Given the widespread use of esters and polyesters in products like cosmetics, fishing nets, lubricants and adhesives, whose specific application(s) may cause their dispersion in open environments, there is a critical need for stringent eco-design criteria based on biodegradability and ecotoxicity evidence. Our approach integrates experimental and computational methods based on short oligomers, offering a screening tool for the rapid identification of sustainable monomers and oligomers, with a special focus on bio-based alternates. We provide insights into the relationships between the chemical structure and properties of bio-based oligomers in terms of biodegradability in marine environments and toxicity in benchmark organisms. The experimental results reveal that the considered aromatic monomers (terephthalic acid and 2,5-furandicarboxylic acid) accumulate under the tested conditions (OECD 306), although some slight biodegradation is observable when the inoculum derives from sites affected by industrial and urban pollution, which suggests that ecosystems adapt to non-natural chemical pollutants. While clean seas are more susceptible to toxic chemical buildup, biotic catalytic activities offer promise for plastic pollution mitigation. Without prejudice to the fact that biodegradability inherently signifies a desirable trait in plastic products, nor that it automatically grants them a sustainable “license”, this study is intended to facilitate the rational design of new polymers and materials on the basis of specific uses and applications. Full article

In South China, the large quantity of rainfall in the pre-summer rainy season can easily lead to natural disasters, which emphasizes the importance of improving the accuracy of precipitation forecasting during this period for the social and economic development of the region. In this paper, the back-propagation neural network (BPNN) is used to establish the model for precipitation forecasting. Three schemes are applied to improve the model performance: (1) predictors are selected based on individual meteorological stations within the region rather than the region as a whole; (2) the triangular irregular network (TIN) is proposed to preprocess the observed precipitation data for input of the BPNN model, while simulated/forecast precipitation is the expected output; and (3) a genetic algorithm is used for the hyperparameter optimization of the BPNN. The first scheme reduces the mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the simulation by roughly 5% and more than 15 mm; the second reduces the MAPE and RMSE by more than 15% and 15 mm, respectively, while the third improves the simulation inapparently. Obviously, the second scheme raises the upper limit of the model simulation capability greatly by preprocessing the precipitation data. During the training and validation periods, the MAPE of the improved model can be controlled at approximately 35%. For precipitation hindcasting in the test period, the anomaly rate is less than 50% in only one season, and the highest is 64.5%. According to the anomaly correlation coefficient and Ps score of the hindcast precipitation, the improved model performance is slightly better than the FGOALS-f2 model. Although global climate change makes the predictors more variable, the trend of simulation is almost identical to that of the observed values over the whole period, suggesting that the model is able to capture the general characteristics of climate change. Full article

Knowing the tolerance interval capabilities (TICs) of a manufacturing process is of prime interest, especially if specifications link the manufacturer to a customer. These TICs can be determined using the machine performance concept of ISO 22514. However, few works have applied this to Additive Manufacturing printers, while testing most of the printing area as recommended takes a very long time (nearly 1 month is common). This paper, by proposing a novel part design called COMPAQT (Component for Machine Performances Assessment in Quick Time), aims at giving the same level of printing area coverage, while keeping the manufacturing time below 24 h. The method was successfully tested on a material extrusion printer. It allowed the determination of potential and real machine tolerance interval capabilities. Independently of the feature size, those aligned with the X axis achieved lower TICs than those aligned with the Y axis, while the Z axis exhibited the best performance. The measurements specific to one part exhibited a systematic error centered around 0 mm ± 0.050 mm, while those involving two parts reached up to 0.314 mm of deviation. COMPAQT can be used in two applications: evaluating printer tolerance interval capabilities and tracking its long-term performance by incorporating it into batches of other parts. Full article

Accurate quantification of uncertainty in solar photovoltaic (PV) generation forecasts is imperative for the efficient and reliable operation of the power grid. In this paper, a data-driven non-parametric probabilistic method based on the Naïve Bayes (NB) classification algorithm and Dempster–Shafer theory (DST) of evidence is proposed for day-ahead probabilistic PV power forecasting. This NB-DST method extends traditional deterministic solar PV forecasting methods by quantifying the uncertainty of their forecasts by estimating the cumulative distribution functions (CDFs) of their forecast errors and forecast variables. The statistical performance of this method is compared with the analog ensemble method and the persistence ensemble method under three different weather conditions using real-world data. The study results reveal that the proposed NB-DST method coupled with an artificial neural network model outperforms the other methods in that its estimated CDFs have lower spread, higher reliability, and sharper probabilistic forecasts with better accuracy. Full article

In recent years, there has been a surge in demand for and research focus on cell therapy, driven by the tissue-regenerative and disease-treating potentials of stem cells. Among the candidates, dental pulp stem cells (DPSCs) or human exfoliated deciduous teeth (SHED) have garnered significant attention due to their easy accessibility (non-invasive), multi-lineage differentiation capability (especially neurogenesis), and low immunogenicity. Utilizing these stem cells for clinical purposes requires careful culture techniques such as excluding animal-derived supplements. Human platelet lysate (hPL) has emerged as a safer alternative to fetal bovine serum (FBS) for cell culture. In our study, we assessed the impact of hPL as a growth factor supplement for culture medium, also conducting a characterization of SHED cultured in hPL-supplemented medium (hPL-SHED). The results showed that hPL has effects in enhancing cell proliferation and migration and increasing cell survivability in oxidative stress conditions induced by H₂O₂. The morphology of hPL-SHED exhibited reduced size and elongation, with a differentiation capacity comparable to or even exceeding that of SHED cultured in a medium supplemented with fetal bovine serum (FBS-SHED). Moreover, no evidence of chromosome abnormalities or tumor formation was detected. In conclusion, hPL-SHED emerges as a promising candidate for cell therapy, exhibiting considerable potential for clinical investigation. Full article

Background: Rare diseases, predominantly caused by genetic factors and often presenting neurological manifestations, are significantly underrepresented in research. This review addresses the urgent need for advanced research in rare neurological diseases (RNDs), which suffer from a data scarcity and diagnostic challenges. Bridging the gap in RND research is the integration of machine learning (ML) and omics technologies, offering potential insights into the genetic and molecular complexities of these conditions. Methods: We employed a structured search strategy, using a combination of machine learning and omics-related keywords, alongside the names and synonyms of 1840 RNDs as identified by Orphanet. Our inclusion criteria were limited to English language articles that utilized specific ML algorithms in the analysis of omics data related to RNDs. We excluded reviews and animal studies, focusing solely on studies with the clear application of ML in omics data to ensure the relevance and specificity of our research corpus. Results: The structured search revealed the growing use of machine learning algorithms for the discovery of biomarkers and diagnosis of rare neurological diseases (RNDs), with a primary focus on genomics and radiomics because genetic factors and imaging techniques play a crucial role in determining the severity of these diseases. With AI, we can improve diagnosis and mutation detection and develop personalized treatment plans. There are, however, several challenges, including small sample sizes, data heterogeneity, model interpretability, and the need for external validation studies. Conclusions: The sparse knowledge of valid biomarkers, disease pathogenesis, and treatments for rare diseases presents a significant challenge for RND research. The integration of omics and machine learning technologies, coupled with collaboration among stakeholders, is essential to develop personalized treatment plans and improve patient outcomes in this critical medical domain. Full article

Due to the long-term deformation settlement of foundations, issues such as damage and functional failure of buildings and structures have long been a concern in the engineering field. The creep of soil is one of the primary causes leading to long-term deformation of foundations. In this paper, the consolidation deformation, creep characteristics, and creep model of reconstituted saturated silty clay were studied using the isotropic consolidation creep test and triaxial compression creep test. The results show that for the isotropic consolidation creep test, although the applied load adopted different stages of loading, as long as the final applied confining pressure was the same, the number of stages applied by the confining pressure had little effect on the final isotropic consolidation deformation of the sample and the triaxial undrained shear strength after creep. However, for the triaxial shear creep test, it was found that under the same final deviatoric stress, the final deviatoric strain of the sample was closely related to the number of loading stages of deviatoric stress. The test showed that the more loading stages with the same deviatoric stress, the smaller the final deviatoric strain, and the triaxial undrained shear strength of the sample after creep increased. In addition, it was reasonable to set the pore pressure dissipation of the sample at 95% ((u₀ − u)/u₀ = 95%) as the time (t₁₀₀) at which the primary consolidation of the soil sample was completed. The isotropic consolidation creep curves and the triaxial compression creep curves showed certain non-linearity. Then, the logarithmic model and the hyperbolic model were used to fit the creep curves of the samples. It was found that the hyperbolic model had a better fitting effect than the logarithmic model, but for the triaxial compression creep test, the creep parameters of the sample changed greatly. Therefore, studying the creep characteristics of soil under different pre-loading steps is of significant engineering importance for evaluating the long-term deformation of underground structures. Full article

This research presents a new, eco-friendly, and swift method combining solid-phase extraction and hydrophobic deep eutectic solvents (DES) with high-performance liquid chromatography (SPE-DES-HPLC) for extracting and quantifying catechin and epicatechin in Shanxi aged vinegar (SAV). The parameters, such as the elution solvent type, the XAD-2 macroporous resin dosage, the DES ratio, the DES volume, the adsorption time, and the desorption time, were optimized via a one-way experiment. A central composite design using the Box–Behnken methodology was employed to investigate the effects of various factors, including 17 experimental runs and the construction of three-dimensional response surface plots to identify the optimal conditions. The results show that the optimal conditions were an HDES (tetraethylammonium chloride and octanoic acid) ratio of 1:3, an XAD-2 macroporous resin dosage of 188 mg, and an adsorption time of 11 min. Under these optimal conditions, the coefficients of determination of the method were greater than or equal to 0.9917, the precision was less than 5%, and the recoveries ranged from 98.8% to 118.8%. The environmentally friendly nature of the analytical process and sample preparation was assessed via the Analytical Eco-Scale and AGREE, demonstrating that this method is a practical and eco-friendly alternative to conventional determination techniques. In summary, this innovative approach offers a solid foundation for the assessment of flavanol compounds present in SAV samples. Full article

As a transitional flow pattern, slug–annular flow occurs over a wide range of operating conditions in micro-channels while its distribution in parallel micro-channels has not been well characterized. Herein, we conducted an experiment to study the phase distribution of slug–annular flow in parallel micro-channels. The test section consists of a header with a diameter of 0.48 mm and six branch channels with a diameter of 0.40 mm. Nitrogen and 0.03 wt% sodium dodecyl sulfate (SDS) solution were used as the test fluids. It was found that the phase distribution of the slug–annular flow was unstable and the duration of the varying process showed regularity with different inlet conditions. Increasing the liquid superficial velocity facilitated the liquid phase to flow into channels at the fore part of the header, while the channels at the rear part of the header were more supplied with liquid as the gas superficial velocity, volume fraction of gas, and volume flow rate increased. Furthermore, the results indicated that the channels located at the rear part of the header experienced a pronounced enhancement in the supply of both the liquid and gas phases, with the spacing between the branches increasing. A predictive correlation was formulated to ascertain the distribution of the liquid phase within slug–annular flow across parallel micro-channels. Full article

This study theoretically investigates the temperature and velocity spatial distributions in the flow of a copper–water nanofluid induced by a rotating rigid disk in a porous medium. Unlike previous work on similar systems, we assume that the disk surface is well polished (coated); therefore, there are velocity and temperature slips between the nanofluid and the disk surface. The importance of considering slip conditions in modeling nanofluids comes from practical applications where rotating parts of machines may be coated. Additionally, this study examines the influence of heat generation on the temperature distribution within the flow. By transforming the original Navier–Stokes partial differential equations (PDEs) into a system of ordinary differential equations (ODEs), numerical solutions are obtained. The boundary conditions for velocity and temperature slips are formulated using the effective viscosity and thermal conductivity of the copper–water nanofluid. The dependence of the velocity and temperature fields in the nanofluid flow on key parameters is investigated. The major findings of the study are that the nanoparticle volume fraction significantly impacts the temperature distribution, particularly in the presence of a heat source. Furthermore, polishing the disk surface enhances velocity slips, reducing stresses at the disk surface, while a pronounced velocity slip leads to distinct changes in the radial, azimuthal, and axial velocity components. The study highlights the influence of slip conditions on fluid velocity as compared to previously considered non-slip conditions. This suggests that accounting for slip conditions for coated rotating disks would yield more accurate predictions in assessing heat transfer, which would be potentially important for the practical design of various devices using nanofluids. Full article

Storing energy in hydrogen has been recognized by scientists as one of the most effective ways of storing energy for many reasons. The first of these reasons is the availability of technology for producing hydrogen from water using electrolytic methods. Another aspect is the availability of relatively cheap energy from renewable energy sources. Moreover, you can count on the availability of large amounts of this energy. The aim of this article is to support the decision-making processes related to the production of yellow hydrogen using a strategic model which exploits the metalog family of probability distributions. This model allows us to calculate, with accuracy regarding the probability distribution, the amount of energy produced by photovoltaic systems with a specific peak power. Using the model in question, it is possible to calculate the expected amount of electricity produced daily from the photovoltaic system and the corresponding amount of yellow hydrogen produced. Such a strategic model may be appropriate for renewable energy developers who build photovoltaic systems intended specifically for the production of yellow and green hydrogen. Based on our model, they can estimate the size of the photovoltaic system needed to produce the assumed hydrogen volume. The strategic model can also be adopted by producers of green and yellow hydrogen. Due to precise calculations, up to the probability distribution, the model allows us to calculate the probability of providing the required energy from a specific part of the energy mix. Full article

In recent years, an increasing number of viruses have triggered outbreaks that pose a severe threat to both human and animal life, as well as caused substantial economic losses. It is crucial to understand the genomic structure and epidemiology of these viruses to guide effective clinical prevention and treatment strategies. Nanopore sequencing, a third-generation sequencing technology, has been widely used in genomic research since 2014. This technology offers several advantages over traditional methods and next-generation sequencing (NGS), such as the ability to generate ultra-long reads, high efficiency, real-time monitoring and analysis, portability, and the ability to directly sequence RNA or DNA molecules. As a result, it exhibits excellent applicability and flexibility in virus research, including viral detection and surveillance, genome assembly, the discovery of new variants and novel viruses, and the identification of chemical modifications. In this paper, we provide a comprehensive review of the development, principles, advantages, and applications of nanopore sequencing technology in animal and human virus research, aiming to offer fresh perspectives for future studies in this field. Full article

Digital economy has emerged as one of the primary driving forces for economic globalization. However, assessing digital economy development in a robust and scientific manner remains a great challenge. This paper proposes an evaluation system with measurement errors correction to accurately research the regional differences in and the spatial-temporal evolution characteristics of digital economy development in the Yangtze River Economic Belt (YEB), combining the entropy method, the Dagum–Gini coefficient, an σ convergence model and grey correlation analysis. The results present that the digital economy development index in the YEB rose from 2012 to 2021, with the greatest weight being social livelihood benefits. Meanwhile, there were noticeable regional differences in digital economy development in the YEB; in particular, the middle reaches showed obvious convergence. The grey correlation degree between the influence factors and the digital economy development ranged from 0.5286 to 0.9144, demonstrating a robust positive correlation. The theoretical framework of this paper integrates economic development models with advanced statistical analysis techniques, providing a robust analytical perspective for examining the complexities of digital economy evolution. The insights offer a blueprint for policymakers seeking to foster a robust and equitable digital economy, underscoring the potential of data-driven policy formulations in navigating the intricate landscape of economic globalization. Full article

Building information modeling (BIM) and finite element method (FEM) models have a wide range of applications in underground engineering design, construction, and operation and maintenance. This study employs a BIM-FEM framework to numerically simulate the impact of excavation on existing subway stations, using the Yanjiang New City Station TOD project as a case study. This framework simplifies the smooth integration of BIM and FEM models, automating functions such as assigning material properties, conducting construction simulations, and generating high-quality meshes. Simulation results reveal significant horizontal and vertical displacements in diaphragm walls, support structures, and subway station structures, with the greatest impacts occurring closest to the excavation site. The BIM-FEM framework is validated as an effective tool for designing foundation pit support structures, enhancing numerical modeling accuracy and efficiency in underground engineering. The findings contribute to a better understanding of the dynamic interactions between excavation and underground structures, informing the development of construction strategies and protective measures to ensure structural safety. Full article

This paper presents a novel class of interior solutions for anisotropic stars under the imposition of a self-similar symmetry. This means proposing exact solutions to the Einstein field equations to describe charged matter distribution with radiation flow. The Einstein–Maxwell system by employing specific choices of mass function is formulated to describe the gravitational collapse of charged, anisotropic, spherically symmetric distributions using the Schwarzschild metric. Two ordinary differential equations governing the dynamics are derived by matching a straightforward solution of the symmetry equations to the charged exterior (Reissner–Nordström–Vaidya). Models with satisfactory physical behavior are constructed by extensively exploring self-similar solutions for a set of parameters and initial conditions. Finally, the paper presents the evolution of physical variables and the collapsing radius, demonstrating the inevitable collapse of the matter distribution. Full article

In the evolving landscape of portable electronics, there is a critical demand for components that meld stretchability with optical transparency, especially in supercapacitors. Traditional materials fall short in harmonizing conductivity, stretchability, transparency, and capacity. Although poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) stands out as an exemplary candidate, further performance enhancements are necessary to meet the demands of practical applications. This study presents an innovative and effective method for enhancing electrochemical properties by homogeneously incorporating Ru(III) into PEDOT:PSS. These Ru(III) PEDOT:PSS complexes are readily synthesized by dipping PEDOT:PSS films in RuCl₃ solution for no longer than one minute, leveraging the high specific capacitance of Ru(III) while minimizing interference with transmittance. The supercapacitor made with this Ru(III) PEDOT:PSS complex demonstrated an areal capacitance of 1.62 mF cm⁻² at a transmittance of 73.5%, which was 155% higher than that of the supercapacitor made with PEDOT:PSS under comparable transparency. Notably, the supercapacitor retained 87.8% of its initial capacitance even under 20% tensile strain across 20,000 cycles. This work presents a blueprint for developing stretchable and transparent supercapacitors, marking a significant stride toward next-generation wearable electronics. Full article

Background/Objectives: This study explores the impact of QMAC-DST, a rapid, fully automated phenotypic drug susceptibility test (pDST), on the treatment of tuberculosis (TB) patients. Methods: This pre–post comparative study, respectively, included pulmonary TB patients who began TB treatment between 1 December 2020 and 31 October 2021 (pre-period; pDST using the Löwenstein–Jensen (LJ) DST (M-kit DST)) and between 1 November 2021 and 30 September 2022 (post-period; pDST using the QMAC-DST) in five university-affiliated tertiary care hospitals in South Korea. We compared the turnaround times (TATs) of pDSTs and the time to appropriate treatment for patients whose anti-TB drugs were changed based on these tests between the groups. All patients were permitted to use molecular DSTs (mDSTs). Results: A total of 182 patients (135 in the M-kit DST group and 47 in the QMAC-DST group) were included. The median TAT was 36 days for M-kit DST (interquartile range (IQR), 30–39) and 12 days for QMAC-DST (IQR, 9–15), with the latter being significantly shorter (p < 0.001). Of the total patients, 10 (5.5%) changed their anti-TB drugs based on the mDST or pDST results after initiating TB treatment (8 in the M-kit DST group and 2 in the QMAC-DST group). In the M-kit DST group, three (37.5%) patients changed anti-TB drugs based on the pDST results. In the QMAC-DST group, all changes were due to mDST results; therefore, calculating the time to appropriate treatment for patients whose anti-TB drugs were changed based on pDST results was not feasible. In the QMAC-DST group, 46.8% of patients underwent the first-line line probe assay compared to 100.0% in the M-kit DST group (p < 0.001), indicating that rapid QMAC-DST results provide quicker assurance of the ongoing treatment by confirming susceptibility to the current anti-TB drugs. Conclusions: QMAC-DST delivers pDST results more rapidly than LJ-DST, ensuring faster confirmation for the current treatment regimen. Full article

The hepatobiliary system is vital for the biotransformation and disposition of endogenous molecules. Any impairment in the normal functioning of the hepatobiliary system leads to a spectrum of hepatobiliary diseases (HBDs), such as liver cirrhosis, fatty liver, biliary dyskinesia, gallbladder cancer, etc. Especially in pregnancy, HBD may result in increased maternal and fetal morbidity and mortality. Maternal HBD is a burden to the fetus’s growth, complicates fetal development, and risks the mother’s life. In fetal programming, the maternal mechanism is significantly disturbed by multiple factors (especially diet) that influence the development of the fetus and increase the frequency of metabolic diseases later in life. Additionally, maternal under-nutrition or over-nutrition (especially in high-fat, high-carbohydrate, or protein-rich diets) lead to dysregulation in gut hormones (CCK, GLP-1, etc.), microbiota metabolite production (SCFA, LPS, TMA, etc.), neurotransmitters (POMC, NPY, etc.), and hepatobiliary signaling (insulin resistance, TNF-a, SREBPs, etc.), which significantly impact fetal programming. Recently, biotherapeutics have provided a new horizon for treating HBD during fetal programming to save the lives of the mother and fetus. This review focuses on how maternal impaired hepatobiliary metabolic signaling leads to disease transmission to the fetus mediated through the gut–brain axis. Full article

Due to their efficient renewable energy consumption performance, AC/DC hybrid microgrids have become an important development form for future power grids. However, the fault response will be more complex due to the interconnected structure of AC/DC hybrid microgrids, which may have a serious influence on the safe operation of the system. Based on an AC/DC hybrid microgrid with an integrated bidirectional power converter, research on the interaction impact of faults was carried out with the purpose of enhancing the safe operation capability of the microgrid. The typical fault types of the DC sub-grid were selected to analyze the transient processes of fault circuits. Then, AC current expressions under the consideration of system interconnection structure were derived and, on this basis, we obtained the response results of non-fault subnets under the fault process, in order to reveal the mechanism of DC fault propagation. Subsequently, a current limitation control strategy based on virtual impedance control is proposed to address the rapid increase in the DC fault current. On the basis of constant DC voltage control in AC/DC hybrid microgrids, a virtual impedance control link was added. The proposed control strategy only needs to activate the control based on the change rate of the DC current, without additional fault detection systems. During normal operations, virtual impedance has a relatively small impact on the steady-state characteristics of the system. In the case of a fault, the virtual impedance resistance value is automatically adjusted to limit the change rate and amplitude of the fault current. Finally, a DC fault model of the AC/DC hybrid microgrid was built on the RTDS platform. The simulation and experimental results show that the control strategy proposed in this paper can reduce the instantaneous change rate of the fault state current from 19.1 kA/s to 2.73 kA/s, and the error between the calculated results of equivalent modeling and simulation results was within 5%. The obtained results verify the accuracy of the mathematical equivalent model and the effectiveness of the proposed current limitation control strategy. Full article

Software vulnerability detection aims to proactively reduce the risk to software security and reliability. Despite advancements in deep-learning-based detection, a semantic gap still remains between learned features and human-understandable vulnerability semantics. In this paper, we present an XAI-based framework to assess program code in a graph context as feature representations and their effect on code vulnerability classification into multiple Common Weakness Enumeration (CWE) types. Our XAI framework is deep-learning-model-agnostic and programming-language-neutral. We rank the feature importance of 40 syntactic constructs for each of the top 20 distributed CWE types from three datasets in Java and C++. By means of four metrics of information retrieval, we measure the similarity of human-understandable CWE types using each CWE type’s feature contribution ranking learned from XAI methods. We observe that the subtle semantic difference between CWE types occurs after the variation in neighboring features’ contribution rankings. Our study shows that the XAI explanation results have approximately 78% Top-1 to 89% Top-5 similarity hit rates and a mean average precision of 0.70 compared with the baseline of CWE similarity identified by the open community experts. Our framework allows for code vulnerability patterns to be learned and contributing factors to be assessed at the same stage. Full article

Population aging is a global issue. China is facing the same challenge, especially in its megacities, with more than 10 million permanent urban residents. These densely populated cities urgently need the scientific planning and optimization of the layout of service facilities for older people. Taking Guangzhou, a megacity in China, as an example, this study uses point-of-interest (POI) data and the ID3 machine learning decision tree algorithm to train a site selection model for service facilities for older people. The model can help to select appropriate locations for new service facilities for older people more scientifically and accurately, and it can provide targeted suggestions to optimize the layout of the service facilities for older people in Guangzhou. First, Guangzhou city is divided into 29,793 grids of 500 m × 500 m based on the range of activities of older people, and 985 grids are found to contain service facilities for older people. Then, the POI data of the grid are fed into the ID3 algorithm for training to obtain a prediction model for the selection of sites for service facilities for older people. The effective prediction rate of the model reaches 87.54%. Then, we apply the site selection model to predict the whole city of Guangzhou, and 4534 grids are suitable for service facilities for older people. In addition, considering the degree of concentration of the elderly population in each street, we further filter out 1066 priority grids as the final site selection. Finally, taking into account the situation of the streets in different districts, we propose several strategies to optimize the layout of the construction of service facilities for older people. Full article

Over the last decade, researchers have developed a variety of new analytical and clinical diagnostic devices. These devices are predominantly based on microfluidic technologies, where biological samples can be processed and manipulated for the collection and detection of important biomolecules. Polydimethylsiloxane (PDMS) is the most commonly used material in the fabrication of these microfluidic devices. However, it has a hydrophobic nature (contact angle with water of 110°), leading to poor wetting behavior and issues related to the mixing of fluids, difficulties in obtaining uniform coatings, and reduced efficiency in processes such as plasma separation and molecule detection (protein adsorption). This work aimed to consider the fabrication aspects of PDMS microfluidic devices for biological applications, such as surface modification methods. Therefore, we studied and characterized two methods for obtaining hydrophilic PDMS surfaces: surface modification by bulk mixture and the surface immersion method. To modify the PDMS surface properties, three different surfactants were used in both methods (Pluronic^® F127, polyethylene glycol (PEG), and polyethylene oxide (PEO)) at different percentages. Water contact angle (WCA) measurements were performed to evaluate the surface wettability. Additionally, capillary flow studies were performed with microchannel molds, which were produced using stereolithography combined with PDMS double casting and replica molding procedures. A PDMS microfluidic device for blood plasma separation was also fabricated by soft lithography with PDMS modified by PEO surfactant at 2.5% (v/v), which proved to be the best method for making the PDMS hydrophilic, as the WCA was lower than 50° for several days without compromising the PDMS’s optical properties. Thus, this study indicates that PDMS surface modification shows great potential for enhancing blood plasma separation efficiency in microfluidic devices, as it facilitates fluid flow, reduces cell aggregations and the trapping of air bubbles, and achieves higher levels of sample purity. Full article