Limitations In Terms Research Articles

Non-Invasive Plant Root Tomography Through Optimized Sonar Array Transducer Antenna Design Using Genetic Swarm Metaheuristic

Plant root imaging is crucial for progress in various domains such as plant breeding and crop optimization. Traditionally, root tomography involves invasive methods that disrupt plant systems and yield non-reproducible results. As a result, non-invasive techniques, particularly electrical tomography, have gained significant attention. Despite the advantages, these techniques have limitations in terms of radiation efficiency and directivity due to suboptimal antenna design. This paper presents a comprehensive simulation on antenna design optimization focusing on dimensions, spacing, and integration of advanced algorithms. A micropatch transducer antenna was engineered for an existing in-silico plant root setup operating within a 3–5 MHz frequency range. The optimized dimensions of the antenna are 109.32 mm × 140.67 mm × 2.55 mm, and it resonates effectively within a frequency range of 3.1–5.68 MHz. Using scalar minimization techniques, patch transducers were interconnected into an antenna array with an optimized 3 mm spacing. Utilizing multi-objective optimization algorithm based on sperm fertilization procedure and shuffled frog leaping algorithm, optimal frequencies were obtained at 3,989,796.88 Hz and 3,989,951.83 Hz, respectively. Validated using CADFEKO software, the proposed antenna design demonstrated distinctive voltage distribution, superior directivity of 9.24 dBi, gain of 9.15 dBi, and 98.6% radiation efficiency when compared to the existing silicon-based root tomography antenna setups.

High-resolution 3D imaging in light-field microscopy through Stokes matrices and data fusion.

The trade-off between the lateral and vertical resolution has long posed challenges to the efficient and widespread application of Fourier light-field microscopy, a highly scalable 3D imaging tool. Although existing methods for resolution enhancement can improve the measurement result to a certain extent, they come with limitations in terms of accuracy and applicable specimen types. To address these problems, this paper proposed a resolution enhancement scheme utilizing data fusion of polarization Stokes vectors and light-field information for Fourier light-field microscopy system. By introducing the surface normal vector information obtained from polarization measurement and integrating it with the light-field 3D point cloud data, 3D reconstruction results accuracy is highly improved in axial direction. Experimental results with a Fourier light-field 3D imaging microscope demonstrated a substantial enhancement of vertical resolution with a depth resolution to depth of field ratio of 0.19%. This represented approximately 44 times the improvement compared to the theoretical ratio before data fusion, enabling the system to access more detailed information with finer measurement accuracy for test samples. This work not only provides a feasible solution for breaking the limitations imposed by traditional light-field microscope hardware configurations but also offers superior 3D measurement approach in a more cost-effective and practical manner.

Closure of gastrointestinal perforations using an endoloop system and a single-channel endoscope: description of a simple, reproducible, and standardized method.

Several endoscopic treatments for iatrogenic perforations are currently available, with some limitations in terms of size, location, complexity, or cost. Our aims were to introduce a novel technique for closure, using an endoloop and clips, to assess its rate of technical success and post-resection complications. For closure of large perforations (diameter ≥ 10mm), two similar techniques were implemented, using a single-channel endoscope. An endoloop was deployed through the operating channel or towed by an endoclip alongside the endoscope. Several clips were utilized to fix it on the muscular layer of defect's margins. The defect was closed, by fastening the loop either directly or after being reattached to the mobile hook. This analysis included eleven patients (72% women, median age 68years). Eight colorectal, one appendiceal, and two gastric lesions were resected, with a median perforation size of 15mm. As confirmed by computed tomography, closure of wall defects was achieved successfully in all cases, using a median of 6 clips. Pneumoperitoneum was evacuated in 4 cases. The median hospitalization duration was 4days, prophylactic antibiotics being prescribed for a median of 7days. One patient had a small abdominal collection, without requiring drainage, while another presented post-resection bleeding from the mucosal defect. The novel techniques, utilizing a single-channel endoscope, clips, and an endoloop, ensuring an edge-to-edge suture of muscular layer, proved to be safe, reproducible, and easy to implement. They exhibit an excellent technical success rate and a minimal incidence of non-severe complications.

Visual assessment of fashion merchandising based on scene saliency

PurposeFashion merchandising hold a paramount position within the realm of retail marketing. Currently, the purpose of this article is that the assessment of display effectiveness predominantly relies on the subjective judgment of merchandisers due to the absence of an effective evaluation method. Although eye-tracking devices have found extensive used in tracking the gaze trajectory of subject, they exhibit limitations in terms of stability when applied to the evaluation of various scenes. This underscores the need for a dependable, user-friendly and objective assessment method.Design/methodology/approachTo develop a cost-effective and convenient evaluation method, the authors introduced an image processing framework for the assessment of variations in the impact of store furnishings. An optimized visual saliency methodology that leverages a multiscale pyramid model, incorporating color, brightness and orientation features, to construct a visual saliency heatmap. Additionally, the authors have established two pivotal evaluation indices aimed at quantifying attention coverage and dispersion. Specifically, bottom features are extract from 9 distinct scale images which are down sampled from merchandising photographs. Subsequently, these extracted features are amalgamated to form a heatmap, serving as the focal point of the evaluation process. The authors have proposed evaluation indices dedicated to measuring visual focus and dispersion, facilitating a precise quantification of attention distribution within the observed scenes.FindingsIn comparison to conventional saliency algorithm, the optimization method yields more intuitive feedback regarding scene contrast. Moreover, the optimized approach results in a more concentrated focus within the central region of the visual field, a pattern in alignment with physiological research findings. The results affirm that the two defined indicators prove highly effective in discerning variations in visual attention across diverse brand store displays.Originality/valueThe study introduces an intelligent and cost-effective objective evaluate method founded upon visual saliency. This pioneering approach not only effectively discerns the efficacy of merchandising efforts but also holds the potential for extension to the assessment of fashion advertisements, home design and website aesthetics.

Rolling bearing fault diagnosis based on Gramian angular difference field and improved channel attention model.

Fault diagnosis of rolling bearings is a critical task, and in previous research, convolutional neural networks (CNN) have been used to process vibration signals and perform fault diagnosis. However, traditional CNN models have certain limitations in terms of accuracy. To improve accuracy, we propose a method that combines the Gramian angular difference field (GADF) with residual networks (ResNet) and embeds frequency channel attention module (Fca) in the ResNet to diagnose rolling bearing fault. Firstly, we used GADF to convert the signals into RGB three-channel fault images during data preprocessing. Secondly, to further enhance the performance of the model, on the foundation of the ResNet we embedded the frequency channel attention module with discrete cosine transform (DCT) to form Fca, to effectively explores the channel information of fault images and identifies the corresponding fault characteristics. Finally, the experiment validated that the accuracy of the new model reaches 99.3% and the accuracy reaches 98.6% even under an unbalanced data set, which significantly improves the accuracy of fault diagnosis and the generalization of the model.

MixedSCNet: LiDAR-Based Place Recognition Using Multi-Channel Scan Context Neural Network

In the realm of LiDAR-based place recognition tasks, three predominant methodologies have emerged: manually crafted feature descriptor-based methods, deep learning-based methods, and hybrid methods that combine the former two. Manually crafted feature descriptors often falter in reverse visits and confined indoor environments, while deep learning-based methods exhibit limitations in terms of generalization to distinct data domains. Hybrid methods tend to fix these problems, albeit at the cost of an expensive computational burden. In response to this, this paper introduces MixedSCNet, a novel hybrid approach designed to harness the strengths of manually crafted feature descriptors and deep learning models while keeping a relatively low computing overhead. MixedSCNet starts with constructing a BEV descriptor called MixedSC, which takes height, intensity, and smoothness into consideration simultaneously, thus offering a more comprehensive representation of the point cloud. Subsequently, MixedSC is fed into a compact Convolutional Neural Network (CNN), which further extracts high-level features, ultimately yielding a discriminative global point cloud descriptor. This descriptor is then employed for place retrieval, effectively bridging the gap between manually crafted feature descriptors and deep learning models. To substantiate the efficacy of this amalgamation, we undertake an extensive array of experiments on the KITTI and NCLT datasets. Results show that MixedSCNet stands out as the sole method showcasing state-of-the-art performance across both datasets, outperforming the other five methods while maintaining a relatively short runtime.

Early detection of cardiac fibrosis in diabetic mice by targeting myocardiopathy and matrix metalloproteinase 2

Early detection of myocardial fibrosis in diabetic cardiomyopathy (DCM) has significant clinical implications for diabetes management. In this study, we identified matrix metalloproteinase 2 (MMP2) as a potential biomarker for early fibrosis detection. Based on this finding, we designed a dual-targeting nanoparticle CHP-SPIO-ab MMP2 to specifically target myocardiopathy and MMP2, enabling sensitive fibrosis detection using magnetic resonance imaging (MRI). Our results demonstrate that collagen hyperplasia (early fiber formation) begins to develop in diabetic mice at 12 weeks old, with observable fibrosis occurring at 16 weeks old. Additionally, MMP2 expression significantly up-regulates around collagen starting from 12 weeks of age. T2 MRI analysis revealed significant T2% enhancement in the hearts of 12-week-old diabetic mice following administration of the CHP-SPIO-ab MMP2 probe, indicating noninvasive detection of fiber formation. Furthermore, after fibrosis treatment, a reduction in T2% signal was observed in the hearts of 16-week-old diabetic mice. These findings were supported by Sirius red and Prussian blue staining techniques. Overall, our study presents a promising strategy for early identification of myocardial fibrosis. Statement of significanceMyocardial damage typically exhibits irreversibility, underscoring the paramount importance of early fibrosis diagnosis. However, the clinical used T1 mapping for fibrosis detection still exhibits limitations in terms of sensitivity. Therefore, it is imperative to develop highly sensitive strategies for early cardiac fibrosis detection. Here, we investigated the development of myocardial fibrosis in diabetic mice, and designed a highly sensitive probe that specifically targets cardiomyopathy and high expression of MMP2 for the early diagnosis of fibrosis. The probe enables non-invasive detection of abnormalities through MRI imaging as soon as fiber deposition appear, which can be detected earlier than T1 mapping. This advancement holds great potential for clinical diagnosis of myocardial fibrosis using cardiac magnetic resonance.

Evaluation of micromotion in multirooted root analogue implants embedded in synthetic bone blocks: an in vitro study

BackgroundWhile conventional threaded implants (TI) have proven to be effective for replacing missing teeth, they have certain limitations in terms of diameter, length, and emergence profile when compared to customised root analogue implants (RAI). To further investigate the potential benefits of RAIs, the aim of this study was to experimentally evaluate the micromotion of RAIs compared to TIs.MethodsA 3D model of tooth 47 (mandibular right second molar) was segmented from an existing cone beam computed tomography (CBCT), and a RAI was designed based on this model. Four RAI subgroups were fabricated as follows: 3D-printed titanium (PT), 3D-printed zirconia (PZ), milled titanium (MT), milled zirconia (MZ), each with a sample size of n = 5. Additionally, two TI subgroups (B11 and C11) were used as control, each with a sample size of n = 5. All samples were embedded in polyurethane foam artificial bone blocks and subjected to load application using a self-developed biomechanical Hexapod Measurement System. Micromotion was quantified by analysing the load/displacement curves.ResultsThere were no statistically significant differences in displacement in Z-axis (the loading direction) between the RAI group and the TI group. However, within the RAI subgroups, PZ exhibited significantly higher displacement values compared to the other subgroups (p < 0.05). In terms of the overall total displacement, the RAI group showed a statistically significant higher displacement than the TI group, with mean displacement values of 96.5 µm and 55.8 µm for the RAI and TI groups, respectively.ConclusionsThe RAI demonstrated promising biomechanical behaviour, with micromotion values falling within the physiological limits. However, their performance is less predictable due to varying anatomical designs.

Investigation of Phishing Susceptibility with Explainable Artificial Intelligence

Phishing attacks represent a significant and growing threat in the digital world, affecting individuals and organizations globally. Understanding the various factors that influence susceptibility to phishing is essential for developing more effective strategies to combat this pervasive cybersecurity challenge. Machine learning has become a prevalent method in the study of phishing susceptibility. Most studies in this area have taken one of two approaches: either they explore statistical associations between various factors and susceptibility, or they use complex models such as deep neural networks to predict phishing behavior. However, these approaches have limitations in terms of providing practical insights for individuals to avoid future phishing attacks and delivering personalized explanations regarding their susceptibility to phishing. In this paper, we propose a machine-learning approach that leverages explainable artificial intelligence techniques to examine the influence of human and demographic factors on susceptibility to phishing attacks. The machine learning model yielded an accuracy of 78%, with a recall of 71%, and a precision of 57%. Our analysis reveals that psychological factors such as impulsivity and conscientiousness, as well as appropriate online security habits, significantly affect an individual’s susceptibility to phishing attacks. Furthermore, our individualized case-by-case approach offers personalized recommendations on mitigating the risk of falling prey to phishing exploits, considering the specific circumstances of each individual.

Boosting the Performance of PEDOT:PSS Based Electronics Via Ionic Liquids.

The conducting polymer poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) offers superior advantages in electronics due to its remarkable combination of high electrical conductivity, excellent biocompatibility, and mechanical flexibility, making it an ideal material among electronic skin, health monitoring, and energy harvesting and storage. Nevertheless, pristine PEDOT:PSS films exhibit limitations in terms of both low conductivity and stretchability; while, conventional processing techniques cannot enhance these properties simultaneously, facing the dilemma that highly conductive interconnected PEDOT:PSS domains are susceptible to tensile strain. Via modifying PEDOT:PSS with ionic liquids (ILs), not only a synergistic enhancement of the electrical and mechanical properties can be achieved but also the requirements for the printable bioelectronic are satisfied. In this comprehensive review, the task of providing a thorough examination of the mechanisms and applications of ILs as modifiers for PEDOT:PSS is undertaken. First, the theoretical mechanisms governing the interactions between ILs and PEDOT:PSS are discussed in detail. Then, the enhanced properties and the elucidation of the underlying mechanisms achieved through the incorporation of ILs are reviewed. Next, specific applications of ILs-modified PEDOT:PSS relevant to bioelectronic devices are presented. Last, there is a concise summary and a discussion regarding the opportunities and challenges in this exciting field.

Digital Participatory Model as Part of a Data-Driven Decision Support System for Urban Vibrancy

Digital participation relies on computational systems as the instruments for expert engagement, data-driven insight, and informed decision-making. This study aims to increase expert engagement with the Bayesian-based decision support model in evaluating urban vibrancy decisions. In this study, urban vibrancy parameters are defined using “economic, use, and image value” measures. This article focuses on the visual aspect of vibrancy, defined as the image value of place. The image value is evaluated through likability and likability features. The case study area is the Eminönü Central Business District in the Istanbul Historic Peninsula due to its distinctive urban dynamics derived from the duality of being a cultural and cosmopolitan city center. This research presents a method as a decision support system (DSS) model based on the Bayesian belief network (BBN) and spatial BBN for supporting urban vibrancy decisions. The spatial BBNs monitor spatial outcomes of variables’ dependencies that form through the BBN relationship network. Spatial BBN tools monitors the spatial impact of decisions for informed urban interventions. The results demonstrate that urban greening, pedestrianization, and human-scaled streetscapes should be prioritized to make streets more likable. The most significant intervention areas are Tahtakale for signboard regulation, Sultanahmet and Vefa for cultural landscape improvement, and Vefa and Mahmutpaşa for planning building enclosures. The participation is achieved by evaluating urban vibrancy with what-if scenarios using BBN. The developed DSS model addresses which parameters should be prioritized, and what are their spatial consequences. The use of spatial BBN tools presents certain limitations in terms of interoperability and user interaction. Overall, this research contributes to participatory urban planning by incorporating both conditional and spatial dependencies. This unique approach not only promotes a more holistic understanding of urban vibrancy but also contributes to the advancement of digital participation in urban planning decisions.

Evaluation of the Microbiome Identification of Forensically Relevant Biological Fluids: A Pilot Study.

In forensic sciences, body fluids, or biological traces, are a major source of information, and their identification can play a decisive role in criminal investigations. Currently, the nature of biological fluids is assessed using immunological, physico-chemical, mRNA and epigenetic methods, but these have limits in terms of sensitivity and specificity. The emergence of next-generation sequencing technologies offers new opportunities to identify the nature of body fluids by determining bacterial communities. The aim of this pilot study was to assess whether analysis of the bacterial communities in isolated and mixed biological fluids could reflect the situation observed in real forensics labs. Several samples commonly encountered in forensic sciences were tested from healthy volunteers: saliva, vaginal fluid, blood, semen and skin swabs. These samples were analyzed alone or in combination in a ratio of 1:1. Sequencing was performed on the Ion Gene StudioTM S5 automated sequencer. Fluids tested alone revealed a typical bacterial signature with specific bacterial orders, enabling formal identification of the fluid of interest, despite inter-individual variations. However, in biological fluid mixtures, the predominance of some bacterial microbiomes inhibited interpretation. Oral and vaginal microbiomes were clearly preponderant, and the relative abundance of their bacterial communities and/or the presence of common species between samples made it impossible to detect bacterial orders or genera from other fluids, although they were distinguishable from one another. However, using the beta diversity, salivary fluids were identified and could be distinguished from fluids in combination. While this method of fluid identification is promising, further analyses are required to consolidate the protocol and ensure reliability.

SGS: SqueezeNet-guided Gaussian-kernel SVM for COVID-19 Diagnosis

AbstractThe ongoing global pandemic has underscored the importance of rapid and reliable identification of COVID-19 cases to enable effective disease management and control. Traditional diagnostic methods, while valuable, often have limitations in terms of time, resources, and accuracy. The approach involved combining the SqueezeNet deep neural network with the Gaussian kernel in support vector machines (SVMs). The model was trained and evaluated on a dataset of CT images, leveraging SqueezeNet for feature extraction and the Gaussian kernel for non-linear classification. The SN-guided Gaussian-Kernel SVM (SGS) model achieved high accuracy and sensitivity in diagnosing COVID-19. It outperformed other models with an impressive accuracy of 96.15% and exhibited robust diagnostic capabilities. The SGS model presents a promising approach for accurate COVID-19 diagnosis. Integrating SqueezeNet and the Gaussian kernel enhances its ability to capture complex relationships and classify COVID-19 cases effectively.

Neural radiance fields for multi-scale constraint-free 3D reconstruction and rendering in orchard scenes

Efficiently, accurately, and realistically reconstructing large-scale 3D orchard scenes in a virtual world is an immensely challenging task. This complexity stems from the intricate and expansive of real orchard scenes. Traditional 3D reconstruction and rendering methods have encountered limitations in terms of modeling efficiency and computational costs, hindering the ability to provide users with immersive experiences. In response to these challenges, this study introduces a strategy for 3D scene reconstruction and rendering grounded in implicit neural representation: the NeRF-Ag model. Building upon the baseline NeRF, this model integrates a multi-resolution latent feature encoding technique, notably heightening training efficiency and elevating modeling precision. Furthermore, by means of environmental factor embedding, the model's robustness and practical applicability are further enhanced. The experimental outcomes illustrate that NeRF-Ag attains photo-realistic rendering outcomes across small, medium, and large scales. Moreover, it surpasses NeRF concerning the evaluation metrics of PSNR, SSIM, and LPIPS. Notably, the training speed of NeRF-Ag is roughly 39 times faster than NeRF. In 3D reconstruction tasks, NeRF-Ag showcases enhanced texture detail representation and higher modeling accuracy compared to the COLMAP-based 3D reconstruction method. Additionally, this study accomplishes free-viewpoint rendering of 3D scenes employing NeRF-Ag and provides evidence substantiating the connection between the quantity of training images and the precision of 3D rendering. The conclusions of this study will contribute to supporting and referencing the implementation of immersive visual interactive features within agricultural digital twin systems.

Laser-Induced Breakdown Spectroscopy Optimization Using Response Surface Methodology

Laser-induced breakdown spectroscopy (LIBS) is a modern analytical technique that is capable of fast, multi-elemental, lowcost and environmental friendly analysis, which does not require complex sample preparation. Albeit its potential, LIBS analysis still presents many limitations in terms of sensitivity and reproducibility, especially when analyzing complex matrices such as of sediments and soil samples. In order to reduce these matrix related effects, it is highly recommended that the system temporal parameters, which are responsible for controlling plasma evolution and signal collection, are optimized beforehand. In this work, we proposed the design of experiments (DOE) tool – specifically, the response surface methodology (RSM) – as an approach to optimize LIBS’s most important parameters (delay-time, interpulse delay, gate width and accumulated pulse). Signal-to-noise ratio (SNR) of the emission lines for Zn, Cd, Mg, Al, Ni, Cu, Ca, Cr, Sr, Fe were the response variable assessed during the procedure. The results showed that the RSM was an effective optimization tool for LIBS parameters and the final condition improved SNR ratios by up to a 48 ratio, when comparing to the not-optimal conditions.

Multiple roles of agricultural cooperatives in improving farm technical efficiency: A case study of rural Gansu, China

AbstractThe establishment of agricultural cooperatives is widely viewed as an institutional arrangement to remove constraints that prevent smallholders from accessing profitable business opportunities in agriculture. Agricultural cooperatives in China offer a wide range of services within the framework of contract farming and vertical integration. This study empirically examines the multiple roles of China's cooperatives in improving farm production efficiency and conserving the agricultural environment. Considering that agricultural cooperatives' services can be broadly divided into biological and machinery services, we specify a stochastic frontier production function in a special form, called the separated Cobb–Douglas. The empirical results present clear evidence that Chinese agricultural cooperatives play essential roles in increasing biochemical technical efficiency and protecting the agroecological environment. However, the involvement of cooperatives as vertical integrators is demonstrated to have a detrimental effect on biochemical technical efficiency. The pressing challenge for agricultural cooperative development in China is to accommodate the conflicting consequences that their entry into farming has on biochemical and machinery technical efficiencies. This has strong policy implications, although our study has certain limitations in terms of the narrow focus on a single specific region. [EconLit Citations: C26, Q12, Q13, Q15, Q18].

Psychological Biases and Democratic Anxiety: A Comment on Little and Meng (2023)

In their timely article, Andrew Little and Anne Meng make an important point. Despite widespread alarm over democratic backsliding, objective evidence suggests that the scale of the phenomenon is much more limited than many seem to think. Recent power holders around the world have not been entrenching themselves more effectively than in the past. Incumbents continue to lose elections about as often as they used to, and those who win have not been doing so by larger margins. Opposition parties are allowed to compete about as frequently today as 10 or 20 years ago. Moreover, there has been no increase in leaders’ ability to evade term limits.

Effectiveness of Data Augmentation for Localization in WSNs Using Deep Learning for the Internet of Things.

Wireless sensor networks (WSNs) have become widely popular and are extensively used for various sensor communication applications due to their flexibility and cost effectiveness, especially for applications where localization is a main challenge. Furthermore, the Dv-hop algorithm is a range-free localization algorithm commonly used in WSNs. Despite its simplicity and low hardware requirements, it does suffer from limitations in terms of localization accuracy. In this article, we develop an accurate Deep Learning (DL)-based range-free localization for WSN applications in the Internet of things (IoT). To improve the localization performance, we exploit a deep neural network (DNN) to correct the estimated distance between the unknown nodes (i.e., position-unaware) and the anchor nodes (i.e., position-aware) without burdening the IoT cost. DL needs large training data to yield accurate results, and the DNN is no stranger. The efficacy of machine learning, including DNNs, hinges on access to substantial training data for optimal performance. However, to address this challenge, we propose a solution through the implementation of a Data Augmentation Strategy (DAS). This strategy involves the strategic creation of multiple virtual anchors around the existing real anchors. Consequently, this process generates more training data and significantly increases data size. We prove that DAS can provide the DNNs with sufficient training data, and ultimately making it more feasible for WSNs and the IoT to fully benefit from low-cost DNN-aided localization. The simulation results indicate that the accuracy of the proposed (Dv-hop with DNN correction) surpasses that of Dv-hop.

Deep convolutional neural network for glaucoma detection based on image classification

PURPOSE: Many researchers have found that the improvement in computerised medical imaging has pushed them to their limits in terms of developing automated algorithms for the identification of illness without the need for human participation. The diagnosis of glaucoma, among other eye illnesses, has continued to be one of the most difficult tasks in the area of medicine. Because there are not enough skilled specialists and there are a lot of patients seeking treatment from ophthalmologists, we have been encouraged to build efficient computer-based diagnostic methods that can assist medical professionals in early diagnosis and help reduce the amount of time and effort they spend working on healthy situations. The Optic Disc position is determined with the help of the LoG operator, and a Disc Image map is projected with the help of a U-net architecture by utilising the location and intensity profile of the optic disc. After this, a Generative adversarial network is suggested as a possible solution for segmenting the disc border. In order to verify the performance of the model, a well-defined investigation is carried out on many retinal datasets. The usage of a multi-encoder U-net framework for optic cup segmentation is the second key addition made by this proposed work. This framework greatly outperforms the state-of-the-art in this area. The suggested algorithms have been tested on public standard datasets such as Drishti-GS, Origa, and Refugee, as well as a private community camp-based difficult dataset obtained from the All-India Institute of Medical Sciences (AIIMS), Delhi. All of these datasets have been verified. In conclusion, we have shown some positive outcomes for the detection of diseases. The unique strategy for glaucoma treatment is called ensemble learning, and it combines clinically meaningful characteristics with a deep Convolutional Neural Network.

Surgery for female urethral stricture.

Female urethral stricture (FUS) is a rare entity that causes great morbidity and suffering in those affected. As the available scientific data is sparce, there are no formal guidelines or standard of care for this disease. This is a narrative review of the surgical management for female urethral stricture. The literature review was performed on PubMed. Articles were limited to English, but there was no limitation in terms of date. Management of FUS is divided between endoscopic and open surgical repair. Urethral dilation with or without urethrectomy can be offered as a first-line treatment. However, the rate of success of this procedure remains inferior to open surgical repair, and its efficacy decreases with the number of previous dilations. For distal urethral strictures, distal urethrectomy and advancement meatoplasty may be considered. Vaginal flaps are readily available, easy to harvest, well-vascularized, and allow for a dorsal or ventral orientation urethroplasty. The results of this procedure are promising, but most studies are small and retrospective. Labia flaps are easily accessible, wet, hairless, and elastic. The main limitations with the use of vaginal or labial tissues are co-existing conditions such as lichen sclerosis or vaginal atrophy, which may affect future results. Vaginal and labial graft urethroplasty can be used when it is not possible to mobilize an adequate flap. Stricture-free rates of this technique are variable. In cases of more severe stricture, an augmentation urethroplasty using buccal mucosa graft may be necessary. The techniques used in FUS replicate those for male urethral strictures, where both ventral and dorsal approaches can be utilized. Although there is growing interest in the field, the optimal management of FUS remains to be determined.