Acceptable Level Of Performance Research Articles

Targeting Full-Hydrogen Operation on Industrial-Scale Gas Turbines: Impact of Unconventional Fuels on Turbine Module Performance and Aeromechanics

Abstract With the trend to full decarbonization, a full hydrogen economy development is a key industrial objective. Gas turbines, currently one of the cleanest fossil fuel-based power generation solutions, provide reliable and on-demand power. The introduction of hydrogen into the fuel mix of existing gas turbines represents a solution with great potential to provide low-carbon or even carbon-free energy. High-hydrogen content fuels, however, challenge the efficient operation of the gas turbine expander, as crucial aspects such as increased flow rate, different gas properties and temperature operating range may affect performance and structural integrity. To evaluate the impact of this conversion, a numerical investigation of five cases with increasing percentages of hydrogen in the fuel was carried out for an industrial four stages gas turbine module. The variation of turbine inlet temperature distribution from combustion chamber was considered, to assess the impact of the unconventional fuel on the well-known aeromechanical behavior of the last stage blades. Variations in capacity, efficiency, power and potential limits due to aeroelasticity were evaluated, identifying the most relevant differences with respect to the full methane case. All these analyses confirm the possibility to employ high-hydrogen fuel operation in a current gas turbine without the need of a further redesign, while maintaining acceptable performance levels.

Improving Data Security in Cloud Computing through the Implementation of a Hybrid Encryption Algorithm

As cloud computing continues to gain popularity, the security of sensitive data stored in the cloud remains a paramount concern for organizations and individuals alike. Traditional encryption methods, while effective, often struggle to balance security, efficiency, and usability. This paper presents a hybrid encryption algorithm that combines the strengths of symmetric and asymmetric encryption to enhance data security in cloud computing environments. The proposed hybrid approach leverages symmetric encryption for fast and efficient data encryption, while asymmetric encryption is utilized for secure key exchange. By implementing this dual-layer encryption strategy, we can significantly improve data confidentiality and integrity during transmission and storage. Additionally, the algorithm incorporates robust key management practices, ensuring that encryption keys are safeguarded against unauthorized access. Through a series of performance evaluations and security assessments, we demonstrate that our hybrid encryption algorithm not only provides superior security but also maintains acceptable levels of performance, making it suitable for real-time applications. The results indicate that this approach effectively mitigates risks associated with data breaches and unauthorized access, positioning it as a viable solution for enhancing data security in cloud computing. This research contributes to the ongoing discourse on cloud security, offering a practical framework for organizations to protect their sensitive information in increasingly complex digital landscapes.

Delineating the drought vulnerability zones in Bangladesh

The research aims to explore the vulnerability of Bangladesh to drought by considering a comprehensive set of twenty-four factors, classified into four major categories: meteorological, hydrological, agricultural, and socioeconomic vulnerability. To achieve this, the study utilized a knowledge-based multi-criteria method known as the Analytic Hierarchy Process (AHP) to delineate drought vulnerability zones across the country. Weight estimation was accomplished by creating pairwise comparison matrices for factors and different types of droughts, drawing on relevant literature, field experience, and expert opinions. Additionally, online-based interviews and group discussions were conducted with 30 national and foreign professionals, researchers, and academics specializing in drought-related issues in Bangladesh. Results from overall drought vulnerability map shows that the eastern hills region displays a notably high vulnerability rate of 56.85% and an extreme low vulnerability rate of 0.03%. The north central region shows substantial vulnerability at high levels (35.85%), while the north east exhibits a significant proportion (41.68%) classified as low vulnerability. The north west region stands out with a vulnerability rate of 40.39%, emphasizing its importance for drought management strategies. The River and Estuary region displays a modest vulnerability percentage (38.44%), suggesting a balanced susceptibility distribution. The south central and south east regions show significant vulnerabilities (18.99% and 39.60%, respectively), while the south west region exhibits notable vulnerability of 41.06%. The resulting model achieved an acceptable level of performance, as indicated by an area under the curve value of 0.819. Policymakers and administrators equipped with a comprehensive vulnerability map can utilize it to develop and implement effective drought mitigation strategies, thereby minimizing the losses associated with drought.

Data-driven assessment of business process resilience

Process resilience represents a core competence for organizations in light of an increasing number of process disruptions, such as sudden increases in case arrivals or absences in the workforce. It reflects an organization’s ability to restore a process to its acceptable performance level after a disruption. In this regard, the first key step for organizations towards achieving resilience is to understand how resilient their processes actually are. Although recognized as important, few works focus on such resilience assessment in a data-driven manner, thus barring organizations from gaining the necessary insights into how much their processes are affected by disruptions and how long it takes them to recover. To address this problem, we propose an approach for automated resilience assessment, based on recorded event data. Our approach interprets relevant process characteristics, such as the average lead time or arrival rate, as time series, which capture the development of the process execution over time. Based on these time series, it uses statistical modeling, specifically a vector autoregressive model, to determine the inter-relations between those characteristics and assess how the process performance responds to a disruption, i.e., a significant and temporal change in one of the process characteristics. We validate our approach by comparing its accuracy with a what-if analysis using a simulation model and demonstrate its effectiveness by assessing the resilience of the same process to diverse disruptions across different organizations.

Performance of ParaHIT® HRP2-Based Rapid Diagnostic Test and Proportions of Plasmodium falciparum Histidine-Rich Protein 2/3 Gene Deletions in Togo.

In areas where malaria is endemic and microscopes are unavailable, rapid diagnostic tests (RDTs) are essential tools for early diagnosis and prompt and effective treatment. However, HRP2-based RDTs are threatened by the emergence of Plasmodium falciparum parasites that do not carry the pfhrp2 or pfhrp3 gene, leading to false-negative results. Therefore, the aim of this study was to evaluate the performance of the ParaHIT RDT together with the proportion of pfhrp2/3 gene-deleted P. falciparum parasites in Togo. The performance of RDTs compared with microscopy and polymerase chain reaction (PCR) was determined using capillary blood collected by finger prick during a cross-sectional study conducted from September 2021 to January 2022 in children aged 6-59 months at two sentinel sites. Blood spots were collected for molecular analysis. Amplicons from the target regions (exon 2 of hrp2 and hrp3 genes) were generated by multiplex nested PCR and sequenced using Illumina's MiSeq protocol. A total of 278 samples were analyzed for ParaHIT RDT evaluation. The sensitivity and specificity of the RDT test compared with microscopy were 96.4% and 85.7%, respectively, which increased to 97.9% and 90.7%, respectively, when compared with PCR. Of the microscopically and PCR-positive P. falciparum samples, 138 were sequenced to detect pfhrp2/3 deletions. None of the parasites had a single pfhrp2 deletion or a single pfhrp3 deletion. The ParaHIT RDT demonstrated an acceptable level of performance in this evaluation, confirming the use of HRP2-based RDTs for the detection of P. falciparum infection in areas where microscopy is not available in Togo.

Code of ethical conduct and core competencies for medical educators: Fostering integrity and excellence appraisal.

Ethics refers to a branch of philosophy that deals with the study of moral principles and values that directs individual and group behaviour. It is apprehensive to what is right and what is wrong under certain circumstance and in different situations. The code of ethics are the fundamental values that guides code of conduct.1 There is imperative need to develop code of ethics and code of conduct for medical educators in Pakistan. Code of ethics plays a guiding role to implement code of conduct. There should be some standards to set levels of acceptable medical educator performance. Performance benchmark should be created to assess level of competence in medical educators by defining core competencies1. ---Continue

Balancing accuracy and efficiency: A lightweight deep learning model for COVID 19 detection

The novel coronavirus disease in human was detected in Wuhan, China and spread rapidly across countries each time mutating itself as a new variant. It has impacted not only the patients but also medical infrastructure a lot. Several artificial intelligence-based systems for detection of covid-19 in chest images have been proposed. This study aims to develop a deep learning-based approach that efficiently detect Covid-19 using chest Computed Tomography (CT) images. In this work, we propose an efficient and lightweight deep-learning architecture based on the concept of a densely connected network for Covid-19 detection. The proposed architecture is designed by stacking dense modules in which each layer is directly connected to the other layers, with very fewer number of neurons at each layer. The dense connection is used to improve the learning capability of the proposed model and the fewer number of neurons make network computationally efficient. CT images for covid-19 detection are effectively used for the detection of Covid-19 patients. We train and test our model on SARS-CoV-2 CT-scan datasets. The experimental results are evaluated on accuracy, sensitivity and F1 score. The Adam optimizer with binary cross entropy loss function gives 97.2% accuracy with 0.98 sensitivity, 0.97 precision and 0.97 F1 score. The model achieves maximum accuracy of 88% at precision 0.97%, specificity 0.82 and F1 score 0.89 with Covid-CT dataset. The proposed model is compared with other state-of-the-art methods. The results of the proposed methods and its comparison shows acceptable level of performance on chest CT images.

Multi-objective optimization of active control system using population guidance and modified reference-point-based NSGA-II

The optimization of the active control system is pivotal in achieving an acceptable performance level while minimizing control costs. The optimal location of actuators, efficient control force, and adequate vibration reduction are critical objectives in control system optimization. This study introduces a novel optimization algorithm, the Population-Guided and Modified Reference-Point Based Non-Dominated Sorting Genetic Algorithm-II (PMR-NSGA-II), tailored for actuator placement and tuning that incorporates input and output user preference. The analysis focuses on optimizing the vibration control of a 20-story steel frame building. The PMR-NSGA-II could reduce the computational expenses of three objectives optimization by guiding the initial population using prospective population prediction and orienting the search towards a reference point. The pre-calculated input energy distribution guides the determination of the prospective initial population. The reference point, along with the allowable drift and actuator capacity constraints, focuses the search process to efficiently obtain the Pareto fronts with less effort wasted to explore the less preferred areas in the search space. The non-dominated sorting and elitist operators are also employed to fasten the convergence. The structural analysis is conducted via non-linear time history analysis with seven ground motions considered. The PMR-NSGA-II exhibits significant computational efficiency in the active control system optimization. Results demonstrate that PMR-NSGA-II could provide the near-optimal solution closest to the reference point with a smaller population size and a faster convergence rate than the NSGA-II. The computational time per generation of the proposed PMR-NSGA-II is 1.36 to 2.43 times faster than NSGA-II for the seven ground motions analyzed. By applying PMR-NSGA-II, the number of individuals that need to be assessed to reach convergence is reduced by 50 % to 88 % compared to NSGA-II. Ultimately, the near-optimal configurations could significantly reduce the building responses and increase the performance level of the building based on FEMA 356 and ASCE 41 acceptance criteria.

Seismic evaluation and retrofit of a typical reinforced concrete hospital building in Indonesia with DCFP isolation system

In this paper, a study on seismic evaluation and retrofit of a typical reinforced concrete (RC) hospital building in Indonesia is presented. Seismic evaluation is performed against basic structural performance objectives outlined in the current code for structural seismic evaluation. The target building is a typical 5-story RC building designed by the 1970s Indonesia seismic and building codes, which imposed lower seismic demand and were not as strict as the current codes in the requirements for strength and ductility. The first part of the paper describes a seismic evaluation procedure against specific target performance levels. Nonlinear time history analysis (NLTHA) is conducted and structural performances were evaluated against structural responses (base shear force and inter-story drift ratio) and acceptance of seismic performance level for local (element rotational limit) and global (roof drift ratio) performance criteria under 225, 975, and 2475-year return period earthquake scenarios. Results reveal that seismic performances of some structural elements are unsatisfactory and under the acceptable performance level, especially for 2475-year return period (BSE-2 N/MCER) earthquake scenario. Seismic retrofit is thus recommended and the use of double-concave friction pendulum (DCFP) base-isolation system as a retrofit strategy is explored. The seismic performance of DCFP-retrofitted building is examined by NLTHA, where effectiveness of DCFP base-isolation system in improving seismic performance is confirmed by comparing structural responses, local and global structural performance of the existing building and the DCFP-retrofitted building under 2475-year return period earthquake scenario. Results of NLTHA demonstrate that local structural performance level has improved from Collapse Prevention to Immediate Occupancy level, whereas the global structural performance has improved from Limited Safety level to Operational level.

Position control of a digital electrohydraulic system with limited sensory data using double deep q-network controller

In this study, we propose a reinforcement learning controller for position control of a digital electrohydraulic system with independent metering valve structure. Our study addresses the inherent challenges of traditional control methods, such as the need for continuous pressure measurement and complex control structures. As a solution, a double deep q-network (DDQN) controller which relies solely on position feedback from D-EHS is suggested. A simulation environment was developed to train the DDQN controller, wherein the artificial neural network structure, training hyperparameters, and reward function were optimized through preliminary studies. The DDQN controller was trained using a sinusoidal position reference signal of a single frequency and evaluated experimentally for its position control performance. Tracking performance was assessed against various position reference signals, including ramp and sinusoidal signal with different frequencies. Robustness against external disturbances was also tested by increasing the nominal supply pressure by 20% and 40%. The results demonstrate that the DDQN controller successfully controls the digital electrohydraulic system and is achieving good tracking performance for the training position reference signal. Furthermore, the controller exhibits satisfactory tracking performance for non-training position reference signals, highlighting its ability to learn the digital electrohydraulic system dynamics with limited training data. Even under varying supply pressure conditions, the DDQN controller maintains an acceptable level of control performance. Our findings emphasize the generalizability and reliability of the DDQN reinforcement learning controller, making it a suitable candidate for application in digital electrohydraulic systems and contributing to the advancement of control theory research.

Assessing the Performance of Artificial Intelligence Models: Insights from the American Society of Functional Neuroradiology Artificial Intelligence Competition.

Artificial intelligence models in radiology are frequently developed and validated using data sets from a single institution and are rarely tested on independent, external data sets, raising questions about their generalizability and applicability in clinical practice. The American Society of Functional Neuroradiology (ASFNR) organized a multicenter artificial intelligence competition to evaluate the proficiency of developed models in identifying various pathologies on NCCT, assessing age-based normality and estimating medical urgency. In total, 1201 anonymized, full-head NCCT clinical scans from 5 institutions were pooled to form the data set. The data set encompassed studies with normal findings as well as those with pathologies, including acute ischemic stroke, intracranial hemorrhage, traumatic brain injury, and mass effect (detection of these, task 1). NCCTs were also assessed to determine if findings were consistent with expected brain changes for the patient's age (task 2: age-based normality assessment) and to identify any abnormalities requiring immediate medical attention (task 3: evaluation of findings for urgent intervention). Five neuroradiologists labeled each NCCT, with consensus interpretations serving as the ground truth. The competition was announced online, inviting academic institutions and companies. Independent central analysis assessed the performance of each model. Accuracy, sensitivity, specificity, positive and negative predictive values, and receiver operating characteristic (ROC) curves were generated for each artificial intelligence model, along with the area under the ROC curve. Four teams processed 1177 studies. The median age of patients was 62 years, with an interquartile range of 33 years. Nineteen teams from various academic institutions registered for the competition. Of these, 4 teams submitted their final results. No commercial entities participated in the competition. For task 1, areas under the ROC curve ranged from 0.49 to 0.59. For task 2, two teams completed the task with area under the ROC curve values of 0.57 and 0.52. For task 3, teams had little-to-no agreement with the ground truth. To assess the performance of artificial intelligence models in real-world clinical scenarios, we analyzed their performance in the ASFNR Artificial Intelligence Competition. The first ASFNR Competition underscored the gap between expectation and reality; and the models largely fell short in their assessments. As the integration of artificial intelligence tools into clinical workflows increases, neuroradiologists must carefully recognize the capabilities, constraints, and consistency of these technologies. Before institutions adopt these algorithms, thorough validation is essential to ensure acceptable levels of performance in clinical settings.

Modeling Wind and Obstacle Disturbances for Effective Performance Observations and Analysis of Resilience in UAV Swarms

UAV swarms have multiple real-world applications but operate in a dynamic environment where disruptions can impede performance or stop mission progress. Ideally, a UAV swarm should be resilient to disruptions to maintain the desired performance and produce consistent outputs. Resilience is the system’s capability to withstand disruptions and maintain acceptable performance levels. Scientists propose novel methods for resilience integration in UAV swarms and test them in simulation scenarios to gauge the performance and observe the system response. However, current studies lack a comprehensive inclusion of modeled disruptions to monitor performance accurately. Existing approaches in compartmentalized research prevent a thorough coverage of disruptions to test resilient responses. Actual resilient systems require robustness in multiple components. The challenge begins with recognizing, classifying, and implementing accurate disruption models in simulation scenarios. This calls for a dedicated study to outline, categorize, and model interferences that can be included in current simulation software, which is provided herein. Wind and in-path obstacles are the two primary disruptions, particularly in the case of aerial vehicles. This study starts a multi-step process to implement these disruptions in simulations accurately. Wind and obstacles are modeled using multiple methods and implemented in simulation scenarios. Their presence in simulations is demonstrated, and suggested scenarios and targeted observations are recommended. The study concludes that introducing previously absent and accurately modeled disruptions, such as wind and obstacles in simulation scenarios, can significantly change how resilience in swarm deployments is recorded and presented. A dedicated section for future work includes suggestions for implementing other disruptions, such as component failure and network intrusion.

Variable Guidelines for Pediatric Medial Patellofemoral Ligament Reconstruction Rehabilitation Exists Across PRiSM Member Organizations.

Medial patellofemoral ligament (MPFL) reconstruction, typically used to restore stability to the patellofemoral joint after dislocation, often requires extensive rehabilitation to address acute impairment related to surgical intervention and also underlying non-anatomical deficits that may have contributed to the index injury. Rehabilitation guidelines, including objective functional performance assessment criteria, are lacking in the literature. We sought to summarize the clinical guidelines for rehabilitation and return to activity assessment after MPFL reconstruction as advocated by the member organizations of the Pediatric Research in Sports Medicine (PRiSM) Patellofemoral Research Interest Group (PF-RIG). We obtained and reviewed MPFL rehabilitation guidelines from 11 member organizations of the PRiSM PF-RIG, extracting information on weight-bearing advancement, bracing, use of supplemental strengthening modalities, and any objective criteria for advancing rehabilitation phases. We found highly variable agreement among guideline parameters at each treatment stage, with time-based criteria most widely used for early progression. Although functional metrics like strength or movement tests were more widely used in later phases of rehabilitation, there was substantial variation in testing mode and level of acceptable performance. Our review found that significant variability exists in current practice among PRiSM and PF-RIG member institutions regarding rehabilitation standards after MPFL reconstruction. Although we found broad consensus that objective strength or performance criteria should be employed to establish a better framework for clinical decision-making, most current guidelines lack standardization and sufficient detail to guide ideal clinical practice.

Determinants of Supply Chain Performance in the Energy Sector in Kenya: A Case Study of the Kenya Power.

The current supply chain management in Kenya energy sector is deficient of an acceptable SCM performance level due to the inability to connect every willing buyer, and the cost of rural electrification by grid development due to dispersion as is expected in the service charter. The general objective of the study was to establish the determinants of supply chain performance in the energy sector in Kenya with an inclination to KPLC. The specific objectives of the study were to; determine the effect of information communication technology on supply chain performance of KPLC; examine the effect of buyer-supplier relationship on supply chain performance of KPLC; determine the effect of procurement professionalism on supply chain performance of KPLC and establish the effect of contract management on performance of KPLC. The study employed a descriptive research design. The researcher used stratified random sampling technique. The study population was 698 employees working in KPLC from which 254 respondents were selected. Questionnaires were used as the main data collection instrument and a pilot study was undertaken to pretest the questionnaires for validity and reliability. For the purpose of analyzing the data that was gathered, and the Statistical Package for Social Sciences Version 24 was utilized in analysis. In order to establish the statistical link between the independent factors and the predicted variable, the study made utilization of the regression model. Descriptive statistics were computed whereby frequencies, percentages and means were clearly shown in the form of both tables and figures. The study revealed that a unit increase in the elements of information communication technology, procurement professionalism, buyer-supplier relationship and contract management will translate to improvement of supply chain performance in KPLC. the study recommends that KPLC and other firms in the energy sector should exploit the aforementioned determinants in a hierarchical order. This would enable KPLC and other firms in the energy sector to focus on the determinants with the most positive significant effect on supply chain performance and prioritize resources allocation and deployment appropriately. The study recommends this order, buyer-supplier relationships, followed by contract management while information communication technology and procurement professionalism should be exploited concurrently.

Análise de falha localizada em uma grande parede de concreto armado

Abstract Reinforced concrete structures must be designed in a manner that assures an acceptable level of safety and performance. However, accidents still occur nowadays causing interdiction, financial losses, or even human casualties. Therefore, succinct studies are required to give answers to such an undesirable situation, helping to prevent repetition and enlightening new design and construction methods. This paper analyzes the failure mechanism in a large reinforced concrete panel composed of one L-shaped cantilevered wall and structural walls in Campinas. The investigation was carried out using in-situ destructive and non-destructive tests and also through numerical simulation. In-situ tests revealed that the region where the fracture happened had no sufficient resisting steel area. The fracture line developed through the end of the wall’s horizontal rebars anchored within the column. It was concluded that the reinforced concrete panel failure happened due to structure misconception and incorrect design, once columns stirrups were responsible for the equilibrium of the cantilevered part of the building, as demonstrated by the nonlinear finite element study and in-situ investigations.

Reconfiguration of the real distribution network and providing a new resilience index, considering the uncertainty of renewable energy sources and digital protection devices in order to improve the resilience

Electric energy, as the most widely used form of energy, has always captured human attention to itself; however, with the climate change and the increase of natural phenomena, it is now necessary to provide solutions to improve the resilience of the network. Resilience is defined as the capability of the system to maintain an acceptable level of performance against a severe disturbance and return to its original condition in an appropriate period of time, which demonstrates the degree of endurance, vulnerability, and reversibility of an infrastructure system. The occurrence of adverse weather conditions and natural disasters causes extensive losses and power outages in distribution networks; and the number and severity of such incidents have seen an increase in recent years. Therefore, it is of great significance to evaluate the resilience of the network and its reversibility capability in dealing with adverse weather conditions. In this paper, after defining the concept of resilience in distribution networks, its characteristics are determined; and then, modeling common natural events such as floods and storms, and introducing a new index to evaluate resilience in distribution networks with digital directional overcurrent relays (DOCR) and Digital dual-setting directional overcurrent relays (DS-DOCR) in the presence of renewable energy sources (RES), the proposed scheme is presented.

234 Can We Use Moderate Quality Round Bale Bermudagrass Silage to Stretch Wheat pasture?

Abstract Grazing wheat pasture with growing calves is an economically important enterprise in the Southern Great Plains and Southeastern U.S. Wheat forage is available when there are few other high quality forage options, and a offers a favorable seasonality of net returns for the stocker cattle enterprise. Our objective was to determine if a moderate quality wrapped round bale silage can be used to profitably increase stocking rates during the fall and winter. This research was conducted at the University of Arkansas System Division of Agriculture Livestock & Forestry Research Station near Batesville, AR (35°50′N, 91°48′W; elevation 150 m) over 3-yr from fall 2018 to spring 2021. Treatments were: HCON, where steers were stocked at 2.47 steers/ha in the fall without bermudagrass baleage or were stocked at 1× (H1.0), 1.5× (H1.5), or 2× (H2.0) the HCON stocking rate with ad libitum access to bermudagrass round bale wrapped silage during the fall. In the spring all pastures were stocked at 5.56 steers/ha without baleage (HCON) or with access to baleage (H1.0, H1.5, H2.0). Steer performance, forage mass and nutritive quality were analyzed as a randomized complete block design with SAS 9.4 (SAS Inst. Inc. Cary, NC). The bermudagrass baleage was analyzed to be 48.8% DM in yr 1, 40.2% DM in yr 2, and 46.8% DM in yr 3; and ranged from 13 to 14% CP and 55 to 56% TDN across the 3-yr (Table 1). Baleage intake based on estimated disappearance from feeders increased (P < 0.01) with increasing stocking rate in the fall and tended (P = 0.07) to increase during the spring with increased fall stocking rate. Steer ADG showed a positive associative effect of baleage feeding in the fall with greater (P = 0.01) ADG for H1.0 than HCON, while HCON and H1.5 did not differ (P = 0.71), but were greater (P ≤ 0.03) than H2.0. Gain per hectare in the fall tended (P = 0.06) to be greater for H1.0 than HCON, H1.5 and H2.0 were greater (P < 0.01) than H1.0 but did not differ from each other. Performance and BW did not differ (P ≥ 0.73) during the spring, but steer grazing days/ha and BW gain/ha were greater when baleage was offered in the spring compared with HCON. Supplemental efficiency of feeding silage did not differ (P ≥ 0.47) among treatments, on the average gains were increased by 0.22 kg/kg baleage DM fed in the fall and 0.11 kg/kg baleage DM in the spring. Offering moderate quality warm-season grass baleage to steers on wheat pasture enabled increased stocking rates in the fall while maintaining acceptable levels of performance and extending the length of the grazing season in the spring.

Exploring visual representations of computer mouse movements for bot detection using deep learning approaches

Nowadays, bots can be seen everywhere on the Internet and are responsible for a large percentage of website traffic. The problem of bot detection has increasingly gained attention since more and more bots have been abused from click fraud in online advertisements to launching credential stuffing attacks for harvesting user accounts at a large scale. In this work, we present an end-to-end deep framework for bot detection based on computer mouse movements. Specifically, we propose a novel visual representation scheme that can simultaneously encode spatial and kinematic information in mouse movements into an image which can then be used as the input to Convolutional Neural Networks (CNN). Various strategies to encode kinematic features into images are investigated to obtain a better scheme of visual representation. Experimental results show that the proposed representation scheme in combination with CNN outperforms several baseline models with a TPR of 99.34% in detecting known bots and can be generalized to unknown bots with a highest accuracy of 99.20%. We also demonstrate that the proposed approach can reach acceptable performance levels even for models trained with a small number of training samples. This makes the deployment of our approach easier in real-world scenarios.

Prediction Of Carboxylic Acid Toxicity Using Machine Learning Model

Carboxylic acids are organic compounds characterized by the presence of a carboxyl functional group capable of donating a proton and forming carboxylate ions in aqueous solutions. The carboxylic acid has widely been used in in manufacturing and medical applications. The rapid growth in carboxylic acid has established a need to predict its toxicity. The purpose of this paper to build predictive toxicity of carboxylic acid models by using five molecular descriptors (refractive index, The octanol/water partition coefficient (log P), acid dissociation constant (pKa), density, and dipole moment) through Machine Learning algorithms. The accuracy of the Machine Learning algorithm was determined by using three different types of models which are Decision Tree, Random Forest and k-Nearest Neighbour (k-NN). Among the machine learning algorithms used, we have determined that the decision tree is the best model for predicting the toxicity of carboxylic acid. This finding demonstrates that the decision tree model exhibits an acceptable level of performance in predicting toxicity within the field of toxicology.

Enhancing robustness and noise rejection in flexible joint manipulators: an optimized sliding mode controller with enhanced gray wolf optimization for trajectory tracking

The objective of this study is to enhance the performance of a nonlinear three-rigid-link manipulator (RLM) with a focus on trajectory tracking, robustness against disturbances and noises, and adaptability to joint flexibility. To achieve this, we have employed an optimized sliding mode controller with a proportional integral derivative (PID) sliding manifold. The tuning process involves selecting the critical gains of the controller that minimizes the integral time absolute error (ITAE), serving as the objective function (OBJF) to optimize the performance of the robot manipulator. To identify the optimal gains of the controller, we have utilized a new optimization algorithm known as memory enhanced linear population size reduction gray wolf optimization (MELGWO). The efficacy of this algorithm is compared to other existing optimization methods in the literature. Moreover, this research has delved into the impact of joint flexibility on the robot system’s performance. Encouragingly, the results demonstrate that the optimized SMC–PID with MELGWO adaptation can effectively address joint flexibility while maintaining acceptable performance levels.