Common Techniques Research Articles

The Effect of Missing Item Data on the Relative Predictive Accuracy of Correctional Risk Assessment Tools.

Missing data are pervasive in risk assessment but their impact on predictive accuracy has largely been unexplored. Common techniques for handling missing risk data include summing available items or proration; however, multiple imputation is a more defensible approach that has not been methodically tested against these simpler techniques. We compared the validity of these three missing data techniques across six conditions using STABLE-2007 (N = 4,286) and SARA-V2 (N = 455) assessments from men on community supervision in Canada. Condition 1 was the observed data (low missingness), and Conditions 2 to 6 were generated missing data conditions, whereby 1% to 50% of items per case were randomly deleted in 10% increments. Relative predictive accuracy was unaffected by missing data, and simpler techniques performed just as well as multiple imputation, but summed totals underestimated absolute risk. The current study therefore provides empirical justification for using proration when data are missing within a sample.

Improved outcome models with denoising diffusion

PurposeRadiotherapy outcome modelling often suffers from class imbalance in the modelled endpoints. One of the main options to address this issue is by introducing new synthetically generated datapoints, using generative models, such as Denoising Diffusion Probabilistic Models (DDPM). In this study, we implemented DDPM to improve performance of a tumor local control model, trained on imbalanced dataset, and compare this approach with other common techniques. MethodsA dataset of 535 NSCLC patients treated with SBRT (50 Gy/5 fractions) was used to train a deep learning outcome model for tumor local control prediction. The dataset included complete treatment planning data (planning CT images, 3D planning dose distribution and patient demographics) with sparsely distributed endpoints (6–7 % experiencing local failure). Consequently, we trained a novel conditional 3D DDPM model to generate synthetic treatment planning data. Synthetically generated treatment planning datapoints were used to supplement the real training dataset and the improvement in the model’s performance was studied. Obtained results were also compared to other common techniques for class imbalanced training, such as Oversampling, Undersampling, Augmentation, Class Weights, SMOTE and ADASYN. ResultsSynthetic DDPM-generated data were visually trustworthy, with Fréchet inception distance (FID) below 50. Extending the training dataset with the synthetic data improved the model’s performance by more than 10%, while other techniques exhibited only about 4% improvement. ConclusionsDDPM introduces a novel approach to class-imbalanced outcome modelling problems. The model generates realistic synthetic radiotherapy planning data, with a strong potential to increase performance and robustness of outcome models.

Niosomes: Composition, Formulation Techniques, and Recent Progress as Delivery Systems in Cancer Therapy.

Niosomes are vesicular nanocarriers, biodegradable, relatively non-toxic, stable, and inexpensive, that provide an alternative for lipid-solid carriers (e.g., liposomes). Niosomes may resolve issues related to the instability, fast degradation, bioavailability, and insolubility of different drugs or natural compounds. Niosomes can be very efficient potential systems for the specific delivery of anticancer, antioxidant, anti-inflammatory, antimicrobial, and antibacterial molecules. This review aims to present an overview of their composition, the most common formulation techniques, as well as of recent utilizations as delivery systems in cancer therapy.

Void-Engineered Metamaterial Delay Line with Built-In Impedance Matching for Ultrasonic Applications

Metamaterials exhibit unique ultrasonic properties that are not always achievable with traditional materials. However, the structures and geometries needed to achieve such properties are often complex and difficult to obtain using common fabrication techniques. In the present research work, we report a novel metamaterial acoustic delay line with built-in impedance matching that is fabricated using a common 3D printer. Delay lines are commonly used in ultrasonic inspection when signals need to be separated in time for improved sensitivity. However, if the impedance of the delay line is not perfectly matched with those of both the sensor and the target medium, a strong standing wave develops in the delay line, leading to a lower energy transmission. The presented metamaterial delay line was designed to match the acoustic impedance at both the sensor and target medium interfaces. This was achieved by introducing graded engineered voids with different densities at both ends of the delay line. The measured impedances of the designed metamaterial samples show a good match with the theoretical predictions. The experimental test results with concrete samples show that the acoustic energy transmission is increased by 120% and the standing wave in the delay line is reduced by over a factor of 2 compared to a commercial delay line.

Text Document Clustering Using Modified Particle Swarm Optimization with k-means Model

In the present digital era, vast amounts of data are generated by millions of Internet users in the form of unstructured text documents. The clustering and organizing of text documents play a crucial role in the applications of data analysis and market research. In this research manuscript, a new modified version of metaheuristic-based optimization technique is proposed with k-means for clustering the text documents. In the initial phase, the input data are acquired from the three-benchmark databases such as Reuters-21578, 20-Newsgroup and British Broadcasting Corporation (BBC)-sport. Further, the data denoising is accomplished by using the common techniques: stemming, lemmatization, tokenization, and stop word removal. In addition to this, the denoised data are transformed into feature vectors by utilizing Term Frequency (TF)-Inverse Document Frequency (IDF) technique. The computed feature vectors are given to the Modified Particle Swarm Optimization (MPSO) with k-means to group the closely related text documents by minimizing the similarity in different clusters. The experimental examination showed that the proposed MPSO with k-means model achieved accuracy of 0.85, 0.85 and 0.86 on the Reuters-21578, 20-Newsgroup and BBC-sport databases, which are superior to the comparative models.

Microwave‐Assisted Rapid Synthesis of MOF‐Based Single‐Atom Ni Catalyst for CO2 Electroreduction at Ampere‐Level Current

AbstractCarbon‐based single‐atom catalysts (SACs) have attracted tremendous interest in heterogeneous catalysis. However, the common electric heating techniques to produce carbon‐based SACs usually suffer from prolonged heating time and tedious operations. Herein, a general and facile microwave‐assisted rapid pyrolysis method is developed to afford carbon‐based SACs within 3 min without inert gas protection. The obtained carbon‐based SACs present high porosity and comparable carbonization degree to those obtained by electric heating techniques. Specifically, the single‐atom Ni implanted N‐doped carbon (Ni1−N−C) derived from a Ni‐doped metal–organic framework (Ni‐ZIF‐8) exhibits remarkable CO Faradaic efficiency (96 %) with a substantial CO partial current density (jCO) up to 1.06 A/cm2 in CO2 electroreduction, far superior to the counterpart obtained by traditional pyrolysis with electric heating. Mechanism investigations reveal that the resulting Ni1−N−C presents abundant defective sites and mesoporous structure, greatly facilitating CO2 adsorption and mass transfer. This work establishes a versatile approach to rapid and large‐scale synthesis of SACs as well as other carbon‐based materials for efficient catalysis.

Microwave-Assisted Rapid Synthesis of MOF-Based Single-Atom Ni Catalyst for CO2 Electroreduction at Ampere-Level Current.

Carbon-based single-atom catalysts (SACs) have attracted tremendous interest in heterogeneous catalysis. However, the common electric heating techniques to produce carbon-based SACs usually suffer from prolonged heating time and tedious operations. Herein, a general and facile microwave-assisted rapid pyrolysis method is developed to afford carbon-based SACs within 3 min without inert gas protection. The obtained carbon-based SACs present high porosity and comparable carbonization degree to those obtained by electric heating techniques. Specifically, the single-atom Ni implanted N-doped carbon (Ni1 -N-C) derived from a Ni-doped metal-organic framework (Ni-ZIF-8) exhibits remarkable CO Faradaic efficiency (96 %) with a substantial CO partial current density (jCO ) up to 1.06 A/cm2 in CO2 electroreduction, far superior to the counterpart obtained by traditional pyrolysis with electric heating. Mechanism investigations reveal that the resulting Ni1 -N-C presents abundant defective sites and mesoporous structure, greatly facilitating CO2 adsorption and mass transfer. This work establishes a versatile approach to rapid and large-scale synthesis of SACs as well as other carbon-based materials for efficient catalysis.

The risk and benefit of revisional vs. primary metabolic- bariatric surgery and drug therapy - A narrative review

Metabolic and bariatric surgery (MBS) leads to long-term weight loss, reduced risk of cardiovascular events and cancer, and reduced mortality. Sleeve gastrectomy and Roux-en-Y gastric bypass are currently the most common surgical techniques.Weight loss after MBS was previously believed to work through restriction and malabsorption, however, mechanistic studies show that MBS techniques with long term efficacy instead alter physiological signaling between the gut and the brain.In revisional MBS, the initial surgical technique is corrected, modified, or converted to a new one. The indication for revisional MBS can be to achieve further weight loss or improvement in obesity comorbidity, but it may be necessary due to complications (e.g., gastroesophageal reflux or obstruction). Revisional MBS is associated with an increased risk of surgical complications and often less weight loss compared to the results following primary surgery.This narrative review summarizes data from revisional MBS where information is often presented with inconsistent definitions for indications and outcomes, making comparison between strategies difficult. In summary, we suggest careful weighing of potential benefits and risks with revisional MBS, bearing in mind the option of add-on therapy with new anti-obesity drugs.

Prevalence of intestinal parasites and comparison of detection techniques for soil-transmitted helminths among newly arrived expatriate labors in Jeddah, Saudi Arabia.

Diversity in clinical signs and symptoms are associated with soil transmitted diseases (STD), which are spread to humans by intestinal worms and transmitted in a variety of ways. There is a need for the present study, which aimed to investigate the prevalence of intestinal parasites and to compare between the common detection techniques for soil-transmitted helminths (STHs) among newly arrived expatriate labors in Jeddah, Saudi Arabia. A total of 188 stool samples were analyzed by macroscopic examination, and microscopic examination using direct iodine smear and the formal ether sedimentation technique. Trichrome and modified Kinyoun's stains were used to confirm the morphology of any detected protozoa stages and oocyst of Cryptosporidium, respectively. A chromatographic immunoassay kit was used for Entamoeba histolytica, Giardia lamblia and Cryptosporidium. In addition, real-time PCR was employed only to identify various STHs. Out of 188, several types of parasites were detected in 35 samples (18.62%), of which some with multiple infections. Nine samples (4.79%) were positive for Entamoeba coli, seven samples (3.72%) for Trichuris trichiura, six samples (3.19%) for Necator americanus, four samples (2.13%) for Strongyloides stercoralis, four samples (2.13%) for Ascaris lumbricoides, four samples (2.13%) for E. histolytica, three samples (1.60%) for Blastocystis hominis and two samples (1.06%) for Ancylostoma duodenale. In comparison between laboratory techniques for STHs, real-time PCR was able to detect the DNA of 19 samples (10.1%) followed by Ritchie sedimentation technique (18, 9.6%), and direct smear (7, 3.7%) (p > 0.05). The high rate of newly arrived foreign workers infected with intestinal parasites could lead to a risk to society. Continuous and regular surveys are needed to deal with the occurrence of intestinal parasitic infections including STHs. To improve the identification of these infections, we recommend a supporting infrastructure for the application of concentration methods and molecular assays.

ESTSS—energy system time series suite: a declustered, application-independent, semi-artificial load profile benchmark set

This paper introduces an univariate application-independent set of load profiles or time series derived from real-world energy system data. The generation involved a two-step process: manifolding the initial dataset through signal processors to increase diversity and heterogeneity, followed by a declustering process that removes data redundancy. The study employed common feature engineering and machine learning techniques: the time series are transformed into a normalized feature space, followed by a dimensionality reduction via hierarchical clustering, and optimization. The resulting dataset is uniformly distributed across multiple feature space dimensions while retaining typical time and frequency domain characteristics inherent in energy system time series. This data serves various purposes, including algorithm testing, uncovering functional relationships between time series features and system performance, and training machine learning models. Two case studies demonstrate the claims: one focused on the suitability of hybrid energy storage systems and the other on quantifying the onsite hydrogen supply cost in green hydrogen production sites. The declustering algorithm, although a bys study, shows promise for further scientific exploration. The data and source code are openly accessible, providing a robust platform for future comparative studies. This work also offers smaller subsets for computationally intensive research. Data and source code can be found at https://github.com/s-guenther/estss and https://zenodo.org/records/10213145.

Quantitative Skeletal Imaging and Image-Based Modeling in Pediatric Orthopaedics.

Musculoskeletal imaging serves a critical role in clinical care and orthopaedic research. Image-based modeling is also gaining traction as a useful tool in understanding skeletal morphology and mechanics. However, there are fewer studies on advanced imaging and modeling in pediatric populations. The purpose of this review is to provide an overview of recent literature on skeletal imaging modalities and modeling techniques with a special emphasis on current and future uses in pediatric research and clinical care. While many principles of imaging and 3D modeling are relevant across the lifespan, there are special considerations for pediatric musculoskeletal imaging and fewer studies of 3D skeletal modeling in pediatric populations. Improved understanding of bone morphology and growth during childhood in healthy and pathologic patients may provide new insight into the pathophysiology of pediatric-onset skeletal diseases and the biomechanics of bone development. Clinical translation of 3D modeling tools developed in orthopaedic research is limited by the requirement for manual image segmentation and the resources needed for segmentation, modeling, and analysis. This paper highlights the current and future uses of common musculoskeletal imaging modalities and 3D modeling techniques in pediatric orthopaedic clinical care and research.

A stochastic approach for modelling the in-vitro effect of osmotic stress on growth dynamics and persistent cell formation in Listeria monocytogenes

Persistent bacteria (or persisters) can be defined as a microbial subpopulation that, exposed to bactericidal treatment, is killed more slowly than the rest of the population they are part of. They stochastically originate in response to environmental stressors or spontaneously without external signals. When transferred into a fresh medium, persisters can resume active replication although they spend more time adapting to the new conditions remaining in the lag phase longer. They were studied for decades for their ability to survive antibiotic treatments while studies on their formation in food and potential impact on their safety are lacking. The most common food preservation techniques may act as stressors that trigger the formation of persistent bacteria able to survive bactericidal treatments and grow later in foods during storage. This study aimed to investigate a possible relationship between exposure to different salt concentrations (osmotic stress) and the amount of persisters triggered in a strain of Listeria monocytogenes. Furthermore, we described this phenomenon from a mathematical perspective through predictive microbiology models commonly used in the food field. The lag time distribution of a L. monocytogenes ATCC 7644 strain grown in broth with additional 2 %, 4 % and 6 % NaCl was evaluated using the software ScanLag. It uses office scanners to automatically record the colony growth on agar plates and evaluate the frequency distribution of their appearance times (lag phase) by automated image analysis. The same broth cultures were diluted to equalize salt concentration and transferred into a fresh broth to evaluate how the previous salt exposure impacted their growth kinetics. The observed growth curves were reproduced using predictive models in which the mean duration of the lag phase of the whole population took into account the occurrence of persisters with a longer lag phase. The models were solved first using a deterministic approach and then a stochastic one introducing a stochastic term that mimics the variability of lag phase duration due to the persisters occurrence. Results showed that the growth of L. monocytogenes in broth with additional NaCl might trigger the formation of persistent cells whose number increased consistently with salt concentrations. The proposed predictive approach reproduced the observed real curves in strong agreement, especially through the stochastic resolution of the models. Persistence is currently a neglected bacterial defence strategy in the food sector but the persisters' formation during production cannot be excluded; therefore, further insights on the topic are certainly desirable.

PREDICTIVE ANALYSIS OF CLIMATE DISASTER DATA

In this paper, the Total deaths and Cost per Index (CPI) of worldwide climate disaster dataset has been modelled. The time period of the dataset is from 1900 to 2021. The Autoregressive Integrated Moving Average (ARIMA) has been applied to forecast the Total Deaths and CPI of the study area. The total of 75% of the train data is used for construction of the model and the remaining 25% dataset is used for testing the model. The ARIMA model is general provides more accurate projection especially interval forecast and is more reliable than other common statistical techniques. The best-fitted model is identified as ARIMA(2,0,1) and (2,1,2) for Cost per Index CPI and Total Deaths respectively, generated on the basis of minimum values of Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) procedures. The accuracy parameter considered as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) both parameters shows the model is accurate respectively. There is a 7% difference between the auto and manual models for the CPI feature, similarly, there is a 4% difference for Total Deaths, indicating that CPI plays a significant impact in climatic disasters. In order to identify best fitted model, we applied the model manually and automatic processing. By means of Auto Correlation Function (ACF) and Partial Auto Correlation Function (PACF) plots, the most appropriate order of the ARIMA model are determine and evaluated. Accordingly the created model can help in determining future strategies related to climate disaster dataset of the world. From the forecast result it is found that the results seems to show an increasing trend in CPI values and the minimal decreasing in total death condition and economic activities of the world.

AI-driven improvement of monthly average rainfall forecasting in Mecca using grid search optimization for LSTM networks

ABSTRACT Predicting the average monthly rainfall in Mecca is crucial for sustainable development, resource management, and infrastructure protection in the region. This study aims to enhance the accuracy of long short-term memory (LSTM) deep regression models used for rainfall forecasting using an advanced grid-search-based hyperparameter optimization technique. The proposed model was trained and validated on a historical dataset of Mecca's monthly average rainfall. The model's performance improved by 5.0% post-optimization, reducing the root-mean-squared error (RMSE) from 0.1201 to 0.114. The results signify the value of grid search optimization in improving the LSTM model's accuracy, demonstrating its superiority over other common hyperparameter optimization techniques. The insights derived from this research provide valuable input for decision-makers in effectively managing water resources, mitigating environmental risks, and fostering regional development.

Hyperspectral image classification based on a novel Lush multi-layer feature fusion bias network

Convolutional neural networks (CNNs) exhibit excellent performance in hyperspectral image classification (HSIC) and have attracted significant interest. Nevertheless, the common CNN-based classification techniques still suffer from the following drawbacks. 1) Although deep CNNs can effectively extract features from hyperspectral images, a network that is too deep often leads to negative effects such as overfitting, vanishing gradients, and decreased accuracy. 2) Most of these models for HSIC do not fully consider the strong complementarity and correlation between features at different levels. To address these issues, firstly, a self-regularization parameter correction non-monotonic activation function, called Lush, was first proposed. Then, this study proposes a novel bias update method that can update network bias parameters more efficiently. On the basis of the above, a Lush multi-layer feature fusion polarization network (LMFFBNet) for hyperspectral image classification was proposed in this study. It designs a new after-melting technology to fuse deep and shallow features in multiple stages and layers, which can obtain more discriminative features, thereby improving the classification performance of hyperspectral images. Extensive experiments on five challenging datasets (Indian Pines, Pavia University, Kennedy Space Center, Salinas Valley, and Houston) have demonstrated that compared to some state-of-the-art methods, the proposed LMFFBNet method can provide better classification performance for hyperspectral images and has strong robustness. This fully proves the effectiveness of the LMFFBNet method.

Developments in deep learning for change detection in remote sensing: A review

AbstractDeep learning (DL) algorithms have become increasingly popular in recent years for remote sensing applications, particularly in the field of change detection. DL has proven to be successful in automatically identifying changes in satellite images with varying resolutions. The integration of DL with remote sensing has not only facilitated the identification of global and regional changes but has also been a valuable resource for the scientific community. Researchers have developed numerous approaches for change detection, and the proposed work provides a summary of the most recent ones. Additionally, it introduces the common DL techniques used for detecting changes in satellite photos. The meta‐analysis conducted in this article serves two purposes. Firstly, it tracks the evolution of change detection in DL investigations, highlighting the advancements made in this field. Secondly, it utilizes powerful DL‐based change detection algorithms to determine the best strategy for monitoring changes at different resolutions. Furthermore, the proposed work thoroughly analyzes the performance of several DL approaches used for change detection. It discusses the strengths and limitations of these approaches, providing insights into their effectiveness and areas for improvement. The article also discusses future directions for DL‐based change detection, emphasizing the need for further research and development in this area.

Ethanolysis of nitrogen mustards: A novel strategy for nitrogen mustard identification in environmental matrices by liquid chromatography-electrospray ionization-tandem mass spectrometry.

Nitrogen mustards (NMs) are blistering chemical warfare agents. The ability to detect NMs in environmental samples is very important for obtaining forensic evidence. The most common analytical techniques for NM detection are gas chromatography-mass spectrometry, which detects NMs in their intact form but is disadvantaged by high limits of detection (LODs), and liquid chromatography-electrospray ionization-tandem mass spectrometry (LC/ESI-MS/MS) of their hydrolysis products, which do not provide robust evidence to support NM use. We developed a novel approach to detect and identify NMs using LC/ESI-MS/MS after chemical derivatization. The method is based on ethoxide-promoted ethanolysis prior to analysis. The effects of reaction time, temperature, ethoxide concentration and chromatography behavior were studied and optimized. In the developed procedure, 0.1% (v/v) sodium ethoxide solution is added to the NMs in ethanol and agitated for 2h at 50°C, followed by LC/ESI-MS/MS, without any other pretreatment. The ethanolysis reaction efficiencies were evaluated in ethanolic extracts from soil, asphalt, and ethanol contaminated with 0.5% (v/v) diesel fortified with NMs at a five-point calibration curve. The calibration curves showed good linearity in the range of 0.05-1ng/mL, with an R2 value of 0.99, and were similar to those of LC/MS-grade ethanol, with almost no observable matrix effects. The derivatization products were stable at room temperature, with LODs of 10pg/mL, in all investigated extracts. Through this newly developed strategy, the derivatization of active NMs by ethanolysis was achieved for the first time, and these derivatization products can serve as specific indicators for the use and presence of NMs. The methodology can also verify trace levels of NM chemical warfare agents collected in war or terror scenarios in forensic investigations.

Conservation of Defensive Military Structures Built with Rammed Earth

Earth is a complex material with mechanical and physical behaviors that differ from modern building materials. The conservation of rammed-earth (RE) constructions presents specific difficulties that are challenging to overcome. A lack of knowledge about RE due to its abandonment for decades and interventions adopting materials and repair methodologies designed for conventional constructions have led to inadequate interventions. In the case of historic defensive constructions, the doubts and technical difficulties are even greater due to the nature of so-called military RE (with physical and mechanical characteristics which differ significantly from those of civil, more common RE) and, not least, due to the historical and cultural heritage value of these constructions. Some important interventions have been carried out recently, while others are underway or in the planning stage, and there is a constant lack of information and technical data regarding the best ways to intervene. To fill this gap, the state of conservation of defensive RE structures and the results of interventions carried out throughout history in the southwest of the Iberian Peninsula are being assessed. This article sets out a framework for the subject, identifies material and construction techniques and recognises the main causes of RE constructions’ deterioration and decay. With special focus on the most frequent damages detected in historic military defensive structures built with RE, it analyses and discusses the most common techniques that have been used for the repair and conservation of these particular structures.

A Review of Design and Modeling of Pneumatic Artificial Muscle

Soft robots, which are often considered safer than rigid robots when interacting with humans due to the reduced risk of injury, have found utility in various medical and industrial fields. Pneumatic artificial muscles (PAMs), one of the most widely used soft actuators, have proven their efficiency in numerous applications, including prosthetic and rehabilitation robots. PAMs are lightweight, responsive, precise, and capable of delivering a high force-to-weight ratio. Their structure comprises a flexible, inflatable membrane reinforced with fibrous twine and fitted with gas-sealing fittings. For the optimal design and integration of these into control systems, it is crucial to develop mathematical models that accurately represent their functioning mechanisms. This paper introduces a general concept of PAM's construction, its various types, and operational mechanisms, along with its key benefits and drawbacks, and also reviews the most common modeling techniques for PAM representation. Most models are grounded in PAM architecture, aiming to calculate the actuator's force across its full axis by correlating pressure, length, and other parameters that influence actuator strength.

Research Progress on Superhydrophobic Surface Preparation Methods and Mechanical Durability

Abstract: Superhydrophobic surfaces have great application prospects due to their unique surface- wetting characteristics. However, superhydrophobic surfaces' micro-nano binary rough structure and low surface energy components are easily damaged or lost by wear and grease, which affects their durability and limits their practical application, so it is of great significance to study the durability of superhydrophobic surfaces. The review first introduces the preparation methods and application fields of superhydrophobic surfaces, then sorts out the test methods for the durability performance of superhydrophobic surfaces, methods to improve the durability of superhydrophobic surfaces are summarized， and finally points out some problems in the current research on the durability of superhydrophobic surfaces, aiming to have a comprehensive understanding of the research progress of durable superhydrophobic surfaces, provide some theoretical guidance for the development of durable superhydrophobic surfaces, and look forward to the development direction and trend of durable superhydrophobic surface research in the future. There have been substantial improvements achieved in the preparation techniques to increase the mechanical durability of superhydrophobic surfaces and widen their applications. This review examines pertinent patents and articles on the fabrication of superhydrophobic surfaces from both domestic and foreign sources. The paper introduces the fundamental preparation methods for superhydrophobic surfaces and examines three common techniques: the template method, the spraying method, and the etching method. Additionally, the study discusses these preparation methods and presents future development trends based on the latest research findings. Aiming at the mechanical durability of superhydrophobic surfaces, the test methods for the durability performance of superhydrophobic surfaces under mechanical action are reviewed. The paper also suggests four key methods for enhancing the durability of superhydrophobic surfaces. The refinement of superhydrophobic surface preparation techniques plays a crucial role in enhancing durability and broadening the applications of these surfaces. Additionally, it holds the potential to unlock new possibilities across various domains. The future holds promising prospects for the invention of additional patents and papers focused on superhydrophobic surfaces.