Effective Technique Research Articles

Direct Right Axillary Artery Cannulation as First Choice Strategy during Aortic Surgery Procedures: Results from a Single Experienced Surgical Centre

Objectives: Since early nineties axillary artery has been proposed as alternative to femoral artery for peripheral arterial cannulation during cardiopulmonary bypass (CPB) in aortic surgery. The aim of this study is to report our single-centre experience using direct axillary artery cannulation during aortic surgery. Methods: One hundred consecutive patients, undergoing aortic surgery using direct axillary artery cannulation were enrolled. Patient population ranged between 29 to 87 years and mean age was 65 ± 13 years. Seventy-seven patients (77%) underwent scheduled complex surgical procedure and 23 patients (23%) underwent emergent surgical procedure. Sixteen patients, furthermore, have had a previous cardiac surgery procedure. Results: CPB management using direct axillary artery cannulation was satisfactory in all cases. No case of malperfusion (cerebral and/or visceral) was reported. Out of 100 patients undergoing direct cannulation of axillary artery, no major complications related to the site or the technique of the cannulation were recorded. Local minor complications were reported in 6 patients (6%). In all 6 patients such complications were transitory and there was no residual compromission at the discharge. Overall 30-days mortality was 16%. Twenty-two patients (22%) conversely were discharged directly from intensive care unit (ICU) to chronic rehabilitation center. Sixty-seven patients (67%) were successfully discharged home. Conclusions: Our experience clearly shows that direct axillary artery cannulation is a safe, effective and reproducible technique for arterial cannulation during complex surgical procedures involving thoracic aorta. We do support such approach as a first choice strategy in such circumstances.

Controlled release herbicide formulation for effective weed control efficacy

Controlled release formulation (CRF) of herbicide is an effective weed management technique with less eco-toxicity than other available commercial formulations. To maximise the effectiveness of CRFs however, it is crucial to understand the herbicide-releasing behaviour at play, which predominately depends on the interaction mechanisms between active ingredients and carrier materials during adsorption. In this study, we investigated and modelled the adsorption characteristics of model herbicide 2,4-D onto two organo-montmorillonites (octadecylamine- and aminopropyltriethoxysilane-modified) to synthesise polymer-based CRFs. Herbicide-releasing behaviour of the synthesised CRF microbeads was then analysed under various experimental conditions, and weed control efficacy determined under glasshouse conditions. Results revealed that adsorption of 2,4-D onto both organo-montmorillonites follows the pseudo-second-order kinetics model and is predominately controlled by the chemisorption process. However, multi-step mechanisms were detected in the adsorption on both organoclays, hence intra-particle diffusion is not the sole rate-limiting step for the adsorption process. Both organoclays followed the Elovich model, suggesting they have energetically heterogeneous surfaces. Herbicide-releasing behaviours of synthesised beads were investigated at various pH temperatures and ionic strengths under laboratory and glasshouse conditions. Furthermore, weed control efficacy of synthesised beads were investigated using pot studies under glasshouse condition. Desorption studies revealed that both synthesised microbeads have slow releasing behaviour at a wide range of pHs (5–9), temperatures (25–45 °C), and ionic strengths. The results also revealed that synthesised microbeads have excellent weed control efficacy on different broad-leaf weed species under glasshouse conditions.

Treating Multifocal Ureteral Strictures with Combined Techniques: 14 Cases of Initial Experience.

Purpose: To evaluate the safety and feasibility of lingual mucosal graft ureteroplasty (LMGU) combined with ureteral reimplantation (UR) for repairing managing multifocal ureteral strictures (MUS). Methods: Between December 2020 and December 2022, 14 patients underwent LMGU combined with UR. Their perioperative data were collected retrospectively and analyzed. For the proximal diseased ureter, the narrow segment was incised longitudinally to open the ventral wall of ureter, and a lingual mucosal graft was placed as an onlay graft. Meanwhile, UR was applied to treat distal ureteral strictures. Results: Of 14 patients, three (21.4%) had previously undergone a failed ureteral reconstruction. The mean (standard deviation [SD]) proximal stricture length was 4.0 cm (1.56), and distal ureteral stricture length was 4.3 cm (0.94). The mean (SD) operative time was 236 minutes (57), the estimated blood loss was 78 mL (41.5), and the length of postoperative stay was 6 days. One (7%) patient underwent double LMGU to treat proximal 2 segments of ureteral stricture. No open conversions and intraoperative complications occurred. With a mean follow-up of 15 months (range 6-29), the recurrence-free rate was 14/14 (100%). Conclusions: LMGU combined with UR is a feasible and effective technique for managing MUS and can be an alternative to ileal ureteral replacement or renal autotransplantation in some selected patients with MUS.

Recent Advances of DNA-Templated Metal Nanoclusters for Food Safety Detection: From Synthesis, Applications, Challenges, and Beyond.

Food safety concerns have become a significant threat to human health and well-being, catching global attention in recent years. As a result, it is imperative to research conceptually novel biosensing and effective techniques for food matrices detection. Currently, DNA-templated metal nanoclusters (DNA-MNCs) are considered as one of the most promising nanomaterials due to their excellent properties in biosensing. While DNA-MNCs have garnered increasing interest, the reviews of design strategies, applications, and futuristic prospects for biosensing have been hardly found especially in food safety. The synthesis of DNA-MNCs and their use as biosensing materials in food contamination detection, including pathogenic bacteria, toxins, heavy metals, residues of pesticides, and others were comprehensively reviewed. In addition, we summarize the properties of DNA-MNCs briefly and discuss the challenges and future trends. The application of DNA-MNCs powered biosensing has been demonstrated and actively studied, which is a promising paradigm for food safety testing that can supplement or even replace current existing methods. Despite the challenges of difficulty regulating accurately, poor stability, low quantum yield, and difficult commercial transformation, the application prospects of DNA-MNCs biosensors are promising. This review aims to provide insights and directions for the future development of DNA-MNCs based food detection technology.

Surface Modification of Diatomite-Based Micro-Arc Coatings for Magnesium Implants Using a Low-Energy High-Current Electron Beam Processing Technique

The present study showcases a novel effective technique for the surface modification of micro-arc diatomite coatings using low-energy, high-current electron beams (LEHCEBs). A variety of methods such as scanning electron microscopy, energy-dispersive X-ray spectroscopy, the X-ray diffraction method, scratch testing, the potentiodynamic polarization method, immersion testing in SBF, and flow cytometry have been used to study the coatings. During processing, the electron beams’ energy density ranged between 2.5–7.5 J/cm2. After the LEHCEB treatment, the surface morphology of the coatings changed completely. The corrosion resistance of the LEHCEB-treated coated samples increased significantly, as evidenced by the decrease in corrosion current to 4.6 × 10−10 A·cm−2 and the increase in polarization resistance to 1.4 × 108 Ω·cm2. The electron beam treatment also increased the adhesion strength of the coatings to the magnesium substrate by 1.8–2.5 times compared to untreated coatings. Additionally, biological studies have shown the high viability of the NIH/3T3 cell line after contact with the samples of the coating extracts.

Magnetic Metallic Nanoparticles Coated with Carbon for the Catalytic Removal of Bromate from Water

Bromate, often detected in drinking water, is associated with a significant risk of cancer. Catalytic reduction has been recognized as an effective treatment technique to remove ions by reducing them over metal catalysts in the presence of a reducing agent, usually hydrogen. This work aims to synthesize metallic magnetic nanoparticles of iron oxide (FeO) and mixed iron oxides with manganese (MnFeO), cobalt (CoFeO), and copper (CuFeO) coated with carbon via chemical vapor deposition (C-MNP) to be applied as catalysts to the reduction of bromate in water. The use of magnetic nanoparticles coated with carbon enables catalyst recovery via magnetic separation and takes advantage of the catalytic properties of the carbon materials. The iron particles proved to be the most promising catalysts for the reduction of bromate into bromide, the highest removal being obtained with the CFeO@CVD750 sample, resulting in a 99% conversion after 120 min of reaction under the conditions tested. Due to its magnetic nature, the catalytic material was easily removed after the reaction and applied in four consecutive cycles without losing its catalytic properties. These results highlight the great potential of carbon-coated magnetic nanoparticles for reducing bromate in water.

Automatic Feature Construction-Based Genetic Programming for Degraded Image Classification

Accurately classifying degraded images is a challenging task that relies on domain expertise to devise effective image processing techniques for various levels of degradation. Genetic Programming (GP) has been proven to be an excellent approach for solving image classification tasks. However, the program structures designed in current GP-based methods are not effective in classifying images with quality degradation. During the iterative process of GP algorithms, the high similarity between individuals often results in convergence to local optima, hindering the discovery of the best solutions. Moreover, the varied degrees of image quality degradation often lead to overfitting in the solutions derived by GP. Therefore, this research introduces an innovative program structure, distinct from the traditional program structure, which automates the creation of new features by transmitting information learned across multiple nodes, thus improving GP individual ability in constructing discriminative features. An accompanying evolution strategy addresses high similarity among GP individuals by retaining promising ones, thereby refining the algorithm’s development of more effective GP solutions. To counter the potential overfitting issue of the best GP individual, a multi-generational individual ensemble strategy is proposed, focusing on constructing an ensemble GP individual with an enhanced generalization capability. The new method evaluates performance in original, blurry, low contrast, noisy, and occlusion scenarios for six different types of datasets. It compares with a multitude of effective methods. The results show that the new method achieves better classification performance on degraded images compared with the comparative methods.

The Effectiveness of Group Guidance Services Using Simulation Techniques to Improve Student Interpersonal Communication

Background: Communication requires a reciprocal relationship between the delivery of the message and the recipient. In the family, school and social environment, individuals really need communication to support their daily activities.
 Purpose: This study aims to test the effectiveness of simulation technique group guidance to improve interpersonal communication of grade VII students of Mataram Kasihan Bantul Junior High School for the 2021/2022 Academic Year.
 Method: This research use a pre-experimental type quantitative experiment method with a one group pretest-posttest design. The data collection method used a questionnaire with a population of 99 students in class VII B, VII C and VII E. While the sample in this study took 12 students from the population using purposive sampling techniques, and the data analysis technique of this study used paired simple t-test analysis.
 Results: Based on the results of the paired simple t-test analysis, the average pretest result = 72.00 and the average posttest result = 89.83, which means that there is an average increase of 17.83. While the value of t = 24.908 with a sig value. (2 tailed) = 0.000 which means < 0.05. Thus Ha is accepted, meaning that there is a positive and significant influence so that the provision of group guidance services for effective discussion techniques to improve interpersonal communication of grade VII students at Mataram Kasihan Bantul Junior High School for the 2021/2022 Academic Year.
 Conclusion: The results of this study indicate that the provision of group guidance services using simulation techniques is effective for improving interpersonal communication for class VII students of Mataram Kasihan Middle School, Bantul, 2021/2022 Academic Year. Based on the results of the t-test analysis, the alternative hypothesis (Ha) is accepted, meaning that there is a positive and significant influence regarding the provision of group guidance services using simulation techniques to improve interpersonal communication for class VII students of SMP Mataram Kasihan Bantul Academic Year 2021/2022.

Recurrences of advanced sessile and lateral spreading colorectal adenoma after endoscopic mucosal resection (EMR) thermal ablation versus no adjuvant therapy (RESPECT): a protocol of an international randomized controlled trial

BackgroundNowadays, large benign lateral spreading lesions (LSLs) and sessile polyps in the colorectum are mostly resected by endoscopic mucosal resection (EMR). A major drawback of EMR is the polyp recurrence rate of up to 20%. Snare tip soft coagulation (STSC) is considered an effective technique to reduce recurrence rates. However, clinical trials on STSC have mainly been conducted in expert referral centers. In these studies, polyp recurrence was assessed optically, and additional adjunctive techniques were excluded. In the current trial, we will evaluate the efficacy and safety of STSC in daily practice, by allowing adjunctive techniques during EMR and the use of both optical and histological polyp recurrence to assess recurrences during follow-up.MethodsThe RESPECT study is a multicenter, parallel-group, international single blinded randomized controlled superiority trial performed in the Netherlands and Germany. A total of 306 patients undergoing piecemeal EMR for LSLs or sessile colorectal polyps sized 20–60 mm will be randomized during the procedure after endoscopic complete polyp resection to the intervention or control group. Post-EMR defects allocated to the intervention group will be treated with thermal ablation with STSC of the entire resection margin. Primary outcome will be polyp recurrence by optical and histological confirmation at the first surveillance colonoscopy after 6 months. Secondary outcomes include technical success and complication rates.DiscussionThe RESPECT study will evaluate if STSC is effective in reducing recurrence rates after piecemeal EMR of large colorectal lesions in daily clinical practice performed by expert and non-expert endoscopists. Moreover, endoscopists will be allowed to use adjunctive techniques to remove remaining adenomatous tissue during the procedure. Finally, adenomatous polyp recurrence during follow-up will be defined by histologic identification.Trial registrationClinicalTrials.gov NCT05121805. Registered on 16 November 2021. Start recruitment: 17 March 2022. Planned completion of recruitment: 31 April 2025.

Teaching Mathematics with Creativity

This article explores and reviews the literature about the latest research-based teaching and learning techniques or strategies that are used by passionate and enthusiastic classroom teachers, often for the purpose of enlightening, firing up, or illuminating classroom lessons to grab students’ immediate attention, curiosity, and overall interest in the learning process. The question used to guide the exploration of the existing literature is: How do effective classroom teachers manage to always create a conducive, welcoming, and exciting rather than depressive learning environment? Participants’ engagement, observation and informal-oral interviews (or conversations) were the methodologies employed for this review study. While the current research output is often forward-looking, that is, it is too busy moving forward as it comes up daily with suggested new ways of teaching and learning, almost nothing or little attention is given to what is already available. This means there is an implication that a valuable knowledge already discovered could remain largely un-utilized, unused knowledge domain which can be put into practice in classroom settings. Hence, this qualitative, cumulative, and narrative review article highlights some of the latest, creative, and effective teaching techniques that the authors explored and found impressive. These are effective strategies that might be overlooked, overshadowed, or blurred; but they are effective techniques that can be utilized by the most passionate and enthusiastic classroom teachers for the purpose of making mathematics attractive. It is therefore recommended that the classroom teachers be allowed certain decree of freedom to experiment with new ways of teaching.

Single-Position Prone Lateral Lumbar Interbody Fusion Technique Guide: Surgical Tips and Tricks.

Lateral lumbar interbody fusion (LLIF) is a popular technique as it allows for the placement of a large interbody implant through a retroperitoneal, transpsoas working corridor. Historically, the interbody is placed with the patient in lateral decubitus and then repositioned to prone for the posterior instrumentation. While this has been an effective and successful technique, removing the interoperative flip would improve the efficiency of these cases. This has led to modified LLIF approaches including single-position prone LLIF (pLLIF). This modification has shown to be an efficient and powerful technique; however, learning to navigate the LLIF approach in the prone position has its own challenges. The purpose of this article is to provide a detailed description of our pLLIF technique while simultaneously introducing surgical tips to overcome the challenges of the approach and optimize the implantation of the interbody device.

Deep investigation of muography in discovering geological structures in mineral exploration: a case study of Zaozigou gold mine

Summary Muography is a promising and rapidly developing physical prospecting technique based on the attenuation of muon flux. The feasibility and potential of applying muography to mining were presented in a small number of previous case studies. In this work, we aimed to address three challenges that limit the applicability and efficiency of muography in mineral exploration: (1) application to low-density-contrast ore body exploration, (2) analysis of altitudinal impacts on measurements, and (3) precise and efficient reconstruction. We conducted the first case of applying muography to the exploration for low-density-contrast ore bodies. Six muon imaging systems were placed underground to collect surviving muons for roughly half a year. We analyzed the altitudinal impact on the data measurements and proposed a simplified method to eliminate it. We also developed the seed algorithm, a novel three-dimensional reconstruction algorithm, that can reconstruct anomalies located far away from the detectors and avoid their elongation along the observed directions. Benefiting from the seed algorithm, a low-density-contrast orebody and a limonitic siliceous slate structure were reconstructed, demonstrating the sensitivity of this technique in density distinction; discoveries of several mined-out areas are important for accident avoidance; and reconstruction of the stope and scarps served as strong circumstantial evidence of the reliability of the result. The success of this experiment shows the great value of muography in the economic, research and safety aspects of mineral exploration and inspection. Moreover, the overcoming of challenges is helpful for the development of muography, making it an effective and competitive technique in this field.

Advances in Cancer Research: Current and Future Diagnostic and Therapeutic Strategies.

Cancers of unknown primary (CUP) exhibit significant cellular heterogeneity and malignancy, which poses significant challenges for diagnosis and treatment. Recent years have seen deeper insights into the imaging, pathology, and genetic characteristics of CUP, driven by interdisciplinary collaboration and the evolution of diagnostic and therapeutic strategies. However, due to their insidious onset, lack of evidence-based medicine, and limited clinical understanding, diagnosing and treating CUP remain a significant challenge. To inspire more creative and fantastic research, herein, we report and highlight recent advances in the diagnosis and therapeutic strategies of CUP. Specifically, we discuss advanced diagnostic technologies, including 12-deoxy-2-[fluorine-18]fluoro-D-glucose integrated with computed tomography (18F-FDG PET/CT) or 68Ga-FAPI (fibroblast activation protein inhibitor) PET/CT, liquid biopsy, molecular diagnostics, self-assembling nanotechnology, and artificial intelligence (AI). In particular, the discussion will extend to the effective treatment techniques currently available, such as targeted therapies, immunotherapies, and bio-nanotechnology-based therapeutics. Finally, a novel perspective on the challenges and directions for future CUP diagnostic and therapeutic strategies is discussed.

Post-Traumatic Renal Artery Pseudoaneurysm Managed Successfully with Angioembolization

Renal artery pseudoaneurysm is a rare complication of blunt renal trauma. Super-selective angioembolization is aminimally invasive, effective procedure to manage traumatic renal artery pseudoaneurysm, in patients who are hemodynamicallystable. We report our experience in the management of patients with post-traumatic bleeding pseudoaneurysm withangioembolization. Materials and methods: We retrospectively reviewed the inpatient and outpatient records and imagingdata of our hospital, of all patients with history of blunt injury to kidneys, admitted with massive hematuria and underwentangioembolization to control the bleeding. Results: During the study period, 3 patients (all male) with a mean age of 26.66 yearspresented to us with blunt injury abdomen. Contrast computed tomography (CT) revealed Grade IV renal injury in 2 patientsand Grade V injury in 1 patient. Focal intensively enhancing areas were noted at the site of renal parenchymal lacerationssuggestive of pseudoaneurysms. Super-selective catheterization was performed and embolization done using 50% N-butyl-2-cyanoacrylate (NBCA) + Lipiodol. Conclusions: Angioembolization, a radiographic intervention, is an effective and minimallyinvasive technique to stop active bleeding from renal artery pseudoaneurysms.

Dynamic S-Box Construction Using Mordell Elliptic Curves over Galois Field and Its Applications in Image Encryption

Elliptic curve cryptography has gained attention due to its strong resilience against current cryptanalysis methods. Inspired by the increasing demand for reliable and secure cryptographic methods, our research investigates the relationship between complex mathematical structures and image encryption. A substitution box (S-box) is the single non-linear component of several well-known security systems. Mordell elliptic curves are used because of their special characteristics and the immense computational capacity of Galois fields. These S-boxes are dynamic, which adds a layer of complexity that raises the encryption process’s security considerably. We suggest an effective technique for creating S-boxes based on a class of elliptic curves over GF(2n),n≥8. We demonstrate our approach’s robustness against a range of cryptographic threats through thorough examination, highlighting its practical applicability. The assessment of resistance of the newly generated S-box to common attack methods including linear, differential, and algebraic attacks involves a thorough analysis. This analysis is conducted by quantifying various metrics such as non-linearity, linear approximation, strict avalanche, bit independence, and differential approximation to gauge the S-box’s robustness against these attacks. A recommended method for image encryption involves the use of built-in S-boxes to quickly perform pixel replacement and shuffling. To evaluate the efficiency of the proposed strategy, we employed various tests. The research holds relevance as it can provide alternative guidelines for image encryption, which could have wider consequences for the area of cryptography as a whole. We believe that our findings will contribute to the development of secure communication and data protection, as digital security is becoming increasingly important.

Fluorescence microscopy shadow imaging for neuroscience.

Fluorescence microscopy remains one of the single most widely applied experimental approaches in neuroscience and beyond and is continuously evolving to make it easier and more versatile. The success of the approach is based on synergistic developments in imaging technologies and fluorophore labeling strategies that have allowed it to greatly diversify and be used across preparations for addressing structure as well as function. Yet, while targeted labeling strategies are a key strength of fluorescence microscopy, they reciprocally impose general limitations on the possible types of experiments and analyses. One recent development that overcomes some of these limitations is fluorescence microscopy shadow imaging, where membrane-bound cellular structures remain unlabeled while the surrounding extracellular space is made to fluoresce to provide a negative contrast shadow image. When based on super-resolution STED microscopy, the technique in effect provides a positive image of the extracellular space geometry and entire neuropil in the field of view. Other noteworthy advantages include the near elimination of the adverse effects of photobleaching and toxicity in live imaging, exhaustive and homogeneous labeling across the preparation, and the ability to apply and adjust the label intensity on the fly. Shadow imaging is gaining popularity and has been applied on its own or combined with conventional positive labeling to visualize cells and synaptic proteins in their parenchymal context. Here, we highlight the inherent limitations of fluorescence microscopy and conventional labeling and contrast these against the pros and cons of recent shadow imaging approaches. Our aim is to describe the brief history and current trajectory of the shadow imaging technique in the neuroscience field, and to draw attention to its ease of application and versatility.

Coastal groundwater quality prediction using objective-weighted WQI and machine learning approach.

The water quality index (WQI) is a globally accepted guideline to indicate the water quality standard of any groundwater resource. Water levels in existing groundwater sources are declining in several coastal zones. Therefore, for monitoring water quality and improving water management, the prediction and identification of groundwater status by an effective technique with higher accuracy is urgently needed. Therefore, this research aims to find an effective model for WQI prediction by comparing entropy and critic weight-based WQI (ENW-WQI and CRITIC-WQI) with multi-layer perceptron artificial neural network (MLP-ANN) technique and also to identify contaminated zones using GIS. Initially, 1000 water sampling datasets with concentrations of several water quality parameters of different coastal blocks of eastern India during 2018 to 2022 are considered for the estimation of ENW-WQI and CRITIC-WQI. It shows 65% and 67% of the samples are excellent to good for drinking. ENW-WQI and CRITIC-WQI-based MLP-ANN models have been established considering different data portioning and hidden neuron numbers. Input variables and appropriate dataset partitioning with hidden neurons for models obtained from correlation and trial-error analysis. Spatial distribution maps are also produced for calculated WQIs using inverse distance weighted interpolation approaches. Three fitting models are obtained: ENW-WQI-MLP-ANN, CRITIC-WQI-MLP-ANN-I and CRITIC-WQI-MLP-ANN-II. CRITIC-WQI-MLP-ANN-II model (data ratio 85:15, network structure 6-12-1, R2 = 0.986, NSE = 0.98, and error rate 0.49%) provides the best accuracy in WQI prediction. The GIS-based WQI maps record several areas related to drinking water quality. The results of this research can help in planning the provision of safe drinking water in the future.

Stacked neural nets for increased accuracy on classification on lung cancer

Lung cancer is regarded as one of the most lethal diseases endangering human survival. It is difficult to detect lung cancer in its early stages, because of the ambiguity in the lung regions in the medical images. Healthcare business is automating itself with the use of image recognition and data analytics, much as the computing sector has completely automated. This article proposes a novel architecture, the Stacked Neural Network (SNN), for the detection and classification of lung cancer using CT scan data. The goal of the proposed technique is to investigate the accuracy levels of different Neural Networks (NN) and determine the early stage of lung cancer. The most effective technique for processing medical images, classifying and detecting lung nodules, extracting features, and predicting the stage of lung cancer is deep learning. First, lung areas are extracted using image processing techniques. SNN is used for the segmentation process. Various neural network techniques are utilised for the classification process once the features are retrieved from the segmented pictures. The suggested methods' performances are assessed using F1-Measure, accuracy, precision, and recall metrics. 96% classification accuracy is shown in the testing findings, which is comparatively greater than other methods currently in use. Proposed algorithm is clearly supported for real-world clinical practice.

Highly Efficient Inverted Light-Emitting Diodes Based on Vertically Aligned CdSe/CdS Nanorod Layers Fabricated by Electrophoretic Deposition.

Inverted colloidal-nanocrystal-based LEDs (NC-LEDs) are highly interesting and invaluable for large-scale display technology and flexible electronics. Semiconductor nanorods (NRs), in addition to the tunable wavelengths of the emitted light (achieved, for example, by the variation of the NR diameter or the diameter of core in a core-shell configuration), also exhibit linearly polarized emission, a larger Stokes shift, faster radiative decay, and slower bleaching kinetics than quantum dots (QDs). Despite these advantages, it is difficult to achieve void-free active NR layers using simple spin-coating techniques. Herein, we employ electrophoretic deposition (EPD) to make closely packed, vertically aligned CdSe/CdS core/shell nanorods (NRs) as the emissive layer. Following an inverted architecture, the device fabricated yields an external quantum efficiency (EQE) of 6.3% and a maximum luminance of 4320 cd/m2 at 11 V. This good performance can be attributed to the vertically aligned NR layer, enhancing the charge transport by reducing the resistance of carrier passage, which is supported by our finite element simulations. To the best of our knowledge, this is the first time vertically aligned NR layers made by EPD have been reported for the fabrication of NC-LEDs and the device performance is one of the best for inverted red NR-LEDs. The findings presented in this work bring forth a simple and effective technique for making vertically aligned NRs, and the mechanism behind the NR-LED device with enhanced performance using these NRs is illustrated. This technique may prove useful to the development of a vast class of nanocrystal-based optoelectronics, including solar cells and laser devices.

Dual view deep learning for enhanced breast cancer screening using mammography

Breast cancer has the highest incidence rate among women in Ethiopia compared to other types of cancer. Unfortunately, many cases are detected at a stage where a cure is delayed or not possible. To address this issue, mammography-based screening is widely accepted as an effective technique for early detection. However, the interpretation of mammography images requires experienced radiologists in breast imaging, a resource that is limited in Ethiopia. In this research, we have developed a model to assist radiologists in mass screening for breast abnormalities and prioritizing patients. Our approach combines an ensemble of EfficientNet-based classifiers with YOLOv5, a suspicious mass detection method, to identify abnormalities. The inclusion of YOLOv5 detection is crucial in providing explanations for classifier predictions and improving sensitivity, particularly when the classifier fails to detect abnormalities. To further enhance the screening process, we have also incorporated an abnormality detection model. The classifier model achieves an F1-score of 0.87 and a sensitivity of 0.82. With the addition of suspicious mass detection, sensitivity increases to 0.89, albeit at the expense of a slightly lower F1-score of 0.79.