Irrigation water management components evaluation is mandatory for sustainable irrigated agriculture production in the era of water scarcity. In this research spatio-temporal distribution of irrigation water components were evaluated at canal command area in Indus Basin Irrigation System (IBIS) using remote sensing based geo-informatics approach. Satellite derived MODIS product-based Surface Energy Balance Algorithm for Land (SEBAL) was used for the estimation of the Actual Evapotranspiration (ETa). Satellite derived SEBAL based ETa was calibrated and validated using the ground data-based advection aridity method (AA). Statistical analysis of the SEBAL based ETa and AA shows the mean 87.1 mm and 47.9 mm and, 100 mm and 77 mm, Standard deviation of 27.7 mm and 15.9 mm and, 34.9 mm and 16.1 mm, R of 0.93 and 0.94, NSE of 0.72 and 0.85, PBIASE -12.9 and -4.4, RMSE 34.9 and 5.76 for the Kharif and Rabi season, respectively. Rainfall data was acquired from the Tropical Rainfall Measuring Mission (TRMM). TRMM based rainfall was calibrated with the point observatory data of the Pakistan Metrological Department Stations. Canal water data was collected from the Punjab Irrigation department for the assessment of canal water availability. Water The water balance approach was applied in the unsaturated zone for the quantification of the gross and net Groundwater irrigation. Mmonthly variation of ETa with the minimum average value of 63.3 mm in January and the maximum average value of 110.6 mm in August was found. While, the average annual of four cropping years (2011-12 to 2014-15) ETa was found 899 mm. Average of the sum of Net Canal Water Use (NCWU) and Rainfall during the study period of four years was only 548 mm (36% of ETa) and this resulted the 739.6 mm of groundwater extraction. While the annual based variation in groundwater extraction of 632 mm and 780 mm was found. Seasonal analysis revealed 39% and 61% of groundwater extraction proportion during Rabi and Kharif season, respectively. The variation in four cropping year’s monthly groundwater extraction was found 28.7 mm to 120.3 mm. This variation was high in the 2011-12 to 2012-13 cropping year (0 mm to 148.7 mm), dependent upon the occurrence of rainfall and crop phenology. Net groundwater irrigation, estimated after incorporating the efficiencies was 503 mm year-1 on average for the four cropping years.

Glaciers in northern Pakistan are a prime source of freshwater, providing headwater in the Indus river system and serving as a lifeline to millions of people in the region. These glaciers undergo continuous changes by melting due to global warming or accumulation due to snowfall/precipitation at higher altitudes. In this study, we used remote sensing data to quantify glacier changes in spatiotemporal variability in the past three decades. Five glaciers in the Gilgit region (near the junction of the Hindukush and Karakoram Mountains) with an extent of less than 5 square kilometers were selected, namely Phakor glacier, Karamber glacier, East Gammu glacier, Bhort glacier, and Bad-e-Swat glacier. The fluctuations in these glaciers were monitored using a digital elevation model (DEM) and a cloud-free continuous series of Landsat satellite pictures from the minimal snow cover season. The annual climatic trends were studied through spatially interpolated gridded climate data WοrldClim version-1 climate database for 1970 – 2000. We used it to study the variations of minimum and maximum temperature, solar radiation, and precipitation through the preparation of sub-sets from the original global grids. Because of its exact delineation in the Gilgit sub-basin, the characterized watersheds were visually compared to optical Landsat 8 OLI data for mountainous ridge matching, revealing that SRTM 30m (radar-based) demonstrated greater accuracy than other DEMs. The temporal assessment of Bhort, Bad-e-Sawat, East Gammu, Karamber, and other rivers was also carried out. It is observed that the glaciers in the Gilgit watershed are rather stable. The little variability of glaciers is due to their geographic condition, altitude, topography, and orientation. Validation of the mapped glacier classes has been performed to check the accuracy assessment through an error matrix method. The kappa coefficient from the error matrix has been calculated to be 84 %. The study makes a critical input to a greater understanding of watershed controlling and hydrological processes in the upper Indus catchment's Gilgit watershed.

This article will provide an overview on internet of things (IoT), 5G communication System and Distributed Clouds. The basic concepts and benefits will be briefly presented, along with current standardization activities. In a nutshell, but the research will focus on relating internet of things, 5G and Distributed Cloud Computing.

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.

The electrical, mechanical and thermal conductivity of ethylene butene copolymer (EBC) composites with carbon fibers were studied. EBC/carbon-fiber composites can be utilized as an electro-mechanical material which is capable of changing it electric resistance with mechanical strain. Carbon fibers were introduced to EBC with different concentrations (5-25 wt%). The results showed that the addition of carbon fibers to EBC could increase the electric resistance up to 10 times. Increasing the load to 2.9 MPa could increase the electric resistance change by 4500% compared 25% fiber sample with pure EBC. It is also noted that the electric resistance of the EBC/CF composites underwent a dramatic increase with raising the strain, for example, the resistance change was around 13 times more at 15% strain in comparison to 5% of strain; The thermal conductivity tests showed that the addition of carbon fibers could increase the thermal conductivity by 40%, from 0.19 to 0.27 (Wm-1K-1). It was also observed that the addition of carbon fibers to EBC could increase the thermal conductivity.

Residual stress (RS) is the most challenging problem in metal additive manufacturing (AM) since the build-up of high tensile RS may influence the fatigue life, corrosion resistance, crack initiation, and failure of the additively manufactured components. While tensile RS is inherent in all the AM processes, fast and accurate prediction of stress state within the part is extremely valuable and would result in optimization of the process parameters in achieving a desired RS and control of the build process. This paper proposes a physics-based analytical model to rapidly and accurately predict the RS within the additively manufactured part. In this model, a transient moving point heat source (HS) is utilized to determine the temperature field. Due to the high temperature gradient within the proximity of the melt pool area, material experience high thermal stress. Thermal stress is calculated by combining three sources of stresses known as stresses due to the body forces, normal tension, and hydrostatic stress in a homogeneous semi-infinite medium. The thermal stress determines the RS state within the part. Consequently, by taking the thermal stress history as an input, both the in-plane and out of plane RS distributions are found from incremental plasticity and kinematic hardening behavior of the metal by considering volume conservation in plastic deformation in coupling with the equilibrium and compatibility conditions. In this modeling, material properties are temperature-sensitive since the steep temperature gradient varies the properties significantly. Moreover, the energy needed for the solid-state phase transition is reflected by modifying the specific heat employing the latent heat of fusion. Furthermore, the multi-layer and multi-scan aspects of metal AM are considered by including the temperature history from previous layers and scans. Results from the analytical RS model presented excellent agreement with XRD measurements employed to determine the RS in the Ti-6Al-4V specimens.

Uncertainties in initial water saturation and early water breakthroughs of un-known sources and paths, which occurred in the low resistivity Lower Haushi sand-stone (LHS) reservoir at the beginning of oil production, imply that the bypassed oil can yet remain in the reservoir. In addition, the upper part of LHS is a tight oil reser-voir, which can still contain a significant amount of unrecovered oil. This opens the opportunity to produce the remaining oil instead of abandoning this reservoir. Low resistivity can exaggerate water saturation, leading to misinterpretation and premature water flooding due to inadequate oil-water contrast. Our analysis revealed a correlation between low resistivity and robust porous network connectivity, along-side elevated formation water salinity in highly porous layers. In regions of high po-rosity, sufficient oil-water contrast facilitates precise water saturation determination, affirming oil presence in all wells. However, within tight oil zones, near-complete wa-ter saturation contradicts actual oil presence. Factors contributing to saturation un-certainty and water breakthroughs were reassessed. Utilizing a 1:1 verti-cal-to-horizontal permeability ratio derived from core data enhanced simulation alignment with water production rates. Investigation into the impact of relative per-meabilities on water production was conducted. Water saturation maps were gener-ated to pinpoint remaining oil reserves. Depletion around wells is evident in lower po-rous regions, while oil persists across the entire tight reservoir, notably in two unex-plored sections.

In this paper, a plasmonic refractive index sensor based on MIM waveguide (metal_insulation_metal) with two plasmonic waveguides and five rings and two teeth and four rectangular cavities is proposed and designed. The refractive index of the resonators as well as the resonant wavelengths will be investigated by the time difference finite difference method. To achieve an optical sensor with excellent quality and performance, we change the number and type of amplifiers and their dimensions. In each stage of the simulation, we will only change the refractive index of the middle ring located in the middle of the two waveguides, and the refractive index of the other amplifiers remains the same. This challenge will help to form a more appropriate structure for optical sensors. The sensor built in this simulation has a balanced and suitable function for integrated circuits and helps researchers to better understand the design of plasmonic structures. It also has important applications in medical research, health care, drug manufacturing, security monitoring and environmental protection, internal security of countries and the battlefield.

In this study, we seek to analyze and numerically evaluate a plasmonic sensor. To form the sensor structure, we use several amplifiers, such as two rings attached to each other and a cavity, as well as two metal insulating metal waveguides (MIM). At the beginning of this simulation, we must examine the resonant wavelengths and the refractive index of the resonators using the finite difference time domain method. By changing the refractive index and changing the dimensions of the cavity and the rims, we seek to investigate the sensor performance and the conduction characteristics of the plasmonics and to obtain the effect of these parameters. To evaluate the sensor performance, we calculate the three factors of sensitivity coefficient S, quality factor Q and figure of merit (FOM), here we reach the sensitivity of 987.6 nm / RIU. Such a plasmonic sensor with a simple framework and high optical resolution can be very useful for sensor systems on optical circuits.

Use of Machine Learning (ML) methodologies in optical communications has paved a new pathway. In this paper, firstly, we discuss the use of ML methodologies for reducing optical fiber nonlinearities, nonlinearity compensation, fault detection and optical performance monitoring. Then we present our recent work where we compare RL-SARSA and SVM based method with conventional method. The results show that RL-SARSA and SVM methods are successful candidates in mitigating the nonlinearities in proposed system as compared to conventional optical communication system.

We propose a 10-Gb/s 64-quadrature amplitude modulation (QAM) signal-based Radio over Fiber (RoF) system for 50 km of standard single mode fiber length which utilizes Reinforcement Learning (RL) SARSA based decision method to indicate an effective decision which mitigates nonlinearity. By utilizing RL-SARSA algorithm, the results demonstrate that significant reduction can be obtained in terms of bit error rate.

Machine learning (ML) methodologies have been looked upon recently as a potential candidate for mitigating nonlinearity issues in optical communications. In this paper, we experimentally demonstrate a 40-Gb/s 256-quadrature amplitude modulation (QAM) signal-based Radio over Fiber (RoF) system for 50 km of standard single mode fiber length which utilizes support vector machine (SVM) decision method to indicate an effective nonlinearity mitigation. The influence of different impairments in the system is evaluated that includes the influences of Mach-Zehnder Modulator nonlinearities, in-phase and quadrature phase skew of the modulator. By utilizing SVM, the results demonstrated in terms of bit error rate and eye linearity suggest that impairments are significantly reduced and licit input signal power span of 5dBs is enlarged to 15 dBs.

This paper investigates how Islamic tolerance contributes to solve religious conflict today that has impacted on various aspects such as economic collapse, insecurity in life, lack of professional society, academic retrogressions, and so on. Knowledge of these issues has become an urgent to build a community that is full of peace, harmony and unity. The paper suggests that Quranic views on tolerance should be understood and practiced in order to avoid disunity in society which is due to failure to appreciate tolerance values in association. The paper concludes problems or misunderstandings always occur in different religious societies, since every religion has its own teachings and principles, as well as each of them is interpreted in different ways.

In this work, we report the engineering of a sub-70 nm nanocomposite of ZnO/Zn2TiO4 using a low-temperature solution-phase method. Titanium isopropoxide and zinc acetate were used as precursors, and isopropyl alcohol and water were used as solvents. The investigation focused on the growth of nanocomposites by varying the concentrations of precursors and surfactants, as well as their efficiency within different pH ranges. All three ZnO/Zn2TiO4 nanocomposites consist of hexagonal wurtzite ZnO and Zn2TiO4 structures in the three nanocomposites. The crystallite size in three ZnO/Zn2TiO4 nanocomposites, with a hexagonal wurtzite ZnO structure, were measured to be 62.67, 40.34, and 39.50 nm. The crystallite size values in three ZnO/Zn2TiO4 nanocomposites were 26.15 nm, 25.36 nm, and 21.24 nm, respectively, with Zn2TiO4. Morphological observations, conducted using FESEM, reveal the formation of three ZnO/Zn2TiO4 nanocomposites across the entire sample. These nanocomposites resemble dispersed cotton packets, with sizes ranging from 18 to 350 nm. Peaks in the range of 610-1250cm-1 are attributed to Ti-O bond formation and peak located at 1350-1450 cm-1 are attributed to Zn-O bond formation. Also, EDX spectrum reveals peaks for various sample components such as Ti, O and Zn. UV spectrums confirmed the formation of three nanocomposite of ZnO/Zn2TiO4 as showing two peaks for each sample. Photocatalytic investigation carried out by analyzing the UV-Visible absorption spectra of methylene blue dye on the surface of ZnO/Zn2TiO4 nanocomposites, dye degradation at various irradiation time intervals, dye degradation at varied catalyst concentrations and the influence of pH on dye-degradation. Furthermore, the said nanocomposites were used to degrade environmental pollutant (Methylene blue dye). Different sets of experiments were conducted under ambient conditions which show an excellent photo catalytic degradation efficiency.

EOR is a process that helps maximize recoverable oil reserves, extend field life and increase recovery rates, and is an important tool for companies to maintain production and increase return on investment. In this article, the performance of nanoclay on aqueous phase viscosity, wettability, and recycled oil in core fluidization in polyacrylamide nanofluid has been investigated in different experiments and EOR. We have used polymers due to the properties of polymers in increasing rheology and effective mobility and controlling stability and variability to resume growth oil in nanofluids. Adding 5% by weight of salt to polymer-clay nanofluid has also been investigated in samples comparable to pure nano clay without salt. The viscosity of this suspension increases by about 50% by adding a certain amount of salt. If the same amount of salt is added to the polymer diluent, the viscosity decreases drastically. The results of the wettability test also show that clay has a better potential to change the wettability in the tendency to salt. In core fluidization tests, the highest efficiency of 30% was obtained in the third recycling in the combination of clay nanoparticles and sulfone copolymer. The highest efficiency was obtained in the third scenario in core fluidization tests, that is; A combination of resin nanoparticles and sulfone copolymer. Their detailed behavior in the characteristics of wettability, rheology, and core fluidization is analyzed in this article.

Natural astaxanthin is a precious substance obtained from some organisms such as microalgae. This plant has many benefits for humans, so research into its cost-effective and economical production has recently increased. For this purpose, some methods such as the use of different culture media, gene engineering, different stresses, nanoparticles, bio acids, and phytohormones are important. Accordingly, this review study was conducted to demonstrate the effect of the factors mentioned above for the high production of astaxanthin in microalgae, especially Haematococcus pluvialis (H.p).

Agricultural intensification has been adopted as a viable option for Poverty eradication and hunger, attainment of food security, socio-economic wellbeing as well as sustainable livelihood across the West Africa sub-humid and semi-arid zones. Thus, there is need to identify and address the challenges of sustainable livelihoods in the agriculture-intensive semi-arid and dry sub-humid zones of West Africa. This was addressed through review of relevant literature and analysis of environmental parameters from recent research by the authors to unveil the environmental uncertainties that threaten the sustainability of agriculture and human livelihoods across the ecologically fragile regions of West Africa, using pointers from Nigeria. The research identified the fundamental challenges, adaptation and mitigation approaches to environmental constraints, and requirements for reducing risk, enhance agricultural resilience as well as livelihoods in the zones. This was based on the premise that primarily, the use of adequate and accurately derived environmental information is crucial for increased agricultural productivity, sustainability of economic diversification and human livelihoods and by implication, enhanced regional and national security.

Using two metal insulated metal waveguides (MIM) and five resonator (a quadrilateral cavity and three rings with different dimensions), we design a plasmonic refractive index sensor. Using the time domain finite difference method, we examine and analyze the sensor performance (numerical analysis) and using the transmission line model method, we theoretically examine the performance of the sensor By summarizing these two methods and calculating the sensitivity coefficient S, quality coefficient Q and figure of merit (FOM), a plasmonic sensor with good performance is obtained. Using the supermods of the resonators, we will achieve a stable wavelength spectrum and a suitable and stable sensitivity coefficient spectrum. Challenges and changes that occur in different modes of this sensor will lead to a balanced and relatively good structure. This designed sensor can be useful in the development of integrated optical circuits.

In Sub-Saharan African Countries such as Nigeria with high prevalence rate, Child HIV/AIDs acquired through Mother-to-Child transmission (MTCT) can be largely prevented by using a well-established prevention programme and scheme. This study examined factors that can enhanced Prevention of Mother-to-Child transmission (PMTCT) in Nasarawa State. To achieve this, structured questionnaire were used to collect data from one hundred and sixteen (116) women attending two (2) primary facilities and two (2) secondary facilities in the State. This study utilized methods of Poisson Regression, Negative Binomial Regression and Logistic Regression Analyses. Results revealed that women with at least a secondary school education, women with husband in military and women with perceived confidentiality of their HIV status significantly enhanced PMTCT of HIV in Nasarawa State while significant proportion of the women attest to the fact that drugs are available in the facilities (p-value=0.0000<0.05) . Other factors include mother income level, willingness to continue with PMTCT programme and women in support group can also enhanced PMTCT though they are not significant. This study recommends that the factors identified should be explored by NGOs, Ministry of Health and, Support groups and other relevant agencies since they have the capacity to enhanced PMTCT of HIV in Nasarawa State, Nigeria.

The recently emerged severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a major outbreak of coronavirus disease 2019 (COVID-19) and instigated a widespread fear and has threatened global health security. Although phenomenal efforts are in progress to effectively combat this COVID-19 outbreak. Still, no licensed antiviral drugs or vaccines are available, and treatment is limited to supportive care and few repurposed drugs. In this urgency situation, computational drug discovery methods provide both an alternative and a supplement to tiresome high-throughput screening, particularly in the hit-to-lead-optimization stage. Identification of small molecules that specifically target viral replication apparatus has shown the most successful strategy in antiviral drug discovery. The present study deals with the identification of potential compounds that specifically interact with SARS-CoV-2 vital proteins, including main protease (Mpro), Nsp12 RNA-dependent-RNA-polymerase (RdRp) and Nsp13 helicase. A constructive and integrated virtual screening efforts together with molecular dynamics simulations identified potential binding modes and favourable molecular interaction profile of corresponding compounds. Moreover, structurally important binding site residues in conserved motifs located inside the active site are elucidated, which displayed relative importance in ligand binding based on residual energy decomposition analysis. Although the current study lacks experimental validation, the structural information obtained from this computational study paved the way to identify and design specific targeted inhibitors to combat COVID-19 outbreak.

Manufacturing firms need to streamline their initiatives in line with 17 sustainable development goals (SDGs) 2030 as a means for improving performance. This research adapts SDG’s goals 8 decent job and economic growth’ as a measure of performance, in examine the effect of corporate sustainability strategies on performance of manufacturing firms. Census sampling technique was used in cross sectional survey of 900 multi-industry managers from one hundred and fifty (150) manufacturing firms in Kano, Nigeria. A five Likert scale, well-structured questionnaire was used in collecting data, analysed by Structural Equation Modelling SEM PLS Package. The study shows positive and significant effect of eco-efficiency, and positive and insignificant effect of both legitimation and risk mitigation strategies on performance respectively. The study concluded that the positive and significant effects of eco efficiency initiatives could be due to benefits derived from total quality management system, energy monitoring, and process safety management reflected in sustainability oriented visionary leadership of the managers. The positive and insignificant effects of legitimation initiatives could be as a result of huge spending on sustainability reporting, and corruption. The positive and insignificant effect of risk mitigation could be due to pro-activeness of the firms’ managers in tackling risk through sustainability oriented visionary leadership. The study recommended firms should adopt conservative eco-efficiency initiatives for improving performance and stop adopting extroverted legitimation and introverted risk mitigation strategies as both have no significant effect on performance. Future researchers should use longitudinal research design using financial metrics adopting the same independent variables in other economic sectors.

To sustain and advance the development of our laboratory, it is imperative for the laboratory 1 personnel to consistently deliver high-quality products and services that meet customer expectations. 2 The progress of productivity and the pace of technological advancements result in constant evolution 3 of measurement procedures, analytical techniques, and overall work methods. Our research within 4 the Neutron Activation Analysis (NAA) laboratory is aimed at contributing to the establishment of 5 quality assurance that complies with the ISO 17025 standard. As such, the laboratory implements 6 quality-oriented approaches to foster a culture of continuous improvement in its detector operations 7 and enhance customer satisfaction.

The contemporary landscape of eHealth is characterized by major concerns related to electronic medical records (EMRs). In an EMR, critical patient health information gets its own repository, which is the core of the eHealth system. Notably, the EMR not only retrieves health information generated in ambulatory clinical settings but also includes court-generated records. These overlapping records assume the role of a foundational data source for a comprehensive electronic health record (EHR). This scholarly review sheds light on recent trends in medical records and associated challenges, while at the same time providing insights into Saudi Arabia’s targeted approaches to healthcare delivery. Additionally, the talk expands to include a description of blockchain technology and its underlying conceptual framework. This demonstration positions blockchain technology as a promising approach that requires consideration for future use in the healthcare system.

Background: Poisoning is a growing public problem that has shown increased prevalence in many countries worldwide, contributing to increased costs and mortalities. A lack of or insufficient information regarding the circumstances, chemicals, drugs and people at risk due to imperfect reporting system is regarded as a barrier to successful poisoning prevention and intervention efforts. In Saudi Arabia, poisoning cases are monitored at the central level in the Ministry of Health, with meticulous follow-up of cases across country areas through public health departments. The current study's objective was to describe the poisoning notification and reporting system in Makkah.Subjects and methods: All reports of chemical and drug overdose poisoning delivered to the environmental health and occupational safety department in Makkah Almukarramah over two years (2018-2019) were reviewed, together with verification of all systems, rules and guidelines organizing the performance of the department.Results: The total number of cases reported in 2018 was 209 cases, while it reached 42 cases in 2019. The overwhelming majority of the cases were Saudi (91.6%), with a slight predominance of males (57.4%) over females (42.6%). Generally, almost two-thirds of the cases occurred in children below 13 years (61.4%), most of the poisoning cases resulted from drug overdose (63.3%), while 34.7% were chemical poisoning. The most common drug poisoning was attributed to analgesics followed by antiepileptics, antihypertensive drugs, antipsychotics and antimicrobials, while cleaning and detergent agents were the most commonly poisoned chemicals. The majority of these agents were taken by the oral route. Children's chemical poisoning was significantly higher than that in older age groups (p<0.05). All intentional poisoning occurred in adults; the great majority of them (88.9%) used drug overdosing rather than chemical substances.Conclusion and recommendations: The current study results are consistent with most of the previous studies carried out in different regions in Saudi Arabia. Reviewing the performance and adherence of the “Environmental Health and Occupational Safety Department” to the guidelines and instruction regulating monitoring, notification and reporting of poison cases are satisfactory and the poisoning surveillance system is effective. Further in-depth studies are needed to elaborate other socioeconomic and demographic factors associated with chemical and drug overdose poisoning. Public health plans, policies and legislations should be implemented to reduce these factors, including prohibition over-the-counter pharmaceutical sales, selling the possibly harmful substances in kid-proof bottles and additional stringent rules governing chemical sale and storage. Intensive supervision of children is needed. Suicidal poisoning patients should seek psychiatric help to limit the possibility of attempting suicide again in the future. Furthermore, efforts are needed to plan and implement health education programs and campaigns about the factors proven in the current study to be significantly related to chemical and drug overdose poisoning to raise public knowledge about exposure to chemicals and its consequences. Popular communication media, commercial malls, road ads, learning institutions and workplaces can be used to deliver health education campaigns and programs. Advice on how to store chemicals safely and how to keep kids safe should be focused on during such programs.

Resource pricing in cloud computing has become one of the main challenges for cloud providers. The challenge is determining a fair and appropriate price to satisfy users and resource providers. To determine a fair price, it is necessary to consider the conditions and needs of both parties: provider and user. This research tries to provide a pricing mechanism for cloud computing based on game theory. The proposed mechanism takes into account three factors including fault possibility, interaction between virtual machines, and energy consumption to reach a fair price. In the proposed game, the provider decides on the price of the virtual machine which can be offered to the user on each physical machine and the user decides between the virtual machines offered to run its application. The whole game is implemented as a function of the resource broker component. The proposed mechanism is simulated and evaluated using the CloudSim simulator. Its performance is compared with several previous recent mechanisms. According to the results, the proposed mechanism has been able to determine a more reasonable price for the user and the provider which increases the overall profitability of the cloud system.

Scientific studies on the impact of wood species on solid fuel production, performance, and sustainability are grossly inadequate. The knowledge of this is imperative as users of solid fuels are increasing rapidly, especially in Africa. On this note, it becomes necessary to explore measures that will improve its efficiency and sustainability as an energy source. This study investigates some properties of selected wood species used as an energy source in Nigeria and their pelleting potential. Nine (9) samples were characterized and assessed for suitability of pelleting following four wood pellet quality standards. The findings revealed Anogeissus leiocarpus as the species with the best rating score in terms of fuel properties while Vitellaria paradoxa as the lowest. However, in terms of conformity with the four wood pellets standards, Khaya senegalensis, Parkia biglobosa and Anogeissus leiocarpus having presented density, calorific value, sulfur, arsenic, cadmium, and lead within the limits of the wood pellet quality standards were considered the best wood species in terms of fuel suitability and pelleting potential. The findings therefore suggest that not all wood species are suitable as fuel. Thus, for species that do not meet the standard wood pellet requirements, alternatives such as the use of biomass blends, additives, or process adjustments can be employed to adapt the quality to the standards or by using the fuels in improved cookstoves.

Tree decline resulting from climate change results in physiological weaknesses, attack by harmful pests and pathogen, and threatens forest ecosystem stability. In the work described here, the effects of drought on wood density, tree ring width, and variations in vessel morphology were investigated in Persian oak (Quercus brantii) in the forests of the Zagros Mountains, Ilam province, western Iran. Radial discs were cut from trunks of declined and healthy trees and wood blocks cut radially from the sapwood, heartwood, and juvenile core. Observations were made on transverse sections from the blocks using microscopy. In trees with decline symptoms, wood density was greater than the healthy trees. Furthermore, declining trees had the narrowest ring width, reduced vessel diameter, vessel area, and highest number of vessels and tylose in pith towards bark. It was concluded that changes in anatomical features are associated with the weakening of the trees and are components in declining tree health.

A Zn(II) perchlorate complex has been prepared and characterized by single X-ray crystallography, nuclear magnetic resonance spectroscopy, thermogravimetric analysis, infrared spectroscopy, elemental analysis, and UV-vis spectroscopy. The complex crystallizes in the monoclinic space group P21/c (Z = 4) with a pentacoordinated zinc center. Interestingly, the Zn complex was found be to a potential fluorophore that could sense acetone and other ketones with high selectivity and sensitivity.

In this paper, a plasmonic refractive index sensor based on metal insulated metal waveguide (MIM) with three rings and a resonant cavity is first proposed and numerically evaluated. Next, we add four teeth to the sensor structure. We study and simulate the resonant wavelengths and refractive index of resonators using the time difference finite difference method. Given that the sensor and the conduction characteristics of plasmonic waves are influenced by the structure parameters,Therefore, by changing the refractive index and changing the dimensions and coordinates of the cavity and rims, we can change the passage coefficient in the resonant modes and measure the sensor performance. As a result, we obtain the sensitivity coefficient, the figure of merit (FOM) and the quality factor Q of the sensor. We modulate the resonance wavelength FWHM using the generated modes and reach a sensitivity of 2166 nm / RIU. These plasmonic sensors with a simple framework and balanced performance and high optical resolution can be used to measure the refractive index in the medical, chemical and food industries.

Measuring pulmonary nodules accurately can help with early diagnosis of lung cancer, which can improve a patient’s chances of survival. Many methods for segmenting nodules have been developed, but they all rely on input from radiologists in the form of a 3D volume of interest (VOI) or use a constant region of interest (ROI) and only consider the presence of nodule within the given VOI. These approaches limit the networks’ ability to detect nodules outside the given VOI and can also include unnecessary structures in the VOI, leading to potentially inaccurate segmentation. In this work, we propose a novel approach for 3D lung nodule segmentation by using 2D region of interest (ROI) inputted from radiologist or computer-aided detection (CADe) system. Particularly, we design a dual-encoder-based hard attention network (DEHA-Net) which incorporates the full slice of thoracic computed tomography scan along with the ROI mask to produce an accurate segmentation mask of lung nodule in the given slice. The proposed architecture exploits the adaptive region of interest (A-ROI) algorithm to automatically investigates the penetration of lung nodule into surrounding slices while eliminating the need to drawing separate ROIs in each slice. Further, the framework performs the multi-view analysis, i.e., in sagittal and coronal views, to improve the segmentation performance. The proposed scheme has been rigorously evaluated on the lung image database consortium and image database resource initiative (LIDC/IDRI) dataset and an extensive analysis of results have been performed. The quantitative analysis shows that the proposed method not only improves the existing state-of-the-art in term of dice score but also, significantly robust against the different types, shape and dimensions of lung nodules.

For a graph G, let P(G, λ) be its chromatic polynomial. Two graphs G and H are said to be chromatically equivalent if P(G,λ) = P(H,λ). A graph is said to be chromatically unique if no other graph shares its chromatic polynomial. In this paper, chromatic polynomial of the necklace graph N_n, for n ≥ 2 has been determined. It is further shown that N₃ is chromatically unique.

As with many of the non-deterministic search algorithms, particularly those are analogous to complex biological systems, there are a number of inherent difficulties, and the Bees Algorithm (BA) is no exception. Basic versions and variations of the BA have their own drawbacks. Some of these drawbacks are a large number of parameters to be set, lack of methodology for parameter setting and computational complexity. This paper describes a Grouped version of the Bees Algorithm (GBA) addressing these issues. Unlike its conventional version, in this algorithm bees are grouped to search different sites with different neighbourhood sizes rather than just discovering two types of sites, namely elite and selected. Following a description of the GBA, the results gained for 12 benchmark functions are presented and compared with those of the basic BA, enhanced BA, standard BA and modified BA to demonstrate the efficacy of the proposed algorithm. Compared to the conventional implementations of the BA, the proposed version requires setting of fewer parameters, while producing the optimum solutions much faster.

Recently there is a growing concern regarding potential health hazards linked with 5G deployment and radio frequency emitted by these stations. It becomes fundamentally important to scientifically address these concerns. In this paper, the health risks incorporated with 5G are discussed critically in the light of scientific work and debunk these tittle-tattles where required. We confute scientifically the “5G appeal”, discuss effects of 5G on health, its implications on privacy, pandemics, ecosystem and role of world health organization in fighting these narratives. Finally, we provide future direction to negate these false claims linked with 5G.

Nowadays, we live in a world in which people are facing with a lot of data that should be stored or displayed. One of the key methods to control and manage this data refers to grouping and classifying them in clusters. Today, clustering has a critical role in information retrieval methods for organizing large collections inside a few significant clusters. One of the main motivations for the use of clustering is to determine and reveal the hidden and inherent structure of a set of data. Ensemble clustering algorithms combine multiple clustering algorithms to finally reach an overall clustering system. Ensemble clustering methods by lack of information fusing utilize several primary partitions of data to find better ways. Since various clustering algorithms look at the different data points, they can produce various partitions from such data. It is possible to create a partition with high performance by combining the partitions obtained from different algorithms, even if the clusters to be very dense from each other. Most studies in this area have examined all the initial clusters. In this study, a new method is used in which the most sustainable clusters are utilized instead of all primary produced clusters. Consensus function based on co-association matrixes used to select more stable clusters. The most stable clusters selection method is done by cluster stability criterion based on F-measure. Optimization functions are used to optimize the obtained final clusters. The genetic algorithm is the optimizer used in this article to find the ultimate clusters participated in a consensus. Experimental results on several datasets show that the output of proposed method is various clusters with high stability.

It has now known that at least 10% of samples with pancreatic cancers (PC) contain a causative mutation in the known susceptibility genes, suggesting the importance of identifying cancer-associated genes that carry the causative mutations in high-risk individuals for early detection of PC. In this study, we develop a statistical pipeline using a new concept, called gene-motif, that utilizes both mutated genes and mutational processes to identify 4,211 3-nucleotide PC-associated gene-motifs within 203 significantly mutated genes in PC. Using these gene-motifs as distinguishable features for pancreatic cancer subtyping results in identifying five PC subtypes with distinguishable phenotypes and genotypes. Our comprehensive biological characterization reveals that these PC subtypes are associated with different molecular mechanisms including unique cancer related signaling pathways, in which for most of the subtypes targeted treatment options are currently available. Some of the pathways we identified in all five PC subtypes, including cell cycle and the Axon guidance pathway are frequently seen and mutated in cancer. We also identified Protein kinase C, EGFR (epidermal growth factor receptor) signaling pathway and P53 signaling pathways as potential targets for treatment of the PC subtypes. Altogether, our results uncover the importance of considering both the mutation type and mutated genes in the identification of cancer subtypes and biomarkers.

Accountable Care Organizations (ACOs) are healthcare delivery systems that aim to coordinate and improve the quality of care for patients while reducing costs. ACOs bring together doctors, hospitals, and other healthcare providers to work as a team to care for a defined population of patients. The idea behind ACOs is to shift the focus from paying for each individual service provided to patients, known as fee-for-service, to paying for the overall care and well-being of patients.ACOs typically operate under a shared savings model and also encourages providers to use data and technology to better coordinate care and improve patient outcomes.ACOs can be physician-led organizations, hospital-led organizations, and collaborations between healthcare providers. The success of ACOs depends on many factors, including the level of cooperation and collaboration among healthcare providers, the quality of care provided, and the ability to effectively manage costs.Despite their potential benefits, ACOs also face challenges, including the difficulty of coordinating care across multiple healthcare providers, the need to invest in technology and data management systems, and the challenge of changing provider behavior.This study assessed a number of systemic reviews and journals to assess the impact of ACO’s on population health. ACO’s have shifted away from costly care patterns and thus helped many countries.

This paper explores the application of various machine learning techniques for predicting customer churn in the telecommunications sector. We utilized a publicly accessible dataset and implemented several models, including Artificial Neural Networks, Decision Trees, Support Vector Machines, Random Forests, Logistic Regression, and gradient boosting techniques (XGBoost, LightGBM, and CatBoost). To mitigate the challenges posed by imbalanced datasets, we adopted different data sampling strategies, namely SMOTE, SMOTE combined with Tomek Links, and SMOTE combined with Edited Nearest Neighbors. Moreover, hyperparameter tuning was employed to enhance model performance. Our evaluation employed standard metrics such as Precision, Recall, F1-Score, and the Receiver Operating Characteristic Area Under Curve (ROC AUC). Regarding the F1-Score metric, CatBoost demonstrates superior performance compared to other machine learning models, achieving an outstanding 93% following the application of Optuna hyperparameter optimization. In the context of the ROC AUC metric, both XGBoost and CatBoost exhibit exceptional performance, recording remarkable scores of 91%. This achievement for XGBoost is attained after implementing a combination of SMOTE with Tomek Links, while CatBoost reaches this level of performance after the application of Optuna hyperparameter optimization.

Patients satisfaction with fast and high-quality services is the most important in the healthcare settings and specially in outpatient departments and private clinics. High quality services on health sector are indicated by continuous quality improvement (CQI). CQI is a progressive incremental process focused on safety of all participants, outcomes, systematic process, regulated and improved working environment at the later than earlier stages. Among the various suggested strategies, we adopted the patents feedback to improve the quality of services our clinic. All of the patient visited hospital were sent a massage on mobile and requested to provide their feedback on our services. Roughly around 5% of all visitors responded and filled up questionnaire. Majority of them were satisfied with the services in various department. However, they provided us some suggestions to identify the further gaps and improve the services in improving patients experience at our clinic. We considered their feedback, identified problems, redesigned the policies and implemented. After implementation of new strategies, we preliminary again evaluated the patients’ feedback on our services. Patients feedback explores that optimized methods of services for the has considerably increased the patients’ satisfaction. Taken together, our this shows that patients’ feedback is very important factor to improve patients’ experience at outpatient clinics using continuous quality improvement tools. For that reason, this study would serve as reference for public health stack holders, administrators and researchers.

The aim of this study was to investigate the effect of β-Hydroxy-β-methylbutyrate (HMB) supplementation on anti-inflammatory cytokines including IL-4, IL-10 and TGF-β during an acute bout of resistance exercise (RE) in young resistance trained men. Ten resistance-trained men in a randomized, double-blind, placebo-controlled and crossover study, were administered a 7-day HMB supplementation (3×1 g.d-1 of HMB) and placebo (3×1 g.d-1 of Maltodextrin) with a 7 days washout period. After supplementation periods, subjects performed three sets of bench press, lat pull down, leg extension, leg curl, biceps curl, triceps curl and shoulder press to failure with 85% of one repetition to maximum (1RM). Blood samples were obtained before- (Pre), immediately post- (IP) and 1 hour-post RE (1h P) to assess serum concentrations of IL-4, IL-10 and TGF-β1. The data were analyzed using 2 (treatment: HMB and PL) × 3 (time points: Pre, IP and 1hP) repeated measures analysis of variance (ANOVA) followed by the Bonferroni post hoc test with a significant level of p<0.05. Serum IL-4 was significantly higher at IP resistance exercise in HMB compared to placebo. Circulating IL-4 and TGF-β1 were significantly raised at IP compared to Pre in both HMB and placebo treatments. No significant differences between treatments were observed for IL-10 and TGF-β1at any time points. In conclusion, HMB supplementation increased the circulating level of IL-4 during RE in resistance-trained men, which may attenuate inflammation and facilitate adaptation to RE.

Mucosal vaccines can target specific mucosal surfaces, such as the respiratory, genital, or intestinal mucosa. Conventional systemic immunization, such as intramuscular injections, do not provide mucosal immunity, including IgA, nor do they prevent pathogens from entering the mucous membranes. Nasal immunization protects mucosal membranes, but nasal antigen presentation appears to entail adjuvant to stimulate immunogenicity. Current treatments for mucosal diseases such as inflammatory bowel disease (IBD) and allergic dermatitis only aimed to temporarily relieve symptoms. Moreover, these treatments not only have drug resistance or side effects, but the disease can recur, leading to switching to other treatments. Therefore, there is a need to develop safe and effective treatments that provide better long-term outcomes to reduce the risk of recurrence. Regulatory T (Treg) cells have been used to suppress inflammation. To find advances in mucosal treatment, we investigated the therapeutic effects of intranasal pep27 mutant immunization. Here, a new method of Treg induction through intranasal immunization without adjuvant has been developed to potentially overcome allergies and mucosal inflammation of the lung-gut axis in animal models. The implementation of the pep27 mutant for these therapies should be preceded by studies of Treg resilience through clinical translational studies of dietary changes.

Big data has revolutionised science and technology leading to the transformation of our societies. High Performance Computing (HPC) provides the necessary computational power for big data analysis using artificial intelligence and methods. Traditionally HPC and big data had focused on different problem domains and had grown into two different ecosystems. Efforts have been underway for the last few years on bringing the best of both paradigms into HPC and big converged architectures. Designing HPC and big data converged systems is a hard task requiring careful placement of data, analytics, and other computational tasks such that the desired performance is achieved with the least amount of resources. Energy efficiency has become the biggest hurdle in the realisation of HPC, big data, and converged systems capable of delivering exascale and beyond performance. Data locality is a key parameter of HPDA system design as moving even a byte costs heavily both in time and energy with an increase in the size of the system. Performance in terms of time and energy are the most important factors for users, particularly energy, due to it being the major hurdle in high performance system design and the increasing focus on green energy systems due to environmental sustainability. Data locality is a broad term that encapsulates different aspects including bringing computations to data, minimizing data movement by efficient exploitation of cache hierarchies, reducing intra- and inter-node communications, locality-aware process and thread mapping, and in-situ and in-transit data analysis. This paper provides an extensive review of the cutting-edge on data locality in HPC, big data, and converged systems. We review the literature on data locality in HPC, big data, and converged environments and discuss challenges, opportunities, and future directions. Subsequently, using the knowledge gained from this extensive review, we propose a system architecture for future HPC and big data converged systems. To the best of our knowledge, there is no such review on data locality in converged HPC and big data systems.

There is clinical concern about the combined use of alcohol and opiates. Several lines of evidence support an interaction between alcohol and the endogenous opioid system. Thus, we hypothesized that ethanol by causing the release of opioid peptides may sensitize the system to the action of exogenous opioids such as morphine. In this study, using the place conditioning paradigm as an animal model of reward, we determined whether a morphine challenge would alter the pre-established preference induced by ethanol conditioning in mice, and whether this response was mediated by the mu opioid receptor (MOP). Given that ethanol exposure stimulates the release of opioid peptides, we also assessed the role of beta-endorphin (β-END) and enkephalins (ENKs) in this response. Mice lacking MOP, β-END and/ or ENKs as well as their respective wild-type controls were tested for baseline place preference on day 1, conditioned with ethanol (2 g/kg) versus saline on days 2 to 4 and then tested under a drug-free state for postconditioning place preference on day 5. On day 8, mice received a single injection of morphine (5 mg/kg) and were tested for place preference. On each test day, mice were placed in the central neutral chamber and allowed to freely explore the conditioning chambers. The amount of time that mice spent in each chamber was recorded. We found that a challenge dose of morphine given on day 8 enhanced the CPP response in mice previously conditioned with ethanol. This response was abolished in MOP null mice, confirming the role of MOP in this response. Although this enhanced response was not altered in mice lacking either β-end or ENKs compared to their wild-type littermates/controls, it was completely blunted in mice lacking both β-end and enkephalins. Together, these results suggest that these opioid peptides jointly mediate the crosstalk between the rewarding actions of morphine and ethanol.

Nowadays, emerging technologies have changed the places of work through computers and ICT tools, which have revolutionized teaching and learning environments in different ways. In spite of the fact that computers as ICT tools have become part and progressively instrument for teachers and instructors used in teaching and learning, but most educators can't incorporate them into their teaching and learning process, which results in students being ill-equipped or lacking some necessary skills in the world of work, which leads to low performance and poor handling of tools whose lead to low production. To tackle this issue, it is essential to develop the technical and vocational education and training (TVET) system by determining the quality of technical and vocational education (TVE) teachers. In this paper, the literature concerning the competence required by TVET teachers towards computer-related instructional tech-nology for classroom teaching and learning was examined through the technological pedagogical content knowledge (TPACK) model. Sixty (60) questionnaires were ad-ministered to TVE teachers within six technical colleges of education in north-eastern Nigeria. The data was quantitatively analyzed using traditional linear models, namely multilinear regression (MLR) and artificial intelligence (AI) models, namely artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS), which were developed using MATLAB 9.3 (R2019a), while the classical linear MLR model was developed using SPSS software. The results from our classical study indicated that TVE teachers are competent in computer and some instructional technology usage and show a high correlation between competence and teaching experience and a lower correlation between competence and gender. The goodness of fit shows a good fit of the model. Future studies should examine the application of other linear and non-linear AI techniques.

Fingerhuthia africana Lehm belongs to family Poaceae, is an erect perennial grass used traditionally to cure different diseases. The current study was carried out to find the bioactive compounds by GC-MS, elemental analysis through atomic absorption spectrophotometry, proximate analysis by AOAC, genotoxic activity through comet assay, antioxidant activity through DPPH and antidiarrheal activity through castor oil induce diarrhea followed by charcoal meal on mice model. GC-MS chromatogram represents different compounds detected in the crude extract. Major pharmacological bioactive compounds are 4H-Pyran-4-one, 2,3-dihydro-3,5-dihydroxy-6-methyl-; 4-Methyl itaconate; .alpha.-Terpineol; 1,2,3-Propanetriol, 1-acetate; Benzofuran, 2,3-dihydro-; Caryophyllene oxide; Hinesol; n-Hexadecanoic acid; 9,12-Octadecadienoic acid (Z,Z)-, methyl ester; Hexadecanoic acid and methyl ester. Elemental analysis of stem, leaves and roots of the plant show the presence of (macro and micro) nutrients such as Ca, Mg, K, Na, Cu, Pb, Zn, Mn, Fe, Cd and Co were absent in all parts. Proximate analysis of different parts revealed the presence of moisture contents (8.57%), ash (9.77%), fats contents (5.96%), fibers contents (45.03%), protein (3.62%), carbohydrates (39.71%) and Gross energy (Kcal/100g) (220.10). The ethanolic extract show dose-dependent genoprotective potential (127.38 ± 1.16), (83.28 ± 0.65), and (44.08 ± 1.40) at 50, 75, and 100 mg/100 ml, respectively, as compared to the methanolic extract which show genotoxic activity (85.13 ± 1.23) at high dose. After 90 minutes at a concentration of 300 (g/ml), methanolic extract demonstrated the greatest antioxidant potential (79.5%), followed by aqueous extract (76.5%). The methanolic extract of F. africana demonstrated antidiarrheal efficacy, with the maximum inhibition of charcoal movement (83.78%) occurring at a dosage of 300 mg/kg, followed by doses of 200 and 100 mg/kg, 73.26% and 59.56%, respectively. The current research highlights that the plant contains a substantial quantity of micro and macro nutrients, possesses high nutritional value, and exhibits pharmacological activities. These properties render the plant suitable for developing remedies to treat various therapeutic diseases.

In this work, quiescence is added to the Susceptible-Infectious-Recovered (SIR) model with demography. In order to investigate consequences of quiescence in the infection process in more depth, we use stochastic simulations on the stochastic version of model that we built. This method provides a more accurate picture of the dynamics of infectious diseases by taking into consideration the inherent randomness in the disease processes. We examine the effects of quiescence on the number of infected people using simulations. The results, presented in histograms depicting the distribution of infected individuals, reveal a notable trend: the mean number of infected individuals is higher when quiescence is incorporated into the dynamics. These finding emphasizes the dynamic influence of quiescence on infectious disease spread. The higher mean number of infections during periods of quiescence highlights the need for public health strategies that are flexible enough to focused interventions during these times to reduce the possibility of an increase in infections.

The stability between the limited available energy resources and their consumption is an important concern of the modern world. The study deals with the development of the methods for measuring the energy performance of different renewable and non-renewable resources used for electricity generation with the help of an energy performance indicator called the Primary Energy Factor (PEF). This study takes account of all energy stages from the resources extraction to its transportation, conversion, combustion and then finally to utilization. The study conducted a comparative approach to calculate the PEF values of electricity generated in South Korea from the period of 1980 to 2017. Four different methodologies namely Eurostat methodology, Life cycle methodology, Modified Eurostat methodology, and upper-end methodology have been used. A set of Programs has been written in MATLAB to calculate the i. PEF values of electricity generated. ii. Impact of different renewable and non-renewable energy sources on PEF values. iii. Future trend of PEF values of electricity in South Korea At the national and local level, this study will help the government and other law-making institutions to make policies regarding annual purchase consumptions of energies by different industrial, residential, public and transportation sectors and at the Global level, the implementation of this study will be the catastrophe of maintaining the stability between the available energy resources, and consumptions. This, in turn, will lead to suitable living conditions for future generations

In this paper, we discuss voltage control method for buck converter operating in continuous conduction mode (CCM) using analog feedback system. The aim of this work is to control the output voltage of a buck converter during the variation in load current. This is obtained using analog feedback made with operational amplifier (Opamp). However, the same technique can be applied to other DC-DC converters (e.g boost, buck-boost, cuk converter, etc) in CCM mode, but for the purpose of analysis buck converter is chosen as an example.

IgA nephropathy (IgAN) is a kidney disease that is characterized by the deposition of IgA (immunoglobulin A) in the kidney tissue, leading to inflammation and damage. This disease is a common chronic glomerulopathy that affects people worldwide (1). Therefore, it is crucial to identify factors that worsen the disease, monitor its activity, and predict appropriate treatments. Complement component 4d (C4d) is a protein that is involved in the activation of the complement system, which is a part of the immune system.

The article reviews Iranian sports in three periods: prehistoric, ancient, and modern. In prehistoric times, remaining cases have shown the existence of movement activities based on recreational and religious ceremonies. In the period of ancient history, documents such as Hasanlu Golden Cup and Arjan Cup, and in the continuation of architectures leftover from different eras, refer to the prosperity of sports activities with different approaches. Among all kinds of activities, Iranian zoorkhaneh sport has a significant cultural background and effect. Modern sports entered Iran through embassies, oil contractors, Western-educated students, and military representatives in other countries (in the modern era). Iran became a member of the International Olympic Committee in 1947 and won its first Olympic medal in 1948. Entering the 21st century, the sports industry in Iran began to grow and the number of clubs, sports careers, and sports science students in universities increased significantly. The growing dimension of attention and interest in sports among Iranians shows that sports will be one of the main areas in Iran's development in the future.

β-hydroxy β-methylbutyrate (HMB) is a bioactive metabolite formed from breakdown of the branched-chain amino acid leucine. Given the popularity of HMB supplements among different athletes, specifically, those who engage in regular resistance training, this review was performed to summarize current literature on some aspects of HMB supplementation that have received less attention. Because of the small number of published studies, it has not been possible to conclude the exact effects of HMB on cardiovascular parameters, oxidative stress and inflammatory markers. Thus, the interpretation of outcomes should be taken cautiously. However, the data presented here suggest that acute HMB supplementation may attenuate pro-inflammatory response following an intense resistance exercise in athletes. Also, the available findings collectively indicate that chronic HMB consumption in conjunction with resistance training has no more adaptive advantages associated with decreasing cardiovascular risk factors and oxidative stress markers. Taken together, there is clearly a need for further well-designed, longer duration studies to support these findings and determine whether HMB supplementation affects the adaptations induced by resistance training associated with body’s inflammatory condition, antioxidative defense system, and cardiovascular risk factors in humans.

Although, promotion of urbanization culture in recent decades has made inevitable development of cities in the world, however, the development can be guided in a direction that leave, to the extent possible, minimum socioeconomic and environmental impacts. For this, it is required to first forecast auto-spreading orientation of cities and suburbs in rural areas over time and then avoid shapeless growth of cities. This paper is an attempt to develop a dynamic hybrid model based on logistic regression (LR), Markov chain (MC), and cellular automata (CA) for prediction of future urban sprawl in fast-growing cities. The model was developed using 12 widely-used urban development criteria, whose significant coefficient was determined by logistic regression, and validated by relative operating characteristic (ROC) analysis. The validated model was run in Guilan, a tourist province in northern Iran with a very high rate of urban development. For this, changes in the area of urban land use were detected over the period of 1989 to 2013 and then, future sprawl of the province was forecasted by the years 2025 and 2037. The analysis results revealed that the area of urban land use was increased by more than 1.7 % from 36012.5 ha in 1989 to 59754.8 ha in 2013, and the area of Caspian Hyrcanian forestland was reduced by 31628 ha. The results also predicted an alarming increase in the rate of urban development in the province by the years 2025 and 2037, during which urban land use is predicted to develop 0.9 % and 1.38 %, respectively. The development pattern is expected to be uneven and scattered, without following any particular direction. The development will occur close to the existing or newly-formed urban basements as well as around major roads and commercial areas. This development, if not controlled, will lead to the loss of 13863 ha of Hyrcanian forests and if the trend continues, 21013 ha of Hyrcanian forests and 20208 ha of Barren/open lands are expected to be destroyed by the year 2037. In general, the proposed model is an efficient tool for the support of urban planning decisions and facilitates the process of sustainable development of cities by providing decision-makers with an overview on future development of cities where the growth rate is very fast.

In recent years, there has been a growing interest in the prediction of facial age due to its diverse applications in fields such as security, entertainment, and healthcare. The current scourge of underaged voting in Nigeria is a problem and this research delves into the realm of facial age prediction by employing four well-known convolutional neural network (CNN) architectures, namely VGG16, ResNet50, Mobile Net, and VGG19 to predict chronological ages from facial pictures of person. The objective is to achieve precise age estimation from facial images, utilizing two datasets: UTKFace and CASIA African Facial Datasets. The results of this investigation are noteworthy. Specifically, the VGG16 model which demonstrated remarkable performance, yielding a Mean Absolute Error (MAE) of 1.76 when applied to the UTK-Face dataset. Additionally, when utilizing Mobile-Net, an unprecedented MAE of 1.10 was achieved for the Casia Africa Face Dataset. Notably, this marks the first instance of employing the dataset for facial age detection with CNNs, and this approach outperformed previous works, yielding the lowest MAE among all the studies reviewed.

Historically, the lack of comprehensive and real-time data is the main challenge in city sustainability assessments. However, with the increase in the availability of crowdsourced data over the last decade, a rich and real-time data source provides detailed images of urban systems. This has opened up new opportunities for practitioners, including geographers, social researchers, and data scientists, to gather and utilize social media data as a core source for studying cities and assessing their sustainability. This paper proposes an assessment model for the sustainability of cities based on crowdsourced data and the principles of General System Theory. The model aims to utilize crowdsourced data for the analysis and decision-making processes to enhance the sustainability and resilience of smart future cities.

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

The Mazenod Lake region of the southern Great Bear Magmatic Zone (GBMZ) of the Northwest Territories, Canada comprises the north-central portion of the Faber volcano-plutonic belt. Mazenod geology is dominated by rhyodacite to basaltic-andesite ignimbrite sheets with interlayered volcaniclastic sedimentary rocks dominated by fine-grained laminated tuff sequences. Much of the intermediate to mafic nature of volcanic rocks is disguised by low-intensity but pervasive metasomatism. The region is affected by a series of coalescing magmatic-hydrothermal systems that host the Sue-Dianne magnetite-hematite IOCG deposit and several related showings including magnetite, skarn, and IOA styles of alteration ± mineralization. Exposed at surface are dominantly mid to upper levels of these systems, with underlying batholith, pluton and stocks exposed along the periphery, as well as locally within volcanic rocks associated with more intense alteration and mineralization. Widespread alteration includes potassic and sodic metasomatism, and silicification with structurally controlled giant quartz complexes. Localized tourmaline, skarn, magnetite-actinolite, and iron-oxides occur within structural breccias, and where most intense formed the Sue-Dianne Cu-Ag-Au diatreme-like breccia deposit. Magmatism, volcanism, hydrothermal alteration and mineralization formed during a negative tectonic inversion within the Wopmay Orogen. This generated a series of oblique offset rifted basins with continental style arc magmatism and extensional structures unique to GBMZ rifting. All significant hydrothermal centres in the Mazenod region occur along and at the intersections of crustal faults either unique to or put under tension during the GBMZ inversion.

This study focusses on the geological formations of the Alfallé massif located approximately 75km east of the town of N'Goura, in the Hadjer-Lamis Province of Chad (13°N, 17°E), and their ability to provide good quality aggregates in an area with poor hard rock ressources. We investigated the petrographic and mechanical characteristics of these formations in a resource perspective. The massif is made up of three lithologies: alkali feldspar megacrystalline granite, biotite-rich fine grained quartz monzonite and biotite-rich fine grained gneiss. Alkali feldspar megacrystalline granite is composed of quartz (30%), orthoclase (30%), plagioclase (20%), biotite (15%) and opaque minerals (≈5%). Micro-monzonite is composed of quartz (15-20%), orthoclase (20-25%), plagioclase (18-25%), biotite (20-25%) and opaque minerals (≈4%). The biotite gneiss is composed of quartz (25-30%), alkali feldspars (25-27%), plagioclase (17%), biotite (25-27%) and opaque minerals (≈5%). The mechanical characteristics of the aggregates show that the aggregates studied have specific density values of between 2.58 and 2.75g/cm3 with an average value of 2.66g/cm3, a Los-Angeles coefficient of 11.2% to 28.9% with an average value of 20.1% and a wet Micro-Deval coefficient of 5.2% to 10.8% with an average value of 7.6%. The values obtained indicate that the materials studied have excellent to satisfactory properties, and that Alfallé massif provide the best characteristics among six different quarrying area in Central Chad. We propose that such rare residual reliefs within the mega-Chad paleolake area reach optimal mechanical properties through a natural selection process, specific of the paleolake erosional conditions.

Background: The incidences of COVID-19 related suicide among adolescents and youths have been reported across the world. There is no cumulative study focusing on nature, patterns, and causative factors that lead to the present investigation. Methods: A purposive sampling of google news between 15 February to 6 July was performed. After excluding duplicate reports, the final list comprised a total of 37-suicide cases across 11 countries. Results: More male suicides were reported (21-cases, i.e., 56.76%), and the mean age of the total victims was 16.6±2.7 years (out of a total of 29-cases). About two-thirds of the suicides were from three countries named India (11-cases), United Kingdom (8-cases), and the USA (6-cases). Out of 23-student victims, 14 were school-going students. Hanging was the most common suicide method accounting in 51.4% of cases. The most common suicide causalities were related to mental sufferings such as depression, loneliness, psychological distress, etc., whereas either online schooling or overwhelming academic distress was placed as the second most risk factors followed by TikTok addiction-related psychological distress, and tested with COVID-19. Conclusion: The finding of the temporal distribution of suicides concerning lockdowns may help in exploring and evolving public measures to prevent/decrease pandemic-related suicides in young people.

One of the most crucial and economically beneficial tasks for energy customer is peak load curtailment. On account of the fast response of renewable energy resources (RERs) such as photovoltaic (PV) units and battery energy storage system (BESS), this task is closer to be efficiently implemented. Depends on the customer peak load demand and energy characteristics, the feasibility of this strategy may warry. When adaptive neuro-fuzzy inference system (ANFIS) is exploited for forecasting, it can provide many benefits to address the above-mentioned issues and facilitate its easy implementation, with short calculating time and re-trainability. This paper introduces a data driven forecasting method based on fuzzy logic for optimized peak load reduction. First, the amount of energy generated by PV is forecasted using ANFIS which conducts output trend, and then, the BESS capacity is calculated according to the forecasted results. The trend of the load power is then decomposed in Cartesian plane into two parts, left and right from load peak, searching for BESS capacity equal. Network switching sequence over consumption is provided by a fuzzy logic controller (FLC) with respect to BESS capacity and PV energy output. Finally, to prove the effectiveness of the proposed ANFIS-based peak shaving method, offline digital time-domain simulations have been performed on a real-life practical test micro grid system in MATLAB/Simulink environment and the results have been experimentally verified by testing on a practical micro grid system with real-life data obtained from smart meter and also, compared with several previously-reported methods.

The sustainability of the planet and its inhabitants is in dire danger and is among the highest priorities on global agendas such as the Sustainable Development Goals (SDGs) of the United Nations (UN). Solar energy -- among other clean, renewable, and sustainable energies -- is seen as essential for environmental, social, and economic sustainability. Predicting solar energy accurately is critical to increasing reliability and stability, and reducing the risks and costs of the energy systems and markets. Researchers have come a long way in developing cutting-edge solar energy forecasting methods. However, these methods are far from optimal in terms of their accuracies, generalizability, benchmarking, and other requirements. Particularly, no single method performs well across all climates and weathers due to the large variations in meteorological data. This paper proposes SENERGY (an acronym for Sustainable Energy), a novel deep learning-based auto-selective approach and tool that, instead of generalising a specific model for all climates, predicts the best performing deep learning model for GHI forecasting in terms of forecasting error. The approach is based on carefully devised deep learning methods and feature sets through an extensive analysis of deep learning forecasting and classification methods using ten meteorological datasets from three continents. We analyse the tool in great detail through a range of metrics and methods for performance analysis, visualization, and comparison of solar forecasting methods. SENERGY outperforms existing methods in all performance metrics including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Forecast Skills (FS), Relative Forecasting Error, and the normalised versions of these metrics. The proposed auto-selective approach can be extended to other research problems such as wind energy forecasting and predict forecasting models based on different criteria (in addition to the minimum forecasting error used in this paper) such as the energy required or speed of model execution, different input features, different optimisations of the same models, or other user preferences.

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

X-ray tomography is an effective non-destructive testing method for industrial quality control. Limited angle tomography can be used to reduce the amount of data that needs to be acquired and thus speed up the process. In some industrial applications, however, objects are flat and layered, and laminography is preferred. It can deliver 2D images of the structure of a layered object at a particular depth from very limited data. An image at a particular depth is obtained by summing those parts of the data that contribute to that slice. This produces a sharp image of that slice while superimposing a blurred version of structures present at other depths. In this paper, we investigate an optimal experimental design (OED) problem for laminography that aims to determine the optimal source positions. Not only can this be used to mitigate imaging artifacts, it can also speed up the acquisition process in case where moving the source and detector is time consuming (e.g., in robotic arm imaging systems). We investigate the imaging artifacts in detail through a modified Fourier Slice Theorem. We address the experimental design problem within the Bayesian risk framework using empirical Bayes risk minimization. Finally, we present numerical experiments on simulated data.

In the present study, we implemented species distribution models (SDMs) to determine the potential invasive distribution range of a cichlid fish, Nile tilapia, Oreochromis niloticus, in a very sensitive inland water system in the southeastern Arabian Peninsula where it is now established and spreading. By using the “Biomod2” package, and the latest CMIP6 climate data, four single species distribution models and EM were built, and the results were evaluated by operating characteristic (ROC) and the true skill statistic (TSS). Based on the relative importance of climate factor analysis the mean diurnal range and precipitation seasonality were useful predictors of habitat suitability for O. niloticus. The response curve analysis indicated that the presence probability of O. niloticus decreased with increasing mean diurnal range and decreasing precipitation seasonality. The suitable distribution ranges for O. niloticus in the studied area were mainly distributed in the northeast of this region. The ensemble model results specified a significant impact of climate change on O. niloticus distribution, so highly suitable areas for this species will be reduced, while areas with low to moderate suitability increase slightly or remain unchanged. Based on the available data, Nile tilapia fulfills all four criteria to be considered as an invasive species: colonization in a new area, direct or indirect impacts of anthropological activity on its distribution range, geographical discontinuity between its native area (Africa) and the Arabian Peninsula (remote dispersal), and finally, its successful breeding in situ without human assistance resulting establishment of this biopollutant. Species distribution modeling provides insights into the factors that can reduce the risks and negative impacts of this biological pollutant on aquatic ecosystems of the southeastern Arabian Peninsula which harbors about 22 recognized fish species in 16 genera, 10 families, seven orders, and a class including 20 natives (seven endemic) species.

In this paper, we propose the set-membership quaternion normalized least-mean-square (SM-QNLMS) algorithm. For this purpose, first, we review the quaternion least-mean-square (QLMS) algorithm, then go into the quaternion normalized least-mean-square (QNLMS) algorithm. By having the QNLMS algorithm, we propose the SM-QNLMS algorithm in order to reduce the update rate of the QNLMS algorithm and avoid updating the system parameters when there is not enough innovation in upcoming data. Moreover, the SM-QNLMS algorithm, thanks to the time-varying step-size, has higher convergence rate as compared to the QNLMS algorithm. Finally, the proposed algorithm is utilized in wind profile prediction and quaternionic adaptive beamforming. The simulation results demonstrate that the SM-QNLMS algorithm outperforms the QNLMS algorithm and it has higher convergence speed and lower update rate.

In this paper, the set-membership affine projection (SM-AP) algorithm is utilized to censor non-informative data in big data applications. To this end, the probability distribution of the additive noise signal and the excess of mean-squared error (EMSE) in steady-state are employed in order to estimate the threshold parameter of the single threshold SM-AP (ST-SM-AP) algorithm aiming at attaining the desired update rate. Furthermore, by defining an acceptable range for the error signal, the double threshold SM-AP (DT-SM-AP) algorithm is proposed to detect very large errors due to the irrelevant data such as outliers. The DT-SM-AP algorithm can censor non-informative and irrelevant data in big data applications, and it can improve misalignment and convergence rate of the learning process with high computational efficiency. The simulation and numerical results corroborate the superiority of the proposed algorithms over traditional algorithms.

Advancements in technology, policies, and cost reductions have led to rapid growth in wind power production. One of the major challenges in wind energy production is the instability of wind power generation due to weather changes. Efficient power grid management requires accurate power output forecasting. New wind energy forecasting methods based on deep learning are better than traditional methods, like numerical weather prediction, statistical models, and machine learning models. This is more true for short-term prediction. Since there is a relationship between methods, climates, and forecasting complexity, forecasting methods do not always perform the same depending on the climate and terrain of the data source. This paper proposes a novel model that combines the variational mode decomposition method with a long short-term memory model, developed for next-hour wind speed prediction in a hot desert climate, such as the climate in Saudi Arabia. We compared the proposed model performance to two other hybrid models, six deep learning models, and four machine learning models using different feature sets. Also, we tested the proposed model on data from different climates, Caracas and Toronto. The proposed model showed a forecast skill between 61% to 74% based on mean absolute error, 64% to 72% based on root mean square error, and 59% to 68% based on mean absolute percentage error for locations in Saudi Arabia.

Molecular dynamics(MD) simulations are carried out to characterize the mechanicalproperties and failure behavior of van der Waals heterostructures composed ofgraphene and hexagonal boron nitride(hBN) single layer sheets. The graphene–hBNand hBN–graphene–hBN heterostructures simulations are carried out under tensileand shear deformation. Accordingly, stress versus strain curves of each systemare plotted and various properties of heterostructures, namely elastic modulusand shear modulus as well as failure stress and failure strain, are evaluatedand compared with one another as well as with the pristine graphene and hBNsheets. Subsequently, the failure mechanism/characterization of each sheet in theheterostructures is described. Alternatively, the elastic and shear modulus of eachheterostructure are computed by means of rule of mixture(ROM) which are in goodagreement with results that are obtained from MD simulations.

Rapidly changing dynamics of globalization and increasing market competition are causing the companies all around the world confronting several new challenges and opportunities. To be competitive and successful apart from relative importance of physical resources, companies must adapt modern strategies and policies regarding market flexibility and development. The purpose of this study is to empirically investigate the relationship between intellectual capital and firm value. Furthermore, the moderating role of managerial ownership has been evaluated with the help of regression analysis. The sample included the panel data taken from non-financial firms listed on Pakistan stock exchange (PSX) covering the period 2010-2015. A sample of 79 firms out of 384 firms have been selected with the help of systematic sampling technique. VAIC (Value Added Intellectual Coefficient) model has been used for the calculation of intellectual capital. Tobin's Q has been taken as a measure of firm value. Managerial ownership has been tested as moderator. Based on data analysis, it is concluded that the relationship between intellectual capital and firm value is positively significant. It is also concluded that managerial ownership moderates the relationship between intellectual capital and firm value negatively.

Chemical and mechanical properties of plasma-assisted nitrided (PAN), TiN cathodic arc coated, and PAN with TiN coated samples were thoroughly evaluated on tool steel samples of 1.2379. The evaluation involved SEM, nano-indentation, profilometer, micro scratch, and corrosion tests. The results revealed that PAN treatment alters the roughness of the surface, while it has minimal impact on the coefficient of friction. However, it increases the substrate's hardness, making it twice as hard and three times more resilient against scratch tests, along with improved corrosion resistance. TiN-coated samples are four times harder than untreated ones, but they exhibit a higher elastic modulus (E), leading to a lower H/E index. The most promising results were obtained with PAN+TiN treatment, combining high hardness (H) with lower E, resulting in a high H/E index, a crucial criterion for wear resistance. These samples also demonstrated superior scratch hardness and corrosion resistance, despite being thinner and rougher.

In this article, ‎we examine an allocation issue faced by European Health Union (EHU)‎. ‎Given this situation, ‎we introduce the issue of vaccine selection. ‎The model proposed in this paper helps to implement vaccination optimally using matching theory. ‎By adopting the mechanism presented in this article, ‎this is done more easily and more quickly, ‎so that mortality in society is lower and public satisfaction is increased. ‎This is similar to the issues analyzed in the literature on the design mechanism of non-perishable goods.

The multidisciplinary nature of the work required for research in the Covid-19 pandemic has created new challenges for health professionals in the battle against the virus. They need to be equipped with novel tools and resources ---that have emerged during the pandemic--- to gain access to breakthrough findings, know the latest developments, and to address their specific needs for rapid data acquisition, analysis, evaluation, and reporting. Because of the complex nature of the virus, the healthcare systems worldwide are severely impacted as the treatment and the vaccine for Covid-19 disease are not yet discovered. This leads to frequent changes in regulations and policies by governments and international organizations. Our analysis suggests that given the abundance of information sources, finding the most suitable tool for a given task is one of such challenges. But health professionals and policymakers need access to the most relevant, reliable, trusted, and latest information and tools that can be used in their day-to-day tasks of Covid-19 research and analysis. In this article, we present our analysis of various novel and important tools that have been specifically developed during the Covid-19 pandemic and that can be used by the health professionals community to help in advancing their analysis and research. These tools comprise of search engines, information repositories for literature and clinical trials, data sources, dashboards, and forecasting models. We present list of the minimally essential tools to serve a multitude of purposes, from hundreds of those developed since the beginning of the pandemic. A critical analysis is provided for the selected tools based on 17 parameters that can be useful for researchers and analysts for their evaluations. These parameters make up our evaluation framework and have not been used previously for analysis and evaluation. Hence, knowledge of the tools will not only increase the productivity but will also allow to explore new dimensions for using existing tools with more control, better management, and greater outcome of their research. In addition, the parameters used in our framework can be applied for future evaluations of similar tools and health professionals can adapt them for evaluation of other tools not covered in this analysis.

This article aims at investigating the complex relationship between the local population and its natural and urban environment. The study of area which, is previously dominated by cereal cultivation, facing profitability challenges due to climate change, water scarcity Rapid urban expansion and overexploitation of aquifers, influenced by changes in agricultural practices, are disrupting the local ecosystem. The study relies on three interconnected indicators: population growth, climate change effects on agriculture, and the NDVI (Normalized Difference Vegetation Index) reveal the impact of these factors on agricultural practices. Google Earth Engine was employed to determine urbanization and greenness indices using scripts. Key findings include a shift towards intensive and protected agriculture, driven by individual and collective choices of official and unofficial actors, deviating from government directives. Additionally, the study highlights the unsustainability of this new agricultural direction, as the available water re-sources cannot meet the demands, potentially leading to recurrent agricultural shortages. The study underscores the importance of incorporating agricultural production into future urban planning and development programs to maintain a renewed balance between built environ-ments and agriculture on the outskirts of cities.

Text book ought to be viewed as one of the numerous sources educators can attract after making a powerful exercise and may offer a system of direction and introduction. The analysis of textbook forms a necessary part in academics so as to devise the most appropriate content. The present study made a comparative account of textbooks for Grade 5 Social studies taught in public (i.e., Punjab textbook) and private sector (Oxford textbook) of Pakistan. The comparison was made on key features and on themes suggested by National Curriculum Council (2017) as a standard for textbook publishers. The findings demonstrated that both books contain activities and are clearly written, however, PTB is cheaper, possess low paper quality than OTB and was revised in 2018 whereas OTB has not revised since 2010. It is also suggested that both textbooks do not contain enough information as per NCC’s standard. It is suggested that government should take notice about it and publishers should be bound to deliver only that material in education sector that is according to the devised set of standards.

Biotic stressors pose serious threats to crop yield, jeopardizing food security and resulting in over US$220 billion per year losses to the agriculture industry. Plants activate innate defense mechanisms upon pathogen perception and invasion. The plant immune response comprises numerous concerted steps, including the recognition of invading pathogens, signal transduction, and activation of defensive pathways. However, pathogens have evolved various structures to evade plant immunity. Given these facts, genetic improvements of plants are required for sustainable disease management to ensure global food security. Advanced genetic technologies have provided new opportunities to revolutionize and boost plant disease resistance against devastating pathogens. Furthermore, targeting susceptibility (S) genes, such as OsERF922 and BnWRKY70, through CRISPR methodologies offers novel frontiers to disrupt the molecular compatibility of pathogens and introduce durable resistance against them in plants. Here, we provide a critical overview of advances in understanding disease resistance mechanisms. The review also critically examines management strategies under challenging environmental conditions and R gene-based plant genome engineering systems intending to enhance plant responses against emerging pathogens. This work underscores the transformative potential of modern genetic engineering practices in revolutionizing plant health and crop disease management while emphasizing the importance of responsible application to ensure sustainable and resilient agricultural systems.

Providing healthcare workers with cost information about the medications they prescribe can influence their decisions, particularly when that information is provided at the very moment they are faced with a prescribing decision. The current study aimed to analyse the impact of nudges that presented cost information to prescribers through their organisation's electronic prescribing system. The nudges were co-created by the research team (composed of behavioural scientists) and the lead hospital pharmacist. One nudge provided simple cost information (percentage difference between two brands of Mesalazine – Asacol and Octasa). The second nudge provided the potential annual cost-saving that could result if the cheaper medication was selected across the organisation. While the statistical analyses revealed that these nudges were not effective, several administrative barriers were overcome, which may inform future research. For example, presenting aggregated cost information to the prescribers is possible even when the actual cost of medicine is confidential and can not be displaced. In addition, future research could reveal more behavioural factors that facilitate medication optimisation.

: Background: Pseudomonas aeruginosa is an opportunistic pathogen involved in number of hospital acquired infections such as catheter-associated urinary tract infections, bacteremia, septicemia, skin infections and ventilator-associated pneumoniae. Biofilm formation is an important trait implicated in chronic infections, such as cystic fibrosis and chromic pulmonary obstruction. Method: A total of 266 isolates were collected from the Armed Forces Institute of Pathology (AFIP). Antibiotic susceptibility was assessed by double disk synergy testing. ESBL and Modified Hodge testing was performed for phenotypic confirmation of carbapenemases. Molecular screening of the genes was done by PCR. Micro-dilution broth method was used to determine minimum inhibitory concentrations of antibiotics. Biofilm formation was done by micro-titer plate assay. Results: Overall, 20% of the P. aeruginosa isolates were extensively drug resistant (XDR-PA) and 25% were multi-drug-resistant (MDR-PA). Likewise, 43% of the isolates were ESBL producers and carbapenemase production was detected in 40% of the isolates. Molecular analysis confirmed occurrence of different resistant factors in ESBL positive isolates; 67% carried bla-TEM, 62% bla-CTXM-15, 41% bla-SHV, 34% bla-CTXM-14 and 33% bla-OXA-1. In addition, 68% of carbapenem-resistant isolates were positive for bla-NDM-1, 25% for bla-OXA-48 and 22% for bla-KPC-2. Biofilm formation was tested for 234 isolates, out of which 28% were strong biofilm formers. Moderate and weak biofilm formers were 46% and 23% respectively. Overall, ciprofloxacin, levofloxacin and cefepime showed inhibitory effects on P. aeruginosa biofilms. Antibiotics in combination showed strong synergistic effects (ciprofloxacin &amp; cefepime); while gentamicin and cefepime resulted into complete eradication of P. aeruginosa biofilm. Conclusion: We confirm strong synergistic effects of gentamicin and cefepime that completely eradicated P. aeruginosa biofilm. We further confirm inhibitory effects of ciprofloxacin, levofloxacin and cefepime on P. aeruginosa biofilms.

In this paper a protection scheme is provided to protect microgrid by considering the problems that are generated by addition of distributed generators to distribution networks and change these networks from passive to active. At first, changes in microgrid conditions that can affect short-circuit current is explained. Then Based on these changes, an algorithm is proposed to update relays settings. The algorithm can be used for both instantaneous and inverse time relays. In this protection scheme, central unit has no place and relays are responsible for monitoring microgrid and update their settings. In other words, this protection scheme is an adaptive and decentralized microgrid protection scheme. Instantaneous overcurrent relays are used in this paper. To avoid storing large amounts of setting data in relays memory, a method for calculating pickup current of instantaneous relay is provided. Since digital relays used, a new characteristic curve for instantaneous relay for better performance in the field of backup protection is defined. This new characteristic curve has two peakup currents: one of them for main protection and the other one for backup protection. Then coordination of instantaneous relay using the new characteristic curve is explained. At the end, this protection scheme is implemented on a microgrid.

The research aims to discover the natural occurrence of patulin (PAT) in selected citrus fruits from the central cities of Punjab and Pakistan's Northern cities. Total 2970 samples of twelve citrus fruits; kinnow, orange, grapefruits, bitter orange, mausami, red blood, pineapple, sweet orange, rough lime, sweet lime, kagzi lime and lemon were examined using liquid chromatography fitted with UV detector. The limit of detection (LOD) and quantification limit was 0.04 and 0.12 µg/kg, respectively. The results have shown that 56% of samples of citrus fruits from Punjab's central cities, Pakistan, were discovered to be infected from PAT. The elevated amounts of PAT ranging from 0.04 to 1150 µg/kg were found in citrus fruit samples from Multan cities. Furthermore, 31.7% of samples of citrus fruits from Northern cities of Pakistan were discovered to be infected with PAT, and the elevated amounts were found ranging from 0.04-320 µg/kg from Swat city. About 22.1% of samples of citrus fruits have levels of PAT greater than the suggested limits established by the European Union (EU). PAT's dietary intake levels ranged from 0.10-1.11 µg/kg bw/day from the central cities of Punjab, Pakistan, and 0.13-1.93 µg/kg bw/day were documented from Northern cities of Pakistan.

The most important plastic resins used for wire coating are Polyvinyl Chloride (PVC), Nylon, Polysulfone and Low-high density polyethylene (LDPE / HDPE). In this article,the coating process is performed using elastic-viscous fluid as a coating material for wire coating in a pressure type coating die. The elastic-viscous fluid is electrically conducted in the presence of an applied magnetic field. The governing non-linear equations are modeled and then solved analytically by utilizing an Adomian decomposition method (ADM). The convergence of the series solution is established. The results are also verified by Optimal Homotopy Asymptotic Method (OHAM). The effect of different emerging parameters such as non-Newtonian parameters α and β, magnetic parameter M and the Brinkman number Br on solutions (velocity and temperature profiles) are discussed through several graphs. Additionally, the current result also compares with the published work already available in the literature.

The study focused on investigating the natural incidence of deoxynivalenol (DON) in corn and products from corn producing districts of Punjab, Pakistan. The analysis was carried out using HPLC with UV detector and immunoaffinity cleanup columns. The detection limit (LOD) and limit of quantification were 25 and 50 µg/kg, respectively. Total 1220 samples of corn and products were analyzed to detect the DON, and 539 (44.2%) samples were observed to be contaminated with DON (n ≥ LOD). Furthermore, 92 (7.5%) samples of corn & products have DON levels, elevated than the proposed limits of the EU. The data is significantly different from a normal distribution for DON in corn and products samples and from different locations (p < 0.05) for Shapiro-Wilk and Kolmogorov-Smirnov values. However, a significant difference in DON levels was found between corn and corn derived-products types (p ≤ 0.05). The lowest and highest exposure & hazard quotient (HQ) of 0.92 and 9.68 µg/kg bw/d were documented in cornflour samples.

Coronavirus disease 2019 (COVID-19), the emissary behind the worst global pandemic of the 21st century, is primarily a respiratory disease-causing virus called SARS-CoV-2 which is responsible for millions of new cases (incidence) and deaths (mortalities), worldwide. Many factors have played a role in the differential morbidity and mortality experienced by nations and ethnicities against SARS-CoV-2, such as the quality of primary medical health facilities or enabling economies. Nevertheless, the most important variable, i.e., the subsequent ability of individuals to be immunologically sensitive or resistant to the infection, was not properly discussed before. Therefore, an astounding issue arose when some developed countries experienced higher morbidity and mortality, compared with their relatively underdeveloped counterparts, despite having excellent medical health facilities. Hence this investigative review attempts to analyze the issue from an angle of previously undiscussed genetic, epigenetic, and molecular immune resistance mechanisms in correlation with the pathophysiology of SARS-CoV-2 and varied ethnicity-based immunological responses against it. The biological factors discussed here include the overall landscape of human microbiota, endogenous retroviral genes spliced into the human genome, copy number variation, and how they could modulate the innate and adaptive immune systems, which put a particular ethnic genetic architecture at a higher risk of SARS-CoV-2 infection than others. Considering an array of these factors in their entirety may help explain the geographic disparity of disease incidence, severity, and subsequent mortality associated with the disease while at the same time encouraging scientists to design new experimental approaches to investigation.

The ubiquitin-specific protease 2 (USP) belongs to the family of deubiquitinases and plays a critical role in tumors cells’ survival and therefore signifies an important therapeutic target. Previous studies have indicated promising efficacies of potent human USP2 inhibitors including, thiopurine analogues against SARS-CoV papain-like proteases (PLpro). The PLpro have significant functional implications in the innate immune response during SARS-CoV-2 infection and considered an important antiviral target. Both proteases share strikingly similar USP fold with right-handed thumb–palm–fingers structural scaffold and conserved catalytic triad Cys-His-Asp/Asn. In this urgency situation of COVID-19 outbreak, there is a lack of in-vitro facilities readily available to test SARS-CoV-2 inhibitors in whole-cell assays. Therefore, we adopted an alternate route to identify potential USP2 inhibitor through integrated structure-based virtual screening efforts. After a subsequent virtual screening protocol, the best compounds were selected and tested. The compound Z93 showed significant IC₅₀ value against Jurkat (9.67 µM) and MOTL-4 cells (11.8 µM). The binding mode of Z93 was extensively analyzed through molecular docking, followed by MD simulations, and molecular interactions were compared with SARS-CoV-2. The relative binding poses of Z93 fitted well in the binding site of both proteases and showed consensus π-π stacking and H-bond interactions with histidine and aspartate/asparagine residues of the catalytic triad. These results led us to speculate that compound Z93 might be the first potential chemical lead against SARS-CoV-2 PLpro, which warrants in-vitro evaluations.

Prostate cancer (PC) is the most frequently diagnosed non-skin cancer in the world. Previous studies showed that genomic alterations represent the most common mechanism for molecular alterations that cause the development and progression of PC. Great efforts have been done to identify common protein-coding genetic variations; however, the impact of non-coding variations including regulatory genetic variants is not still well understood. To gain an understanding of the functional impact of genetic variants, particularly, regulatory variants in PC, we developed an integrative pipeline (AGV) that used whole genome/exome sequences, GWAS SNPs, chromosome conformation capture data, and ChIP-Seq signals to investigate the potential impact of genomic variants on the underlying target genes in PC. We identified 646 putative regulatory variants, of which 30 of them significantly altered the expression of at least one protein-coding gene. Our analysis of chromatin interactions data (Hi-C) revealed that the 30 putative regulatory variants may affect 131 coding and non-coding genes. Interestingly, our study showed the 131 protein-coding genes are involved in disease-related pathways including Reactome and MSigDB in which for most of them targeted treatment options are currently available. Together, our results provide a comprehensive map of genomic variants in PC and revealed their potential contribution to prostate cancer progression and development.

We assessed if there were any sex-related differences in the ability of nicotine to increase plasma corticosterone secretion after single or repeated nicotine administration. For single-dose studies, male and female mice were habituated to the test room for 1 h and injected with saline or nicotine (0.25 or 1 mg/kg, s.c.). In repeated-dosing studies, mice were injected with saline or nicotine (1 mg/kg, s.c.) once daily for six days, and, on day 7, received nicotine (1 mg/kg, s.c.). The mice were euthanized 15 min later, and trunk blood was collected for the measurement of corticosterone, nicotine, and cotinine. Our results showed that saline or nicotine each significantly increased plasma corticosterone levels in both male and female mice, with a greater response in female mice. Plasma corticosterone levels were increased in male but not female mice after repeated compared to single nicotine administration. The level of cotinine, a biomarker of nicotine use, was significantly higher in female than in male mice. Taken together, these novel findings suggest that female mice responded to nicotine and stress of handling more than male mice and provide for the first-time quantitative data on the male-female differences in nicotine-induced elevations of corticosterone and of cotinine.

As technology advances, the field of electrical and computer engineering continuously demands the introduction of innovative new tools and methodologies to facilitate effective learning and comprehension of fundamental concepts. This research addresses an identified gap in technology-augmented education capabilities and researches the integration of Virtual Reality (VR) technology with real-time electronic circuit simulation to enable and enhance the visualization of non-observable concepts such as voltage distribution and current flow within these circuits. In this paper, we describe the development of our immersive educational platform, which makes understanding these abstract concepts intuitive and engaging. This research also involved the design and development of a VR-based circuit simulation environment. By leveraging VR’s immersive capabilities, our system enables users to physically interact with electronic components, observe the flow of electrical signals, and manipulate circuit parameters in real-time. Through this immersive experience, learners can gain a deeper understanding of fundamental electronic principles, transcending the limitations of traditional two-dimensional diagrams and equations. Furthermore, this research focuses on the implementation of advanced and novel visualization techniques within the VR environment for non-observable electrical and electromagnetic properties, providing users with a clearer and more intuitive understanding of electrical circuit concepts. Examples include color-coded pathways for current flow and dynamic voltage gradient visualization. Additionally, real-time data representation and graphical overlays are researched and integrated to offer users insights into the dynamic behavior of circuits, allowing for better analysis and troubleshooting.

Ultrasound (US) has become a widely used imaging modality in clinical practice, characterized by its rapidly evolving technology, advantages, and unique challenges such as low imaging quality and high variability. There is a critical need to develop advanced automatic US image analysis methods to enhance diagnostic accuracy and objectivity. Vision transformer, a recent innovation in machine learning, has demonstrated significant potential in various research fields, including general image analysis and computer vision, due to its capacity to process large datasets and learn complex patterns. Its suitability for automatic US image analysis tasks, such as classification, detection, and segmentation, has been recognized. This review provides an introduction to vision transformer and discusses its applications in specific US image analysis tasks, while also addressing the open challenges and potential future trends in its application in medical US image analysis. Vision transformer has shown promise in enhancing the accuracy and efficiency of ultrasound image analysis and is expected to play an increasingly important role in the diagnosis and treatment of medical conditions using ultrasound imaging as technology progresses.

Abstract Purpose – The purpose of this study is to investigate the impact of spiritual leadership on knowledge hiding behavior of employees in agriculture research institutes of Khyber Pakhtunkhwa, Pakistan. The study also aims to analyze theoretically and then investigate empirical evidence concerning the mediation pathway (namely, professional commitment) to clarify the significant association amid spiritual leadership and subordinate's knowledge hiding behavior. Design/methodology- The cross-sectional data using five point lickert scale was used in this survey based study to investigate the given hypotheses. In order to account for the prim effect and any potential for common method bias, data was gathered at two different time points with a gap of four weeks. Smart PLS4 was used to assess a sample of 298 complete and valid responses for hypotheses testing. Findings – The results show that spiritual leadership impacts negatively the employees’ knowledge hiding behavior. Moreover, this relationship is mediated by professional commitment. Originality/value – First, in contrast to the majority of past studies that concentrated on antecedents of knowledge sharing, the present study examined the impact of spiritual leadership on employee’s knowledge hiding behaviors, which are two opposing concepts. Secondly, the study examined empirically, the mediation effect of professional commitment. These three variables have not been studied together previously.

Recently, with the rapid advancement of technology, Internet of Things (IoT‏‏‏‏) security has become more efficient and more complex, especially for resource-limited devices such as embedded devices, wireless sensors and radio frequency identification tags (RFID). Lightweight block ciphers (LBCs) provide security for these technologies to protect them against adversaries, but the need for low power consumption in LBCs is one of the most important challenges for IoT‏ technologies. Furthermore, these LBCs are subject to multiple attacks and side channel attacks (SCAs) are among the mentioned threats to these cryptosystems. A type of SCA is correlation power analysis (CPA) in which the attacker tries to reach the key using the relationship between the power consumption of the chip during the algorithm running, data processing, and operations. In this article, a CPA attack is designed to discover master key of the Midori-64 block cipher. According to the proposed method, an attack is done to the first round S-boxes to get half of the key bits. Then, the second round S-boxes were attacked to other half of key bits just use 300 plain text samples. Finally, the most important physical attacks performed on the Midori, are compared to our proposed CPA attack.

Faba bean (Vicia faba L.) was considered a minor crop in the Canadian prairies until recently, but its potential for cultivation is increasing due to its positive environmental impact and economic value. This review provides a historical summary of faba bean improvement in western Canada. Although traditional breeding methods have proved useful, in the last decade faba bean improvement has benefited from advances in genetics, biochemistry and molecular breeding tools. The overall breeding goal is to develop high yielding germplasm with improved agronomic characteristics that will be of economic value to the emerging faba bean sectors, including the plant protein industry. To maximize value and acceptance by producers, processors and the food industry as a source of protein and dietary fibre, future faba bean varieties need to be high-yielding, have diverse seed size classes, disease resistance, genetically low vicine-convicine concentration, and have wider adaptation to different agro-ecological zones of Canada. The experiences over the last 40 years of faba bean improvement in western Canada may be useful to other breeding programs globally located in regions with similar agroecology. In the past 10–15 years, faba bean genetic development in Canada has benefited greatly from research and development interactions with most of the faba bean research programs in northern Europe.

In this paper, the effect of an exterior shading element (Iwan) on energy consumption in four different climatic regions, and for different geographical directions, has been investigated numerically and experimentally. By applying different materials and techniques and creating various elements and spaces, architects make hard climatic conditions more tolerable for residents. Iwan is one of the cooling elements which is used in different forms and dimensions in the Islamic architecture. In the present research, Iwan has been introduced as a climatic element in traditional and contemporary architectures and its role in reducing the energy consumption in buildings has been studied. In this respect, first, the thermal loads of a building without Iwan are computed by means of EnergyPlus software. Then, four different forms of Iwan are added to the above-mentioned structure along the four principal geographical directions, and the effect of Iwan on the reduction of thermal loads is analyzed for four different climates. Finally, the design parameters of Iwan, in terms of depth and form, that can help reduce the thermal loads in different climatic conditions are presented. The results show that the best position for using an Iwan is the south direction and the use of Iwan in temperate & humid, hot & humid, cold & mountainous and hot & dry climates could reduce the energy consumption in buildings by 32%, 26%, 14%, and 29%, respectively.

The advent of high throughput sequencing has enabled researchers to systematically evaluate the genetic variations in cancer, resulting in identifying many cancer-associated genes. Although cancers in the same tissue are widely categorized in the same group, they demonstrate many differences concerning their mutational profiles. Hence there is no “silver bullet” for the treatment of a cancer type. This reveals the importance of developing a pipeline to identify cancer-associated genes accurately and re-classify patients with similar mutational profiles. Classification of cancer patients with similar mutational profiles may help discover subtypes of cancer patients who might benefit from specific treatment types. In this study, we propose a new machine learning pipeline to identify protein-coding genes mutated in a significant portion of samples to identify cancer subtypes. We applied our pipeline to 12270 samples collected from the International Cancer Genome Consortium (ICGC), covering 19 cancer types. Here we identified 17 different cancer subtypes. Comprehensive phenotypic and genotypic analysis indicates distinguishable properties, including unique cancer-related signaling pathways, in which, for most of them, targeted treatment options are currently available. This new subtyping approach offers a novel opportunity for cancer drug development based on the mutational profile of patients. We also comprehensive study the causes of mutations among samples in each subtype by mining the mutational signatures, which provides important insight into their active molecular mechanisms. Some of the pathways we identified in most subtypes, including the cell cycle and the Axon guidance pathways, are frequently observed in cancer disease. Interestingly, we also identified several mutated genes and different rates of mutation in multiple cancer subtypes. In addition, our study on “gene-motif” suggests the importance of considering both the context of the mutations and mutational processes in identifying cancer-associated genes. The source codes for our proposed clustering pipeline and analysis are publicly available at: https://github.com/bcb-sut/Pan-Cancer.

Temperature field is an essential attribute of metal additive manufacturing in view of its bearings on the prediction, control, and optimization of residual stress, part distortion, fatigue, balling effect, etc. This work provides an analytical physics-based approach to investigate the effect of scan strategy parameters including time delay between two irradiations and hatching space on thermal material properties and melt pool geometry. This approach is performed through the analysis of the distribution of material properties and temperature profile in three-dimensional space. The moving point heat source approach is used to predict the temperature field. To predict the temperature field during the additive manufacturing process some important phenomena are considered. 1) Due to the high magnitude of temperature in the presence of the laser, the temperature gradient is usually high which has a crucial influence on thermal material properties. Consequently, the thermal material properties of stainless steel grade 316L are considered to be temperature-dependent. 2) Due to the repeated heating and cooling, part usually undergoes several melting and solidification cycles. This physical phenomenon is considered by modifying the heat capacity using the latent heat of melting. 3) The multi-layer aspect of metal AM process is considered by incorporating the temperature history from the previous layer since the interaction of the successive layers has an impact on heat transfer mechanisms. 4) Effect of heat affected zone on thermal material properties is considered by the superposition of material properties in regions where the temperature fields of two consecutive irradiations have an overlap since the consecutive irradiations change the behavior of the material properties. The goals are to 1) investigate the effects of temperature-sensitive material properties and constant material properties on the temperature field. 2) Study the behavior of thermal material properties under different scan strategies. 3) Study the importance of considering the effect of heat affected zone on thermal material through the prediction of melt pool geometry. 4) Investigate the effect of hatching space on melt pool geometry. This work is purely employed physics-based analytical models to predict the behavior of material properties and temperature field under different process conditions, and no finite element modeling is used.

Excessive high fat diet (HFD) consumption can induce food addiction which is believed to involve the communication between the hypothalamus and mesolimbic dopaminergic neurons, originating in the ventral tegmental area (VTA) and projecting to the nucleus accumbens (NAc). These brain regions are densely populated with opioid receptors, raising the possibility that these receptors, and particularly mu opioid receptors (MORs), are involved in mediating reward induced by palatable foods. This study sought to investigate the involvement of MORs in high fat diet (HFD)-induced reward and if there is any difference between male and female subjects in this response. We also assessed if exposure to HFD would potentiate the rewarding action of oxycodone, a relatively selective MOR agonist. The place conditioning paradigm was used to determine if conditioning for a short time (STC, 2 h) or long time (LTC, 16 h) with HFD induces reward or alters the rewarding action of oxycodone. Male and female C57BL/6J mice as well as MOR knockout and wildtype mice of both sexes were tested for basal place preference on day 1 and then conditioned with HFD in one chamber and regular chow diet (RCD) in another chamber and tested for place preference again after three sets of STC and again after a set of LTC. Each set consisted of two conditioning with RCD and two conditioning with HFD. Controls were conditioned with RCD in both conditioning chambers. Following the last place preference test, mice were treated with oxycodone and conditioned in the HFD-paired chamber and saline in the RCD-paired chamber for one hour once a day to explore the possibility if the HFD could alter oxycodone reward. The result showed that HFD induced conditioned place preference (CPP) in both male and female subjects following the LTC. This response was further potentiated after oxycodone conditioning. The latter response was mediated via MORs, as it was blunted in MOR knockout mice. However, HFD-induced CPP was observed following LTC in only female MOR knockout mice, suggesting for sexual dimorphism in this response.

Solar energy adoption worldwide has expanded exponentially due to a surge in international interest in producing clean energy and the declining cost of solar power plants and their technology. It is anticipated that by 2050, solar will have surpassed fossil fuels to become the primary source of energy. However, one of the main challenges associated with solar energy production is the instability of photovoltaic (PV) power generation because of weather changes. Short-term forecasting of the power output of photovoltaic systems is essential for efficient management of the power grid and energy markets. This paper aims to evaluate the ability of deep learning (DL) models to provide accurate forecasting of hourly global horizontal irradiance (GHI) using different sets of features, including weather and aerosol variables along with solar radiation components. The results show that the best forecast skills are achieved by the long short-term memory autoencoder (LSTM-AE) model.

PurposeThis study was designed to characterize the psychometric properties of the Persian version of the Fear of Hospitalization Scale (P-FHS).Design and methodsIn order to evaluate the validity and reliability of the translated scale, a cross-sectional design was employed. Ten experts evaluated the content validity of the Fear of Hospitalization Scale (FHS) after it had been back-translated into Persian. With 612 patients having emergency surgery, construct validity was assessed using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The STROBE checklist for cross-sectional studies was followed.FindingsThe results of EFA (n = 306) showed that the fear of hospitalization had three factors. These three factors accounted for 45.28% of the total variance. Also, these factors were confirmed by CFA (n = 306) (root-mean-square error of approximation = (90%. confidence interval) = 0.050 [0.041, 0.058], goodness-of-fit index = 0.945, comparative fit index = 0.968, Non-Normal Fit Index = 0.948, incremental fit index = 0.968, Tucker-Lewis Index = 0.959). The coefficients of Cronbach’s alpha, McDonald’s omega, composite reliability, and maximum reliability for all three factors were greater than 0.7, demonstrating satisfied internal consistency.Practice implicationAccording to the published results, the P-FHS is effective at measuring hospitalization anxiety in patients undergoing emergency surgery. It is advised that nurses in Iranian culture use a legitimate and trustworthy technique to pinpoint the causes of hospitalization anxiety in patients undergoing emergency surgery to give optimal care.

Alcoholism is attributed to regular or excessive drinking of alcohol and leads to the disturbance of the neuronal system in the human brain. This results in certain malfunctioning of neurons that can be detected by an electroencephalogram (EEG) using several electrodes on a human skull at appropriate positions. It is of great interest to be able to classify an EEG activity as that of a normal person or an alcoholic person using data from the minimum possible electrodes (or channels). Due to the complex nature of EEG signals, accurate classification of alcoholism using only a small data is a challenging task. Artificial neural networks, specifically convolutional neural networks (CNN), provide efficient and accurate results in various pattern-based classification problems. In this work, we apply CNN on raw EEG data, and demonstrate how we achieved 98% average accuracy by optimizing a baseline CNN model and outperforming its results in a range of performance evaluation metrics on the UCI-KDD EGG dataset. This article explains the step-wise improvement of the baseline model using the dropout, batch normalization, and kernel regularization techniques, and provides a comparison of the two models that can be beneficial for aspiring practitioners who aim to develop similar classification models in CNN. A performance comparison is also provided with other approaches using the same dataset.

Considering the great growth of IoT networks and the need for highly reliable networks and also considering the manufacturing divides of IoT equipment which are highly limited by their memory, processing power and battery; we need a highly efficient routing for guaranteeing our network's high life span. So, this paper has suggested an efficient energy routing method based on the overlapping clustering method which is inspired from the Grey theory. The Overlap clustering method means that some Things collect data that must be sent to two or more Fog nodes for processing. In the suggested method the best node is selected as the cluster head based on factors such as remaining energy, distance, link expiration time, signal power for receiving data from things by the Fog nodes. In the next step the Fog node's data are sent in a hierarchical method using a symmetrical tree of processed data to the server. Thus, the main issue here is making using a proper routing method for data sending to the Cloud that doesn't just focus on energy, but also considers other factors such as delay and network life span. The simulation results show that the HR-IoT reduces the average end to end delay more than 17.2% and 23.1%, decreases the response time more than 20.1% and 25.78% and increase packet delivery rate more than 23.1% and 28.78% and lifetime more than 25.1% and 28.78% compared to EECRP and ERGID approaches.

Sensors became integrated through the control condition arrangement, either for visual, mechanical, biological, or chemical applications. New stuff is designed for detection, as Diluted Magnetic Semiconductors (DMS), and are considered attractive candidates that consist of a traditional 111- V, II-VI, or group IV semiconductor. Manganese Mn-doped GaN (Mn.Gac.N) epitaxial velum has a unique magnetic, visual and chemical properties in order to control system intelligent for detector design. The subject area of the magnetic properties of MnxGal-xN is on a large scale available, there are only a few studies on the visual properties and electrochemical properties of MnxGal-xN epitaxial velums. Where MnGaN velums were used in spintronic and opto-electronic applications according to their magnetic characterization and constructed MnGaN electrode are drop fabric potential for potentiometric sensor applications since they have good performance as ion-selective electrodes. The electrical and magnetic properties that allow the control of electron spin as well as complaint period, makes the materials ideal for spintronic applications. Designing such spintronic and optoelectronic devices based on MnxGal-xN requires a broader agreement of physical, visual, electrical and chemical properties epitaxial velums that are still seldom in the literature. This bailiwick displays the potential use of MnGaN semiconductor as an all solid-state potentiometric sensor for measuring anions in solutions in the control engineering field.

A new class of tetrasubstituted imidazole based compounds was synthesized using a multicomponent one-pot synthesis scheme through a cyclo condensation reaction of benzil, aromatic primary amines, aldehydes and ammonium acetate in glacial acetic acid. The synthesized compounds have been analyzed and characterized by melting point, color, conductivity method, CHN analysis, FT-IR, and UV-Visible. The reaction proceeding was examined by TLC after regular intervals of period. To test biological activity, the synthesized compounds have been examined against various bacterial strains. From the analysis of the antibacterial activity of these synthesized compounds demonstrated that all three imidazole compounds have considerable to significant activity against the strains, and compound K2 was found potent comparatively.

The Kabul River basin (KRB) of Afghanistan, a lifeline of around 10 million people, has multiplicity of governance, management and development related challenges leading to inequity, inadequacy and unreliability of irrigation water distribution. Prior to any uplifting intervention, there is a need to evaluate the performance of irrigation system on long term basis to identify the existing bottlenecks. Although there are several indicators used for the performance evaluation of the irrigation schemes, but we used the coefficient of variation (CV) of actual evapotranspiration (ETa) in space (basin, sub-basin, and provincial level), relative evapotranspiration (RET) and temporal CV of RET to assess the equity, adequacy and reliability of water distribution respectively, from 2003 to 2013. The ETa was estimated through surface energy balance system (SEBS) algorithm and the ETa estimates were validated using advection aridity (AA) method with R2 value of 0.81 and 0.77 at Nawabad and Sultanpur stations respectively. The global land data assimilation system (GLDAS) and moderate-resolution imaging spectroradiometer (MODIS) products were used as main inputs to the SEBS. Results show that mean seasonal sub-based RET values during summer (May – September) (0.37 ± 0.06) and winter (October – April) (0.40 ± 0.08) are below the target values (RET ≥0.75) during 2003-2013. The CV of mean ETa within sub-basins and provinces for the entire study period has equitable distribution of water from October-January (0.09±0.04) whereas the highest inequity (0.24±0.08) in water distribution is during early summer. The range of the CV of mean ETa (0.04-0.06) on monthly and seasonal basis shows the unreliability of water supplies in several provinces or sub-basins. The analysis of temporal CV of mean RET highlights unreliable water supplies across the entire basin. The maximum ETa during the study period was estimated for Shamal sub-basin (552±43mm) while among provinces Kunar experienced the highest ETa (544±39mm). This study highlights the dire need for interventions to improve the irrigation performance in time and space. The proposed methodology can be used as a framework for monitoring and implementing the water distribution plans in future.

CO₂ emissions have been identified as the main driver for climate change, with devastating consequences for the global natural environment. The steel industry is responsible for ca. 8%-11% of global CO₂ emissions, due to high fossil-fuel and energy consumption. The onus is therefore on industry to remedy the environmental damage caused and to decarbonise production. This desk research report follows a two-tiered route: 1) exploring the Bio Steel Cycle; 2) proposing a seven-step strategy to overcome the challenges within the iron and steel industry. The true levels of CO₂ emissions in the steelmaking process, during the blast-furnace/basic-oxygen-furnace operation, will be detailed first, at 4.61t of CO₂ emissions/t of steel produced. The manuscript proposes a solution for reduced carbon emissions steelmaking with the Bio Steel Cycle components and detail on how to reach net-zero carbon emissions steel production. The 7-step-implementation-strategy as a potential solution to achieve net and true zero carbon emissions steel production in the short to medium-term. The findings of this are pointing towards the conclusion that CO₂ emissions seem to have been under-reported and under-estimated in the past, but the emission can be addressed if the correct scenarios for net-zero steel manufacturing are implemented by 2050.