Distinct Samples Research Articles

The Influence of Iron Particle Contamination on the Breakdown Characteristics of Circulating Mineral Oil under DC Voltage

The presence of metallic particles can lead to the degradation of transformer oil due to the intensification of the electric fields near the conductive components. The primary objective of this research was to investigate the impact of iron impurities on the electrical properties of Mineral Oil (MO), particularly under conditions of continuous flow. Five distinct samples with varying levels of contaminants were carefully prepared for analysis. A specialized chamber was designed to replicate the circulation conditions of oil within an operating transformer. The focus of the investigation was on the breakdown characteristics under DC voltages. The results indicated that higher concentrations of iron impurities were associated with a reduction in breakdown voltage although the circulation of oil exhibited a beneficial effect. To validate these findings, Finite Element Method (FEM)-based simulations were conducted. The analysis of the electric field distribution revealed that iron impurities amplified the electric field intensity, while circulation served to mitigate this effect. Furthermore, the simulations tracking the trajectory of iron particles demonstrated that circulation hindered the particles from reaching the electrodes, thereby diminishing discharge events and lowering the risk of dielectric failure. In conclusion, the circulation of MO enhanced its breakdown voltage, although the presence of iron contamination could still pose a risk under DC voltage conditions.

Mine Your Own Anatomy: Revisiting Medical Image Segmentation With Extremely Limited Labels.

Recent studies on contrastive learning have achieved remarkable performance solely by leveraging few labels in the context of medical image segmentation. Existing methods mainly focus on instance discrimination and invariant mapping (i.e., pulling positive samples closer and negative samples apart in the feature space). However, they face three common pitfalls: (1) tailness: medical image data usually follows an implicit long-tail class distribution. Blindly leveraging all pixels in training hence can lead to the data imbalance issues, and cause deteriorated performance; (2) consistency: it remains unclear whether a segmentation model has learned meaningful and yet consistent anatomical features due to the intra-class variations between different anatomical features; and (3) diversity: the intra-slice correlations within the entire dataset have received significantly less attention. This motivates us to seek a principled approach for strategically making use of the dataset itself to discover similar yet distinct samples from different anatomical views. In this paper, we introduce a novel semi-supervised 2D medical image segmentation framework termed Mine yOur owNAnatomy (MONA), and make three contributions. First, prior work argues that every pixel equally matters to the model training; we observe empirically that this alone is unlikely to define meaningful anatomical features, mainly due to lacking the supervision signal. We show two simple solutions towards learning invariances-through the use of stronger data augmentations and nearest neighbors. Second, we construct a set of objectives that encourage the model to be capable of decomposing medical images into a collection of anatomical features in an unsupervised manner. Lastly, we both empirically and theoretically, demonstrate the efficacy of our MONA on three benchmark datasets with different labeled settings, achieving new state-of-the-art under different labeled semi-supervised settings. MONA makes minimal assumptions on domain expertise, and hence constitutes a practical and versatile solution in medical image analysis. We provide the PyTorch-like pseudo-code in supplementary.

Enhanced super capacitance performance of V₂O₅·nH₂O/g-C₃N₄ nanocomposites: Synthesis, characterizations, and electrochemical properties

The primary problem addressed in this study is to enhance the performance of supercapacitor electrode materials by developing a cost-effective, and efficient synthesis method for nanocomposites with high capacitance. Hence, in this study, we report the synthesis of V2O5·nH2O/g-C3N4 nanocomposites using a cost-effective hydrothermal method, resulting in four distinct samples: pristine V2O5·nH2O nanoribbons (VO) and three composites with varying amounts of g-C3N4 (10, 20, and 30 mg), labelled as VCN1, VCN2, and VCN3. Structural, morphological, optical, and electrochemical characterizations were conducted to assess their potential as supercapacitor electrode materials. Electrochemical analysis, including cyclic voltammetry (CV) and galvanostatic charge-discharge (GCD), confirmed pseudocapacitance behavior in all samples. VCN1, containing 10 mg of g-C3N4 in V2O5·nH2O matrix, exhibited the highest specific capacitance of 230 F/g at a scan rate of 10 mV/s in a 3M KOH electrolyte. This enhancement in capacitance is attributed to the added g-C3N4, which increases active sites, as confirmed by Electrochemically Active Surface Area (EASA) measurements. Additionally, electrochemical impedance spectroscopy (EIS) supported VCN1's superior performance, identifying it as a promising, cost-effective electrode material for super capacitor applications.

Using the phenotype differences model to identify genetic effects in samples of partially genotyped sibling pairs

The identification of causal relationships between specific genes and social, behavioral, and health outcomes is challenging due to environmental confounding from population stratification and dynastic genetic effects. Existing methods to eliminate environmental confounding leverage random genetic variation resulting from recombination and require within-family dyadic genetic data (i.e., parent-child and/or sibling pairs), meaning they can only be applied in relatively small and selected samples. We introduce the phenotype differences model and provide derivations showing that it-under plausible assumptions-provides consistent (and, in certain cases, unbiased) estimates of genetic effects using just a single individual's genotype. Then, leveraging distinct samples of fully and partially genotyped sibling pairs in the Wisconsin Longitudinal Study, we use polygenic indices and phenotypic data for 24 different traits to empirically validate the phenotype differences model. Finally, we utilize the model to test the effects of 40 polygenic indices on lifespan. After a 10% false discovery rate correction, we find that polygenic indices for three traits-body mass index, self-rated health, chronic obstructive pulmonary disease-have a statistically significant effect on an individual's lifespan.

Changes in quality characteristics and inactivation of Salmonella in cake, including oleogel used as a fat replacer, baked with two different methods.

This is the first study to assess the impact of substitution of shortening (50%) with sunflower-beeswax oleogel in cake formulations on the inactivation kinetics of Salmonella spp. and the quality attributes of cakes baked in conventional (CO) and microwave (MWO) ovens. Four distinct cake samples were examined in the study: Cake samples containing oleogel prepared in two different ovens (Oleo-Cake-CO and Oleo-Cake-MWO) and control cake samples baked in two different ovens (Cont-Cake-CO and Cont-Cake-MWO). The control-batter and oleogel-batter demonstrated shear thinning behavior (pseudoplastic, n<1), with a good fit to the Power Law model, but viscosity and viscoelastic moduli decreased when the oleogel was used in place of shortening in cake recipes. In addition, the Cont-Cake-MWO had the greatest special volume value (2.31±0.04mL/g), whereas the Oleo-Cake-CO had the lowest (1.65±0.02mL/g) (p<0.05). Furthermore, compared to the samples baked with CO, lower water activity and moisture values (p<0.05) were observed in the samples baked in MWO due to their higher cooking loss values (p<0.05). In all baking techniques, the addition of oleogel to the cake formula, used as a fat substitute, resulted in higher values for cohesiveness, hardness, springiness, and chewiness (p<0.05). As a result, the inactivation of Salmonella in cakes slightly reduced with using oleogel as a fat substitute (p>0.05), whereas it affected some quality properties of cakes baked with both heat treatments.

Narratives of worth: analyzing the effects of worthy and anti-immigrant narratives on immigration attitudes

ABSTRACT Political elites use contrasting narratives about immigrants to shape public attitudes and influence immigration policy support. This study examines how pro- and anti-immigrant narratives affect public preferences for reducing immigration in the United States. Using two distinct samples – a non-Hispanic White sample from YouGov and a racially diverse sample from NORC – the experiment reveals that exposure to these narratives significantly shapes attitudes toward immigration. Effects vary depending on the type of narrative, as well as respondents’ political affiliations and racial backgrounds. Specifically, the pro- (‘worthy’) immigrant narrative, which portrays immigrants as hardworking, law-abiding members of the in-group, reduces support for lowering immigration levels, especially among White respondents, with Republicans and Independents showing the greatest shift. In contrast, the anti-immigrant narrative, which characterizes unauthorized immigrants as undeserving outsiders, increases support for reducing immigration across racial groups. This research advances our understanding of how political rhetoric shapes public attitudes toward immigration by employing large, diverse samples and realistic portrayals of pro- and anti-immigrant narratives.

Hybrid convolutional network with enhanced graph attention mechanism (HCN-EGAM) for hyperspectral image classification

ABSTRACT Due to their reliance on superficial spectral or spatial information, current hyperspectral image classification techniques frequently fail to generate similarity measures that are accurate enough to create graph structures. We have developed a new end-to-end model, the hybrid convolutional network with enhanced graph attention mechanism (HCN-EGAM), to address this issue. It integrates an enhanced graph attention mechanism with 3D–2D hybrid convolution. In order to improve classification accuracy, this model synergistically uses the graph attention network module with the hybrid convolutional feature extraction module. The model starts by creating a 3D–2D hybrid convolutional network. This lets it quickly pull out deep spatial-spectral features that make different ground objects stand out in hyperspectral images. The model then employs these deep spatial-spectral properties to construct a graph structure, thereby enhancing its feature representation capabilities. Lastly, we use the Enhanced Graph Attention Mechanism (EGAM) to learn long-range spatial associations, successfully distinguishing between intra-class differences and inter-class similarities between distinct samples. Comparing our suggested method to other state-of-the-art methods, experimental assessments on three datasets, including the University of Pavia, Kennedy Space Center (KSC) and Indian Pines (IP) datasets, show a considerable improvement in classification accuracy.

Revealing the impact of CO2 exposure during calcination on the physicochemical and electrochemical properties of LiNi0.8Co0.1Mn0.1O2.

The synthesis atmosphere plays a fundamental role in determining the physicochemical properties and electrochemical performance of NMC811 cathode materials used in lithium-ion batteries. This study investigates the effect of carbonate impurities generated during synthesis by comparing three distinct samples: NMC811 calcined in ambient air, NMC811 calcined in synthetic air to mitigate carbonate formation, and NMC811 initially calcined in ambient air followed by annealing in synthetic air to eliminate carbonate species. Physicochemical characterization through XRD, SEM, FTIR, and TGA techniques revealed noticeable differences in the structural and chemical properties among the samples. Electrochemical assessments conducted via coin-cell testing demonstrate superior performance for materials synthesized in synthetic air, exhibiting an enhanced discharge capacity of 145.4 ± 4.8 mA h g-1 compared to materials synthesized in normal air (109.4 ± 4.3 mA h g-1) at C/10. More importantly, sample annealing in synthetic air after air calcination partially recovers the electrochemical performance of the cathode (142.1 ± 4.6 mA h g-1 at C/10) and this is related to the elimination of carbonate species from the ceramic powder. These findings highlight the importance of controlling synthesis conditions, particularly the atmosphere, to tailor the properties of NMC811 cathode materials for optimal lithium-ion battery performance.

BIOM-22. SPATIAL, SINGLE-CELL, AND BULK RNA SEQUENCING IDENTIFY MOLECULAR PROGRAMS UNDERLYING MENINGIOMA BRAIN INVASION

Abstract Brain invasion is a negative prognostic factor for meningiomas, but the molecular programs underlying meningioma brain invasion are poorly understood. To address this, we performed transcriptomic analyses of 3 independent datasets: Bulk RNA sequencing from 199 meningiomas (n=33 with brain invasion), single-cell RNA sequencing of 2 regionally distinct samples from the brain tumor interface (BTI) versus tumor core (TC) (n=22,118 single-cell transcriptomes), and spatial RNA sequencing from 6 samples with (n=4) versus without (n=2) brain invasion (n=18,471 spatial transcriptomes). Single-cell and spatial datasets were integrated with Harmony, cell interactions were defined using CellChat, and cross-platform comparisons were performed with the Seurat data integration pipeline. Bulk RNA sequencing revealed enrichment of 1835 genes in meningiomas with brain invasion compared to those without brain invasion, including glial markers (GFAP, NCAN) and markers of extracellular matrix remodeling (PDGFRA, COL9A1). Single-cell RNA sequencing comparison of BTI versus TC meningioma cells identified 2793 genes that were enriched in BTI cells, 268 of which were conserved across bulk and single-cell transcriptomes from brain-invasive meningiomas. Gene ontology enrichment analysis showed molecular programs underlying cell migration and regulation of neurogenesis at the BTI, while glycolysis and antigen presentation programs were enriched in the TC. Cholesterol, CSPG4, and LIF signaling were active in the TC, while SLIT signaling was enriched at the BTI. Spatial RNA sequencing showed enrichment of 439 genes at the BTI, and a conserved set of 12 genes were enriched in BTI meningioma cells across bulk, single-cell, and spatial datasets (TGM2, APOC1, IGFBP6, PLEKHO1, GFPT2, ZYX, IQGAP3, LUM, S100A11, ARPC1B, CDK1, CYCS). Comparison of tumor microenvironment monocytes at the BTI versus TC demonstrated enrichment of 1571 genes that were associated with macrophage migration and activation. These data elucidate molecular programs underlying meningioma brain invasion and provide insights into meningioma intratumor heterogeneity.

DeSide: A unified deep learning approach for cellular deconvolution of tumor microenvironment

Cellular deconvolution via bulk RNA sequencing (RNA-seq) presents a cost-effective and efficient alternative to experimental methods such as flow cytometry and single-cell RNA-seq (scRNA-seq) for analyzing the complex cellular composition of tumor microenvironments. Despite challenges due to heterogeneity within and among tumors, our innovative deep learning-based approach, DeSide, shows exceptional accuracy in estimating the proportions of 16 distinct cell types and subtypes within solid tumors. DeSide integrates biological pathways and assesses noncancerous cell types first, effectively sidestepping the issue of highly variable gene expression profiles (GEPs) associated with cancer cells. By leveraging scRNA-seq data from six cancer types and 185 cancer cell lines across 22 cancer types as references, our method introduces distinctive sampling and filtering techniques to generate a high-quality training set that closely replicates real tumor GEPs, based on The Cancer Genome Atlas (TCGA) bulk RNA-seq data. With this model and high-quality training set, DeSide outperforms existing methods in estimating tumor purity and the proportions of noncancerous cells within solid tumors. Our model precisely predicts cellular compositions across 19 cancer types from TCGA and proves its effectiveness with multiple additional external datasets. Crucially, DeSide enables the identification and analysis of combinatorial cell type pairs, facilitating the stratification of cancer patients into prognostically significant groups. This approach not only provides deeper insights into the dynamics of tumor biology but also highlights potential therapeutic targets by underscoring the importance of specific cell type or subtype interactions.

Determination of Nanoparticles and Elements in Blue Mussels (Mytilus edulis) along the Norwegian Coastline.

Our work aimed to examine nanoparticle levels in 69 distinct pooled mussel samples along the Norwegian coastline, considering samples from different environmental contexts, including natural locations, potentially polluted hotspots, and mussel farms. Single-particle ICP-MS was utilized to determine particle mass and number concentrations at environmentally relevant levels in addition to the total content of 11 elements: aluminum, barium, cerium, copper, iron, manganese, lead, silicon, silver, titanium, and zirconium. Results showed nanoparticle mass concentrations of few ng/g up to tens of μg/g and number concentrations of 106 to 109 particles/g (wet weight). Certain urban and industrially impacted locations were linked to increased levels of, e.g., silver, lead, cerium, zirconium, and titanium NPs. Farmed mussels exhibited lower concentrations. However, natural variations were considerable, and impacted locations mostly did not differ from the highest levels in pristine areas. The study presents the first extensive survey of NPs of 11 different elements in marine biota and provides evidence of increased levels of NPs in areas with anthropogenic activities.

Fast spectroscopic and multisensor methods for analysis of glucosamine and hyaluronic acid in dietary supplements

There is a lack of fast and inexpensive analytical methods for quantification of key ingredients in dietary supplements. Here we explore the potential of near infrared (NIR) spectrometry, attenuated total reflection infrared (ATR-IR) spectrometry and potentiometric multisensor system (MSS) in quantitative determination of glucosamine and hyaluronic acid in commercial samples of dietary supplements. All three methods have demonstrated their applicability for this task when combined with chemometric data processing. Principal Component Analysis (PCA) revealed similarities across the three techniques, indicating the presence of distinct sample composition. Partial least squares (PLS) models were constructed for glucosamine and hyaluronic acid quantification. The root mean square error of cross validation (RMSECV) for glucosamine quantification varied between 7.7 wt% and 8.9 wt%. NIR spectrometry has demonstrated the best accuracy for hyaluronic acid (RMSECV = 9.9 wt%), while ATR-IR and MSS yielded somewhat worse performance with RMSECV values of 12.1 and 11.3 wt%, respectively. The findings of this study indicated that NIR, ATR-IR and MSS exhibit reduced accuracy in comparison to complex and high-precision analytical techniques. However, they can be employed for the rapid, semi-quantitative evaluation of glucosamine and hyaluronic acid in dietary supplements, with the possibility of integration into routine quality control procedures.

Genetics in the Ocean's Twilight Zone: Population Structure of the Glacier Lanternfish Across Its Distribution Range.

The mesopelagic zone represents one of the few habitats that remains relatively untouched from anthropogenic activities. Among the many species inhabiting the north Atlantic mesopelagic zone, glacier lanternfish (Benthosema glaciale) is the most abundant and widely distributed. This species has been regarded as a potential target for a dedicated fishery despite the scarce knowledge of its population genetic structure. Here, we investigated its genetic structure across the North Atlantic and into the Mediterranean Sea using 121 SNPs, which revealed strong differentiation among three main groups: the Mediterranean Sea, oceanic samples, and Norwegian fjords. The Mediterranean samples displayed less than half the genetic variation of the remaining ones. Very weak or nearly absent genetic structure was detected among geographically distinct oceanic samples across the North Atlantic, which contrasts with the low motility of the species. In contrast, a longitudinal gradient of differentiation was observed in the Mediterranean Sea, where genetic connectivity is known to be strongly shaped by oceanographic processes such as current patterns and oceanographic discontinuities. In addition, 12 of the SNPs, in linkage disequilibrium, drove a three clusters' pattern detectable through Principal Component Analysis biplot matching the genetic signatures generally associated with large chromosomal rearrangements, such as inversions. The arrangement of this putative inversion showed frequency differences between open-ocean and more confined water bodies such as the fjords and the Mediterranean, as it was fixed in the latter for the second most common arrangement of the fjord's samples. However, whether genetic differentiation was driven by local adaptation, secondary contact, or a combination of both factors remains undetermined. The major finding of this study is that B. glaciale in the North Atlantic-Mediterranean is divided into three major genetic units, information that should be combined with demographic properties to outline the management of this species prior to any eventual fishery attempt.

Opportunities with Multi-Layer Weave Structures in Woven E-Textile Design

Most textiles in day-to-day use are products of weaving. The versatility of this manufacturing technique, which readily supports a multi-layered structure, inclusion of several yarn types, malleability and other valuable characteristics, has attracted attention from HCI researchers intrigued by its potential to expand the interaction capabilities of e-textiles. Research nonetheless has barely scratched the surface of the wealth of weaving techniques and woven structures available. Therefore, a design-research project anchored in practice investigated how touch-sensitive e-textiles’ capabilities might be enriched via advanced multi-layer weaving techniques. The research process, which drew inspiration from literature both on textile design and on woven e-textiles, produced 25 distinct e-textile samples. Results from evaluating the structural properties, electrical capabilities and overall utility of each point to numerous unexplored opportunities from woven multi-layer e-textiles. Even holding potential for entirely new forms of interaction, these represent promising starting points for in-depth investigation.

Measuring depressive symptoms among Latinos in the US: a psychometric evaluation of the CES-D Boston form.

We present a psychometric evaluation of the Center for Epidemiologic Studies Depression Boston Form (CES-D-B) for use with different Latino subgroups as there is inconsistency regarding its performance across subgroups of Latinos, a large and rapidly growing cultural group in the United States. We evaluated the reliability and structural validity of the scores generated by the CES-D-B using four distinct Latino samples residing in US: Mexicans, Venezuelans, Cubans, and "other Latinos" (total N = 1033). To further explore structural validity of CES-D-B scores, we conducted measurement invariance analyses across different countries of origin, gender groups, educational levels, and languages of assessment (English, Spanish). For all four samples, CES-D-B scores were highly reliable as indicated with the coefficients ranging from 0.82 to 0.88, and the factor structure provided an adequate fit to the data with the fit indices CFI/TLI ranging from 0.96 to 0.99, RMSEA estimates between 0.02 and 0.07, and SRMR estimates between 0.02 and 0.04. While measurement invariance analyses for different educational levels indicated scalar invariance across all samples, the same level of measurement equivalency was achieved only for Mexicans and Venezuelans with varying gender and languages of assessment. The findings indicated that CES-D-B scores are internally consistent, possess a strong four-factor structure, and have somewhat equivalent psychometric properties across diverse Latino groups. Findings from this study highlight the importance of considering gender and languages of assessment when assessing depressive symptoms of various Latino subgroups.

Using the Capture–Recapture Technique to Supplement a Point-in-Time Count of Homeless Adults in Kittitas County, Washington

Abstract Capture–recapture (CRC) is a method of population estimation pioneered in biology that allows the surveyor to approximate the proportion of individuals who evaded sampling efforts (i.e., captures) based on repeated samples from the same population. The current national standard for estimating homelessness is a single-day enumeration called the point-in-time (PIT) count. By using a local community action agency’s homeless outreach program and a cold weather shelter as two distinct sampling sources to capture data using CRC methodology, the current study sought to estimate the homeless population in Kittitas County, Washington, and compare CRC with PIT count estimates. Four months of data collection for the two sampling sources yielded an estimate of 115 (95% CI [93, 137]) homeless individuals in the county using CRC methodology. The PIT count conducted in the same year sampled 61 individuals, supporting the idea that cross-sectional enumeration (i.e., PIT counts) underestimate the homeless population and could be supplemented by CRC methods, particularly in rural areas where resources to conduct PIT counts may be limited. Greater accuracy in homelessness enumeration may aid in advocacy efforts, provide baseline data for researching interventions designed to reduce homelessness, and yield statistics to better guide legislative and policy initiatives.

Comparison and chemometrics analysis of phenolic compounds and mineral elements in Artemisia Argyi Folium from different geographical origins

The quality of Artemisia Argyi Folium (AAF), a traditional Chinese food ingredient, is intrinsically linked to its geographical origin, which this study explores through phenolic compounds and mineral elements. The contents of 17 phenols and 18 minerals differed significantly between geographically distinct samples according to UHPLC and ICP-MS, respectively. Chemometrics indicated that a supervised model, orthogonal partial least squares discriminant analysis (OPLS-DA), outperformed unsupervised methods at classifying AAF samples by their origins. Phenols were more effective at distinguishing samples from seven provinces, while minerals were adept at differentiating samples from the Dabie Mountain region (three provinces) and those from four other provinces. Six phenols and 10 minerals were important variables for discrimination. Complex correlations were observed between the contents of various phenols and minerals in AAF, with minerals possibly affecting the accumulation of phenols. This study provides an approach for distinguishing geographically distinct AAF samples and determining their geographical origins.

Investigating the chemical space coverage of multiple chromatographic and ionization methods using non-targeted analysis on surface and drinking water collected using passive sampling

Multiple non-targeted analysis tools were used to look for a broad range of possible chemical contaminants present in surface and drinking water using liquid chromatography separation and high-resolution mass spectrometry detection, including both quadrupole time of flight (Q-ToF) and Orbitrap instruments. Two chromatographic techniques were evaluated on an LC-Q-ToF with electrospray ionization in both positive and negative modes: (1) the traditionally used reverse phase C18 and (2) the hydrophilic interaction liquid chromatography (HILIC) aimed to capture more polar contaminants that may be present in water. Multiple ionization modes were evaluated with an LC-Orbitrap, including electrospray (ESI) and atmospheric pressure chemical ionization (APCI), also in both positive and negative modes. A suspect screening library of over 1300 possible environmental contaminants, including pesticides, pharmaceuticals, personal care products, illicit drugs/drugs of abuse, and various anthropogenic markers was made with experimentally collected data with the LC-Q-ToF with both column types, with 227 chemicals being retained by the HILIC column. The non-targeted methods using multiple chromatographic and ionization modes were applied to environmental water samples collected with polar organic chemical integrative samplers (POCIS), including surface water upstream and downstream from wastewater effluent discharge, and the downstream drinking water intake and treated drinking water for three distinct sampling events. For the LC-Q-ToF, 442 chemical features were detected on the C18 column and 91 with the HILIC column in the POCIS extracts, while 556 features were found on the Orbitrap workflow by ESI and 131 features detected by APCI. Over 100 chemicals were tentatively identified by suspect screening and database searching. The comprehensive and systematic evaluation of these methods serve as a step in characterizing the chemical space covered when utilizing different chromatography and ionization methods, or different instrument workflows on complex environmental mixtures.

An auto-focusing method for the lens-free single-shot digital holography based on the dissimilar state

Lens-free digital holography enables multi-focus quantitative phase microscopy for imaging both reflective surfaces and transparent objects. For high-resolution reconstruction in lens-free holographic microscopy, precisely and quickly determining the defocusing distance is essential. In this work, we propose a new single-shot method that relies on the disparity between the captured image and the reconstructions on ergodic planes corresponding to different defocusing distances. This approach effectively generates a unimodal curve, providing a robust solution for auto-focusing and avoiding the need to select different values for discriminative factors across distinct sample types. Furthermore, the simulations and experiments demonstrate that the entire focal-plane search process is simpler and quicker, potentially at least doubling the speed compared to traditional methods. The experiments, conducted in a traditional lens-free system, utilize amplitude and phase resolution targets to validate this method’s applicability. We believe that this method will be an alternative refocusing method for lens-free on-chip microscopes.

Tribological Analysis of Surface Textures in Porcelain Tiles for Enhancing Pedestrian Walkway Safety

Due to their durability and esthetic appeal, porcelain tiles have become a staple in modern construction and interior design. However, the persistent challenge of mitigating slip hazards, particularly in moist conditions, remains a critical concern for pedestrian safety. This study investigates the slip resistance performance of porcelain tiles, focusing on the influence of varying surface textures. The study clarifies the intricate relationship between surface texture and slip resistance through comprehensive dynamic friction tests involving four distinct porcelain tile samples and three different shoe samples conducted under arid, damp, and lathered conditions. Whereas dry conditions showcase robust traction, moist environments underscore the significance of surface textures in enhancing grip. Moreover, the study introduces an operational scale of surface roughness, delineating optimal texture boundaries for maximal slip resistance. The implications of these findings extend beyond mere material characterization, offering actionable insights for architects, designers, and safety professionals to enhance pedestrian safety in diverse settings. By bridging the gap between material science and real-world safety concerns, this research paves the way for innovative porcelain tile designs that prioritize safety without compromising esthetic appeal.