Informing policy makers in developing countries:Practices and limitations of geriatric home medication review in Malaysia-A qualitative inquiry

Objective:To explore existing practices and challenges in the delivery of geriatric home medication review(HMR).The study was part of a larger study aimed to offer solution to expand the range of geriatric HMR.Methods:This study employed qualitative exploratory design through semi-structured individual in-depth interviews with the public pharmacists involved in the delivery of geriatric HMR at public hospitals.The purpose of the interviews was to explore challenges faced by them in the delivery of geriatric HMR.Results:Based on the emerging themes from the qualitative data,the study reveals that geriatric HMR in Malaysia is integrated as part of multidisciplinary home care visits,encompassing a diverse patient population with various healthcare needs.However,it faces challenges such as the lack of outcome monitoring,formal training,and workforce constraints.Despite these hurdles,there is a pressing need for the expansion of this service to better serve the community,and collaboration with community pharmacists holds potential to broaden its scope.Ultimately,the findings suggest that pharmacist-led HMR is both warranted and feasible within the Malaysian healthcare context.In order to optimize medicine-use among older people living in the community,approaches for expanding geriatric HMR services in Malaysia must be developed.Conclusions:This study holds profound implications as it attempts to illuminate policy makers in developing countries,enabling them to formulate effective HMR plans.By considering the challenges highlighted within this research,policy makers can design a comprehensive HMR service that caters adeptly to the healthcare needs of the mass population.

Sentiment Analysis with Tweets Behaviour in Twitter Streaming API

Twitter is a radiant platform with a quick and effective technique to analyze users’perceptions of activities on social media.Many researchers and industry experts show their attention to Twitter sentiment analysis to recognize the stakeholder group.The sentiment analysis needs an advanced level of approaches including adoption to encompass data sentiment analysis and various machine learning tools.An assessment of sentiment analysis in multiple fields that affect their elevations among the people in real-time by using Naive Bayes and Support Vector Machine(SVM).This paper focused on analysing the distinguished sentiment techniques in tweets behaviour datasets for various spheres such as healthcare,behaviour estimation,etc.In addition,the results in this work explore and validate the statistical machine learning classifiers that provide the accuracy percentages attained in terms of positive,negative and neutral tweets.In this work,we obligated Twitter Application Programming Interface(API)account and programmed in python for sentiment analysis approach for the computational measure of user’s perceptions that extract a massive number of tweets and provide market value to the Twitter account proprietor.To distinguish the results in terms of the performance evaluation,an error analysis investigates the features of various stakeholders comprising social media analytics researchers,Natural Language Processing(NLP)developers,engineering managers and experts involved to have a decision-making approach.

Chlorella across latitudes: investigating biochemical composition and antioxidant activities for biotechnological applications

With the present day rise of interest in acquiring sustainability in the pharmaceutical industry,there has been an emphasis on finding natural resources to replace the use of synthetic compounds used in products.Microalgae have garnered significant attention owing to their natural and sustainable capability to produce a diverse array of bioactive compounds.Therefore,this study aims to evaluate the biochemical composition and antioxidant properties of Chlorella strains from a tropical region(Chlorella UMACC 051 and Chlorella UMACC 038)and a polar region(Chlorella UMACC 250 and Chlorella UMACC 234).The cultures were grown for 10 d.At the end of the experiment,the specific growth rate,chlorophyll-a content,carotenoid content,biomass,and biochemical composition such as carbohydrate,protein and lipid content were determined.In addition,the phytochemical properties were determined using a total phenolic assay while the antioxidant activities were determined using 2,2-diphenyl-1-picrylhydrazyl(DPPH).Of all Chlorella strains tested,the tropical Chlorella UMACC 051 showed the fastest growth rate and biomass while the polar Chlorella UMACC 234 contained the highest pigment content and tropical Chlorella UMACC 038 has the highest total phenolic content.The biochemical composition analysis showed all strains have a high lipid content ranging from 45.36%to 60.30%dry weight.All Chlorella strains exhibited a small amount of antioxidant activity(15.42%to 30.15%)and total phenolic content ranging from 1.91±0.04 to 4.43±0.10 mg GAE·g–1 dry weight.The results indicated that polar Chlorella UMACC 234 has the most potential in containing significant amounts of bioactive compounds.

A Secure Multi-factor Authentication Protocol for Healthcare Services Using Cloud-based SDN

Cloud-based SDN(Software Defined Network)integration offers new kinds of agility,flexibility,automation,and speed in the network.Enterprises and Cloud providers both leverage the benefits as networks can be configured and optimized based on the application requirement.The integration of cloud and SDN paradigms has played an indispensable role in improving ubiquitous health care services.It has improved the real-time monitoring of patients by medical practitioners.Patients’data get stored at the central server on the cloud from where it is available to medical practitioners in no time.The centralisation of data on the server makes it more vulnerable to malicious attacks and causes a major threat to patients’privacy.In recent days,several schemes have been proposed to ensure the safety of patients’data.But most of the techniques still lack the practical implementation and safety of data.In this paper,a secure multi-factor authentication protocol using a hash function has been proposed.BAN(Body Area Network)logic has been used to formally analyse the proposed scheme and ensure that no unauthenticated user can steal sensitivepatient information.Security Protocol Animator(SPAN)–Automated Validation of Internet Security Protocols and Applications(AVISPA)tool has been used for simulation.The results prove that the proposed scheme ensures secure access to the database in terms of spoofing and identification.Performance comparisons of the proposed scheme with other related historical schemes regarding time complexity,computation cost which accounts to only 423 ms in proposed,and security parameters such as identification and spoofing prove its efficiency.

Deep Learning Based Sentiment Analysis of COVID-19 Tweets via Resampling and Label Analysis

Twitter has emerged as a platform that produces new data every day through its users which can be utilized for various purposes.People express their unique ideas and views onmultiple topics thus providing vast knowledge.Sentiment analysis is critical from the corporate and political perspectives as it can impact decision-making.Since the proliferation of COVID-19,it has become an important challenge to detect the sentiment of COVID-19-related tweets so that people’s opinions can be tracked.The purpose of this research is to detect the sentiment of people regarding this problem with limited data as it can be challenging considering the various textual characteristics that must be analyzed.Hence,this research presents a deep learning-based model that utilizes the positives of random minority oversampling combined with class label analysis to achieve the best results for sentiment analysis.This research specifically focuses on utilizing class label analysis to deal with the multiclass problem by combining the class labels with a similar overall sentiment.This can be particularly helpful when dealing with smaller datasets.Furthermore,our proposed model integrates various preprocessing steps with random minority oversampling and various deep learning algorithms including standard deep learning and bi-directional deep learning algorithms.This research explores several algorithms and their impact on sentiment analysis tasks and concludes that bidirectional neural networks do not provide any advantage over standard neural networks as standard Neural Networks provide slightly better results than their bidirectional counterparts.The experimental results validate that our model offers excellent results with a validation accuracy of 92.5%and an F1 measure of 0.92.

Improved Video Steganography with Dual Cover Medium,DNA and Complex Frames

The most valuable resource on the planet is no longer oil,but data.The transmission of this data securely over the internet is another challenge that comes with its ever-increasing value.In order to transmit sensitive information securely,researchers are combining robust cryptography and steganographic approaches.The objective of this research is to introduce a more secure method of video steganography by using Deoxyribonucleic acid(DNA)for embedding encrypted data and an intelligent frame selection algorithm to improve video imperceptibility.In the previous approach,DNA was used only for frame selection.If this DNA is compromised,then our frames with the hidden and unencrypted data will be exposed.Moreover the frame selected in this way were random frames,and no consideration was made to the contents of frames.Hiding data in this way introduces visible artifacts in video.In the proposed approach rather than using DNA for frame selection we have created a fakeDNA out of our data and then embedded it in a video file on intelligently selected frames called the complex frames.Using chaotic maps and linear congruential generators,a unique pixel set is selected each time only from the identified complex frames,and encrypted data is embedded in these random locations.Experimental results demonstrate that the proposed technique shows minimum degradation of the stenographic video hence reducing the very first chances of visual surveillance.Further,the selection of complex frames for embedding and creation of a fake DNA as proposed in this research have higher peak signal-to-noise ratio(PSNR)and reduced mean squared error(MSE)values that indicate improved results.The proposed methodology has been implemented in Matlab.

Application of Deep Learning to Production Forecasting in Intelligent Agricultural Product Supply Chain

Production prediction is an important factor influencing the realization of an intelligent agricultural supply chain.In an Internet of Things(IoT)environment,accurate yield prediction is one of the prerequisites for achieving an efficient response in an intelligent agricultural supply chain.As an example,this study applied a conventional prediction method and deep learning prediction model to predict the yield of a characteristic regional fruit(the Shatian pomelo)in a comparative study.The root means square error(RMSE)values of regression analysis,exponential smoothing,grey prediction,grey neural network,support vector regression(SVR),and long short-term memory(LSTM)neural network methods were 53.715,6.707,18.440,1.580,and 1.436,respectively.Among these,the mean square error(MSE)values of the grey neural network,SVR,and LSTM neural network methods were 2.4979,31.652,and 2.0618,respectively;and theirRvalues were 0.99905,0.94,and 0.94501,respectively.The results demonstrated that the RMSE of the deep learning model is generally lower than that of a traditional prediction model,and the prediction results are more accurate.The prediction performance of the grey neural network was shown to be superior to that of SVR,and LSTM neural network,based on the comparison of parameters.

Review on Development of Pressure Injury Prevention Fabric

Pressure injury is a common postoperative complication of wheelchair users. It has a high incidence rate and can induce sepsis and even death. The fabrics that touch patients’ skin directly have a profound impact on their skin surfaces. This review summarizes the research in the last five years on the relationship between pressure injuries and fabrics. Following the preferred reporting items for systematic reviews and meta-analyses(PRISMA) statement, a systematic literature search is conducted in nine databases, including SCI(Web of Science), IEEE, ASTM, Taylor & Francis, ProQuest, Wiley, SpringerLink, PubMed and SAGE. The research objects, experimental equipment and survey results are investigated in this review, in which twenty-four different textiles and seven different test methodologies are involved. The test methodologies include five kinds of performance tests and two kinds of observational studies. Two studies showed that the particular fabric was helpful to reduce the pressure on the skin surface, and twelve studies revealed that the multi-layer fabric with good air permeability, heat dissipation and moisture dissipation was helpful to prevent pressure injury. This review confirms the correlation between the fabric properties and the occurrence of postoperative pressure injury in wheelchair users. Further experiments are needed to reasonably integrate the fabric characteristics of pressure injury prevention into the mass-produced pressure injury prevention clothes for wheelchair users.

Alpha Fusion Adversarial Attack Analysis Using Deep Learning

The deep learning model encompasses a powerful learning ability that integrates the feature extraction,and classification method to improve accuracy.Convolutional Neural Networks(CNN)perform well in machine learning and image processing tasks like segmentation,classification,detection,identification,etc.The CNN models are still sensitive to noise and attack.The smallest change in training images as in an adversarial attack can greatly decrease the accuracy of the CNN model.This paper presents an alpha fusion attack analysis and generates defense against adversarial attacks.The proposed work is divided into three phases:firstly,an MLSTM-based CNN classification model is developed for classifying COVID-CT images.Secondly,an alpha fusion attack is generated to fool the classification model.The alpha fusion attack is tested in the last phase on a modified LSTM-based CNN(CNN-MLSTM)model and other pre-trained models.The results of CNN models show that the accuracy of these models dropped greatly after the alpha-fusion attack.The highest F1 score before the attack was achieved is 97.45 And after the attack lowest F1 score recorded is 22%.Results elucidate the performance in terms of accuracy,precision,F1 score and Recall.

A Novel Machine Learning-Based Hand Gesture Recognition Using HCI on IoT Assisted Cloud Platform

Machine learning is a technique for analyzing data that aids the construction of mathematical models.Because of the growth of the Internet of Things(IoT)and wearable sensor devices,gesture interfaces are becoming a more natural and expedient human-machine interaction method.This type of artificial intelligence that requires minimal or no direct human intervention in decision-making is predicated on the ability of intelligent systems to self-train and detect patterns.The rise of touch-free applications and the number of deaf people have increased the significance of hand gesture recognition.Potential applications of hand gesture recognition research span from online gaming to surgical robotics.The location of the hands,the alignment of the fingers,and the hand-to-body posture are the fundamental components of hierarchical emotions in gestures.Linguistic gestures may be difficult to distinguish from nonsensical motions in the field of gesture recognition.Linguistic gestures may be difficult to distinguish from nonsensical motions in the field of gesture recognition.In this scenario,it may be difficult to overcome segmentation uncertainty caused by accidental hand motions or trembling.When a user performs the same dynamic gesture,the hand shapes and speeds of each user,as well as those often generated by the same user,vary.A machine-learning-based Gesture Recognition Framework(ML-GRF)for recognizing the beginning and end of a gesture sequence in a continuous stream of data is suggested to solve the problem of distinguishing between meaningful dynamic gestures and scattered generation.We have recommended using a similarity matching-based gesture classification approach to reduce the overall computing cost associated with identifying actions,and we have shown how an efficient feature extraction method can be used to reduce the thousands of single gesture information to four binary digit gesture codes.The findings from the simulation support the accuracy,precision,gesture recognition,sensitivity,and efficiency rates.The Machine Learning-based Gesture Recognition Framework(ML-GRF)had an accuracy rate of 98.97%,a precision rate of 97.65%,a gesture recognition rate of 98.04%,a sensitivity rate of 96.99%,and an efficiency rate of 95.12%.

A Pattern Classification Model for Vowel Data Using Fuzzy Nearest Neighbor

Classification of the patterns is a crucial structure of research and applications. Using fuzzy set theory, classifying the patterns has become of great interest because of its ability to understand the parameters. One of the problemsobserved in the fuzzification of an unknown pattern is that importance is givenonly to the known patterns but not to their features. In contrast, features of thepatterns play an essential role when their respective patterns overlap. In this paper,an optimal fuzzy nearest neighbor model has been introduced in which a fuzzifi-cation process has been carried out for the unknown pattern using k nearest neighbor. With the help of the fuzzification process, the membership matrix has beenformed. In this membership matrix, fuzzification has been carried out of the features of the unknown pattern. Classification results are verified on a completelyllabelled Telugu vowel data set, and the accuracy is compared with the differentmodels and the fuzzy k nearest neighbor algorithm. The proposed model gives84.86% accuracy on 50% training data set and 89.35% accuracy on 80% trainingdata set. The proposed classifier learns well enough with a small amount of training data, resulting in an efficient and faster approach.

Information-Centric IoT-Based Smart Farming with Dynamic Data Optimization

Smart farming has become a strategic approach of sustainable agriculture management and monitoring with the infrastructure to exploit modern technologies,including big data,the cloud,and the Internet of Things(IoT).Many researchers try to integrate IoT-based smart farming on cloud platforms effectively.They define various frameworks on smart farming and monitoring system and still lacks to define effective data management schemes.Since IoT-cloud systems involve massive structured and unstructured data,data optimization comes into the picture.Hence,this research designs an Information-Centric IoT-based Smart Farming with Dynamic Data Optimization(ICISF-DDO),which enhances the performance of the smart farming infrastructure with minimal energy consumption and improved lifetime.Here,a conceptual framework of the proposed scheme and statistical design model has beenwell defined.The information storage and management with DDO has been expanded individually to show the effective use of membership parameters in data optimization.The simulation outcomes state that the proposed ICISF-DDO can surpass existing smart farming systems with a data optimization ratio of 97.71%,reliability ratio of 98.63%,a coverage ratio of 99.67%,least sensor error rate of 8.96%,and efficient energy consumption ratio of 4.84%.

FSE2R:An Improved Collision-Avoidance-based Energy Efficient Route Selection Protocolin USN

The 3D Underwater Sensor Network(USNs)has become the most optimistic medium for tracking and monitoring underwater environment.Energy and collision are two most critical factors in USNs for both sparse and dense regions.Due to harsh ocean environment,it is a challenge to design a reliable energy efficient with collision free protocol.Diversity in link qualities may cause collision and frequent communication lead to energy loss;that effects the network performance.To overcome these challenges a novel protocol Forwarder Selection Energy Efficient Routing(FSE2R)is proposed.Our proposal’s key idea is based on computation of node distance from the sink,Residual Energy(RE)of each node and Signal to Interference Noise Ratio(SINR).The node distance from sink and RE is computed for reliable forwarder node selection and SINR is used for analysis of collision.The novel proposal compares with existing protocols like H2AB,DEEP,and E2LR to achieve Quality of Service(QoS)in terms of through-put,packet delivery ratio and energy consumption.The comparative analysis shows that FSE2R gives on an average 30%less energy consumption,24.62%better PDR and 48.31%less end-to-end delay compared to other protocols.

Attitudes and understanding of complementary and alternative medicine in cancer care:An exploratory study of patients’perspectives in Karachi,Pakistan

Objective:To evaluate the prevalence and types of complementary and alternative medicine(CAM)modalities among patients with cancer in Karachi,Pakistan.Methods:This descriptive cross-sectional study was conducted from March 2021 to December 2021.Five hundred patients with cancer were invited to participate in the study.Electronic databases,namely,Google scholar,Publons,EMBASE,PubMed,Chinese National Knowledge Infrastructure Database,and ResearchGate was used for questionnaire designed.The self-administered survey included questions on demographic characteristics,education level,socio-economic conditions and information about CAM therapies,prevalence,effectiveness,and common CAM modalities.Statistical analysis was conducted using SPSS software version 22.Results:Out of the 500 invited patients,433(86.6%)successfully completed and returned the questionnaires.In contrast to patients who were with younger,highly educated,professionally active,higher income,and had advanced cancer,time since diagnosis,type of treatment,cancer types and family history are significantly associated with CAM use.The results showed that 59.8%of the participants were acquainted with complementary and/or alternative medicine and considered safe owing to its natural ingredients.The prevalence of CAM usage among cancer patients was 40.9%and the most widely used CAM modality was herbal medicine(27.7%)and dietary supplements(28.8%).Patients used CAM as a complementary therapy to improve the morphological parameter(28.2%),strengthen the immune system(6.8%),and to decrease the side effects of conventional treatment(18.1%).Most of the respondents get the information regarding CAM therapy from the electronic media(43.2%)and the family members(48%)rather than healthcare personnel.Conclusions:Participants used CAM modalities along with the conventional health care practices.Further multicentre studies should be conducted to provide information regarding the usage of CAM therapies and their eventual benefits in patients with cancer.

Analysis of Tourism Demand Difference Based on Data Mining and Intelligent Analysis

To serve as a reference for future foreign tourism study,relevant tourist sectors have done in-depth investigations on foreign tourism both domestically and internationally.A study of outbound tourism activities from the viewpoint of tourists can examine its development law and create successful marketing tactics based on the rise in the number of foreign tourists.Based on this,this study suggests a data mining technique to examine the variations in travel needs and marketing tactics among various consumer groups.The combined example analysis demonstrates how logical and useful our data mining analysis is.Our data tests demonstrate that the tourism strategy outlined in this paper can enhance the number of tourists by piquing their interest based on the rise in the number of international travellers travelling overseas.

Performance analysis of 20 Pole 1.5 KW Three Phase Permanent Magnet Synchronous Generator for low Speed Vertical Axis Wind Turbine

This paper gives performance analysis of a three phase Permanent Magnet Synchronous Generator (PMSG) connected to a Vertical Axis Wind Turbine (VAWT). Low speed wind condition (less than 5 m/s) is taken in consideration and the entire simulation is carried in Matlab/Simulink environment. The rated power for the generator is fixed at 1.5 KW and number of pole at 20. It is observed under low wind speed of6 m/s, a turbine having approximately1 mof radius and2.6 mof height develops 150 Nm mechanical torque that can generate power up to 1.5 KW. The generator is designed using modeling tool and is fabricated. The fabricated generator is tested in the laboratory with the simulation result for the error analysis. The range of error is about 5%-27% for the same output power value. The limitations and possible causes for error are presented and discussed.

Intrusion Detection Systems in Internet of Things and Mobile Ad-Hoc Networks

Internet of Things(IoT)devices work mainly in wireless mediums;requiring different Intrusion Detection System(IDS)kind of solutions to leverage 802.11 header information for intrusion detection.Wireless-specific traffic features with high information gain are primarily found in data link layers rather than application layers in wired networks.This survey investigates some of the complexities and challenges in deploying wireless IDS in terms of data collection methods,IDS techniques,IDS placement strategies,and traffic data analysis techniques.This paper’s main finding highlights the lack of available network traces for training modern machine-learning models against IoT specific intrusions.Specifically,the Knowledge Discovery in Databases(KDD)Cup dataset is reviewed to highlight the design challenges of wireless intrusion detection based on current data attributes and proposed several guidelines to future-proof following traffic capture methods in the wireless network(WN).The paper starts with a review of various intrusion detection techniques,data collection methods and placement methods.The main goal of this paper is to study the design challenges of deploying intrusion detection system in a wireless environment.Intrusion detection system deployment in a wireless environment is not as straightforward as in the wired network environment due to the architectural complexities.So this paper reviews the traditional wired intrusion detection deployment methods and discusses how these techniques could be adopted into the wireless environment and also highlights the design challenges in the wireless environment.The main wireless environments to look into would be Wireless Sensor Networks(WSN),Mobile Ad Hoc Networks(MANET)and IoT as this are the future trends and a lot of attacks have been targeted into these networks.So it is very crucial to design an IDS specifically to target on the wireless networks.

Effects of elevated temperatures on growth and photosynthetic performance of polar Chlorella

Global warming has been the subject of concern in today’s world with elevating temperature causing the melting of polar ice and increasing sea level.The aim of this study was to investigate the physiological and photosynthetic performance of two polar Chlorella,namely Chlorella UMACC 250 and Chlorella UMACC 234 to elevating temperatures as might be experienced under future warming scenarios.The cultures were exposed to three different temperatures of 4℃,8℃and 12℃.The growth and photosynthetic activity were determined every 2 d for a period of 10 d.At the end of the experiment,the cultures were harvested and analysed for biochemical composition.Both Chlorella strains were able to tolerate higher temperatures than their ambient temperature.The final pigments content showed an increasing trend with increased temperatures for both strains.The photosynthetic activities were measured using pulse-amplitude modulation(PAM)fluorometer.The photosynthetic parameters including maximum quantum efficiency(Fv/Fm),maximum relative electron transport rate(r ETRmax),light harvesting efficiency(a)and photoadaptive index(Ek)were derived from the rapid light curves(RLCs).Both Chlorella strains showed a slight decline in growth and photosynthetic activities at the initial part of the experiment.However,they showed the ability to recuperate with Chlorella UMACC 250 recovers better compared to Chlorella UMACC 234.Both Chlorella strains showed similar trend in their carbohydrate content at 12℃,while the protein content of Chlorella UMACC 234 decreased when exposed to increasing temperatures.The results indicated that polar Chlorella are able to survive at increased temperatures throughout the experiment.

Convalescent plasma: A potential therapeutic option for COVID-19 patients

The new coronavirus disease(COVID-19)outbreak has challenged us to take unprecedented steps to bring this pandemic under control.In view of the urgency of this situation,convalescent plasma which was used in previous coronavirus outbreaks has emerged as one of the treatment options in this current pandemic.This is mainly due to the fact that convalescent plasma has been studied in a few case series with promising outcomes.In addition,on-going large clinical trials aimed to further evaluate the effectiveness,safety,and optimal dosage,duration and timing of administration of convalescent plasma are indeed revealing a certain level of promising results.Therefore,this article aims to provide an overview of possible mechanisms of actions of convalescent plasma,its benefits and its level of usage safeness by summarizing the existing evidence on the use of convalescent plasma in COVID-19 patients.

A Secure Communication Protocol for Unmanned Aerial Vehicles

Mavlink is a lightweight and most widely used open-source communication protocol used for Unmanned Aerial Vehicles.Multiple UAVs and autopilot systems support it,and it provides bi-directional communication between the UAV and Ground Control Station.The communications contain critical information about the UAV status and basic control commands sent from GCS to UAV and UAV to GCS.In order to increase the transfer speed and efficiency,the Mavlink does not encrypt the messages.As a result,the protocol is vulnerable to various security attacks such as Eavesdropping,GPS Spoofing,and DDoS.In this study,we tackle the problem and secure the Mavlink communication protocol.By leveraging the Mavlink packet’s vulnerabilities,this research work introduces an experiment in which,first,the Mavlink packets are compromised in terms of security requirements based on our threat model.The results show that the protocol is insecure and the attacks carried out are successful.To overcomeMavlink security,an additional security layer is added to encrypt and secure the protocol.An encryption technique is proposed that makes the communication between the UAV and GCS secure.The results show that the Mavlink packets are encrypted using our technique without affecting the performance and efficiency.The results are validated in terms of transfer speed,performance,and efficiency compared to the literature solutions such as MAVSec and benchmarked with the original Mavlink protocol.Our achieved results have significant improvement over the literature and Mavlink in terms of security.