Background: Although the link between environmental lead exposure and several patterns of cardiovascular disease (CVD) has been reported, its association with silent myocardial infarc-tion (SMI) has not been explored. We aimed to assess the association between blood lead levels (BLL) and risk of SMI. Methods: This analysis included 7283 (mean age 56.1±2.52 years, 52.5% women) participants from the Third National Health and Nutrition Examination Survey who were free of clinical CVD. Ascertainment of BLL done using graphite furnace atomic absorption spectrophotometry. SMI defined as ECG evidence of MI without history of MI. Multivariable lo-gistic regression analysis was used to examine the association between SMI and BLL. Results: SMI was detected in 120 participants corresponding to an unweighted prevalence of 1.65%. Par-ticipants with higher levels of BLL had higher prevalence of SMI (0.4%, 0.9%, and 2.4% across BLL tertiles; p-value

The fundamental mechanism underlying negative-ion catalysis involves bond-strength breaking in the transition state (TS). Doubly-charged atomic/molecular anions are proposed as novel dynamic tunable catalysts and demonstrated in water oxidation to peroxide. Density Functional Theory TS calculations have found tunable energy activation barrier reduction ranging from 0.030 eV to 2.070eV, with Si²ˉ, Pu²ˉ, Pa²ˉ and Sn²ˉ the best catalysts; the radioactive elements usher in new application opportunities. C₆₀²ˉ reduces significantly the standard C₆₀ˉ TS energy barrier while graphene increases it, behaving like cationic systems. Rank-ordered catalysts according to their reaction barrier reduction efficiency, variation across charge states and systems reveal their tunable and wide applications, ranging from water purification to biocompatible anti-viral and anti-bacterial sanitation systems.

The propeller jet from a ship has a significant component directed upwards towards the free surface of the water, which can be used for ice management. This paper describes a comprehensive laboratory experiment where the influences of operational factors affecting a propeller wake velocity field were investigated. The experiment was done on a steady wake field to investigate the characteristics of the axial velocity of the fluid in the wake and the corresponding variability downstream of the propeller. The axial velocities and the variability recorded were time-averaged. Propeller rotational speed was found to be the most significant factor, followed by propeller inclination. The experimental results also provide some idea about the change of the patterns of the mean axial velocity distribution against the factors considered for the test throughout the effective wake field, as well as the relationships to predict the axial velocity for known factors.

Sulfur dioxide (SO2) reduction remains an area of global necessity further enhanced by the current international focus on pandemic diseases mitigation, elimination of air pollution, and promotion of renewable green energy. The dynamics of chemical bond-strength breaking and reformation in the transition state (TS) is a fundamental process in the reduction of SO2 by CO. Density Functional Theory (DFT) has been used to determine optimal TS reaction pathway via a novel triple-hybrid catalyst utilizing doubly-charged negative atomic V, Mn, and Au. The triple-hybrid catalyst is furthermore tailored to the subsequent minimization of each individual step of the 3-Step SO2 reduction by CO chemical reaction. Each optimized step 1, 2, and 3 is minimized with doubly-charged V, Mn, and Au, respectively, with TS barrier reductions ranging from 1.18 eV to 0.002 eV. Super-benzene, armchair (6, 6) single wall carbon nanotube, and fullerene TS reaction pathways have also been calculated to compare the nanoscale catalytic effectiveness with that of the atomic scale transition metals.

For water-stressed regions like Iran improving the effectiveness and productivity of agricultural water-use is of utmost importance due to climate change and unsustainable demands. Therefore, a hydro-economic model has been developed here for the Zarrine River Basin with the central concept of that demands are value-sensitive functions, where quantities of water-uses at different locations and times have a changeable economic benefits. To do this, the potential crop yields and the surface and groundwater resources, especially Boukan Dam inflow are simulated using the hydrologic model, SWAT, based on predicted climatic scenarios i.e. quantile mapping-downscaled projections. Then, to allocate the agricultural water based on the agro- economic crop water productivity (AEWP) of crops, a basin-wide water management tool, MODSIM, is customized. Next, a simulation- optimization model has been developed using a coupled CSPSO-MODSIM, to optimize the total AEWP, considering climatic impact and crop pattern scenarios, for 2020-2038, 2050-2068 and 2080-2098 periods. Finally, the optimum crop pattern and crop water irrigation depths are presented for different RCPs and periods. The results indicated that this approach will improve considerably the AEWPs and decrease the agricultural water-use up to 40%. Thus, this integrated model is able to support water authorities and other stakeholder in a water-scarce basin, as is the study area.

There is little understanding of patients' experiences and perceptions with satisfaction by health professionals such as medical and dental clinicians. Furthermore, patient satisfaction is not well understood. The objective of this article is to better understand patients' satisfaction with their medical and dental care. The methods of the current article are based on a narrative review of the literature strategy. Patient satisfaction's multidimensional nature has been established since the perceived reasons for satisfaction varied widely among patients. Many aspects of the treatment influence participant satisfaction at different stages of the intervention's process. An improved understanding of the basis for managing patients' expectations with information reiteratively and efficiently may ultimately reduce patients' potential for negative feelings toward the medical and dental treatment experience. The consumerist method may misrepresent the concept of satisfaction in health service.

Most empirical work on sources of economic growth for different countries lack country-specific empirical evidence to guide policy choices in individual developing countries and previous studies of factor productivity tend to focus on the entire economy or a single sector. In this study, we use the recently developed growth accounting tools to explicitly determine the sources of economic growth at both national and sectoral levels in Zambia between 1970 and 2013. We use data from World Development Indicators and Zambia’s Central Statistical Office. On average, total factor productivity (TFP) contributes about 5.7% to economic growth. Sectoral analysis shows that agriculture contributes the least to GDP and that within each sector; factors that contribute to growth differ. Structural transformation has been slow and contributed to the observed inefficiency. We outline the implications of the observed growth and provide recommendations.

Background: RSV, or respiratory syncytial virus, is a deadly condition that is present all over the world today. The infection is initially contained in the lower respiratory tract without systemic transmission. The objective of the study: Bioinformatics was used in Egypt to create an mRNA vaccine against the Respiratory syncytial virus (RSV). Methodology: In the present screening experimental study, a lipid nanoparticles vaccine of mRNA of the surface fusion protein of Respiratory syncytial virus was manufactured. The present vaccine delivery system was lipid nanoparticles with a particle size of approximately 90 nanometres, manufactured by the hot micro-emulsion method. In stages 1 and 2 of clinical trials, the immunogenicity of the current mRNA RSV vaccine was evaluated. Results: The current vaccine demonstrated 81% immunogenicity in animal testing, but only 69% in stages 1 and 2 of clinical trials. Its side effects were manageable. The results persisted for a while. In the current trial, the vaccination proved effective as a preventative measure against RSV infection. The current vaccination lacked antibody-dependent enhancement, which causes non-protective antibodies to develop. These non-neutralizing antibodies exacerbate infection by activating cytokines and complement cascade through the formation of immune complexes or enhancement of the virus entry and replication in the host cells. On the other hand, the current RSV mRNA vaccine of the surface fusion protein consequent on the production of powerful neutralizing antibodies with prolonged immunity especially to infants and elderly candidates who received the optimal dosage regimen.

This review provides an overview of the current understanding of the neural systems involved in regulating wakefulness, non-rapid eye movement sleep (NREMS), and rapid eye movement sleep (REMS) in mammals. Specifically, we focus on the anatomical connections between the subiculum, a component of the hippocampal formation, and the regions responsible for regulating the sleep-wake cycle. The subiculum exhibits direct connections with key areas involved in sleep regulation, such as the lateral hypothalamus, tuberomammillary nucleus, basal forebrain, ventrolateral preoptic nucleus, ventrolateral tegmental area, and suprachiasmatic nucleus. Additionally, second-order projections from the subiculum are received by the laterodorsal tegmental nucleus and locus coeruleus, suggesting potential involvement of the subiculum in the regulation of circadian rhythms, particularly the circadian sleep-wake cycle. We also discuss alterations in the subiculum observed in individuals with sleep disorders and sleep-deprived mice, underscoring the significance of investigating neuronal communication between the subiculum and pathways promoting both sleep and wakefulness.

This paper presents some results from a computational fluid dynamics (CFD) model of a multi-megawatt crosswind kite spinning on a circular path in a straight downwind configuration. The unsteady Reynolds averaged Navier-Stokes equations closed by the k−ω SST turbulence model are solved in the three-dimensional space using ANSYS Fluent. The flow behaviour is examined at the rotation plane, and the overall (or global) induction factor is obtained by getting the weighted average of induction factors on multiple annuli over the swept area. The wake flow behaviour is also discussed in some details using velocity and pressure contour plots. In addition to the CFD model, an analytical model for calculating the average flow velocity and radii of the annular wake downstream of the kite is developed. The model is formulated based on the widely-used Jensen’s model (Technical Report Risø-M; No. 2411, 1983), which was developed for conventional wind turbines, and thus has a simple form. Expressions for the dimensionless wake flow velocity and wake radii are obtained by assuming self-similarity of flow velocity and linear wake expansion. Comparisons are made between numerical results from the analytical model and those from the CFD simulation. The level of agreement was found to be reasonably good. Such computational and analytical models are indispensable for kite farm layout design and optimization, where aerodynamic interactions between kites should be considered.

The Sulfur Dioxide (SO2) compound is a primary environmental pollutant worldwide, whereas elemental Sulfur (S) is a global commodity possessing a variety of industrial as well as commercial functions. The chemical relationship between poisonous SO2 and commercially viable elemental S has motivated this investigation using Density Functional Theory calculation of the relative transition state barriers for the 2-step Dehydro-sulfurization oxidation-reduction reaction. Additionally, doubly-charged nanoscale platelet Molybdenum Disulfide (MoS2), Armchair (6,6) Carbon Nanotube, 28-atom Graphene nanoflake (GR-28), and Fullerene C-60 are utilized as catalysts. The optimal heterogeneous and homogeneous catalysis pathways of the 2-step oxidation-reduction from SO2 to elemental S are further inspired by the biomimicry of the honeybee species multi-step bio-catalysis of pollen conversion to organic honey. Potential applications include environmental depollution, the mining of elemental sulfur, and the functionalization of novel technologies such as the recently patented aerial and amphibious Lynchpin TM drones.

Farming systems prevalent in sub-Saharan Africa are exposed and vulnerable to climate change due to their high dependence on rainfall. However, most studies have only estimated the impacts of climate change on agricultural productivity at a regional or national level. We add to this literature by focussing on the sub-national impacts. This study uses 30 years (1981–2011) of yield and weather data in Zambia and applies the Just and Pope model to determine how rainfall and temperature affect yield and yield variability of maize and beans at the national and subnational levels. Results show a negative impact of temperature rise on yield and a positive impact of rainfall rise on yield, above the current mean levels. These results differ by agro-ecological region. Worst-case-scenario predicted impacts using HadGEM-ES2 global circulation model show that major yield decreases (25% for maize and 34% for beans) by 2050 will be in region II and will be driven mainly by temperature increase offsetting the positive gains from rainfall increase. The model mainly under-predicts yield for maize and overpredicts yield for beans. These findings call for agro-ecological region-specific adaptation strategies and well-planned policy interventions to make agriculture more resilient to climate change.

Biological, environmental, behavioral, and social factors can influence sleep and lead to sleep disorders or diseases. Sleep disorders are common, numerous and heterogeneous in terms of their etiology, pathogenesis, and symptomatology. Management of sleep-wake circadian disorders (SWCD) includes education to sleep hygiene, behavioral strategies, psychotherapy (cognitive behavioral therapy (CBT), particularly), instrument-based treatments (i.e. positive airway pressure therapy, hypoglossal nerve stimulation), and pharmacotherapy. Depending on the disease, therapy varies and is executed sequentially, or can be a combination of several forms of therapy. Drugs used for SWCD include traditional sleep or wake-promoting agents, chronotherapeutic agents. Recently, novel medications, which are more precisely acting on specific neurochemical systems (i.e. orexin system) important for sleep and wake, are also increasingly being used. In this review, the pharmacotherapy of common sleep disorders (insomnia, sleep-related breathing disorder, central disorders of hypersomnolence, circadian rhythm sleep wake disorders, parasomnias, and sleep-related movement disorders) embedded in the overall therapeutic concept of each disorder is presented. There is also an outlook on possible future pharmacotherapies.

High-quality ionization injection methods for Wake Field Acceleration driven by lasers or charged beams (LWFA/PWFA) can be optimised so as to generate high-brightness electron beams with tuneable duration in the attosecond range. We present a model of the minimum bunch duration obtainable with low-emittance ionization injection schemes, by spotting the roles of the ionization pulse duration, of the wake field longitudinal shape and of the delay of the ionization pulse position with respect to the node of the accelerating field. The model is tested for the resonant multi-pulse ionization injection (ReMPI) scheme, showing that bunches having length of about 300as can be obtained with a ionization pulse having duration of 30fs FWHM

The General Data Protection Regulation (GDPR) protects the personal data of natural persons and at the same time allows the free movement of such data within the European Union (EU). Hailed as majestic by admirers and dismissed as protectionist by critics, the Regulation is expected to have a profound impact around the world, including in the African Union (AU). For European–African consortia conducting research that may affect the privacy of African citizens, the question is ‘how to protect personal data of data subjects while at the same time ensuring a just distribution of the benefits of a global digital ecosystem?’ We use location privacy as a point of departure, because information about an individual’s location is different from other kinds of personally identifiable information. We analyse privacy at two levels, individual and cultural. Our perspective is interdisciplinary: we draw from computer science to describe three scenarios of transformation of volunteered/observed information to inferred information about a natural person and from cultural theory to distinguish four privacy cultures emerging within the EU in the wake of GDPR. We highlight recent data protection legislation in the AU and discuss factors that may accelerate or inhibit the alignment of data protection legislation in the AU with the GDPR.

Intelligent Tutoring Systems (ITS) are increasingly popular for online learning. These systems use adaptive algorithms to recommend relevant content based on students' profiles. However, instructors need to periodically assess students' performance to ensure learning outcomes and adjust strategies accordingly. Our objective is to predict students' progress in advance, enabling teachers to make quicker decisions and facilitating the iterative process of adaptive algorithms. For this study, we collected a dataset from ALIN, an online learning platform, consisting of over 5,000 students' learning records and test results. Using this dataset, we conducted experiments employing various machine learning algorithms. The results indicate that learning behavior contributes to improving forecast performance, while students' progress strongly correlates with their previous test results. Additionally, we discovered that students' progress can be indirectly predicted by forecasting their scores. Furthermore, by breaking down overall scores into several distinct components and predicting individual scores for each component, the accuracy of the forecasts can be improved.

Data mining techniques have garnered significant attention within the realm of education. However, the procurement of ample student data poses a formidable challenge. In response to this challenge, we present a student dataset characterized by its size and distinctive attributes. This dataset encompasses various task-related topics interconnected through a learning pathway, thereby enabling researchers to delve into the data from novel perspectives. Moreover, it encompasses extensive longitudinal student behavioral data, a rarity that adds substantial value. Spanning the years from 2010 to 2021, our dataset comprises a cohort of 7,933 students, 64,344 test scores, and 183,390 behavior records, solidifying its status as a valuable resource for educational research. In our experiments, we achieved successful predictions of students' test outcomes based on behavioral learning data. The strengths of our dataset render it apt for analyzing the nexus between student conduct and academic performance, crafting personalized learning recommendations, and pursuing various other research pursuits.

Cardiovascular disease (CVD) is the number one cause of death worldwide. This condition resulted in huge research on CVD increasing the need for animal models suitable for the in vivo research. Daphnia and Zebrafish are good animal models for cardiovascular research due to their relative body transparency and easy culture property. Several methods have been developed to conduct cardiac performance measurement in Daphnia and Zebrafish. However, most of the methods only able to obtain heartbeat rate. The other important cardiac endpoints like stroke volume, ejection fraction, fraction shortening, cardiac output and heartbeat regularity must use other programs for measurement. To overcome this limitation, in this study, we successfully developed a one-stop ImageJ-based method using kymograph macros language that able to obtain multiple cardiac performance endpoints simultaneously for the first time. To validate its utility, we incubate Daphnia magna at different ambient temperatures and exposed zebrafish with astemizole to detect the corresponding cardiac performance alterations. In summary, the kymograph method reported in this study provides a new, easy to use, and inexpensive one-stop method on obtaining multiple cardiac performance endpoints with high accuracy and convenience.

Arboviruses are infections caused by arboviruses that may be either symptomatic or asymptomatic, according to individuals' resistance and immune response, which dietary patterns can influence. Thus, this study aimed to investigate the possible association between the occurrence of clinical manifestations of arboviruses and the dietary pattern of the population studied. The study was conducted in a rural population in the metropolitan area of Belém, Pará State, Brazil. The sampling consisted of 95 individuals during a febrile outbreak. Two investigations were conducted: clinical-epidemiological-dietary and laboratory investigation based on blood samples taken for arbovirus analysis. Out of the 95 individuals studied, more than half (57.89%) showed clinical manifestations. Following the laboratory serology (HI and IgM ELISA) analyses, viral isolation in C6/36 cells, and viral genome detection (RT-qPCR), the final diagnosis of the cases was 47.37% Oropouche Fever, 2.11% Oropouche Fever and Chikungunya Fever, and 1.05% Mayaro Fever. Regarding dietary pattern, as for the comparison analyses, statistical significance was observed between dietary pattern and classification regarding the presence of symptoms (p<0.05); after the correspondence analysis, symptomatic cases were more associated with a bad dietary pattern, while asymptomatic cases were more associated with a good dietary pattern.

We first explore negative-ion formation in fullerenes C44, C60, C70, C98, C112, C120, C132 and C136 through low-energy electron elastic scattering total cross sections calculations using our Regge-pole methodology. Water oxidation to peroxide and water synthesis from H₂ and O₂ are then investigated using the anionic catalysts C44ˉ to C136ˉ. The fundamental mechanism underlying negative-ion catalysis involves hydrogen bond strength-weakening in the transition state. DFT transition state calculations found C60ˉ numerically stable for both water and peroxide synthesis, C100ˉ increases the energy barrier the most and C136ˉ the most effective catalyst in both water synthesis and oxidation to H2O2.

Porous metals possess low bulk density, large specific strength, high stiffness and special thermal, electromagnetic and acoustic conductivity, so that porous metals are widely used in filtration and separation, flame retardant explosion-proof, biomedical application, etc. Compared with its corresponding dense metal, the presence of porous structures also leads to different corrosive performances of porous metal. So, some researches have utilized weight loss method, electrochemical impedance to evaluate porous metals’ corrosion behavior; however, the influence of pore structure on metal corrosion is still controversial, and present methods used for analyses of porous metals’ corrosion are statistical averages of the corrosion behavior of the entire porous material, which cannot accurately reflect the corrosion behavior inside the pore structure. Herein, we prepare porous copper samples with 0, 24, 72, 96 pores using mechanical process, and employ scanning kelvin probe combined with electrochemical polarization curve and impedance spectroscopy to test the corrosion performance of the porous copper in static and dynamic NaCl solution. The relevant results indicate that in static solution, the corrosion resistance of samples gradually increases with the rise of the number of pores. By contrast, the sample with 24 pores exhibits more susceptible to corrosion than the sample without pore.

Recently, medaka has been used as a model organism in various research fields. However, even though it possesses several advantages over zebrafish, fewer studies were done in medaka than zebrafish, especially its behaviour. Thus, to provide more information regarding its behaviour and to demonstrate the be-havioural differences between several species of medaka, we compared the behavioural performance and biomarker expression in the brain between four medaka fishes, which were Oryzias latipes, O. dancena, O. woworae, and O. sinensis. From the results, we found that each medaka species explicitly exhib-ited different behaviours to each other, which might be related to the different basal levels of several biomarkers. Furthermore, by phenomics and ge-nomic-based clustering, the differences between these medaka fishes were further investigated. Interestingly, even though both phenomics and ge-nomic-based clustering showed some resemblances to each other in terms of the interspecies relationship between medaka and zebrafish, however, in the medaka interspecies comparisons, this similarity was not displayed. Therefore, these results suggest a re-interpretation of several prior studies in comparative biology. We hope that these results contribute to the growing database of medaka fish phenotypes and provide one of the foundations for future phe-nomics studies of medaka fish.

Recent studies investigated the detectability of ship wake signatures on SAR imagery using a large number of SAR images collocated with Automatic Identification System data for training machine learning models. These detectability models are in agreement with oceanographic expectations from preceding studies and can therefore be used for comparing the performance of different SAR sensors in terms of wake detectability. Previous model comparisons showed better wake detection performance of TerraSAR-X (TS-X) than of RADARSAT-2 (RS2) and Sentinel-1 (S1). A comparison between CosmoSkymed (CSK) and RS2 is performed here, to examine the hypothesis that X-Band is generally better for wake detection than C-Band. Finally, this hypothesis is not confirmed, as the detectability models for TS-X, CSK and RS2 reveal similar performances. A comparison of wake detection performance should take the individual wake components into account separately.

Changes in the amount and the distribution of mean and turbulent quantities in the free shear layer wake of a 2D NACA 0012 airfoil and AR 4 NACA 0012 wing with passive segmented rigid trailing edge (TE) extensions was investigated at the University of Dayton Low Speed Wind Tunnel (UD-LSWT). The TE extensions were intentionally placed at zero degrees with respect to the chord line to study the effects of segmented extensions without changing the effective angle of attack. Force based experiments was used to determine the total lift coefficient variation of hte wing with seven segmented trailing edge extensions distributed across the span. The segmented trailing edge extensions had negligible effect of lift coefficient but showed measurable decrement in sectional and total drag coefficient. Investigation of turbulent quantities (obtained through Particle Image Velocimetry (PIV)) such as Reynolds stress, streamwise and transverse RMS in the wake, reveal a significant decrease in magnitude when compared to the baseline. The decrease in the magnitude of turbulent parameters was supported by the changes in coherent structures obtained through two-point correlations. Apart from the reduction in drag, the lower turbulent wake generated by the extensions has implications in reducing structural vibrations and acoustic tones.

This case study focuses on exploring the potential of biogas as a direct substitute for conventional fossil fuels in industrial applications. Instead of being limited to its common uses as a vehicle fuel or for power generation, this research aims to investigate how biogas can be integrated into industrial processes as an advanced and sustainable energy solution. By conducting a detailed analysis of the financial implications associated with the utilization of landfill gas and gas derived from biomass, a comprehensive understanding of their practical applications in the industrial sector can be obtained. This research goes beyond a superficial examination and delves into the intricate aspects of incorporating biogas as a viable alternative fuel source. When we say that utilizing biogas is technically feasible, it means that the necessary infrastructure and technologies are available to support its integration into industrial processes. This implies that there are existing methods and systems in place that can efficiently harness and utilize biogas for various industrial applications. These technologies ensure a seamless transition from conventional fossil fuels to renewable energy sources, leading to a more sustainable and environmentally friendly industrial landscape. Moreover, the statement that utilizing biogas is economically feasible emphasizes that the financial viability of employing biogas in industrial processes is advantageous. This research explores the potential cost savings and benefits associated with substituting conventional fuels with biogas. By carefully examining the economic aspects, such as initial investments, operational costs, and potential revenue streams, a comprehensive understanding of the financial impact of adopting biogas solutions in the industrial sector can be gained. By expanding and broadening the understanding of the technical and economic feasibility of biogas integration, this research aims to provide valuable insights into the potential of advanced biogas solutions for industrial applications. Indeed, to make biogas a viable and sustainable option as a raw material, it is necessary to establish farming subsidies and financial support specifically tailored for biogas production, similar to the existing support provided for food production. These subsidies and financial incentives play a crucial role in promoting and encouraging the growth of the biogas industry. By establishing such support systems, farmers and other stakeholders in the agricultural sector can be incentivized to participate in the production of biogas. This can involve the utilization of agricultural waste, organic residues, or dedicated energy crops for biogas generation. The financial assistance can help offset the initial investment costs, provide incentives for production, and ensure the economic viability of biogas projects. Moreover, when considering large-scale landfills or situations where industrial demands for energy are relatively low, landfill gas becomes a viable and practical option. Landfill gas, which is generated by the decomposition of organic waste in landfills, can be harnessed and utilized for various industrial purposes. This not only helps in managing waste but also provides a valuable source of renewable energy. In summary, while farming subsidies and financial support are crucial for promoting biogas production, landfill gas remains a viable option for large landfills or situations with minor industrial demands. The combination of these two approaches offers a comprehensive outlook on utilizing biogas as a renewable energy source for industrial applications.

The paper first compares the turn-off performance of two package solutions (3-lead vs 4-lead) in Super-Junction MOSFETs. It is commonly assumed that the better performance (lower switching losses) of the 4-lead is obtained thanks to the decoupling of the power and driving loop. On the contrary, in this work, the experimental results, circuit models, and Kirchhoff laws have shown that the turn-off improvement (lower turn-off losses) obtained by adopting the Kelvin source is due to the lower inductance of the driver loop in comparison with the power loop inductance, instead of the decoupling between these loops.

The Sulfur Dioxide (SO2) compound is a primary environmental pollutant worldwide, whereas elemental Sulfur (S) is a global commodity possessing a variety of industrial as well as commercial functions. The chemical relationship between poisonous SO2 and commercially viable elemental S has motivated this investigation using Density Functional Theory calculation of the relative transition state barriers for the 2-step Dehydro-sulfurization oxidation-reduction reaction. Additionally, doubly-charged nanoscale platelet Molybdenum Disulfide (MoS2), Armchair (6,6) Carbon Nanotube, 28-atom Graphene nanoflake (GR-28), and Fullerene C-60 are utilized as catalysts. The optimal heterogeneous and homogeneous catalysis pathways of the 2-step oxidation-reduction from SO2 to elemental S are further inspired by the biomimicry of the honeybee species multi-step bio-catalysis of pollen conversion to organic honey. Potential applications include environmental depollution, the mining of elemental sulfur, and the functionalization of novel technologies such as the recently patented aerial and amphibious Lynchpin TM drones.

Enhancing the Efficiency of Natural Gas Liquid (NGL) Extraction and Fractionation Trains: An Integrated Approach of Simulation Analysis, Advanced Modifications, and Technological Advancements. Through comprehensive research, innovative solutions such as the heat pump aided distillation (HPAD) system and self-heat recuperation technology (SHRT) have been developed to significantly reduce the energy consumption associated with conventional distillation systems. To identify a practical system for NGL fractionation trains, this study extensively examined and compared various HPAD and SHRT options to retrofit a single column. The objective was to find the most suitable and efficient solution for the fractionation process of natural gas liquids (NGLs). The retrofit options analyzed in this study encompassed a range of techniques, including vapor compression (VC), mechanical vapor re-compression (MVR), thermal vapor re-compression (TVR), bottom flashing (BF), side heat exchanger (SHE), intermediate heating and cooling (IHC), self-heat recuperative (SHR), and modified self-heat recuperative (MSHR) distillation. These methods were carefully examined to determine their suitability and effectiveness in improving the performance of NGL fractionation trains.In this study, a depropanizer column, typically employed in conventional NGL plants, was selected as a case study. The simulation software Aspen HYSYS V7.3 was employed to model and analyze eight retrofit designs based on predefined criteria. The simulation data was carefully evaluated to identify the most efficient design for minimizing energy consumption. Among the retrofit options, the mechanical vapor re-compression (MVR) technique demonstrated the most significant energy cost reduction, with a remarkable 68.11 percent improvement compared to the base case conventional column. These findings highlight the potential of MVR as an effective solution for lowering energy costs in NGL fractionation trains.Following the MVR retrofit option, the study found that vapor compression (VC) achieved a considerable energy cost reduction of 66.65 percent, closely followed by modified self-heat recuperative (MSHR) at 64.02 percent, bottom flashing (BF) at 62.88 percent, self-heat recuperative (SHR) at 55.85 percent, side heat exchanger (SHE) at 54.23 percent, intermediate heating and cooling (IHC) at 39.54 percent, and thermal vapor re-compression (TVR) also at 39.54 percent. These findings highlight the significant potential for energy savings offered by these retrofit options, with VC being the most popular choice, closely followed by MSHR, BF, SHR, SHE, IHC, and TVR.

This research investigates the implementation of complex exponential-based neurons in FPGA, which can pave the way for implementing bio-inspired spiking neural networks to compensate for the existing computational constraints in conventional artificial neural networks. The increasing use of extensive neural networks and the complexity of models in handling big data lead to higher power consumption and delays. Hence, finding solutions to reduce computational complexity is crucial for addressing power consumption challenges. The complex exponential form effectively encodes oscillating features like frequency, amplitude, and phase shift, streamlining the demanding calculations typical of conventional artificial neurons through levering simple phase addition of complex exponential functions. The article implements such a two-neuron and a multi-neuron neural model using Xilinx system generator and Vivado design suite, employing 8-bit, 16-bit, and 32-bit fixed-point data format representations. The study evaluates the accuracy of the proposed neuron model across different FPGA implementations while also providing a detailed analysis of operating frequency, power consumption, and resource usage for the hardware implementations. BRAM-based Vivado designs outperformed Simulink regarding speed, power, and resource efficiency. Specifically, the Vivado BRAM-based approach supported up to 128 neurons, showcasing optimal LUT and FF resource utilization. Such outcomes accommodate choosing the optimal design procedure for implementing spiking neural networks on FPGAs.

Ship wave is of great interest for wave drag, coastal erosion and ship detection. In this paper, a highly-paralleled numerical scheme is proposed for simulating three-dimensional (3-D) nonlinear Kelvin ship waves effectively. First, a numerical model for nonlinear ship waves is established based on potential flow theory, boundary integral method and Jacobian-free Newton-Krylov (JFNK) method. In order to improve computational efficiency and reduce data storage of JFNK method, a banded preconditioner method is then developed by formulating the optimal bandwidth selection rule. After that, a Graphics Process Unit (GPU) based parallel computing framework is designed, and a GPU solver is developed by using Compute Unified Device Architecture (CUDA) language. Finally, numerical simulations of 3-D nonlinear ship waves under multiple scales are performed by using the GPU and CPU solvers. Simulation results show that the proposed GPU solver is more efficient than the CPU solver with the same accuracy. More than 66% GPU memory can be saved and the computational speed can be accelerated up to 20 times. Hence, the computation time for Kelvin ship waves simulation can be significantly reduced by applying the GPU parallel numerical scheme, which lays a solid foundation for practical ocean engineering.

To overcome the time-consuming drawbacks of Computational Fluid Dynamics (CFD) numerical simulations, this paper proposes a hybrid model based on a parallel architecture based on the concept of intelligent aerodynamics, named PA-TLA (Parallel Architecture combining TCN, LSTM, and Attention mechanism). This model utilizes CFD data to drive efficient predictions of aircraft wake evolution at different initial altitudes during the approach phase. Initially, CFD simulations of continuous initial altitudes during the approach phase are used to generate aircraft wake evolution data, which are then validated against real-world LIDAR data to verify their reliability. The PA-TLA model is designed based on a parallel architecture, combining Long Short-Term Memory networks (LSTM), Temporal Convolutional Networks (TCN), and a tensor concatenation module based on the attention mechanism, which ensures computational efficiency while fully leveraging the advantages of each component in a parallel processing framework. The study results show that the PA-TLA model outperforms both the LSTM and TCN models in predicting the three characteristic parameters of aircraft wake: vorticity, circulation, and Q-criterion. Compared to the serially structured TCN-LSTM, PA-TLA achieves an average reduction in mean squared error (MSE) by 6.80%, mean absolute error (MAE) by 7.70%, and root mean square error (RMSE) by 4.47%, with an average increase in the coefficient of determination (R²) by 0.36%, and a 35% improvement in prediction efficiency. Lastly, this study combines numerical simulation and the PA-TLA deep learning architecture to analyze the characteristics of wake evolution during the near-ground phase, providing theoretical value for further reducing aircraft wake intervals and enhancing airport operational efficiency.

Yaw misalignment can make a wake steer, which is an effective method to increase the power of wind farms, but it also increases the fatigue load of the turbines. In this paper, the method of combining yaw misalignment and pitch control (CYMP) is proposed to investigate the potential to mitigate wake interference, the wind velocity distribution and turbulence distribution in the wake, as well as the power and load changes of the wind turbine are studied, focusing on power in-crement and fatigue damage reduction. Although some studies have found that the method of CYMP are beneficial to increase power generation and reduce fatigue loads, there are no papers that have made a detailed analysis of the effects of wake turbulence, wind speed distribution, power, especially fatigue damage under this control. Simulation tool FAST.Farm is used to model and analyze wind farms, obtain the variation details of wake and provide simulation data for calculating power and fatigue load. The pitch control brings bias to the CYMP optimization results was neglected, the case studies show that the method of CYMP can reduce the fatigue load by 10.29% and increase the total power by 1.7% compared with only wake steering. The Model Predictive Control collaborative method based on CYMP is proposed, which can increase power by more than 2% and reduce thrust by more than 4% under 10m/s turbulent wind.

In a vacuum tube two identical and parallel Ag-O-Cs emitters A and B (work function 0.8eV) ceaselessly emit thermal electrons at room temperature. The thermal electrons are controlled by a static uniform magnetic field so that the number of electrons migrate from A to B exceeds the one from B to A (or vice versa). The net migration of thermal electrons from A to B quickly results in a charge distribution of A charged positively and B negatively, and a potential difference between A and B emerges, enabling a continuous output current and a stable power to an external load (e.g., a resistor). Thus, the tube cools down (slightly). The (slightly) cooled tube extracts heat from ambient air, and all the heat is converted into electric energy without other effect. We believe the experiment is in contradiction to the Kelvin statement of the second law.

This paper explores the application of machine learning techniques for automated object grasping. Current electronic prosthetics often require extensive training for users to gain fine motor control over the prosthetic fingers, hindering their usability and acceptance. To address this challenge, this paper proposes an automated method that leverages computer vision-based techniques and machine learning algorithms. In this study, three reinforcement learning algorithms, namely Soft Actor-Critic (SAC), Deep Q-Network (DQN), and Proximal Policy Optimization (PPO), are employed to train agents for automated grasping tasks. The results indicate that the SAC algorithm achieves the highest success rate of 99% among the three algorithms at just under 200,000 timesteps. This research also shows that object’s physical characteristics can affect the agent’s ability to learn an optimal policy. Moreover, the findings highlight the potential of the SAC algorithm in developing intelligent prosthetic hands with automatic object gripping capabilities.

Schizophrenia is widely seen as a disorder of dysconnectivity. Neuroimaging studies have examined both structural and functional connectivity in the disorder, but these modalities have rarely been integrated directly. We scanned 29 patients with schizophrenia and 25 healthy control subjects and acquired resting state fMRI and diffusion tensor imaging. The Functional and Tractographic Connectivity Analysis Toolbox (FATCAT) was used to estimate functional and structural connectivity of the default mode network. Correlations between modalities were investigated, and multimodal connectivity scores (MCS) were created using principal components analysis. Nine of 28 possible region pairs showed consistent (>80%) tracts across participants. Correlations between modalities were found among those with schizophrenia for the prefrontal cortex, posterior cingulate, and lateral temporal lobes with frontal and parietal regions, consistent with frontotemporoparietal network involvement in the disorder. In patients, MCS values correlated with several aspects of the Positive and Negative Syndrome Scale, positively with those involving inwardly directed psychopathology, and negatively with those involving external psychopathology. In this preliminary sample, we found FATCAT to be a useful toolbox to directly integrate and examine connectivity between imaging modalities. A consideration of conjoint structural and functional connectivity can provide important information about the network mechanisms of schizophrenia.

A rigid protruding surface, agitator and vortex generator often dissipate more energy than to produce a satisfied turbulence production. In contrast, a passively-oscillating flexible protruding surface has the ability in generating greater turbulence level. In the current study, a circular finite cylinder (cantilever) was used as the geometry of the rigid and protruding surface. They were varied through the aspect ratio (AR) and materials. Also, a local Reynolds number within the subcritical flow range (102<ReD<105) was considered. The results from the rigid protruding surface (finite cylinder) is served as a validation to the published results and benchmark to the improve-ment of the turbulence generated by the flexible protruding surface. It was mentioned in the pre-vious work that the increment of turbulence production by the flexible cylinder is speculated to be oscillation, this work investigates the validity of the speculation through manipulating the struc-tural stiffness of the flexible cylinder by employing different materials. The results have further demonstrated that the flexible cylinder is capable of generating a greater turbulence through the examination of turbulence intensity, turbulence production term and Reynolds stress. All the flex-ible cylinders that oscillate show increment in turbulence production but at a different percentage. The cylinders studied in this work ranged from the least structural stiffness (EVA), moderate (al-uminium) to the highest structural stiffness (carbon steel). Through studying the normalized am-plitude responses graph of the flexible cylinder, it is identified that the oscillating motion does in-deed contribute to increment. A further investigation into the results, it is found that the factor which governs the increment is the structural velocity instead of just oscillating motion alone.

Sedation is a ubiquitous practice in ICUs and NCCUs. It has the benefit of reducing cerebral energy demands, but also precludes an accurate neurologic assessment. Because of this, sedation is intermittently stopped for the purposes of a neurologic assessment, which is termed a neurologic wake-up test (NWT). NWTs are considered to be the gold-standard in continued assessment of brain-injured patients under sedation. NWTs also produce an acute stress response that is accompanied by elevations in blood pressure, respiratory rate, heart rate, and ICP. Utilization of cerebral microdialysis and brain tissue oxygen monitoring indicates that this is not mirrored by alterations in overall cerebral metabolism, and seldom affects oxygenation. The hard contraindications for the NWT are preexisting intracranial hypertension, barbiturate treatment, status epilepticus, and hyperthermia. However, hemodynamic instability, sedative use for primary ICP control, and sedative use for severe agitation or respiratory distress are considered significant safety concerns. Despite ubiquitous recommendation, it is not clear if additional clinically relevant information is gleaned through its use, especially with the contemporaneous utilization of multimodality monitoring. Various monitoring modalities provide unique and pertinent information about neurologic function, however, their role in improving patient outcomes and guiding treatment plans has not been fully elucidated. There is a paucity of information pertaining to the optimal frequency of NWTs, and if it differs based on type of injury. Only one concrete recommendation was found in the literature, exemplifying the uncertainty surrounding its utility. The most common sedative used and recommended is propofol because of its rapid onset, short duration, and reduction of cerebral energy requirements. Dexmedetomidine may be employed to facilitate serial NWTs, and should always be used in the non-intubated patient or if propofol infusion syndrome (PRIS) develops. Midazolam is not recommended due to tissue accumulation and residual sedation confounding a reliable NWT. Thus, NWTs are well tolerated in most patients and remain recommended as the gold-standard for continued neuromonitoring. Predicated upon one expert panel, they should be performed at least one time per day. Propofol or dexmedetomidine are the main sedative choices, both enabling a rapid awakening and consistent NWT.

A modified free-wake vortex ring model is proposed to compute the dynamics of a floating horizontal-axis wind turbine. The model is divided into two parts. The near wake model uses a blade bound vortex model and trailed vortex model, which is developed based on vortex filament method. By contrast, the far wake model is based on the vortex ring method. The proposed model is a good compromise between accuracy and computational cost, for example when compared with more complex vortex methods. The present model is used to assess the influence of floating platform motions on the performance of a horizontal-axis wind turbine rotor. The results are validated on the 5MW NREL rotor and compared with other aerodynamic models for the same rotor subjected to different platform motions. It was found that the results from the proposed method are more reliable than the results from BEM theory especially at small angles of attack in the region of low wind speeds, on the one hand, and high wind speeds with blade pitch motions, on the other hand. And also the proposed method is less time consuming and has similar accuracy when comparing with more advanced vortex based methods.

Donepezil (DPZ) is an acetylcholinesterase inhibitor used for the clinical treatment of mild cognitive impairment. However, DPZ has been reported to have adverse effects, including abnormal cardiac rhythm, insomnia, vomiting, and muscle cramps. However, the existence of these effects in subjects without Dementia is unknown. In this study, we use zebrafish to conduct a deeper analysis of the potential adverse effects of DPZ on the short-term memory and behaviors of normal zebrafish by performing multiple behavioral and biochemical assays. Adult zebrafish were exposed to 1 ppm and 2.5 ppm of DPZ. From the results, DPZ caused a slight improvement in the short-term memory of zebrafish and induced significant elevation in aggressiveness, while the novel tank and shoaling tests revealed anxiolytic-like behavior to be caused by DPZ. Furthermore, zebrafish circadian locomotor activity displayed a higher reduction of locomotion and abnormal movement orientation in both low- and high-dose groups, compared to the control group. Biomarker assays revealed that these alterations were associated with an elevation of oxytocin and a reduction of cortisol levels in the brain. Moreover, the significant increases of reactive oxygen species (ROS) and malondialdehyde (MDA) levels in muscle tissue suggest DPZ exposure induced muscle tissue oxidative stress and muscle weakness, which may underlie the locomotor activity impairment. In conclusion, we show, for the first time, that the chronic waterborne exposure of DPZ can severely induce adverse effects on normal zebrafish in a dose-dependent manner. These unexpected adverse effects on behavioral alteration should be carefully addressed in future studies considering DPZ conducted on zebrafish or other animals.

The II law of thermodynamics is most often given in three supposedly equivalent formulations: two Clausius (I and II) and one Kelvin. The most general and indisputable entropy formulation belong to Clausius (II). The earlier Clausius I principle determines the natural direction heat flow between bodies at different temperatures. On the other hand, the Kelvin principle states that it is impossible to completely convert heat into work. The author argues that the Kelvin principle is a weaker statement (or more strictly non-equivalent) than Clausius I principle, and the latter is a weaker statement than Carnot principle, which is equivalent to Clausius II principle. As a result, the Kelvin principle and the Clausius I principle are not exhaustive formulations of the II law of thermodynamics. At the same time, it turns out that the Carnot principle becomes such a formulation. Apart from providing a complete set of proof and disproof, the author, indicates where the methodological errors were made in the alleged proof of the equivalence of the Kelvin principle and both Clausius principles.

This document introduces methods and algorithms to characterize a series of values as patterns of repeating symbols or sequences selected with the criterion to maximize the information retrieved. The method converts a series of real-number values into texts. Then, it interprets the text from a point of view specified by several parameters. Located patterns are linked nested, with shorter sequences within longer ones. Texts are then synthesized and organized in a tree-like structure, substantially reducing entropy. The characterization of processes serves as the basis for constructing models to estimate likely projected values. Alternative ways to assess the multiscale complexity of unidimensional processes are applications of the results obtained here.

Dietary patterns were recently applied to examine the relationship between eating habits and prostate cancer (PC) risk. While the associations between PC risk with glycemic index and Mediterranean score have been reviewed, no meta-analysis is currently available on dietary patterns defined by “a posteriori” methods. Search was carried out (PubMed, Web of Science) to identify studies reporting the relationship between dietary patterns and PC risk. Relevant dietary patterns were selected and the risk estimated were calculated by a random-effect model. Multivariable-adjusted odds ratios (ORs) for a 1st-percentile increase in dietary pattern score were combined by a dose response meta-analysis. 12 observational studies were included in the meta-analysis which identified “Healthy pattern” and “Western pattern”. The Healthy pattern was not related to PC risk (OR=0.96; 95% CI: 0.88-1.04) while the Western pattern significantly increased it (OR=1.34; 95% CI: 1.08-1.65). In addition, a “Carbohydrate pattern”, identified in four articles, was positively associated with a higher PC risk (OR=1.64; 95% CI: 1.35-2.00). A significant linear trend between the Western (p=0.011) and the Carbohydrate (p=0.005) pattern and the increment of PC risk was observed. The small numbers of studies included suggest that further investigations are necessary to support these findings.

In this paper the dynamics of spatially extended infected phytoplankton with the Holling type II functional response and logistically growing susceptible phytoplankton system is studied. The proposed model is an extension of temporal model available [6], in spatiotemporal domain. The reaction diffusion system exhibits spatiotemporal chaos in phytoplankton dynamics. This is particularly important for the spatially extended systems that are studied in this paper as they display a wide spectrum of ecologically relevant behavior, including chaos. In this system stability of the system is studied with respect to disease contact rate and the growth fraction of infected phytoplankton indirectly rejoin the susceptible phytoplankton population. The results of numerical experiments in one dimension and two dimensions in space as well as time series in temporal models are presented using MATLAB simulation. Moreover, the stability of the corresponding temporal model is studied analytically. Finally, the comparison of the three types of numerical experimentations are discussed in conclusion.

Background: Eid al-Adha is celebrated every year in Muslim countries; however, it is a time when patients present to the emergency department with serious injuries. In our study, we examined current injuries at the time of Eid al-Adha in the largest patient population in one of the largest hospitals in Turkiye, including tendon injury map and maxillofacial injuries for the first time in the literature. Methods: Between 2019 and 2023, patients admitted to Ankara City Hospital during Eid al-Adha were examined. The demographic characteristics, injury patterns, and injury sites of the patients were evaluated by analyzing the patients admitted on the four days of Eid al-Adha. Statistical analysis were performed using IBM SPSS Statistics for Windows. Results: Six hundred and ten patients, including 101 female and 509 male patients, were included in this study. There was a statistically significant increase (p

In this study, deep neural network (DNN) and transfer learning (TL) techniques were employed to predict the viscous resistance and wake distribution based on the positions of flow control fins (FCFs) applied to containerships of various sizes. Both methods utilized data collected through Computational Fluid Dynamics (CFD) analysis. The position of the flow control fin (FCF) and hull-form information were utilized as input data, and the output data included viscous resistance coefficients and components of propeller axial velocity. The base DNN model was trained and validated using a source dataset from a 1000 TEU containership. Grid search cross-validation technique was employed to optimize the hyperparameters of the base DNN model. Then, transfer learning was applied to predict the viscous resistance and wake distribution for containerships of varying sizes. To enhance the accuracy of feature prediction with a limited amount of dataset, learning rate optimization was conducted. Transfer learning involves retraining and reconfiguring the base DNN model, and the accuracy was verified based on the fine-tuning method of the learning model. The results of this study can provide hull designers for containerships with performance evaluation information by predicting wake distribution, without relying on CFD analysis.

This research delves into the impact of complex low-speed wake flow structures on the aerodynamic characteristics of objects downstream. It employs the proper orthogonal decomposition (POD) method and the domain precursor simulation method to compare traditional methods and validate this approach. The study generates several flow structures of parallel dual-cylinder wakes with different scales and spacing. The variations in the aerodynamic coefficient of three downstream objects at various times passing through wakes of varying scales are appropriately compared and analyzed. The study establishes that the wake with a cylinder spacing of G=1.5 has a more compact and concentrated modal structure than that with a cylinder spacing of G=0.35. Smaller objects are more responsive to the wake flow structure with a spacing of G=1.5, whereas larger objects respond more to the flow structure with a spacing of G=0.35. The achieved results also reveal that the aerodynamic force coefficients of objects passing through the wakefield at different times are closely related to the temporal characteristics of the wake flow structure with different scales.

Differentiated instruction has been implemented in classrooms where students who come from various backgrounds require appropriate lessons that are tailored to their varied learning preferences. As the spread of coronavirus 19 (Covid-19) is becoming more rampant, online learning has been lifted up as the main platform of teaching and learning, forcing the need to shift from the conventional face-to-face classroom to online, or virtual, mode of knowledge transfer. Having such global phenomenon affecting education, how does it affect the implementation of differentiated instruction that has been practised regularly by teachers before the wake of Covid-19? Previous studies have shown how it is done in the normal classroom setting, and that the practice of differentiated instruction contributes to students’ motivation as well as academic performance. However, there is a need to explore how do teachers go about with online differentiated instruction and how does it affect the students, since online learning poses various threads to both teachers and students; and thus, one might argue the effectiveness of not only the teaching approach but also the overall teaching and learning outcomes. Borg’s framework of teacher cognition, which has been extensively used in exploring how teachers teach, can be utilized to document the practice of online differentiated instruction. This study aimed to explore the practice of online differentiated instruction by teachers and examined its impact on students’ motivation and academic performance during the Covid-19 outbreak. The participants of this study consists of 247 gifted students from a public school in Malaysia. To meet these aims, this study employed a mixed method research design, utilizing the framework of teacher cognition to explore the teachers’ practice of online differentiated through interviews; and, utilizing a survey design using a questionnaire to determine the impact of online differentiated instruction towards students’ motivation and academic performance. The findings revealed that although online differentiated instruction is feasible, however, appropriate combination of differentiation constructs need to be applied in order to achieve higher motivation and better academic performance among the students.

Rapid eye movement sleep behavior disorder (RBD) is often prodromal to Parkinson’s disease (PD). Thus there should be detectable in vivo functional signatures shared between RBD and PD that aid in disease classification. To assess common in-vivo phenotypes, resting state data was collected on a 3T clinical MRI platform and a novel functional connectivity magnetic resonance imaging (fcMRI) approach, which combined independent component analysis (ICA) and graph theory, was used to evaluate deficits in interconnectivity among 15 PD, 14 RBD and 13 control participants. Whole brain and network-level analyses revealed the largest deficits in network connectivity in PD compared with controls, with less severe differences between RBD and controls. Importantly, the network-level analysis demonstrated decreased network interconnectivity, with the greatest aberrant networks in PD, and a subset in RBD. Additionally, a disease classification algorithm predicted PD cases by being trained on RBD cases with 0.87 sensitivity and 0.68 specificity. The functional alterations in cortical networks in RBD extended beyond the brainstem. These findings demonstrate progressive reductions in connectivity between brain networks, with less severe deficits in RBD than PD. Moreover, RBD phenotypes can be used to predict PD status in a cross-sectional sample, which suggests RBD is an intermediate phenotype.

The main objective of this study is to conduct a bibliometric analysis of scholarly publications of Authorship Pattern. The present study covers 1723 research papers published in the area of authorship pattern and indexed in Scopus database from the year 2013 to 2022. These research publications considered for the present study have been analysed based on their year wise growth, pattern of authorship, times citations, type of publication, most productive publication source as well as countries and institutions. The study shows the positive growth of the literatures with collaborative authorship pattern and good citation status. This original research paper will be helpful to the researchers of library and information science, especially who are working in the area of bibliometrics studies.

Elevation has been a cornerstone of biodiversity research, and changes in the environmental factors behind it influence biodiversity and community patterns. Exploring the potential reasons behind liverwort community patterns has been a matter of multiplied interest for ecologists. In the present study, we assessed the liverwort plants of the Sygera Mountain from 3100 m to 4500 m decaying wood, tree, soil, and rock using complex field surveys in 2017 and 2019, and investigated the effects of altitude and various climatic factors on the diversity and community composition of the Sygera Mountain. Also, we used including ANOVA, mixed effects modeling, NMDS, ANOSIM, and CCA, which helped to critically assess the liverwort diversity with respect to environmental variables. The results of the study showed a bimodal variation in the richness of liverwort communities with altitude, with peaks occurring at 3500 m and 4100 m. The variation with altitude was significant, with communities at 4100 m and 4300 m being associated with arid environments, and those at 3100 m and 3300 m favoring areas of higher mean annual temperatures and precipitation. Among the climatic factors, the variation in mean diurnal rang, precipitation, and annual temperature was found to be the determinant of liverwort communities. The results suggest that elevation and daily temperature fluctuations play a crucial role in the distribution and community structure formation of mountain liverwort plants. This study deepens our understanding of liverwort ecology and emphasizes the importance of climatic variables in determining plant community composition.

In the investment casting process, the pattern made of wax is obtained in a die for further formation of shell mold. This work investigates commercial pattern waxes fluidity with a newly developed injection fluidity test. A good correlation between wax fluidity and its viscosity is observed, which is different from metallic alloys, where the solidification behavior is more critical. The difference in investigated filled waxes fluidity can be associated with the type and amount of filler.

MRI shows in-vivo the three archetypal patterns of CNS volume loss underlying progressive ataxias, namely spinal atrophy (SA), cortical cerebellar atrophy (CCA) and olivopontocerebellar atrophy (OPCA). The MRI-based CNS atrophy pattern was reviewed in 128 progressive ataxias. A CNS atrophy pattern was identified in 91 conditions: SA in Freidreich’s ataxia, CCA in 5 acquired and 72 (24 dominant, 47 recessive,1 X-linked) inherited ataxias, OPCA in Multi-System Atrophy and 12 (9 dominant, 2 recessive,1 X-linked) inherited ataxias. The MRI-based CNS atrophy pattern may be useful for genetic assessment, identification of shared cellular targets, and repurposing therapies or enlargement of drugs indications in progressive ataxias.

Drafting is a common technique to reduce the drag experienced by elite runners on races, leading to better running efficiency and energy savings. The tactic has been successfully used in previous races where world records were set, especially by Eliud Kipchoge in the 2018 and 2022 editions of the Berlin marathon. In the 2023 edition of the Chicago Marathon, Kenyan runner Kelvin Kiptum broke the Marathon record after a 2:00:35 finish. This feat becomes even more impressive by the lack of use of drafting formations, despite the availability of two pacers for the majority of the race. In this study, the drag faced by Kiptum and his pacers during the race is calculated by means of computational fluid dynamics (CFD). The performance of each runner is evaluated from an energetic standpoint, and the analysis is extended to include more efficient drafting formations. Our results indicate that, by properly using the advantages of drafting, Kiptum could have finished the race at a staggering 1:57:34, a full three minutes better than his own record and 215 seconds better than the previous world record.

Clopidogrel, a prescription drug to reduce ischemic events in cardiovascular patients, has been extensively studied in mostly European individuals but not among Caribbean Hispanics. This study evaluated the low abundance and reduced activity of paraoxonase-1 (PON1) in clopidogrel-resistant patients as a predictive risk biomarker of poor responders and disease severity in this population. Thirty-six patients on clopidogrel (cases; divided into poor and normal responders) were enrolled along with 11 cardiovascular patients with no clopidogrel indications (positive control) and 13 healthy volunteers (negative control). Residual on-treatment platelet function (PRU), PON1 abundance by western blotting, and PON1 activity by enzymatic assays were measured. PON1 genotyping and computational haplotype phasing were performed for two genetic loci (rs662 and rs854560). No statistical differences in mean relative PON1 abundance were found among the groups (p&gt; 0.05). However, a significantly lower enzymatic activity was found in poor responders (10.57 6.79 µU/mL) when compared to controls (22.66 8.30 µU/mL and 22.21 9.66 µU/mL; p= 0.004). PON1 activity among carriers of the most prevalent PON1 haplotype (AA|AA) was significantly lower than in wild-types (7.90 µU/mL vs 22.03 µU/mL; p= 0.005). Our findings suggested that PON1 is a potential biomarker of cardiovascular disease severity in Caribbean Hispanics.

Inflammation is the body’s response to infection, injury, or other stimuli. Acute inflammation is a crucial component of the immune system’s defense, aimed at eliminating infected or damaged cells and halting disease progression. A subtle interaction between pro- and anti-inflammatory processes determines its progression to inflammation resolution or to chronic inflammation. In this study, we propose a generic model of inflammation through a system of reaction-diffusion equations involving various inflammatory and anti-inflammatory cells and cytokines. We investigate the formation of patterns, determined by the emergence of Turing structures, through linear stability analysis and numerical simulations. These theoretical findings are further supported by observations of similar patterns in skin diseases.

This article introduces a novel approach, termed the Asymmetric Probabilistic Tsetlin (APT) Machine, which incorporates the Stochastic Point Location (SPL) algorithm with the Asymmetric Steps technique into the Tsetlin Machine (TM). APT introduces stochasticity into the state transitions of Tsetlin Automata (TA) by leveraging the SPL algorithm, thereby enhancing pattern recognition capabilities. To enhance random search processes, we introduced a decaying normal distribution into the procedure. Meanwhile, the Asymmetric Steps approach biases state transition probabilities towards specific input patterns, further elevating operational efficiency. The efficacy of the proposed approach is assessed across diverse benchmark datasets for classification tasks. The performance of APT is compared with traditional machine learning algorithms and other Tsetlin Machine models, including the Asymmetric Tsetlin (AT) Machine, characterized by deterministic rules for Asymmetric transitions, and the Classical Tsetlin (CT) Machine, employing deterministic rules for symmetric transitions. Strikingly, the introduced APT methodology demonstrates highly competitive outcomes compared to established machine learning methods. Notably, both APT and AT exhibit state-of-the-art performance, surpassing the Classical Tsetlin Machine, emphasizing the efficacy of asymmetric models for achieving superior outcomes. Remarkably, APT exhibits even better performance than AT, particularly in handling complex datasets.

Security patterns encompass security-related issues in secure software system development and operations that often appear in certain contexts. Since the late 1990s about 500 security patterns have been proposed. Although the technical components are well investigated, the direction, overall picture, and barriers to implementation are not. Here, a systematic literature review of 240 papers is used to devise a taxonomy for security pattern research. Our taxonomy and the survey results should improve communications among practitioners and researchers, standardize the terminology, and increase the effectiveness of security patterns.

Ovarian tissue cryopreservation (OTC) or testicular tissue cryopreservation (TTC) are effective and often the only options for fertility preservation in female or male patients due to oncological, medical, or social aspects. While TTC and resumption of spermatogenesis, either in vivo or in vitro, has still be considered an experimental approach in humans, OTC and autotransplantation has been applied increasingly to preserve fertility with more than 200 live births worldwide. However, the cryopreservation of reproductive cells followed by the resumption of gametogenesis, either in vivo or in vitro, may interfere with sensitive and highly regulated cellular processes. In particular, the epigenetic profile, which includes not just reversible modifications of the DNA itself but also post-translational histone modifications, small non-coding RNAs, gene expression and availability, and storage of related proteins or transcripts, have to be considered in this context. Due to complex reprogramming and maintenance mechanisms of the epigenome in germ cells, growing embryos, and offspring, OTC and TTC are carried out at very critical moments early in the life cycle. Given this background, the safety of OTC and TTC taking into account the epigenetic profile has to be clarified. Cryopreservation of mature germ cells (including Metaphase II oocytes and mature spermatozoa collected via ejaculation or more invasively after testicular biopsy) or embryos has been used successfully for many years in Medically Assisted Reproduction (MAR). However, tissue freezing followed by in vitro or in vivo gametogenesis has become more attractive in the past, while few human studies have analysed the epigenetic effects, with most data deriving from animal studies. In this review, we highlight the potential influence of the cryopreservation of immature germ cells and subsequent in vivo or in vitro growth and differentiation on the epigenetic profile in humans and animals.

Influenza virus imprinting is now understood to significantly the influence immune responses and clinical outcome of influenza virus infections that occur later in life. Due to the yearly cycling of influenza viruses, humans are imprinted with the circulating virus of their birth year to subsequently build a complex influenza virus immune history but very little is known about how the imprinting strain influences vaccine responses. To investigate the imprinted host immune responses to split-virion vaccination, we imprinted ferrets with a sublethal dose of the historical seasonal H1N1 strain A/USSR/90/1977. After a +60 day recovery period was given to build immune memory, ferrets were immunized and the challenge at Day 123. Samples were collected throughout the time course and immunological assays were performed to investigate recall mechanisms. The preimmune-vaccinated ferrets did not experience significant disease during challenge while naïve-vaccinated ferrets had severe disease. Hemagglutination inhibition assays showed preimmune ferrets had a more robust antibody response post vaccination, increased virus neutralization activity. Virus specific immunoglobulins were of predominantly the IgG isotype suggesting B cell maturity and plasticity at vaccination. These results are important and should be considered for vaccine design.

Background: The Covid-19 pandemic abruptly disrupted global healthcare systems, necessitating rapid adaptations. This study evaluates the impact on health systems and services in the aftermath of the first wave in the Indian context. It analyses the disruptions, adaptive measures, and changes made, as well as the challenges faced by healthcare providers and seekers. Valuable insights from this study will enhance the resilience and preparedness of healthcare systems for future challenges. Methods: The eligible studies included primary studies conducted in the Indian context that explore the impact of COVID-19 on health services provision, utilisation, and the well-being of healthcare providers and seekers. Electronic searches were conducted in six databases: PubMed, MEDLINE, Embase, Global Health, CINAHL, and the WHO database on COVID-19. The results were analysed using detailed narrative synthesis. Results: The review includes 38 articles, incorporating a total of 22,502 subjects. There has been a substantial impact on health service provision, particularly in outpatient departments (OPD) (n=19) and elective services (n=16), while emergency services continued at sub-optimal levels (n=20). Various adaptations and changes were implemented in precautionary measures, protocols, staff allocation and training, usage of personal protective equipment (PPE), preoperative, operative, and postoperative measures, as well as physical infrastructure and resources. Depression and stress (n=14), fear of contracting the infection (n=6), stigmatisation (n=5), and financial repercussions (n=5) significantly affected the mental health of healthcare providers, and healthcare seekers also faced significant challenges (n=11). Conclusion: The study reveals COVID-19's substantial impact on health services. The healthcare system responded by quickly adjusting staff management, resource allocation, and infection prevention measures. The study also highlights the mental health challenges faced by healthcare providers and the concerns of healthcare seekers regarding delays and suboptimal care. Looking ahead, the findings underscore the importance of preparedness for future pandemics, including improved healthcare infrastructure, resource optimisation, and comprehensive protocols. Lessons learned from COVID-19 should inform strategies to mitigate disruptions and ensure the well-being of healthcare providers and seekers in future outbreaks.

Footprints are powerful indicators for evaluating the impact of the bioeconomy of a country on environmental goods, domestically and abroad. In this study, we apply a hybrid approach combining a Multi-Regional Input-Output model and land use modelling to compute the agricultural land footprint (aLF). Furthermore, we added information on land-use change to the analysis and allocated land conversion to specific commodities. The German case study shows that the aLF abroad is larger by a factor of 2.5 to 3 than the aLF in Germany. In 2005 and 2010, conversion of natural and semi-natural land-cover types abroad allocated to Germany due to import increases was 2.5 times higher than the global average. Import increases to Germany slowed down in 2015 and 2020, reducing land conversion attributed to the German bioeconomy to the global average. The case study shows that the applied land footprint provides clear and meaningful information for policymakers and other stakeholders. The presented methodological approach can be applied to other countries and regions covered in the underlying database EXIOBASE. It can be adapted, also for an assessment of other ecosystem functions, such as water or soil fertility.

The effective management of Fusarium wilt of bananas (FW) depends on the knowledge of the disease dynamics in time and space. The objectives of this work were: To estimate disease intensity and impact, and to investigate the spatial and temporal dynamic of FW. Fields planted with Silk (n = 10), Pome (n = 17) or Cavendish (n = 3) banana subgroups were surveyed in Brazil, totaling 95 ha. In each field, all plants were visually assessed and diseased plants were georeferenced. The incidence of FW and the impact of the disease on yield on a regional scale were estimated. Spatial patterns were analyzed using quadrat- and distance-based methods. FW incidence ranged from 0.09 to 41.42%, being higher in Silk fields (median = 14.26%). Impacts of epidemics on yield ranged from 18.4 to 8,192.5 kg.ha-1.year-1, with a median of 935.2 kg.ha-1.year-1. The higher economic impact of the disease was observed on Silk cultivar with a median loss of US$ 910.5 ha-1.year-1. Overall, estimated losses increased on average by US$ 109.8 ha-1.year-1 at each 1% of incidence. Aggregation of FW was detected by all analytical methods in 13 fields (1 of Cavendish, 11 of Pome and 1 of Silk). In the other 17 fields, at least one analytical method did not reject the null hypothesis of randomness. One field (5 ha), composed of six plots, was selected for spatial and temporal studies during two years with bi-monthly assessments. A sigmoidal curve represented the FW progress and the Gompertz model best fitted disease progress. The level of aggregation varied over time, and evidence of secondary infection to neighboring and distant plants were detected. FW is a widespread problem in Brazil and yield losses can be of high magnitude. Epidemiology-based management strategies can now be better established.

The expression microarray technique was performed to investigate the differences in gene expression between Czech Large White pigs and wild boars in the longissimus lumborum et thoracis and biceps femoris muscle tissues. The NREP gene (neuronal regeneration related protein homolog) was selected for detailed study as an expressional and functional candidate gene. NREP plays a role in the transformation of neural, muscle and fibroblast cells and in smooth muscle myogenesis. Quantitative real-time PCR results confirmed that the porcine NREP gene was expressed in both skeletal muscles and significantly overexpressed in Czech Large White pigs compared to wild boars (P &lt; 0.05). We identified 9 polymorphic sites in genomic DNA of NREP gene. Six of these polymorphisms were in complete linkage disequilibrium and therefore only 4 polymorphisms were informative. Associations of these 4 polymorphisms (HF571253:g.103G&gt;A, HF571253:g.134G&gt;A, HF571253:g.179T&gt;C and HF571253:g.402_409delT) with meat performance traits were assessed in Czech Large White pigs. New polymorphisms in NREP gene were significantly associated with parameters of daily weight gain, lean meat and backfat thickness in Czech Large White pigs. Our primary study suggested that porcine NREP may play an important role in skeletal muscle growth, fat metabolism and meat performance traits.

Agriculture and farming worldwide are responsible for numerous environmental threats, including degradation of land and water resources depletion. Underlining the dynamic interaction between bio-physical and socio-economic drivers is the key towards a more sustainable land and water management. With regard to a highly developed agricultural area in southern Italy, multi-regression models were developed to interpret the observed inter-annual variability of cropped land. Main drivers related to Common Agricultural Policy support, product market prices, crop yield and irrigation water availability were investigated. The adopted models revealed the different weighs of each driver. The findings reported the role that direct payments played in supporting the extension of irrigated crops, such as processing tomato. Likewise, the models pointed to decoupled payment scheme as the most important driver of change in the crop pattern over the last years.

We investigated if the abnormal sleep-wake rhythms in infants are related to the future development of autism spectrum disorder (ASD), using questionnaire from September to October 2016. The parents of a total 160 children with ASD (male, n=123; female, n=37) recruited from the two hospitals in K and H cities and as a control group, 145 children (male, n=75; female, n=70) recruited from four nursery schools in T city. Fisher’s exact test was used for the comparison of categorical variables. The associations of bedtime and waking time on weekdays and holidays in infancy, at 1-3≦ (1-3) years of age and at 3-5 ≦ (3-5)years of age with the prevalence of ASD were analyzed using a multivariable logistic regression analysis. Especially by 3 years of age, the following factors were associated with an increased risk of developing ASD in the future: 1) short sleep (<8h), 2) long time to fall asleep (>60min), 3) sleep onset time after 22:00h, 4) wake-up time after 07:00h and/or 08:00h on weekdays and holidays, respectively, 5) frequent (>3 times) and long-term awakening (>60min). Misalignment and/or shift of the sleep-wake circadian rhythm in infant may be one of the precursors and/or risk factor to the future development of ASD.

Daily thousands of people and goods move along Brazilian Federal highways. Traffic accidents are numerous on these highways and have a significant impact, whether on the economy or the health system. Identifying predictor variables, the probability of an event occurring and how to mitigate them are of paramount importance for the actions of the transit authorities that manage these roads. The main contribution of this study is the development of a predictive machine learning model which uses open data to shows graphically the critical points in the highways. This model is fully reproducible and can be applied to any region worldwide helping to minimize the number of accidents and to prevent deaths by automotive collisions. For this study, 43 variables were analyzed supporting the identification of the causes of accidents with fatal victims on the main highways in the south of Brazil. RoadLytics is proposed as a supervised machine learning model, using the Random Forest algorithm to analyze about 33 thousand occurrences between 2017 and 2020. An exploratory analysis of the data was carried out to support the modeling and to facilitate data visualization. In this sense, heat maps were developed to support the analysis and identification of potential risk areas. The results show that BR386 highway registers the highest number of fatal occurrences, regardless of the season. Additionally, concerning the weather conditions, the analysis shows that 52% of accidents occurred in favorable conditions, such as clear skies, victimizing 501 people. The driver’s lack of attention is the main reason for the accidents’ occurrences. Applying the developed model, an accuracy of 77% was achieved for the classification of fatal accidents.

Generally, when cognitive radio users are in Underlay mode, through the analysis of the electromagnetic spectrum in time, frequency, space, and field of multiple dimensional data accounts for judging the inherent correlation of the radio spectrum used/idle state, this is the realization of cognitive radio users (secondary users, SUs) efficient access to the foundation of the limited spectrum resources. Therefore, how to efficiently use of spectrum status of each SU implementation of reception multidimensional combination forecasting is the core of this paper addressing the problem. In this paper, we propose a deep-learning hybrid model called TensorGCN-LSTM based on the tensor data structure. The model treats SUs deployed at different spatial locations under the same frequency and the spectrum status of SUs themselves under different frequencies in the task area as nodes and constructs two types of graph structures. Graph convolutional operations are used to sequentially extract corresponding spatial-domain and frequency-domain features from the two types of graph structures. Then, the long short-term memory (LSTM) model is used to fuse the spatial, frequency, and temporal features of the cognitive radio environment data. Finally, the prediction task of the spectrum distribution situation is accomplished through fully connected layers. Specifically, the model constructs a tensor graph based on the spatial similarity of SUs' locations and the frequency correlation between different frequency signals received by SUs, which describes the electromagnetic wave's dependency relationship in spatial and frequency domains. LSTM is used to capture the electromagnetic wave's dependency relationship in the temporal domain. To evaluate the effectiveness of the model, we conducted ablation experiments on LSTM, GCN, GC-LSTM, and TensorGCN-LSTM models using simulated data. The experimental results show that our model achieved better prediction performance in RMSE, and the correlation coefficient R2 of 0.8753 also confirmed the feasibility of the model.

Music acoustics is an interdisciplinary field and mathematics is the basis in the music art form. Music and mathematics correlation exist since the inception of music. Various philosophers, scientists, mathematicians and musicians have expressed their views about this relationship. This paper attempts to explore this association with focus on melodic pattern identification. Mathematics in Indian Classical music with raga as the basis and just intonation tuning system is discussed. Indian vocal music clips are used for different pitch estimation algorithms in the experimentation. Harmonic product spectrum and auto-correlation algorithms are tested for accurate pitch estimation. Enhanced auto-correlation function using audio segmentation is compared with other approaches for effective pitch extraction. Results indicate pitch extraction with enhanced auto-correlation function provides accurate results as compared with other approaches tested.

Despite increased protection globally, climate change and other anthropogenic factors have caused a significant decline in seagrass cover. Identifying the specific causes of this decline is paramount if they are to be addressed. Therefore, we identified the causes of long-term change in seagrass/submerged aquatic vegetation (SAV) percentage cover and extent in the Bluefields Bay Special Fish Conservation Area (BBSFCA), a marine protected area on Jamaica’s southern coast. We used two random forest regression (RFr) models that included 2013 hydroacoustic survey SAV percentage cover data (dependent variable), and auxiliary data, reflectance data from level 2 processed 2013 Landsat 7 and 8 images and image band texture statistics maps (mean and variance) as predictors to generate 24 SAV percentage cover maps for the period 1984 – 2021 (37 years) using data from Landsat 4-5, 7 and 8 images., from which benthic features maps (SAV present, absent and coral reef) were created. Rainfall and map data were used to determine if SAV extent/area (km2) and average percentage cover and annual rainfall changed significantly over time and to evaluate the influence of rainfall. Rainfall impact on the overall spatial patterns of SAV loss, gain, and percentage cover change was assessed using a pixel-based regression. Finally, the most important spatial pattern predictors (two rainfall proxies (distance and direction from river mouth), benthic topography, depth, and hurricane exposure (a measure of hurricane disturbance)) of SAV loss, gain, and percentage cover change during 23 successive 1-to-4-year periods were identified using spatial Bayesian INLA generalized linear mixed models. SAV area/extent was largely stable with &gt; 70% mean percentage cover for multiple years. However, Hurricane Ivan (in 2004) caused a significant decline in SAV area/extent (by 1.62 km2, or a 13%) during 2002 – 2006 and a second hurricane (Dean) in 2007 delayed recovery until 2015. Additionally, rainfall declined significantly by &gt;1000 mm since 1901, and mean monthly rainfall positively influenced SAV percentage cover change. The pixel-based regression highlighted areas where mean monthly rainfall had a positive overall effect on SAV cover percentage change (across the entire bay) and gain (closer to the mouth of a river). The most frequently selected important spatial pattern predictors were the two rainfall proxies (areas closer to the river mouth were more likely to experience SAV loss and gain) and depth, with shallow areas generally having a higher probability of SAV loss and gain. Three hurricanes had significant but different impacts depending on their distance from the southern coastline. Hurricane Gilbert, which made landfall in 1988, resulted in higher SAV percentage cover loss in 1987 - 1988. Benthic locations with a northwestern/northern facing aspect (the predominant direction of Ivan’s leading edge wind bands) experienced higher SAV losses during 2002 – 2006. Although some locations impacted by Ivan and not by Dean recovered during 2006 – 2008, exposure to Ivan explained percentage cover loss during 2006 – 2008 and average exposure to (the cumulative impact of) Ivan and Dean (both with tracks close to the southern coastline) explained SAV loss during 2013 – 2015. Therefore, despite historic lows in annual rainfall, overall, higher rainfall was beneficial, multiple hurricanes impacted the site, and despite two hurricanes in three years SAV recovered within a decade. Hurricanes and a further reduction in rainfall may pose a serious threat to SAV persistence in the future.

In offshore drilling, accidents such as gas invasion, overflow, and kicks are inevitable, and they can escalate into blowouts and other catastrophic events that result in casualties and substantial economic losses. Therefore, maintaining drilling safety requires accurate monitoring of gas invasion and overflow. The majority of overflow monitoring methods currently utilized at drilling sites are threshold-based. However, monitoring parameters acquired during actual drilling operations frequently contain noise signals, making it difficult for threshold-based methods to strike a balance between improving accuracy and minimizing false positives. In this paper, Pattern Recognition-based Kick Detection (PRKD) is proposed as a novel method for diagnosing overflow in offshore drilling. This method utilizes the overflow evolution process by integrating multi-phase flow calculations, data filtering theory, pattern recognition theory, the Bayesian framework, and other theoretical models. The PRKD effectively detects and monitors gas intrusion and overflow based on single parameters by analyzing the shape and wave characteristics of the curves. The case analysis demonstrates that the proposed method for monitoring drilling overflow achieves high precision while maintaining a low false positive rate. By combining advanced computational techniques with pattern recognition algorithms, the PRKD improves the accuracy and reliability of kick detection, enabling proactive responses to potential risks, protecting the environment and human lives, and optimizing drilling operations.

Ground penetrating radar (georadar, GPR), widely used in geology, archeology and road construction, is an efficient tool for searching and studying subsurface objects and structures. When planning GPR missions, it is necessary to predict the resolution of the device and the achievable probing depth. The article discusses the methods and results of assessing these characteristics of the Loza-V and Loza-N radars obtained in the course of archaeological and geographical expedition works.

For personalized recommender systems,matrix factorization and its variants have become mainstream in collaborative filtering.However,the dot product in matrix factorization does not satisfy the triangle inequality and therefore fails to capture fine-grained information. Metric learning-based models have been shown to be better at capturing fine-grained information than matrix factorization. Nevertheless,most of these models only focus on rating data and social information, which are not sufficient for dealing with the challenges of data sparsity. In this paper,we propose a metric learning-based social recommendation model called SRMC.SRMC exploits users' co-occurrence pattern to discover their potentially similar or dissimilar users with symmetric relationships and change their relative positions to achieve better recommendations.Experiments on three public datasets show that our model is more effective than the compared models.

Existing implementations of game design patterns have largely been confined to theoretical or research settings. Weaknesses in these implementations have prevented game design patterns from being properly evaluated as an educational and practical development tool. This paper examines these weaknesses, describes a method of developing and applying patterns that overcome the weaknesses, and evaluates use of the method for game design education and practice. Weaknesses in existing pattern implementations are: omission of design problems, presumption of functional completeness at the level of pattern languages, narrow topical focus, and lack of a concise, repeatable method for pattern production. Several features of the proposed method were specifically built to address these weaknesses, namely the pattern template, the process for connecting patterns into a language and assessing the language’s scope, a rubric for assessing pattern confidence and interconnectivity confidence, and pattern-building exercises. This method was applied in a classroom setting. Results, as assessed by the evaluation of student work, suggest that creating patterns/pattern languages is an effective pedagogical approach. De-signs produced using designer-created patterns closely align with existing design theory and are clearly understood by students. The above results may indicate that the path to gaining wider acceptance of pattern theory as a design framework within game design is not to produce a universal pattern language but to facilitate the creation of case-specific languages, by students and professional designers, that use a shared ontology and thus can be combined easily to solve the diverse sets of problems faced by these groups.

The study aimed to evaluate the association between the changes in ventilatory variables (tidal volume (Vt), respiratory rate (RR), and lung ventilation (VE)) and deoxygenation of m.intescostales (∆SmO2-m.intercostales) during a maximal incremental exercise in nineteen male competitive marathon runners. The ventilatory variables and oxygen-consumption (VO2) were recorded breath-by-breath by exhaled gas analysis. A near-infrared spectroscopy device (MOXY) located in the right-hemithorax allowed recording SmO2-m.intercostales. To explore changes in oxygen levels in muscles with high demand during exercise, a second MOXY records SmO2-m.vastus laterallis. The triphasic model of exercise intensity was used for evaluating changes in SmO2 in both muscle groups. We found that ∆SmO2-m.intercostales correlated with VO2-peak (r=0.65; p=0.002) and the increase of VE (r=0.78; p=0.001), RR (r=0.54; p=0.001), but not Vt (p=0.210). The interaction of factors (muscles X exercise-phases) in SmO2 expressed as an arbitrary unit (a.u) was significant (p=0.005). At VT1 were no difference (p=0.177), but SmO2-m.intercostales was higher at VT1 (p&lt;0.001) and VO2-peak (p&lt;0.001). In competitive marathon runners, the m.intercostales deoxygenation during incremental exercise is directly associated with the aerobic capacity and increased lung ventilation and respiratory rate, but not tidal volume. Besides, it shows less deoxygenation than m.vastus laterallis at intensities above the aerobic ventilatory threshold.

Recent advancements in web-based geospatial software and smartphone technology have popularized the process of voluntary production and sharing of geospatial data by individual citizens. Through such Volunteered Geographic Information (VGI) activities, people across the world participate in online mapping projects (such as OpenStreetMap) to insert their spatial information. The quality of data generated by such VGI activities has profound impacts on online mapping projects and their spatial database. In this study, we examine the VGI contribution pattern in OpenStreetMap through three case study neighborhoods located in three major cities: Tehran, London, and Los Angeles, and investigate how it might affect the process of quality assessment of VGI.

Age-related Macular Degeneration (AMD) is the major cause of elders’ vision loss, early screening and treatment are the most efficient way to reduce the rate of blindness. AI-based methods based on ophthalmic images play great potential for AMD diagnosis. However, low levels of accuracy, robustness, and explainability are challenges for AI approaches applied in clinics. Thus, this study proposed a multi-type of data source fusion method and a multi-model fusion approach for AMD detection. Typical unsupervised (Hierarchical Clustering and K-Means), typical supervised (SVM, VGG-16, and ResNet) methods, and proposed methods (multi-source data fusion-based method and multi-model fusion-based approach) are compared based on Optical Coherence Tomography (OCT), Fundus Autofluorescence (FAF), regular color fundus photography (CFP) and Ultra-Wide field Fundus (UWF) images. Data preprocessing and enhancements of each type of data are discussed. A feature extraction based on unsupervised ML models, feature combination and normalization, and multi-layer perception (MLP) algorithm are involved in the proposed multi-source data fusion-based method. Supervised ML and DL models and a voting mechanism are involved in the multi-model fusion-based approach. Findings show that the proposed methods present a high performance of accuracy and robustness. A real-world UWF database is involved from Shenzhen Aier Hospital. Practical and theoretical contributions are delivered. A reference value for medical diagnosis based on multiple digital images is contributed.

Influenza virus infection causes severe respiratory illness in people worldwide, disproportionately affecting infants. The immature respiratory tract coupled with the developing immune system is thought to synergistically play a role in the increased disease severity in younger age groups. Although vaccines remain the best solution for protecting this vulnerable population, no vaccines are available for those under 6 months, and for infants aged 6 months to 2 years, the vaccine elicits a dampened immune response. Dampened immune responses may be due to unique features of the infant immune system and a lack of pre-existing immunity. Unlike older children and adults, the infant immune system is Th2 skewed and has less antigen presenting cells and soluble immune factors. Paradoxically, we know that a person’s first infection with the influenza virus during infancy or childhood leads to the establishment of life-long immunity toward that particular virus strain. This is called influenza imprinting. To provide better protection against influenza virus infection and disease in infants, more research must be conducted to understand the imprinting event. We contend that by understanding influenza imprinting in the context of the infant immune system and the infant’s immature respiratory tract, we will be able to design more effective influenza vaccines for both infants and adults. Working through the lens of imprinting, using infant influenza animal models such as mice and ferrets, which have proven useful for infant immunity studies, we will gain a better understanding of imprinting and its implications regarding vaccine design. This review examines literature regarding infant immune development, current vaccine strategies, respiratory development, and the importance of researching the imprinting event in infant animal models to develop more effective and protective vaccines for young children.

The restrictions imposed by the Covid-19 pandemic left many older adults isolated and confined. Under active aging theory, self-management is crucial for well-being among older adults coping with aging. The current study examines how (a) initial self-management, and (b) changes in self-management due to independent physical training, affect psychological outcomes in a sample of care home residents following outbreak of the pandemic. 64 older adults (53 females, 11 males), mean age is 82.23, reported on their self-management abilities, then embarked on six months of training in chair exercises (one session per week). The training exercises were halted after 22 sessions due to the pandemic, but some residents continued to practice independently. Eight weeks after the outbreak of the pandemic, residents who had continued to practice at least once per week (n = 35) and those who had not continued to practice (n = 29) were questioned again about their self-management, and about five psychological outcomes: anxiety, traumatic stress, satis-faction, general mood, and post-traumatic growth (PTG). Self-management improved among older adults who independently practiced the exercises, and declined among those who did not. Pre-pandemic self-management significantly predicted post-outbreak traumatic stress symptoms, anxiety, general mood, and satisfaction with life, but not PTG. However, the difference in self-management between the pre-pandemic and post-outbreak measures was associated with PTG, and made a unique contribution to prediction of the other effects. Self-management abilities among older adults can be seen as a protective factor against adverse psychological outcomes at times of trauma. Further, the improvement in self-management among older adults who independently practiced physical excises made a unique contribution beyond initial self-management abilities.

The once-held wisdom of the supreme efficiency of one-way streets has been gradually sup-planted by the perceived sustainability of two-way streets in the design of livable cities that prioritizes the safety of pedestrians and thriving of local businesses. However, it is rarely dis-cussed on whether one-way street conversions have truly improved the long-term traffic effi-ciencies on urban street networks, as conflating socioeconomic factors such as vehicular popula-tion growth and induced travel demand may render empirical analysis inconclusive. In this study, microscopic traffic simulations implemented on SUMO platform was performed to ana-lyze the effect of street conversion in Downtown Brickfields, Kuala Lumpur. This approach can control and standardize travel demand in both one-way and two-way street networks, and would therefore give a fairer evaluation by precluding all socioeconomic factors. It was found that one-way streets do not necessarily improve the traffic efficiency of the network, as it is very dependent on the traffic scenario evolution over time. One-way streets perform better at the on-set of traffic congestion due to its higher capacity, but on average, the 4-fold longer travel times that made it harder to clear traffic by getting vehicles to their destinations compared to two-way streets. As time progresses, congestion in one-way streets may become twice as worse compared to two-way streets. This study may contribute to a more holistic assessment of traffic circulation plan designed for smart and livable cities

Background: Poisoning is a challenging medical condition facing health care providers, as it usually results in serious outcomes in terms of morbidities or mortalities. Poisoning is a challenging medical condition facing health care providers, as it usually results in serious outcomes in terms of morbidities or mortalities. The lack of or inadequate information about the circumstances, substances and populations at risk are considered barriers to the actual prevention of poisoning and effective intervention strategies in Saudi Arabia. The aim of the current study was to describe the pattern and determinants of chemical and drug overdose poisoning in Makkah.Subjects and methods: All reports of chemical and drug overdose poisoning delivered to the Environmental Health and Occupational Safety Department in Makkah over two years (2018-2019) were reviewed.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 of Saudi Arabia. Further in-depth studies are needed to elaborate other social and demographic factors associated with chemical and drug overdose poisoning. Public health plans, policies and legislations should be implemented to reduce these factors. 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 about the factors proven in the current study to be significantly related to chemical and drug overdose poisoning.

Pressure drop and flow pattern of oil-water flows were investigated in a 19 mm ID clear polyvinyl chloride pipe consisting of U-bend with radius of curvature of 100 mm. The range for oil and water superficial velocities tested were and respectively. Measurements were carried out under different flow conditions in a test section that consisted of four different parts: upstream of the bend, at the bend and at two redeveloping flow locations after the bend. The result indicated that the bend had limited influence on downstream flow patterns. However, the shear forces imposed by the bend caused some shift flow pattern transition and bubble characteristics in the redeveloping flow section after the bend relative to develop flow before the bend. Generally, pressure gradient at all the test sections increased with both oil fraction and water superficial velocity and there was a sharp change of pressure gradient profile during phase inversion. The transition point where phase inversion occurred was always within the range of . Pressure losses differed at the various test sections and the difference was strongly linked to the superficial velocity of the phases and the flow pattern. At high mixture velocity, pressure losses at the redeveloping section after the bend were higher than that at the bend and that for fully developed flows. At low mixture velocity, pressure losses at the bend are higher than in the straight sections. Pressure drop generally decreased with level of flow development downstream of the bend.

The paper introduces a control hybrid model that is designed on the basis of OOSEM (Object-Oriented Systems Engineering Method), MDA (Model-Driven Architecture) concepts, RealTime UML/SysML (Unified Modeling Language/Systems Modeling Language), and an algorithm based on UKF (Unscented Kalman Filter). This hybrid model enables the implementation of control elements for autonomous underwater vehicles (AUVs) and can be adapted to reuse for most standard AUV platforms. To obtain this goal, the dynamic model of AUV is integrated with the following specializations of OOSEM/MDA, in which the analysis model is clarified via the use-case model definition and then combines with HA (hybrid automata) to precisely define the requirements for control. Next, the designed model is specialized via real-time UML/SysML to get the core control blocks, which describe the behaviors and structures of control parts in detail. This design model is then transformed into the model of implementation with the assistance of round-trip engineering to conveniently realize the controller of AUVs. Based on this new model, an AUV controller for low-cost turtle-shaped AUVs is implemented that performs tracking for a planar trajectory with accepted feasibility.

Pregestational and gestational diabetes mellitus are relevant complications of pregnancy and antidiabetic drugs are prescribed to obtain glycemic control and improve perinatal outcomes. The objective of this study was to highlight the prescription pattern of antidiabetics before, during and after pregnancy in Italy and to evaluate its appropriateness. A multi-database cross sectional population study using a Common Data Model was performed. In a cohort of about 450,000 women, the prescribing profile of antidiabetics seemed to be in line with the Italian standards for the treatment of GDM which currently do not recommend the use of oral antidiabetics and non-insulin injection, even if a small share of new users (up to 3.8% in the third trimester) used oral antidiabetics. A substantial variability in the prescription pattern was observed among the Italian regions considered: the highest increase was registered in Tuscany (4.2%), Umbria (3.5%) and Emilia-Romagna (2.6%) while the lowest in Lombardy (1.5%), Veneto (1.7%) and Apulia (1.7%). Analysing the prevalence of use of antidiabetics between foreign women we observed that the consumption during pregnancy was higher than Italians (second trimester: 1,8% vs 0,9%, third trimester: 3,6% vs 1,8%).

Traditionally, neutron scattering is an essential method for the analysis of spin structures and spin excitations in bulk materials. Over the last 30 years, polarized neutron scattering in terms of reflectometry has also contributed largely to the analysis of magnetic thin films and magnetic multilayers. More recently it has been shown that polarized neutron reflectivity is, in addition, a suitable tool for the study of thin films laterally patterned with magnetic stripes or islands. We provide a brief overview of the fundamental properties of polarized neutron reflectivity, considering different domain states, domain fluctuations, and different domain sizes with respect to the neutron coherence volume. The discussion is exemplified by a set of simulated reflectivities assuming either complete polarization and polarization analysis, or a reduced form of polarized neutron reflectivity without polarization analysis. Furthermore, we emphasize the importance of the neutron coherence volume for the interpretation of specular and off-specular intensity maps, in particular when studying laterally non-homogeneous magnetic films. Finally, experimental results, fits, and simulations are shown for specular and off-specular scattering from a magnetic film that has been lithographically patterned into a periodic stripe array. These experiments demonstrate the different and mutually complementary information that can be gained when orienting the stripe array parallel or perpendicular to the scattering plane.

The human proteome is composed from diverse and heterogeneous gene products/proteoforms. Previously, we have been discussing the main technical aspects in developing for inventory of human proteoforms that would be visually attractive, clear, and easy to search (Naryzhny S. J. Proteomics 2018, S1874-3919(18) 30220-3). Here, we present our first draft of the database of proteoforms that is based on this discussion. The database principles and structure are described. The database is called “2DE-pattern” as it contains multiple isoform-centric patterns of proteoforms separated according to 2DE principles.

This paper presents a study and implementation of a convolutional neural network to identify and recognize humpback whale specimens from the unique patterns of their tails. Starting from a dataset composed of images of whale tails, all the phases of the process of creation and training of a neural network are detailed – from the analysis and pre-processing of images to the elaboration of predictions, using TensorFlow and Keras frameworks. Other possible alternatives are also explained when it comes to tackling this problem and the complications that have arisen during the process of developing this paper.

Power Electronic development determines introduction of nonlinear devices in Electric Power Systems. Introduction of nonlinear devices increase current harmonics in Transmission and Distribution Power Systems. Distribution transformers and feeders increase power losses and their nominal parameters are reduced. Present work introduces a procedure to evaluate maximum permissible load in single phase distribution transformers with massive introduction of a new type of nonlinear load which changes daily demand graphs.

Polymeric drag reducers have been developed over many years due to the great number of practical applications. In all of them, the molecular stability is an essential requirement. Usually, polymers break down under turbulent flows, which causes a decrease in their efficiency as drag reducers. Besides that, some specific applications, in agro and biomedical fields, impose a specific requirement that must be fulfilled, which is the use of non-toxic materials. A suitable stable material that is elected to accomplish this necessity is the mucilage of aloe vera, which is a bio-polymer that can be used as an alternative to the synthetic ones. Here, we investigate the role played by the aging of aloe vera on its capacity to reduce drag. The results obtained by ¹H nuclear magnetic resonance indicate that the compositions of young and mature leaves of aloe vera are different and such a difference plays an important role on their efficiency as drag reducers. Tests were performed to analyse the drag reduction in a rotating apparatus and in a pipeline system and the efficiencies of leaves of different ages were compared to their composition. The main conclusion of these experiments is that the young mucilage samples, which are richer in complex polysaccharides and exhibit lower acid contents, are more efficient drag reducers.

Forensic anthropologists play a key role in skeletal trauma analysis and commonly use macroscopic features to distinguish between trauma types. However, this approach can be challenging, particularly in cases of highly comminuted or incompletely recovered fractures. Histological analysis of microscopic fracture characteristics in fractured bones may thus help provide additional information on trauma type and bone fracture biomechanics in general. This study analysed the extent of microcrack damage to osteons in long bones with blunt force trauma (BFT) and gunshot trauma (GST), from both traumatic death cases and post-mortem experimental fractures. We identified four types of osteonal damage (OD). In traumatic death cases, OD affecting the inside of the osteon and compromising the Haversian canal (type 1) was found to be indicative of BFT. Moreover, OD affecting the cement line (type 3) and interstitial lamellae (type 4) was more common in the GST samples. OD affecting the inside of the osteon without compromising the Haversian canal (type 2) was not found to be indicative of either trauma type. In cases of experimental fractures, our study revealed that post-mortem fractures in dry bone samples featured the highest amount of OD, particularly of type 4. This study also found that the experimentally produced GST featured similar OD pattern to GST death cases. These findings support our hypothesis that there are distinct osteonal damage patterns in human long bones with BFT and GST, which are of relevant value for trauma analysis in forensic anthropology.

Background: Myocardial infarction (MI) is a major cause of mortality worldwide. Its presentation as ST-segment elevation MI (STEMI) and non-ST-segment elevation MI (NSTEMI) is influence by atherosclerosis risk factors. Aim: To assess the patterns of presentation and predicting risk factors of acute MI in Kosovo. Methods: This is a cross sectional study from the Clinic of Cardiology of the University Clinical Center of Kosovo, which included all patients hospitalized with acute MI over a period of 7 years (January 1st, 2014 to December 31st, 2020). Data was collected using a structured questionnaire and epidemiological patterns and risk factors were analyzed. Results: Among 7353 patients admitted with acute MI (age 63 ± 12 years, 29% female), 4366 (59.4%) patients had STEMI, and 2987 (40.6%) NSTEMI. Patients age was not different between the two groups (p=0.077). NSTEMI patients smoked less (48.3% vs. 54%, p<0.001), but were more diabetics (37.8% vs. 33.6%, p<0.001), more hypertensives (69.6% vs. 63%, p<0.001), frequently had family history for coronary artery disease (CAD) (40% vs 38%, p=0.009), and included more females compared to STEMI patients (32% vs 27%, p<0.001). The two groups of patients, did not differ in the prevalence of significant CAD on angiography (66.8% vs. 67.8%, p = 0.396), but NSTEMI patients underwent less primary percutaneous interventions compared with STEMI patients (43.6% vs. 55.2%, p < 0.001). Conclusion: In Kosovo, STEMI is commoner than NSTEMI, who were mostly males, more likely to have diabetes, hypertension and family history for CAD compared to those with NSTEMI. Smoking and arterial hypertension proved the strongest predictors of acute MI, in Kosovo, thus highlighting the urgent need for better atherosclerosis risk control and education strategy.

Galectins have been shown to have roles in cancer progression via contributions to angiogenesis, metastasis, cell division, and evasion of immune destruction. This study analyses galectin-1, -3, and -9 serum concentrations in breast cancer patients by enzyme-linked immunosorbent assay (ELISA) against the characteristics of the patient and tumor such as stage, molecular subtype, and receptor expression. Galectin-9 was found to be statistically significantly increased in HER2 enriched tumors as well as being reduced in patients with hormone receptor positive tumors and increased in patients with HER2 amplified tissues. Galectin-1 was found to be statistically significantly increased in the serum of patients who had undergone hormonal, immunotherapy, or chemotherapy. These findings provide insight as to the changes in galectin levels during cancer progress, response to treatment and molecular phenotype. These findings are valuable in the further understanding of the relationships of galectin and tumor biology and can inform future research on therapeutic targets for galectin inhibitors and the utility of galectin biomarkers.

The nutraceutical value of pomegranate in the treatment of many neoplastic, cardiovascular, viral, inflammatory, metabolic, microbial, intestinal, reproductive and skin diseases is well-documented and is linked to its richness in phenolic compounds. This study aims to evaluate nutraceutical and genetic diversity of novel pomegranate genotypes (G1-G5) in comparison to leading commercial pomegranate varieties i.e. ‘Wonderful’, ‘Primosole’, ‘Dente di Cavallo’ and ‘Valenciana’. Morphometric measurements were carried out on fruits, accompanied by chemical characterization and the development of four new polymorphic SSR markers involved in the flavonoid pathway. The cultivars displayed a marked variability in the weight and shape of fruits, as well as in the weight of arils and juice yield. The highest level of total phenolic content and antioxidant activity was found in ‘Wonderful’ and G4, while the lowest was in ‘Dente di Cavallo’. Furthermore, the results showed that the pomegranate juice, is an excellent source of minerals, especially potassium, which plays a key role in organ functioning. The new flavonoid-related markers effectively differentiated the cultivars with the same diversity pattern as morpho-chemical characterization, so the SSRs developed in the present study can be used as a rapid tool for the identification of pomegranate cultivars with relevant nutraceutical traits.

Data on the KIT mutation rate in skin melanoma in the central European region is missing. Accordingly, in a cohort of 79 double wild type (BRAF/NRAS) skin melanoma KIT mutation was assessed by Sanger sequencing of exons 9,11,13,17 and 18. In this skin melanoma cohort KIT mutation frequency was found to be 34/79 (43%) with a significantly higher rate in acrolentiginous melanoma (ALM) as compared to UV-induced common variants (20/34, 58.8% versus 14/45, 31.1%, p=0.014). Exon 11 was the most frequent mutation site (44.7%) followed by exon 9 (21.1%) equally characterizing UV-induced common histotypes and ALM tumors. KIT mutation hotspots were identified in exon 9 (c491/492), in exon 11 (c559,c572, c570), in exon 13 (c642), in exon 17 (c822) and in exon 18 (c853). The relatively high KIT mutation rate in skin melanoma in this central-European cohort justifies regular testing of this molecular target.

The effect of divergence of chute sidewalls with three different baffle block geometries namely USBR, trihedral and semicircular blocks, as well as the depth and dimensions of the scour hole downstream of the chute were studied using a physical model. For this purpose, 9 models of baffled chutes were designed and constructed with divergence ratios of 1.45, 1.75, and 2.45 and without divergence (with a divergence ratio of 1). Comparing the results on the effect of block geometry at different divergence ratios revealed that the use of blocks proposed in this study instead of standard USBR blocks reduced the mean and maximum scour hole by 50%. For a given block geometry, the mean depth, maximum depth, and length of scour hole were reduced by 75%, 58%, and 50%, respectively.

Stingless bee beekeeping provides new opportunities to improve the incomes of many households in Malaysia through the sale of honey and other bee products. While Heterotrigona itama is one of the most commonly cultured species of stingless bees, its behavior is not very well understood. Hence, we conducted this study to investigate the behavior of H. itama in exploiting food sources by ascertaining the nectar sugar concentration preferred by the bee. We also aimed to determine the preferred distance of food source from the bee hive. Our results suggest that H. itama prefers high sugar concentrations of 35% and above, and they would fly up to 7 m from the hive to collect food. We discuss how nectar concentration and food distance influence the number of bees exploiting food sources and the overall foraging pattern of H. itama.

This paper aims to investigate carbon monoxide (CO) concentrations on roadways of Karachi, potential blood levels carboxy-hemoglobin (COHb) in Karachi. Geographical information system (GIS) was used for spatial analysis of diseases potentiality while an interpolation technique has been applied for surface generation with town boundaries and later evaluates risk areas.The higher concentration of carbon monoxide in the ambient is mainly due to automobile emissions. The City center and CBD areas are more perilous.

Brain plasticity is induced by learning during wakefulness and is consolidated during sleep. But the molecular mechanisms involved are poorly understood and their relation to experience-dependent changes in brain activity remains to be clarified. Localized mRNA translation is im-portant for the structural changes at synapses supporting brain plasticity consolidation. Sleep has been shown activate the translation mTOR pathway, via phosphorylation of 4E-BPs, during brain plasticity, but whether this activation is specific to synapses is not known. We investigated this question using acute exposure of rats to an enriched environment (EE). We measured brain activity with EEGs and 4E-BPs phosphorylation at cortical and cerebellar synapses with Western Blot. Sleep significantly increased the conversion of 4E-BPs to its hyperphosphorylated form at synapses, especially after EE exposure. EE exposure increased oscillations in the alpha band dur-ing active exploration and in the theta to beta (4-30Hz) range, as well as spindle density, during NREM sleep. Theta activity during exploration and NREM spindle frequency predicted changes in 4E-BPs hyperphosphorylation at synapses. Our results provide a link between EEG and mo-lecular markers of plasticity across wake and sleep.

Cardiovascular disease (CVD) is one of the leading causes of death in Puerto Rico, where clopidogrel is commonly prescribed to prevent ischemic events. Genetic contributors to both a poor clopidogrel response and the severity of CVD have been identified mainly in Europeans. However, the non-random enrichment of single nucleotide polymorphisms (SNPs) associated with clopidogrel resistance within risk loci linked to underlying CVDs, and the role of admixture, has yet to be tested. This study aimed to assess possible interaction between genetic biomarkers linked to CVDs and those associated with clopidogrel resistance among admixed Caribbean Hispanics. We identified 50 SNPs significantly associated with CVDs in previous genome-wide association studies (GWAS). These SNPs were combined with another ten SNPs related to clopidogrel resistance in Caribbean Hispanics. We developed Python scripts to determine whether SNPs related to CVDs are in close proximity to those associated with clopidogrel response. Average and individual local ancestry (LAI) within each locus were inferred, and 60 random SNPs with their corresponding LAI were generated for enrichment estimation purposes. Our results showed no CVD-linked SNPs in close proximity to those associated with clopidogrel response among Caribbean Hispanics. Consequently, no genetic loci with a dual predictive role for risk of CVD severity and clopidogrel resistance were found in this population. Native American ancestry was the most enriched within risk loci linked to CVDs in this population. Non-random enrichment of disease susceptibility loci with drug response SNPs is a new frontier in Precision Medicine that needs further attention.

With recent advancements in network technology and spread of Internet, use of social network services and Internet of Things devices has flourished, leading to a continuous generation of large volumes of graph stream data, where changes, such as additions or deletions of vertices and edges occur over time. Additionally, owing to the need for efficient use of storage space and security requirements, graph stream data compression has become essential in various applications. Even though various studies on graph compression methods have been conducted, most of them do not fully reflect the dynamic characteristics of graph streams and complexity of large graphs. In this paper, we propose a compression scheme using provenance data to efficiently process and analyze large graph stream data. It obtains provenance data by analyzing graph stream data and builds a pattern dictionary based on this to perform dictionary-based compression. By improving the existing dictionary-based graph compression methods, it enables more efficient dictionary management through tracking pattern changes and evaluating their importance using provenance. Furthermore, it considers the relationships among sub-patterns using an FP-tree and performs pattern dictionary management that updates pattern scores based on time. Our experiments show that the proposed scheme outperforms existing graph compression methods in key performance metrics, such as compression rate and processing time.

As the world increasingly becomes more interconnected, the demand for safety and security is ever-increasing, particularly for industrial networks. This has prompted numerous researchers to investigate different methodologies and techniques suitable for intrusion detection systems (IDS) requirements. Over the years, many studies have proposed various solutions in this regard including signature-based and machine-learning (ML) based systems. More recently, researchers are considering deep learning (DL) based anomaly detection approaches. Most proposed works in this research field aimed to achieve either one or a combination of high accuracy, considerably low false alarm rates (FARs), high classification specificity and detection sensitivity, achieving lightweight DL models, or other ML and DL-related performance measurement metrics. In this study, we propose a novel method to convert a raw dataset to an image dataset to magnify patterns. Based on this we devise an anomaly detection for IDS using a lightweight convolutional neural network (CNN) that classifies denial of service and distributed denial of service. The proposed methods were evaluated using a modern dataset, CSE-CIC-IDS2018, and a legacy dataset, NSL-KDD. We have also applied a combined dataset to assess the generalization of the proposed model across various datasets. Our experimental results have demonstrated that the proposed methods achieved high accuracy and considerably low FARs with high specificity and sensitivity. The resulting loss and accuracy curves have also demonstrated the excellent generalization of the proposed lightweight CNN model, effectively avoiding overfitting. This holds for both the modern and legacy datasets, including their mixed version.