Traditional Research Articles

Setting Optimization of Distribution Network Current Protection Based on Constrained Multi‐Objectives Backbone Particle Swarm Optimization

With the increase in residential electricity consumption, the number of branch lines connected to the 10 kV main line and the load all increase. When the main line is segmented into several shorter lines, the current protections of these lines are difficult to coordinate with each other. This condition may cause the number of protection mal‐operation increases, the power outage scope expands, and the reliability of the power supply decreases. Therefore, this paper studies the setting optimization method of distribution network current protections. First, a mathematical model of protection setting optimization is established with three objective functions keeping the fault clearing time minimal, the protection sensitivity highest, and the outage load minimal. Some constraints are considered, such as the cooperation between the protections of the adjacent lines. Second, the multi‐objective backbone particle swarm optimization algorithm is used. The problems of the traditional particle swarm optimization algorithm that lacks population diversity and easily falls into local optimization are solved by adopting the time‐varying variation and linear decreasing dynamic allocation strategy. Finally, the K‐means clustering algorithm is used to get the most representative optimal solution providing a variety of feasible solutions for the practical application. This paper takes a 10 kV line as an example to test the setting optimization method and compares the optimized settings with the traditional ones. The results show that the setting optimization method based on the constrained multi‐objective backbone particle swarm can effectively reduce the protection mal‐operations and improve the overall performance of distribution network protections. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Optimization of energy consumption in smart homes using firefly algorithm and deep neural networks

Electronic gadget advancements have increased the demand for IoT-based smart homes as the number of connected devices grows rapidly. The most prevalent connected electronic devices are smart environments in houses, grids, structures, and metropolises. Smart grid technology advancements have enabled smart structures to cover every nanosecond of energy use. The problem with smart, intelligent operations is that they use a lot more energy than traditional ones. Because of the growing growth of smart cities and houses, there is an increasing demand for efficient resource management. Energy is a valuable resource with a high unit cost. Consequently, authors are endeavoring to decrease energy usage, specifically in smart urban areas, while simultaneously ensuring a consistent terrain. The objective of this study is to enhance energy efficiency in intelligent buildings for both homes and businesses. For the comfort indicator ("thermal, visual, and air quality"), three parameters are used: temperature, illumination, and CO2. A hybrid rule-based Deep Neural Network (DNN) and Fire Fly (FF) algorithm are used to read the sensor parameters and to operate the comfort indication, as well as optimize energy consumption, respectively. The anticipated user attributes contributed to the system's enhanced performance in terms of the ease of use of the smart system and its energy usage. When compared to traditional approaches in expressions of Multi View with 98.23%, convolutional neural network (CNN) with 99.17%, and traffic automatic vehicle (AV) with 98.14%, the activities of the contributed approach are negligibly commanding.

Modeling and Simulation of Auxetic Materials for Balistic Protection

A type of structural metamaterials known as auxetics has a negative Poisson's ratio. Auxetic structurals have been found to possess a number of better qualities when compared to traditional ones, including: greater energy absorption, stronger indentation resistance, and enhanced mechanical properties. As a result, auxetic structures are becoming more known as a high-performance, lightweight defensive construction that can survive collisions and blasts.

Energy-efficient BS sleeping, user association, and resource allocation in full-duplex ultra-dense networks

Full-duplex ultra-dense network (FD-UDN) is a promising technology in 5G mobile networks for handling the increase in network capacity. However, interference management and energy efficiency are two of the most important challenges that must be addressed. With the small base stations (SBSs) densification, employing sleeping strategy coupled with resource management becomes an effective approach to manage interference and power consumption in FD-UDN. To the best of our knowledge, this aspect has not been addressed in previous work. To this end, we develop a framework to optimize the BS sleeping and resource management with the aim of maximizing energy efficiency and maintaining the quality of service (QoS) requirements of users. The problem is formulated as a non-convex mixed-integer non-linear programming problem, which is difficult to handle. Employing the Dinkelbach method, the objective function of the problem is converted to an equivalent parametric subtractive form. Then, the problem is decoupled into two sub-problems: user association and resource allocation, as well as BSs on/off switching. The former is solved using the iterative reweighted lq-norm minimization (IRM) method, and the latter is solved using the Lagrangian dual method and the constrained concave-convex procedure (CCCP). The simulation results demonstrate that the proposed method is more effective than traditional ones in simultaneously improving the EE, reducing power consumption, and keeping fewer SBSs active, especially in high network loads.

The Effect of Transaction Costs on the Sustainability of Milkfish Aquaculture in North Coast of Java, Indonesia

The potential of Milkfish Aquaculture on the North Coast of Java is very promising as the source of economy to both the farmers and Indonesia. However, the challenge faced by milkfish farmers is the fluctuation of input and selling prices may threaten business. Gresik as one of the center of milkfish production faces issues in accessing the market to sell their products, hence they require intermediaries that carries transaction costs which implicitly increase their final costs. Moreover, lack of cold storage and advance processing in the area complicate and reduce the bargaining value of milkfish farmers towards its buyer. This is because milkfish has a relatively very short shelf-life without proper storage or additional processing. The research is aimed to analyze the cost structure of milkfish farmers on the north coast of Java towards farmers income. Quantitative method is employed using two analyses: Ordinary Least Square (OLS) and Mann-Whitney Comparative Test. This study found that input cost is significantly affect the milkfish farmer, while the investment and transaction costs do not affect the milkfish farmer. Although the transaction cost variable is not significant, it shows negative coefficient value which implies reverse relationship with income. The result of Comparative Test is that the transaction cost of traditional farmers is higher compared to the semi-intensive. This is because the semi-intensive aquaculture has a higher bargaining position compared to the traditional ones, so that the cost of information, negotiation and other transaction cost are lower compared to the traditional ones. This study may useful for the milkfish farmer to increase efficiencies in the production by reducing the hidden costs and improve supply chain of the milkfish industry for the sustainability of milkfish farmers and its sub-industries in Gresik.

Design and Simulate Intracranial Support to Guide Maxillo Surgery: A Study Based on Bioengineering.

Intraoperative navigation allows for the creation of a real-time relationship between the anatomy imagined during diagnosis/planning and the site of surgical interest. This procedure takes place by identifying and registering trustworthy anatomical markers on planning images and using a point locator during the operation. The locator is calibrated in the workspace by placing a Dynamic Reference Frame (DRF) sensor. This study aims to calculate the localization accuracy of an electromagnetic locator of neuro-maxillofacial surgery, moving the standard sensor position to a different position more suitable for maxillofacial surgery. The upper dental arch was chosen as an alternative fixed point for the positioning of the sensor. The prototype of a bite support device was designed and generated via 3D printing. CT images of a skull phantom with 10 anatomical landmarks were acquired. The testing procedure consisted of 10 measurements for each position of the sensor: precisely 10 measurements with the sensor placed on the forehead and 10 measurements with the sensor placed on the bite support device. It also evaluated the localization error by comparing the two procedures. The localization error, when the sensor was placed on the bite support device, was lower in the sphere located on the temporal bone. It was the same in the spheres located on the maxillary bone. The test analysis of the data of the new device showed that it is reliable; the tests are reproducible and can be considered as accurate as the traditional ones. In addition, the sensor mounted on this device has proven to be slightly superior in terms of accuracy and accuracy in areas such as the middle third of the face and jaw. The realization of the bite support device allowed the sensor to change position concerning its natural site. This procedure allows us to explore structures, such as the frontal site, which were initially difficult to approach with neuronavigation and improves the approach to midface structures, already studied with neuronavigation. The new calibration, with the position of the sensor on the support device in the same reference points sphere, highlighted the reduction in the location error. We can say that the support proposed in this study lays the foundations for a new navigation approach for patients in maxillofacial surgery, by changing the position of the sensor. It has strong points in improving the localization error for some reference points without determining disadvantages both in the calibration and in the surgical impediment.

REMOTE ACADEMIC EDUCATION IN THE OPINION OF TEACHERS

The article presents an assessment of the quality and effectiveness of remote academic education by teachers, including its advantages and disadvantages, affecting the preparation of students for professional work. The analysis was based on research on the quality of distance learning during the pandemic (2020–2021), conducted among academic teachers at selected public universities. Other publications on distance learning at various levels of education and reports of the Polish Accreditation Committee were also used. The following issues were analysed and evaluated: preparation of universities and academic teachers to conduct studies in a remote form, effectiveness of various ways and forms of conducting classes and objectivity of colloquia and exams, commitment and quality of students' work, assessment of the effectiveness of remote studies against the background of traditional ones, advantages and disadvantages of distance learning and teachers' opinions on the further use of the remote form in studies. Almost half of the surveyed academic teachers assessed the effectiveness of remote studies worse than traditional ones, especially in the field of teaching skills important in the future professional work of students. Slightly fewer respondents believed that there is no difference between the effectiveness of remote and traditional studies. The least respondents assessed the effectiveness of remote studies better than traditional ones.

A smartphone-based tool for screening diabetic neuropathies: A mHealth and 3D printing approach

Diabetic neuropathy, a nerve damage associated with diabetes mellitus, can lead to severe disabilities, morbidity, and mortality, if not diagnosed in a timely manner. Diabetic neuropathies represent a huge economic burden and are a growing problem in sub-Saharan Africa, where they affect up to 61% of the diabetic population. Therefore, the United Nations (UN) has included the reduction of the diabetes-related mortality, as a priority in the Sustainable Development Agenda. A review of the current existing solutions for diabetic patients highlighted the fact that many are focused on lifestyle management and glycemia monitoring, while less are available for diabetic neuropathies screening, in particular in the digital health field. Beyond cutting-edge screening methods, which are time-consuming and equipment-heavy, traditional ones are effective, but they require specialised knowledge, which often lacks in low-resource settings. These settings, specifically those in low-income countries, are challenged by the lack of expertise, funds, spare parts, and consumables and harsh environmental conditions, which hinder the safe use of medical devices. This paper proposes a smart-tool for the screening of diabetic neuropathies based on the effective combination of three already established methods, through 3D-printed accessories and a smartphone app, aiming at contributing towards the UN’s Sustainable Development Goal 3, as well as the fourth industrial revolution in healthcare. Moreover, an on-field evaluation for this smart-tool is ongoing. So far, we recruited 11 normosubjects as a pilot study. The results demonstrate that it could be a viable solution to improve the standard of care of diabetic patients, specifically in the field of diabetic neuropathy screening, globally, as well as locally in low-resource settings.

Comparison of machine learning approaches for the identification of top-performing materials for hydrogen storage

Unique properties of Metal-Organic Frameworks (MOFs), such as their extremely high surface areas and porosity, render them as one of, if not the most promising adsorbents for gas storage applications. However, their extremely flexible and tunable nature has resulted in an enormous expansion of the available material pool which in turn begs the question as to whether an efficient identification of the best materials is feasible. In the last years, Machine Learning (ML) techniques have been extensively applied for the exploration of large material databases, since they can significantly accelerate this process. In this work, “traditional” ML models and models based on our recently developed iterative self-consistent approach are compared with respect to their ability to efficiently identify the best materials of a database. As a case study, we have used hydrogen adsorption in MOFs at different thermodynamic conditions. Despite their high accuracy, traditional models struggle to pinpoint the best materials, regarding usable gravimetric uptake, without compromising computational resources. On the other hand, self-consistent models can even reduce by two orders of magnitude the amount of reference data required for the identification of the best gravimetric materials compared to traditional ones. Notably, 300 training samples are enough for the SC models to correctly identify the top-100 gravimetric materials of a database. Nevertheless, both type of models underperform when they are queried for the top-performing materials with regards to usable volumetric uptake.

The effect of valve design on the pressure losses in a high-pressure homogenizer – An improved pressure drop correlation for estimating gap height

Understanding how the design of high-pressure homogenizer valves influence pressure losses in the device is of general interest for performance optimizing, and of special importance for developing correlations for predicting gap heights and gap velocities. The traditionally used correlation is from 1975 and based on a limited experimental dataset on gaps that are longer and often of lower capacity than contemporary ones. This study uses well-validated best-practice CFD to develop an improved correlation for how homogenizing pressure depends on valve designs, extending previous investigations to also include the effect of seat and forcer incline angles. The suggested correlation differs substantially from the traditional ones, shows a marked effect on inlet and exit loss coefficients of Reynolds number and of inlet seat incline angle. For cases with Reynolds number comparable to pilot- and production-scale homogenizers, the gap height can be predicted to within 10% of the set value, using the suggested correlation.

Feed-forward networks using logistic regression and support vector machine for whole-slide breast cancer histopathology image classification

The performance of an image classification depends on the efficiency of the feature learning process. This process is a challenging task that traditionally requires prior knowledge from domain experts. Recently, representation learning was introduced to extract features directly from the raw images without any prior knowledge. Deep learning using a Convolutional Neural Network (CNN) has gained massive attention for performing image classification, as it achieves remarkable accuracy that sometimes exceeds human performance. But this type of network learns features by using a back-propagation approach. This approach requires a huge amount of training data and suffers from the vanishing gradient problem that deteriorates the feature learning. The forward-propagation approach uses predefined filters or filters learned outside the model and applied in a feed-forward manner. This approach is proven to achieve good results with small size labeled datasets. In this work, we investigate the suitability of using two feed-forward methods such as Convolutional Logistic Regression Network (CLR), and Convolutional Support Vector Machine Network for Histopathology Images (CSVM-H). The experiments we have conducted on two small breast cancer datasets (Sultan Qaboos University Hospital (SQUH) and BreaKHis dataset) demonstrate the advantage of using feed-forward approaches over the traditional back-propagation ones. On those datasets, the proposed models CLR and CSVM-H were faster to train and achieved better classification performance than the traditional back-propagation methods (VggNet-16 and ResNet-50) on the SQUH dataset. Importantly, our proposed approach CLR and CSVM-H efficiently learn representations from small amounts of breast cancer whole-slide images and achieve an AUC of 0.83 and 0.84, respectively, on the SQUH dataset. Moreover, the proposed models reduce memory footprint in the classification of Whole-Slide histopathology images since their training time is significantly reduced compared to the traditional CNN on the SQUH and BreaKHis datasets.

Modeling the interest rates term structure using Machine Learning: a Gaussian process regression approach

The correct modeling of the interest rates term structure should definitely be considered an aspect of primary importance since the forward rates and the discount factors used in any financial and risk analysis are calculated from such structure. The turbulence of the markets in recent years, with negative interest rates followed by their recent substantial rise, the period of the COVID pandemic crisis, the political instabilities linked to the war between Ukraine and Russia have very often led to observe anomalies in the shape of the interest rate curve that are difficult to represent using traditional econometric models, to the point that researchers have to address this modeling problem using Machine Learning methodologies. The purpose of this study is to design a model selection heuristic which, starting from the traditional ones (Nelson-Siegel, Svensson and de Rezende-Ferreira) up to the Gaussian Process (GP) Regression, is able to define the best representation for a generic term structure. This approach has been tested over the past five years on term structures denominated in five different currencies: the Swiss Franc (CHF), the Euro (EUR), the British Pound (GBP), the Japanese Yen (JPY) and the U.S. Dollar (USD).

Plant-based oral nutritional supplements: can they be an alternative to traditional ones? A sensory evaluation

Plant-based oral nutritional supplements: can they be an alternative to traditional ones? A sensory evaluation

Somatic Antonymy: Common and Specific Features of Lexical and Phraseological Antonyms

The evolution of society and the changes that occur in the surrounding reality also imply continuous changes in the means of verbal expression of a language. Thus, language with its phraseological micro-universe is a very complex phenomenon, susceptible to evolution and change, which is very slow to develop and leads to the emergence of new words and phrases, syntactic structures parallel to the traditional ones. In its uninterrupted dynamic, it enhances the means it offers to express our ideas and feelings more clearly, concisely and expressively. It is well known that there are phraseological units with close meanings (synonymic phraseological expressions) and those with opposite meanings (antonymic phraseological expressions), phenomena that are widespread in the creation of the phraseological treasury stricto modo. In this article we will investigate the phenomenon of somatic phraseological antonymic series and in particular we are to lay emphasis on their common and specific features. We start from the hypothesis that the use of these somatic phraseological antonymic series allows us, on the one hand, to avoid cases of ambiguity of the lexical units themselves, and on the other hand, to identify either bipolar cultural ideas or concepts within a culture. We set out to study the two types of lexical and phraseological antonymy since they complement each other and liquidate the gaps in both systems of English and Romanian polar units.

Accurate metaheuristic deep convolutional structure for a robust human gait recognition

<span lang="EN-US">Gait recognition has become a developing technology in various security, industrial, medical, and military applications. This paper proposed a deep convolutional neural network (CNN) model to authenticate humans via their walking style. The proposed model has been applied to two commonly used standardized datasets, Chinese Academy of Sciences (CASIA) and Osaka University-Institute of Scientific and Industrial Research (OU-ISIR). After the silhouette images have been isolated from the gait image datasets, their features have been extracted using the proposed deep CNN and the traditional ones, including AlexNet, Inception (GoogleNet), VGGNet, ResNet50, and Xception. The best features were selected using genetic, grey wolf optimizer (GWO), particle swarm optimizer (PSO), and chi-square algorithms. Finally, recognize the selected features using the proposed deep neural network (DNN). Several performance evaluation parameters have been estimated to evaluate the model’s quality, including accuracy, specificity, sensitivity, false negative rate (FNR), and training time. Experiments have demonstrated that the suggested framework with a genetic feature selector outperforms previous selectors and recent research, scoring accuracy values of 99.46% and 99.09% for evaluating the CASIA and OU-ISIR datasets, respectively, in low time (19 seconds for training).</span>

Failure mechanism and impact resistance of a novel all-composite double-corrugated sandwich plate under low-velocity impact

A novel composite double trapezoidal corrugated sandwich plate (CDTCSP) was prepared by mold hot pressing and secondary bonding of carbon fiber reinforced polymer (CFRP) prepregs. The DIT302E drop hammer testing machine with a hammerhead of 5.5 kg was selected to realize the impact test of the new corrugated sandwich structure and the all-composite trapezoidal corrugated sandwich plate (CTCSP) with three different energies of 10 J,30 J, and 50 J by changing the height. The failure modes of the two samples were further studied by experimental investigation along with the finite element model established in Abaqus/Explicit. Based on the verified numerical model, the failure process of CDTCSP under 30 J and 50 J impact energy is analyzed. At the same time, the effects of different impact positions and layup angles on the impact resistance of CDTCSP at 10 J energy were evaluated. It can be observed that as the impact energy increases, the peak load of the composite corrugated sandwich plate decreases. Compared with the traditional ones, the impact position has no noticeable effect on the overall impact resistance of CDTCSP. Based on the analysis of CDTCSP with five different layup parameters, it is found that the matrix compression failure extends along the fiber direction, and the peak load of the sample with all the surfacing layup of ± 45° is 17% larger than that of the sample with 0°.

TEXT IN NEW MEDIA: SPECIFICITY OF THE VERBAL COMPONENT

The priority tasks in the article are 1) understanding the phenomenon of new media as a technical, social and communicative phenomenon, 2) describing the specifi cs of new media in terms of semiotic, functional features, features of information decoding and derived features of the text of new media, genetically related to the traditional text, but transforming in the conditions of a new sociality. To solve the second problem, the texts of the news community “Lentach” (social network “VKontakte”) for the period from March 2022 to March 2023 were considered as empirical material and a number of their specifi c features were established. The thematic fi eld of the analyzed texts was not a relevant feature for solving the problems of this article, but in order to achieve a long-term goal (determining the communicative strategies of a media text), the selection of texts was based on the criterion of the presence of political topics in the text (aspects of foreign and domestic policy were taken into account). In the polycode media text of new media, within the framework of this article, the object of attention was its verbal component: the traditional nature of its presence in the media message was noted and a number of specifi c features were established at diff erent levels of text organization. At the phonetic-graphic level, visual techniques for segmenting information and simulating oral speech are observed; on morphemic – intensifi cation of creative word-formation processes; on lexical – stylistic and speech interference, instrumental use of tropes and lexical resources, specifi c metaphorization of the text; at the syntactic level – activation of expressive syntax. In the texts of new media, as in traditional ones, variations of the language game are found, the functions of which can be considered the self-identifi cation of the linguistic personality, the eff ects of media infl uence, the activation of the dialogue potential of the media text, the destruction of the classical linearity of the media text.

Dynamics of Marketing Communication Platforms Used by Farmers to Access Information on Agriculture in Kakamega County, Kenya

Agriculture is fundamental to Kenya’s development as anchored in the in the Bottom-Up Economic blue print and Vision 2030. Despite the increasing promotional attempts by marketers, manufacturers and even government agencies, agricultural production is marred by low outputs, low uptake of farm inputs such as fertilizer, quality seeds, equipment, chemicals, and reluctance in adopting new agricultural innovations. The presence of various marketing communication approaches, several information platforms, and financial incentives to the clientele notwithstanding, the uptake of farm inputs is still very low. The objective of this study was to describe the dynamics of integrated marketing communication platforms that are used by farmers in accessing information on agriculture. The Uses and Gratifications Theory by Katz, Blumler, and Gurevitch was adopted because of its ability to examine diverse media and communication channel preferences among audiences. The theory advances the use of alterative media platforms where traditional ones are not effective. The researcher relied on Mixed-methods in actualizing the study. Simple random sampling was used to get 259 farmers, while purposive sampling was used to select Agricultural Society of Kenya officials and agricultural extension officers. Six agro-dealers were randomly selected while quota sampling was used to get media personnel. Data were collected through questionnaires, interviews, and focus group discussions. Qualitative and quantitative approaches were used in analysis and organization of results. Findings were presented descriptively and complemented by graphs, tables, percentages, and charts. This enabled the researcher to draw inferences that later informed the conclusions of the study which indicate that participants had access to multiple marketing communication platforms that they relied upon to access information on agriculture. The channels included both traditional and new media platforms such as radio, TV, SMS, interpersonal communication, and public barazas, which were effective in sharing information on agriculture. The study recommended the following; embracement of targeted marketing; empowerment of Agricultural field officers and increase collaborations with vibrant agricultural organizations; make use of integrated communication platforms that meet regional needs; utilization of social media to advertise; and finally, embrace continuous provision of agricultural information at all times.

Filesystem Fragmentation on Modern Storage Systems

Filesystem fragmentation has been one of the primary reasons for computer systems to get slower over time. However, there have been rapid changes in modern storage systems over the past decades, and modern storage devices such as solid state drives have different mechanisms to access data, compared with traditional rotational ones. In this article, we revisit filesystem fragmentation on modern computer systems from both performance and fairness perspectives. According to our extensive experiments, filesystem fragmentation not only degrades I/O performance of modern storage devices, but also incurs various problems related to I/O fairness, such as performance interference. Unfortunately, conventional defragmentation tools are designed primarily for hard disk drives and thus generate an unnecessarily large amount of I/Os for data migration. To mitigate such problems, this article present FragPicker, a new defragmentation tool for modern storage devices. FragPicker analyzes the I/O behaviors of each target application and defragments only necessary pieces of data whose migration can contribute to performance improvement, thereby effectively minimizing the I/O amount for defragmentation. Our evaluation with YCSB workload-C shows FragPicker reduces the total amount of I/O for defragmentation by around 66% and the elapsed time by around 84%, while showing a similar level of defragmentation effect.

TEE-Graph: efficient privacy and ownership protection for cloud-based graph spectral analysis.

Big graphs like social network user interactions and customer rating matrices require significant computing resources to maintain. Data owners are now using public cloud resources for storage and computing elasticity. However, existing solutions do not fully address the privacy and ownership protection needs of the key involved parties: data contributors and the data owner who collects data from contributors. We propose a Trusted Execution Environment (TEE) based solution: TEE-Graph for graph spectral analysis of outsourced graphs in the cloud. TEEs are new CPU features that can enable much more efficient confidential computing solutions than traditional software-based cryptographic ones. Our approach has several unique contributions compared to existing confidential graph analysis approaches. (1) It utilizes the unique TEE properties to ensure contributors' new privacy needs, e.g., the right of revocation for shared data. (2) It implements efficient access-pattern protection with a differentially private data encoding method. And (3) it implements TEE-based special analysis algorithms: the Lanczos method and the Nystrom method for efficiently handling big graphs and protecting confidentiality from compromised cloud providers. The TEE-Graph approach is much more efficient than software crypto approaches and also immune to access-pattern-based attacks. Compared with the best-known software crypto approach for graph spectral analysis, PrivateGraph, we have seen that TEE-Graph has 103-105 times lower computation, storage, and communication costs. Furthermore, the proposed access-pattern protection method incurs only about 10%-25% of the overall computation cost. Our experimentation showed that TEE-Graph performs significantly better and has lower costs than typical software approaches. It also addresses the unique ownership and access-pattern issues that other TEE-related graph analytics approaches have not sufficiently studied. The proposed approach can be extended to other graph analytics problems with strong ownership and access-pattern protection.