Droplet digital polymerase chain reaction (ddPCR) is a method used to detect and quantify nu-cleic acids even when present in exceptionally low numbers. While it has proven to be valuable for clinical studies, it has failed to be widely adopted for environmental and applied studies. Due to the complexity of the chemical and biological composition of environmental samples, protocols tailored to clinical studies are not appropriate, and results are difficult to interpret. We used en-vironmental DNA samples originating from field studies to determine a protocol for environ-mental samples. Samples included field soils which had been inoculated with the soil fungus Rhizophagus irregularis (environmental positive control), field soils that had not been inoculat-ed and the targeted fungus was not naturally present (environmental negative control), and root samples from both field categories. To control for the effect of soil inhibitors, we also in-cluded DNA samples of an organismal control extracted from pure fungal spores (organismal positive control). Finally, we included a no-template control consisting only of the PCR reaction reagents and nuclease free water instead of template DNA. Using original data, we examined which factors contribute to poor resolution in root and soil samples and propose best practises to ensure accuracy and repeatability. Furthermore, we evaluated manual and automatic threshold determination methods and we propose a novel protocol based on multiple controls that is more appropriate for environmental samples.

For the in-house validation of a droplet digital PCR method, a factorial experimental design was implemented. This design serves different purposes. On the one hand, it is an efficient design in relation to the workload involved in achieving a desirable level of reliability of variance estimates. On the other hand, it allows a partitioning of total variance into different components, thus providing information regarding the dominant sources of random variation. The statistical modelling reflects the actual measurement mechanism, establishing relationships between nominal target DNA copies per well, the range of variation of copy numbers per droplet, probability of detection values, and estimated numbers of copies.

The viral nervous necrosis virus (VNNV) is the causative agent of an important disease affecting fish species cultured worldwide. Early and accurate diagnosis is at present the most effective control and prevention tool, and the molecular techniques have been strongly introduced and accepted by official organizations. Among those, real time quantitative polymerase chain reaction (rt-qPCR) is nowadays displacing other molecular techniques. However, another PCR-based technology, the droplet digital PCR (ddPCR), is on the increase. It has many advantages over qPCR, such as higher sensitivity and more reliability of the quantification. Therefore, we decided to design and validate a protocol for diagnosis and quantification of SJ and RG type VNNV, using reverse transcription-ddPCR (RT-ddPCR). We obtained an extremely low limit of detection, 10 to 100-folds lower than with RT-qPCR. Quantification by RT-ddPCR, with a dynamic range of 6.8 – 6.8 x 104 (SJ type) or 1.04 x 101 – 1.04 x 105 (RG) cps/rctn, was more reliable than with RT-qPCR. The procedure was tested and validated in field samples, providing the high clinical sensitivity and negative predictive values. In conclusion, we propose this method to substitute RT-qPCR protocols because it exceeds the expectations of qPCR in the diagnosis and quantification of VNNV.

Background: The reference test for SARS-CoV-2 detection is the reverse transcriptase real time PCR (real time RT-PCR). However, evidences reported that real time RT-PCR has a lower sensitivity compared with the droplet digital PCR (ddPCR) leading to possible false negative in low viral load cases. Methods: We used ddPCR for viral genes N1 and N2 on 20 negative (no detection) samples from symptomatic hospitalized COVID-patients presenting fluctuating real time RT-PCR results and 10 suspected samples (Ct value>35) from asymptomatic not hospitalized subjects. Results: ddPCR performed on RNA revealed 65% of positivity for at least one viral target in the hospitalized patients group of samples (35% for N1 and N2, 10% only for N1 and 20% only for N2) and 50% in the suspected cases (30% for N1 and N2, while 20% only for N2). On hospitalized patients’ samples, we applied also a direct ddPCR approach on the swab material, achieving an overall positivity of 83%. Conclusion: ddPCR, in particular the direct quantitation on swabs, shows a sensitivity advantage for the SARS-CoV-2 identification and may be useful to reduce the false negative diagnosis, especially for low viral load suspected samples.

Surface water distribution extracted from remote sensing data has been used in water resource assessment, coastal management, and environmental change studies. Traditional manual methods for extracting water bodies cannot satisfy the requirements for mass processing of remote sensing data; therefore, accurate automated extraction of such water bodies has remained a challenge. The histogram bimodal method (HBM) is a frequently used objective tool for threshold selection in image segmentation. The threshold is determined by seeking twin peaks, and the valley values between them; however, automatically calculating the threshold is difficult because complex surfaces and image noise which lead to not perfect twin peaks (single or multiple peaks). We developed an operational automated water extraction method, the modified histogram bimodal method (MHBM). The MHBM defines the threshold range of water extraction through mass static data; therefore, it does not require the identification of twin histogram peaks. It then seeks the minimum values in the threshold range to achieve automated threshold. We calibrated the MHBM for many lakes in China using Landsat 8 Operational Land Imager (OLI) images, for which the relative error (RE) and squared correlation coefficient (R2) for threshold accuracy were found to be 2.1% and 0.96, respectively. The RE and root-mean-square error (RMSE) for the area accuracy of MHBM were 0.59% and 7.4 km2. The results show that the MHBM could easily be applied to mass time-series remote sensing data to calculate water thresholds within water index images and successfully extract the spatial distribution of large water bodies automatically.

Achieving effective control over microbial contamination necessitates the precise and concurrent identification of numerous pathogens. In this research, we have devised a remarkably sensitive duplex droplet digital PCR (dddPCR) reaction system to simultaneously detect Pseudomonas aeruginosa (P. aeruginosa) and Pseudomonas fragi (P. fragi). Employing comparative genomics, we identified four genes of P. fragi. By specific analysis, RS22680 gene was selected as the detection target of P. fragi and LasR gene was chosed as P. aeruginosa, which were applied to construct a dddPCR reaction. In terms of specificity, sensitivity and anti-interference ability, the constructed dddPCR detection system was verified and analyzed. The assay showed excellent sensitivity and applicability, as evidenced by a limit of detection of 100 CFU/mL. When the concentration of natural background bacteria in milk or fresh meat was 100 times that of the target detection bacteria, the method was still capable of completing the absolute quantification. In the simulation of actual sample contamination, P. aeruginosa could be detected after 3 h of enrichment culture, and P. fragi could be detected after 6 h. The established ddPCR detection system exhibits exceptional performance, serving as a foundation for the simultaneous detection of various pathogenic bacteria in food products.

Head and neck squamous cell carcinomas (HNSCCs) develop from the mucosa of the oral cavity, pharynx, and larynx. The methylation levels of Septin 9 (SEPT9) and short stature homeobox 2 (SHOX2) genes in ccfDNA are considered pan-cancer biomarkers and have shown prognostic value in preliminary reports in HNSCC. Liquid biopsy is a non-invasive procedure that collects tumor-derived molecules including circulating cell-free DNA (ccfDNA). Here, we developed a ddPCR-based assay to detect the DNA methylation levels of plasma circulating SEPT9 and SHOX2 in patients with HNSCC. We first set up the assay on commercial methylated and un-methylated DNA. Then, the dynamic changes of the methylation levels of SEPT9 and SHOX2 were quantified in 20 patients during the follow-up. The results highlighted: i) the capability of the ddPCR-based assay in detecting very low copies of methylated molecules; ii) the significant decrease in methylation levels of SEPT9 and SHOX2 in plasma of HNSCC patients at the first time points of follow-up respect to T0; iii) a different trend of longitudinally DNA methylation variations associable to the clinicopathological features of the patients. The absolute quantification of the methylation levels of SEPT9 and SHOX2 in HNSCC may be used for risk stratification and disease monitoring.

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.

The device and method in accordance with the constant pressure regulation of micro droplet PCR in microfluidic chips are developed to explore the optimization way for the micro droplet movement, fragmentation, and bubble generation in microfluidic chips in existing digital PCR technology. In the developed device, an air source device is adopted to regulate the pressure in the chip, such that micro droplet generation and PCR amplification without bubbles can be achieved. In 3 min, the sample in 20ul will be distributed into nearly 50,000 water-in-oil droplets exhibiting a diameter of about 87μm, and the micro droplet will be subjected to a close arrangement in the chip without air bubbles. The device and chip are adopted to quantitatively detect the human gene . As indicated by the experimental results, a good linear relationship exists between detection signal and DNA concentration ranging from 101~ 105 copies /μL (R2=0.999). The micro droplet PCR devices based on constant pressure regulation chips exhibit a wide variety of advantages (e.g., achieving high pollution resistance, micro droplet fragmentation and integration avoidance, reducing human interference, and standardizing results). Thus, micro droplet PCR devices based on constant pressure regulation chips have promising applications in nucleic acid quantification.

A Infectious diseases account for about 3 million deaths per year. The advent of molecular techniques has led to an enormous improvement in their diagnosis, both in terms of sensitivity and specificity and in terms of the speed with which a clinically useful result can be obtained. Digital PCR, or 3rd generation PCR, is based on a series of technical modifications that result in more sensitive techniques, more resistant to the action of inhibitors and capable of direct quantification, without the need for standard curves.This review presents the main applications that have been developed for the diagnosis of viral, bacterial and parasitic infections, and the potential prospects for the clinical use of this technology.

With the increasing concern of environment, the energy-efficiency scheduling of manufacturing industry is becoming urgent and popular. In turning processes, both spindle speed and processing time can affect the final energy consumption and thus the spindle speed and scheduling scheme need to be optimized simultaneously. Since the turning workshop can be regarded as the flexible flow shop, this paper formulates a mixed integer linear programming model for energy-efficient scheduling of flexible flow shop. Accordingly, a new decoding method is developed by considering of the optimization of spindle speed and scheduling scheme simultaneously, and an estimation of distribution algorithm adopting the new decoding method is proposed to solve large-size problems. The parameters of this algorithm are determined by statistical technique with a simplified practical case. In order to validate the proposed method, a case from practical factories is studied, in which the makespan can be shortened by 25.22%, and the consumed energy can be saved by 5.48%. These results demonstrate the effectiveness of the proposed mathematical model and algorithm.

This paper presents a study of the 2D roughness profiles on a flat surface generated on a steel work piece, by ball nose end milling, with linear equidistant tool paths (pick-intervals). The milled surface exploration with a surface roughness tester (on pick and feed directions) produces usually 2D roughness profiles having periodical evolutions. These evolutions can be seen as time-dependent signals, describable as a sum of sinusoidal components (the wavelength of each component being regarded as a period). In order to detect a well approximated description of these components, two appropriate signal processing techniques are used in this work: first technique provides a direct mathematical (analytical) description and is based on computer aided curve (signal) fitting (more accurate), the second technique (providing an indirect and incomplete description) is based on the spectrum generated by fast Fourier transform (less accurate). This study can be seen as a way for a better understanding the interaction between the tool and the work piece or to achieve a mathematical description of the machined surface microgeometry in terms of roughness (e. g. its description being a collection of closely spaced 2D roughness profiles).

This work presents a novel approach for the study of the movement of droplets on inclined surfaces under the influence of gravity and chemical heterogeneities. The developed numerical methodology uses data-driven modeling to extend the applicability limits of an analytically derived reduced-order model for the contact line velocity. More specifically, while the reduced-order model is able to capture the effects of the chemical heterogeneities to a satisfactory degree, it does not account for gravity. To alleviate this shortcoming, datasets generated from direct numerical simulations are used to train a data-driven model for the contact line velocity, which is based on the Fourier neural operator and corrects the reduced-order model predictions to match the reference solutions. This hybrid surrogate model, which comprises of both analytical and data-driven components, is then integrated in time to simulate the droplet movement, offering a speedup of five orders of magnitude compared to direct numerical simulations. The performance of this hybrid model is quantified and assessed in different wetting scenarios, by considering various inclination angles and values for the Bond number, demonstrating the accuracy of the predictions as long as the adopted parameters lie within the ranges considered in the training dataset.

Plant leaf area index (LAI) is a key characteristic affecting field canopy microclimate. In addition to traditional professional measuring instruments, smartphone camera sensors have been used to measure plant LAI. However, when smartphone methods were used to measure conifer forest LAI, very different performances were obtained depending on whether the smartphone was held at the zenith angle or at a 57.5° angle. To validate further the potential of smartphone sensors for measuring conifer LAI and to find the limits of this method, this paper reports the results of a comparison of two smartphone methods with an LAI-2000 instrument. It is shown that both methods can be used to reveal the conifer leaf-growing trajectory. However, the method with the phone oriented vertically upwards always produced better consistency in magnitude with LAI-2000. The bias of the LAI between the smartphone method and the LAI-2000 instrument was explained with regard to four aspects that can affect LAI: gap fraction, leaf projection ratio, sensor field of view (FOV), and viewing zenith angle (VZA). It was concluded that large FOV and large VZA cause the 57.5° method to overestimate the gap fraction and hence underestimate conifer LAI, especially when tree height is greater than 2.0 m. For the vertically upward method, the bias caused by the overestimated gap fraction is compensated for by an underestimated leaf projection ratio.

This paper will discuss Data, Information, Knowledge, and Wisdom, which is commonly referred to as DIKW. The DIKW Pyramid Model that is often referenced in both academics and practitioner circles will be discussed and shown to be faulty on several levels, including. a lack of definitional agreement. A new DIKW framework will be proposed that focuses on what the DIKW elements do in the way humans think, not what they are by definition. Information as a replacement for wasted physical resources in goal-oriented tasks will be a central organizing point. The paper will move the DIKW discussion to the computer-based concept of Digital Twins (DTs) and its augmentation of how we can use DIKW to be more effective and efficient. This will especially be the case as we move to Intelligent Digital Twins (IDT) with Artificial Intelligence (AI).

Induction heating is a fast, reproducible, and efficient heating method used in various manufacturing processes. However, there is no established additive manufacturing (AM) process based on induction heating using wire as feedstock. This study investigates a novel approach to AM based on inductive heating, where a steel wire is melted and droplets are detached periodically using a two-winding induction coil. The process parameters and energy input into the droplets are characterized. The induction generator exhibits sluggish response to the excitation voltage, resulting in a lag in the coil current. The process is captured using a high-speed camera, revealing regular droplet formation and uniform size and shape when operated within an appropriate process window. Larger drops and increased spatter formation occur outside this window. The proposed method allows for the production of droplets with almost spherical shapes. Further analysis and characterization of droplet formation and energy input provide insights into process optimization and overall efficiency.

L-Theanine, a unique non-protein amino acid, is an important bioactive component of green tea. Previous studies have shown that L-theanine has many potent health benefits, such as anti-anxiety effect, regulation of immune response, relaxing neural tension, reducing oxidative damage. However, little is known concerning whether L-theanine can improve the clearance of mitochondrial DNA (mtDNA) damage in organisms. Here, we reported that L-theanine treatment increased ATP production and improved mitochondrial morphology to extend the lifespan of UVC-exposed nematodes. Mechanistic investigations showed that L-theanine treatment enhanced the removal of mtDNA damage and extended lifespan by activating autophagy, mitophagy, mitochondrial dynamics, and mitochondrial unfolded protein response (UPRmt) in UVC-exposed nematodes. In addition, L-theanine treatment also up-regulated the expression of genes related to mitochondrial energy metabolism in UVC-exposed nematodes. Our study provided a theoretical basis for the possibility that tea drinking may prevent mitochondrial related diseases.

PIK3CA is a gene usually mutated in breast cancer and has an important role in tumor progression and treatment. Therefore, there is required a technique to detect low-rate PIK3CA mutations improving the clinical conduct. This study aimed to compare chip-based dPCR and Sanger sequencing to detect PIK3CA mutations in breast cancer patients. Fifty-seven tumor samples from breast cancer patients were collected and analyzed by Sanger sequencing and dPCR for PIK3CA mutations (E545K, H1047R, and H1047L). Digital PCR sensitivity, specificity, and overall performance were estimated by contingency tables, receptor operator characteristic (ROC), and area under the curve (AUC). Sanger sequencing identified PIK3CA mutations in six patients (10.5%), two with H1047R, and four with E545K. Digital PCR confirmed those mutations and identified 19 additional patients with at least one mutation. Comparison between dPCR and Sanger sequencing showed a sensitivity of 100% (95% CI 53-100%), and a specificity of 84.2% (95% CI 83 - 84.2%). Besides, H1047R mutation showed a significant association with breast cancer phenotype (p =0.019) and lymphatic node infiltration (p =0.046). Digital PCR showed a high sensitivity to detect mutations in tumor samples and it might be capable to detect low-rate mutations and tumor subpopulations not detected by Sanger sequencing.

The mineralogical indices, e.g., Quartz Index (QI), Carbonate Index (CI), Mafic Index (MI) for ASTER multispectral thermal infrared (TIR) data have been applied to various geological materials. The indices are proved to be robust in extracting geological information at the local scale. Considering the relatively narrow spatial coverage of each ASTER scene compared to LANDSAT, many scenes are needed when mosaicking the images to be mapped at regional scales. We develop a system to search ASTER data for a target area given the vast and expanding ASTER data archive. The data is then conditioned, prioritized, and the indices are calculated before finally mosaicking the imagery. The maps of the indices covering the very wide region of the central Tibetan Plateau are produced with this system. The characteristic features of the indices relating to their geology in the study area are analyzed and discussed. Many interesting lithological and structural information that are not currently well understood in the central Tibetan Plateau, the highest and most extensive plateau in the world, with an average elevation of over 4,500 meters above sea level, for example, distributions of the mafic-ultramafic rocks along the suture zones, the quarzitic and marine sedimentology accreted to the Eurasian continent and sulfate layers related to the Tethys and neo-Tethys geological setting can be retrieved from the processed ASTER images.

Establishment of diagnostic methods with low detection limits plays a critical role in the maintenance of early diagnosis, prevention of serious neurological complications, and control of the spread of ZIKA. In this study, we established the micro-droplet digital polymerase chain reaction (ddPCR) and real-time fluorescent quantification PCR (qPCR) protocols for the detection of Zika virus based on the NS5 gene. For the Zika standard plasmid, the standard curve of R² was 0.999, and the amplification efficiency was 92.203%, as determined by qPCR. Both ddPCR and qPCR were positive for cell culture of Zika nucleic acid.The minimum detection limit of ddPCR is 1–2 times lower than qPCR. Moreover, all tests of Dengue virus (1–4 serotypes) were negative in cell culture. Overall, these results suggested than ddPCR may have a lower limit of detection than qPCR.

For our research on droplet deformation and breakup in scaled high-pressure homogenizing units we developed a pressure stable inline droplet generator. It consists of an optically accessible flow channel with a combination of stainless steel and glass capillaries and a 3D printed orifice. The droplet size is determined online by live image analysis. The influence of the orifice diameter, the mass flow of the continuous phase and the mass flow of the disperse phase on the droplet diameter was investigated. Furthermore, the droplet detachment mechanisms were identified. Droplet diameters with small diameter fluctuation between 175 µm and 500 µm could be realized, which allows a precise adjustment of the Ca and We Number in the subsequent scaled high pressure homogenizer disruption unit. The determined influence of geometry and process parameters on the resulting droplet size and droplet detachment mechanism agreed well with literature on microfluidics. Furthermore, droplet trajectories in an exemplary scaled high-pressure homogenizer disruption unit are presented which show that the droplets can be reinjected on a trajectory close to the center axis or close to the wall, which should result in different stresses on the droplets.

The described data set contains features from the machine control of a five-axis milling machine. The features were recorded during thirteen series productions. Each series production includes a changeover process in which the machine was set up for the production of a different product. In addition to the timestamps and the twenty recorded features derived from Numerical Control (NC) control variables, the data set also contains labels for the different production phases. For this purpose, up to 23 phases were assigned, which are based on a generalized milling process. The data set consists of thirteen .csv files, each representing a series production. The data set was recorded in a production company in the contract manufacturing sector for components with real series orders in ongoing industrial production.

Electrowetting – induced detachment of droplets from solid surfaces is important for numerous applications in the fields of heat transfer and fluid mechanics. The forced oscillations of droplets on solid surfaces and their ability to detach are studied. In this study the process is efficiently simulated by implementing a powerful methodology developed by our team. Our results agree with experiments, showing that optimal detachment, in terms of actuation energy, is achieved when the application of voltage is synchronized with the spreading time of the droplet. Under these conditions, the droplet oscillates with a period close to that of a mirrored Rayleigh droplet. The relation between the droplet’s oscillation period and its physical properties is examined. During voltage-droplet synchronization, the droplet’s ability to detach depends mostly on its contact angle, its viscosity, and the applied voltage. An energy analysis is also conducted, revealing how energy is supplied to the droplet by electrowetting-induced detachment.

Salmonella is a foodborne pathogen associated with localized outbreaks that potentially pose a huge risk to the public health in some countries such as Africa and India, especially in the regions lacking health care measures. The focus of the present study is the molecular detection method of salmonella. The study was conducted to identify Salmonella strains by PCR targeting flagellin specific gene from suspected patient’s clinical blood samples. Serologically, Widal test results showed that all serum samples except one patient (ID 7) were positive for O antigen. The serum of three patients (ID 4, 5 and 10) were found to be positive for H and O antigens while one patient (ID 9) showed agglutination for antigen H. The PCR approach used in this study was successful for fast and precise detection of clinically related Salmonella Typhi. Seven samples out of 10 were positive for Salmonella Typhi as fragments of 458-497bp were observed on the gel-agarose 0.8% corresponds to flagellin gene of S. Typhi. Relatively, smaller amplicons of 366-343bp were observed utilizing nested PCR as well, which seems more sensitive than the conventional PCR. Therefore, we recommend the PCR approach performed in this study to be used as a fast, cost-effective, and time-consuming tool for diagnostic purposes of Salmonella.

We investigated humoral immune responses in 222 unvaccinated Japanese people after recovery from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in 2021. Anti-spike protein IgG antibody levels and neutralizing antibody titers were measured in serum samples obtained within 20–180 days after diagnosis. The geometric mean of antibody titers was 1555 ELU/mL (95% confidence interval [CI] = 1257-1923), and the neutralizing activities (50% inhibitory dilution) was 253 (95% CI = 204-313). The antibody titer and neutralizing activity both increased with increasing disease severity, and both values were approximately 4-fold higher for hospitalized patients than for non-hospitalized patients. However, these differences were smaller in older patients. The humoral immune response, which increased with increasing disease severity, gradually decreased over time after SARS-CoV-2 infection. Most patients with mild or moderate symptoms sustained neutralizing activity for up to 180 days after the infection, the decay of the neutralizing activity in the patients with asymptomatic was rather faster than other groups. 11.7% (26/222) of patients had very low neutralizing activity, and half of these were in the age of 20s. Our study results show the importance of measuring the neutralizing activity to confirm the immune status and also estimate the timing of vaccine.

This paper explores Ghana's two-decade journey in developing a digital land information system (LIS), exemplified by Ghana Enterprise Land Information System (GELIS) and Enterprise Land Information System (ELIS). Despite advancements by Ghana’s Lands Commission, manual processes persist and coexist with digital ones. Our study uses a qualitative approach to assess the effectiveness of the LIS implemented in Accra. The study found that Accra’s LIS is highly aligned with clear institutional mandates, roles, and responsibilities as well as the availability of laws and policies to support analogue to digital conversion. Furthermore, a robust approach for protecting data, operating system and software underpins the system. Subsequently, the integration of a digital LIS has enhanced service delivery and accountability. Nonetheless, Accra’s LIS is still at the beginning of a comprehensive learning and development curve. To sustain the LIS requires furtherance in the implementation plan, funding, law implementation, ICT strategy, divisional integration, work processes, data quality, and communication strategy. These findings will inform the next steps of improvement for Accra's LIS and guide its nationwide scaling, contributing to discussions on technology acceptance dynamics in predominantly analogue environments.

The purpose of this study is to establish whether digital literacy and insurtech adoption influence insurance inclusion in Uganda. Principally, we sought to determine whether insurtech adoption mediates the nexus between digital literacy and insurance inclusion. This study adopted a cross-sectional and quantitative correlational approach. The study’s sample was 391 individuals who had used digital platforms such as mobile phones and computers to access insurance products and services in Uganda. Data were collected using structured survey questionnaires. Partial Least Squares Structural Equation Modelling (PLSEM) was employed to test the hypothesised relationships. The results demonstrate that both digital literacy and insurtech adoption significantly and positively influence insurance inclusion. We also found digital literacy to be a significant and positive determinant of insurtech adoption. Markedly, it was found that insurtech adoption mediates the association between digital literacy and insurance inclusion in Uganda. However, this study was conducted in a developing country with an underdeveloped insurance market and with low technological advancement. This may affect generalisation of the study findings. This study’s novelty lies in establishing how digital literacy and insurtech adoption interplay to influence insurance inclusion in Uganda. This is the first study to examine the effect of digital literacy and insurtech adoption on insurance inclusion.

The conventional well placement was done manually by using a numerical reservoir simulator, and it required a lengthy trial-and-error process. It required great experience and expertise to manipulate the variables and uncertainties that lie on the reservoir to determine the best placement of the well. In addition, the traditional gradient-based methods such as Line-search and Trust-region were not viable in terms of maximum results obtained. Gradient-based methods were too dependent on the surface gradient of the solution and may only converge to local optima instead of global optima. Complex reservoirs have rough surfaces with high uncertainties, which hinders the traditional gradient-based method from converging to global optima. Thus, genetic algorithms were utilized to automate the manual trial-and-error process and to overcome the limitations of the traditional gradient-based method. The objectives of this study were to analyse the effect of different initial well placement distributions, the number of random solution sizes, and the crossover rate on cumulative oil production. A synthetic reservoir model built using CMG Builder was used as the testing platform for the optimization problems. Well-placement parameterization and optimization set-up were carried out using the CMG CMOST optimization tool. The integration of CMG IMEX and CMOST optimized cumulative oil production based on the objectives of the study. The results obtained showed that the higher number of random solutions used resulted in higher cumulative oil production, with more generations needed to reach the optimum solution. It can be concluded that the larger number of random solutions used increased the probability of reaching the optimum solution, but will take more generations.

Lipid droplet (LD) accumulation in hepatocytes is one of the major symptoms associated with fatty liver disease. Mitochondria play a key role in catabolizing fatty acids for energy production through β-oxidation. The interplay between mitochondria and LD assumes a crucial role in lipid metabolism, while it is obscure how mitochondrial morphology affects systemic lipid metabolism in the liver. We previously reported that cilnidipine, an already existing anti-hypertensive drug, has potency to prevent pathological mitochondrial fission by inhibiting protein-protein interaction between dynamin-related protein 1 (Drp1) and filamin, an actin-binding protein. Here, we found that cilnidipine and its new dihydropyridine (DHP) derivative, 1,4-DHP, which lacks Ca2+ channel blocking action of cilnidipine, prevent the palmitic ac-id-induced Drp1-filamin interaction, LD accumulation and cytotoxicity of human hepatic HepG2 cells. Cilnidipine and 1,4-DHP also suppressed the LD accumulation accompanied by reducing mitochondrial contact with LD in obese model and high fat diet-fed mouse livers. These results propose that targeting Drp1-filimin interaction becomes a new strategy for the prevention or treatment of fatty liver disease.

In this work, we proposed a novel design of microfluidic mixer utilizing the amplified Marangoni chaotic advection induced by AC continuous electrowetting of a metal droplet situated in electrolyte solution, due to the linear and quadratic voltage-dependence of flow velocity at small or large voltages, respectively. Unlike previous researchers exploiting the unidirectional surface stress with DC bias at droplet/medium interface for pumping of electrolyte where the resulting flow rate is linearly proportional to the field intensity, dominance of another kind of dipolar flow pattern caused by local Marangoni stress at the drop surface in sufficiently intense AC electric field is demonstrated by both theoretical analysis and experimental observation, which exhibits a quadratic growth trend as a function of the applied voltage. The dipolar shear stress merely appears at larger voltages and greatly enhances the mixing performance by inducing chaotic advection between the neighboring laminar flow. The mixer design developed herein, on the basis of amplified Marangoni chaotic advection around a liquid metal droplet at larger AC voltages, has great potential for chemical reaction and MEMS actuator applications, because of generating high-throughput and excellent mixing performance at the same time.

In this comprehensive review, we delve into the realm of intention recognition within the context of digital forensics and cybercrime. The rise of cybercrime has become a major concern for individuals, organizations, and governments worldwide. Digital forensics is a field that deals with the investigation and analysis of digital evidence in order to identify, preserve, and analyze information that can be used as evidence in a court of law. Whereas, Intention recognition is a subfield of artificial intelligence that deals with the identification of agents’ intentions based on their actions and change of states. In the context of cybercrime, intention recognition can be used to identify the intentions of cybercriminals and even to predict their future actions. Employing a meticulous six-step systematic review approach, we curated research articles from reputable journals and categorized them into three distinct modeling approaches: logic-based, classical machine learning-based, and deep learning-based. Notably, intention recognition has transcended its historical confinement to network security, now addressing critical challenges across various subdomains, including social engineering attacks, AI black box vulnerabilities, and physical security. While deep learning emerges as the dominant paradigm, its inherent lack of transparency poses unique challenges in the digital forensics landscape. We advocate for hybrid solutions that blend deep learning’s power with interpretability. Furthermore, we propose the creation of a comprehensive taxonomy to precisely define intention recognition, paving the way for future advancements in this pivotal field.

(1) Background: The aim of this study was to investigate the circadian rhythms of tongue features according to the effects of physiological phases over a 24-hour period. (2) Methods: Fifteen healthy participants aged 20 to 69 years were recruited. The participants did not have current chronic diseases or past diseases and had to meet the inclusion and exclusion criteria. The participants stayed at the Gil Hospital for a duration of 2 nights and 3 days. On the first day, at 18:00, they consumed their allocated portions of food and water and then completed a questionnaire. At approximately 21:00, their tongue images were acquired using a computerized tongue image acquisition system, following which they slept for 8 hours, commencing at 23:00. Measurements were taken from 07:00 through 21:00 on the second day, and the final acquisition was taken at 07:00 on the following morning, resulting in a total of eight images. The circadian rhythm was authenticated and quantified utilizing the single cosinor analysis, a technique for periodic regression analysis for fitting a 24-hour cosine curve. (3) Results: Cosinor analysis revealed that all tongue features were significantly related to circadian rhythm. (4) Conclusions: The results of this study may be important for considering the time of day at which the tongue is observed and tongue status is evaluated.

Diabetic foot ulcers (DFUs) significantly affect the lives of patients and increase the risk of hospital stay and amputation. We suggest a remote-monitoring platform for better DFU care. This system uses digital health metrics (scaled from 0 to 10, where higher scores indicate a greater risk of slow healing) to provide a comprehensive overview through a visual interface. The platform features smart offloading devices that capture behavioral metrics such as offloading adherence, daily steps, and cadence. Coupled with remotely measurable frailty and phenotypic metrics, it offers an in-depth patient profile. Additional demographic data, wound characteristics, and clinical parameters, such as cognitive function, were integrated, contributing to a comprehensive risk factor profile. We evaluated the feasibility of this platform with 124 DFU patients over 12 weeks; 39% experienced unfavorable outcomes such as dropout, adverse events, or non-healing. Digital biomarkers were benchmarked (0-10), categorized into low, medium, and high risk for unfavorable outcomes, and visually represented using color-coded radar plots. The initial results of case reports illustrate the value of this holistic visualization to pinpoint the underlying risk factors for unfavorable outcomes, including a high number of steps, poor adherence, and cognitive impairment. Although future studies are needed to validate the effectiveness of this visualization in personalizing care and improving wound outcomes, early results in identifying risk factors for unfavorable outcomes are promising.

This paper proposed a three-layer distributed simulation network architecture: 1. Distributed virtual simulation network, 2. Perception and control sub-network, and 3. Collaborative communication service network. This simulation architecture runs on a distributed platform. The platform can provide a unique virtual scenario and several simulation services to verify the individual robot system's basic perception, control, and planning algorithms and the distributed collaborative algorithms of heterogeneous multi-robot systems. Furthermore, we designed a simulation experiment scenario for classical heterogeneous robot systems such as unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). At last, We gave a rough estimation model of the bandwidth.

This review summarises the main numerical models of fuel droplet heating and evaporation (DHE) in combustion engines across the different scales by accessing the nano/micro, meso and macroscopic fluid elements. The phenomena of multi-physics, multi-scale and multi-phase fluid flow and heat transfer are fully investigated when the fuel droplet (dodecane) is heating and evaporated into a background gas (nitrogen) crossing the liquid-vapour (LV) interface, kinetic region (i.e., Knudsen layer) and the bulk regions of liquid and gas in terms of molecular dynamics (MD) simulations, kinetic theory modelling (i.e., direct numerical solutions of Boltzmann equations) and convectional fluid dynamics approach, respectively. The evaporation coefficient of fuel evaporating molecules and their velocity distributions at the LV interface derived from MD simulations formulate a new kinetic boundary condition (KBC). Moreover, a novel kinetic model considering the inelastic collision between fuel molecules alongside the new KBC enables us to describe the non-equilibrium gas dynamics of fuel vapour and gas mixture in Knudsen layer (KL). Heat and mass flux analysis of the fuel droplet under combustion engine conditions can be accurately assessed by implementing the inelastic collision between fuel molecules in KL and a temperature-dependent evaporation coefficient at the LV interface into DHE. The surface temperature of fuel droplet and its evaporation time, which play a significant role in resolving the ignition delay and hence the combustion phasing in engines, can also be well estimated. The multi-scale modelling of fuel DHE will make significantly potential input into the cleaner engine targeting the low-carbon emissions and enhance the capability of the existing computational fluid dynamics (CFD) solvers.

DNA Topoisomerase IIa (Top2A) is a nuclear enzyme that is a cancer drug target, and there is interest in identifying novel sites on the enzyme to inhibit cancer cells more selectively and to reduce off-target toxicity. The C-terminal domain (CTD) is one potential target, but it is an intrinsically disordered domain, which prevents structural analysis. Therefore, we set out to analyze the sequence of Top2A from 105 species using bioinformatic analysis including the PSICalc algorithm, entropy analysis, and other approaches. Our results demonstrate that large (10th-order) interdependent clusters are found including non-proximal positions across the major domains of Top2A. Further, CTD-specific clusters of 3rd, 4th, and 5th-order were identified that included positions that had been previously analyzed via mutation and biochemical assays. Some of these clusters coincided with positions that when mutated either increased or decreased relaxation activity. Finally, sites of low entropy (i.e., high information content) were identified and mapped as key positions in the CTD. Included in the low entropy sites are phosphorylation sites and charged positions. Together, these results help to build a clearer picture of the critical positions in the CTD and provide potential sites/regions for further analysis.

The rapid growth of the global wearable technologies market is projected to reach $61.30 billion by 2030 which reflects a shift towards personalised health monitoring embedded in everyday activities. This paper explores the potential of embroidered interdigitated transducer (IDT)-based sensors for non-invasive, continuous monitoring of human biomarkers, particularly glucose levels, in human sweat. The study employs innovative embroidery techniques to create flexible fabric-based sensors with gold-coated IDTs. Experimental results demonstrate the sensor’s capability to detect water droplets and glucose concentrations, showcasing promising applications for health monitoring. The embroidered IDT-based sensors offer a unique approach for seamless integration into clothing, paving the way for personalised, continuous health data capture.

Motivated by the increasing need of optimised micro-devices for droplets production in medical and biological applications, this paper introduces an integrated approach for the study of the liquid-liquid droplet creation in flow-focusing micro cross-junctions. The micro-junction considered is characterised by a restriction of the channels cross-sections in the junction, which has the function of focusing the flow in the region of the droplet formation. The problem is studied numerically in OpenFOAM environment and validated by the comparison with experimental results obtained by high-speed camera images and micro-PIV measurements. The analysis of the forces acting on the dispersed phase during the droplet formation and the diameter of the droplets obtained numerically are considered for the development of a model of the droplet breakup under the squeezing regime. On the basis of energy balancing during the breakup, a relation between interfacial tension, the size of the cross-sections in the junction, and the time interval needed for droplet creation is obtained, which yields a novel correlation between the dimensionless length of the droplet and the dimensionless flow rate. This correlation is general and can be used to predict the droplet diameters produced by similar micro-junctions with different aspect ratios and extended to different geometries of flow-focusing micro-junctions.

The temperature-assisted laser shock process has shown promising prospects in the fields of forming manufacturing and surface strengthening. However, large-scale application of this process is limited by the instability and failure of confinement medium at high temperatures (≥300 ℃). Aiming at this problem, we propose a novel laser shock strategy based on Leidenfrost effect, where the suspended droplets are utilized as the confinement medium. According to the sequence of images acquired by time delay system and high-speed camera, the droplet dynamics behavior is studied. The focusing enhancement effect of the droplet is comprehensively explored. And the correlations between droplet size, ambient temperature, vapor layer thickness and focusing effect are investigated. Combining the dynamics and focusing enhancement effect of droplets, a theoretical model of laser shock pressure under droplet confinement is established. Finally, the effectiveness and feasibility of the droplet-based laser shock strategy in high temperature processing environments are verified by typical applications in laser shock forming and laser shock peening fields. The results show that the droplet-based laser shock process presents better forming effect. And the mechanical property tests demonstrate that this process can obtain the simultaneous improvement of the strength (~51%) and ductility (~6.4%) of annealed Cu. The multiscale plasticity mechanisms of the strengthened material are comprehensively investigated. We believe that this low-energy, low-cost and high-quality process can provide a new solution for the industrial application of laser shock at high temperatures.

This study examined the efficiency of a combined diagnostic approach in which bioamplification of Arizona 861 S1 citron was integrated with RT‒PCR to detect Citrus tristeza virus (CTV) and Hop Stunt Viroid (HSVd) in Citrus plants. The capacity of the combined method to diagnose mixed infections of CTV and HSVd, along with early single infections of both pathogens, was assessed by evaluating the incubation periods and differential detection capabilities between the combined and direct methods. The bioamplification method exhibited high sensitivity (100%) and specificity (100%) for CTV, enabling early detection even in patients with low-titer infections compared to direct RT‒PCR. However, for HSVd, the combined method achieved 50% sensitivity in early infections, highlighting the need for additional replicates to enhance diagnostic reliability. The relative concentrations of pathogens in mixed and simple infections were evaluated; the analysis revealed no significant differences, suggesting minimal interference between CTV and HSVd. This approach allows early detection of CTV but requires further refinement for HSVd diagnosis. These findings underscore the complementary roles of the cost-effectiveness of direct RT‒PCR and the sensitivity of the combined method in implementing effective disease control strategies in citrus crops.

Tungiasis is a neglected tropical disease caused by skin-penetrating female Tunga penetrans fleas. Although tungiasis causes severe health problems, its ecology is poorly understood and morphological descriptions of larvae are unavailable. To identify T. penetrans immature stages and sites where they develop, diagnostic PCRs are required. However, flea larvae feed on soil organic matter rich in PCR inhibitors. Here, three DNA preparation methods, a soil DNA kit removing inhibitors, a simple ammonium acetate precipitation approach (AmAcet) and a crude lysate of larvae (CL), were combined with amplification by the highly processive FIREPol® Taq or the inhibitor-resistant Phusion® polymerase. Independent of the polymerase used, frequency of successful amplification, Cq values and PCR efficacies for the low-cost CL and AmAcet methods were superior to the commercial kit for amplification of a 278 bp partial internal transcribed spacer-2 (ITS-2) and a 730 bp pan-Siphonaptera cytochrome oxidase I PCR. For the CL method combined with Phusion® polymerase, costs were approximately 20-fold lower than for methods based on the soil DNA kit, which is a considerable advantage in resource-poor settings. The ITS-2 PCR did not amplify Ctenocephalides felis genomic or Tunga trimammilata ITS-2 plasmid DNA allowing it to be used to specifically identify T. penetrans.

Digital twins are an emerging technology that could be harnessed for the digitalization of the industry. Steel industry systems contain a large number of electro-hydraulic components, as proportional valves. An input-output model for a novel water proportional cartridge valve is derived from physical modelling based on fluid mechanics, dynamics, and electrical principles. The valve is a two-stage valve with two 2/2-way water proportional valves as the pilot stage and a marginally stable poppet-type cartridge valve as the main valve. The orifice equation, which is based on Bernoulli principles, is approximated by a polynomial which makes parameter estimation easier and makes modelling possible without measuring pressure of the varying control volume situated in the pilot stage part of the valve. Data were collected when the valve was operating as a component in a closed-loop system as part of a press-mill machine in a steel manufacturing plant. Parameter estimation was made by convex optimization with physical constraints to overcome problems caused by poor system excitation. For comparison, a simple linear model was derived and the least squares method used for parameter estimation. A thorough estimation of the parameters relative errors is presented. The model contains 5 parameters related to the design parameters of the valve. The modelled position output is in good agreement with experimental data for training and test data. The model can be used for real time monitoring of the valve’s status by the model parameters. One of the model parameters varies linearly with production cycles. Thus, aging of the valve can be monitored.

The changing of banking customer preferences after Covid-19 pandemic encourages banking transformation. Neobanks emerges as innovation in banking that enter the market to compete with the incumbent banks by offering new digital experience for customer. This paper analyzes the neobanks which transformed from traditional bank, specifically its efficiency after they implement digital transformation. Efficiency is measured by using Stochastic Frontier Analysis (SFA). This study also uses Pooled Mean Group (PMG) of Panel ARDL (Autoregressive Distributed Lagged) to investigate digital transformation as one of determinants neobank’s efficiency, as well as to examine the existence of short-term and long-term relationship between digital transformation and efficiency. The SFA results show that the efficiency of the neobanks increase after they carry out digital transformation. Furthermore, from PMG Panel ARDL, it can be concluded that digital transformation is one of determinant of efficiency and there is longevity relationship between digital transformation and efficiency. In the short run, digital transformation has a significant and negative correlation with efficiency and in the long run, it has a significant positive relationship because initially digital transformation cost for operational and risk management innovation decreases the profit efficiency, but afterwards it increases the profit efficiency.

Bridges enable communications and transportation of goods nationally and internationally, underpinning economic and social activities, thus they are pylons of our prosperity and mobility. Bridges worldwide are constantly subjected to physical wear, ageing, deterioration, hazards, environmental influences, and increased loading. Loss of performance and functionality of bridge structures would have a crucial impact on overall infrastructural resilience and would cause significant negative economic and social consequences. Monitoring their behaviour for different loading conditions relies on accurate estimations of the stress-strain state of various critical components and remaining capacities. These activities are of high importance for better planning lifespan prolongation, that is the extension of their service life and prevention of unforeseen collapses, in line with sustainability principles of well-informed maintenance. In many cases, access to the structure is limited or even impossible, which causes the need for the deployment of remote and contactless methods. One such innovative technique, which has recently attracted attention in scientific and practical applications is the digital image correlation (DIC). DIC is a contactless approach applicable for long-term measurements, which enables obtaining the full field of strains and deformations. Although the DIC approach has been widely used in world engineering practice for monitoring bridges and has proved to be a reliable and accurate method, there is a lack of systematic integral review on previous practical applications, revealing limitations and perspectives. This is the main motivation and novelty of this study, which will describe selected case studies, in which DIC was used on real full-scale bridge structures and propose improvements for the method.

We provide an extensive and systematic investigation of exciton dynamics in droplet epitaxial quantum dots comparing the cases of (311)A, (001) and (111)A surfaces. In spite of a similar s-shell exciton structure common to the three cases, the absence of a wetting layer for (311)A and (111)A samples leads to a larger carrier confinement with respect to (001), where a wetting layer is present. Moreover, this leads to a more pronounced dependence of the binding energies of s-shell excitons on the quantum dot size and to a strong anti-binding character of the positive charged exciton for smaller quantum dots. In-plane geometrical anisotropies of (311)A and (001) quantum dots lead to a large electron-hole fine interaction (fine structure splitting, FSS ~ 100 ueV) whereas for the three-fold symmetric (111)A counterpart this figure of merit is reduced of about one order of magnitude. In all these cases we do not observe any size dependence of the fine interactions. Heavy-hole/light-hole mixing is present in all the studied cases leading to a broad spread of linear polarization anisotropy (from 0 up to about 50%) irrespective of surface orientation (symmetry of the confinement), fine interactions and nanostructure size. These results are important for the further development of ideal single and entangled photon sources based on semiconductor quantum dots.

The objective of this research is to establish a better understanding of the current landscape of digital learning research and sustainable development by using CiteSpace. First, we retrieved published publications from the Web of Science (2004–2022). Following that, we examined the primary research strengths and important subjects of digital learning from two perspectives: collaboration networks (including collaborative networks across countries, institutions, and authors) and co-citation networks. We examined the co-citation network from three perspectives: cluster analysis, the most active citers, top references. Furthermore, referenced journals, popular themes, and rising trends were examined. These findings indicate the primary study subjects in the field of digital learning, the most intriguing research literature, and each period's emerging research hotspots. Finally, we proposed further study ideas for future paths.

Proposed study aims to investigate the digital transformation of Greek small and medium enterprises (SMEs), with a particular focus on their digital maturity and the strategic and organizational factors contributing to digital transformation. The research issue of digital transformation is attracting considerable interest among academics and business practicians since COVID-19 accelerated the procedure of implementing Industry’s 4.0 principles all over global economies. Quantitative research on 147 Greek SMEs revealed most important issues on how these businesses implement digital transformation, factors accelerating or decelerating the process, barriers and expected outcomes. Research enlights how SMEs face the transition to digital business and reveals the factors that can facilitate the whole process. Results can be valuable for stakeholders enabled to digital transformation process from both business and academic point of view, while there exist aspects that can contribute to policy makers / motivation developers in state’s level as well.

Genetic engineering and directed evolution are effective methods to address the low yield and poor industrialization level of microbial target products. The current research focus is on how to efficiently and rapidly screen beneficial mutants from constructed large-scale mutation libraries. Traditional screening methods such as plate screening and well plate screening are severely limited in their development and application due to their low efficiency and high costs. In the past decade, microfluidic technology has become an important high-throughput screening technology due to its fast speed, low cost, high automation, and high screening throughput, and it has developed rapidly. Droplet-based microfluidic high-throughput screening has been widely used in various fields such as strain/enzyme activity screening, pathogen detection, single-cell analysis, drug discovery, and chemical synthesis, and has been widely applied in industries such as materials, food, chemicals, textiles, and biomedicine. In particular, in the field of enzyme research, droplet-based microfluidic high-throughput screening has shown excellent performance in discovering enzymes with new functions, improved catalytic efficiency or stability, acid-base tolerance, etc. Currently, droplet-based microfluidic high-throughput screening technology has achieved high-throughput screening of enzymes such as glycosidase, lipase, peroxidase, protease, amylase, oxidase, and transaminase, as well as high-throughput detection of products such as riboflavin, coumarin, 3-dehydroquinate, lactic acid, and ethanol. This article reviews the application of droplet-based microfluidics in high-throughput screening, with a focus on high-throughput screening strategies based on UV, visible, and fluorescence spectroscopy, including labeled optical signal detection screening, as well as label-free electrochemical detection, mass spectrometry, Raman spectroscopy, nuclear magnetic resonance, etc. Furthermore, the research progress and development trends of droplet-based microfluidic technology in enzyme modification and strain screening are also introduced.

Understanding the equilibrium saturation level is crucial to Binder Jetting (BJ). Saturation level influences dimensional accuracy, print time, green strength, and final material properties. Improved understanding of the saturation level can reduce development time for new materials and improve existing processes in BJ. Attempts have been made to predict saturation levels of parts with simple calculations from droplet primitives and capillary pressure. There is, however, limited experimental validation for these methods and they do not include the impact of drop velocity and droplet spacing. This study incorporates the influences of drop velocity and droplet spacing on the saturation level of the part. Drop primitives of varying droplet velocity and droplet spacing were compared. Results show that velocity impacts the feasible parameter space.

Oil floating on the sea surface can be detected by both passive and active methods using the ultraviolet-to-microwave spectrum, whereas oil immersed below the sea surface can signal its presence only in visible light. This paper presents an optical model of a sea area deeply polluted by an oil suspension (10 ppm) located in a layer (thickness 5 m) separated from the sea surface by a clear layer (thickness 1 m). The impact of wavelength and state of the sea surface on reflectance changes is shown based on the results of Monte Carlo ray tracing. A two-wavelength index of reflectance is proposed to detect oil suspended in the water column (645-469 nm).

A total of five protoclones were successfully cultured on PDA medium out of regenerated twenty two colonies of Termitomyces protoplast and further studied. Liquid MYG grown mycelial tissue is used for protoplast isolation by enzymatic digestionin a mixture containing Lysing enzyme 2% and Cellulase R10 2% in 0.6 M mannitol. The incubation conditions like temperature, shaking and time were standardized at 24ºC, 60 rpm and 10 hours, respectively for healthy protoplasts liberation. The purified protoplasts showed an average yield of 1.2 × 10⁷ cells/gm tissue with 31.60 ± 9.31% regeneration efficiency on specific medium and 77.12 ± 2.72% viability by FDA test. Four ISSR primers were used in this study resulting a total of 27 reproducible bands with mean value of 6.75. They showed similar banding pattern in all the lines with zero percent polymorphism ranged from 280 bp–2700 bp. The amplified rRNA-ITS gene showed ~600 bp size in gel and found a single restriction site for enzyme HaeIII in all the protoclones and parent with similar fragment size in all.

Background: Learning arrangements in health care profession education are increasingly taking place in digital environments. VR in nursing education, as one of the digital elements, is the subject of controversial debate. On the one hand it supports the authenticity of case studies by adding realistic perspectives and information. On the other hand, the costs of developing and maintaining software and hardware hinder long-term implementation. Methods: In this paper, we describe our findings and insights from two different research projects focused on the incorporation of digital tools, particularly virtual reality (VR), into nursing education. Based on the German context, our aim is to promote the adoption of innovative digital methods in nursing education and to offer invaluable experiences from the field. Results: Starting with a brief recapitulation of the projects, we then elucidate the pedagogical strategies for embedding VR-driven scenarios in nursing education. Based on our experiences during the projects we identify various positive aspects, such as change of perspective and simulating acute situations. Key findings: Although potential drawbacks remain, we advocate long-term implementation and specific use of VR at the interface between theory and practice. Nevertheless it is crucial to establish regular evaluation observing the value of digitalisation, especially VR; for nursing education.

Astaxanthin (ATX) is a carotenoid nutraceutical with a poor bioavailability due to its high lipophilicity. We tested a new tailored nano-droplet capable of solubilizing ATX in an oil-in-water micro-environment (LDS-ATX, manufactured by Lyotropic Delivery Systems (LDS)), for its capacity to improve ATX pharmacokinetic profile and anti-oxidant efficacy. We used liquid chromatography tandem mass spectrometry (LC-MS/MS) to compare the pharmacokinetic profile of ATX and LDS-ATX and protein carbonylation and lipid peroxidation assays to compare their basal and lipopolysaccharide (LPS)-induced oxidative damage. Our results show that only LPS-induced oxidative damage was corrected by ATX and LDS-ATX. While in the liver and muscle LDS-ATX was more efficacious than ATX in attenuating oxidative damage to both proteins and lipids, only oxidative damage to lipids was preferably corrected by LDS-ATX in the brain. These results strongly suggest improvement of ATX bioavailability and efficacy by the LDS-ATX nano-formulation.

This article delves into the intersection of generative AI and digital twins within drug discovery, exploring their synergistic potential to revolutionize pharmaceutical research and development. Through various instances and examples, we illuminate how generative AI algorithms, capable of simulating vast chemical spaces and predicting molecular properties, are increasingly integrated with digital twins of biological systems to expedite drug discovery. By harnessing the power of computational models and machine learning, researchers can design novel compounds tailored to specific targets, optimize drug candidates, and simulate their behavior within virtual biological environments. This paradigm shift offers unprecedented opportunities for accelerating drug development, reducing costs, and, ultimately, improving patient outcomes. As we navigate this rapidly evolving landscape, collaboration between interdisciplinary teams and continued innovation will be paramount in realizing the promise of generative AI and digital twins in advancing drug discovery.

The centrifugal force field in the hydrocyclone was affected by the concave-wall curvature radius R0, the mechanism of droplet deformation was closely related to the mass transfer efficiency. Numerical simulation and experimental data were collected to reveal the deformation characteristics and mechanism of single droplet crossing concave-wall jet. Normalized interfacial energy γ and stretching performance were provided to investigate the droplet deformation process. The results showed that the droplet was stretched along the streamwise and shrank along the spanwise in the concave-wall jet. The droplet interfacial energy and deformation were the largest when the droplet crossed the jet boundary at t = 0.20 s. Maximum γ value increased with the increase of R0 by 57.3% to 71.4%, and the distance between droplet and concave-wall increased with R0. Q-criterion was exported to show the vortex strength increasing as the decrease of R0 in the jet boundary. The pressure distribution inside the droplet showed that the pressure decreased as R0 increased, while the pressure difference increased along the streamwise and wall-normal direction. The study suggested that the droplet breakup was more difficult for the smaller R0, which was beneficial for liquid-liquid heterogeneous separation.

The liquid droplet cooling technique for fast scanning chip calorimetry (FSC) is introduced, increasing the cooling rate for large samples on a given sensor. Reaching higher cooling rates and using a gas as the cooling medium, the common standard for ultra-fast temperature control in cooling, requires reducing the lateral dimensions of the sample and sensor. The maximum cooling rate is limited by the heat capacity of the sample and the heat exchange between the gas and the sample. The enhanced cooling performance of the new liquid droplet cooling technique is demonstrated for both metals and polymers, on examples of solidification of large samples of indium, high-density polyethylene (HDPE), and poly (butylene 2,6-naphthalate) (PBN). It was found that the maximum cooling rate can be increased up to 5 MK/s in room temperature environment, that is, by 2 orders of magnitude, compared to standard gas cooling. Furthermore, modifying the droplet size and using coolants at different temperatures provide options to adjust the cooling rate in the temperature ranges of interest.

Droplet epitaxy allows the efficient fabrication of a plethora of 3D, III-V-based nanostructures on different crystalline orientations. Quantum dots grown on (311)A-oriented surface are obtained with record surface density, with or without a wetting layer. These are appealing features for quantum-dot lasing, thanks to the large density of quantum emitters and a truly 3D lateral confinement. However, the intimate photophysics of this class of nanostructures has not yet been investigated. Here we address the main optical and electronic properties of s-shell excitons in individual quantum dots grown on (311)A substrates with photoluminescence spectroscopy experiments. We show the presence of neutral exciton and biexciton as well as positive and negative charged excitons. We investigate the origins of spectral broadening, identifying them in spectral diffusion at low temperature and phonon-interaction at higher temperature, the presence of fine interactions between electron and hole spin, and a relevant heavy-hole/light-hole mixing. We interpret the level filling with a simple Poissonian model reproducing the power excitation dependence of the s-shell excitons. These results are relevant for the further improvement of this class of quantum emitters and their exploitation as single photon sources for low density samples as well as for efficient lasers for high density samples.

The novel COVID-19 pandemic prompted an unprecedented Institutional reaction to aggregate existing capacity from silos of research laboratories to establish a multidisciplinary research laboratory for COVID19 testing. In less than two weeks, resources were mobilized from the community to strengthen public health response and epidemic control. Such strengthening of institutional research capacity to support public health response contributes to a natural knowledge transfer, facilitates collaboration, and generates new frontiers for knowledge production that should ultimately lead to professional development and retention of skilled human resources. This report describes the pre-established mechanisms and involvement of the authors that made it possible to set-up a multidisciplinary laboratory in a remarkably short period of time. We also discuss the opportunities and sustainability of multidisciplinary laboratory research post-COVID19. Existing institutional capacity can be repurposed to establish multidisciplinary research laboratories to support the strengthening of basic and clinical translational research capacity in resource limited settings and impact on public health and scientific knowledge for socioeconomic development.

Early onset neonatal sepsis is a significant contributor to neonatal morbidity and mortality. Although blood cultures remain the diagnostic gold standard, they detect pathogens in only a minority of suspected cases. This study compared the accuracy of blood cultures with a rapid multiplex PCR test. Newborns at risk for neonatal sepsis were prospectively screened as recommended per national guidelines. Evaluations included laboratory parameters (CrP, IL6, differential blood count), blood culture, and a molecular multiplex PCR test (ROCHE LightCycler SeptiFast) identifying 20 common microbial agents. Blood samples were taken simultaneously from umbilical cord or venous sources on the first day of life. Of 229 infants included, 69% were born preterm. Blood culture and multiplex PCR sensitivity were 7.4% and 11.1% respectively. Specificity, negative and positive predictive values between methods showed no significant variance, though multiplex PCR had more false positives due to contamination. The limited sensitivity of blood cultures for early onset neonatal sepsis is concerning. Despite quicker results, multiplex PCR does not enhance diagnostic accuracy or antibiotic therapy guidance, thus it can not be recommended for this indication.

Floods, recognized for their erratic behavior and devastating effects, are a significant threat as a natural disaster. In Brunei Darussalam, the impact of climate change is becoming increasingly evident through higher temperatures and an expected increase in annual rainfall (5.0mm per year from 2021 to 2050), highlighting the critical need for updated flood risk management techniques. In line with the climate resilience and adaptation objectives of WAWASAN 2035, this paper introduces an innovative approach—a cutting-edge flood risk management framework that utilizes the capabilities of big data analytics, sophisticated machine learning algorithms, and smart Internet of Things (IoT) sensors. Traditional methods, which rely on numerical and physical data modeling, often do not provide precise short-term flood forecasts. Our suggested framework uses machine learning to analyze real-time flood forecasting, sensor data and enhance warning systems, addressing the shortcomings of traditional approaches. The key advantage of this approach is its potential to drastically decrease flood-related damages, protect lives, and boost infrastructure resilience. This proposal is not only in aligns with Brunei's vision for a safe and climate-adaptable future but also marks a significant step forward in adopting innovative and sustainable flood risk management solutions amidst an evolving climate.

The two-phase flow of water and reactive gas in the channel of proton exchange membrane fuel cell (PEMFC) plays an important role in high-quality water management. In this study, the removal effect of 3D wave channel and 2D straight channel for liquid water was studied by volume of fluid method. The dynamic behavior of droplet emerge from the gas diffusion layer (GDL) into the channel under the influence of gas flow was investigated. The effects of droplet growth, deformation, detachment, force, pore size on water critical behavior and water content in the channel are discussed. The results show that the 3D wave channel is superior to the removal of liquid water than the 2D straight channel. The increase of velocity is conducive to the discharge liquid water. While infinite increase of velocity will cause more parasitic power loss. The larger pore size of GDL is not advantageous to the performance of PEMFC, but the appropriate increase of pore size is conducive to the discharge of liquid water. The basic knowledge obtained in this study will help to deepen the understanding of droplet dynamics in PEMFC gas channels, and help to optimize the design and working conditions of these channels.

In recent years, the digital economy has played an increasingly important role in enhancing productivity, fostering growth, and facilitating the emergence of digital banking. With the widespread adoption of digital financial services for financial inclusion, the banking sector is experiencing a demand for digital transformation. It is important to note that despite the many advantages of this change, it also disrupts the familiar banking experience that customers have grown to expect, as well as introducing potential risks to maintaining customer protection at previous levels. Many critics have raised concerns regarding customer protection and potential exposure to various risks that may arise from digital financial services, which may result in both financial and non-financial losses. These risks can impact customer retention as dissatisfied customers may switch to alternative service providers. Additionally, reputation can be affected since merely offering digital financial services may not sufficiently safeguard customers. To remain competitive, financial institutions must deliver a secure experience that aligns with customer expectations while considering economic factors and societal needs. This is why it is essential to comprehend the severity of the risk factors that influence the protection of customers in the realm of digitalized banking services and products. In this study, five factors have been identified that influence customer protection when using digital financial services in Pakistan. Based on the analysis conducted using SmartPLS, it appears that all the factors proposed in this study have a significant and positive impact on the protection of customers. In particular, the information provided remains of paramount importance when it comes to determining the level of protection that a customer is entitled to within digital financial services, followed by responsiveness, privacy, authentication, and encryption mechanisms. Due to these findings, the implementation of enhanced information security management principles becomes imperative for the development and progress of the Pakistani digital banking industry.

The massive amount of human biological, imaging and clinical data produced by multiple and diverse sources, necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels as well as their orchestration and links are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof of concept study cases. Personalized simulations over the actual anatomy of a patient have been carried out. The hypermodel has also applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin based clinical decision support system and the core of future in silico trial platforms, although additional retrospective adaptation and validation is necessary.

Verification of inclusivity of molecular detection methods gives indications on the reliability of viral infections diagnosis because of the tendency of viral pathogens to undergo sequence variation. This study was aimed to select inclusive probe based reverse transcription quantitative PCR (RT-qPCR) assays for the diagnosis of the most widespread and detrimental viruses infecting honeybees, namely the acute bee paralysis virus (ABPV), the black queen cell virus (BQCV), the chronic paralysis bee virus (CBPV), the deformed wing virus variants A (DWVA) and B (DWVB), and the sacbrood virus (SBV). Therefore, detection methods previously described were re-evaluated in silico for specificity and inclusivity. Based on this evaluation, selected methods were modified or new ones were designed and tested in duplex RT-qPCR reactions. The limits of detection (LODs), effect of multiplexing on sensitivity and viral RNA quantification potential in bees and hive debris were assessed. This study made available diagnostic assays able to detect an increased number of virus variants compared to previously described tests and two viral pathogens in a single PCR reaction.

It is important to be able to detect and differentiate between distinct porcine enteric coronaviruses that can cause similar diseases. However, the existence of naturally occurring recombinant coronaviruses such as swine enteric coronavirus (SeCoV) can give misleading results with currently used diagnostic methods. Therefore, we have developed and validated three duplex real-time quantitative RT-PCR assays for the simultaneous detection of, and differentiation between, porcine epidemic diarrhea virus (PEDV) and SeCoV. Transmissible gastroenteritis virus (TGEV) is also detected by two out of these three assays. In addition, a novel triplex assay was set up that was able to detect and differentiate between these alphacoronaviruses and the porcine deltacoronavirus (PDCoV). The validated assays have low limits of detection, close to 100% efficiency and were able to correctly identify the presence of PEDV and SeCoV in 55 field samples, while 20 samples of other pathogens did not give a positive result. Implementing one or more of these multiplex assays into the routine diagnostic surveillance for PEDV will ensure that the presence of SeCoV, TGEV and PDCoV will not go unnoticed.

A brown seaweed consumed worldwide, Fucus vesiculosus, has been used to prevent atherosclerosis and hypercholesterolemia, among other uses, however, the mechanisms of action that lead to these effects are not yet fully understood. An F. vesiculosus aqueous extract, prepared as a soup was characterized as rich in sev-eral bioactive compounds, mainly phlorotannin and peptides. This work aims to study the in vitro effect of this extract on the expression of different proteins involved in the synthesis and transport of cholesterol in HepG2 cells, though a proteomic analysis, western blot and qRT-PCR. The results demonstrated that, in liver cells, the extract decreases the expression of 4 proteins involved in cholesterol biosynthesis process and also decrease the expression of two important transporters proteins of cholesterol, NPC1L1 and ABCG5, as well as decrease the NPC1L1 mRNA levels. Our study demonstrates some mechanisms of action of bioactive com-pounds from F. vesiculosus that may explain its previously reported hypocholesterolemic effect.

Background and Objectives: Most individuals infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are asymptomatic or have mild symptoms of COVID-19, which usually resolve after few days. Regardless of symptoms, infected people can transmit the virus to others especially on the first days of infection. Quantitative reverse transcription-polymerase chain reaction (RT-qPCR) is used to confirm SARS-CoV-2 infection; some individuals show persistent PCR-positivity after recovering from COVID-19. In this study, 12 individuals who showed persistence of COVID-19 symptoms and of SARS-CoV-2 PCR-positivity were followed-up. Methods: nasopharyngeal samples were collected for SARS-CoV-2 detection by RT-qPCR; clinical and epidemiological data were analyzed. Results: that persistence of SARS-CoV-2 PCR positivity was associated with duration of symptoms (r_s 0.81338), which varied between one and 49 days, with 75% of the individuals reporting symptoms for more than two weeks; 83.33% of cases remained positive after two weeks of onset of symptoms, despite decreases in viral load. Conclusion: neither RT-qPCR test nor a symptom-based approach alone are sufficient to evaluate discontinuation of patient isolation; other factors such as viral loads and symptom severity should also be considered. Additional studies are needed to understand how RT-PCR-positivity is related to symptoms and the risk of viral transmission, and to better support isolation guidelines.

This paper provides an overview of the current state-of-the-art digital twin and digital thread technology in industrial operations. Both are transformational technologies that have the advantage of improving the efficiency of current design and manufacturing. Digital twin is an important element of the Industry 4.0 digitalization process; however, the huge amount of data that are generated and collected by digital twin offer challenges in handling, processing and storage. The paper aims to report on the development of a new framework that combines the digital twin and digital thread for better data management in order to drive innovation, improve the production process and performance, and to ensure continuity and traceability of information. The digital twin/thread framework incorporated behavior simulation and physical control components, in which these two components rely on the connectivity between the twin and thread for information flow and exchange to drive innovation. The twin/thread framework encompasses specifications that include organizational architecture layout, security, user access, cloud computing set-up, and hardware and software requirements. It is envisaged that the framework will be applicable to enhancing optimization of operational processes and traceability of information in the physical world, especially in Industry Shipyard 4.0.

The quantification of human DNA extracts from forensic samples plays a key role in the forensic genetics process. Quantification guarantees maximum efficiency and avoids repeated analyses, over amplified samples, or unnecessary examinations. Here, we present our experience using an extremely sensitive real-time-based method with the Quantifiler® Trio DNA quantification kit, which we have explored in the ISO/IEC17025 accredited test validation process conducted in our laboratory. In this study, our focus was on establishing a minimum threshold beyond which DNA typing is unnecessary, by defining the correlation of negative quantification results with the absence of genetic profiles resulting from DNA typing. We investigated the results in the range in which the technique is used (50-0.005 ng/μl). In the validation process of the internally accredited method, we used data from the commercial quantification system, based on the average values of the standard curve using a wide range of samples, including artificially degraded samples. This method was extensively used continuously for nine years to quantify the DNA extract coming from a wide range of samples from criminal cases. The validation allowed us to verify the limits of the technique, especially in the use of the minimum quantities necessary to obtain genetic profiles acceptable for sending to the Italian DNA database. This optimization of the protocol for analysing forensic samples allows us to optimise the protocol for analysing forensic samples. However, the technique is used in our laboratory with a completely manual preparative phase, and we have decided to investigate the contribution of human error more accurately. Thus, in our experience, we have found that it is preferable to use quantification ranges rather than exact limits before deciding how to analyse the extracts via PCR. In addition, here we present some preliminary data related to the analysis of certain samples that initially appeared negative during quantification, applying a concentration method for maximum yield from forensic samples of judicial interest. Our aim is to ensure that no source of evidence is lost in the reconstruction of a criminal event.

Klebsiella pneumonia (K. pneumoniae) is a Gram-negative bacterium that causes nosocomial infections in the lung, bloodstream, and urinary tract. Therefore, detecting K. pneumoniae in early time is important in preventing severe infections. However, clinical detection of K. pneumoniae requires a long time of agar plate culture. Nucleic acid detection like qPCR is precise but requires expensive equipment. Recent research reveals that collateral cleavage activity of CRISPR-LbCas12a has been applied in nucleic acid detection. In this study, PCR combined with CRISPR-LbCas12a targeting the K. pneumoniae system was established. This system showed excellent detection specificity and sensitivity in both bench work and clinical samples. Due to its advantages, its application can meet different detection requirements in health centers where qPCR is not accessible.

Numerous protocols have been published for extracting DNA from phlebotomines. Nevertheless, their small size is generally an issue in terms of yield, efficiency, and purity, for large-scale individual sand fly DNA extractions when using traditional methods. Even though this can be circumvented with commercial kits, these are generally cost-prohibitive for developing countries. We encountered these limitations when analysing parasite infection in Lutzomyia spp. by PCR [1] and, for this reason, we evaluated various modifications on a previously published protocol ([2] and Acardi personal communication). The most significant variation was the use of a different lysis buffer [3] to which added Ca2+ (buffer TESCa), because this ion protects proteinase K against autolysis, increases its thermal stability, and could have a regulatory function for its substrate-binding site [4]. Individual sand fly DNA extraction success was confirmed by amplification reactions using internal control primers that amplify a fragment of the cacophony gene [5,6]. To the best of our knowledge, this is the first time a lysis buffer containing Ca2+ has been reported for the extraction of DNA from sand flies.

In this study, ignition delay and burning rate are experimentally investigated in relation to the effect of pristine carbon nanotubes (CNTs), sodium dodecylbenzene sulfonate (SDBS), and diesel blends. Single droplet combustion tests were conducted in a combustion system for 21 days using CNTs at concentrations of 50 ppm and 100 ppm, that were dispersed in Colombian commercial diesel and stabilized by SDBS. Additionally, recordings of the combustion were collected, and the Shadowgraph optical method was utilized for monitoring the evolution of droplet size during combustion. The measurements were done over a four-week period. The results demonstrated that the temporal stability has a direct impact on the single droplet combustion tests because a concentration of CNTs of 100 ppm showed higher stability than those achieved by 50 ppm. Consequently, improvements were found with a concentration of 100 ppm, for instance, thermal conductivity increased by about 20%, ignition delay time by 16.2%, and burning rate by 30.5%.

Based on screening in computational biology and biological in vitro assays, five natural products isolated from extracts of the herbal medicine toad skin, such as cinobufagin (CBFi), bufalin (BFi), arenobufagin (ABFi), telocinobufagin (TBFi), bufotalin (BFTi), were subjected to molecular docking calculations with the use of SARS-CoV-2 main protease (PDB 6LU7 and 7BTF) and top-scoring ligand-receptor complexes were obtained. The results showed that the binding energy of ABFi to the 3CL protein was -17.044kcal/mol, which was higher than CBFi and TBFi. However, the binding energy of ABFi to the RdRp protease was -23.250 kcal/mol, which was much lower than that of CBFi and TBFi, EVEN lower than that of ABFi to the 3CL protein. ABFi also has polar interactions with amino acids such as Glu811, Ser814, Ser681 and Thr680 of RdRp enzyme. The results revealed that ABFi had a moderate inhibitory effect on the cell proliferation of SARS-CoV-2 in vitro, with an inhibition rate of 61.12%, even weaker than Remdesivir. This new discovery provides us with new ideas for in-depth studies on the development of natural products with this class of structural generalizations as inhibitors of SARS-CoV-2, and provides an experimental basis for the next step of mechanistic studies.

The liquid-vapor phase change lattice Boltzmann method is used to investigate the pinning-depinning mechanism of the contact line during droplet evaporation on the stripe-patterned surfaces in 3D space. Considering the curvature of the contact line and the direction of the unbalanced Young’s force, the local force balance theory near the stripe boundary is proposed to explain the steady state of the droplets on the stripe-patterned surfaces. An equation is proposed to evaluate the characteristic contact angle of the stabilized droplets. During the evaporation of the droplet, the stick-slip-jump behavior and the CCR-Mixed-CCA mode can be well captured by the lattice Boltzmann simulation. When the contact line is pinned to the stripe boundary, the contact line in the direction perpendicular to the stripes is slowly moving while the curvature of the contact line is gradually increasing. The gradually increasing curvature of the contact line accelerates the movement of the contact line, and the final contact line is detached from the stripe boundary. The research results provide theoretical support and guidance for the design, improvement and application of patterned surfaces in the field of micro-fluidic and evaporation heat transfer.

The Herpotrichiellaceae family is an important group of dematiaceous filamentous fungi, associated with a variety of pathogenic fungal species causing Chromoblastomycosis (CBM) and Phaeohyphomycosis (PHM), both with polymorphic clinical manifestations and worldwide incidence. Currently, the identification of this family and determination of the causative agent is challenging due to the subjectivity of morphological identification methods, necessitating the use of molecular techniques to complement diagnosis. In this context, genetic sequencing of the Internal Transcribed Spacer (ITS) has become the norm due to a lack of alternative molecular tools for identifying these agents. Therefore, this study aimed to develop PCR-Multiplex methodologies to address this gap. Sequences from the ITS and Large subunit (LSU) of ribosomal DNA were used, and after manual curation and in vitro analyses, primers were synthesized for the identification of the aforementioned targets. The primers were optimized and validated in vitro, resulting in two PCR-Multiplex methodologies: one for identifying the Herpotrichiellaceae family and the bantiana clade, and another for determining the species Fonsecaea pedrosoi and Fonsecaea monophora. Ultimately, the assays developed in this study aim to complement other identification approaches for these agents, reducing the need for sequencing, improving the management of these infections, and enhancing the accuracy of epidemiological information.

Respiratory tract infections (RTIs) are one of the main causes of hospitalization and mortality causing substantial economic burden to healthcare systems globally. As opposed to the previous belief that respiratory infections are caused by a single pathogen, studies have shown that most RTIs are a result of a combination of bacterial and/or viral pathogens infecting the host. The clinical manifestation of RTIs is very similar (i.e., syndrome), often showing Influenza-like illness (ILI) symptoms. While COVID-19 has dominated ILI over the past few years, there are many other pathogens that are responsible for ILI. In addition, it is not uncommon to have coinfections with multiple pathogens in patients presenting with ILI and that such coinfections can even exacerbate the disease severity of RTIs. Therefore, an insight into coinfections can help with accurate disease prognosis, patient care management and outcomes. The goal of this study was to identify the different organisms in symptomatic patients presenting with ILI.

Accurate and timely testing has become an essential measure in combatting the COVID-19 global pandemic. Currently, polymerase chain reaction (PCR) based assays are the most relied on methods for SARS-CoV-2 detection. This traditional workflow involves a viral RNA extraction from the viral transport media storing nasopharyngeal swabs collected from patients, followed by PCR based detection. While accurate, this methodology is time consuming and resource heavy, causing for delays in receiving results or limited access to testing. Herein, we demonstrate a validated method for SARS-CoV-2 detection from viral transport media using a two-step, direct-to-PCR workflow revolving around shaker-mill homogenization. This method completely bypasses the extraction steps of the traditional workflow, replacing it with 30 seconds of mechanical disruption sufficient to allow for COVID-19 detection with a 96.43% sensitivity and 100% specificity when compared to traditional extraction to PCR based methods.

A reinforcement learning algorithm is proposed to improve the accuracy of short-term load forecasting (STLF) in this article. The proposed model integrates radial basis function neural network (RBFNN), support vector regression (SVR), and adaptive annealing learning algorithm (AALA). In the proposed methodology, firstly, the initial structure of RBFNN is determined by using SVR. Then, an AALA with time-varying learning rates is used to optimize the initial parameters of SVR-RBFNN (AALA-SVR-RBFNN). In order to overcome the stagnation for searching optimal RBFNN, a particle swarm optimization (PSO) is applied to simultaneously find promising learning rates in AALA. Finally, the short-term load demands are predicted by using the optimal RBFNN. The performance of the proposed methodology is verified on the actual load dataset from Taiwan Power Company (TPC). Simulation results reveal that the proposed AALA-SVR-RBFNN can achieve a better load forecasting precision as compared to various RBFNNs.

There is no doubt that the COVID-19 pandemic has greatly affected the higher education sector in South Africa. However, academics and students’ preparedness and competencies have been considered a major conundrum concerning the integration of online learning in the classroom. The purpose of this paper is to examine the academics’ perspectives on a blend of a metaverse in the higher education sector in the post-COVID-19 era at one of the South African rural-based universities. A survey was administered in 2022 to ten (10) academics and the reports were consulted from four (4) E-learning practitioners to solicit their perceptions regarding the use of online learning and contact classes in the post-pandemic era. A qualitative research approach was employed to comprehend the challenges encountered in a historically disadvantaged rural-based university in Limpopo Province. The findings of this study show that there are major issues and challenges impeding the establishment of effective e-learning education. Consequently, technological continuity as a new normal in the post-COVID-19 era appears to be unattainable due to the challenges encountered by the students and lecturers. The use of Learning Management Systems among academics seems to have deteriorated due to the adjustments in COVID-19 rules and regulations. Furthermore, the findings revealed that most academics believed that contact learning remains irreplaceable throughout generations and ages as some are technophilic. This paper recommends that the incompetence and technological resistance and reluctance in adopting transformative pedagogies may not be used as a stumbling block to consider e-learning as a viable option to discontinue online education.

Sepsis is a serious disease that can lead to mortality. The etiology of the disease is connected to how the body as a host reacts toward microbes such as bacteria, viruses, and fungi invasion. Current research shows that during sepsis, the reaction toward microbe attacks takes two main phases. The first is known as a cytokine storm, while the second is a vicious cycle of pro-inflammatory responses followed by a strict anti-inflammatory response led by CD4+ Tregs. Various immunomodulatory therapies have been proposed to break the cycle of pro- and anti-inflammatory reactions to sepsis. However, clinical trials did not show promising results, indicating further research into the mechanism that Treg uses to inhibit proinflammatory reactions. We used next-generation sequencing (NGS) analysis of bulk RNA-seq and single-cell RNA-seq data to examine the exact molecular pathways used by Treg to inhibit the immune response in sepsis. Our approach identified ACOD1 (Aconitate Decarboxylase 1) as a primary mediator of Treg suppression of immune cells. We identified and validated CD36 as a downstream target of ACOD1. CD36 is a known metabolite regulator in Tregs. Taken together, our results indicate that targeting ACOD1 could prove valuable in regulating Treg function during sepsis.

Inspired by Dirac's large number hypothesis (LNH), Zeldovich proposed a relation between the cosmological constant (CC), the mass of a proton, the Planck constant and Newton's gravitational constant. Zeldovich's formula suggests that the substance which is responsible for CC may interact with elementary particles. Since Newton's gravitational constant and the Planck constant appear in Zeldovich's formula, these interactions may be related to gravitational phenomena and quantum phenomena. Following this clue, we propose a liquid droplet model of electron. Applying the Laplace's equation, Zeldovich's formula of CC is derived. Weinberg proposes a relation between the mass of a pion, the Planck constant, the Hubble constant, Newton's gravitational constant and the velocity of light in vacuum when discussing cosmological models with a varying constant of gravitation. Weinberg pointed out that this relation may not merely be regarded as a meaningless random combinations of these fundamental constants in physics. The Weinberg's relation is derived based on the critical mass density formula in the Friedmann model of the universe and Zeldovich's formula.

In the current era of ‘big data’, scientists are able to quickly amass enormous amount of data in a limited number of experiments. The investigators then try to hypothesize about the root cause based on the observed trends for the predictors and the response variable. This involves identifying the discriminatory predictors that are most responsible for explaining variation in the response variable. In the current work, we investigated three related multivariate techniques: Principal Component Regression (PCR), Partial Least Squares or Projections to Latent Structures (PLS), and Orthogonal Partial Least Squares (OPLS). To perform a comparative analysis, we used a publicly available dataset for Parkinson’ disease patien ts. We first performed the analysis using a cross-validated number of principal components for the aforementioned techniques. Our results demonstrated that PLS and OPLS were better suited than PCR for identifying the discriminatory predictors. Since the X data did not exhibit a strong correlation, we also performed Multiple Linear Regression (MLR) on the dataset. A comparison of the top five discriminatory predictors identified by the four techniques showed a substantial overlap between the results obtained by PLS, OPLS, and MLR, and the three techniques exhibited a significant divergence from the variables identified by PCR. A further investigation of the data revealed that PCR could be used to identify the discriminatory variables successfully if the number of principal components in the regression model were increased. In summary, we recommend using PLS or OPLS for hypothesis generation and systemizing the selection process for principal components when using PCR.rewordexplain later why MLR can be used on a dataset with no correlation

The construction industry is a resource and energy intensive industry and thus, has been criti-cized due to rising environmental concerns. As a result of that it has gained heightened interest in the concept of Circular Economy (CE) over the last decade to its ability to promote the slowing, reducing, and closing production and consumption cycles of materials and products used in the construction practices. Digital technologies hold potential to enhance the construction industry's ability to integrate the concept of CE in its practices. However, a clear understanding of digital technology (DT) related barriers that hinder practical implementation of CE, appears to be lacking within the sector. In light of this, this study aims to identify the barriers to adopting DTs to im-plement CE practices in the construction industry. To achieve this aim, a systematic literature review was conducted by reviewing twenty-three (23) relevant papers published until January 2024 in Scopus database. The VOS viewer software was used to perform a co-occurrence analysis of keywords to identify new and popular study areas in the field and content analysis was used to analyse the major barriers to adapt DTs to implement CE in the construction industry. The study identified thirty-four (34) barriers to use DTs to implement CE and these were categorized into nine areas such as: infrastructure, organizational, regulatory, standardization, investment, nature of the construction industry, technological, stakeholder and data related barriers. These findings will be beneficial for construction practitioners and policy makers to adopt DTs to integrate CE practices in the construction industry.

As technology and multimedia production have advanced, there has been a significant rise in attacks on digital media, resulting in duplicated, fraudulent, and altered data and the infringement of copyright laws. This paper presents a robust and secure digital image watermarking technique that has been implemented in the spatial domain and exploits the erratic and chaotic behaviour of the powerful elementary cellular automata rule-30. The crucial characteristics of the watermarking system, i.e., imperceptibility, capacity, and robustness, have been perfectly balanced by the suggested blind watermarking technique. In this approach, prior to embedding, the grayscale watermark image is downsized to its two Most Significant Bits (MSBs). Then, the 2-MSBs watermark is encrypted using an ECA rule-30 so as to level up the security attribute of the system. Then, the host image is scrambled using ECA rule-30 to distribute the watermark pixels throughout the host image and thus achieve the highest robustness against geometrical attacks. Finally, the encrypted watermark data is embedded into the scrambled host image using the ECA rule-30-based embedding key. The proposed method performs better in terms of imperceptibility, capacity, and robustness when compared to several systems with similar competencies. The simulation's findings demonstrate strong imperceptibility as evaluated by the Peak Signal-to-Noise Ratio (PSNR), which has an average value of 58.3735 dB and a high payload. The experimental outcomes, observed across a diverse range of standardized attack scenarios, unequivocally establish the ascendancy of the proposed algorithm over competing methodologies in the realm of image watermarking.

Based on the data of A-share listed companies in Shanghai and Shenzhen of China from 2011 to 2018 and the digital inclusive finance index of Peking University (2011-2018), this paper empirically tests the impact of digital finance development on corporate social responsibility in various provinces of China and its impact mechanism. The results show that: (1) the development of digital finance helps to promote the fulfillment of corporate social responsibility; (2) the influence mechanism of the development of digital finance to promote the fulfillment of corporate social responsibility lies in that it reduces the cost of debt financing and leads to the improvement of corporate social responsibility.(3) Further research shows that the positive relationship between digital finance and corporate social responsibility is more significant in private enterprises. At the same time, the impact is more significant in areas with poor market environment. The above research shows that the development of digital finance has a significant positive effect, which will improve the level of corporate social responsibility.

There are clear signs that the COVID-19 pandemic has had a straining impact on the higher education sector in South Africa and the far reaches of the world. This chapter examines (Science, Technology, Engineering, Mathematics) STEM lecturers’ experiences with digital assessments and perspectives on the continuity of digital transformation in the higher education sector in the post-COVID-19 era at one of South Africa’s rural universities. In 2022, a survey of ten (10) lecturers was conducted and the reports were also consulted by four (4) e-learning practitioners to obtain their views on the use of online learning and contact courses in the post-pandemic period. A qualitative research approach was used to understand the puzzling issues faced by STEM lecturers at a previously disadvantaged institution in Limpopo Province. The results of this study show that there is a puzzling conundrum that hinders the integration of effective online learning. Furthermore, online education as the new normal in the post-COVID-19 era seems unattainable due to extremely complex and difficult-to-understand mathematical symbols. Software and tools proved to be limitations in terms of establishing an interactive learning environment for students and lecturers on online learning platforms. Lecturers’ use of learning and teaching platforms deteriorated due to numerous unknown challenges, but also the tightening of COVID-19 restrictions. Furthermore, the results showed that science and mathematics lecturers believe that contact learning remains irreplaceable across generations and eras as some are not technology enthusiasts. This paper recommends that the lack of technological systems as well as technological resistance and reluctance to adopt transformative pedagogy and innovative practices, should not be viewed as a thorny issue to end the dominance of online education in STEM courses. Keywords: connectivism, Learning Management Systems, affordances, technological resistance, Moodle, transformative pedagogy

In an era driven by technology and connectivity, where the sphere of learning goes beyond conventional classrooms, e-Service-Learning (e-SL) merges the possibilities of technology with the principles of experiential education in a powerful blend [1]. Service-learning (SL) is an educational proposal that has been cited by UNESCO [2] as an innovative educational tool through which to address the complex challenges of our society. The Sustainable Development Goals [3] provide a framework around which to structure the SL projects and thus develop the skills needed to act on the full range of goals. Embedding digital means open a debate on the intricate relationship between technology, education, and societal impact [4]. By analyzing two focus groups involving faculty, students and community partners, this contribution aims to explore the potential of combining SL and digital empowerment (DE). The focus group participants discussed the opportunities for innovation, the expected impact on them and the society at large, their needs and the pre-conditions needed to make SL and DE successful, and limitations. The results are presented according to the six principles of sustainability education in the university environment, highlighting the transformative potential of combining these principles within SL and DE.

The most popular schemes of digital potentiometers (DP) and digital-to-analog converters (DAC) are considered. DP and DAC use how elements of parameter adjustment of active RC-filters. The circuits of the DAC on the Kelvin-Varley divider, the Kelvin divider based on the R-2R matrix, and the architecture of the DAC with a segmented circuit are shown. The internal structure of DP an Analog Devices and Intersil company is shown, and also the DP switching circuits in a tunable low-pass filter and a band-pass filter, a programmable inverting amplifier and a voltage regulator are presented.

(1) Background: The purpose of this study is to evaluate the full arch scan accuracy (precision and trueness) of nine digital intra-oral scanners and four lab scanners. Previous studies have compared the accuracy of some intra-oral scanners, but as this is a field of quickly developing technologies, a more up-to-date study was needed to assess the capabilities of currently available models.; (2) Methods: The present in vitro study compared nine different intraoral scanners (Omnicam 4.6; Omnicam 5.1; Primescan; CS 3600; Trios 3; Trios 4; Runyes; i500 and DL206) as well as four lab light scanners (Einscan SE; 300e; E2 and Ineos X5) to investigate the accuracy of each scanner by examining the overall trueness and precision. Ten aligned and cut scans from each of the intra-oral and lab scanners in the in vitro study were brought into CloudCompare. A comparison was made with the master STL using the CloudCompare 3D analysis best-fit algorithm. The results were recorded along with individual standard deviation and a colorimetric map of the deviation across the surface of the STL mesh; a comparison was made to the master STL, quantified at specific points. ; (3) Results: In the present study, the Primescan had the best overall trueness (17.3 ± 4.9). Followed by (in order of increasing deviation) the Trios 4 (20.8 ± 6.2), i500 (25.2 ± 7.3), CS3600 (26.9 ± 15.9), Trios 3 (27.7 ± 6.8), Runyes (47.2 ± 5.4), Omnicam 5.1 (55.1 ± 9.5), Omnicam 4.6 (57.5 ± 3.2) and Launca DL206 (58.5 ± 22.0). Regarding the lab light scanners, the Ineos X5 had the best overall trueness with (0.0 ± 1.9). Followed by (in order of increasing deviation) the 3Shape E2 (3.6 ± 2.2), Up3D 300E (12.8 ± 2.7), and Einscan SE (14.9 ± 9.5); (4) Conclusions: This study confirms that all current generations of intra-oral digital scanners can capture a reliable, reproducible full arch scan in dentate patients. Out of the intra-oral scanners tested, no scanner produced results significantly similar in trueness to the Ineos X5. However, the Primescan was the only one to be statistically of a similar level of trueness to the 3Shape E2 lab scanner. All scanners in the study had mean trueness of under 60-micron deviation. While this study can compare the scanning accuracy of this sample in a dentate arch, the scanning of a fully edentulous arch is more challenging. The accuracy of these scanners in edentulous cases should be examined in further studies.

Droplet-based microfluidics has revolutionized numerous fields such as biomedical research, pharmaceuticals, drug discovery, food engineering, flow chemistry, and cosmetics. This paper presents a comprehensive study focusing on the detection and characterization of droplets with volumes in the nanoliter range. Leveraging the precise control of minute liquid volumes, we introduced a novel spectroscopic On-Chip microsensor equipped with integrated microfluidic channels for droplet generation, characterization, and sensing, simultaneously. The microsensor, designed with Interdigitated-Ring-Shaped Electrodes (IRSE) and seamlessly integrated with microfluidic channels, offers enhanced capacitance and impedance signal amplitudes, reproducibility, and reliability in droplet analysis. We were able to make analyses of droplets length in the range 1.0-6.0 mm, velocity 0.66-2.51 mm/s, droplet volume 1.07nL-113.46nL. Experimental results demonstrated that the microsensor's has a great performance in terms of droplet size, velocity, and length, with a significant signal amplitude of capacitance and impedance, and real-time detection capabilities, thereby highlighting its potential for facilitating microcapsule reactions and enabling on-site real-time detection for chemical and biosensor analyses on-chip.

During the recent pandemics of COVID-19, sequencing technics became powerful tool for gaining information about the SARS-CoV-2 virus and using this knowledge in our advantage. Thanks to this advantage, scientists over the world were able to search for emerging variations, watching the virus evolving in real time. Assembly of the virus genomes is crucial part of getting this kind of useful information. In our study, we sequenced 79 samples from nasopharyngeal swabs of COVID-19 patients. Positivity on disease was evaluated by qRT-PCR. By studying assembly success rate, we noticed, that Ct-value of qRT-PCR can predict success of genome assembly and SARS-CoV-2 lineage assignment. In this work we described the relationship between Ct-value and further steps of genome construction and its assignment to specific viral strain.

Wastewater surveillance for pathogens using the reverse transcription-polymerase chain reaction (RT-PCR) is an effective, resource-efficient tool for gathering additional community-level public health information, including the incidence and/or prevalence and trends of coronavirus disease-19 (COVID-19). Surveillance of SARS-CoV-2 in wastewater may provide an early-warning signal of COVID-19 infections in a community. The capacity of the world’s environmental microbiology and virology laboratories for SARS-CoV-2 RNA characterization in wastewater is rapidly increasing. However, there are no standardized protocols nor harmonized quality assurance and quality control (QA/QC) procedures for SARS-CoV-2 wastewater surveillance. This paper is a technical review of factors that can lead to false-positive and -negative errors in the surveillance of SARS-CoV-2, culminating in recommendations and strategies that can be implemented to identify and mitigate these errors. Recommendations include, stringent QA/QC measures, representative sampling approaches, effective virus concentration and efficient RNA extraction, amplification inhibition assessment, inclusion of sample processing controls, and considerations for RT-PCR assay selection and data interpretation. Clear data interpretation guidelines (e.g., determination of positive and negative samples) are critical, particularly during a low incidence of SARS-CoV-2 in wastewater. Corrective and confirmatory actions must be in place for inconclusive and/or potentially significant results (e.g., initial onset or reemergence of COVID-19 in a community). It will also be prudent to perform inter-laboratory comparisons to ensure results are reliable and interpretable for ongoing and retrospective analyses. The strategies that are recommended in this review aim to improve SARS-CoV-2 characterization for wastewater surveillance applications. A silver lining of the COVID-19 pandemic is that the efficacy of wastewater surveillance was demonstrated during this global crisis. In the future, wastewater will play an important role in the surveillance of a range of other communicable diseases.

Abstract-Digital Phase Plane is presented in this paper. For constant input second order digital systems with arbitrary initial conditions for arbitrary quantizaters, this digital phase plane shows, in plot form, the state trajectory and the asymptotic stability, including the existence and the position of arbitrary period limit cycles.

The co-circulation of duck hepatitis A virus subtypes 1 (DHAV-1) and 3 (DHAV-3) in ducklings has resulted in significant economic losses. Because ducklings infected with DHAV-1 or DHAV-3 show similar clinical signs and gross lesions, it is important to discriminate these subtypes as early as possible for better clinical management. On the basis of multiple alignments of the 5′-noncoding region sequences of strains DHAV-1 and DHAV-3, universal and type-specific primers were designed and synthesized. Using the primers in a one-tube reverse transcription-PCR (RT-PCR) assay, reference strains of DHAV-1 and DHAV-3 (isolated over a span of 60 years and covering many different countries) were successfully amplified, indicating that the primer sequences were completely conserved. The amplicon sequences results and the sizes of amplicons from reference DHAV-1 and DHAV-3 isolates correlated completely with their genotypes. Moreover, with this one-tube RT-PCR system, the amplicon sizes of liver samples of reference DHAV-1- or DHAV-3-infected birds matched perfectly with their respective genotypes, as determined by virus isolation and neutralization tests. No other RNA viruses of duck origin were detected with the synthesized primers. The sensitivity of viral RNA detection was 10 pg. With this system, 20% genotype 1, 45% genotype 3, and 9% co-infection of the two genotypes were detected in 55 clinical samples. This novel approach could be used for the rapid genotyping DHAV-1 and/or DHAV-3 infection in routine clinical surveillance or epidemiologic screening.

This manuscript explores the significance of continuous improvement in software development and digital product management, highlighting its role in addressing challenges and achieving superior results in today's dynamic business landscape. Drawing on Lean Thinking and Agile principles, the paper emphasizes the importance of customer focus, multidisciplinary collaboration, respect for people, inspection and adaptation, experimentation, incremental delivery, and continuous feedback in driving continuous improvement. It identifies key problems associated with the lack of continuous improvement, including quality issues, stagnation, inefficiency, lack of learning, lack of participation and collaboration, and lack of adaptability. To overcome these challenges, the paper proposes a solution that leverages agile events and interactions, such as sprint retrospectives and daily scrum meetings, and introduces a "kaizen board" as a visual tool to manage and monitor the continuous improvement process. The manuscript highlights the benefits of continuous improvement in terms of quality enhancement, efficiency gains, improved customer satisfaction, and adaptability to change. It stresses the need for fostering a culture of learning and constant adaptation, and provides insights on applying continuous improvement across different dimensions: product, process, team, and culture. The paper concludes by emphasizing the significance of commitment and active participation from all team members for achieving sustainable progress and success in digital transformation efforts.

The quality of photogrammetric-based derived products like orthophotos, digital terrain models (DTMs) and digital line maps as well as the global digital elevation models (DEM) are rigorously dependent on the accuracy of image orientation. This paper evaluates the vertical accuracy of aerial photogrammetric Digital Terrain Model (DTM), Shuttle Radar Topography Mission (SRTM), Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER), and TerraSAR-X's twin satellite of TanDEM-X (TDX) datasets against in-situ orthometric heights computed from ellipsoidal heights and the 2008 Earth Gravitational Model (EGM2008) derived geoid heights in Ethiopia. The quality of the four global digital elevation models was also validated against the aerial photogrammetric DTM measurements. Besides, the accuracies of the photogrammetric DTM and the four DEM products were checked for their compliance to the American Society for Photogrammetry and Remote Sensing (ASPRS) standards as well as the Ethiopian national vertical data evaluation standards. The study showed that the photogrammetric DTM is in a good agreement with the reference orthometric heights compared to SRTM, ASTER and TDX datasets. More precisely, the result has an absolute accuracy of 1.67 m at Linear Error (LE) 95% confidence level, while the absolute accuracy of SRTM3 arc seconds (SRTM3) at LE 90% (11.91 m) is better than its product specification (16 m). The absolute accuracy of SRTM1 arc second (SRTM1) (7.70 m at LE 90%) surpasses that of SRTM3, whereas the absolute accuracy of ASTER DEM is somehow below its product specification. TDX also has the same vertical accuracy (10.29 m at LE 90%) compared to its product specification (10 m). Furthermore, the vertical accuracy of the photogrammetric DTM meets the100 cm vertical accuracy of the 2015 ASPRS standard. However, it does not meet the Ethiopian national vertical data accuracy requirement standard, i.e., RMSEz of ± 0.45 m. In general, the photogrammetric DTM, SRTM1, and TDX have been proven a superior product over the SRTM3 and ASTER DEMs, and better to use these products for high-level precision and accuracy required applications.

This study investigates the impact of temperature-induced quantum proton tunneling probability on DNA amplification during polymerase chain reactions (PCR). Using a simulation model based on a Gaussian wavefunction and finite-difference time-domain method, quantum tunneling of protons across square potential barriers is examined. The results unveil consistent probability distributions for quantum tunneling across various PCR temperatures, with distinct oscillation patterns emerging post-barrier crossing. Acknowledging limitations in initial conditions due to temperature-dependent proton energy, the study highlights the need for refined models and experimental validation. These findings accentuate the potential interplay between quantum mechanics and biological systems, prompting further research to understand quantum tunnelling’s comprehensive effect on genetic variations and molecular processes.

Polymerase chain reaction (PCR) technique is one of the molecular methods in amplifying DNA for the detection of malaria. However, the limitations of PCR especially when used for routine clinical practice can hamper its sensitivity and specificity. With that, this study focuses on designing a microfluidic device that will mimic the function of a conventional genus-specific PCR based on 18S rRNA gene to detect malaria parasites (Plasmodium falciparum) at low grade parasitemia. The design was drawn and simulated using ANSYS 14.5 Computational Fluid Dynamics (CFD). The simulation shows that adding loops to the design increases its relative deviation but to a minimal extent as compared to having only a straight path design, which indicates that looping is acceptable in designing a microfluidic device to minimize chip length. Also, increasing the cross-sectional area of the fluid path decreases the efficiency of the design, thus, the design with a relatively smaller cross-sectional area is favored. And lastly, among the three materials utilized, the chip made of polypropylene is the most efficient with a relative deviation of 0.94 as compared to polycarbonate and polydimethylsiloxane which have relative deviations of 2.78 and 1.92, respectively.

In Kawasaki disease (KD), convulsions are rare and mostly attributed to latent and coexisting encephalitis/encephalopathy due to KD itself. Therefore, we retrospectively investigated concomitant viral infections in patients with KD. Between January 2012 and December 2020, among 523 patients with KD, 7 (1.53%), 1 (0.19%), 6 and 1 (1.15% and 0.19%) were positive for adenovirus (AdV-Ag), respiratory syncytial virus (RSV-Ag), and influenza A or B (Flu A-Ag or Flu B-Ag) antigens, respectively. Among them, two were positive for both AdV-Ag and FluA-Ag. Seven patients with KD (1.3%) presented febrile convulsions, and only one was diagnosed with encephalitis/encephalopathy. Between January 2021 and August 2022, 57 patients with KD were newly diagnosed among whom FilmArray® respiratory panel (FARP) was applied on 24 patients, and 14 (58.3%) were positive for at least one virus. During the same period, 32 out of 45 patients (71.1%) with febrile convulsions examined using the FARP were positive for at least one virus. The trend of viruses in patients with KD and febrile convulsion reflected the regional epidemic viral infection. Respiratory virus detected at a much higher rate using the FARP suggests that most convulsions in patients with KD might be related to concomitant viral infection. The possible involvement and association of concomitant respiratory viral infections in patients with KD with prognosis or encephalopathy/encephalitis remain to be investigated.

During the last 25 years the scientific community has coexisted with the most fascinating protocol due to Quantum Physics: quantum teleportation (QTele), which would have been impossible if quantum entanglement, so questioned by Einstein, did not exist. In this work, a complete architecture for the teleportation of Computational Basis States (CBS) is presented. Such CBS will represent each of the possible 24 classical bits commonly used to encode every pixel of a 3-color-channel-image (red-green-blue, or cyan-yellow-magenta). For this purpose, a couple of interfaces: classical-to-quantum (Cl2Qu) and quantum-to-classical (Qu2Cl) are presented with two versions of the teleportation protocol: standard and simplified.