Unveiling the Interactions between Water Molecule Clusters and Conical Structures via Molecular Dynamics Simulations.

Water scarcity presents a pressing global challenge, necessitating innovative solutions, such as the collection of water from the air using conical structures. However, current research primarily focuses on mist collection rather than on nanoscale clusters of water molecules. Under standard atmospheric conditions, water vapor predominantly exists as imperceptible clusters. Therefore, it is crucial to investigate the interactions between these water molecule clusters and conical structures, particularly regarding whether the conical shape induces Laplace pressure difference on the adhering cluster formations. To gain deeper insights and determine optimal droplet collection structures, we conducted molecular dynamics simulations to investigate interactions between water molecule clusters and conical structures. Our investigations focused on studying the interactions between conical structures and water molecule clusters with varying densities, as well as the impact of surface energies on the collection of water by these conical structures. Notably, our simulations unveiled the significant roles played by van der Waals forces and Laplace pressure in the process of collecting water molecule clusters. Furthermore, our simulations revealed that Janus conical structures, featuring two distinct surface energy regions, played a crucial role in promoting the aggregation of water molecules, resulting in the formation of larger droplets. This aggregation was driven by surface tension gradients, which arise from the contrasting wetting properties in different regions of the Janus structure. As a consequence, under the influence of gravitational forces, these larger droplets could eventually detach from the structure. Through the combined effects of surface tension gradients and gravitational forces, Janus conical structures offer a promising avenue for enhancing the collection efficiency of water from the air. Our research sheds light on the fundamental mechanisms governing water molecule cluster-based water collection and provides valuable insights for the design of more efficient and effective water collection systems.

A comparative environmental analysis of conventional and organic rice farming in Thailand in a life cycle perspective using a stochastic modeling approach.

System stochasticity is an inherent characteristic of agricultural systems. Many studies have been conducted in Thailand to analyze the rice production systems. However, most of the prior work just focused on deterministic approach to investigate the rice production systems while disregarding the system variability. In this study, the conventional and organic rice farming systems in Thailand were compared considering the uncertainties associated with parameters. The system variability was taken into account by employing a stochastic modeling approach. The considered impact categories include global warming, ozone formation (human health), freshwater ecotoxicity, terrestrial acidification, fine particulate matter formation, freshwater eutrophication, and fossil resource scarcity. The results showed that yield had considerable influence on the environmental profiles of the two systems; organic and conventional farming showed similar results in terms of global warming on a per hectare basis, but the considerable difference was observed on a per tonne basis. The field emissions due to farm inputs were the most significant contributor to most of the impact categories. The fuel used for irrigation, land preparation, and harvesting also contributed significantly to several impact categories. On the other hand, the impacts of inputs production and material transportation were modest. Uncertainty analysis outcomes indicated that there was a noticeable deviation from the deterministic results in terms of global warming and freshwater ecotoxicity. However, when considering the associated uncertainties, no significant difference was observed between the environmental profiles of the two systems.

End-to-End Deep Learning Method for Detection of Invasive Parkinson's Disease.

Parkinson's disease directly affects the nervous system are causes a change in voice, lower efficiency in daily routine tasks, failure of organs, and death. As an estimate, nearly ten million people are suffering from Parkinson's disease worldwide, and this number is increasing day by day. The main cause of an increase in Parkinson's disease patients is the unavailability of reliable procedures for diagnosing Parkinson's disease. In the literature, we observed different methods for diagnosing Parkinson's disease such as gait movement, voice signals, and handwriting tests. The detection of Parkinson's disease is a difficult task because the important features that can help in detecting Parkinson's disease are unknown. Our aim in this study is to extract those essential voice features which play a vital role in detecting Parkinson's disease and develop a reliable model which can diagnose Parkinson's disease at its early stages. Early diagnostic systems for the detection of Parkinson's disease are needed to diagnose Parkinson's disease early so that it can be controlled at the initial stages, but existing models have limitations that can lead to the misdiagnosing of the disease. Our proposed model can assist practitioners in continuously monitoring the Parkinson's disease rating scale, known as the Total Unified Parkinson's Disease Scale, which can help practitioners in treating their patients. The proposed model can detect Parkinson's disease with an error of 0.10 RMSE, which is lower than that of existing models. The proposed model has the capability to extract vital voice features which can help detect Parkinson's disease in its early stages.

Automatic Detection of Liver Cancer Using Hybrid Pre-Trained Models.

Liver cancer is a life-threatening illness and one of the fastest-growing cancer types in the world. Consequently, the early detection of liver cancer leads to lower mortality rates. This work aims to build a model that will help clinicians determine the type of tumor when it occurs within the liver region by analyzing images of tissue taken from a biopsy of this tumor. Working within this stage requires effort, time, and accumulated experience that must be possessed by a tissue expert to determine whether this tumor is malignant and needs treatment. Thus, a histology expert can make use of this model to obtain an initial diagnosis. This study aims to propose a deep learning model using convolutional neural networks (CNNs), which are able to transfer knowledge from pre-trained global models and decant this knowledge into a single model to help diagnose liver tumors from CT scans. Thus, we obtained a hybrid model capable of detecting CT images of a biopsy of a liver tumor. The best results that we obtained within this research reached an accuracy of 0.995, a precision value of 0.864, and a recall value of 0.979, which are higher than those obtained using other models. It is worth noting that this model was tested on a limited set of data and gave good detection results. This model can be used as an aid to support the decisions of specialists in this field and save their efforts. In addition, it saves the effort and time incurred by the treatment of this type of cancer by specialists, especially during periodic examination campaigns every year.

A Novel Binary Hybrid PSO-EO Algorithm for Cryptanalysis of Internal State of RC4 Cipher.

Cryptography protects privacy and confidentiality. So, it is necessary to guarantee that the ciphers used are secure and cryptanalysis-resistant. In this paper, a new state recovery attack against the RC4 stream cipher is revealed. A plaintext attack is used in which the attacker has both the plaintext and the ciphertext, so they can calculate the keystream and reveal the cipher's internal state. To increase the quality of answers to practical and recent real-world global optimization difficulties, researchers are increasingly combining two or more variations. PSO and EO are combined in a hybrid PSOEO in an uncertain environment. We may also convert this method to its binary form to cryptanalyze the internal state of the RC4 cipher. When solving the cryptanalysis issue with HBPSOEO, we discover that it is more accurate and quicker than utilizing both PSO and EO independently. Experiments reveal that our proposed fitness function, in combination with HBPSOEO, requires checking 104 possible internal states; however, brute force attacks require checking 2128 states.

Study of Different Deep Learning Methods for Coronavirus (COVID-19) Pandemic: Taxonomy, Survey and Insights.

COVID-19 has evolved into one of the most severe and acute illnesses. The number of deaths continues to climb despite the development of vaccines and new strains of the virus have appeared. The early and precise recognition of COVID-19 are key in viably treating patients and containing the pandemic on the whole. Deep learning technology has been shown to be a significant tool in diagnosing COVID-19 and in assisting radiologists to detect anomalies and numerous diseases during this epidemic. This research seeks to provide an overview of novel deep learning-based applications for medical imaging modalities, computer tomography (CT) and chest X-rays (CXR), for the detection and classification COVID-19. First, we give an overview of the taxonomy of medical imaging and present a summary of types of deep learning (DL) methods. Then, utilizing deep learning techniques, we present an overview of systems created for COVID-19 detection and classification. We also give a rundown of the most well-known databases used to train these networks. Finally, we explore the challenges of using deep learning algorithms to detect COVID-19, as well as future research prospects in this field.

Insight into single-element nobel metal anisotropic silver nanoparticle shape-dependent selective ROS generation and quantification.

The biocidal action mechanism of single element noble metal anisotropic nanoparticles has remained a perplexing challenge. Herein, we investigated the photogenerated anisotropic AgNP ROS production kinetics and each ROS species' direct impact on Gram-negative and Gram-positive bacteria. Three shapes (Triangular, Cubes, Rods) of AgNP with excellent morphology were fabricated via plasmon mediated synthesis. The results demonstrated a distinct bactericidal capacity of each NP shape where Ag-Tri outperformed Ag-Cub and Ag-Rod by displaying complete bacterial mutilation at a very low dose of 18 µg mL-1 for the shortest exposure time of 180 min. In contrast, Ag-Cub needed 66.6% higher NP concentration, while Ag-Rod was unable to achieve complete bacterial mutilation. In contrast to O2˙-, (Ag-Tri 69 ± 3.2, Ag-Cub 72 ± 2.9, Ag-Rod 68.5 ± 3.7 µM), the amount of ˙OH production was considerably lower (Ag-Tri 11 ± 1.6, Ag-Cub 10.4 ± 1.9, Ag-Rod 11.3 ± 2.2 µM), while 1O2 remained undetected for all NP shapes. Moreover, antimicrobial activity of selective ROS species revealed O2˙- as a dominant species among ROS. However, O2˙- was not found as a decisive factor in microbial mutilation. SEM images affirmed the significance of the specific geometrical shape and its resultant attachment to bacterial surface to be of paramount significance. The sharp-tip morphology with high-atom density active {111} facets played a pivotal role in physically deteriorating bacterial cells. Ag-Tri morphology in synchronization with ROS species assisted its wedging into the bacterial cell, translating into superior and multifaceted antibacterial performance.

Under-oil superhydrophilic salt particle filter for the efficient separation of water-in-oil emulsions.

In this study, a surfactant stabilized water-in-oil emulsion has been successfully separated by using only NaCl particles as a filter. This novel strategy is suitable for continuous filtration of a large quantity of water-in-oil emulsion with a volume of up to 1500 mL. Moreover, a filtration flux of up to 40 000 L m-2 h-1 is reported, which is around ten times higher than the conventional filtration methods.

Corrigendum to "Tabu Search and Machine-Learning Classification of Benign and Malignant Proliferative Breast Lesions".

[This corrects the article DOI: 10.1155/2020/4671349.].

Underoil Superhydrophilic Metal Felt Fabricated by Modifying Ultrathin Fumed Silica Coatings for the Separation of Water-in-Oil Emulsions.

Although various superhydrophobic/superoleophilic porous materials have been developed and successfully applied to separate water-in-oil emulsions through the size-sieving mechanism, the separation performance is restricted by their nanoscale pore size severely. In this study, the wettability of underoil water on fumed silica was experimentally observed, and the underlying mechanism was investigated by carrying out theoretical analysis and molecular dynamic (MD) simulations. Further, we present a novel, facile, and an inexpensive technique to fabricate an underoil superhydrophilic metal felt with microscale pores for the separation of water-in-oil emulsions using SiO2 nanoparticles (NPs) as building blocks. The as-prepared underoil superhydrophilic coating is closed-packed and ultrathin (the thickness is approximately hundreds of nanometers), as well as capable of being coated on a metal felt with complex structures without blocking its pores. The as-prepared metal felt could adsorb water droplets directly from oil, which endowed it with the ability to separate both surfactant-free and surfactant-stabilized water-in-oil emulsions with high separation efficiency up to 99.7% even though its pore size is larger than that of the emulsified droplet. The filtration flux for the separation of span 80-stabilized emulsion is up to ∼4000 L·m-2·h-1. Its separation performance is better than most of the other traditional membranes and superwettable materials used for the separation of water-in-oil emulsions. Moreover, the as-prepared metal felt retained outstanding separation performance even after 30 cycles of use, which demonstrated its excellent reusability and durability. Additionally, the distinctive wettability of underoil superhydrophilicity endued coated metal felt with superior antifouling properties toward crude oil. Overall, this study not only provides a new perspective on separating water-in-oil emulsions but also gives a universal approach to develop unique wettability surfaces.

Tabu Search and Machine-Learning Classification of Benign and Malignant Proliferative Breast Lesions.

Breast cancer is the most diagnosed cancer among women around the world. The development of computer-aided diagnosis tools is essential to help pathologists to accurately interpret and discriminate between malignant and benign tumors. This paper proposes the development of an automated proliferative breast lesion diagnosis based on machine-learning algorithms. We used Tabu search to select the most significant features. The evaluation of the feature is based on the dependency degree of each attribute in the rough set. The categorization of reduced features was built using five machine-learning algorithms. The proposed models were applied to the BIDMC-MGH and Wisconsin Diagnostic Breast Cancer datasets. The performance measures of the used models were evaluated owing to five criteria. The top performing models were AdaBoost and logistic regression. Comparisons with others works prove the efficiency of the proposed method for superior diagnosis of breast cancer against the reviewed classification techniques.

Spontaneous propulsion of a water nanodroplet induced by a wettability gradient: a molecular dynamics simulation study.

The directional propulsion of liquid droplets at the nanoscale is quite an interesting topic of research in the fields of micro/nano-fluidics, water filtration, precision medicine, and cooling of electronics. In this study, the unidirectional spontaneous transport of a water nanodroplet on a solid surface with a multi-gradient surface (MGS) inspired by natural species is modeled and analyzed using molecular dynamics (MD) simulations. There are three different MGSs considered in this study containing different wedge angles of the hydrophilic region of the solid surface. The MGSs contain two regions: a hydrophilic wedge-shaped region with a constant surface energy parameter equal to 50 meV and a hydrophobic region with a tuned surface energy parameter. The energy parameter of the hydrophobic region is set equal to 1, 5, 10, 20, 30, and 40 meV in order to alter the intensity of the wettability gradient of the two surfaces and its effect on the propulsion of the water nanodroplet is analyzed. Furthermore, three different sizes of water droplets containing 6000, 8000, and 10 000 water molecules are also used in this study and their effect on the transport behavior of the water nanodroplet is also measured. Moreover, two different designs on a solid surface with a continuous wettability gradient are modelled and the impact of solid surface geometry on the transport of the water droplet is calculated by means of mean square displacement (MSD) and average velocity data. In addition, the wedge-shaped surface is found to be more superior to the parallel-shaped surface for the spontaneous propulsion of the water droplet.

Automated Breast Cancer Diagnosis Based on Machine Learning Algorithms.

There have been several empirical studies addressing breast cancer using machine learning and soft computing techniques. Many claim that their algorithms are faster, easier, or more accurate than others are. This study is based on genetic programming and machine learning algorithms that aim to construct a system to accurately differentiate between benign and malignant breast tumors. The aim of this study was to optimize the learning algorithm. In this context, we applied the genetic programming technique to select the best features and perfect parameter values of the machine learning classifiers. The performance of the proposed method was based on sensitivity, specificity, precision, accuracy, and the roc curves. The present study proves that genetic programming can automatically find the best model by combining feature preprocessing methods and classifier algorithms.

Life-Like Motion of Oil Drops at the Air-Liquid Interface.

Generally, interactions of oil drops at the air-liquid interface mainly have two features, namely, attraction and repulsion. However, in our study, we find that the oil drops at the air-liquid interface have other interacting features, that is, the atomic-like motion and the "capture" motion. For the atomic-like motion, oil drops attract each other at a long distance, but repel when they are about to come into contact with each other. For the "capture" motion, a big oil drop can actively "capture" oil droplets like a zooplankton. In our research, we analyze interfacial forces among the oil drops. Based on the experiments and analyses, we demonstrate that the atomic-like motion of oil drops is mainly due to the lateral capillary force and the surface tension force, and the "capture" motion is mainly due to the unbalanced impact force of flow fluid around the drops. In addition, based on our results, we use the oil drops to perform many functions at the air-liquid interface. For example, the oil drops can drive an object with linear and rotational motion. When a carbon tetrachloride drop is suspended above the air-liquid interface, it can be used to control an oil droplet to pass through serpentine grooves and obstacles. In addition, the suspended carbon tetrachloride drops also can be used to rank multiple droplets with a special shape. Based on the results, our study makes it possible to use oil drops to transport materials, drive objects, and even collect droplets at the air-liquid interface.

Development of Durable, Fluorine-free, and Transparent Superhydrophobic Surfaces for Oil/Water Separation.

Although artificial superhydrophobic materials have extensive and significant applications in antifouling, self-cleaning, anti-icing, fluid transport, oil/water separation, and so forth, the poor robustness of these surfaces has always been a bottleneck for their development in practical industrial applications. Here, we report a facile, economical, efficient, and versatile strategy to prepare environmentally friendly, mechanically robust, and transparent superhydrophobic surfaces by combining adhesive and hydrophobic paint, which is applicable for both hard and soft substrates. The coated substrates exhibit excellent superhydrophobic property and ultralow adhesion with water (contact angle ≈ 160° and sliding angle <2°). Additionally, the coated surface maintained its superhydrophobicity even after 325 sandpaper abrasion cycles, showing remarkable mechanical robustness. Furthermore, the coated surfaces were applied to separate oil/water mixtures because of their unique characteristics of being simultaneously superhydrophobic and superoleophilic. In addition, it is believed that this fabrication method is significant, promising, and feasible for mass production of superhydrophobic surfaces for industrial applications.

Vapor-Driven Transport of Different Types of Objects at the Air-Liquid Interface.

Transportation and position control of objects on the surface of liquids is an important part of automation. To drive an object on the surface of a liquid, many methods have been proposed. However, these methods mainly focus on the driving of the object itself, and it is still difficult to precisely control its position. In our study, we propose a new method that uses vapor released from a suspended drop to achieve precise position control and transport of different types of objects at the air-liquid interface. These objects can be a plastic plate, a liquid marble, or an oil drop. The mechanism for controlling objects is that vapor released from a suspended drop causes a surface tension gradient around the object. When the vapor dissolves on the surface of a liquid, the surface tension of the liquid increases. Due to the surface tension gradient, the object moves from the surrounding area to the area below the suspended drop and follows the motion of the suspended drop with the trajectory of a letter. To show that the position of the objects can be precisely controlled by our method, we control the object on the center of a circle, and the maximum offset distance from the center of the circle is less than 3 mm. In addition, we also use vapor released from a suspended drop to transport an oil drop close to an object. After the drop adhered with the object, the object is driven by the oil drop. Compared with other methods that drive the motion of objects by reducing the surface tension of a liquid, our method is easy and the position of objects can be precisely controlled.

Separation Mechanism and Construction of Surfaces with Special Wettability for Oil/Water Separation.

Oil leakage and the discharge of oil/water mixtures by domestic and industrial consumers have caused not only severe environmental pollution and a threat to all species in the ecosystem but also a huge waste of precious resources. Therefore, the separation of oil/water mixtures, especially stable emulsion, has become an urgent global issue. Recently, materials containing a special wettability feature for oil and water have drawn immense attention because of their potential applications for oil/water separation application. In this paper, we systematically summarize the fundamental theories, separation mechanism, design strategies, and recent developments in materials with special wettability for separating stratified and emulsified oil/water mixtures. The related wetting theories that unveil the physical underlying mechanism of the oil/water separation mechanism are proposed, and the practical design criteria for oil/water separation materials are provided. Guided by the fundamental design criteria, various porous materials with special wettability characteristics, including those which are superhydrophilic/underwater superoleophobic, superhydrophobic/superoleophilic, and superhydrophilic/in-air superoleophobic, are systemically analyzed. These superwetting materials are widely employed to separate oil/water mixtures: from stratified oil/water to emulsified ones. In addition, the materials that implement the demulsification of emulsified oil/water mixtures via the ingenious design of the multiscale surface morphology and construction of special wettability are also discussed. In each section, we introduce the design ideas, base materials, preparation methods, and representative works in detail. Finally, the conclusions and challenges for the oil/water separation research field are discussed in depth.

Unidirectional transport of water nanodroplets entrapped inside a nonparallel smooth surface: a molecular dynamics simulation study.

The unidirectional transport of liquid nanodroplets is an important topic of research in the field of drug delivery, labs on chips, micro/nanofluidics, and water collection. Inspired by nature a nonparallel surface (NPS) is modelled in this study for pumpless water transport applications. The dynamics of water transport is analyzed with the aid of Molecular Dynamics (MD) simulations. There were five different types of NPSs namely A1, A2, A3, A4, and A5 utilized in this study, with separation angles equal to 5°, 7°, 9°, 11°, and 13° respectively. The water droplet was placed at the beginning of the open end of the NPS and it moved spontaneously towards the cusp of the surface in all cases except for the 13° NPS. The size of the water droplet, too, was altered and four different sizes of water droplets (3000, 4000, 5000, and 6000 molecules) were utilized in this study. Furthermore, the surface energy parameter of the NPS was also changed and four different values, i.e. 7.5 eV, 17.5 eV, 27.56 eV, 37.5 eV were assigned to the surface in order to represent a surface with hydrophobic to hydrophilic characteristics. In addition the importance of water bridge formation for its spontaneous propulsion with the influence of surface energy and droplet size is also discussed in this study. Moreover, a unique design is modelled for the practical application of water harvesting and a large size water droplet is formed by combining two water droplets placed inside a NPS.

Multidirectional regression (MDR)-based features for automatic voice disorder detection.

BACKGROUND AND OBJECTIVE: Objective assessment of voice pathology has a growing interest nowadays. Automatic speech/speaker recognition (ASR) systems are commonly deployed in voice pathology detection. The aim of this work was to develop a novel feature extraction method for ASR that incorporates distributions of voiced and unvoiced parts, and voice onset and offset characteristics in a time-frequency domain to detect voice pathology. MATERIALS AND METHODS: The speech samples of 70 dysphonic patients with six different types of voice disorders and 50 normal subjects were analyzed. The Arabic spoken digits (1-10) were taken as an input. The proposed feature extraction method was embedded into the ASR system with Gaussian mixture model (GMM) classifier to detect voice disorder. RESULTS: Accuracy of 97.48% was obtained in text independent (all digits' training) case, and over 99% accuracy was obtained in text dependent (separate digit's training) case. The proposed method outperformed the conventional Mel frequency cepstral coefficient (MFCC) features. CONCLUSION: The results of this study revealed that incorporating voice onset and offset information leads to efficient automatic voice disordered detection.