Glass Slides Research Articles

Background removal for debiasing computer-aided cytological diagnosis.

To address the background-bias problem in computer-aided cytology caused by microscopic slide deterioration, this article proposes a deep learning approach for cell segmentation and background removal without requiring cell annotation. A U-Net-based model was trained to separate cells from the background in an unsupervised manner by leveraging the redundancy of the background and the sparsity of cells in liquid-based cytology (LBC) images. The experimental results demonstrate that the U-Net-based model trained on a small set of cytology images can exclude background features and accurately segment cells. This capability is beneficial for debiasing in the detection and classification of the cells of interest in oral LBC. Slide deterioration can significantly affect deep learning-based cell classification. Our proposed method effectively removes background features at no cost of cell annotation, thereby enabling accurate cytological diagnosis through the deep learning of microscopic slide images.

Partially Bio-Based Benzoxazine Monomers Derived from Thymol: Photoluminescent Properties, Polymerization Characteristics, Hydrophobic Coating Investigations, and Anticorrosion Studies

In this work, four thymol-based benzoxazines were synthesized using four primary amines with different chain lengths, namely methylamine, ethylamine, 1-propylamine, and 1-butylamine, which are then named T-m, T-e, T-p, and T-b, respectively. The optical properties of the synthesized thymol-based benzoxazines were examined via the photoluminescent study of their solutions in acetone. The results show that all the prepared benzoxazines emitted blue light with the maximum wavelengths from 425 to 450 nm when irradiated by the excitation wavelengths from 275 to 315 nm. The maximum excitation wavelengths are found to be 275 nm. The polymerization of the thymol-based benzoxazines is triggered by heat treatments with different conditions (160, 180, and 200 °C for 1 h). According to the FTIR results, the heat-curing process introduces a presence of the OH peak, of which intensity increases as the curing temperature increases. Thermal decompositions of thymol-based benzoxazines regarding TGA analyses reveal the enhancement of thermal stability of the benzoxazines with respect to the N-substituent chain length, as significantly observed the change in the first thermal decomposition at temperature ranged from 253 to 260 °C. Synthesized benzoxazine derivatives are further employed to coat the substrate, e.g., the glass slides. The investigation of the water contact angle shows that the coating of the benzoxazines onto the surface improves the hydrophobicity of the substrate, resulting in the enlargement of the contact angle from 25.5° to 93.3°. Moreover, the anticorrosion performance of the polybenzoxazine coatings is examined using potentiodynamic polarization techniques. The results illustrate the anticorrosion efficiency of the thymol-based polybenzoxazine up to 99.99%. Both hydrophobic and electrochemical studies suggest the feasibility for employing benzoxazines in anticorrosion coating applications.

Self-stabilized styrene-acrylic copolymer latex with heteroatom function for improved water and corrosion resistant coatings

This approach investigates the preparation of a set of styrene-acrylic copolymers incorporated with N-heteroatom in the form of N-vinyl pyrrolidone (NVP) as the functional co-monomer at different concentrations within the range of 17–33 mol%. In this case, self-stabilized copolymer latex was obtained without using any conventional surfactant in the system. The beneficial effect of NVP for the emulsion copolymerization of styrene and butyl acrylate was quantified by preparing and analyzing a control sample of the same copolymer latex that was stabilized by a conventional surfactant. The prepared self-stabilized copolymer latexes having particle sizes of 325–371 nm showed excellent colloidal stability with a zeta potential of >+44.5 mV. The air-dried films were found to be optically transparent and comparable to the control sample. The thermal, structural, microscopic, and water/alkali resistance properties of the films were characterized with respect to the NVP content in the copolymer. The coating characteristics of the copolymers on glass slides and mild steel panels were explored by analyzing the hydrophobicity (water contact angle up to 79°) and corrosion resistance (corrosion rate: 6.03 × 10−5 mm/year) that are significantly improved from the copolymer prepared by conventional emulsion polymerization. This study reveals the impact of N-heteroatom to obtain a self-stabilized polymer latex with improved properties and additional functionalities.

A large scale bacterial attraction assay: A new quantitative bacterial migration assay suitable for genetic screens.

Bacteria use various motility mechanisms to explore their environments. Chemotaxis is the ability of a motile bacterial cell to direct its movement in response to chemical gradients. A number of methods have been developed and widely used to study chemotactic responses to chemoeffectors including capillary, agar plug, microscopic slide, and microfluidic assays. While valuable, these assays are primarily designed to monitor rapid chemotactic responses to chemoeffectors on a small scale, which poses challenges in collecting large quantities of attracted bacteria. Consequently, these setups are not ideal for experiments like forward genetic screens. To overcome this limitation, we developed the Large Scale Bacterial Attraction assay (LSBA), which relies on the use of a Nalgene™ Reusable Filter Unit and other materials commonly found in laboratories. We validate the LSBA by investigating chemoeffector kinetics in the setup and by using chemoattractants to quantify the chemotactic response of wild-type, and motility impaired strains of the plant pathogenic bacterium Xanthomonas campestris pv. campestris and the environmental bacterium Shewanella oneidensis. We show that the LSBA establishes a long lasting chemoeffector gradient, that the setup can be used to quantify bacterial migration over time and that the LSBA offers the possibility to collect high numbers of attracted bacteria, making it suitable for genetic screens.

Patterns of Use and Barriers to STI Point-of-care Tests for Military Obstetrician Gynecologists.

Sexually transmitted infections (STIs) are commonly reported in military populations. Point-of-care tests (POCTs) are commercially available, but their use is variable in the civilian sector. Their use among military providers has not been evaluated. We sought to identify the pattern of use and barriers to using STI POCTs for military obstetrician/gynecologists (OBGYNs). We adapted a survey of civilian OBGYNs on patterns of use and barriers to STI POCTs for military OBGYNs. We sent an online questionnaire to 479 military OBGYNs via Army, Air Force, and Navy specialty leaders in May 2023. The questionnaire included 14 demographic questions and up to 52 questions regarding availability, use, and barriers to STI POCTs. The USU Institutional Review Board deemed the study exempt from Institutional Review Board review. Of the 479 solicited, 117 participated in the survey (24.4%). Of respondents, 64.1% were women and 79.5% non-Hispanic white. Sexually transmitted infections were detected once to twice weekly by 13.0% of respondents and once or twice monthly by 52.8%. The most available STI-related POCTs were wet mount prep (68.7%), rapid HIV (43.3%), and urine dipstick (38.6%). Gram stain was available for 30.3%, the Affirm VPIII (Becton, Dickinson and Company, Franklin Lakes, NJ) for 24.5%, and stat RPR for 16.3%. Economic barriers to using POCTs included cost of the test from manufacturer/distributor (57.9%) and military funding/stocking decisions (10.3%). The greatest barriers to use were the purchasing of an instrument (60.8%) and the interruption to workflow in clinic (57.8%). Military OBGYNs rely on several STI-related POCTs. Economic factors and interruption to workflow were cited as the most significant barriers to using POCTs for military OBGYNs. Test cost and impact on workflow should be considered in future development and procurement of POCTs for the Military Health System.

Tailoring bistability in optical tweezers with vortex beams and spherical aberration

We demonstrate a bistable optical trap by tightly focusing a vortex laser beam. The optical potential has the form of a Mexican hat with an additional minimum at the center. The bistable trapping corresponds to a nonequilibrium steady state, where the microsphere continually hops, due to thermal activation, between an axial equilibrium state and an orbital state driven by the optical torque. We develop a theoretical model for the optical force field, based entirely on experimentally accessible parameters, combining a Debye-type nonparaxial description of the focused vortex beam with Mie scattering by the microsphere. The theoretical prediction that the microsphere and the annular laser focal spot should have comparable sizes is confirmed experimentally by taking different values for the topological charge of the vortex beam. Spherical aberration introduced by refraction at the interface between the glass slide and the sample is considered and allows us to finetune between axial, bistable, and orbital states as the sample is shifted with respect to the objective focal plane. We find overall agreement between theory and experiment for a rather broad range of topological charges. Our results open the way for applications in stochastic thermodynamics, as they establish a control parameter—the height of the objective focal plane with respect to the glass slide—that allows us to shape the optical force field in real time and in a controllable way. Published by the American Physical Society 2024

Salivary duct carcinoma with squamous differentiation: histomorphological and immunophenotypical analysis of six cases.

Salivary duct carcinoma (SDC) is an aggressive salivary malignancy with multiple morphological subtypes. Primary salivary squamous cell carcinoma (SCC) requires exclusion of high-grade salivary malignancies and metastatic disease and is considered exceptionally rare. We report six cases of SDC with resemblance to SCC on account of variable, but often extensive, squamous differentiation. A retrospective review (2009-2023) at two institutions of SDC with histological and immunophenotypical evidence of squamous differentiation identified six cases. Medical charts and available glass slides were reviewed. There were five males and one female with a median age of 63 years, with tumours involving the parotid (five of six) and submandibular (one of six) glands. All six tumours showed a conventional SDC component comprising < 5-90% of viable tumour. Squamous differentiation comprised 10-95%+ (> 75% in three of six cases) of total viable tumour, and demonstrated CK5/6, p63 and/or p40 immunoexpression in all cases. A sarcomatoid component, comprising 10-60% of viable tumour, was present in three of six (50%) cases. All tumours were androgen receptor (AR)-positive, but only two of six (33.3%) retained AR immunoreactivity in the squamous component. Metastatic SDC to regional lymph nodes exhibited exclusive squamous differentiation in two of six (33.3%) cases. Squamous differentiation, histologically and immunophenotypically, can be extensive in SDC. AR expression may be lost in the squamous component and metastases may demonstrate only squamous differentiation. These findings cast further doubt on the existence of primary salivary SCC. SDC should be considered whenever encountering a carcinoma with squamous differentiation in major salivary glands or within cervical lymph nodes in the setting of a salivary mass.

A Minimal Approach to Demonstrate Concordance of Digital and Conventional Microscopy in Toxicologic Pathology.

Digitalization of pathology workflows has undergone a rapid evolution and has been widely established in the diagnostic field but remains a challenge in the nonclinical safety context due to lack of regulatory guidance and validation experience for good laboratory practice (GLP) use. One means to demonstrate that digital slides are fit for purpose, that is, provide sufficient quality for pathologists to reach a diagnosis, is conduction of comparison studies, which have been published both, for veterinary and human diagnostic pathology, but not for toxicologic pathology. Here, we present an approach that uses study material from nonclinical safety studies and that allows for the statistical comparison of concordance rates for glass and digital slide evaluation while minimizing time and effort for the involved personnel. Using a benchmark study design, we demonstrate that evaluation of digital slides fits the purpose of nonclinical safety evaluation. These results add to reports of successful workflow validations and support the full adaptation of digital pathology in the regulatory field.

A deep learning-based model for detecting Leishmania amastigotes in microscopic slides: a new approach to telemedicine

BackgroundLeishmaniasis, an illness caused by protozoa, accounts for a substantial number of human fatalities globally, thereby emerging as one of the most fatal parasitic diseases. The conventional methods employed for detecting the Leishmania parasite through microscopy are not only time-consuming but also susceptible to errors. Therefore, the main objective of this study is to develop a model based on deep learning, a subfield of artificial intelligence, that could facilitate automated diagnosis of leishmaniasis.MethodsIn this research, we introduce LeishFuNet, a deep learning framework designed for detecting Leishmania parasites in microscopic images. To enhance the performance of our model through same-domain transfer learning, we initially train four distinct models: VGG19, ResNet50, MobileNetV2, and DenseNet 169 on a dataset related to another infectious disease, COVID-19. These trained models are then utilized as new pre-trained models and fine-tuned on a set of 292 self-collected high-resolution microscopic images, consisting of 138 positive cases and 154 negative cases. The final prediction is generated through the fusion of information analyzed by these pre-trained models. Grad-CAM, an explainable artificial intelligence technique, is implemented to demonstrate the model’s interpretability.ResultsThe final results of utilizing our model for detecting amastigotes in microscopic images are as follows: accuracy of 98.95 1.4%, specificity of 98 2.67%, sensitivity of 100%, precision of 97.91 2.77%, F1-score of 98.92 1.43%, and Area Under Receiver Operating Characteristic Curve of 99 1.33.ConclusionThe newly devised system is precise, swift, user-friendly, and economical, thus indicating the potential of deep learning as a substitute for the prevailing leishmanial diagnostic techniques.

A scalable, passive sampling method for the quantification of airborne allergens

Traditionally, airborne concentrations of aeroallergens are sampled in a single location by an active sampler, which requires electricity and regular maintenance. However, aeroallergen concentrations may vary widely over urban and rural environments, requiring a method that is cost-effective and scalable so that many measurements can be made across an air shed. We developed such a method that uses passive sampling and light microscopy for analysis. Inexpensive and easy to operate, passive samplers rely on the gravitational settling of particles onto microscope slides. This determines airborne pollen concentration through: 1) sample collection using a modified Durham sampler, 2) preparation of samples for microscopy and strategic sample imaging, and 3) simplified particle measurements and calculation of pollen concentration following deposition velocity models proposed by Scheppegrell [1] and Wagner and Leith [2]. This method was verified with two sampling campaigns during the ragweed season of 2020 and the tree pollen season of 2021. The concentrations determined with the passive and Burkard sampling methods were found to be well-correlated (r > 0.99, r = 0.87) and precise (%CV = 20 %, 21 %). The validation of passive samplers will enable measurements of aeroallergens over wider spatial scales and help determine where aeroallergen exposure risks are greatest.•An inexpensive and low-cost method was developed to determine airborne pollen counts.•The method was evaluated for its accuracy and reproducibility.•The method can be applied to examine the concentrations and spatial variability of airborne pollen.

Influence of collector type and the glass cover cooling on the performance of the solar distiller integrated with the solar collectors

Improving the efficiency of solar distillers is essential to increasing freshwater production. One method to enhance the output of the solar distiller is to connect it with another solar collector. Previous studies did not indicate a comparison of the performance of integrated solar stills with different types of solar collectors. The current article aims to study the performance of solar distillers connected to two different types of solar collectors, study the influencing operational variables, and compare these cases. A practical model was built that included four operational cases. The first case (M1) was a solar heater with a solar concentrator collector and a solar distiller connected in series. The second case (M2): A solar concentrator collector and solar distiller connected in series. The third case (M3): A solar heater and solar distiller connected in series. The fourth case (M4): It is the same as the M1 system, but the solar distiller contains a tube to cool the glass and compare the productivity with the classical solar distiller. The results showed that the M1 case is higher than the M3 and M2 cases. In contrast, the M4 case had the highest productivity in all the cases and systems that were discussed, which is estimated at 7583 ml/ day; this means that the M4 system is the best system studied, and this is due to the glass cooling mechanism. The highest productivity recorded for the M4 system was on 28 June, and the lowest productivity recorded was on 15 July. The highest hourly productivity was reached when the glass surface temperature (4 ˚C) was 1400 ml/h at 14:00.

Pediatric Urinary Tract Infections at a Tertiary Care Center: Microbial Diversity and Antibiotic Resistance Patterns

Abstract Introduction: Urinary tract infection (UTI) remains a prevalent bacterial infection among the pediatric age group, ranking as the third-most common bacterial infection in children in developing countries, following gastrointestinal and respiratory tract infections. It continues to pose a significant health challenge. Materials and Methods: The urine samples underwent gross examination, wet mount, and Gram stain. Semiquantitative cultures were conducted on conventional media (MacConkey agar, blood agar, and CLED agar) and UTI chromogenic media. Uropathogen identification employed standard microbiological techniques. Antibiotic susceptibility was determined using the Kirby–Bauer disk diffusion method, with interpretation based on CLSI guidelines. Plates were incubated overnight, and clear zone diameters around antibiotic discs were measured for sensitivity or resistance determination. Results: The study comprised 64.75% male children and 35.25% female children. Analyzing the age distribution, the majority of participants fell within the age range of 1–5 years (35%), followed by those &lt;1 year (33.75%), and the age group of 5–12 years, accounting for 31.25% of the total 400 participants. The findings from our investigation revealed a UTI prevalence of 17.25%, with Escherichia coli and Klebsiella emerging as the predominant uropathogens. Notably, these pathogens exhibited a significant susceptibility to carbapenems, nitrofurantoin, and amikacin. Conclusion: Our study identified a male predominance and various age distributions among UTI patients. Antibiotic susceptibility patterns revealed E. coli and Klebsiella pneumoniae’s high sensitivity to key antibiotics, whereas Enterococcus spp. exhibited significant high-level aminoglycoside resistance. This study underscores the importance of tailored antibiotic strategies based on local resistance profiles to combat UTIs effectively.

Detection of milk adulteration using coffee ring effect and convolutional neural network

A low-cost and effective method is reported to identify water and synthetic milk adulteration of cow’s milk using coffee ring patterns. The cow’s milk samples were diluted with tap water (TW), distilled water (DW) and mineral water (MW) and drop cast onto glass slides to observe coffee ring patterns. The area of the ring, total particle area and average particle diameter were extracted from these patterns. For each ring, the ratio of total particle area versus total ring area was calculated. The area ratio, regardless of water adulterants, follows an exponential model with respect to average particle diameter. Unlike TW, the ratio for DW and MW adulterated milk are clustered and classified together with respect to the particle diameter. These results were independent of dilution level and are used for adulterant classification. The ring of milk adulterated using synthetic milk gave multiple concentric rings, flower-like structures, and oil globules throughout the dilution level. An Alexnet model was used to classify water and synthetic milk adulterants in authentic milk. The trained model could achieve 96.7% and 95.8% accuracy for binary and tertiary classification respectively. These results enable us to distinguish synthetic milk from pure milk and segregate DW and MW with respect to TW adulterated milk.

Experimental and numerical analyses of water droplet condensation on photovoltaic surfaces under clean and dusty conditions

Several studies have investigated the condensation effect on dust agglomerate located on PV surfaces. The phenomena of dust condensation remain minimally addressed by researchers and not understanding well in the field of PV systems. Using rigorous experimentation, this research study explored the accumulation of dust on photovoltaic units. Furthermore, we thoroughly examined the effect of condensation on the glass surface of the unit. The formation of condensed droplets and their consequences were examined through meticulous experiments and mechanical analyses. The experiment was made possible by an ingeniously designed experimental platform, which enabled monitoring the influences of condensation on dust aggregation. The condensation process was carefully observed and analysed using microscopic techniques, while its direct impact on the dust accumulated on the PV module's surface was also examined. The experimental and numerical investigations of droplets condensation, condensing water droplets speed, condensation film thickness, and mud formation layer on the glass cover of PV module were highlighted. The droplets condensation on a glass plane (similar surface to covering glass for PV module) was recorded at different air conditions. The values of air velocity were chosen 0, 2.4, 4.1, 6.3 m/s, to provide a constant air temperature and humidity of the inflow air, the channel was supplied with heating control system. Furthermore, two dust seeding quantities 150 and 300 g were tested to provide a range for different cases for thickness of dust layers with different air velocities. According to the study, condensate time directly correlates with drop equivalent diameter, demonstrating that drop diameter increases proportionally to condensation time. It is indicated that the droplet formation and condensation period accelerated with increasing dust seeding quantity. It is noteworthy that the period of droplet formation and condensation accelerates with the increase in the amount of dust seeding. The results showed that by increasing the air speed from 2.4 m/s to 6.3 m/s, the weight of accumulated dust was decreased from 0.166 g to 0.151 g. Also, the average mud layer thickness was reduced from 27.6 μm to 25.1 μm.

Applied Artificial Intelligence and Prospect of Internet of Everything (IoE) for the Detection of Tuberculosis

ABSTRACT Mycobacterium tuberculosis is a bacterium that causes disease known as Tuberculosis. Tuberculosis is highly contagious and can result in high mortality rate if left untreated. In order to screen individuals suspected of the disease, medical expert relies on several conventional approaches which are hindered by several limitations which include time consuming, high workload, false positive results, etc. This calls for the need to develop smart and automatic approaches that can address these challenges. Majority of existing studies reported the use of 1 or 2 pretrained models and the use of SoftMax as the classifier. Moreover, majority of the studies trained models using a single type of dataset which are mostly curated from public accessible domains. Thus, this study addressed these challenges by: (1) The use of several pretrained models (2) The use of 2 classifiers which include SVM and KNN and (3) Training and validating pretrained models fused with classifiers on microscopic slide and chest X-ray images. The result achieved in this study highlights the prospect of computer-assisted techniques in triaging and screening of TB. The integration of Internet of Everything (IoE) in medical diagnosis has the potential to increase healthcare outcome, boost productivity, and reduce workload.

Functional and financial analysis of an inclined step solar desalination using phase change nanomaterials.

Recent decades have seen a shortage of water, which has led scientists to concentrate on solar desalination technologies. The present study examines the solar water desalination system with inclined steps, while considering various phase change materials (PCMs). The findings suggest that the incorporation of PCM generally enhances the productivity of the solar desalination system. Additionally, the combination of nanoparticles has been used to PCM, which is a popular technique utilized nowadays to improve the efficiency of these systems. The current investigation involves the transient modeling of a solar water desalination system, utilizing energy conservation equations. The equations were solved using the Runge-Kutta technique of the ODE23s order. The temperatures of the salt water, the absorbent plate of the glass cover, and the PCM were calculated at each time. Without a phase changer, the rate at which fresh water is produced is around 5.15 kg/m2·h. The corresponding mass flow rates of paraffin, n-PCM I, n-PCM III, n-PCM II, and stearic acid are 22.9, 28.9, 5.9, 11.9, and 73 kg/m2·h. PCMs, with the exception of stearic acid, exhibit similar energy efficiency up to an ambient temperature of around 29°. However, at temperatures over 29°, n-PCM II outperforms other PCM.

Second trimester vaginal Candida colonization among pregnant women attending antenatal care in Bukavu, Democratic Republic of the Congo: prevalence, clinical correlates, risk factors and pregnancy outcomes

IntroductionVaginal Candida colonization (CC) can lead to vulvovaginal candidiasis, the second most prevalent vaginal condition worldwide, and has been associated with adverse birth outcomes. However, no data on CC in the Democratic Republic of the Congo are available. We investigated the prevalence, Candida species, clinical correlates, risk factors and pregnancy outcomes in women with CC in the second trimester of pregnancy.Material and methodsIn Bukavu, the Democratic Republic of the Congo, pregnant women were recruited during antenatal care between 16 and 20 weeks of gestation from January 2017 to October 2017 and followed until delivery. Sociodemographics, sexual behavioral, hygienic and clinical characteristics, microbiological data and pregnancy outcomes were collected. Candida detection and speciation was performed with microscopy (Gram-stained smears and wet-mount) and/or quantitative PCR. Multivariate regression models were used to estimate the different associations with CC.ResultsThe prevalence of CC by wet mount, microscopy of Gram-stain smears and qPCR was 27.9%, 28.1% and 38.2%, respectively. C. albicans was the most prevalent Candida species (91.0%). Previous genital infections, an intermediate vaginal microbiota, bacterial vaginosis, and the use of pit toilets were risk factors for CC. Clinically, CC was associated with itching only. Women with CC had twice the odds for preterm birth, if Candida concentration was high, the odds were four times higher.ConclusionsIn Bukavu, the Democratic Republic of the Congo, the prevalence of CC was high and associated with microbiological and modifiable risk factors. Screening and treatment for CC during antenatal care should be investigated as a possible strategy to reduce preterm birth.

Photovoltaic Energy Production in Greenhouses With Spectral Splitting Solar Trackers

The spectral filtering low concentration photovoltaic system developed by Voltiris is an innovative solution for energy production in greenhouses without affecting food production. A first prototype was installed in the greenhouse of the agricultural research center Agroscope in Conthey, Switzerland. During an eight-month agronomic study from March to October, the yield of tomato, pepper bell and basil under this system was on a par with a control group. I-V curves were recorded to evaluate the photovoltaic system performance, and the impact of concentration and filtering. The curves showed that the prototype achieved a direct normal irradiation efficiency of 10.1 %. The specific power output of the Voltiris system inside the greenhouse was comparable to the one of a conventional solar panel placed outside. Filters with two different transmission spectra were used, both of which were matched to the absorption spectra of chlorophyll and reflected 50 % and 60 % of the incident global radiation respectively. To transfer the performance of the system to other greenhouses, the transmittance of the test greenhouse and its glass cover were measured for global and diffuse radiation. This allowed to determine the transmittance of the greenhouse specific metal structure. In the test greenhouse, the overall transmission coefficient for direct solar radiation was 0.28, hence limiting the system yield.

Maternal COVID-19 exposure and placental characteristics.

The impact of COVID-19 on the placenta is poorly described, particularly among minority women. This is a retrospective case-control study. Micro- and macroscopic placental pathologic findings were compared for 15 COVID-19 positive and 36 negative mothers. Cases and controls were frequency matched on gestational age, race, maternal comorbidities, and delivery type. Data from the electronic medical record were supplemented with independent review of microscopic slides. Placentas from cases and controls were similar except the median distance from the site of the cord insertion to the nearest disk margin was statistically significantly shorter among placentas from COVID-19 positive cases (3.5 versus 6.0 cm, p = 0.006). Case status was not associated with an increased risk of placental pathologies. There are few pathologic differences between placentas of COVID-19 positive and negative mothers. Additional studies are needed to investigate the role of timing of infection.

A proposed hybrid model of ANN and KNN for solar cell defects detection and temperature prediction using fuzzy image segmentation

This paper presents a novel hybrid model employing Artificial Neural Networks (ANN) and Mathematical Morphology (MM) for the effective detection of defects in solar cells. Focusing on issues such as broken corners and black edges caused by environmental factors like broken glass cover, dust, and temperature variations. This study utilizes a hybrid model of ANN and K-Nearest Neighbor (KNN) for temperature prediction. This hybrid approach leverages the strengths of both models, potentially opening up new avenues for improved accuracy in temperature forecasting, which is critical for solar energy applications. The significance lies in the interconnectedness of temperature fluctuations and solar cell efficiency, leading to defects. The proposed model aims to predict temperatures accurately, providing insights into potential solar cell efficiency problems. Subsequently, this work studies the transitions to defect detection using Fuzzy C-Means (FCM) clustering and MM techniques. The hybrid model demonstrates accurate temperature prediction with Mean Absolute Percentage Error (MAPE) values of 0.92 %, 0.72 %, and 1.3 % for average, maximum, and minimum temperatures, respectively. The defect detection process yields a detection accuracy (CR) of 96 % and sensitivity of detection (SD) of 89 %. This work is validated compared to the literature work done and by using K-fold cross validation technique. The proposed work emphasizes the improvement in defect detection accuracy and the overall quality enhancement of solar cells.