bluuebunny/crossref_metadata_2025 huggingface dataset:
Analyzing the Effect of Dynamic Pressure Drop on Corona Discharges for Aircraft Applications
Authors: Bas-Calopa Pau , Riba Jordi-Roger , Moreno-Eguilaz Manuel
Published: March 2023

The combination of the low-pressure environment found in aircraft systems and the gradual electrification of aircraft increases the risk of electrical discharges occurrence. This is an undesirable situation that compromises aircraft safety and complicates maintenance operations. Experimental data are needed to understand this problem. However, most of the published studies are based on static pressure conditions, but aircraft systems are exposed to dynamic pressure conditions, especially during the climb and descent phases of flight. This paper analyzes the effect of dynamic pressure during the climb phase on the corona inception voltage because this phase experiences the worst pressure drop rate. The experimental evidence presented in this paper shows that within the analyzed pressure drop rate range, the dynamic pressure conditions do not have a significant effect on the corona inception value under typical conditions found in aircraft systems during the climb phase.
Weed Competition on Soybean Varieties from Different Relative Maturity Groups
Authors: Caldas João Victor dos Santos , Silva Alessandro Guerra da , Braz Guilherme Braga Pereira , Procópio Sergio de Oliveira , Teixeira Itamar Rosa , Souza Matheus de Freitas , Reginaldo Laís Tereza Rêgo Torquato
Published: March 2023

One factor limiting the achievement of high yields in the soybean crop is weed interference. The level of weed interference can vary according to the specificities of the weed community but also due to the agronomic characteristics of the soybean varieties. The objective of this work was to evaluate the effects of weed interference on soybean varieties of different relative maturity groups (RMG). A field experiment was implemented in a randomized complete block design, in a 3 × 4 factorial scheme, with five replications. The treatments were composed of the varieties BMX Flecha® (RMG 6.6), BMX Power® (RMG 7.3), and BMX Bônus® (RMG 7.9), associated with the following four weed managements: weeding throughout the cycle; weeded up to 20 days after emergence (DAE); weeded after 20 DAE until the end of soybean cycle; not weeded throughout the entire cycle. There was no interaction between the effects of the varieties and the weed management for emergence speed index, plant height, chlorophyll, first pod height insertion, plant population, thousand-grain weight and yield. The initial weed management caused changes in the composition of the weed community. The managements weeded throughout the cycle and weeded up until 20 DAE provided higher levels of chlorophyll and grain yield. The management without weeding during the entire cycle negatively influenced yield components. Late interventions in weed control, regardless of the soybean variety, result in yield losses.
Wavelet Scattering Convolution Network-Based Detection Algorithm on Nondestructive Microcrack Electrical Signals of Eggs
Authors: Shi Chenbo , Cheng Yanhong , Zhang Chun , Yuan Jin , Wang Yuxin , Jiang Xin , Zhu Changsheng
Published: March 2023

The detection of poultry egg microcracks based on electrical characteristic models is a new and effective method. However, due to the disorder, mutation, nonlinear, time discontinuity, and other factors of the current data, detection algorithms such as support-vector machines (SVM) and random forest (RF) under traditional statistical characteristics cannot identify subtle defects. The detection system voltage is set to 1500 V in the existing method, and higher voltages may cause damage to the hatched eggs; therefore, how to reduce the voltage is also a focus of research. In this paper, to address the problem of the low signal-to-noise ratio of microcracks in current signals, a wavelet scattering transform capable of extracting translation-invariant and small deformation-stable features is proposed to extract multi-scale high-frequency feature vectors. In view of the time series and low feature scale of current signals, various convolutional networks, such as a one-dimensional convolutional neural network (1DCNN), long short-term memory (LSTM), bi-directional long short-term memory (Bi-LSTM), and gated recurrent unit (GRU) are adopted. The detection algorithm of the wavelet scattering convolutional network is implemented for electrical sensing signals. The experimental results show that compared with previous works, the accuracy, precision, recall, F1-score, and Matthews correlation coefficient of the proposed wavelet scattering convolutional network on microcrack datasets smaller than 3 μm at a voltage of 1000 V are 99.4393%, 99.2523%, 99.6226%, 99.4357%, and 98.8819%, respectively, with an average increase of 2.0561%. In addition, the promotability and validity of the proposed detection algorithm were verified on a class-imbalanced dataset and a duck egg dataset. Based on the good results of the above experiments, further experiments were conducted with different voltages. The new feature extraction and detection method reduces the sensing voltage from 1500 V to 500 V, which allows for achieving higher detection accuracy with a lower signal-to-noise ratio, significantly reducing the risk of high voltage damage to hatching eggs and meeting the requirements for crack detection.
The Journey of 1000 Leagues towards the Decontamination of the Soil from Heavy Metals and the Impact on the Soil–Plant–Animal–Human Chain Begins with the First Step: Phytostabilization/Phytoextraction
Authors: Hegedus Cristina , Pașcalău Simona-Nicoleta , Andronie Luisa , Rotaru Ancuţa-Simona , Cucu Alexandra-Antonia , Dezmirean Daniel Severus
Published: March 2023

Nowadays, there are a multitude of sources of heavy metal pollution which have unwanted effects on this super organism, the soil, which is capable of self-regulation, but limited. Living a healthy life through the consumption of fruits and vegetables, mushrooms, edible products and by-products of animal origin, honey and bee products can sometimes turn out to be just a myth due to the contamination of the soil with heavy metals whose values, even if they are below accepted limits, are taken up by plants, reach the food chain and in the long term unbalance the homeostasis of the human organism. Plants, these miracles of nature, some with the natural ability to grow on polluted soils, others needing a little help by adding chelators or amendments, can participate in the soil detoxification of heavy metals through phytoextraction and phytostabilization. The success of soil decontamination must take into account the collaboration of earth sciences, pedology, pedochemistry, plant physiology, climatology, the characteristics of heavy metals and how they are absorbed in plants, and in addition how to avoid the contamination of other systems, water or air. The present work materialized after extensive bibliographic study in which the results obtained by the cited authors were compiled.
Deep Learning for Laying Hen Activity Recognition Using Wearable Sensors
Authors: Shahbazi Mohammad , Mohammadi Kamyar , Derakhshani Sayed M. , Groot Koerkamp Peter W. G.
Published: March 2023

Laying hen activities in modern intensive housing systems can dramatically influence the policies needed for the optimal management of such systems. Intermittent monitoring of different behaviors during daytime cannot provide a good overview, since daily behaviors are not equally distributed over the day. This paper investigates the application of deep learning technology in the automatic recognition of laying hen behaviors equipped with body-worn inertial measurement unit (IMU) modules in poultry systems. Motivated by the human activity recognition literature, a sophisticated preprocessing method is tailored on the time-series data of IMU, transforming it into the form of so-called activity images to be recognized by the deep learning models. The diverse range of behaviors a laying hen can exhibit are categorized into three classes: low-, medium-, and high-intensity activities, and various recognition models are trained to recognize these behaviors in real-time. Several ablation studies are conducted to assess the efficacy and robustness of the developed models against variations and limitations common for an in situ practical implementation. Overall, the best trained model on the full-feature acquired data achieves a mean accuracy of almost 100%, where the whole process of inference by the model takes less than 30 milliseconds. The results suggest that the application of deep learning technology for activity recognition of individual hens has the potential to accurately aid successful management of modern poultry systems.
Pesticide and Adjuvant Mixture Impacts on the Physical–Chemical Properties, Droplet Spectrum, and Absorption of Spray Applied in Soybean Crop
Authors: Tavares Rafael Marcão , Cunha João Paulo Arantes Rodrigues da
Published: March 2023

Tank mixing of pesticides, a common practice in many countries, when performed incorrectly, can negatively impact the effectiveness of the pesticides. This study aimed to investigate the physical–chemical properties, droplet spectrum, and absorption by soybean plants of mixtures of the azoxystrobin fungicide with glyphosate herbicide and different adjuvants (mineral oil, propionic acid, and orange oil). The study design was completely randomized, with five treatments (T): T1, only fungicide; T2, fungicide + glyphosate; T3, fungicide + mineral oil; T4, fungicide + propionic acid; and T5, fungicide + orange oil. The spray concentrations simulated an application rate of 160 L ha−1 using the TT110015 nozzle. The physical stability, hydrogen ion potential, electrical conductivity, surface tension, droplet spectrum, and azoxystrobin absorption in soybean plants were evaluated. To measure absorption, soybean plants were sprayed at the reproductive growth stage, and leaf samples were collected after 0, 2, and 48 h. The amount of azoxystrobin absorbed was determined using gas chromatography. The results indicated that the spray mixtures were physically compatible. All mixtures produced a medium droplet spectrum. T2 had the lowest absorption percentages, suggesting that these pesticides should not be mixed. Adjuvants increased the amount of azoxystrobin absorbed by the plants, and it took 2 h on average for the soybean leaves to absorb 72.58% of the applied spray.
Application of Machine Learning for Insect Monitoring in Grain Facilities
Authors: Mendoza Querriel Arvy , Pordesimo Lester , Neilsen Mitchell , Armstrong Paul , Campbell James , Mendoza Princess Tiffany
Published: March 2023

In this study, a basic insect detection system consisting of a manual-focus camera, a Jetson Nano—a low-cost, low-power single-board computer, and a trained deep learning model was developed. The model was validated through a live visual feed. Detecting, classifying, and monitoring insect pests in a grain storage or food facility in real time is vital to making insect control decisions. The camera captures the image of the insect and passes it to a Jetson Nano for processing. The Jetson Nano runs a trained deep-learning model to detect the presence and species of insects. With three different lighting situations: white LED light, yellow LED light, and no lighting condition, the detection results are displayed on a monitor. Validating using F1 scores and comparing the accuracy based on light sources, the system was tested with a variety of stored grain insect pests and was able to detect and classify adult cigarette beetles and warehouse beetles with acceptable accuracy. The results demonstrate that the system is an effective and affordable automated solution to insect detection. Such an automated insect detection system can help reduce pest control costs and save producers time and energy while safeguarding the quality of stored products.
Assessment of Cystamine’s Radioprotective/Antioxidant Ability under High-Dose-Rate Irradiation: A Monte Carlo Multi-Track Chemistry Simulation Study
Authors: Penabeï Samafou , Meesungnoen Jintana , Jay-Gerin Jean-Paul
Published: March 2023

(1) Background: cystamine and its reduced form cysteamine have radioprotective/antioxidant effects in vivo. In this study, we use an in vitro model system to examine the behavior of cystamine towards the reactive primary species produced during the radiolysis of the Fricke dosimeter under high dose-rate irradiation conditions. (2) Methods: our approach was to use the familiar radiolytic oxidation of ferrous to ferric ions as an indicator of the radioprotective/antioxidant capacity of cystamine. A Monte Carlo computer code was used to simulate the multi-track radiation-induced chemistry of aerated and deaerated Fricke-cystamine solutions as a function of dose rate while covering a large range of cystamine concentrations. (3) Results: our simulations revealed that cystamine provides better protection at pulsed dose rates compared to conventional, low-dose-rate irradiations. Furthermore, our simulations confirmed the radical-capturing ability of cystamine, clearly indicating the strong antioxidant profile of this compound. (4) Conclusion: assuming that these findings can be transposable to cells and tissues at physiological pH, it is suggested that combining cystamine with FLASH-RT could be a promising approach to further enhance the therapeutic ratio of cancer cure.
Named Entity Recognition Networks Based on Syntactically Constrained Attention
Authors: Sun Weiwei , Liu Shengquan , Liu Yan , Kong Lingqi , Jian Zhaorui
Published: March 2023

The task of named entity recognition can be transformed into a machine reading comprehension task by associating the query and its context, which contains entity information, with the encoding layer. In this process, the model learns a priori knowledge about the entity, from the query, to achieve good results. However, as the length of the context and query increases, the model struggles with an increasing number of less relevant words, which can distract it from the task. Although attention mechanisms can help the model understand contextual semantic relations, without explicit constraint information, attention may be allocated to less task-relevant words, leading to a bias in the model’s understanding of the context. To address this problem, we propose a new model, the syntactic constraint-based dual-context aggregation network, which uses syntactic information to guide query and context modeling. By incorporating syntactic constraint information into the attention mechanism, the model can better determine the relevance of each word in the context of the task, and selectively focus on the relevant parts of the context. This enhances the model’s ability to read and understand the context, ultimately improving its performance in named entity recognition tasks. Extensive experiments on three datasets, ACE2004, ACE2005, and GENIA, show that this method achieves superior performance when compared to previous methods.
Regional Remote Sensing of Lake Water Transparency Based on Google Earth Engine: Performance of Empirical Algorithm and Machine Learning
Authors: Zeng Weizhong , Xu Ke , Cheng Sihang , Zhao Lei , Yang Kun
Published: March 2023

Secchi depth (SD) is a valuable and feasible water quality indicator of lake eutrophication. The establishment of an automated system with efficient image processing and an algorithm suitable for the inversion of transparency in lake-rich regions could provide sufficient temporal and spatial information for lake management. These are especially critical for lake-rich regions where in situ monitoring data are scarce. This study demonstrated the implementation of an atmospheric correction algorithm (ACOLITE algorithm) in conjunction with the Google Earth Engine platform to generate remote-sensing reflectance products of specific points efficiently. The study also evaluated the performance of an algorithm for inverting lake SDs in Yunnan Plateau lakes, which is one of the five lake districts in China, since there is a lack of in situ data for most of the lakes in the region. The in situ data from four lakes with large SD ranges and imagery from Landsat Operational Land Imager were used to train and evaluate the performance of two algorithms: an empirical algorithm (stepwise regression) and machine learning (support vector machines and multi-layer perception). The results revealed that the retrieval accuracy of models with bands and band ratio combinations could be substantially improved compared with models with a single band or band combinations. A negative correlation was also observed between the temporal match between observations and the model accuracy. This study found that the MLP model with sufficient training data was more suitable for transparency estimation of lakes belonging to the dataset; the SVM model was more suitable for transparency prediction outside the training set, regardless of the adequacy of the training data. This study provides a reference for monitoring lakes within the Yunnan region using remote sensing.
Quantum 3D FFT in Tomography
Authors: Koukiou Georgia , Anastassopoulos Vassilis
Published: March 2023

The Radon transform constitutes the conventional tool for tomosynthesis, i.e., the composition of cross-sections of an object from its projections. It is actually a version of the Fourier Transform, which is accompanied by the appropriate digital high pass filters for correct distribution of energy among the reconstructed frequency components. The Radon transform and its inverse are employed in their 2D and 3D versions, respectively, and the whole procedure is verified by the a priori known cross-sections to be reconstructed (known fandom). Usually, 3D medical image cubes, which are to be reconstructed, require powerful computational tools since the 2D projections are of high-resolution containing millions of pixels. Although the 3D FFT is very fast, the large number of projections will result in a 3D spectrum of very large dimensions. Inverting this spectrum with the inverse 3D FFT is extremely time consuming. In this work, the implementation of the 2D Radon transform using the 2D Quantum Fourier Transform is analytically presented. Simultaneously, its inverse version is realized by means of the Quantum inverse 3D FFT. For this purpose, a review of the necessary quantum computational units is presented for the implementation of the quantum 3D FFT and simultaneously simple examples of tomosynthesis are given by means of the quantum version of the 2D Radon transform and its inverse 3D counterpart. The whole procedure of the quantum tomosynthesis is analytically described.
An Advanced Artificial Fish School Algorithm to Update Decision Tree for NLOS Acoustic Localization Signal Identification with the Dual-Receiving Method
Authors: Kan Ruixiang , Wang Mei , Liu Xin , Liu Xiaojuan , Qiu Hongbing
Published: March 2023

For indoor sensor systems, it is essential to implement an extra supporting area notification part. To inform the real-time coordinates, the time difference of arrival (TDOA) algorithm can be introduced. For these indoor localization systems, their main processes are often built based on the line of sight (LOS) scenario. However, obstacles make the off-the-shelf localization system unable to play its due role in the flexible non-line of sight (NLOS) scenario. So, it is necessary to adjust the signals according to the NLOS identification results. However, the NLOS identification methods before were not effective enough. To address these challenges, on the one hand, this paper proposes an adaptive strategy for a dual-receiving signal processing method. On the other hand, the system is matched with the homologous NLOS identification method based on a novel artificial fish school algorithm (AFSA) and the decision tree model. According to our experiments, our novel AFSA optimization method can obtain a better effect and take less time. The NLOS acoustic signal identification accuracy can be improved significantly in flexible scenarios compared with other methods. Based on these processes, the system will achieve more accurate localization results in flexible NLOS situations.
A Flight Parameter-Based Aircraft Structural Load Monitoring Method Using a Genetic Algorithm Enhanced Extreme Learning Machine
Authors: Zhang Yanjun , Cao Shancheng , Wang Bintuan , Yin Zhiping
Published: March 2023

High-precision operational flight loads are essential for monitoring fatigue of individual aircraft and are usually determined by flight parameters. To tackle the nonlinear relationship between flight loads and flight parameters for more accurate prediction of flight loads, artificial neural networks have been widely studied. However, there are still two major problems, namely the training strategy and sensitivity analysis of the flight parameters. For the first problem, the gradient descent method is usually used, which is time-consuming and can easily converge to a local solution. To solve this problem, an extreme learning machine is proposed to determine the weights based on a Moore–Penrose generalized inverse. Moreover, a genetic algorithm method is proposed to optimize the weights between the input and hidden layers. For the second problem, a mean impact value (MIV) method is proposed to measure the sensitivity of the flight parameters, and the neuron number in the hidden layer is also optimized. Finally, based on the measured dataset of an aircraft, the proposed flight load prediction method is verified to be effective and efficient. In addition, a comparison is made with some well-known neural networks to demonstrate the advantages of the proposed method.
Fuzzy Windows with Gaussian Processed Labels for Ordinal Image Scoring Tasks
Authors: Kang Cheng , Yao Xujing , Novak Daniel
Published: March 2023

In this paper, we propose a Fuzzy Window with the Gaussian Processed Label (FW-GPL) method to mitigate the overlap problem in the neighboring ordinal category when scoring images. Many published conventional methods treat this challenge as a traditional regression problem and make a strong assumption that each ordinal category owns an adequate intrinsic rank to outline its distribution. Our FW-GPL method aims to refine the ordinal label pattern by using two novel techniques: (1) assembling fuzzy logic to the fully connected layer of convolution neural networks and (2) transforming the ordinal labels with a Gaussian process. Specifically, it incorporates a heuristic fuzzy logic from the ordinal characteristic and simultaneously plugs in ordinal distribution shapes that penalize the difference between the targeted label and its neighbors to ensure a concentrated regional distribution. Accordingly, the function of these proposed windows is leveraged to minimize the influence of majority classes that mislead the prediction of minority samples. Our model is specifically designed to carefully avoid partially missing continuous facial-age segments. It can perform competitively when using the whole continuous facial-age dataset. Extensive experimental results on three facial-aging datasets and one ambiguous medical dataset demonstrate that our FW-GPL can achieve compelling performance results compared to the State-Of-The-Art (SOTA).
A Constrained Louvain Algorithm with a Novel Modularity
Authors: Yao Bibao , Zhu Junfang , Ma Peijie , Gao Kun , Ren Xuezao
Published: March 2023

Community detection is a significant and challenging task in network research. Nowadays, many community detection methods have been developed. Among them, the classical Louvain algorithm is an excellent method aiming at optimizing an objective function. In this paper, we propose a modularity function F2 as a new objective function. Our modularity function F2 overcomes certain disadvantages of the modularity functions raised in previous literature, such as the resolution limit problem. It is desired as a competitive objective function. Then, the constrained Louvain algorithm is proposed by adding some constraints to the classical Louvain algorithm. Finally, through the comparison, we have found that the constrained Louvain algorithm with F2 is better than the constrained Louvain algorithm with other objective functions on most considered networks. Moreover, the constrained Louvain algorithm with F2 is superior to the classical Louvain algorithm and the Newman’s fast method.
Numerical Investigation of the Relationship between Anastomosis Angle and Hemodynamics in Ridged Spiral Flow Bypass Grafts
Authors: Apan Jhon Jasper , Tayo Lemmuel , Honra Jaime
Published: March 2023

Bypass graft failures are linked to hemodynamic disturbances resulting from poor design. Several studies have tried to improve graft patency by modifying conventional graft designs. One strategy being employed is to induce spiral flow in bypass grafts using an internal ridge which has been proposed to optimize blood flow. However, there is still no study focusing on how the anastomosis angle can affect the hemodynamics of such a design despite its huge influence on local flow fields. To fill this gap, we aimed to understand and optimize the relationship between anastomosis angle and ridged spiral flow bypass graft hemodynamics to minimize disturbances and prolong graft patency. Steady-state, non-Newtonian computational fluid dynamics (CFD) analysis of a distal, end-to-side anastomosis between a ridged graft and idealized femoral artery was used to determine the anastomosis angle that would yield the least hemodynamic disturbances. Transient, pulsatile, non-Newtonian CFD analysis between a conventional and ridged graft at the optimal angle was performed to determine if such a design has an advantage over conventional designs. The results revealed that smaller anastomosis angles tend to optimize graft performance by the reduction in the pressure drop, recirculation, and areas in the host artery affected by abnormally high shear stresses. It was also confirmed that the modified design outperformed conventional bypass grafts due to the increased shear stress generated which is said to have atheroprotective benefits. The findings of the study may be taken into consideration in the design of bypass grafts to prevent their failure due to hemodynamic disturbances associated with conventional designs and highlight the importance of understanding and optimizing the relationship among different geometric properties in designing long-lasting bypass grafts.
Power Losses Investigation in Direct 3 × 5 Matrix Converter Using MATLAB Simulink
Authors: Praženica Michal , Kaščák Slavomír , Resutík Patrik
Published: March 2023

This article addressed the problem of matrix converters (MxC), specifically the investigation of power losses and matrix converter efficiency in a 3 × 5 arrangement. In today’s modern world, efficiency is very important; hence, power loss and efficiency analysis are important throughout the design process of modern semiconductor converters. The ability to evaluate power losses more quickly using the simulation approach can greatly reduce the amount of time necessary for the design, in comparison with numerical analysis. The described model employed contemporary SiC semiconductors, which offer substantial benefits over IGBT transistors. The 3 × 5 converter model was shown, along with a study of power losses in various elements of the converter, such as the power circuit, input filter, and so on. A summary of the simulated findings was offered at the end of the study, along with the benefits and drawbacks of employing SiC semiconductors in bidirectional switches for matrix converters.
Detecting COVID-19 Effectively with Transformers and CNN-Based Deep Learning Mechanisms
Authors: Umejiaku Afamefuna Promise , Dhakal Prastab , Sheng Victor S.
Published: March 2023

The COVID-19 pandemic has been a major global concern in the field of respiratory diseases, with healthcare institutions and partners investing significant resources to improve the detection and severity assessment of the virus. In an effort to further enhance the detection of COVID-19, researchers have investigated the performance of current detection methodologies and proposed new approaches that leverage deep learning techniques. In this article, the authors propose a two-step transformer model for the multi-class classification of COVID-19 images in a patient-aware manner. This model is implemented using transfer learning, which allows for the efficient use of pre-trained models to accelerate the training of the proposed model. The authors compare the performance of their proposed model to other CNN models commonly used in the detection of COVID-19. The experimental results of the study show that CNN-based deep learning networks obtained an accuracy in the range of 0.76–0.92. However, the proposed two-step transformer model implemented with transfer learning achieved a significantly higher accuracy of 0.9735 ± 0.0051. This result indicates that the proposed model is a promising approach to improving the detection of COVID-19. Overall, the findings of this study highlight the potential of deep learning techniques, particularly the use of transfer learning and transformer models, to enhance the detection of COVID-19. These approaches can help healthcare institutions and partners to reduce the time and difficulty in detecting the virus, ultimately leading to more effective and timely treatment for patients.
Pop/Poetry: Dickinson as Remix
Authors: Leyda Julia , Sulimma Maria
Published: March 2023

In its meticulous, freewheeling adaptation of the life and work of celebrated poet Emily Dickinson, the television series Dickinson (Apple TV+, 2019–2021) manifests a twenty-first-century disruption of high and low culture afforded by digital media, including streaming video and music platforms. This article argues that the fanciful series models a mixed-media, multimodal aesthetic form that invites a diverse range of viewers to find pleasure in Dickinson’s poetry itself and in the foibles of its author, regardless of their familiarity with the literary or cultural histories of the US American 19th century. Dickinson showcases creator Alena Smith’s well-researched knowledge of the poet and her work, while simultaneously mocking popular (mis)conceptions about her life and that of other literary figures such as Walt Whitman and Sylvia Plath, all set to a contemporary soundtrack. This analysis of Dickinson proposes to bring into conversation shifting boundaries of high and low culture across generations and engage with critical debates about the utility of the popular (and of studies of the popular) in literary and cultural studies in particular.
One-Stage Multiple Comparisons of the Mean Lifetimes of k Treatments with the Average for Exponential Distributions under Heteroscedasticity
Authors: Wu Shu-Fei
Published: March 2023

Making use of uniformly minimum-variance unbiased estimators for the parameters of two-parameter exponential distributions and the distribution of pivotal quantities, we propose one-stage multiple comparison procedures for k mean lifetimes with the average under heteroscedasticity. The multiple comparison procedures include one-sided and two-sided confidence intervals. These intervals can be applied to identify which treatment’s mean lifetime is better than the average or worse than the average in terms of the mean lifetimes of all treatments. Critical values are obtained in order to assure users that the given confidence coefficient has been reached; they are organized in table format for practical and convenient use. An example is provided to demonstrate the proposed techniques, wherein the mean survival times of four different lung cancer categories are compared with the average.
Deep Learning for Highly Accurate Hand Recognition Based on Yolov7 Model
Authors: Dewi Christine , Chen Abbott Po Shun , Christanto Henoch Juli
Published: March 2023

Hand detection is a key step in the pre-processing stage of many computer vision tasks because human hands are involved in the activity. Some examples of such tasks are hand posture estimation, hand gesture recognition, human activity analysis, and other tasks such as these. Human hands have a wide range of motion and change their appearance in a lot of different ways. This makes it hard to identify some hands in a crowded place, and some hands can move in a lot of different ways. In this investigation, we provide a concise analysis of CNN-based object recognition algorithms, more specifically, the Yolov7 and Yolov7x models with 100 and 200 epochs. This study explores a vast array of object detectors, some of which are used to locate hand recognition applications. Further, we train and test our proposed method on the Oxford Hand Dataset with the Yolov7 and Yolov7x models. Important statistics, such as the quantity of GFLOPS, the mean average precision (mAP), and the detection time, are tracked and monitored via performance metrics. The results of our research indicate that Yolov7x with 200 epochs during the training stage is the most stable approach when compared to other methods. It achieved 84.7% precision, 79.9% recall, and 86.1% mAP when it was being trained. In addition, Yolov7x accomplished the highest possible average mAP score, which was 86.3%, during the testing stage.
A Novel Fusion Protein System for the Production of Nanobodies and the SARS-CoV-2 Spike RBD in a Bacterial System
Authors: Nagy-Fazekas Dóra , Stráner Pál , Ecsédi Péter , Taricska Nóra , Borbély Adina , Nyitray László , Perczel András
Published: March 2023

Antibodies are key proteins of the immune system, and they are widely used for both research and theragnostic applications. Among them, camelid immunoglobulins (IgG) differ from the canonical human IgG molecules, as their light chains are completely missing; thus, they have only variable domains on their heavy chains (VHHs). A single VHH domain, often called a nanobody, has favorable structural, biophysical, and functional features compared to canonical antibodies. Therefore, robust and efficient production protocols relying on recombinant technologies are in high demand. Here, by utilizing ecotin, an Escherichia coli protein, as a fusion partner, we present a bacterial expression system that allows an easy, fast, and cost-effective way to prepare nanobodies. Ecotin was used here as a periplasmic translocator and a passive refolding chaperone, which allowed us to reach high-yield production of nanobodies. We also present a new, easily applicable prokaryotic expression and purification method of the receptor-binding domain (RBD) of the SARS-CoV-2 S protein for interaction assays. We demonstrate using ECD spectroscopy that the bacterially produced RBD is well-folded. The bacterially produced nanobody was shown to bind strongly to the recombinant RBD, with a Kd of 10 nM. The simple methods presented here could facilitate rapid interaction measurements in the event of the appearance of additional SARS-CoV-2 variants.
GSN-HVNET: A Lightweight, Multi-Task Deep Learning Framework for Nuclei Segmentation and Classification
Authors: Zhao Tengfei , Fu Chong , Tian Yunjia , Song Wei , Sham Chiu-Wing
Published: March 2023

Nuclei segmentation and classification are two basic and essential tasks in computer-aided diagnosis of digital pathology images, and those deep-learning-based methods have achieved significant success. Unfortunately, most of the existing studies accomplish the two tasks by splicing two related neural networks directly, resulting in repetitive computation efforts and a redundant-and-large neural network. Thus, this paper proposes a lightweight deep learning framework (GSN-HVNET) with an encoder–decoder structure for simultaneous segmentation and classification of nuclei. The decoder consists of three branches outputting the semantic segmentation of nuclei, the horizontal and vertical (HV) distances of nuclei pixels to their mass centers, and the class of each nucleus, respectively. The instance segmentation results are obtained by combing the outputs of the first and second branches. To reduce the computational cost and improve the network stability under small batch sizes, we propose two newly designed blocks, Residual-Ghost-SN (RGS) and Dense-Ghost-SN (DGS). Furthermore, considering the practical usage in pathological diagnosis, we redefine the classification principle of the CoNSeP dataset. Experimental results demonstrate that the proposed model outperforms other state-of-the-art models in terms of segmentation and classification accuracy by a significant margin while maintaining high computational efficiency.
Live to Die Another Day: Regeneration in Diopatra aciculata Knox and Cameron, 1971 (Annelida: Onuphidae) Collected as Bait in Knysna Estuary, South Africa
Authors: Schoeman Stephanie , Simon Carol A.
Published: March 2023

Regeneration is critical for survivorship after injury, sublethal predation, and asexual reproduction; it allows individuals to recover, potentially enabling populations of bait species to overcome the effects of bait collection through incidental asexual reproduction. Opportunities for regeneration are created when worms break during collection (which happens more often than not) and are thrown back into the estuary. Additionally, the trade and movement of bait could result in the range expansion of invasive species. This study investigated bait collection habits of local fishermen and the in situ incidence of regeneration in the estuarine moonshine worm, Diopatra aciculata. The evidence shows that this species is capable of anterior and posterior regeneration. The disproportionately small percentage of worms that seem to be recovering from the degree of damage that may be inflicted during bait collection suggests that regeneration may not help worms to withstand the effects of bait collection. However, the continuous movement and discarding of even small numbers of bait in other estuaries can lead to range expansion through incremental build-up, forming new populations, if these fragments are large enough to regenerate.
Modulation of Allosteric Control and Evolution of Hemoglobin
Authors: Brunori Maurizio , Miele Adriana Erica
Published: March 2023

Allostery arises when a ligand-induced change in shape of a binding site of a protein is coupled to a tertiary/quaternary conformational change with a consequent modulation of functional properties. The two-state allosteric model of Monod, Wyman and Changeux [J. Mol. Biol. 1965; 12, 88–118] is an elegant and effective theory to account for protein regulation and control. Tetrameric hemoglobin (Hb), the oxygen transporter of all vertebrates, has been for decades the ideal system to test for the validity of the MWC theory. The small ligands affecting Hb’s behavior (organic phosphates, protons, bicarbonate) are produced by the red blood cell during metabolism. By binding to specific sites, these messengers make Hb sensing the environment and reacting consequently. HbI and HbIV from trout and human HbA are classical cooperative models, being similar yet different. They share many fundamental features, starting with the globin fold and the quaternary assembly, and reversible cooperative O2 binding. Nevertheless, they differ in ligand affinity, binding of allosteric effectors, and stability of the quaternary assembly. Here, we recollect essential functional properties and correlate them to the tertiary and quaternary structures available in the protein databank to infer on the molecular basis of the evolution of oxygen transporters.
Identification of a Novel Protein-Based Prognostic Model in Gastric Cancers
Authors: Xiong Zhijuan , Xing Chutian , Zhang Ping , Diao Yunlian , Guang Chenxi , Ying Ying , Zhang Wei
Published: March 2023

Gastric cancer (GC) is the third leading cause of cancer-related deaths worldwide. However, there are still no reliable biomarkers for the prognosis of this disease. This study aims to construct a robust protein-based prognostic prediction model for GC patients. The protein expression data and clinical information of GC patients were downloaded from the TCPA and TCGA databases, and the expressions of 218 proteins in 352 GC patients were analyzed using bioinformatics methods. Additionally, Kaplan–Meier (KM) survival analysis and univariate and multivariate Cox regression analysis were applied to screen the prognosis-related proteins for establishing the prognostic prediction risk model. Finally, five proteins, including NDRG1_pT346, SYK, P90RSK, TIGAR, and XBP1, were related to the risk prognosis of gastric cancer and were selected for model construction. Furthermore, a significant trend toward worse survival was found in the high-risk group (p = 1.495 × 10−7). The time-dependent ROC analysis indicated that the model had better specificity and sensitivity compared to the clinical features at 1, 2, and 3 years (AUC = 0.685, 0.673, and 0.665, respectively). Notably, the independent prognostic analysis results revealed that the model was an independent prognostic factor for GC patients. In conclusion, the robust protein-based model based on five proteins was established, and its potential benefits in the prognostic prediction of GC patients were demonstrated.
Comparative Analysis of Mechanical Properties and Microbiological Resistance of Polyfilament and Monofilament Suture Materials Used in the Operation “Tooth Extraction”
Authors: Pcheliakov Alexey A. , Diachkova Ekaterina Yu. , Vasil’ev Yuriy L. , Svitich Oxana A. , Poddubikov Alexander V. , Evlashin Stanislav A. , Volel Beatrice A. , Bakhmet Anastasia A. , Klochkova Svetlana V. , Velichko Ellina V. , Tiunova Natalia , Tarasenko Svetlana V.
Published: March 2023

In surgical dentistry, suture material is the only foreign body that remains in the tissues after surgery, and it can lead to several negative reactions, for example, infection of the wound. The purpose of this study was to compare the mechanical properties and microbiological resistance of mono- and polyfilament suture materials used in tooth extraction operations. The study of elongation and knot force was carried out on an Instron 5969 Dual Column Testing System device. The capillarity of the materials was studied on a setup assembled by the authors manually by immersing the ends of the filaments in a colored manganese solution. A microbiological study was carried out on the threads taken for the experiment immediately after wound suturing, and on day 7, at which time they were removed. The comparison was made according to Rothia mucilaginosa, Streptococcus sanguinis, Staphylococcus epidermidis. Results: monofilament suture materials (Prolene and Glycolon), after calculating the Kruskal–Wallis and Mann–Whitney indices, showed better performance in all experiments compared to polyfilament sutures (Vicryl and PGA). In capillarity comparison, there was a significant difference between groups (p = 0.00018). According to the sum of the results of three microbiological studies on day 7, monofilament suture materials absorbed less of the studied bacteria on their surface compared to the polyfilament ones (p < 0.05). Conclusions: Of the studied suture materials, Prolene had the best microbiological resistance and good mechanical properties.
Multiplex Detection of Infectious Diseases on Microfluidic Platforms
Authors: Chen Fumin , Hu Qinqin , Li Huimin , Xie Yi , Xiu Leshan , Zhang Yuqian , Guo Xiaokui , Yin Kun
Published: March 2023

Infectious diseases contribute significantly to the global disease burden. Sensitive and accurate screening methods are some of the most effective means of identifying sources of infection and controlling infectivity. Conventional detecting strategies such as quantitative polymerase chain reaction (qPCR), DNA sequencing, and mass spectrometry typically require bulky equipment and well-trained personnel. Therefore, mass screening of a large population using conventional strategies during pandemic periods often requires additional manpower, resources, and time, which cannot be guaranteed in resource-limited settings. Recently, emerging microfluidic technologies have shown the potential to replace conventional methods in performing point-of-care detection because they are automated, miniaturized, and integrated. By exploiting the spatial separation of detection sites, microfluidic platforms can enable the multiplex detection of infectious diseases to reduce the possibility of misdiagnosis and incomplete diagnosis of infectious diseases with similar symptoms. This review presents the recent advances in microfluidic platforms used for multiplex detection of infectious diseases, including microfluidic immunosensors and microfluidic nucleic acid sensors. As representative microfluidic platforms, lateral flow immunoassay (LFIA) platforms, polymer-based chips, paper-based devices, and droplet-based devices will be discussed in detail. In addition, the current challenges, commercialization, and prospects are proposed to promote the application of microfluidic platforms in infectious disease detection.
Concentrations of Manganese in Tufted Titmouse Feathers near Metal Processing Plants
Authors: Sarnowski Rachael , Kellam James S.
Published: March 2023

Human industry and land use has led to the anthropogenic release of manganese (Mn) into the air and soil near manufacturing centers. Overexposure to Mn can cause considerable health problems in birds. We studied whether the concentration of Mn in bird feathers correlates with the distance to point sources of Mn air emissions. Feathers were collected from Tufted Titmice (Baeolophus bicolor) at two sites in western Pennsylvania, USA. One site was in proximity (0.3 km) to a steel plant with documented Mn releases, and the other site was in a different town about 4.0 km away from other steel plants with documented Mn releases. Using the microwave plasma–atomic emission spectrometer (MP–AES), we found that tail feathers collected from nearest to a steel plant had a significantly higher concentration of Mn compared to the samples from the site further from the emission source. A body mass index was calculated for each set of birds; however, the indices did not vary significantly. This is the first published study of Mn sequestration in Tufted Titmouse feathers. This study develops our general understanding of the potential use of bird feathers as non-invasive bioindicators of environmental metal exposure.