Dynamic visualizations have been developed to exchange information that transforms over time across a broad range of professional and academic contexts. However, these visual tools may impose substantial demands on the learner’s cognitive resources that are very limited in current knowledge. Cognitive load theory has been used to improve learning from dynamic visualizations by providing certain design techniques to manage learner cognitive load without adding any oral/written explanations. This systematic review examined a series of experimental studies assessing the roles of these design techniques in learning tactical scenes of play through dynamic visualizations. Electronic databases PubMed and Google Scholar were used to search relevant articles. Eleven studies were eventually included for the systematic review based on the eligibility criteria. The present review revealed that adapting design techniques to the level of learners’ expertise, type of depicted knowledge, and level of content complexity is a crucial part of effective learning.

The variations in socioeconomic status (SES) between different social classes of a population correspond to differences in accessibility to all resources available and able to improve global health. SES can influence global health trajectory for an individual or a community, depending if SES is low or high. Sleep is sensitive to environmental stimuli, as well as living conditions. Plenty of studies linked sleep complaints with mood disorders, allostatic load or circadian disruption; but very few or none investigated deeply what happened earlier to sleep depending of SES. While SES is now known as one of the main determinants for a good health and a good aging, its influence on sleep disorders (SD) is not well understood. SES is a concept, not directly observable but estimated using indicators like income, education, occupational status and area of living. Even if recent evidence suggested that few of SES indicators like occupational status are linked with sleep disturbances, the relation between SES and health in general with sleep as an outcome or a mediator is not well documented. This scoping review synthetized studies which investigated physiological and psychological mechanisms resulting from a low SES and linked them with sleep disturbances as consequences or as mediators. This review also explore a possible role played by sleep in the relation between socioeconomic status and health inequalities.

The aim of this paper is to at first evaluate the influence of three key parameters including weld current, weld time and electrode force on nugget diameter and tensile strength in resistance projection welding. Then, a 2-D axis-symmetric finite element model is developed to simulate the projection welding and predict the nugget diameter. Finally, the FEM results are compared to experimental data to verify the simulation model and simulated results. In the finite element model, the temperature-dependent material properties were taken into account.

Policing duties may inherently be dangerous due to stab, blunt trauma and ballistic threats. The addition of individual light armour vests (ILAVs) has been suggested as a means to protect officers. However, the addition of the extra load of the ILAV may affect officer ability to conduct occupational tasks. The purpose of this study was to determine if wearing any of 3 different ILAVs (ILAV A, ILAV B, & ILAV C) affected occupational task performance when compared to that in normal station wear. A prospective, within-subjects repeated measures design was employed, using a counterbalanced randomization in which each ILAV was worn for an entire day while officers completed a variety of occupationally relevant tasks. These tasks included a victim drag, car exit and 5 meter sprint, step down and marksmanship task. Results showed that performance in each task did not vary between any of the ILAV or normal station wear conditions. There was less variability in the marksmanship task with ILAV B, however. The results suggest that none of the ILAVs used in this study were heavy enough to significantly affect task performance in the assessed tasks when compared to wearing normal station wear.

In this study, the probabilistic exergoeconomic analysis was performed for four industrial gas turbine (GT) units comprising of two (GT16 and GT19) units of 100MW GE engine and two (GT8 and GT12) units of 25MW Hitachi engine at Transcorp Power Limited, Ughelli. These four industrial GT engine units were modelled and simulated using natural gas as fuel. The design point (DP) simulation results of the modelled GT engines were validated with the available DP thermodynamic data from original equipment manufacturer (OEM). This was done before the off-design point (ODP) simulation was carried out which represents the plant operations. The results obtained from exergy analysis at full load operation show that the turbine has the highest exergy efficiency followed by compressor and combustion having the least. For turbines these were 96.13% for GT8 unit, 98.02% for GT12 unit, 96.26% for GT16 unit, and 96.30% for GT19 unit. Moreover, the combustion chamber has the highest exergy destruction efficiency of 55.16% GT8 unit, 56.58% GT12 unit, 43.90% GT16 unit, and 43.30% GT19 unit respectively. The exergy analysis results obtained from the four units show that the combustion chamber (CC) is the most significant exergy destruction with lowest exergy efficiency and highest exergy destruction efficiency of plant components. The exergoeconomic analysis results from four units showed combustion chamber energy destruction cost of 531.08 $/h GT8 unit, 584.53 $/h GT12 unit, 2351.81$/h GT16, and 2315.93$/h GT19 unit. The probabilistic results analysis based on the input parameters distributions evaluated and discussed.

The article presents a methodology for modeling the impact of factors of both on-farm activity and environmental factors on the resulting indicators of an international company. The dataset includes the company's profit, revenue, valuation, share price, and market share from 2012 through 2022. This empirical period is optimal for such a type of modeling. Modeling the impact of carbon emissions factors on the resulting indicators is considered based on complex systems theory. Endogenous model variables include indicators of the development of the international company. Predefined model variables reflect the influence of both the external environment and internal factors on the development indicators of an international company. An approach of picture fuzzy rough sets based on time series of endogenous and exogenous variables can provide an opportunity to analyze and consider the consequences of feedback changes in the systems of which they are a part. Based on the results of the analysis of the model construction, it is concluded that picture fuzzy rough sets can be an excellent way to model interdependent social processes.

Isothermal Nucleic Acid Amplification based-Lateral Flow Testing (INAA-LFT) has emerged as a robust technique for on-site pathogen detection, providing a visible indication of pathogen nucleic acid amplification that rivals or even surpasses the sensitivity of real-time quantitative PCR. The isothermal nature of INAA-LFT ensures consistent conditions for nucleic acid amplification, establishing it as a crucial technology for rapid on-site pathogen detection. However, despite its considerable promise, the widespread application of isothermal INAA amplification-based lateral flow testing faces several challenges. This review provides an overview of the INAA-LFT procedure, highlighting its advancements in detecting plant viruses. Moreover, the review underscores the imperative to address existing limitations and emphasizes ongoing research efforts dedicated to enhancing the applicability and performance of this technology in the realm of rapid on-site testing.

Recent literature has seen a rapid expansion in the reference to front-end forensics as an indication of the untapped value of forensic science. While some of these contributions have centered on development of forensic intelligence from a single area of investigation, others call for a more fundamental change in the relationship between crime laboratories and policing, particularly relating early laboratory analysis with big datasets to provide leads to investigators. We highlight several recently implemented tactical strategies of crime laboratories that contribute to the body of forensic intelligence. Beyond the scientific gains from these tactical applications, the corresponding details on associated efficiencies, costs, time savings, and quality improvements offer insights towards patterns of success for the community of crime laboratories. Further details expand an interpretation of what constitutes success with an eye on the contributions of the crime laboratory towards public health, safety, and protection of the innocent in addition to societal gains from conviction of the guilty. The economic interpretation of the value provided by the forensic laboratory assists in the cost-benefit review of strategic and tactical decisions and supports the justification for laboratory public funding with measures of the return on investment from public support of the forensic laboratory

The short-lived positron-emitter carbon-11 (t1/2 = 20.4 min; b+, 99.8%) is prominent for labeling tracers for use in biomedical research with positron emission tomography (PET). Carbon-11 is produced for this purpose with a cyclotron, nowadays almost exclusively by the 14N(p,a)11C nuclear reaction, either on nitrogen containing a low concentration of oxygen (0.1–0.5%) or hydrogen (~ 5%) to produce [11C]carbon dioxide or [11C]methane, respectively. These primary radioactive products can be produced in high yields and with high molar activities. However, only [11C]carbon dioxide has some utility for directly labeling PET tracers. Primary products are required to be converted rapidly and efficiently into secondary labeling synthons to provide versatile radiochemistry for labeling diverse tracer chemotypes at molecular positions of choice. This review surveys known gas phase transformations of carbon-11 and summarizes the important roles that many of these transformations now play for producing a broad range of labeling synthons in carbon-11 chemistry.

The mechanical effects of membrane compositional inhomogeneities are analyzed in a process analogous of neck formation in cellular membranes. We cast on the Canham-Helfrich model of fluid membranes with both the spontaneous curvature and the surface tension being non-homogeneous functions along the cell membrane. The inhomogeneous distribution is determined by the equilibrium mechanical equations and the boundary conditions as considered in the axisymmetric setting compatible with the necking process. To establish the role played by mechanical inhomogeneity, we focus on the catenoid, a surface of zero mean curvature. Analytic solutions are shown to exist for the spontaneous curvature and the constrictive forces in terms of the border-radii. Our theoretical analysis shows that the inhomogeneous distribution of spontaneous curvature in a mosaic-like neck constrictional forces potentially contributing to the membrane scission under minimized work in living cells.

The way Norway is spearheading electrification in the transport sector is of global interest, and details of its policies and emission impacts represent a reference. We used the NERVE model, a bottom-up high-resolution traffic emission model, to calculate all exhaust emissions in Norway (2009-2020). This allows us to evaluate the co-benefit of policies to target climate change mitigation and air quality. We have analyzed local municipal data with regards to traffic growth, road network influences, vehicle composition, emissions and energy consumption. Light vehicle CO2 emissions per kilometre have been reduced by 22% since 2009, mainly driven by an increaseing bio-fuel mixing and BEV share. BEVs are mostly located in and around the main cities, areas with young vehicle fleets, and strong local incentives. Beneficiaries of all BEVs incentives have been a subset of the population with strong economic indicators. The incentivized growth in the share of diesel-fuelled passenger vehicle has been turned, and together with Euro6 emission standards, light vehicle NOx emissions have been halved since peaking in 2014. BEV represent an investment in emission reductions in years to come and current sales set up for an accelerated decline in emissions despite growth in traffic.

The on-state voltage of MOSFETs is a convenient and powerful temperature-sensitive electric parameter (TSEP) to determine the junction temperature, thus enabling device monitoring, protection, diagnostics and prognostics. The main hurdle in the use of the on-state voltage as a TSEP is the per-device characterization procedure, to be carried out in a controlled environment, with high costs. In this paper we compare two novel techniques for MOSFET junction temperature estimation: controlled shoot-through and direct heating by resistive heaters embedded in two Kapton (polyimide) films. Both allow in-place characterization of the TSEP curve with the device mounted in its final circuit and assembly, including the working heat sink. The two methods are also validated against the conventional procedure in a thermal chamber.

Introduction: In general, everyone believes that the smallest preterm infants should achieve normal intrauterine growth rates, but many thinks that this is not possible with current nutrition guidelines. There is resistance to giving enough nutrition for fear of "toxicity". The difference in weight Z-score between birth and a corrected gestational age (CGA) at discharge is assess in postnatal growth in our unit. Material and methods: An observational study was done between January 2018 and December 2020 where all cases that had ≤ 29 weeks of GA at birth and survived to 36 weeks corrected GA or that were discharged home. An aggressive nutrition protocol including parenteral as well as enteral nutrition was followed. Patients and their weight trajectory was plotted on the Fenton 2013 growth curve. The patients who had had a smaller WZP difference were also plotted. Results: A total of 32 cases were found. The median change in Z-score between birth and discharge of the whole group was -0.52 (IQR 0.53). Six of 32 (19%) had a more than one WZP, all of whom had severe pathologies. The median decline in Z score for this group with poor growth was 1.24 (IQR 0.22). There were 26 cases with a < 1 WZP (81%) and a median Z score fall of 0.39 (IQR 0.55). No important complications secondary to the ingested volumes or parenteral nutrition were reported. Conclusion: The group of cases with a > 1 WZP drop had severe pathologies. All the other cases had adequate growth parallel to normal weight growth charts and a few cases had some catch-up growth. The study showed that it is possible for many preterm infants to achieve normal intrauterine growth rates if they are given enough nutrition, but bigger multicenter studies are needed to confirm these findings.

Saiga (Saiga tatarica) was extirpated in China. Since Mid-1980s, attempts have been made for revival the species in the country, however, only a breeding herd of Saiga was successfully established at Wuwei, Gansu, China. The reintroduced Saiga population experienced a bumpy growth. Then, the population collapsed following the catastrophe die-off in the Saiga ranging countries in Central Asia, then population started to rebound when 6 new lams were born in 2019. After reviewing the population trend and conservation breeding of Saiga in China, we concluded that to establish a migratory species that needs vast range size like Saiga on central Asia steppe, an international collaboration is needed for introducing new genes. We recommend China to ratify the CMS in order to facilitate international conservation efforts to restoring the species in its former range.

Microsystems are key enabling technologies, with applications found in almost every industrial field, including in-vitro diagnostic, energy harvesting, automotive, telecommunication, drug screening, etc. Microsystems, such as microsensors and actuators, are typically made up of components below 1000 microns in size that can be manufactured at low-unit cost through mass-production. Yet, the development of microsystems for commercial or educational purposes, has typically been limited to upper income countries due to the initial investment costs associated with the microfabrication equipment and processes. However, recent technological advances have enabled the development of low-cost microfabrication tools. In this paper, we describe a range of low-cost approaches and equipment (below £1000), developed or adapted and implemented in our laboratories. We describe processes including photolithography, micromilling, 3D printing, xurography and screen-printing used for the microfabrication of structural and functional materials. The processes that can be used to shape a range of materials with sub-millimetre feature sizes are demonstrated here in the context of Lab-on-Chips, but they can be adapted for other applications. We anticipate that this paper, which will enable researchers to build a low-cost microfabrication toolbox in a wide range of settings, will spark a new interest in microsystems.

Manufacturing of grinding wheels is continuously adapting to new industrial requirements. New abrasives and new wheel configurations, together with wheel wear control allow for grinding process optimization. However, the wear behavior of the new abrasive materials is not usually studied from a scientific point of view due to the difficulty to control and monitor all the variables affecting the tribochemical wear mechanisms. In this work an original design of pin on disk tribometer is developed in a CNC grinding machine. An Alumina grinding wheel with special characteristics is employed and two types of abrasive are compared: White Fused Alumina (WFA) and Sol-Gel Alumina (SG). The implemented tribometer reaches sliding speeds of between 20 and 30 m/s and real contact pressures up to 190 MPa. The results show that the wear behavior of the abrasive grains is strongly influenced by their crystallographic structure and the tribometer appears to be a very good tool for characterizing the wear mechanisms of grinding wheels, depending on the abrasive grains.

Surface Plasmon Resonance (SPR) based sensors have the advantage of being label-free, enzyme-free and real-time. However, their spreading in multidisciplinary research is still limited and almost confined to prism-coupled devices. Plasmonic gratings, combined with a simple and cost-effective instrumentation, have been poorly developed compared to prism-coupled system mainly due to their lower sensitivity. Here we describe the optimization and signal enhancement of a sensing platform based on phase-interrogation method, which entails the exploitation of a nanostructured sensor. This technique is particularly suitable for integration of the plasmonic sensor in a lab-on-a-chip platform and can be used in a microfluidic circuit to ease the sensing procedures and limit the injected volume. The careful optimization of most suitable experimental parameters by numerical simulations leads to a 30 to 50% enhancement of SPR response, opening new possibilities for applications in the biomedical research field while maintaining the ease and versatility of the configuration.

Since the systematic evolution of ligands by exponential enrichment (SELEX) method was developed, aptamers have made significant contributions as bio-recognition sensors. Microdevice systems allow for low reagent consumption, high-throughput of samples, and disposability. Due to these advantages, there has been an increasing demand to develop microfluidic-based aptasensors for analytical technique applications. This review introduces the principal concepts of aptasensors and then presents some advanced applications of microdevice-based aptasensors on several platforms. Highly sensitive detection techniques such as electrochemical and optical detection have been integrated into lab-on-a-chip devices and researchers have moved towards the goal of establishing point-of-care diagnoses for target analyses.

This article presents and discusses analytical data on the scientific publication record from 1910 to 2020 on two topics: "climate" and "climate change/global warming/climate emergency". The goal is to visualize how the publication record on these two topics has evolved over time, from different classification perspectives (year, country, source and organization). Three hypotheses are tested using data collected from Web of Science and various graphical representations of the data. It is found that research output related to the Earth’s contemporary changing climate overtook that of general climate research in 2011, and the publication ratio has been expanding in the last decade. There are significant differences in the publication countries and sources between the two topics, and conversely less significant differences in terms of organizations publishing these works. Differentiation factors that affect the level of research output and engagement on the climate challenge include: island versus landlocked nations, specialized versus general scientific journals, academic versus institutional organizations. The future of the publication records is discussed, such as the emergence of new terms to refer to the climate challenge, such as “climate emergency”.

Background: Previous neuroimaging studies have identified brain regions related to respiratory motor control and perception. However, little is known about the resting-state functional connectivity (FC) associated with respiratory impairment. We aimed to determine the FC involved in mild respiratory impairment without changing the saturation of percutaneous oxygen. Methods: We obtained resting-state functional magnetic resonance imaging data from 36 healthy volunteers during normal respiration and mild respiratory impairment induced by resistive load (effort breathing). ROI-to-ROI and seed-to-voxel analyses were performed using Statistical Parametric Mapping 12 and the CONN toolbox. Results: Compared to normal respiration, effort breathing activated FCs within and between the sensory perceptive area of respiration (the postcentral gyrus, anterior insular cortex (AInsula), and anterior cingulate cortex) and visual cortex (the visual occipital, occipital pole (OP), and occipital fusiform gyrus). Graph theoretical analysis showed strong centrality in the visual cortex. A significant positive correlation was observed between the dyspnoea score (modified Borg scale) and FC between the left AInsula and right OP. Conclusions: These results suggested that the FCs within the respiratory sensory area via the network hub may be neural mechanisms underlying effort breathing and modified Borg scale scores.

The goal of the study presented in this article is to investigate interconnection issues of the Intelligent Electrical Networks (IENs) with the existing ones, as well as the deployment of the appropriate load forecasting demand techniques. The concept of IENs has recently emerged as a potential solution to the global energy problem, due to the more flexible dispersed production units that are involved. In the same context, additional issues, such as the reduction of production-transfer-distribution costs as well as power losses are analyzed as well, via the penetration of Renewable Energy Sources (RESs). To this end, it is necessary to use appropriate advanced infrastructure, where in combination with modern telecommunication networks will make possible the full exploitation of IENs. Moreover, to make the electricity system more efficient, to avoid voltage and frequency imbalance issues and to implement optimal production and consumption planning, the Electric Load Forecast (ELF) is a vital process and plays a key role in the planning as well as in the management of electricity systems. Therefore, recent state of the art approaches in ELF methods are discussed as well.

Commercial buildings situated in hot and humid tropical climates rely significantly on cooling systems to maintain optimal occupant comfort. A well-accurate day-ahead load profile prediction plays a pivotal role in planning the energy requirements of cooling systems. Despite the pressing need for effective day-ahead cooling load predictions, current methodologies have not fully harnessed the potential of advanced deep-learning techniques. This research paper aims to address this gap by investigating the application of innovative deep learning models in day-ahead hourly cooling load prediction for commercial buildings situated in tropical climates. A range of deep learning techniques, including Deep Neural Network, Convolutional Neural Networks, Recurrent Neural Networks, and Long Short-Term Memory networks are employed to enhance prediction accuracy. Furthermore, these individual deep learning techniques are synergistically integrated to create hybrid models, such as CNN-LSTM and Sequence-to-Sequence models. An extensive comparative analysis is conducted to identify the most effective hybrid model. Sequence-to-sequence model provided better performance compared to the other single and hybrid models. Experiments are conducted to choose the time horizons from the past that can serve as input to the models. Further, the influence of various categories of input parameters on prediction performance has been assessed. Historical cooling load, calendar features and outdoor weather parameters are found in decreasing order of influence on prediction accuracy. This research focuses on buildings located in Singapore and presents a comprehensive case study to validate the proposed models and methodologies.

Against the backdrop of the current carbon peaking and carbon neutrality policies, higher requirements have been put forward for the upgrading and construction of smart grids. Non-intrusive load monitoring (NILM) technology is a key technology for advanced measurement systems at the end of the power grid. It obtains detailed power information of the load, without the need for traditional hardware deployment. The key step to solve this problem is load decomposition and identification. This paper first utilized Long Short Term Memory-Denoising Autoencoder(LSTM-DAE) to decompose the mixed current signal on the household busbar and obtain the current signals of multiple independent loads that constituted the mixed current. Then, the obtained independent current signals were combined with voltage signals to generate multi-cycle colored Voltage-Current(VI) trajectories, which were color-coded according to the background. These colored VI trajectories with background colors formed a feature library. When the CNN network was used for load recognition, considering the influence of hyperparameters on recognition results, the BOA algorithm was used for optimization, and the optimized CNN network was employed for VI trajectory recognition. Finally, the proposed method was validated using the PLAID dataset. Experimental results showed that the proposed method exhibited better performance in load decomposition and identification.

The COVID-19 pandemic has led to the growth of hybrid and online learning environments and the trend to introduce more technology into the classroom. One such change would be the use of smart synchronous hybrid learning environments (SSHLE), which are settings with both in-person and online students concurrently, and in which technology plays a key role in sensing, analyzing, and reacting throughout the teaching and learning process. These changing environments and the incorporation of new technologies can place a greater orchestration load on participants and a reduction in teacher agency. In this context, the aim of this paper is to analyse the orchestration load and teacher agency in different SSHLEs. The NASA-TLX model was used to measure the orchestration load in several scenarios. Questionnaires and interviews were used to measure teacher agency. The results obtained indicate that the orchestration load of the teacher tends to be high (between 60 and 70 points out of 100 of the NASA-TLX workload), especially when they lack experience in synchronous hybrid learning environments, and the orchestration load of the students tends to have average values (between 50 and 60) in the SSHLEs analysed. Meanwhile, the teacher agency does not appear to be altered but shows potential for improvement.

This paper presents a comparison of two methods to represent variable blade inertia in two codes for aero-servo-elastic simulations of wind turbines: the nonlinear aeroelastic multi-body model HAWC2 and the nonlinear geometrically exact beam model BeamDyn for OpenFAST. The main goal is to enable these tools to simulate the dynamic behavior of a wind turbine with variable blade inertia. However, in order to simulate the structural response of wind turbines with variable blade inertias, the source code of load simulation tools need to be modified. This is due to the fact that, currently, state-of-the-art load simulation tools for wind turbines are unable to simulate variable rotor blade inertias. The validity of the modified codes is proven based on a simple beam model. The validation shows very good agreement between the modified codes of HAWC2, BeamDyn and an analytical calculation. The add-on of variable blade inertias is applied to reduce the mechanical loads of a 5 megawatt reference wind turbine with an integrated hydraulic-pneumatic flywheel in its rotor blades.

In the course of SARS-CoV-2 infection, the 3CL or nsp5 protease plays a pivotal role as the most important viral protease required for the maturation of viral proteins during host infection. Herein, we simulated for 500 ns 3CLproWT, 3CLproH41A, , 3CLproBeta, and 3CLproOmicron, in complex with the substrates nsp 4|5 and nsp 5|6. Our results show that mutations in the 3CLpro present in the SARS-CoV-2 variant of concerns (VOCs) did not lead to significant conformational changes or changes in substrate binding affinities. However, significantly high cleavage rates for the boundary between nsp4 and nsp5 were obtained for 3CLproBeta and 3CLproOmicron and may play a key role in the viral replication and virus fitness gain. Our molecular dynamics data suggest that the cleavage rate of nsp4|5 may be related to the increased amount of viral load observed for these VOCs, releasing more nsp4 than other non-structural proteins. Based on our hydrogen bonding analyses, we also discovered that Gly143 and Glu166 are key residues in substrate recognition, suggesting that these residues may be incorporated as pharmacophoric centers for Beta and Omicron variants in drug design. Our results suggest that Gly143 and Glu166 are essential residues to interact with Gln6 of the different substrates and, therefore, are potential broad-spectrum pharmacophoric centers of SARS-CoV-2 3CLpro.

Lung cancer is the most common cause of cancer deaths worldwide, and lung adenocarcinoma (LUAD) is the most common histological subtype. However, the prognostic and predictive outcomes differ because of the heterogeneity of programmed cell death. The purpose of this work is to investigate and develop a cuproptosis-associated lncRNA-based LUAD prediction marker. We firstly performed bioinformatic analysis of the Cuprotosis database and The Cancer Genome Atlas (TCGA) database to obtain 19 cuprotosis-related gene datasets and transcriptional data for LUAD. Univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis were utilized to construct cuproptosis-associated lncRNA modes. LUAD patients were thus classified into high-risk and low-risk categories based on prognostic risk values, with a median of It acted as a boundary. Risk models were evaluated and validated using Kaplan-Meier analysis, principal component analysis (PCA), gene set enrichment analysis (GSEA) and nomograms. Utilizing the TCGA-LUAD dataset, we identified seven predicted cuproptosis-associated lncRNAs in tumor microenvironment to create the risk model. 95.54% (214/224) of high-risk category tumor samples included cuproptosis-associated gene alterations, compared to 85.65% (203/237) of low-risk category tumor samples, with TP53 accounting for the bulk of occurrences. According to these findings, risk value was superior to other clinical variables and tumor mutation burden as a predictor of 1-, 3-, and 5-year overall survival (OS). The predictive validity of the cuproptosis-associated lncRNA-based risk model for LUAD is high, and this may have implications for how lung cancer patients are treated individually.

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

Sediment plumes, released to the Bering Sea from the delta front of the Yukon River, Alaska, are initiated mainly by glacier-melt sediment runoffs in the glacierized regions of the Yukon River drainage basin. The surface sediment plumes are extended around the fan-shaped Yukon River delta, which is followed by the northwestward dispersion. During continuous measure-ments of the Yukon River discharge and sediment load, behaviors of the sediment plumes were explored by shipboard observations in the Bering Sea offshore from the Yukon delta. At the high river sediment load of ca. 3000 kg/s, the plume partially plunged into the sea bottom layer. The plunging probably originated in the nepheloid-layer formation from the flocculation of river-suspended sediment, of which more than 90 %wt. is silt and clay (grain size d < 0.063 mm). In order to numerically obtain the area of the surface sediment plumes, a satellite image analy-sis was performed by using three near-infrared bands in MODIS/Aqua or MODIS/Terra. The plume area was significantly correlated (R2=0.735, p<0.01) to the sediment load averaged for the two days with time lags of 20 days and 21 days to the date of a certain satellite image. Hence, the dispersion of plume-suspended sediment appears to be controlled by the sediment runoff events in the Yukon River rather than the northward “Alaskan Coastal Water”.

The tiered laboratory framework for HIV viral load monitoring accommodates a range of HIV viral load testing platforms, with quality assessment critical to ensure quality patient testing. HIV plasma viral load testing is challenged by the instability of viral RNA. An approach using an RNA stabilizing buffer is described for the Xpert® HIV-1 Viral Load (Cepheid) assay and was tested in remote laboratories in South Africa. EDTA-plasma panels with known HIV viral titres were prepared in PrimeStore molecular transport medium for per-module verification and per-instrument external quality assessment. The panels were transported at ambient temperatures to 13 testing laboratories during 2017 and 2018, tested according to standard procedures and uploaded to a web portal for analysis. A total of 275 quality assessment specimens (57 verification panels and two EQA cycles) were tested. All participating laboratories met study verification criteria (n=171 specimens) with an overall concordance correlation coefficient (ρ_c) of 0.997 (95% confidence interval [CI]: 0.996 to 0.998) and a mean bias of -0.019 log cp/mL (95% CI: -0.044 to 0.063). The overall EQA ρ_c (n=104 specimens) was 0.999 (95% CI: 0.998 to 0.999), with a mean bias of 0.03 log cp/mL (95% CI: 0.02 to 0.05). These panels are suitable for use in quality monitoring of Xpert® HIV-1 VL and are applicable to laboratories in remote settings.

The power system vulnerability leads to faults and the severity of the fault may lead to prolonged load-shedding. The power system needs to be configured in extreme failure scenarios for protecting the network from further contingencies and prolonged load-shedding. Distributed generation resources (DGs) can be useful to form intentional islands after faults to maintain the continuity of power supply to loads based on their weightage during faulty periods and to reduce overall load shedding duration. Power system is bound to collapses and secondary collapse in the formed island is possible. This research represents a novel method of impedance based path finding for intentional islanding, which adapts itself with the changes in the demands, DGs outputs or further severities during restoration period. In this adaptive islanding approach, network adjusts itself with the changes in either the load demands or renewable DGs outputs and rearranges the restoration plan by curtailing or adding some of the loads through controllable switches. Further a secondary collapse in the existing island is studied by injecting multiple faults at various positions of the network to validate the system resilience to cope with severities. A short-term load forecasting approach is used to predict changes in load demands and variations in DG outputs during the islanding scheme. During the restoration period, these variations are tracked and the islands are modified accordingly. In order to minimize the overall generation cost by using less fuel, an economical approach is used in the selection of controllable DGs. The proposed approach is formulated as a multi-objective, that incorporates several operational constraints and simulation is carried out using the modified IEEE 69-bus distribution system to assess the efficacy of the proposed model.

An adaptive control technique has been implemented for DSTATCOM for power quality enhancement in three phase four wires(3P4W) distribution system. The reference source currents are extracted from active components of load current for each phase by using this technique. It has also been used for load balancing, harmonic suppression, power factor improvement and reactive power compensation in distribution system. The adaptive control technique has been tested at varying load in steady state and dynamic conditions. This technique is developed using MATLAB/Simulink and source side harmonics are mitigated under different load conditions which are acceptable in accordance with IEEE standard.

This study examined the effects of incorporating 8 weeks of biweekly upper limb loaded plyometric training (elastic band) into the in-season regimen of handball players. Trial participants were assigned between control (n = 15, age: 18.1±0.5 years, body mass: 73.7±13.9 kg) and experimental (n = 14, age: 17.7±0.3 years, body mass: 76.8±10.7 kg) groups. Measures obtained pre- and post- included a cycle ergometer force-velocity test, ball throwing velocity in three types throw, 1-RM bench press and pull-over, and anthropometric estimates of upper limb muscle volumes. Gains in the experimental group relative to controls included absolute muscle power (W) (Δ23.3%; t-test p<0.01; d=0.083), relative muscle power (W.kg-1) (Δ22.3%; t-test p<0.01; d=0.091), and all 3 types of ball throw (Δ18.6%, t-test p<0.01, d=0.097 on jumping shot; Δ18.6%, t-test p<0.01; d=0.101 on 3-step running throw; and Δ19.1%, t-test p<0.01, d=0.072 on standing throw). Furthermore, a significant improvement by time interactions was observed in both groups on 1-RM bench press and pull-over performance. However, upper limb muscle volumes remained unchanged in both groups. We conclude that adding biweekly elastic band plyometric training to standard training improves measures important to game performance. Accordingly, such exercises can usefully be adopted as a part of handball training.

The growing number of the accessed energy-efficient frequency conversion air conditioners is likely to generate a large number of harmonics on the power grid. The following shortage in the reactive power of peak load may trigger voltage collapse. Hence, this conflicts with people’s expectations for a cozy environment. Concerning the problems mentioned above, an active management scheme is put forward to balance the electricity use and the normal operation of air conditioning systems. To be specific, schemes to suppress the low voltage ride through (LVRT) and harmonic are designed firstly. Then to deaden the adverse effects caused by nonlinear group load running on the grid, and to prevent the unexpected accidents engendered from grid malfunction, the dynamic sensing information obtained by an online monitor is analyzed, which can be seen as an actively supervise mechanism. The combined application of active and passive filtering technology is studied as well. Thirdly, the new energy storage is accessed reliably to cope with peak-cutting or grid breaking emergencies, and the fuzzy control algorithm is researched. Finally, system feasibility is verified by functional modules co-operation simulation, and active management target is achieved under scientific and reasonable state-of-charge (SOC) management strategy.

Aerated concrete, which is manufactured from binding material, sand, foaming agent and water, is currently being utilized in the construction industry because of its lightweight and durability. The binding material, cement, along with other materials used in the concrete produces huge carbon footprints during its fabrication. The utilization of natural aggregates name as coarse aggregates depletes the natural resources of the country. Therefore, huge amounts of agricultural wastes have led scholars to investigate the effectiveness of replacing conventional materials used in concrete with agricultural wastes. In the current study, rice husk ash (RHA) was used as supplementary cementing material, thereby reducing the amount of cement used in aerated concrete (AC) mixture will reduce carbon footprints. The experimental and numerical analysis were conducted to investigate structural behavior of reinforced RAC- B beams subjected to flexural load. Parametric study on structural performance of RAC- B beam under flexure were conducted using finite element analysis (FEA). From the experiment and FEA. Results from the parametric study showed that RAC-10%RHA-B with higher depth structurally performed better compared to RAC-B under flexure with greater load carrying capacity, lesser maximum deflection, and less cracks developing in the tension area.

The variations in socioeconomic status (SES) between different social classes of a population correspond to differences in accessibility to all resources available and able to improve global health. While SES is now known as one of the main determinants for a good health and a good aging, its influence on sleep disorders (SD) is not well understood. SES is a concept, not directly observable but estimated using indicators like income, education, occupational status and area of living. This theoretical review explores some theories linking environment of people with occurrence of SD, with different patterns associated to SES. A model of interaction is proposed to summarize and conceptualizes these interactions and to promote more research on the topic.

With large-scale distributed generators (DGs) in an active distribution network (ADN), conventional load flow convergence failure is incurred by heavy power transmission. The Holomorphic Embedding Load Flow Method (HELM) has proven to be more robust than the Newton–Raphson method under heavy power transmission. At present, HELM is mainly designed for balanced transmission networks. In this study, we developed a three-phase HELM model to accommodate DGs, delta connection loads, and ZIP loads for ADN. The effectiveness and better performance of the proposed method under heavy load situations were validated using modified unbalanced IEEE 13, 34, 37, and 123 test feeders.

In this work is represented conceptual model of robot-manipulator for capture and holding no-cooperation client spacecraft, which has Payload Adapter interface PAS 1666 S, PAS 1194 C, PAS 1666 MVS, PAS 1184 VS, when there are dynamic errors of linear and angular position of client spacecraft in the interval +/-5 deg. per minute and +/-0.1 meters per minute respectively.

Emergency control load-shedding is a key solution to prevent blackouts in the power system. This paper proposed a new model of emergency controls load shedding based on the fast identification of the unstable state of the power system. K-means clustering algorithm divided the instability mode into the clusters. The results of analysis of this cluster were used as the basis for classification control. Building load shedding strategies is consisted of the pre-designed rules based on AHP algorithm. When the recognition of the power system “instability” is detected, the signal of load shedding control is triggered immediately, therefore the decision time is greatly shortened comparing to the traditional methods. The effectiveness of the proposed method was tested on the IEEE 39-bus to overcome the limitations of the last traditional methods.

Distribution systems can form islands when faults occur. Each island represents a subsection with variable boundaries subject to the location of a fault(s) in the system. A subsection with variable boundaries is referred to as island in this paper. For operation in autonomous mode, it is imperative to detect the island configurations and stabilize these subsections. This paper presents a novel scheme for the detection of islanding boundaries and stabilizing the system during autonomous operation. In the first stage, a boundary detection method is proposed to detect the configuration of the island. In the second stage a dynamic voltage sensitivity factor (DVSF) is proposed to assess the dynamic performance of the system. In the third stage, a wide area load shedding program is adopted based on DVSF to shed the load in weak busbars and stabilize the system. The proposed scheme is validated and tested on a generic 18-bus system using a combination of EMTDC/PSCAD and MATLAB softwares.

Globally, humans are struggling with the double threat of communicable and non-communicable diseases, which are presenting new challenges to public health. Public health problems are generally studied and addressed at primary, secondary and tertiary levels. Most effective results are seen with primary interventions. Public health is becoming more aware of the importance of environment-gene interactions in the ontology of health and disease using epigenetics. Epigenetics is the study of altered gene expression without change in base pairs. Be it physical, social, behavioral, or economic factors; they all influence quality of life and health of individuals and populations. That environments are changing the human health phenotype and these changed phenotypes are heritable is of concern for the future of the human race. Knowing the causes of non-communicable human diseases using epigenetics will contribute to the development of new policies to encourage prevention using primary public health initiatives. Research and application of epigenetics shows great promise for improving population health. Continued advances in epigenetics will enhance how we understand and address the way environments are affecting the human health phenotype. For some time, health systems have been and continue to be tertiary in nature. Epigenetic changes can provide information necessary to better understand how social determinants of health can be used to build societies focused on equitable health for all people, rather than continuing to focus on treatment of diseases in the tertiary phase that leads to health disparities. Understanding mechanisms of social determinants of disease, will allow society to evolve in a health-oriented rather than a disease-oriented world. For this reason, we must enhance and apply epigenetics (physical, social, behavioral, economic) research to policy development. We will discuss how bringing social determinants together with biology can be used as new tools for public health policy.

(1) Background: Since the pandemic, adolescent girls have increasingly faced mental health challenges. We examined prevalence trends and associated risk factors for depression among adolescent girls. 2) Methods: Data for girls aged 12 to 17 years (N=4346) from the 2021 cross-sectional National Survey on Drug Use and Health were analyzed. Factors associated with depression were examined using multiple regression analysis. (3) Results: Rates of severe depression were significantly higher (p&lt;.001) in older girls (adjusted odds ratio [AOR]: 1.63, 1.61, those who did not have authoritative parents (AOR: 3.40), and those with negative school experiences (AOR: 4.03). Black and Asian/Native Hawaiian or other Pacific Islanders were less likely to report severe depression than white girls. As previously reported, non-White girls were significantly less likely to receive treatment for depression (p&lt;.05). Parents’ characteristics and school experiences had no effect on the likelihood of receiving mental health treatment. (4) Conclusion: Depression has become increasingly common among American adolescent girls, who are now three times as likely as adolescent boys to have had recent experiences with depression. Our results show that family structure, parenting style, and negative school experiences significantly contribute to the rate of depression and that treatment disparities exist with regard to race and ethnicity.

This proposed research explores a novel approach to image classification by deploying a complex-valued neural network (CVNN) on a field-programmable gate array (FPGA), specifically for classifying 2D images transformed into polar form. The aim of this research is to address the limitations of existing neural network models in terms of energy and resource efficiency, by exploring the potential of FPGA-based hardware acceleration in conjunction with advanced neural network architectures like CVNNs. The methodological innovation of this research lies in the Cartesian to polar transformation of 2D images, effectively reducing the input data volume required for neural network processing. Subsequent efforts focused on constructing a CVNN model optimized for FPGA implementation, emphasizing the enhancement of computational efficiency and overall performance. The experimental findings provide empirical evidence supporting the efficacy of the image classification system developed in this study. One of the developed models, CVNN_128, achieves an accuracy of 88.3% with an inference time of just 1.6ms and a power consumption of 4.66mW for the classification of the MNIST test dataset consists of 10,000 frames. While there is a slight concession in accuracy compared to recent FPGA implementations that achieve 94.43%, our model significantly excels in classification speed and power efficiency—surpassing existing models by more than a factor of 100. In conclusion, the paper demonstrates the substantial advantages of FPGA-implementation of CVNNs for image classification tasks, particularly in scenarios where speed, resource, and power consumption are critical. The study’s reproducible results and corresponding code are available on GitHub at the following link: https://github.com/mahmad2005/CVNNonFPGA

Screening and early diagnosis of diseases are crucial for a patient's treatment to be successful and to improve their survival rate, especially in cancer. The development of non-invasive analytical methods able to detect biomarkers of pathologies is a critical point to define a successful treat-ment and a good outcome. This study extensively reviews electrochemical methods used for the development of biosensors in liquid biopsy, owing to their ability to provide rapid response, pre-cise detection, and low detection limits. We also discuss new developments in electrochemical biosensors, which can improve the specificity and sensitivity of standard analytical procedures. Electrochemical biosensors demonstrate remarkable sensitivity in detecting minute quantities of analytes, encompassing proteins, nucleic acids, and circulating tumor cells, even within challeng-ing matrices such as urine, serum, blood, and various other body fluids. Among the various de-tection techniques used for the detection of cancer biomarkers, even in the picogram range, volt-ammetric sensors are deeply discussed in this review because of their advantages and technical characteristics. This widespread utilization stems from their ability to facilitate quantitative de-tection of ions and molecules with exceptional precision. The comparison of each electrochemical technique is discussed to provide the selection of appropriate analytical methods.

Influence of reaction time on silver nanoparticles-catalyzed oxidation of 3,3’,5,5’-tetramethylbenzidine (TMB) was evaluated via probing the oxidation process by recording the UV-Visible absorbance of the resulting colored products of TMB oxidation by hydrogen peroxide in the presence of silver nanoparticles as peroxides mimics. The time curve of the oxidation process was constructed, revealed that the concentration of the oxidation product was increased by increasing the reaction time and then levled off because the nanozyme active nodes were saturated after a certain reaction time and consequently the steady-state condition can be observed. The maximal activity of the silver nanozymes was achieved after a very short reaction time as short as 3.0 min and after this time, the color intensity of the oxidation products remained constant, revealing saturation of active nodes of nanoparticles with nanozyme-substrate.

Keywords: Kenaf; liquid cultivation; solid cultivation; in vitro propagation, life on land

The idea of an extension of life for CubeSats is proposed to reduce space debris in a low-earth orbit. In this work, a gripper is designed for geometry-based grasping in berthing tasks. The grasping operation is outlined for square- and rectangle-profiled CubeSats. Equilibrium conditions are used to choose fingertips shape and parameters for grasping the CubeSat body. A design scheme is proposed to provide the required accuracy. A design concept is developed into a lab prototype by using low-cost 3D printing technique, and a mock-up grasping task that is representative of the berthing operation is evaluated with the lab prototype. Center-mass hanging setup for the prototype and grasped body is used to evaluate the impact of grasping, partially replicating the conditions in space by reducing the effect of gravity on the system.

MultiCal is an affordable, high-precision measuring device designed for the on-site calibration of industrial robots. Its design features a long measuring rod with a spherical tip that is attached to the robot. By restricting the rod’s tip to multiple fixed points under different rod orientations, the relative positions of these points are accurately measured beforehand. A common issue with MultiCal is the gravity deformation of the long measuring rod, which introduces measurement errors into the system. This problem becomes especially serious when calibrating large robots, as the length of the measuring rod needs to be increased to enable the robot to move in a sufficient space. To address this issue, we propose two improvements in this paper. Firstly, we suggest the use of a new design of the measuring rod that is lightweight yet has high rigidity. Secondly, we propose a deformation compensation algorithm. Experimental results have shown that the new measuring rod improves calibration accuracy by 20% to 39%, while by using the deformation compensation algorithm, the accuracy increases by 6% to 16%. In the best configuration, the calibration accuracy is similar to that of a measuring arm with a laser scanner, producing an average positioning error of 0.274 mm and a maximum positioning error of 0.838 mm. The improved design is cost-affordable, robust, and has sufficient accuracy, making MultiCal a more reliable tool for industrial robot calibration.

Considering the actual world economical trends, one of the most important questions is now and in the future: how to reduce power consumption of electronic systems. Since the invention of computers, the electrical energy consumption step by step increased. Now when not only computers, but electric vehicles, robots, automation, and unmanned aerial vehicles play a very important role of our life, the main problem of system designers is how to reduce energy consumption in these systems. But also the existing already working systems must be revised in order to decrease their electric power consumption. The importance of this subject (energy control) shows that a huge number of research publications and survey papers deal with it. Just focusing on the last one or two years (2021 and 2022) the search hit 221000 titles (103000 hits only in 2022). Analyzing all the research areas is almost impossible, but focusing on some important research subjects, where one of the main topic is “energy saving methods” can give an overview about the subject. The paper focuses on the area of industrial robotic systems, electric vehicles, and embedded systems.

This article aims to reflect upon the relevance of Decolonization methodologies with the Theses on Feuerbach. Somehow, all the Indigenous scholars started from new Marxist like Paulo Freire, Frantz Fanon, but not from classic Marx. To us, the German Ideology of young Marx only resembles the pioneering sources of Indigenous methodology. This discussion is thus a reflection of our studies and a philosophic endeavor to talk about the marginal people of the world, and the scholars who engaged in and with the oppressed. However, we are not prepared to turn our attention away from all the vastness of Marx to a collection of potentially equally relevant to Indigenous methodology. This article concludes that the Theses on Feuerbach is the core of Marxist archaeology of knowledge or philosophy as a whole and has been wading in the Indigenous paradigm.

The contactless coalescence of a droplet is of paramount importance for physical and industrial applications. This paper describes a coalescence method in mid-air via acoustic levitation using an ultrasonic phased array system. Acoustic levitation using ultrasonic phased arrays provides promising lab-on-a-drop applications, such as transportation, coalescence, mixing, separation, evaporation, and extraction in a continuous operation. The mechanism of droplet coalescence in mid-air may be better understood by experimentally and numerically exploring the droplet dynamics immediately before the coalescence. In this study, water droplets were experimentally levitated, transported, and coalesced by controlling acoustic fields. We observed that the edge of droplets deformed and attracted each other immediately before the coalescence. Through image processing, the radii of curvature of the droplets were quantified and the pressure difference between the inside and outside the droplet was simulated to obtain the pressure and velocity information on the droplet surface. The results revealed that the sound pressure acting on the droplet clearly decreased before the impact of the droplets. This pressure on the droplets was quantitatively analyzed from the experimental data. Our experimental and numerical results provide deeper physical insights into contactless droplet manipulation for futuristic lab-on-a-drop applications.

Marine mortar was the goal material and its multi-scale physical-chemical-mechanical characteristics were the principal interest in this study. The on-line multi-scale damage detection experiments art was designed to quantify the characteristics mathematically and graphically. The normal cylinder specimen with 70-day age was produced and investigated by dynamically global MSHCT scan and local detection of EDS, SEM and XRD. The experiments results indicated that the marine mortar offered the appreciable strength at the early age at least, although some saline minerals were generated during the preparation. The micro-interfacial behavior and the parental foci controlled the damage development of the marine mortar the performance of which was still the adjustable one by the composition optimization.

An approach employing ultrafast laser hybrid subtractive-additive microfabrication combining ablation, 3D nanolithography and welding is proposed for the realization of Lab-On-Chip (LOC) device. Single amplified Yb:KGW fs-pulsed laser source is shown to be suitable for fabricating microgrooves in glass slabs, polymerization of fine-meshes filter out of hybrid organic-inorganic photopolymer SZ2080 inside them, and, lastly, sealing the whole chip with cover glass into a single monolithic piece. The created microfluidic device proved its particle sorting function by separating 1 μm and 10 μm polystyrene spheres in a mixture. All together, this shows that fs-laser microfabrication technology is a flexible and versatile tool for the manufacturing of mesoscale multi-material LOC devices.

This study was aimed to investigate the effect of capital structure on firms’ profitability with special emphasis on Ethiopian Large Private Manufacturing Firms using panel data of five consecutive years (2006/07-2010/11G.C). The secondary data sources (audited financial statements) have been collected from the randomly selected thirty three large private manufacturing firms in Ethiopia. Linear regression model has been employed to analyze the relationship between firms’ profitability and capital structure. Specifically, Random-effect Generalized Least Square of panel data regression model has been selected to empirically test the literature driven hypotheses. Finally, the findings of this study revealed that a significant positive relationship between firms’ profitability and total debt ratio which indicate firm’s capital structure.

The metrological performance of FBG strain sensing systems is a critical premise for the precision of plastics water support pipeline structural deformation measurement, but the strain transfer loss of FBG attached on the plastics pipe subjected by dynamic force is unclear. This paper presents the first study on the effects of strain transfer between FBG, and acrylate polymer blended with poly resin (ABR)pipe sheets loaded by static and dynamic force. Theoretical methods of dynamic strain transfer theory combine with the exponentially decaying sinusoidal shock force time course is deduced and various dynamic parameters sensitivities studies are carried out, as well as that of the geometric parameters. The result of the dynamic theory is compared with the calibration test outcomes, while the ASTR of FBG attached on ABR sheet is compared with distributed optical fiber sensors (DOFS) and other pipe materials such as PVC, concrete, and steel. Results show that the ASTR of FBG is more accurate than that of the DOFS, and the grows of ASTR depending on the paste length of FBG, the Young's modulus of the middle materials, the thickness of the middle materials and attenuation coefficient. Paste length produced the largest increase, yet the magnitude and loading speed of the external force has unexpectedly little effect on ASTR. FBG seem low error, insensitive with dynamic force and independent with the material of matrix, and this demonstrates benefits that are stronger across the board than DOFS. This is not currently recognized by standards or by most reported studies.

Nanoindentation load-depth (P-h) plots is the basis for determination of mechanical properties of the samples and is especially vital for thin coatings deposited on substrates of various mechanical makeup from amorphous-brittle (borosilicate glass) to Silicon (having different crystallographic texture) to ductile substrates like stainless steel as well other aspects like indenter tip shape. The elastic-plastic response by simulated P-h plots as per modified empirical relations have been presented. The outcomes of this computational studies enlighten the database on the P-h plots for various coating/substrate systems and shall aid in the analysis of experimental P-h plots obtained,

In this paper, a buck DC-DC converter with the proposed twin frequency control scheme (TFCS) and accurate load current sensing (ALCS) have been designed and implemented with 0.18µm CMOS technology for a supply voltage ranging from 2.0-3.0V which is compatible with state-of-the-art batteries (NiCd/NiMH: 1.1-2V, Li-Ion: 2.5-4.2V). The proposed converter yields a peak efficiency of about 92.7% with a load current of 30mA. Furthermore, it only occupies a silicon area of 1.3mm2. The proposed buck converter is dedicated for smartphone applications whereby it spends most of its time in idle, low load conditions.

Zambia recently experienced extended hours of load shedding due to reduced hydroelectricity generation attributed to poor rainfall patterns. The inability to meet energy demand has influenced the government to consider alternative energy sources. This study aims to assess the economic feasibility of the eight sites using a 25-turbine layout for a 4 MW generic wind turbine at 130m hub height. An economic evaluation mathematical model was developed to analyze the economic feasibility using net present value, simple payback period, internal rate of return, and levelized cost of electricity (LCOE). The study shows that the Lusaka wind farm was the most economical with 386 GWh energy yield analysis (EYA), wind speed of 8 m/s, NPV of 316 million dollars, SPP of 2.9 years, IRR of 82%, and LCOE of $0.182/ kWh. The results also show that the other seven sites are economically feasible, with Petauke being the lowest. The study's economic sensitivity analysis conducted by varying the average electricity tariff shows that Lusaka was the most financially viable site. Therefore, policymakers should develop cost-reflective feed-in tariff (FiT) schemes and power purchase agreements for the country to have cost-reflective electricity tariffs that will motivate independent power producers to invest in Zambia.

We investigated whether cigarette smoking and dietary acid load (DAL) are associated with the risk of chronic obstructive pulmonary disease (COPD) in middle-aged healthy Korean men. Healthy men without diagnosed chronic disease (40-64 yrs) from the KNHANES-VI (2013–2015) included in the analysis (n=774) were subdivided by smoking status and DAL levels as estimated by the net endogenous acid production (NEAP) quartile. Current smokers tended to have a higher risk of COPD than non-smokers before and after the adjustment. When divided by the DAL quartile, the Q4 tended to have a higher risk of COPD than the Q1. Additionally, current smokers with lower (Q2), modest (Q3), and highest NEAP scores (Q4) showed more than 4-fold higher risks of COPD than the non-smokers with the lowest NEAP scores (Q1). Ex–smokers with higher NEAP scores (Q3 and Q4) showed more than 4-fold higher risks of COPD than the Q1. Interestingly, the risk of COPD was also more than 6-fold higher in non-smokers with the highest NEAP scores compared to the Q1. NEAP scores and smoking status synergistically increased the risk of COPD in middle-aged healthy Korean men. It suggests that DAL levels are an important factor for the prevention and management of COPD.

To quantitatively estimate the risk of power system operation under extreme rainfall, a multi-scenario stochastic risk assessment method is proposed. First, a scenario generation scheme considering waterlogged faults of power facilities is constructed based on the storm water management model (SWMM) and the extreme learning machine method. These scenarios will be merged to several typical scenario sets for further processing. The outage of power facilities will induce power flow transfer which may consequently lead to transmission lines’ thermal limit violation. Semi-invariant and Gram-Charlier level expansion methods are adopted to analytically depict the probability density function and cumulative probability function of each line’s power flow. The optimal solution is performed by a particle swarm algorithm to obtain proper load curtailment at each bus, and consequently the violation probability of line thermal violations can be controlled within an allowable range. The volume of load curtailment as well as their importance are considered to quantitatively access the risk of power supply security under extreme precipitation scenarios. The effectiveness of the proposed method is verified in case studies based on the IEEE 24-bus system.

The emergence of new SARSCoV-2 variants can affect vaccine efficacy, laboratory diagnosis and therapies already available, triggering interest in the search for antiviral agents for SARSCoV-2 infections. Ribavirin (RBV) is a broad-spectrum antiviral with demonstrated in vitro activity against multiple viruses, including SARSCoV-2. Methods. This retrospective study evaluated the dynamic and viral clearance of SARSCoV-2 in hospitalized adult participants (PTs) with COVID-19 pneumonia who received RBV aerosol within a compassionate use study. Was also assessed the impact of RBV on the clinical outcome and the mutational profile of SARSCoV-2. Results. The median RNA values measured in nine PTs included in the study de-creased from baseline to discharge (at BL, threshold cycle (Ct)=22.4, IQR 19.84-5.07; at discharge, Ct=27.92, IQR 26.43-36.11). Seven/9 PTs experienced a clinical improvement, while two PTs deceased during hos-pitalization. In PTs with a favorable outcome the virus clearance rate at discharge was 28.6%. The cumulative clearance rate was 71.4% within day 14th from discharge. A mutational pattern after RBV was detected in 3/5 PTs in whom was available whole genome sequencing. Conclusions. RBV may limit SARSCOV-2 replication resulting in a favorable clinical outcome. Ribavirin may also contribute to the mutational spectrum of SARSCoV-2.

This work deals with finding out whether the heart rate values of operators of forwarding machines during the work shift are influenced by the individual activities that the operator has to perform during timber skidding, or by the operators themselves. Furthermore, the work deals with determining the difficulty of individual activities in terms of physical load. For this purpose, the work shift of operators carrying out timber skidding was divided into individual activities: Driving, Maintenance, Forwarding, Break. During these work activities, the heart rate of each operator was taken for subsequent evaluation. The results show that the highest pulse rates of the operators were achieved during the Maintenance of the entrusted machine, while the highest pulse fluctuations in the operators were recorded during Forwarding. As part of this activity, the highest heart rate of the entire measurement process was recorded (132.0000 bpm), but also the lowest (42.0000 bpm). Furthermore, it was proven that both the operator and the activity he performs affect the pulse rate. The activities themselves did not differ from each other in only one of the six cases of comparison. Specifically, it was Driving and Forwarding.

The variation of river hydrologic process can not only reflect the impact of natural factors, but also the impact of human activities. The purpose of this study is to reveal the change trend of the hydrologic regime of the Yellow River and its response to ecological protection in the river basin. Based on the daily water and sediment observation data of representative gauging stations in the middle and lower reaches of the Yellow River, it was analyzed the variation trend of the annual and monthly runoff and suspended sediment load (SSL), and monthly mean runoff, suspended sediment transport rate (SSTR), sediment inflow coefficient, and hydrological regime in decadal average of the gauging stations during the period 1960-2019. The results show that the variation of annual runoff and SSL, as well as the monthly mean runoff and SSTR in decadal average, has a significant decreasing trend in the 1960’s-1990’s, which was mainly in response to the gradual implementation of ecological protection measures such as afforestation, grass planting, terrace construction, and check dam construction, etc. in the basin. In 2000’s and 2010’s, the annual runoff increased, while the SSL increased slightly. This was a response to the implementation of new river management measures such as ensuring the ecological water demand of the lower reaches and scouring the riverbed by manually regulated clear water discharged from the Xiaolangdi Reservoir. In the same time, the monthly mean runoff and SSTR for the flood season (Jun.-Oct.) decreased remarkably, while the process curve of the monthly mean discharge and sediment concentration, changed from a clockwise loop to a counterclockwise loop in the river reach below the Xiaolangdi dam. This was a comprehensive response to the environmental protection measures in the Yellow River basin, in which the construction and operation of the Xiaolangdi Reservoir played a key role. This study can provide reference for river basin management.

Electric vehicles (EVs) will have a greater need for the amount of electricity needed to charge them as their popularity grows. It is anticipated that in order to accomplish this objective, it will be essential to implement a variety of solutions for grid transportation that are designed to complement one another and to make significant changes to the transmission infrastructure. It is possible to reduce the amount of energy that is lost on the power network through strategic planning and control, which may include economic models and methods to engage and reward users. This would eliminate the need for grid upgrades. Charging electric vehicles can also assist alleviate problems with transmission systems that are caused by the allocation of electric vehicles (EVs) using bidirectional charging method. The most significant problems that can occur with a transmission network are power loss and unstable voltage. Adding EV units to the transmission network is typically an effective method for resolving these challenges. As a result, EVs need to have the appropriate arrangement and dimensions. This research establishes where and how many electric vehicles (EVs) should be in a radial transmission network both before and after the adjustment is made. An artificially intelligent (AI) approach, known as a hybrid genetic algorithm particle swarm optimization (HGAIPSO), is used both before and after the radial network modification to find the optimal EV location and size. When electric vehicles are coordinated in an active transmission network, power losses are decreased, voltage profiles are raised, and system stability is increased. These benefits can be attributed to the greater use of electric vehicles. The simulation found that incorporating EVs into the testing system resulted in a considerable decrease in the quantity of power that was wasted. The minimal bus voltage of the system also undergoes similar kinds of enhancements. According to the findings of the comparative study, the proposed method mitigates both the voltage fluctuations and the power losses that occur in the transmission system. For type 1, type 2, and type 3 EV allocations, the IEEE-30 bus test system reduced real power loss by 40.70%, 36.24%, and 42.94%, respectively. IEEE-30 bus voltage reaches 1.01 pu.

Newcastle disease (ND) is a highly contagious and usually causes severe illness that affects Aves all over the world, including domestic poultry. Depending on the virus's virulence, it can impact the nervous, respiratory, and digestive systems and causes up to 100% mortality. The chIFITM genes are activated in response to viral infection. The current study was conducted to quantify the mRNA of chIFITM genes in vitro in response to ND viral infection. It also examined its ability to inhibit ND virus replication in Chicken Embryo Fibroblast (CEF) cells of Aseel and Kadaknath breeds. Results from the study showed that the expression of all chIFITM genes was significantly upregulated throughout the period in the infected CEF cells of both breeds compared to uninfected CEF cells. In CEF cells of the Kadaknath breed, elevated levels of expression of the chIFITM3 gene dramatically reduced the ND viral growth and the viral load was 60% lower than in CEF cells of the Aseel breed. The expression level of the chIFITMs in Kadaknath ranged from 2.39 to 11.68 log2 folds higher than that of control CEFs, and was consistently (p<0.01) higher than Aseel CEFs. Similar to this, IFN-γ gene expresses strongly quickly and peaks at 13.9 log2 fold at 48 hpi. The result suggests that the Kadaknath chicken breed may have a higher level of disease tolerance compared to the Aseel chicken.

Load Frequency Control (LFC) is essential to ensure the stability and performance of power systems. In this study, a novel optimization algorithm, the Sea Horse Optimizer (SHO) algorithm, was proposed for optimizing controller parameters in LFC problems of power system. The proposed SHO algorithm was tested on a two-area power system with photovoltaic system and reheat thermal units, under three different scenarios: a 10% load change in both area, large load disturbances, and varying solar radiation. The proposed algorithm optimizes the gain parameters of PI/PID controllers using performance metrics such as Integral of Absolute value of the Error (IAE), Integral of Square Error (ISE), Integral of Time multiplied by Square Error (ITSE), and Integral of Time multiplied by Absolute Error (ITAE). The study compared the performance of the SHO-optimized controller with other reported optimization algorithms such as Genetic Algorithm (GA), Firefly Algorithm (FA), Whale Optimization Algorithm (WOA), and Modified Whale Optimization Algorithm (MWOA). The study also evaluated the accuracy and effectiveness of the controllers using performance metrics such as Settling Time, Overshoot (M+), and Undershoot (M-). The results show that the SHO-tuned controller significantly reduces overshoot, undershoot, and settling time of the system oscillations compared to other algorithms. The study provides valuable insights into the optimization of LFC controllers and presents a significant contribution to the literature with its novel algorithm. The proposed SHO algorithm can be considered an effective alternative solution method for LFC in power systems.

The accurate prediction of heat load profiles with a daily resolution is required for a broad range of applications such as potential studies, design, or predictive operation of heating systems. If the heat demand of a consumer is mainly caused by (production) processes independent of the ambient temperature, existing load profile prediction methods fail. To close this gap, this study develops two ex post machine learning models for the prediction of the heat demand with a daily resolution. The selected input features are commonly available to each consumer connected to public natural gas and electricity grids or operating an energy monitoring system: Ambient temperature, weekday, electricity consumption, and heat consumption of the last seven days directly before the predicted day. The study’s database covers electricity and natural gas consumption load profiles from 82 German consumers over a period of two years. Electricity and heat consumption correlate strongly with individual patterns for many consumers. Both, shallow and deep learning algorithms are evaluated. The highest accuracy is achieved by a Long Short Term Memory (LSTM) model with a median of R² of 0.94. The ex post model architecture makes the model suitable for automated anomaly detection in energy monitoring systems.

The work is devoted to the study of the mechanisms of damage accumulation in a polymer composite material (PCM) during fatigue loading. Mechanical testing of a fiberglass sample was carried out by cyclic tension accompanied by registration of acoustic emission (AE). For the recorded AE signals, the Fourier spectra were calculated and used for clustering with Kohonen self-organizing map. Relations between clusters and types of damage in the PCM structure were established. The analysis of the peak frequencies of the Daubechies D14-wavelet components of AE signals was carried out. Obtained results has allows one to describe the processes of destruction in the PCM sample. It has been established that, on the base of local formation of microdamages in the matrix and the fracture of the fibers detected during recording of the AE data, it is possible to predict the destruction of the polymer composite material, while the beginning of a material destruction can be registered if the damage identified as an adhesion failure is observed. Perspectives of application of adaptive fiber-optic AE sensors for structural monitoring of PCMs on the base of preliminary experimental results are considered and discussed.

This paper, we examined the technology to maximize the use of renewable energy. The passive ventilation system is expected to reduce the energy consumption of the fan power and the maintenance burden. In addition, the wall-mounted solar air heater can supply thermal energy without using any energy at all. Therefore, we propose a "passive ventilation system with a solar air heater" that combines a passive ventilation system with solar air heater to preheat the air supply and reduce the ventilation load. To evaluate the solar air heater performance in a real environment, we developed a simulation for calculating the heat collection capacity of the solar air heater, and then implemented the system in a real building for verification. Based on the measurement results, the effects of heating load reduction and prediction methods are presented.

Internet of things (IoT) is a network of smart things. This indicates the ability of these physical things to transfer information with other physical things. The characteristics of these networks, such as topology dynamicity and energy constraint, challenges the routing problem in these networks. Previous routing methods could not achieve the required performance in this type of network. Therefore, developers of this network designed and developed specific methods in order to satisfy the requirements of these networks. One of the routing methods is utilization of multipath protocols which send data to its destination using routes with separate links. One of such protocols is RPL routing protocol. In this paper, this method is improved using composite metrics which chooses the best paths used for separate routes to send packets. We propose Energy and Load aware RPL (ELaM-IoT) protocol, which is an enhancement of RPL protocol. It uses a composite metric, calculated based on remaining energy, hop count, Link Expiration Time (LET), load and battery depletion index (BDI) for the route selection. In order to evaluate and report the results, the proposed ELaM-IoT method is compared to the ERGID and ADRM-IoT approaches with regard to average remaining energy, and network lifetime. The results demonstrate the superior performance of the proposed ELaM-IoT compared to the ERGID and ADRM-IoT approaches.

During quasi-stationary tensile deformation of ultrafine-grained Cu-0.2 mass%Zr at 673 K and a deformation rate of about 10⁻⁴ s⁻¹ load changes were performed. Relative load reductions by more than about 25% to relative loads R < 0.75 initiate anelastic back flow. Subsequently the creep rate turns positive again and goes through a relative maximum. This is interpreted by a strain rate contribution ε ̇− from recovery of dislocations. Back extrapolation indicates that ε ̇− contributes about (20 ± 10)% to the quasi-stationary strain rate. The stress dependences of the recovery-strain rate ε ̇− and the rate ε ̇+ related with generation and storage of dislocations are discussed in terms of thermally activated processes characterized by different kinetics.

Sleep disorders (SD) have a complex aetiology, and socioeconomic status (SES) as determined by social class, household income, ethnicity and education plays an important role in their development. As SD are associated with cognitive impairment and mood disorders, they in turn impact SES. Socioeconomic status also influences allostatic load caused by chronic accumulation of stress throughout life. Environmental and psychological stressors have a direct effect on SD, and they are modulated by SES, in combination with comorbidities like obesity and cardiovascular disease. This review explores the recent theories about the influence of SES on the development of SD in the general population, whether or not occurring with comorbidities, and also focusses on the interplay between socioeconomic status, circadian rhythms, aging and clinical outcomes like metabolic diseases and cancer.

The aim of this study is to introduce a novel method which can separate sand or gravel dominated bed load transport in rivers with mixed-size bed material. In engineering practice, the Shields-Parker diagram could be used for such purposes, however, the method has certain applicability limits, due to the fact that it is based on uniform bed material and provides information rather on river-scale, instead of reach or local scale. When dealing with large rivers with complex hydrodynamics and morphodynamics the bed load transport modes can also indicate strong variation even locally, which requires a more suitable approach to estimate the locally unique behavior of the sediment transport. Here, we suggest that the decision criteria utilizes the shear Reynolds number (Re*). The method was verified against field and laboratory measurement data, both performed at non-uniform bed material compositions. The comparative assessment of the results show that the shear Reynolds number based method operates more reliably than the Shields-Parker diagram and it is expected to predict the sand or gravel transport domination with a < 5% uncertainty. The introduced results can greatly contribute to the improvement of numerical sediment transport modeling as well as to the field implementation of bed load transport measurements.

Concrete is a very common construction material. As time prevails, cracks often appear in structures that pose a threat to strength and durability, which ultimately affect the structural integrity of concrete. Extensive work has been carried out in past on utilization of Carbon Fiber Reinforced Polymer (CFRP) as a repair material. But, this research particularly focuses on: multiple fiber layers i.e. single and double, traditional concrete mix ratio with locally available materials and varying flexural reinforcement. Furthermore, this work presents an experimental analysis to strengthen the Reinforced Concrete (RC) beams using SikaWrap- 230 C as a CFRP wrap, and Sikadur-330 as a bonding material. The comparison between single and double layers of CFRP is of major interest in this research. Six reinforced concrete beams were repaired with CFRP and tested under three-point bending test. For these specimens, load deflection behavior along with crack propagation and failure of beams were studied. Experimental results show that beams repaired using CFRP wrapping achieved higher flexural capacity and ductility as compared with reference beam. Moreover, results reveal that introduction of second layer of fiber caused around 4-5.2% increment in flexural strength and resulted in higher ductility of RCC beam.

Increased access to highly active antiretroviral therapy (HAART) by HIV⁺ individuals has become a reality worldwide. In Brazil, ART currently reaches over half of the HIV-infected subjects. In the context of a remarkable HIV-1 genetic variability, highly related variants, called quasispecies, are generated. HIV quasispecies generated during infection can influence virus persistence and pathogenicity, representing a challenge to treatment. However, the clinical relevance of minority quasispecies is still uncertain. For this study, we have determined the archived proviral sequences, viral subtype and drug resistance mutations from a cohort of HIV⁺ patients with undetectable viral load undergoing HAART as first-line therapy using next-generation sequencing for near full-length virus genome (NFLG) assembly. HIV-1 consensus sequences representing NFLG were obtained for eleven patients, while for another twelve varying genome coverage rates were obtained. Phylogenetic analysis showed the predominance of subtype B (83%; 19/23). Considering the minority variants, 18 patients carried archived virus harboring at least one mutation conferring antiretroviral resistance; for six patients, the mutations correlated with the current ARVs used. These data highlight the importance of monitoring HIV minority drug resistant variants and their clinical impact, to guide future regimen switches and improve HIV treatment success.

Mechanical properties of cardiomyocytes from different transmural regions are heterogeneous in the left ventricular wall. The cardiomyocyte mechanical environment affects this heterogeneity because of mechano-electric feedback mechanisms. In the present study, we investigated the effects of load upon transmural differences in contraction of subendocardial (ENDO) and subepicardial (EPI) single cells isolated from the murine left ventricle. Various loads were applied to the cells using carbon fiber techniques for single myocytes. To simulate experimentally obtained results and to predict mechanisms underlying the cellular response to change in load, our mathematical models of the ENDO and EPI cells were used. Extent of the transmural gradient in the time course of contractions was independent of the loading conditions where unloaded and heavy loaded (isometric) contractions were examined, but the regional gradient of the relaxation time characteristics tended to decrease when the load decreased. Under auxotonic contractions, time to peak contraction (T_max) was significantly longer in ENDO cells than in EPI cells at low preload. An increase in preload (axial stretch) prolonged T_max in both cell types; however, the prolongation was greater in EPI cells, resulting in a decrease in transmural gradient in T_max at high preload. The [Ca²⁺]_i transient decay time constant was consistent with the greater preload dependency in T_max of EPI cells. Our modified mathematical models reproduced experimental results, suggesting that differences in cooperativity of cross bridges and calcium troponin C complex interactions between the ENDO and EPI cardiomyocytes may contribute to the different cellular responses to stretch, which may provide a decrease in transmural dispersion of cellular shortening in the intact heart.

Biomaterials play an important role in the development of advancing three dimensional (3D) in vitro skin models, providing valuable insights for drug testing and tissue-specific modeling. Commercial materials, such as collagen, fibrin or alginate, have been widely used in skin modelling. However, they do not adequately represent the molecular complexity of skin components. On this regard, the development of novel biomaterials that represent the complexity of tissues are becoming more important in the design of advanced models. In this study, we have obtained aged human decellularized dermal extracellular matrix (dECM) hydrogels extracted from cadaveric human skin and demonstrated their potential as scaffold for advanced skin models. These dECM hydrogels effectively reproduce the complex fibrillar structure of other common scaffolds, exhibiting similar mechanical properties, while preserving the molecular composition of the native dermis. It is worth noting that fibroblasts embedded within human dECM hydrogels exhibit behavior more representative of natural skin compared to commercial collagen hydrogels, where uncontrolled cell proliferation leads to material shrinkage. The described human dECM hydrogel is able to be used as scaffold for dermal fibroblasts in a skin aging on chip model. These results demonstrate that dECM hydrogels preserve essential components of the native human dermis making them a suitable option for the development of 3D skin aging models that accurately represent the cellular microenvironment, improving existing in vitro skin models and allowing for more reliable results in dermatopathological studies.

Phosphorus, an essential rare element in aquatic ecosystems, plays a key role in maintaining ecosystem balance. However, an excess of phosphorus will lead to eutrophication and algal pro-liferation. To prevent eutrophication, it is crucial to pretreat and measure the concentration of total phosphorus (TP). Compared with conventional TP pretreatment equipment (autoclave), a lab-on-a-chip detection device fabricated through micro-electromechanical system technology using titania (TiO2) as a photocatalyst is more convenient, efficient, and cost-effective. However, the wide bandgap of TiO2 (3.2 eV) limits the photocatalytic activity. To address this problem, this paper describes the preparation of a TiO2/Au nanocomposite film by electron-beam evaporation and atomic layer deposition, based on the introduction of a gold film and TiO2 on a quartz substrate. The photocatalytic degradation properties of TiO2/Au nanocomposite films with thicknesses of 1, 2, 3, and 4 nm are assessed using rhodamine B as a pollutant. Experimental results demonstrate that the deposition of a gold film with different thicknesses can enhance the photocatalytic degradation efficiency through synergetic reactions in the charge separation process on the surface. The optimal photocatalytic efficiency is achieved when the deposition thickness is 2 nm, and it decreases with further increase in the thickness. When the photocatalytic reaction time is 15 min, the LOC device with a 2-nm-thick gold layer and autoclave exhibit similar TP pretreatment performance. Therefore, the proposed LOC device based on photocatalysis technology can address the limitation of conventional autoclave equipment, such as large volume, long processing time, and high cost, thereby satisfying the growing demand for on-site evaluation.

Aeromagnetic compensation plays a vital role in geomagnetic navigation and has received considerable attention throughout last few decades. Classical aeromagnetic compensation methods based on the Tolles–Lawson (T-L) model are mainly aimed at permanent, induced, and eddy-current magnetic interferences of aircraft platform, which ignores other stray magnetic field interference on the platform including the interferences caused by on-board electronic (OBE) systems. In order to cooperate with TL model, magnetometers are usually required to be installed on the extension rod outside the cabin, which is widely applied to geophysical magnetic survey. In order to ensure safety and reduce the cost of platform modification in geomagnetic navigation, it’s necessary to place magnetometers inside the cabin. It also further exacerbates the magnetic interferences and improves the difficulty of magnetic compensation. In this paper, a modified aeromagnetic compensation method is proposed, and the in-cabin OBE interferences are respectively modelled to be proportional to the currents and their temporal variations of different electronic devices. To ensure that modified model adapts to strong OBE interference in the cabin, a cut-off frequency determination method based on curvature calculation and a feature selection method based on correlation calculation are proposed. The cut-off frequency determination method helps to select passband filter which suitable for in-cabin OBE interference. The feature selection method can help to effectively select current and voltage inputs for modified model. In addition, principal component analysis (PCA) is adopted to reduce multicollinearity which is intensified by the extension of OBE interferences in coefficient-estimating. Experiments on public dataset are conducted to illustrate the effectiveness of the proposed method, and the best compensation result achieved 1.71nT of root mean square error (RMSE).

This study presents the design and implementation of a PLC microprocessor adhering to the IEC-61131-3 standard, executed on a Cyclone-V FPGA using a DE10-NANO development board. Our microprocessor optimizes the central processing unit by streamlining the data path, achieving a remarkable simulated response time of approximately 60 ns, equivalent to three clock cycles at a 50MHz frequency for Boolean operations. To substantiate our approach, we conducted practical experiments utilizing a FESTO conveyor station, employing relays as actuators, and incorporating optical and inductive sensors. The results underscore the feasibility of our proposed approach and serve as practical validation of its efficacy. This work introduces a promising avenue for the development of cost-effective PLCs employing SoC FPGA variants. Additionally, a thorough comparison of execution times with other early reported architectures. Our microprocessor outperforms even well-established PLCs like the S7-312, with substantial reductions in execution times of 94.54% for floating-point operations, 71.42% to 93.33% for word operations, and up to 78.57% for bit operations.

This study sought to evaluate the awareness, comprehension, and practices concerning Forensic Odontology among dental students and faculty at a Dental School in Cyprus. An online, cross-sectional, descriptive survey was disseminated to the entirety of dental students and faculty of the School of Dentistry, European University Cyprus, in November 2022. Of those surveyed, 47 faculty members and 304 students responded, yielding response rates of 66.2% and 80%, respectively. The survey revealed that 87% of faculty and 65% of students were familiar with Forensic Odontology. A noteworthy 94% of faculty and 85% of students recognized teeth as DNA repositories. A high percentage, 98% of faculty and 89% of students, acknowledged the role of Forensic Odontology in the identification of criminals and deceased individuals. Awareness of age estimation through dental eruption patterns was evident in 85% of faculty and 81.6% of students. A substantial proportion (80% of faculty) maintained dental records, while 78% of students recognized the importance of dental record-keeping in ensuring quality care. Interestingly, 57% of students and 64% of faculty were aware of the possibility of dentists testifying as expert witnesses. The majority, 95.7% of faculty and 85% of students, concurred that physical harm, scars, and behavioral alterations predominantly indicate child abuse. The survey underscored a robust awareness of Forensic Odontology among respondents. Despite faculty's comprehensive understanding, there's a pronounced need to bolster their inclination towards attending relevant seminars. Emphasis on improved record-keeping practices for potential forensic applications is paramount.

This study investigates the complex interplay between public sentiment, as captured through news titles and descriptions, and the stock prices of three major tech companies: Microsoft (MSFT), Tesla (TSLA), and Apple (AAPL). Leveraging advanced analytical methods including Pearson correlation, wavelet coherence, and regression analysis, this research probes the degree to which stock price fluctuations can be attributed to the polarity of media sentiment. The methodology combines statistical techniques to assess sentiment's predictive power on stock opening and closing prices, while wavelet coherence analysis unveils the temporal dynamics of these relationships. Results demonstrate a significant correlation between sentiment polarity and stock prices, with description polarity affecting Microsoft's opening prices, title polarity influencing Tesla's opening prices, and a positive impact of title polarity on Apple's closing prices. However, Tesla's stock showed no significant coherence, indicating a potential divergence in how sentiment affects stock behavior across companies. The study highlights the importance of sentiment analysis in forecasting stock market trends, revealing not only direct correlations but also lagged influences on stock prices. Despite its focus on large-cap tech firms, this research provides a foundational understanding of sentiment's financial implications, suggesting further investigation into smaller firms and other market sectors.

This work investigates the influence of ground albedo on the solar radiation obtained by surfaces mounted on fixed-tilt to south, one-axis, and two-axis systems. To do this, estimation of the solar radiation difference is performed by applying real albedo and zero albedo. This is done within Saudi Arabia, at 82 selected sites. Annual, seasonal, and monthly mean solar energy differences are computed as function of the site’s number, latitude, and local near-real ground albedo. The great variation in the ground-albedo values at the 82 sites (0.1 - 0.46) could be thought as having a significant effect on the solar radiation levels received on the 3 tracking modes. The analysis shows quite the opposite; a zero-albedo ground diminishes solar radiation levels by 1.43%, 3.50%, and 3.20%, respectively for the 3 modes. Therefore, in most solar engineering applications a ground albedo of 0.2 (considered as reference) can be used without losing accuracy. This is the main conclusion of the study, which must, however, be applied with caution in areas with snow cover, especially for mode-III tracking systems. In such situations the increase in solar radiation levels may be up to 15% (but ≈3.5% for mode-I and -II systems instead).

In this work, we present an analytical model of dynamic power losses for enhancement-mode AlGaN/GaN high-electron-mobility transistor power devices (eGaN HEMTs). To build this new model, the dynamic on-resistance (Rdson) is first accurately extracted by our ex-traction circuit based on a double-diode isolation (DDI) method for a high operating frequency up to 1 MHz and a large drain voltage up to 600 V, thus the unique problem of an increase in the dynamic Rdson is presented. Then the impact of the current operation mode on the on/off transi-tion time is evaluated by a dual-pulse-current-mode test (DPCT) which including a discontinu-ous conduction mode (DCM) and a continuous conduction mode (CCM), thus the transition time is revised for different current mode. Afterward the discrepancy between the drain current and the real channel current is qualitative investigated by an external shunt capacitance (ESC) meth-od, thus the losses due to device parasitic capacitance are also taken into account. After these improvements, the dynamic model will be more compatible for eGaN HEMTs. Finally, the dynamic power losses calculated by this model are verified to be in good agreement with experimental results. Based on this model, we propose a superior solution with qua-si-resonant mode (QRM) to achieve lossless switching and accelerate switching speed.

Access and Benefit Sharing (ABS) is a framework which refers to a relatively recent type of legal requirements for access to and/or use of “genetic resources”. They are based on diverse national and regional laws and regulations, which mostly result from the implementation of the United Nations’ Convention on Biological Diversity (CBD) and its supplementary agreement, the Nagoya Protocol. Their ambition is to achieve fair and equitable sharing of benefits arising out of the use of genetic resources as an incentive to conserve and sustainably use them. This paper describes the experiences, practical constraints and complexities encountered by users of genetic resources when dealing with ABS legislation, with a focus on users from the private sector. We provide insights on how ABS laws have fundamentally changed the way of working with genetic resources in the hope that it inspires re-thinking of the ABS framework, to better support the overall objectives of the CBD.

Current, application-driven trends towards larger-scale integration (LSI) of microfluidic systems for comprehensive assay automation and multiplexing pose significant technological and economical challenges to developers. By virtue of their intrinsic capability for powerful sample preparation, centrifugal systems have attracted significant interest in academia and business since the early 1990s. This review models common, rotationally controlled valving schemes at the heart of such “Lab-on-a-Disc” (LoaD) platforms to predict critical spin rates and reliability of flow control mainly based on geometries, location and liquid volumes to be processed, and their experimental tolerances. In absence of larger-scale manufacturing facilities during product development, the method presented here facilitates the provision of efficient simulation tools for virtual prototyping and characterization to greatly expedite design optimization according to key performance metrics. This virtual in silico approach thus significantly accelerates, de-risks and lowers costs along the critical advancement from idea, fluidic testing, bioanalytical validation and scale-up to commercial mass manufacture.

The outdoor field test of the 4-terminal on Si tandem photovoltaic module (specifically, InGaP/GaAs on Si) was investigated and performance model, considering spectrum change affected by fluctuation of atmospheric parameters, was developed and validated. The 4-terminal on Si tandem photovoltaic module had about 40 % advantage in seasonal performance loss compared with standard InGaP/GaAs/InGaAs 2-terminal tandem photovoltaic module. This advantage is expanded in (subarctic zone) < (temperate zone) < (subtropical zone). The developed and validated model used all-climate spectrum model and considered fluctuation of atmospheric parameters, and can be applied every type of on-Si tandem solar cells.

Microfluidics is facing critical challenges in the quest of miniaturizing, integrating, and automating in vitro diagnostics, including the increasing complexity of assays, the gap between the macroscale world and the microscale devices, and the diverse throughput demands in various clinical settings. Here a “3D extensible” microfluidic design paradigm that consists of a set of basic structures and unit operations was developed for constructing any application-specific assay. Four basic structures- check valve (in), check valve (out), double-check valve (in and out), and on-off valve, were designed to mimic basic acts in biochemical assays. By combining these structures linearly, a series of unit operations can be readily formed. We then proposed a “3D extensible” architecture to fulfill the needs of the function integration, the adaptive “world-to-chip” interface, and the adjustable throughput in the X, Y, and Z directions, respectively. To verify this design paradigm, we developed a fully integrated loop-mediated isothermal amplification microsystem that can directly accept swab samples and detect Chlamydia trachomatis automatically with a sensitivity one order higher than that of the conventional kit. This demonstration validated the feasibility of using this paradigm to develop integrated and automated microsystems in a less risky and more consistent manner.

The aim of this study is to provide a detailed strategy for Safe-by-Design (SbD) 3D printed lab-on-a-chip (LOC) device manufacturing, using Fused Filament Fabrication (FFF) technology. At first, the applicability of FFF in lab-on-a-chip device development is briefly discussed. Subsequently, a methodology to categorize, identify and implement SbD measures for FFF is suggested. Furthermore, the most crucial health risks involved in FFF processes are examined, placing the focus on the examination of ultrafine particle (UFP) and Volatile Organic Compound (VOC) emission hazards. Thus, a SbD scheme for lab-on-a-chip manufacturing is provided, while also taking into account process optimization for obtaining satisfactory printed LOC quality. This work can serve as a guideline for the effective application of FFF technology for lab-on-a-chip manufacturing through the safest applicable way, towards a continuous effort to support sustainable development of lab-on-a-chip devices through cost-effective means.

Despite their limited spatial extent, freshwater ecosystems host remarkable biodiversity, including one third of all vertebrate species. This biodiversity is declining dramatically: globally, wetlands are vanishing three times faster than forests and freshwater vertebrate populations have fallen more than twice as steeply as terrestrial or marine populations. Threats to freshwater biodiversity are well- documented but co-ordinated action to reverse this decline is lacking. We present an Emergency Recovery Plan to “bend the curve” of freshwater biodiversity loss. Priorities for action include: 1) accelerating implementation of environmental flows, 2) improving water quality, 3) protecting and restoring critical habitats, 4) managing exploitation of freshwater species and riverine aggregates, 5) preventing and controlling non-native species invasions, and 6) safeguarding and restoring river connectivity. We recommend revised targets and indicators for the Convention on Biological Diversity and the Sustainable Development Goals, and investment in enabling conditions at national, river basin and local scales.

The objective of this work was to investigate the use of remotely sensed vegetation indices to improve the quality of yield maps. The method was applied to the yield data of twelve cornfields from the Data Intensive Farm Management project. The results revealed the need to time shift the yield values up to three seconds to better match the sensor readings with the geographic coordinates. The residuals of the yield prediction model were used to identify points with unlikely yield values for that location, as an alternative to traditional approaches using local spatial statistics, without any assumption of spatial dependence or stationarity. The temporal and spatial distribution of the standardized coefficients for each experimental unit highlighted the presence of trends in the data. At least five out of the twelve fields presented trends that could have been induced by data collection.

In this paper we consider Changhee polynomials of type two, which are motivated from the recent work of Kim and Kim. We investigate some symmetry identities for the Changhee polynomials of type two which are derived from the properties of symmtery for the fermionic p-adic integral on Zp.

Road markings play an important role in road safety because they provide significant information to drivers about the road. For that reason, they must be replaced when they are not correctly perceived by road users. To analyse which are the main factors that affect road marking perception over time, a test section was designed in a two-lane rural highway, running actual traffic over 18 different types of markings fabricated with different combinations of drop-on materials. Chromatic coordinates, luminance and retroreflectivity of each sample were measured during 18 months in order to study their evolution over time. The results obtained show different behaviours depending on the aggregates and application method used. An increment of the durability has been observed with the use of different layers and mixtures of glass microbeads with different sizes.

Amidst the recent energy crisis, the pivotal role of resource efficiency and renewable energy sources (RES) for sustainable development has become apparent. Transition to sustainability involves decentralized energy solutions empowering local communities to generate, store, and utilize their energy, diminishing reliance on centralized systems and potentially transforming them into power flexibility resources. This is particularly crucial in non-interconnected island systems that primarily rely on imported oil for electricity generation. Such reliance not only implies high production costs but also threatens energy security and adds to the environmental footprint of local economic activities. Addressing the above necessitates, amongst other elements, the adoption of advanced energy management with sophisticated forecasting and demand-side management (DSM) strategies. In response, we introduce a versatile algorithm investigating the impact of DSM at the community scale, designed to maximize the utilization of renewable energy produced from local installations. Integrated as an ancillary module in an advanced energy management platform, the algorithm undergoes testing using datasets collected from end-consumers and a small-scale RES installation on the island of Tilos, Greece. This study not only offers insights for energy stakeholders but also establishes theoretical parameters that can inform subsequent decision-making processes.

Thermostatically controlled loads have great potential to make a significant contribution to improving energy efficiency in the building sector, which is responsible for 40% of greenhouse gas emissions in the EU. This, in addition to the environmental damage, represents a huge expense in the electricity bill. Therefore, it is very important to train engineers on how to design energy management systems for TCLs. With this goal in mind, it would be very useful to have a simulation-based educational tool (SBET) to understand thermostatically controlled loads, their characteristics and possibilities in energy efficiency. In addition, it would be very useful if this tool could be introduced in engineering curricula to help students to be better trained and enter the labor market with more opportunities. Based on the shortcomings detected, this work develops a SBET specifically designed to teach about TCLs (SBET-TCLs), both about their intrinsic characteristics and their better management. To verify the developed SBET-TCL, it has been tested in a real scenario: A survey was carried out among the students of the subject 'Alternative Energy Sources' of the degrees of Industrial Engineering. The results show that the use of SBET-TCL has very positive effects on the learning process.

A form of dementia distinct from healthy cognitive aging, Alzheimer's disease (AD) is a complex multi-stage disease that currently afflicts over 50 million people worldwide. Unfortunately, previous therapeutic strategies developed from murine models emulating different aspects of AD pathogenesis have failed. Consequently, researchers are now developing models that express several aspects of pathogenesis that better reflect the clinical situation in humans. As such, this review seeks to provide insight regarding current applications of mammalian models in AD research by addressing recent developments and characterizations of prominent transgenic models and their contributions to pathogenesis as well as discuss the advantages, limitations, and application of emerging models (hAβ-KI) that better capture genetic heterogeneity and mixed pathologies observed in the clinical situation.

Bridge load testing is crucial in evaluating bridge load-carrying capacity and construction quality. However, conventional techniques have many limitations such as the need for direct contact and low acquisition frequency. This study provides a comprehensive examination of the static and dynamic load tests of a multi-span cable-stayed bridge utilizing a ground-based radar inter-ferometer (GB-radar). The case study was conducted at the Fifth Nanjing Yangtze River Bridge (FNYRB), which is recognized as the world's first lightweight steel-concrete composite cable-stayed bridge, with two spans measuring 600 m. To enhance measurement accuracy, a method was proposed to detect and recover radar phase jumps caused by vehicle motion. Furthermore, precise geometry projection was employed to acquire vertical displacements from GB-radar which were taken along the line-of-sight (LOS) direction. Moreover, during the static load test, a continuous deformation was observed with a maximum pace of 0.31 mm/min, indicating a post-construction settlement caused by soft soil consolidation. This case study highlights the high sampling frequency, high measurement accuracy, and exceptional weatherproof ability of GB-radar, thereby demonstrating its potential to be an alternative to the structural health monitoring (SHM) system.

In this paper, a systematic literature review is presented, through a survey in the main digital databases, about modelling methods for Short-Term Load Forecasting (STLF) for hourly electricity demand for residential electricity and to realize the performance evolution and impact of the Artificial Intelligent (AI) in STLF. With these specific objectives, it was developed a conceptual framework on the subject, a systematic review of the literature based on scientific publications with high impact and a bibliometric study directed to the scientific production of AI and STLF. The review of research articles over a 10-year period, which takes place between 2012-2022, uses the Preferred Reporting Items for Systematic and Meta-Analyses (PRISMA) method. This research resulted in more than 300 articles, available in 4 (four) databases: Web of Science, IEEE Xplore, Scopus and Science Direct. The research was organized around 3 (three) central themes, which were defined through keywords: STLF, Electricity, Residential and their corresponding synonyms. 334 research articles were analyzed and identified the year of publication, journal, author, geography by continent and country and the area of application. Of the 334 documents found in the initial research and after applying the inclusion/exclusion criteria, which allowed delimiting the subject addressed in the topics of interest for analysis, 37 (thirty-seven) documents are in English (25 are journal articles and 12 are conference papers). The results point to a diversity of modelling techniques and associated algorithms. The corresponding performance is measured with different metrics and, therefore, cannot be compared directly. Hence, it should be desirable to have a unified data set together with a set of benchmarks with well-defined metrics for a clear comparison of all the modelling techniques and the corresponding algorithms.

Molecular autism research is evolving towards a biopsychosocial framework that is more informed by autistic experiences. In this context, research aims are moving away from correcting external autistic behaviors and towards alleviating internal distress. Autism Spectrum Conditions (ASCs) are associated with high rates of depression, suicidality and other comorbid psychopathologies, but this relationship is poorly understood. Here, we integrate emerging characterizations of internal autistic experiences within a molecular framework to yield insight into the prevalence of psychopathology in ASC. We demonstrate that descriptions of social camouflaging and autistic burnout resonate closely with the accepted definitions for early life stress (ELS) and chronic adolescent stress (CAS). We propose that social camouflaging could be considered a distinct form of CAS that contributes to allostatic overload, culminating in a pathophysiological state that is experienced as autistic burnout. Autistic burnout is thought to contribute to psychopathology via psychological and physiological mechanisms, but these remain largely unexplored by molecular researchers. Building on converging fields in molecular neuroscience, we discuss the substantial evidence implicating mitochondrial dysfunction in ASC to propose a novel role for mitochondrial allostatic load in the relationship between autism and psychopathology. An interplay between mitochondrial, neuroimmune and neuroendocrine signaling is increasingly implicated in stress-related psychopathologies, and these molecular players are also associated with neurodevelopmental, neurophysiological and neurochemical aspects of ASC etiology. Together, this suggests an increased exposure and underlying molecular susceptibility to ELS that increases the risk of psychopathology in ASC. This article describes an integrative framework shaped by autistic experiences that highlights novel avenues for molecular research into mechanisms that directly affect the quality of life and well-being of autistic individuals. Moreover, this framework emphasizes the need for increased access to diagnoses, accommodations, and resources to improve mental health outcomes in autism.

Maritime transport has become very important due to its ability to internationally unite all continents. In turn, during the last two years, we have observed that the increase of consumer goods has resulted in global shipping deadlocks. In addition, the future goes through the role of ports and efficiency in maritime transport, with the aim of decarbonizing its impact on the environment. In order to improve the economy and people's lives, in this work, we propose to enhance services offered in maritime logistics. To do this, a communications system is designed on the deck of ships to transmit data through a constellation of satellites using interconnected smart devices based on IoT. Among the services, we highlight the monitoring and tracking of refrigerated containers, the transmission of geolocation data from GPS, and security through AIS. This information will be used for a fleet of ships to make better decisions and help guarantee the status of the cargo, as well as maritime safety on the routes. The system design, network dimensioning, and a communications protocol for decision-making will be presented.