental data points as testing data. According to the results of the multicollinearity test, the bubble point pressure, temperature, resins, and saturate-to-aromatic ratio were chosen as predictors. The upper onset pressure data with comparable trends were clustered, and unsupervised recognition of three distinct cluster groups was performed. For each cluster identified, a multivariate linear regression model was developed. The model was chosen based on Mallow’s coefficient of determination (Cp), adjusted R² (statistical measure of fit), and S (standard error of the regression slope). The developed model was tested using a data set, and the results showed an adjusted R² of 96.25%, with a mean absolute error of 4.1%. The model was randomly applied to 15 data points to compare it to perturbed-chain statistical associated fluid theory (PC SAFT) and the Peng-Robinson equation of state models and to the multivariate regression models of Fahim (2007) and Ameli et al. (2016). The results showed that the mean absolute error for predicting the asphaltene precipitation onset pressure was 2.82% using Peng-Robinson, 2.36% using the PC SAFT equation of state, 23.96% using the Fahim model, 24.80% using the model reported by Ameli et al., and 2.39% using the newly developed multivariate regression model. The developed multivariate model appears to be as accurate as the PC SAFT equation of state modeling with tuning parameters. The primary advantage of multivariate regression is that, unlike the PC SAFT equation of state model, it does not require saturates, aromatics, resins, and asphaltenes (SARA)-based characterization methodologies or rigorous parameter tuning. It is simple to use, quick, and it produces results in a short period of time.

The United Arab Emirates (UAE) has established itself as the medical device industry’s preferred regional center. Major multinational corporations either have a regional subsidiary or are planning to open up a principal office in the UAE to expand their business. Employees in the industry are in high demand as new opportunities open up frequently and that causes challenges to companies, namely employee turnover. This research helps bridge the gap found in the literature by investigating the factors that impact employee turnover in the medical device industry in the UAE. These factors were tested with their subsequent sub-factors. Using a quantitative approach, the study surveyed 230 participants from 47 medical device companies in the UAE. The result of one-way ANOVA demonstrated a significant relationship between employee salary and employee turnover. The linear regression analysis was statistically significant between perceived external prestige, location, flextime, organizational justice, job satisfaction, organizational commitment, extrinsic rewards, career advancement, lack of alternatives, constituent attachments, and employee turnover. These findings indicate the need for a strategy utilizing these factors for existing and new medical device companies in the UAE to prevent high employee turnover or limit it at least.

In this paper, first we use the optical metrics of black-bounce traversable wormholes to calculate the Gaussian curvature. Then we use the Gauss-Bonnet theorem to obtain the weak deflection angle of light from the black-bounce traversable wormholes. Then we investigate the effect of dark matter medium on weak deflection angle using the Gauss-Bonnet theorem. We show how weak deflection angle of wormhole is affected by the bounce parameter $a$. Using the Gauss-bonnet theorem for calculating weak deflection angle shows us that light bending can be thought as a global and topological effect.

Background: Flattening the curve refers to community isolation measures that keep the daily number of disease cases at a manageable level for medical providers. Efforts to completely contain the COVID – 19 have failed. As there is currently no vaccine or specific medication to treat COVID-19, the only way to flatten the curve is through collective action of mitigation efforts. Objectives: The study was carried out with aim to study the effect of mitigation efforts in reducing the overall COVID -19 attack rate and flattening the curve of COVID-19 in Egypt. Methods: Secondary data in the situation reports of WHO, Worldometer and Egyptian MoHP Report about the COVID-19 epidemic in Egypt were analyzed till June 27, 2020. Survival analysis was carried out to determine the cumulative proportional survival of COVID-19. The Susceptible Infected Recovered (SIR) model was used to study the epidemic dynamics of COVID-19 pandemic. Results: In Egypt in the fourth week after exposure β was 0.17 and γ = 0.07. The fraction of infectious individuals will grow exponentially as the exponential growth rate was 0.10, with an epidemic time of 7 days. R in 22 ^nd June was 0.80 (less than one person infected) where β=0.21 and γ =0.26. There was reduction in the actual overall attack rate at 22^nd June of 96 % of its value without intervention with a cumulative proportional survival of 0.89. There was flattening of the curve. Conclusion and Recommendation: Mitigation efforts of Egypt had succeeded in flattening the curve of COVID-19 Pandemic. Even though, strengthening all the mitigation efforts to keep the exposure probability, as well as the transmission rate as low as possible is mandatory for containment of the epidemic.

This study suggests a novel cooperative multi-simplex algorithm that generalizes a local search optimizer to design a novel global search heuristic algorithm. The proposed algorithm exploits the vertex sharing strategy to enhance the search abilities of the working simplexes. The vertex sharing among the simplexes is carried out through cooperative step that is based on fitness of the underlying simplex. The proposed algorithm is applied to solve some systems of nonlinear equations by transforming them to optimization problems. Comparative analysis of results shows that the proposed method is practical and effective.

Cultural heritage is the most important resource providing communication between the past and future. The societies utilizing this resource in the best way, have had an inventory of cultural heritage and contributed to world culture. The efforts made for being able to the accurate and healthy data in the documentation of cultural heritage led the new techniques to emerge other than documentation and, together with the developing technology, documentation with traditional method replaced with modern documentation techniques using new technological devices. One of these documentation techniques is the use of Unmanned Aerial Vehicles (UAVs) in the documentation studies. In this study, the usability of unmanned aerial vehicles in the studies of cultural heritage was studied.

Maxwell's fish eye has been known to be a perfect lens in optics. In this letter, using the Gibbons-Werner method, namely Gauss-Bonnet theorem on optical geometry of black hole, we extend the calculation of the weak gravitational lensing within the Maxwell's fisheye as a perfect lensing in medium composed of an isotropic refractive index that near-field information can be obtained from far-field distances. Moreover, these results provide an excellent tool to observe compact massive object by weak gravitational lensing within the dark matter medium and to understand the nature of the dark matter that may effect the gravitational waves. Moreover, we show that Gauss-Bonnet theorem is a global effect and this method can be used as a new tool on any optical geometry of compact objects in dark matter medium.

Indoor navigation systems must deal with absence of GPS signals, since they are only available in outdoor environments. Therefore, indoor systems have to rely upon other techniques for positioning users. Recently various indoor navigation systems have been designed and developed to help visually impaired people. In this paper an overview of some existing indoor navigation systems for visually impaired people are presented and they are compared from different perspectives. The evaluated techniques are ultrasonic systems, RFID-based solutions, computer vision aided navigation systems, ans smartphone-based applications.

The rapid urbanization growth of Dubai has resulted in connectivity issues, considered tre-mendous development pressure. That leads the local authorities to set a vision for Dubai as a 15-20 minute city by 2040. The 15-minute city, where all services can be reached with minimum travel time using sustainable mobility means (walking, cycling, or electric bik-ing). This paper aims to assess the current walkability situation within 15 minutes in the most significant parts of Dubai. This study considered 13 communities; Bur-Dubai and Business Bay were selected to represent the ungated communities, and eleven major gated communities were considered to indicate the gated ones. Those neighborhoods are selected based on the developments' socio-economic status and population density. The assessment considered 14 essential services, categorized into five categories: educational, health, social, entertainment, and religious. The research methodology conducted desktop research, site visits, interviewing random residents to collect data, and then using ArcGIS to assess walk-ability. The results show (64.5%) of the ungated neighborhoods population which access essential services within 15 minutes, while most of the gated communities residents must use cars to access many services. Furthermore, services distribution patterns and walkability infrastructures outside these developments should be developed to obtain higher walkability indicators.

Many combinatorial optimization problems are hard to solve within the polynomial computational time or NP-hard problems. Therefore, developing new optimization techniques or improving existing ones still grab attention. This paper presents an improved variant of the Ant Colony Optimization meta-heuristic called Enhanced Hyper Cube Framework ACO (EHCFACO). This variant has an enhanced exploitation feature that works through two added local search movements of insertion and bit flip. In order to examine the performance of the improved meta-heuristic, a well-known structural optimization problem of laminate Stacking Sequence Design (SSD) for maximizing critical buckling load has been used. Furthermore, five different ACO variants were concisely presented and implemented to solve the same optimization problem. The performance assessment results reveal that EHCFACO outperforms the other ACO variants and produces a cost-effective solution with considerable quality.

This paper is devoted to investigate charged vector particles tunneling via horizons of a pair of accelerating rotating charged NUT black hole under the influence of quantum gravitational effects. For this purpose, we use the modified Proca equation incorporating generalized uncertainty principle. Using the WKB approximation to the field equation, we obtain a modified tunneling rate and the corresponding corrected Hawking temperature for this black hole. Moreover, we analyze the graphical behavior of corrected Hawking temperature T'_H with respect to the event horizon for the given black hole. By considering quantum gravitational effects on Hawking temperatures, we discuss the stability analysis of this black hole. For a pair of black holes, the temperature T'_H increases with the increase in rotation parameters α and w, correction parameter β, black hole acceleration α and arbitrary parameter k and decreases with the increase in electric e and magnetic charges g.

In this paper, we analyze the Hawking radiation phenomenon for types of Banados-Teitelboim-Zanelli-like (BTZ-like) black holes. For this purpose, using the Hamilton-Jacobi method, we consider semi-classical WKB approximation to calculate the tunneling probabilities of massive boson particles. For these particles, we use the equation of motion for the Glashow-Weinberg-Salam model. Using quantum tunneling process of charged massive bossons, we compute the corresponding Hawking temperatures. Furthermore, we discuss the effects of rotation parameter on tunneling probability and temperature.

The African bush and forest elephants, Loxodonta Africana and Loxodonta cyclotis, and the Asian elephant, Elephas maximus, are the largest land-dwelling animals. Elephants need to be highly mobile and active in order to find fresh food and water, and in the case of males, to locate females in estrus for breeding. Asian elephants walk up to 21 km each day and African elephants can walk up to 28 km per day. This high level of mobility in the wild is also important for maintaining an optimum musculoskeletal health. However, most zoo elephants live in restricted spaces and cold climates that require extended periods of indoor confinement are therefore unable to be as physically active. Zoo enclosures for elephants are relatively small with hard surfaces (i.e. concrete, tarmac and hard packed dirt), so they cannot exercise and are forced to stand on unnaturally hard surfaces continually. Physical inactivity in captivity makes them more prone to gaining weight and developing bone and joint diseases such as osteomyelitis, joint ankylosis and osteoarthritis (OA). Many health and welfare problems in captive elephants are likely to be caused by the lack of mobility. This perspective article focuses on the possible link between captivity, mobility, physical inactivity and the development of OA in captive elephants.

Culinary herbs and spices are widely used in daily diets. Pakistan flora is enriched with phytochemicals due to a diverse range of land. Phytochemicals, including volatile and non-volatile compounds, have captured much interest due to their numerous health advantages and significance in daily diet. The present study aimed to conduct in-depth metabolomic profiling of Pakistani-grown fenugreek leaves (Trigonella foenum-graecum), fennel seeds (Foeniculum vulgare), mint leaves (Mentha royleana), coriander seeds (Coriandrum sativum) and basil leaves (Ocimum basilicum) by using liquid chromatography–mass spectrometry (LC-MS/MS) and gas chromatography–mass spectrometry (GC-MS). The first study was conducted to optimize extraction using different solvents (methanol, ethanol, chloroform, acetone, and water). Total phenolic content (TPC), total flavonoid content (TFC), and total condensed tannins (TCT) were quantified along with the antioxidant and anti-diabetic activities. The highest TPC (125.42 ± 10.89 mg GAE/g) and the highest antioxidant and anti-diabetic potential were quantified in mint. Seventy-one phytochemical metabolites were identified using LC-MS/MS, while forty-nine volatile constituents were identified using the GC-MS. A positive correlation was identified between phenolic contents and their biological activities. Furthermore, molecular docking helped to find drug molecules with more excellent anti-diabetic activity based on their binding affinities. This study suggests that selected medicinal herbs from Pakistan have significant nutraceutical and phytopharmaceutical potential. This study could further help in drug discovery.

An earthquake is a natural event by its general definition. This natural event is a disaster that causes significant damage, loss of life, and other economic effects that will damage the state. The possibility of predicting a natural event such as an earthquake will minimize the reasons mentioned. Data collection, data processing, and data evaluation were carried out in this study. Earthquake forecasting was performed using the data and the RNN (Recurrent Neural Network) method. The study was carried out on seismic data with a magnitude of 3.0 and above belonging to Düzce Province between 1990 and 2022. In order to increase the learning potential of the method, the b and d values of the earthquake were calculated and included in the data set, except for the earthquake magnitude. The determination of earthquakes in a specific time interval in regions of Turkey, the classification of earthquake-related seismic data using artificial neural networks, and the production of predictions for the future reveal the importance of this study.

Building Information Modelling (BIM) has been more popular in the construction industry as a consequence of the many advantages it offers, such as increased productivity, improved project outcomes, and reduced expenses. However, there are many obstacles that must be overcome before BIM can be widely used. This research uses a poll of construction industry experts to investigate the challenges associated with using BIM in projects. Cost of implementation, inadequate standards and procedures, and a lack of knowledge with BIM were some of the hurdles that were measured in the study. Statistical techniques were used to examine the data, and the findings indicate that the high cost of implementation and the absence of sufficient standards and procedures are the two main obstacles to the widespread adoption of BIM. Problems with supply chain management and a lack of experience with BIM are also mentioned as obstacles. The report also suggests solutions, such as a more focused teaching effort on BIM-related issues and the creation of a specialised BIM curriculum. Professionals in the construction industry may utilise the study’s results to their advantage by expanding their knowledge of the obstacles standing in the way of widespread adoption of BIM.

Enteropathogenic microorganisms like Escherichia coli cause severe intestinal problems by disrupting the gut homeostasis. The live microorganisms, when given in adequate quantities provide several beneficial effects to the host are known as probiotics. One of the pronounced benefits conferred by probiotic is to antagonize the growth of enteropathogens competing for adherence to the intestinal epithelium. Bifidobacterium is the major genera of human especially infant are intestinal microbiota. In current study, Bifidobacterium bifidum was isolated from the infant stools and probiotic potential was assessed using prescribed tolerance tests against low pH, gastric juices and bile salts. Anti-infectious activity of probiotic Bifidobacterium bifidum against enteropathogenic E. coli was checked both in vitro and in vivo using agar well diffusion assay and mice model respectively. Mice feces were evaluated for both Bifidobacterium bifidum and E. coli counts in all groups and analyzed statistically. In vitro results showed Bifidobacterium bifidum possess marked antibacterial activity against E. coli. There was significant decrease in enteropathogenic E. coli burden in the mice group fed with Bifidobacterium bifidum before and after challenge. In conclusion, the endogenous Bifidobacterium bifidum have excellent probiotic potential and can be used prophylactic and treatment option against enteropathogens.

Various products of the Integrated Multisatellite Retrievals for GPM (IMERG) and passive mi-crowave (PMW) sensors are assessed with respect to near-surface wet-bulb temperature (Tw), precipitation intensity, and surface type (i.e., with and without snow and ice on the surface) over the CONUS and using Stage-IV product as reference precipitation. IMERG products include precipitation estimates from infrared (IR), combined PMW, and their combination. PMW products generally have higher skills than IR over snow- and ice-free surfaces. Over snow- and ice-covered surfaces (1) PMW products (except AMSR-2) show a higher correlation coefficient than IR, (2) IR and PMW precipitation products tend to overestimate precipitation, but at colder temperatures (e.g., Tw<-10oC) PMW products tend to underestimate and IR product continues to show large overestimations, and (3) PMW sensors show higher overall skill in detecting precipitation oc-currence, but not necessarily at very cold Tw. The results suggest that the current approach of IMERG (i.e., replacing PMW with IR precipitation estimates over snow- and ice-surfaces) may need to be revised.

The fuel cell particularly the Proton Exchange Membrane Fuel Cell (PEMFC) is a new energy technology that plays a critical role in achieving market viability. The PEMFC has high power density, zero CO₂ emissions, and high conversion efficiency. However, with uniform distribution of fuel in PEMFC, we can improve the performance and reduce the cost. The PEMFC will be more efficient and effective in terms of renewable energy as a byproduct only water. Metal foam is used as an alternative for the channels in PEMFC. The model of PEMFC was established by using the ANSYS Fluent 3D Model. Then we proposed four configurations of PEMFC. The outcome demonstrates that Case IV provided outstanding performance if it is compared with the other three cases. In terms of power density, Case IV provided a high power density that is double that of Case I. The porosity of metal foam was investigated. The 0.6 porosity value is found as the optimal value. The uniform distribution of the fuel can be achieved at this value which leads to the outstanding current density.

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

This paper addresses the cosmological constant problem, a significant discrepancy highlighted by \cite{Weinberg:1988cp} that underlines a fundamental inconsistency between quantum field theory (QFT) and general relativity (GR). QFT examines matter dynamics within Lorentzian spacetime under standard model symmetry, while GR uses Riemannian geometry but adapts it to ensure local Lorentzian behavior in small regions. Despite the experimental successes of both theories, a major challenge exists in theoretical understanding. This letter seeks to clarify this critical misunderstanding. As the universe cools from its hot beginning, the \(SU(3) \times SU(2) \times U(1)\) standard model gauge symmetry evolves. This symmetry breaks to \(SU(3) \times U(1)\) upon cooling at the electroweak scale and ultimately to \(SU(3)\) alone as temperatures approach near-absolute zero kelvin, facilitated by the experimental Meissner effect. This suggests that \(SU(3)\) symmetry forms the foundational "atoms" of vacuum energy. Calculating the number of \(SU(3)\) vacuum atoms across the universe results in a value that perfectly aligns the theoretical predictions with the observed vacuum energy densities, thereby resolving the cosmological constant problem. Since \(SU(3)\) atoms account for the cosmological constant, they must be unbreakable. The third law of thermodynamics, which states that it is impossible to reach absolute zero Kelvin, provides the protection that prevents SU(3) atoms from breaking at zero Kelvin. This reveals the connection between the third law of thermodynamics and the quark confinement. This leads to the \(SU(3)\) atom creating a mass gap within the universe. This solution serves as a symmetric evidence of gauge-gravity duality as well as gravity-superconductor duality.

Because of the rapid advancement in the use of photovoltaic (PV) energy systems, it has become critical to look for ways to improve the energy generated by them. The extracted power from the PV modules is proportional to the output voltage. The relationship between output power and array voltage has only one peak under uniform irradiance, whereas it has multiple peaks under partial shade circumstances (PSC). There is only one global peak (GP) and many local peaks (LPs), where the typical maximum power point trackers (MPPT) may become locked in one of the LPs, significantly reducing the PV system's generated power and efficiency. The metaheuristic optimization algorithms (MOAs) solved this problem, albeit at the expense of the convergence time, which is one of these algorithms' key shortcomings. Most MOAs attempt to lower the convergence time at the cost of the failure rate and the accuracy of the findings because these two factors are interdependent. To address these issues, this work introduces the dandelion optimization algorithm (DOA), a novel optimization algorithm. The DOA's convergence time and failure rate are compared to other modern MOAs in critical scenarios of partial shade PV systems to demonstrate the DOA's superiority. The results obtained from this study showed substantial performance improvement compared to other MOAs, where the convergence time is reduced to 0.4 s with zero failure rate compared to 0.9 s, 1.25 s, and 0.43 s for other MOAs under study. The optimal number of search agents in the swarm, optimal initialization of search agents, and optimal design of the dc-dc converter is introduced for optimal MPPT performance.

Rubber-based composites are widely used especially in transportation. The goal of this paper is to study the mechanical properties of rubber-based composites of carbon black and Nano Aluminum trioxide additive (50 nm). Various percentage of carbon black was used (20,40, and 60 phr). The increase in Carbon black percentage shows an increase in mechanical properties of the composites (for 60 phr properties tensile test improve by 49%, for hardness resistance the improve was 21%, and for the wear test the composite improve by 22%). Various wetting percentage of nano Aluminum trioxide was used (1,1.5,2, and 2.5 %). Increasing the wetting percentage increase tensile strength (27%, hardness resistance increase by 28%, and wear resistance increase nonlinearly with a percentage reaching 70%). Selecting the optimal composition of the two fillers, then study different irradiances for it with the ultraviolet laser of moderately low energy after the vulcanization process. Post ultraviolet laser of (345 nm). Furthermore, laser vulcanization shows improvement in mechanical properties.

In a wireless network, locations of base stations (BSs)/access points (APs)/sensor nodes can be modeled based on stochastic processes, e.g., a Poisson point process (PPP) or a deterministic pattern planned ahead by providers. While deterministic deployment does not provide tractable interference analysis in general, PPP yields tractable analysis for interference. However, PPP allows APs to be deployed very close to each other and gives pessimistic results compared to the field measurements. In this study, in order to address this issue, Lloyd’s algorithm, which functions as a bridge between random and structural APs deployments, is investigated for analyzing coverage probability in a network. The link distance distribution is modeled as a mixture of Weibull distributions and its parameters are obtained by using the expectation-maximization (EM) algorithm for each iteration of Lloyd’s algorithm. The link distance distribution is further utilized for calculating the coverage probability approximately by exploiting the tractability of PPP.

Over the past few decades, aircraft Ground Handling Operations (GHO) have been investigated by numerous researchers. Some aspects of GHO have been more focused on than others due to their importance in the GH processes. For instance, GHO performance has been tackled from different perspectives while workers and vehicles allocation suffer from the lack of research in the area. This paper is a literary review of the research that has been conducted in different areas of GHO. It sheds light on GHO performance, resource allocation and scheduling, operations scheduling, vehicle routing, and workers and vehicles allocation.

Minimal access cardiac surgery appears to be the future. It is increasingly desired by cardiologists and demanded by patients who perceive superiority. Minimal access coronary artery revascularisation has been increasingly adopted throughout the world. Here we review the history of minimal access coronary revascularization and see that it is almost as old as the history of cardiac surgery. Modern minimal access coronary revascularization takes a variety of forms – namely minimal access direct coronary artery bypass grafting (MIDCAB), hybrid coronary revascularisation (HCR) and totally Endoscopic Coronary Artery Bypass Grafting (TECAB). It is noteworthy that there is significant variation in nomenclature and approaches for minimal access coronary surgery and this truly presents a challenge to comparing the different methods. However, these approaches are increasing in frequency and proponents demonstrate clear advantages for their patients. The challenge that remains, as for all areas of surgery, is demonstrating superiority of these techniques over tried and tested open techniques which is very difficult. There is a paucity of randomized controlled trials to help answer this question, and the future of minimal access coronary revascularisation to some extent is dependent on such trials. Thankfully some are underway and the results eagerly anticipated.

In this paper, it is defined that how the IPOs are affected by the underwriter reputation. What will happen to IPOs who are backed by reputed underwriter and who is not? And evaluate the IPOs short-run and long-run performance. Also try to explain the relationship between IPOs and underwriter. Also tried to compare some of the international return and Pakistani returns. Also determine the relation of the investor to the underwriter. There is a set of investors who have there own relationship with the underwriters how they will affect on the IPOs in the market.

With the emergence of the novel corona virus SARS-CoV-2 since December 2019, more than 43 million cases have been reported worldwide. This virus has shown high infectivity and severe symptoms in some cases leading to over 1 million deaths globally. Despite the collaborative and concerted research efforts that has been made, no effective treatment for COVID-19 (corona virus disease-2019) is currently available. SARS-CoV-2 uses the angiotensin converting enzyme 2 (ACE2) as an initial mediator for viral attachment and host cell invasion. ACE2 is widely distributed in human tissues including the cell surface of lung cells which represent the primary site of the infection. Inhibiting or reducing cell surface availability of ACE2 represents a promising therapy for tackling COVID-19. In this context, most ACE2–based therapeutic strategies have aimed to achieve this through the use of angiotensin converting enzyme (ACE) inhibitors or neutralizing the virus by exogenous administration of ACE2. However, through this review, we present another perspective focusing on the subcellular localization and trafficking of ACE2. Membrane targeting of ACE2, shedding and its cellular trafficking pathways including internalization are not well elucidated. Therefore, hereby we present an overview on the fate of newly synthesized ACE2, its post translational modifications, what is known of its trafficking pathways. In addition, we highlight the possibility that some of the identified ACE2 missense variants might affect its trafficking efficiency and localization and hence may explain some of the observed variable severity of SARS-CoV-2 infections. Extensive understanding of these processes is necessary to evaluate the potential use of ACE2 as a credible therapeutic target.

Continuous object tracking in WSNs, such as monitoring of mud-rock flows, forest fires etc., is a challenging task due to characteristic nature of continuous objects. They can appear randomly in the network field, move continuously, and can change in size and shape. Monitoring such objects in real-time generally require tremendous amount of messaging between sensor nodes to synergistically estimate object’s movement and track its location. In this paper, we propose a novel twofold-sink mechanism, comprising of a mobile and a static sink node. Both sink nodes gather information about boundary sensor nodes, which is then used to uniformly distribute energy consumption across all network nodes, thus helping in saving residual energy of network nodes. Numerous object tracking schemes, using mobile sink, have been proposed in the literature. However, existing schemes employing mobile sink cannot be applied to track continuous objects, because of momentous variation of network topology. Therefore, we present in this paper a mechanism, transformed from K-means algorithm, to find the best sensing location of the mobile sink node. It helps to reduce transmission load on the intermediate network nodes situated between static sink node and the ordinary network sensing nodes. The simulation results show that the proposed scheme can distinctly improve life time of the network, compared to one-sink protocol employed in continuous object tracking.

In this work, the Pumped Storage Unit (PSU) is proposed to be integrated with wind-thermal generation system owned by a company whose aim is to maximize its profit through optimal self-scheduling of its units by finding the best bidding strategy in day-ahead energy market. The mathematical formulation of the scenario based unit-commitment and coordinated trading problem is formulated as a stochastic mixed integer linear program. The formulation takes into account several uncertain parameters; such as the wind power generation, energy market prices, and imbalances up/down prices. The expected total profit obtained from energy trading with and without coordinating pumped storage system (PSS) with thermal-wind generation system were compared, and a significant improvement in profit resulted from coordination was observed. Furthermore, coordination between PSS and wind-thermal generation improve the conditional value at risk (CVaR) which monitors the biding risk level.

In a bid to resolve lingering problems in cosmology, more focus is being tilted towards cosmological models in which physical constants of nature are not necessarily real constants, but varying with cosmic time. In this paper we have study cosmology in nonlinear electrodynamics with the Newton's gravitational constant $G$ not a constant but vary in form of power-law of the scale factor of the universe. The evolution of the scale factor $a (t)$ is studied in this model which depends on nonlinear electrodynamics fine tuning term of $\alpha$. Then we check the stability of the model using the speed of sound.

In this paper, we use a new asymptotically flat and spherically symmetric solution in the generalized Einstein-Cartan-Kibble-Sciama (ECKS) theory of gravity to study the weak gravitational lensing and its shadow cast. To this end, we first compute the weak deflection angle of generalized ECKS black hole using the Gauss–Bonnet theorem in plasma medium and in vacuum. Next by using the Newman-Janis algorithm without complexification, we derive the rotating generalized ECKS black hole and in the sequel study its shadow. Then, we discuss the effect of the ECKS parameter on the shadow of the black hole and weak deflection angle. In short, the goal of this paper is to give contribution to the ECKS theory and look for evidences to understand how the ECKS parameter effects the gravitational lensing.

The Internet of Things (IoT) has become one of the most attractive domains nowadays. It works by creating a special network between physical devices such as vehicles, home equipment, and other items. In recent days, the common technologies of communication such as Wi-Fi and 2G/3G/4G cellular are insufficient for the IoT networks because they are designed to serve appliances with immense processing capabilities such as laptops and PCs. Moreover, most of these technologies are centralized and use an existing infrastructure. Currently, the new communication technologies such as Z-Wave, 6LowPAN, and Thread are dedicated to the IoT and have been developed to meet its requirements. These technologies can handle many factors such as range, data requirements, security, power demands, and battery life. Nevertheless, the security issues in IoT systems have major concerns and matters because vulnerabilities in such systems may result in fatal catastrophes. In this paper, an enhanced IoT security framework for authentication and authorization is proposed and implemented to protect the IoT protocols from different types of attacks such as man-in-the-middle attack, reply attack, and brute force attack. The proposed framework combines an enhanced token authentication that has identity verification capabilities and a new sender verification mechanism on the IoT device side based on time stamp, which in turn can mitigate the need for local identity verification methods in IoT devices. The proposed IoT security framework is tested using security analysis with different types of attacks compared with previous related frameworks. The analysis shows the high capability of the proposed framework to protect IoT networks against many types of attacks compared with current available security frameworks. Finally, the proposed framework is developed using Windows application to simulate the framework phases, check its validity through the real network, and calculate the payload time is adds.

The calculation of effective doses is crucial in many medical and radiation fields, in order to ensure safety and compliance with regulatory limits. Traditionally, Monte Carlo codes using detailed human body computational phantoms have been used for such calculations. Monte Carlo dose calculations can be time-consuming and require expertise in different processes when building the computational phantom and dose calculations. This study employs various machine learning (ML) algorithms to predict the organ doses and effective dose conversion coefficients (DCCs) for different anthropomorphic phantoms. A comprehensive data set comprising neutron energy bins, organ labels, masses, and densities is compiled from Monte Carlo studies, and is used to train and evaluate the supervised ML models. This study includes a broad range of phantoms, including those from the International Commission of Radiation Protection (ICRP-110, ICRP-116 phantom), the Visible-Human Project (VIP-man phantom), and the Medical Internal Radiation Dose Committee (MIRD-Phantom), with row data prepared using numerical data and organ categorical labeled data. Extreme gradient boosting (XGB), gradient boosting (GB), and the random forest-based Extra Trees regressor are employed to assess the performance of the ML models against published ICRP neutron DCC values using the mean square error, mean absolute error, and R² metrics. The results demonstrate that the ML predictions significantly vary in lower energy ranges and vary less in higher neutron energy ranges, while showing good agreement with ICRP values at mid-range energies. Moreover, the categorical data models align closely with the reference doses, suggesting the potential of ML in predicting effective doses for custom phantoms based on regional populations, such as the Saudi voxel-based model. This study paves the way for efficient dose prediction using ML, particularly in scenarios requiring rapid results without extensive computational resources or expertise. The findings also indicate potential improvements in data representation and the inclusion of larger data sets to refine model accuracy and prevent overfitting. Thus, ML methods can serve as valuable techniques for the continued development of personalized dosimetry.

A significant electromotive force is induced in the rotor circuit of a doubly fed induction generator (DFIG) due to its high vulnerability to grid faults. Therefore, the system performance must be increased with appropriate control actions that can successfully offset such abnormalities in order to provide consistent and stable operations during grid disturbances. In this regard, this paper presents a solution based on a combination of an energy storage-based crowbar and a rotor-side crowbar that makes the effective transient current and voltage suppression for wind-driven DFIG possible. The core of the solution is its ability to restrict the transient rotor and stator overcurrents and DC-link overvoltages within their prescribed limits, aiming to protect the DFIG and power converters, and consequently, improve the system’ ability to ride-through faults. Further, the capacity of an energy storage device for transient suppression is estimated. Finally, simulation results show that the proposed approach is effective in achieving transient control objectives precisely and maintaining a stable grid connection during the faults.

With recent developments in fusion engineering, interest has sparked in development of fusion devices for deterrent. Enormous amount of energy generated by combining two light nuclei could be contained and manipulated at will to trigger and accelerate micro explosions (from shock wave, x-rays or ion beam focusing) which finally result in full scale blast. Materials required to make such device are critical. They must possess high strength, high hardness, ductility, formability, drawability, and anisotropic properties. High entropy alloys (HEA) are new class of materials which nicely fulfils this requirement. Essentially, they are solid solutions of multi principal elements (usually > 5) eliminating the need of base metal as in conventional alloys. This gives them many unique properties which may be tailored at will (heat treatment, cold rolling, precipitation, irradiation). They also exhibit excellent directional properties with formation of distinct bands along certain preferred crystallographic planes even in hexagonal close packed structures. These anisotropic properties are strong function of rolling, working, or forging (swaging) direction and can be utilized to benefit. This study encompasses making outer shell of a typical fusion device selected on the basis of the weight, which is a function of area of pay load bay of carrier aircraft.