Finish Time Research Articles

Application of general unit hydrograph model for marathon finish time distributions

The marathon finish time distribution is the probability distribution of the time a group of runners completes a marathon race. The knowledge of this distribution is required to optimize a running process when planning and operating a marathon; it is also required to compare different marathons and evaluate individual running performance. This research demonstrated that the marathon finish time distribution can be described by the general unit hydrograph model from applied hydrology. Specifically, we found that marathon finish time data, in terms of distribution density, often show a positively skewed distribution, which implies that a few runners are superfast, many runners are very slow, and most runners are between the two extremes. This asymmetrical feature is similar to a unit hydrograph that describes rainfall-runoff processes in watersheds. We thus hypothesized that the marathon finish time distribution follows Guo’s three-parameter general unit hydrograph model. To test this hypothesis, we fit the general unit hydrograph model to the 2013–2021 New York City (NYC) Marathon finish time distribution data, resulting in determination coefficients r2≥0.9997. We next validated the proposed model with the world marathon database including 35 million results in 2000–2021 from more than 28,000 long-distance foot races, which was then compared with the NYC marathon data. According to the two inflection times of the finish time density function (asymmetrical bell curve) of the world marathons, we classified all marathoners into three categories: super runners who finish before the left inflection time; endurable runners who finish after the right inflection time; and fast runners who finish between the two inflection times. Finally, we compared the general unit hydrograph model and two typical asymmetrical distribution functions with the world marathon data and found that the proposed model describes the data better.

Enhanced hybrid multi-objective workflow scheduling approach based artificial bee colony in cloud computing

This paper presents a hybrid approach based Binary Artificial Bee Colony (BABC) and Pareto Dominance strategy for scheduling workflow applications considering different Quality of Services (QoS) requirements in cloud computing. The main purpose is to schedule a given application onto the available machines in the cloud environment with minimum makespan (i.e. schedule length) and processing cost while maximizing resource utilization without violating Service Level Agreement (SLA) among users and cloud providers. The proposed approach is called Enhanced Binary Artificial Bee Colony based Pareto Front (EBABC-PF). Our proposed approach starts by listing the tasks according to priority defined by Heterogeneous Earliest Finish Time (HEFT) algorithm, then gets an initial solution by applying Greedy Randomized Adaptive Search Procedure (GRASP) and finally schedules tasks onto machines by applying Enhanced Binary Artificial Bee Colony (BABC). Further, several modifications are considered with BABC to improve the local searching process by applying circular shift operator then mutation operator on the food sources of the population considering the improvement rate. The proposed approach is simulated and implemented in the WorkflowSim which extends the existing CloudSim tool. The performance of the proposed approach is compared with Heterogeneous Earliest Finish Time (HEFT) algorithm, Deadline Heterogeneous Earliest Finish Time (DHEFT), Non-dominated Sort Genetic Algorithm (NSGA-II) and standard Binary Artificial Bee Colony (BABC) algorithm using different sizes of tasks and various benchmark workflows. The results clearly demonstrate the efficiency of the proposed approach in terms of makespan, processing cost and resources utilization.

Learning to Coordinate for a Worker-Station Multi-Robot System in Planar Coverage Tasks

For massive large-scale tasks, a multi-robot system (MRS) can effectively improve efficiency by utilizing each robot's different capabilities, mobility, and functionality. In this letter, we focus on the multi-robot coverage path planning (mCPP) problem in large-scale planar areas with random dynamic interferers in the environment, where the robots have limited resources. We introduce a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">worker-station</i> MRS consisting of multiple <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">workers</i> with limited resources for actual work, and one <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">station</i> with enough resources for resource replenishment. We aim to solve the mCPP problem for the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">worker-station</i> MRS by formulating it as a fully cooperative multi-agent reinforcement learning problem. Then we propose an end-to-end decentralized online planning method, which simultaneously solves coverage planning for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">workers</i> and rendezvous planning for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">station</i> . Our method manages to reduce the influence of random dynamic interferers on planning, while the robots can avoid collisions with them. We conduct simulation and real robot experiments, and the comparison results show that our method has competitive performance in solving the mCPP problem for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">worker-station</i> MRS in metric of task finish time.

Effects of ibuprofen during 42-km trail running on oxidative stress, muscle fatigue, muscle damage and performance: a randomized controlled trial

ABSTRACT Up to 75% of marathon runners ingest non-steroidal anti-inflammatory drugs (NSAIDs) during competition. Despite the doubt whether or not they contribute to performance, the effect of NSAID in endurance sports is unclear. We evaluated the effect of ibuprofen (IBU) use on oxidative stress, muscle damage, physical performance, and vertical jump of runners participating in a 42-km-trail running. The sample consisted of 12 men randomly divided into 2 groups: a placebo group (placebo) and an ibuprofen group (IBG). A 400-mg IBU capsule was administered to the IBG 15 min prior to the start of the trial and during the course after 5 h. In the intergroup analysis, placebo 70.1% increase (p < 0.0001; Cohen’s d = 4.77) of the thiobarbituric acid reactive substances (TBARS); the IBG exhibited a 31.46% increase of the sulphhydryl groups (SH) (p = 0.024, Cohen’s d = 0.27), 55% of squat jump (SJ) (p < 0.01; Cohen’s d = 1.41) with no significant effect on creatine kinase (CK), pace, speed, and finish time. In summary, IBU had positive evidence on oxidative stress and muscle fatigue, but had no effect on physical performance and muscle damage.

Effect of Feed rate on Material Removal Rate, Surface Roughness and Machining Time of Aluminum Alloy 6063 in CNC Turning

The main objective of today’s manufacturing industries is to produce low cost and high quality product in short time. Turning is a very important machining process in which a single-point cutting tool removes material from the surface of a rotating cylindrical work-piece. In general, CNC lathe machine is operated with several controllable factors such as spindle speed, depth of cut, feed rate etc. This present work focuses on CNC turning of aluminum alloy 6063 using carbide tool for varying feed rate. The results revealed that the feed rate is the most important parameter that affects the material removal rate, surface finish and machine time in CNC turning process.

Task sequencing in heterogeneous device for improved offloading decision using optimization technique

Edge Computing (EC) has made decisions regarding offloading tasks. Also, it might be a challenging task for a gadget of the multi-hop cause of competing network models due to the limited bandwidth available. The literature provides several studies on the issue of the multi-hop cloud environment. Moreover, partial offloading of multi-hop computing and dynamic network management leads to network problems. And inefficient performance measured in terms of task completion time has not been factored into current studies. To reduce the average finishing time of all jobs, this article proposed joint multi-task complete unloading and a network that supports the scheduling issue. Several variable decision factors such as the partial systematic ratio of offloading, Offloading remote devices, task start time, path of routing & dynamic network start time were optimized by the formulated problem. To manage tasks, we use the features inherent in multiple processors with various CPU frequencies. Under the restrictions of power & latency, we determine the appropriate quantity of task data to be handled immediately or distributed to the preferred nodes on the clouds remotely. Our objective is to solve the issue of optimization of task scheduling on a limited quasi-quadratic function. We offer an Improved Offloading and Heuristic Flow Optimization (IOHFO) solution effectively to solve these problems by using semi-definite relaxation. Finally, a simulator can be used to evaluate our proposed unloading system to reduce the (optimal) cost of unloading profiles for various parameters.

Sex-specific Physiological Responses To Ultramarathon

PURPOSE. Despite a growing body of literature on the physiological response to ultra-marathon, there is a paucity of data in female athletes. This study assessed the female physiological response to ultra-marathon and compared perturbations to a group of race- and performance-matched males. METHODS. Data were examined from a cohort of 53 individuals who contested an ultra-marathon trail race at the Ultra Trail du Mont-Blanc (UTMB®) in 2018/19. Before and within 2-h of the finish, subjects underwent physiological assessments including: Blood sampling (creatine kinase, CK-MB; cardiac troponin I, cTnI; brain natriuretic peptide, BNP, creatinine, Cr); pulmonary function testing (spirometric indices, exhaled NO, diffusing capacities, mouth pressures); and transthoracic ultrasound (lung comet tails, cardiac function). Data from eight female finishers (age = 36.6 ± 6.9 y; finish time = 30:57 ± 11:36 hh:mm) were compared to a group of eight time-matched males (age = 40.3 ± 8.3 y; finish time = 30:46 ± 10:32 hh:mm). RESULTS. Females exhibited significant post-race hematological changes in BNP (25.8 ± 14.6 vs. 140.9 ± 102.7 pg/mL; p = 0.017) and CK-MB (3.3 ± 2.4 vs. 74.6 ± 49.6 IU/L; p = 0.005), whereas males exhibited significant post-race hematological changes in BNP (26.6 ± 17.5 vs. 96.4 ± 51.9 pg/mL; p = 0.007), CK-MB (7.2 ± 3.9 vs. 108.8 ± 37.4 IU/L; p < 0.001), cTnI (0.01 ± 0.01 vs. 0.05 ± 0.05 ng/mL; p = 0.033), and Cr (1.06 ± 0.19 vs. 1.23 ± 0.24 mg/dL; p = 0.014). Post-race peak expiratory flows decreased in both groups; however, only males exhibited reductions in lung diffusing capacities (DLCO = 34.4 ± 5.7 vs. 29.2 ± 6.9 mL/min/mmHg, p = 0.002; DLNO = 179.1 ± 26.2 vs. 152.8 ± 33.4 mL/min/mmHg, p = 0.001) and pulmonary capillary blood volume (77.4 ± 16.7 vs. 57.3 ± 16.1 mL; p < 0.001). Males, but not females, exhibited a significant post-race increase in the number of lung comet tails (2.4 ± 2.2 vs. 8.3 ± 2.7; p = 0.001). CONCLUSION. Participation in an ultra-marathon negatively affects numerous aspects of physiological function but evokes more extensive perturbations in males compared to females with similar race performances.

Performant, Multi-Objective Scheduling of Highly Interleaved Task Graphs on Heterogeneous System on Chip Devices

Performance-, power-, and energy-aware scheduling techniques play an essential role in optimally utilizing processing elements (PEs) of heterogeneous systems. List schedulers, a class of low-complexity static schedulers, have commonly been used in static execution scenarios. However, list schedulers are not suitable for runtime decision making, particularly when multiple concurrent applications are interleaved dynamically. For such cases, the static task execution times and expectation of idle PEs assumed by list schedulers lead to inefficient system utilization and poor performance. To address this problem, we present techniques for optimizing execution of list scheduling algorithms in dynamic runtime scenarios via a family of algorithms inspired by the well-known heterogeneous earliest finish time (HEFT) list scheduler. Through dynamically arriving, realistic workload scenarios that are simulated in an open-source discrete event heterogeneous SoC simulator, we exhaustively evaluate each of the proposed algorithms across two SoCs modeled after the Xilinx Zynq Ultrascale+ ZCU102 and O-Droid XU3 development boards. Altogether, depending on the chosen variant in this family of algorithms, we are able to achieve an up to 39% execution time improvement, up to 7.24x algorithmic speedup, or up to 30% energy consumption improvement compared to the baseline HEFT implementation.

Efficient Prioritization and Processor Selection Schemes for HEFT Algorithm: A Makespan Optimizer for Task Scheduling in Cloud Environment

Cloud computing is one of the most commonly used infrastructures for carrying out activities using virtual machines known as processing units. One of the most fundamental issues with cloud computing is task scheduling. The optimal determination of scheduling criteria in cloud computing is a non-deterministic polynomial-time (NP)-complete optimization problem, and several procedures to manage this problem have been suggested by researchers in the past. Among these methods, the Heterogeneous Earliest Finish Time (HEFT) algorithm is recognized to produce optimal outcomes in a shorter time period for scheduling tasks in a heterogeneous environment. Literature shows that HEFT gives extraordinary results in terms of quality of schedule and execution time. However, in some cases, the average computation cost and selection of the first idle slot may not produce a good solution. Therefore, here we propose modified versions of the HEFT algorithm that can obtain improved results. In the rank generation phase, we implement different methodologies for calculating ranks, while in the processor selection phase, we modify the way of selecting idle slots for scheduling the tasks. This paper suggests enhanced versions of the HEFT algorithm under user-required financial constraints to minimize the makespan of a specified workflow submission on virtual machines. Our findings also suggest that enhanced versions of the HEFT algorithm perform better than the basic HEFT method in terms of lesser schedule length of the workflow problems running on various virtual machines.

Uneven but Conservative Pacing Is Associated With Performance During Uphill and Downhill Running.

To investigate the relationship between pacing strategy and performance during uphill and downhill running-specifically, what distribution of energy corresponds to faster race finish times between and among participants. Eighteen years of race data from a 10.2-mile running race with an uphill first half and a downhill second half were analyzed to identify relationships between pacing and performance. A pacing coefficient (PC), equal to a participant's ascent time divided by finishing time (FT), was used to define each participant's pacing strategy. The American College of Sports Medicine metabolic running equation was used to estimate energy expenditure during the ascent, descent, and total race. Statistical analyses compared participants' PC to their FT and finishing place within their age and gender category. Additionally, FT and finishing place were compared between groups of participants who exhibited similar pacing strategies. PCs were positively associated with faster FTs (r2 = .120, P < .001) and better finishing positions (r2 = .104, P < .001). PCs above .600 were associated with the fastest average FTs and best average finishing position within age and gender categories (all P ≤ .047). Participants performed the best when energy expenditure increased no more than 10.4% during the uphill portion compared to their overall average. It is not possible to state that overly aggressive uphill efforts resulted in premature fatigue and thus slower decent times and worse race performance. However, participants should still avoid overly aggressive uphill pacing, as performance was associated with larger PCs.

Coded Distributed Computing With Predictive Heterogeneous User Demands: A Learning Auction Approach

Coded distributed computing(CDC) has shown great potentials to solve the unexpected delay caused by stragglers in distributed computing. In this paper, we focus on the auction design for efficient resource allocation in CDC. Specifically, we aim to design a learning auction mechanism to handle heterogeneous user demands and also to free users from the complexity of specifying valuations for resource combinations, which increases exponentially with the resource dimensions. The user demand type is heterogeneous according to different variation trends of the value with finish time and workload, which is modeled by deep learning. The platform would allocate resources according to the user value function. Then users do not need to consider the complex relationship between uncertain finish time and resource configuration in CDC. Due to the inference error of the learning model and the complexity of calculating uncertain finish time, the considered social welfare optimization problem is a non-linear and non-convex integer problem. Even worse, the typical VCG-based payment scheme cannot guarantee truthfulness with the inference error. In response to these difficulties, we transform the social welfare optimization problem into a mixed integer programming problem which already has efficient solutions. The social welfare gap caused by the inference error is analyzed theoretically. The relationship between the utility regret of reporting truthfully and the inference error is also analyzed. We prove that our mechanism satisfies incentive alignment and individual rationality. Extensive experiments show the superiority of our mechanism compared with existing ones.

Multi‐objectivetask scheduling in fog computing using improved gaining sharing knowledge based algorithm

SummaryIn the modern Internet of Things (IoT) era, several applications generate a vast amount of data and that needs to be handled appropriately. The conventional cloud computing system delivers us with enormous resources to manage such voluminous data. Despite that, the growing demands of IoT applications on minimal energy consumption, minimal latency, the privacy of data, data processing based on location, and maximum Quality of Service(QoS) impels the advent of fog computing. As the devices in the fog layer are heterogeneous, distributed, and resource‐constrained, how the fog resources are effectively utilized for executing latency and time‐sensitive applications is a primary challenge. The data generated by the IoT devices are voluminous and produce overhead in network bandwidth during transmission and slow down the response time. This article addresses the task scheduling problem in fog computing to minimize the makespan and energy consumption. The proposed work consists of two phases. In the first phase, the tasks are ordered based on the heuristic method, heterogeneous earliest finish time (HEFT). Then the ordered tasks are scheduled by applying the improved gaining sharing knowledge (IGSK) based algorithm. To reduce the energy consumption dynamic voltage frequency scaling (DVFS) is applied in the proposed scheme. To evaluate the proposed scheduling method the simulations are performed on different workflows with varying sizes. The performance evaluation results exhibit that the proposed work outperforms the task scheduling with similar methods in terms of makespan and energy consumption.

Investigations on a hybrid chemo-magnetorheological finishing process for freeform surface quality enhancement

Surface roughness improvement of the freeform surfaces is essential for the enhanced functionality of the components, including implants, turbine blades, microchannels, etc. The exclusion of human involvement during the finishing of freeform surfaces prevents hazardous exposure to particulate and provides uniform surface quality. Different surface finishing techniques, including vibratory finishing, Abrasive Flow Finishing (AFF), Electron Beam (EB) Irradiation, Electrolytic in-process Dressing (ELID) grinding, etc., have been developed to automate the finishing operation. However, some limitations are associated with the preexisting techniques, e.g., the process controllability of AFF and vibratory finishing, recast layer formation during EB irradiation, and low productivity of ELID grinding. Hence, a novel method for surface improvement, namely, Hybrid Chemo-Magnetorheological Finishing (HC-MRF), is proposed in the present study to reduce the surface roughness (Ra) (~up to a few nanometer) without affecting the surface topography of the workpiece. HC-MRF simultaneously utilizes the synergic action between Magnetorheological Finishing (MRF) and chemical etching to reduce surface finishing time and escalate process efficiency. Herein, the carrier medium of the Magnetorheological (MR) fluid is replaced by a reagent to facilitate the chemical etching process, and its synchronized action with mechanical abrasion reduces surface irregularities. Furthermore, the design of the developed apparatus to enable the HC-MRF process, where permanent magnets are used to produce the desired external magnetic field, is also discoursed. A case study on the surface roughness enhancement through the HC-MRF on the WC-Co (tungsten carbide in a cobalt-rich matrix) is investigated using Response Surface Methodology (RSM). Herein, Murakami's reagent is used as the carrier medium in the MR fluid, and a reduction in surface roughness (Ra) from an initial value of 312.87 nm to a final value of 34.50 nm is noticed. Moreover, the inclusion of Murakami's reagent enhances the process efficiency. The change in surface roughness with and without Murakami's reagent in the MR fluid is 88.97 % and 43.12 %, respectively, for a constant finishing time.

Tropical approximation to finish time of activity networks.

We breakdown complex projects into activities and their logical dependencies. We estimate the project finish time based on the activity durations and relations. However, adverse events trigger delay cascades shifting the finish time. Here I derive a tropical algebraic equationfor the finish time of activity networks, encapsulating the principle of linear superposition of exogenous perturbations in the tropical sense. From the tropical algebraic equationI derive the finish time distribution with explicit reference to the distribution of exogenous delays and the network topology and geometry.

Air pollution and individual productivity: Evidence from the Ironman Triathlon results

The study considers how air quality affects the productivity of physically intensive labor. Namely, we analyze the athletes of the Ironman Triathlon, one of the toughest long-distance triathlon races in the world. Moreover, in this competition, both men and women, professional and amateur athletes, can participate. We consider the results of Ironman Triathlon from 2005 to 2019. Using athlete's finishing time as a dependent variable, we estimate the impact of concentrations of two main air pollutants, O3 and PM2.5. We found triathletes performance decrements in swimming due to high ozone concentration and in bicycle riding and running due to high level of PM2.5. We have also found that professional athletes are less sensitive to air pollution, whereas there is almost no gender difference in the impact of air pollution on athletes’ productivity.

DAG Scheduling with Communication Delays Based on Graph Convolutional Neural Network

In vehicular edge computing (VEC), tasks and data collected by sensors on the vehicles can be offloaded to roadside units (RSUs) equipped with a set of servers through the wireless transmission. These tasks may be dependent of each other and can be modeled as a directed acyclic graph (DAG). The DAG scheduling problem is aimed at scheduling the tasks to the servers to minimize the scheduling length (makespan), i.e., the maximum finish time of all tasks. The conventional heuristic algorithms only utilize partial information of the DAG, so the performance of these algorithms is not stable. The state-of-the-art scheduling method employs the graph neural network to further reduce the makespan. However, this method ignores the fact that there are communication delays between tasks scheduled on different servers. In this paper, we tackle the DAG scheduling problem considering communication delays which makes the problem much more challenging. Our method is based on graph convolutional neural network and reinforcement learning. Experimental results show that our scheduling method reduces the DAG scheduling length by 8% to 15% compared with the representative scheduling strategies based on graph neural network models (GAT, GraphSAGE) and 15% to 25% compared with the conventional algorithms (HEFT, LC, and CPOP) and the sequence-to-sequence model.

Getting going on time: reducing neurophysiology set-up times in order to contribute to improving surgery start and finish times

At the Walton Centre we conduct a relatively large number of complex and lengthy elective (booked) spinal operations. Recently, we have had a particular problem with half or more of...

The pollution-routing problem with one general period of congestion

PurposeThis paper aims to address a pollution-routing problem with one general period of congestion (PRP-1GPC), where the start and finish times of this period can be set freely.Design/methodology/approachIn this paper, three sets of decision variables are optimized, namely, travel speeds before and after congestion and departure times on given routes, aiming to minimize total cost including green-house gas emissions, fuel consumption and driver wages. A two-phase algorithm is introduced to solve this problem. First, an adaptive large neighborhood search heuristic is used where new removal and insertion operators are developed. Second, an analysis of optimal speed before congestion is presented, and a tailored speed-and-departure-time optimization algorithm considering congestion is proposed by obtaining the best node to be served first over the congested period.FindingsThe results show that the newly developed operator of congested service-time insertion with noise is generally used more than other insertion operators. Besides, compared to the baseline methods, the proposed algorithm equipped with the new operators provides better solutions in a short time both in PRP-1GPC instances and time-dependent pollution-routing problem instances.Originality/valueThis paper considers a more general situation of the pollution-routing problem that allows drivers to depart before the congestion. The PRP-1GPC is better solved by the proposed algorithm, which adds operators specifically designed from the new perspective of the traveling distance, traveling time and service time during the congestion period.

Artificial intelligence-driven prescriptive model to optimize team efficiency in a high-volume primary arthroplasty practice.

We aimed to improve OR efficiency using machine learning (ML) to find relevant metrics influencing surgery time success and team performance on efficiency to create a model which incorporated team, patient, and surgery-related factors. From 2012 to 2020, five surgeons, 44 nurses, and 152 anesthesiologists participated in 1199 fourjoint days (4796 cases): 1461 THA, 1496 TKA, 652 HR, 242 UKA, and 945 others. Patients were 2461f:2335m; age, 64.1; BMI, 29.93; and ASA, 2.45. Surgical Success was defined as completing four joints within an eight hour shift using one OR. Time data was recorded prospectively using Surgical Information Management Systems. Hospital records provided team, patient demographics, adverse events, and anesthetic. Data mining identified patterns and relationships in higher dimensions. Predictive analytics used ML ranking algorithm to identify important metrics and created decision tree models for benchmarks and success probability. Five variables predicted success: anaesthesia preparation time, surgical preparation time, time of procedure, anesthesia finish time, and type of joint replacement. The model determined success rate with accuracy of 72% and AUC = 0.72. Probability of success based on mean performance was 77-89% (mean-median) if APT 14-15minutes, PT 68-70minutes, AFT four to five minutes, and turnover 25-27minutes. With the above benchmarks maintained, success rate was 59% if surgeon exceeded 71.5-minutes PT or 89% if 64-minutes procedure time or 66% when anesthesiologist spent 17-19.5minutes on APT. AI-ML predicted OR success without increasing resources. Benchmarks track OR performance, demonstrate effects of strategic changes, guide decisions, and provide teamwork improvement opportunities.

Characterization of wear resistance and corrosion during magnetorheological fluid assisted finishing (MFAF) of Ti-6Al-4V and duplex stainless steel for enhanced biocompatibility

The sustainability of biomaterials relies on their interaction with the proteins, fluid, and cells present inside the human body. The biomaterials’ enhanced surface roughness parameters and mechanical properties (i.e. wear resistance) are responsible for reducing degradation (i.e. corrosion rate) to enhance life span during musculoskeletal interaction. The present study compares the biocompatibility between two biomaterials (i.e. Ti-6Al-4V and DSS) polished through the Magnetorheological Fluid Assisted Finishing ( MFAF) process. MFAF process can produce nano-level average surface roughness ( Ra) and is used to improve the quality of the biomaterial's surface. However, apart from Ra, two other surface roughness parameters, namely skewness ( Rsk) and kurtosis ( Rku) analyze the characteristics of the irregularities formed on the polished surface. During the MFAF process, Trochoidal, a high-speed surface finishing toolpath, is used to map the workspace on the Duplex Stainless Steel ( DSS) and Ti alloy (Ti-6Al-4V) biomaterials to enhance their biocompatibility with a reduced finishing time. However, during the pin-on-disc wear tests, an Ultra High Molecular Weight Polyethylene ( UHMWPE) is used as the disc to imitate the interface of the bone with the implants for analyzing wear resistance. During experimentation, it is observed that the wear resistance of DSS and Ti-6Al-4V is improved to 3.19 × 10−6 and 0.123 × 10−5 mm3/min from its initial value of 7.65 × 10−6 and 2.68 × 10−5 mm3/min, respectively. Furthermore, the dependency of initial surface roughness parameters over the wear resistance of the biomaterials is also analyzed. A three-electrode-based electrochemical test with 0.5 M NaCl concentration is utilized to replicate the human body fluid while analyzing the corrosion resistance of the biomaterials. It is observed that the MFAF process enhances the corrosion resistance of the DSS and Ti-6Al-4V to 0.0037 and 0.0114 mm/year, from its initial values of 0.0079 and 0.0130 mm/year, respectively.