Efficient Handling Research Articles

On the Road Again – Using Rule-Oriented and Participative Leadership to Reduce Threats and Violence in the Construction Industry

Threats and violence are acute safety issues in many industries and effect a large proportion of the Swedish workforce. In the construction industry, more than half of constructions workers redirecting traffic at construction sites are subjected to threats or violence at least once a year. To help construction companies to systematically address and handle health and safety issues in accordance with the Provisions of the Swedish Work Environment Authority on Systematic Work Environment Management, an interventions method named Building Health has been developed by Gyllensten and colleagues. The intervention involved rule-oriented and participative leadership practise and was evaluated through a single case effect study in a middle-sized construction company. The results showed significant reductions in the one-year prevalence of threats of violence (pre-intervention = 35.0%; post-intervention = 19.0%; χ² = 7.047; p = .008), feelings of being threatened (pre-intervention = 42.7%; post-intervention = 23.8%; χ² = 9.188; p = .002), and conflicts with third parties (pre-intervention = 48.5%; post-intervention = 30.6%; χ² = 7.913; p = .005). Combining rule-oriented and participative leadership when helping construction companies to improve their systematic work environment management for efficient handling of health and safety issues can have substantial positive effects on employees’ work environment.

Rapid yield optimization of miniaturized microwave passives by response features and variable-fidelity EM simulations

The operation of high-frequency devices, including microwave passive components, can be impaired by fabrication tolerances but also incomplete knowledge concerning operating conditions (temperature, input power levels) and material parameters (e.g., substrate permittivity). Although the accuracy of manufacturing processes is always limited, the effects of parameter deviations can be accounted for in advance at the design phase through optimization of suitably selected statistical performance figures. Perhaps the most popular one is the yield, which provides a straightforward assessment of the likelihood of fulfilling performance conditions imposed upon the system given the assumed deviations of designable parameters. The latter are typically quantified by means of probability distributions pertinent to the fabrication process. The fundamental obstacle of the yield-driven design is its high computational cost. The primary mitigation approach nowadays is the employment of surrogate modeling methods. Yet, a construction of reliable metamodels becomes problematic for systems featuring a large number of degrees of freedom. Our work proposes a technique for fast yield optimization of microwave passives, which relies on response feature technology as well as variable-fidelity simulation models. Utilization of response features enables efficient handling of issues related to the system response nonlinearities. Meanwhile, the incorporation of variable-resolution simulations allows for accelerating the yield estimation process, which translates into remarkably low overall cost of the optimizing the yield. Our approach is verified with the use of three microstrip couplers. Comprehensive benchmarking demonstrates its superiority in terms of computational efficiency over the state-of-the-art algorithms, whereas reliability is corroborated by electromagnetic-driven Monte Carlo simulations.

Paper based microfluidic devices: a review of fabrication techniques and applications.

A wide range of applications are possible with paper-based analytical devices, which are low priced, easy to fabricate and operate, and require no specialized equipment. Paper-based microfluidics offers the design of miniaturized POC devices to be applied in the health, environment, food, and energy sector employing the ASSURED (Affordable, Sensitive, Specific, User-friendly, Rapid and Robust, Equipment free and Deliverable to end users) principle of WHO. Therefore, this field is growing very rapidly and ample research is being done. This review focuses on fabrication and detection techniques reported to date. Additionally, this review emphasises on the application of this technology in the area of medical diagnosis, energy generation, environmental monitoring, and food quality control. This review also presents the theoretical analysis of fluid flow in porous media for the efficient handling and control of fluids. The limitations of PAD have also been discussed with an emphasis to concern on the transformation of such devices from laboratory to the consumer.

Upsurge in biomedical waste due to COVID-19 in India: A statistical correlation, challenges and recommendations

Proper management of Biomedical Waste (BMW) is an essential component of any sustainable healthcare sector. With the burst of COVID-19 pandemic when every hospital and treatment facility was overburdened patients, efficient handling of the huge amount of generated BMW became a task for the entire world. This review compares the BMW generated before and during the second wave of COVID-19, highlights the challenges in managing the exuberated amount of COVID-19 waste and sites recommendations to promote sustainable design thinking, in order to address this grave concern in the current setting of the Indian system. The study indicated that inappropriate management of waste and the lacunae in the entire chain from segregation to collection until its disposal has posed a serious threat to the wellbeing of healthcare workers, sanitation staff as well as the operators and housekeeping staff at the hospitals, isolation centers and Municipal Corporation. Many states had inadequate number of common BMW treatment facilities (CBMWTFs) leading to inefficient treatment of the excess waste. The behavioural and attitudinal barriers of neglect and ignorance of different stakeholders further aggravated the problem of BMW management to manifolds. To achieve better management we recommend spreading awareness regarding the kind and infectious nature of waste generated by COVID-19 patients and their caregivers, segregation and decontamination of such waste at source and increasing the capacity as well as number of CBMWTFs. Creative ways to recycle the waste must be devised so as to reduce the burden on disposal sites.

Electrodialysis process for carbon dioxide capture coupled with salinity reduction: A statistical and quantitative investigation

Rejected brine and CO2 are the two most common pollutants associated with the desalination process. The current study focuses on the sustainable and efficient handling of these emissions. Herein, a method is presented that can effectively manages waste fractions and results in value-added products. A multi-chamber electrodialysis was studied to remove salt from the water and capture CO2 to produce hydrochloric acid, carbonates/bicarbonate salt mixture, and irrigation standard water. The effects of process variables (sodium chloride concentration, voltage/area, and CO2 flow rate on CO2 uptake, current efficiency, % salt removal, waste-to-product ratio, and energy consumption were optimized using a central composite design. It was found that high voltage and high CO2 flow rate were beneficial for high CO2 uptake. Current efficiency for salt removal improved with low voltage and high salt concentration. In contrast, increasing voltage and reducing salt concentration reduced the waste-to-product ratio and improved % salt removal. A salt concentration of 0.76 M, a voltage/area of 2344 V/m2, and a CO2 flow rate of 1.3 l/min were optimum parameters, resulting in a CO2 uptake of 11 g/M NaCl, a current efficiency of 87 %, a salt removal percentage of 28 %, a waste-to-product ratio of 2.2, and energy consumption of 123 Wh/M NaCl. Under optimum conditions, the process was analyzed over time to determine the variations within the system caused by ion transfer. The sodium carbonate and bicarbonate liquid mixture was freeze-dried and quantified using Fourier transform infrared spectroscopy (FTIR).

1-ADM-CNN: A Lightweight In-field Compression Method for Seismic Data

Large-scale seismic acquisition, versatility, flexibility, automation, and scalability are the objectives of future oil and gas exploration technology. An example of emerging technology for seismic monitoring is distributed acoustic sensing (DAS). The significant amount of data produced by DAS is a challenge that necessitates the development of new technologies for its efficient handling and processing. A typical seismic survey, on the one hand, can generate hundreds of terabytes of raw seismic data per day. The demand for wireless seismic data transmission, on the other hand, remains enormous. The massive amount of data transmission from geophones to the on-site data collection center and its storage poses significant challenges. A lightweight compression procedure is required in order to reduce the data traffic and the storage size at the data center without putting an extra burden on a geophone. In this brief, an efficient implementation of a 1D convolutional neural network (CNN) together with 1-bit adaptive delta modulation is presented for in-field seismic data compression. It is worth mentioning here that the training of CNN is done offline on synthetic data set and hence, the proposed approach has potential for real-time implementation. Furthermore, no assumption on the underlying statistics of noise or the seismic signal is imposed and consequently, the proposed method is suitable for a wide range of seismic data. Furthermore, the proposed method works in the time-domain, unlike existing transform-domain methods, making it suitable for quick diagnosis of bad traces at the data center. Simulation results with real data set reveal that the proposed approach achieves a signal-to-noise ratio (SNR) of approximately 30 dB with a compression gain of 35: 1. Finally, significant superiority in terms of compression gain and reconstruction quality is demonstrated when compared to the existing methods.

Simulating Fractures With Bonded Discrete Element Method.

Along with motion and deformation, fracture is a fundamental behaviour for solid materials, playing a critical role in physically-based animation. Many simulation methods including both continuum and discrete approaches have been used by the graphics community to animate fractures for various materials. However, compared with motion and deformation, fracture remains a challenging task for simulation, because the material's geometry, topology and mechanical states all undergo continuous (and sometimes chaotic) changes as fragmentation develops. Recognizing the discontinuous nature of fragmentation, we propose a discrete approach, namely the Bonded Discrete Element Method (BDEM), for fracture simulation. The research of BDEM in engineering has been growing rapidly in recent years, while its potential in graphics has not been explored. We also introduce several novel changes to BDEM to make it more suitable for animation design. Compared with other fracture simulation methods, the BDEM has some attractive benefits, e.g., efficient handling of multiple fractures, simple formulation and implementation, and good scaling consistency. But it also has some critical weaknesses, e.g., high computational cost, which demand further research. A number of examples are presented to demonstrate the pros and cons, which are then highlighted in the conclusion and discussion.

OpenSANS: A Semi-Analytical solver for Nonlocal plasmonicS

Prompted by the renewed interest the past decade in the hydrodynamic model for the description of nanoplasmonic systems, we present OpenSANS, a MATLAB toolbox for the simulation of canonical (that is, standalone spherical and cylindrical) nanoparticles and planar stratified structures whose critical dimensions are of mere nanometres. The method comprises of a generalisation of Mie theory to properly address the hydrodynamic motion of free electrons in metals and a compact S matrix formulation for the efficient treatment of multiple layers. The toolbox is built in a modular manner; each part (routine) has its own important functionality and can be used independently from the others. Our primary goal is the presentation of a highly efficient and accessible solver for the nanoplasmonics community. This is achieved by the very semi-analyticity of the chosen method and by powerful vectorisation techniques optimised in a very popular environment, MATLAB. Program summaryProgram Title: OpenSANSCPC Library link to program files:https://doi.org/10.17632/hmk224xngb.1Code Ocean capsule:https://codeocean.com/capsule/2246190Licensing provisions: GPLv3Programming language: MATLAB R2018b and newer.Supplementary material: See Appendix A.Nature of problem: Solve the scattering problem from multilayered, dielectric, local or nonlocal metallic nanoparticles, excited by a plane wave and placed in a local (dielectric or metallic) background.Solution method: Mie theory, appropriately extended to incorporate nonlocal effects (longitudinal waves) as predicted by the Hard Wall-Hydrodynamic Model, combined with S matrix theory for efficient handling of the multilayered structure.

Identifikasi Kebutuhan Penanganan Jalan Berdasarkan Nilai IRI (International Roughness Index) dan PCI (Pavement Condition Index)

Identification of road handling needs is an activity of searching, collecting, researching, and recording data and information on road needs that emphasize the comfort factor in driving, where these factors can occur from road geometric conditions, unevenness or hardness of the road surface. The identification of road handling needs that I will do in this research is a case study of national road conditions in the Sorong region of West Papua Province, which aims to provide input for the proposed road handling needs program for an area traversed by the national road network so as to achieve an optimal, effective and efficient road handling program. The data used in this analysis are IRI value data and PCI values, from these data can produce a road stability and road conditions, which will then be analyzed for the proposed handling needs program. The proposed national road handling needs program in the Sorong region, West Papua Province from the results of the analysis carried out on asphalt pavement there is 82.71% handling on the length of the road and there are 17.34% of the length of the road on the type of concrete pavement.

Technical solutions for waste classification and management: A mini-review.

The massive growth of population coupled with urbanization over the years has created a significant challenge of increase in waste generation. India has achieved massive developmental growth in economic and social areas but still lacks a proper waste management system. The lack of knowledge about segregation of waste into different categories and proper disposal techniques in a country like India with an accelerated population growth is a critical issue. Since trash has different disposal techniques, according to its type, segregating waste through an automated process at the point of collection will streamline the process and result in effective waste management and utilization. The mini-review article evaluates the recent literature on technologies used for municipal waste segregation and management, with the motive of providing critical information for advancement in current research. This article reviews the use of various convolutional neural network architectures for waste classification and describes in detail as to why image processing methods are preferred over sensors for segregation into respective categories. It is also important to have an efficient waste monitoring and management system for proper disposal. A comprehensive mini-review was undertaken to understand internet of things-based models proposing efficient waste handling, from the perspective of reduced costs, collection time and optimized routes. The proposed systems were compared and evaluated based on the sensors used monitoring, microcontrollers and communication protocols such as Long Range, Global System for Mobile Communication, Zigbee and Wi-Fi, which are employed for the secure and efficient data transmission.

8549 Simplifying Laparoscopic Suturing for the Gynecologic Trainee: A Systematic Approach for Reproducible Needle Handling

<h3>Study Objective</h3> Laparoscopic suturing and needle handling are core components of gynecologic surgical management of many benign and malignant disease processes. Adoption of a reproducible technique for laparoscopic needle loading and handling is beneficial for the gynecologic trainee. <h3>Design</h3> Educational demonstration of a surgical technique. <h3>Setting</h3> The demonstration of this method is shown in a laparoscopic simulator trainer with a vaginal cuff model. <h3>Patients or Participants</h3> N/A. <h3>Interventions</h3> This video demonstrates a 3-step approach for safe and efficient needling loading and handling in a simulation environment. 1. Grasp the suture one to two centimeters from the swaged end of the needle to serve as the fulcrum of movement. 2. Lightly grasp the body of the needle so it may act as a pendulum for the swing. 3. Maintain tension on the suture to allow for needle rotation. <h3>Measurements and Main Results</h3> N/A <h3>Conclusion</h3> Mastering laparoscopic needle handling and suturing are important fundamentals of gynecologic laparoscopic surgery. The swing technique may be used in a variety of port configurations making it adaptable and transferable across a wide setting of operations. Additionally, it uses minimal needle grasps for increased safety and efficiency. Learning this reproducible and methodical swing technique may help develop safe and efficient needle handling practices for the gynecologic trainee.

Studies on Standardization of Production Technology of Oyster Mushroom

In the present investigation, attempts were made to evaluate different substrate quantities to get higherbiological efficiency. Two strains of oyster mushroom (PL-19-05 and PL-19-06) were cultivated by usingdifferent quantities of wheat straw (2 kg, 3 kg, 4 kg and 5 kg). Among four substrate quantities, earlyspawn run (9.50 days and 11.00 days) and pin head initiation (19.25 days and 19.00 days) were observed at2 kg substrate quantity for PL-19-05 and PL-19-06 strains, respectively, followed by 3 kg, 4 kg and 5kgsubstrate quantities. Maximum yield was obtained at 5 kg substrate quantity for both strains. But biologicalefficiency was highest at 3 kg of substrate quantity which was 75.73% and 89.98 % for PL-19-05 and PL-19-06 strains, respectively, increase or decrease in quantity of substrate ensued in low biological efficiency.Based on the results, 3 kg substrate quantity is suggested as optimum substrate quantity forsub-humidconditions of Udaipur region to get higher biological efficiency and easy handling.

Ignoring visual distractors: Habituation to onsets is driven by time-based expectation.

An increasing bulk of evidence shows that through different mechanisms, experienced-based or voluntary, reactive or proactive, human beings can attenuate the distracting impact of salient visual, albeit irrelevant, stimuli. Current mechanisms assume that this is achieved by suppressing the salient distractor's features or location at the priority map level, or at lower dimension-based maps levels. However, this functional architecture has so far ignored the role of time in distractors filtering, a key question that we have addressed in the present study. We found that during a visual discrimination task, a "standard" onset distractor, always appearing at the same interval from the beginning of the trial, was subject to habituation. Crucially, however, when the onset distractor was unfrequently presented with an unexpected 1-second delay, it reboosted capture at full strength, while the "standard" distractor continued to remain overall habituated. As predicted by Sokolov's (1963, Annual Review of Physiology, 25[1], 545-580) theory, our results show that habituation mechanisms filter the irrelevant distracting sensory input also on the basis of its temporal parameters. We conclude that habituation to onsets is controlled also by time-based expectation mechanisms and suggest that more recently proposed theories of distractors filtering should also incorporate the temporal parameter among the factors that allow an efficient handling of visual distraction.

Sketch-based community detection in evolving networks.

We consider an approach for community detection in time-varying networks. At its core, this approach maintains a small sketch graph to capture the essential community structure found in each snapshot of the full network. We demonstrate how the sketch can be used to explicitly identify six key community events which typically occur during network evolution: growth, shrinkage, merging, splitting, birth, and death. Based on these detection techniques, we formulate a community detection algorithm which can process a network concurrently exhibiting all processes. One advantage afforded by the sketch-based algorithm is the efficient handling of large networks. Whereas detecting events in the full graph may be computationally expensive, the small size of the sketch allows changes to be quickly assessed. A second advantage occurs in networks containing clusters of disproportionate size. The sketch is constructed such that there is equal representation of each cluster, thus reducing the possibility that the small clusters are lost in the estimate. We present a new standardized benchmark based on the stochastic block model which models the addition and deletion of nodes, as well as the birth and death of communities. When coupled with existing benchmarks, this new benchmark provides a comprehensive suite of tests encompassing all six community events. We provide analysis and a set of numerical results demonstrating the advantages of our approach both in runtime and in the handling of small clusters.

A dual-population and multi-stage based constrained multi-objective evolutionary

The existence of constrained multi-objective optimization problems (CMOPs) in real-world applications motivate researchers to focus more on developing constrained multi-objective evolutionary algorithms (CMOEAs). Due to the presence of constraints, an efficient constraint handling technique (CHT) is required in CMOEA to balance the constraint satisfaction and optimization of objective functions. Recently, different fitness based, ranking based, multi-population and multi-staged evolutionary approaches are proposed to handle CMOPs. However, most of the approaches still struggle while handling CMOPs with discontinuous feasible regions or whose feasible regions consist infeasible barriers. To overcome these issues, we propose a novel Dual-Population and Multi-Stage based Constrained Multi-objective Evolutionary Algorithm which is termed as CMOEA-DPMS. In CMOEA-DPMS, two populations are used to explore the search space and feasible regions. Along with two populations, an archive is also employed to store feasible, well converged and distributed solutions. To employ appropriate mating selection and environmental selection strategies according to the evolution of the populations, evolutionary process is divided into several stages. A strategy decider mechanism is proposed to determine the appropriate mating and environmental selections depending on the status of the population. In addition, a novel CHT named decomposition based constraint non-dominating sorting (DCDSort) is proposed by combining decomposition based selection with traditional constraint non-dominating sorting to maintain feasibility, convergence and diversity. The proposed algorithm is evaluated on five recent and popular test suites along with 36 real-world constrained multi-objective optimization problems against eight state-of-the-art algorithms. The empirical results suggests that CMOEA-DPMS is significantly superior or comparable to the considered algorithms and can tackle all kinds of CMOPs.

Expert systems and the prospects of artificial intelligence for the automatic supervisory control of salinity gradient solar ponds

Salt Gradient Solar Ponds (SGSPs) are conventionally operated wholly manually. They hence suffer from a loss of efficiency for substantial durations when disturbed by natural diffusion and environmental elements such as temperature variation, solar radiation, and wind. They also impose considerable operational costs and time when a repair is unavoidable. Here, a Zero-Discharge Closed Cycle (ZDClC) is proposed for sustainable implementation of control architectures. We then define the SGSP as a control system and propose an automatic approach that uses Artificial Intelligence (AI) to mimic experts’ actions during repair operations. More specifically, an Automatic Supervisory Controller (ASC-SGSP) is proposed that decomposes the pond into several uniform sublayers according to their thicknesses and salt concentrations. The automated supervisor then uses a decision tree analysis of pond stability to determine the sequences of injection/suction actions to guarantee the pond stability and prevent layer overturning. An on–off subcontroller then directs the chosen sublayer’s salt density to its desired value. The supervisory structure is general and can operate with either the proposed open-loop or closed-loop configurations. Results indicate the potential utility of AI and expert systems as a successful paradigm for SGSP control. Specifically, the proposed approach is examined in a simulated pond under several scenarios challenging its efficiency, adaptivity, robustness, and constraint handling properties. The ASC-SGSP fulfills the automatic control requirements during either open/closed-loop configurations and regulates the desired salinity levels under parameter constraints and uncertainties, even when the SGSP parameters are perturbed by as much as 50%.

An Optimal Decision Support System Based on Crop Dynamic Model for N-Fertilizer Treatment.

The efficient handling of nitrogen has become a critical issue in modern agriculture, from a financial standpoint, as well as in regard to reducing the environmental impacts of using an excessive amount of nitrogen fertilizer. Manure compost is useful for maintaining or raising soil chemical levels without excessive accumulation; however, for the best grain yield, it should be combined with N fertilizer. Via this study, we aimed to develop an optimal decision support system that indicates when to initiate fertilization based on nitrogen-limited (N-limited) crop growth dynamics. An optimal nitrogen fertilizer (N-fertilizer) management system increases crop yield while maintaining a balance between fertilizer supply and crop demand. This study used the N-limited crop growth model (LINTUL3) to develop an optimal decision support system. In this work, we formulated and resolved two optimization challenges: (i) maximization of biomass growth; and (ii) maximization of growth with the least cost paid on N-fertilizer and its application. Furthermore, two case studies were developed based on the number of fields: (i) optimization for a single field, and (ii) optimization for multiple fields. In the case of multiple fields, it is hypothesized that a fertilizer treatment for one field can leak to other fields and affect the nitrogen dynamics of different fields. Finally, numerical simulations were carried out supporting the theory developed in the paper. The simulations showed that when the proposed work was employed to achieve the goal of optimal nitrogen management for a crop, a to increase in biomass growth under certain scenarios was attained.

Algorithms for rescheduling jobs with a LIFO buffer to minimize the weighted number of late jobs

Rescheduling can help to improve the quality of a schedule with respect to an initially given sequence. In this paper, we consider the possibility of rescheduling jobs arriving for processing at a single machine under the following limitations: (a) jobs can only be moved toward the end of the schedule and not toward the front, and (b) when a job is taken out of the sequence, it is put on a buffer of limited capacity before being reinserted in its new position closer to the end of the sequence. The buffer is organized as a stack with a last-in/first-out policy. As an objective function, we consider the minimization of the weighted number of late jobs. For this NP-hard problem, we first provide two different integer linear programming (ILP) formulations. Furthermore, we develop a branch-and-bound algorithm with a branching rule based on the movement of jobs. Then a new pseudo-polynomial dynamic programming algorithm is presented which utilizes dominance criteria and an efficient handling of states. Our computational experiments with up to 100 jobs show that this algorithm performs remarkably well and can be seen as the current method of choice.

Soft-tissue simulation of the breast for intraoperative navigation and fusion of preoperative planning.

Computational preoperative planning offers the opportunity to reduce surgery time and patient risk. However, on soft tissues such as the breast, deviations between the preoperative and intraoperative settings largely limit the applicability of preoperative planning. In this work, we propose a high-performance accurate simulation model of the breast, to fuse preoperative information with the intraoperative deformation setting. Our simulation method encompasses three major elements: high-quality finite-element modeling (FEM), efficient handling of anatomical couplings for high-performance computation, and personalized parameter estimation from surface scans. We show the applicability of our method on two problems: 1) transforming high-quality preoperative scans to the intraoperative setting for fusion of preoperative planning data, and 2) real-time tracking of breast tumors for navigation during intraoperative radiotherapy. We have validated our methodology on a test cohort of nine patients who underwent tumor resection surgery and intraoperative radiotherapy, and we have quantitatively compared simulation results to intraoperative scans. The accuracy of our simulation results suggest clinical viability of the proposed methodology.

282. VALIDITY AND RELIABILITY OF CROWD-SOURCED AND EXPERT VIDEO ASSESSMENT IN MINIMALLY INVASIVE ESOPHAGECTOMY USING GLOBAL RATING SCALES

Abstract Minimally Invasive Esophagectomy (MIE) is a complex and technically demanding procedure and implementation is associated with a substantial learning curve. Suboptimal surgical performance in complex minimally invasive procedures has convincingly been related to less favorable patient outcomes. Structured video-based assessment by other surgeons can objectively evaluate surgical performance, but requires a surgeon’s scarce time. This study investigated whether crowdsourcing, collective surgical performance evaluation by anonymous and untrained laypeople, could be a valid alternative for MIE. Two video assessments types were conducted via the C-SATS platform with eight MIE operative videos. First, videos were cut into eight procedural phase-based video clips of maximum ten minutes. These 64 video clips were assessed with the validated Global Operative Assessment of Laparoscopic Skills (GOALS)‘s four domains (depth perception, bimanual dexterity, efficiency and tissue handling) using a 1-5 Likert scale. Each video clip was assessed by a pair of two MIE experts and &amp;gt; 30 crowd workers, recruited via Amazon Mechanical Turk. Second, the same expert pairs assessed the corresponding complete video with the procedure-specific MIE competency assessment tool (MIE-CAT). Experts assessed 128 clips and sixteen complete videos in 3.5 months. Crowd workers assessed 1984 clips in &amp;lt; two days. Expert scores showed a wider range (expert GOALS score range 7.50-19.50 versus 12.84-15.63 for crowds). When comparing experts and crowd workers scores, a poor correlation was found on all domains and total GOALS scores (ICC ≤ 0.42). When correlating GOALS scores with MIE-CAT scores a strong, positive correlation between experts’ scores was found (r¬s = 0.76, p = 0.03) while between crowd’s GOALS and expert’s MIE-CAT scores a small correlation was found (r¬s = 0.23, p = 0.58) (Figure 1). Despite rapid results from crowd workers, a poor agreement between crowd and experts’ GOALS scores was observed. MIE might be technically too difficult to assess for laypeople. Furthermore, GOALS might not be extensive enough to assess MIE performance in detail, while the procedure-specific MIE-CAT might be more comprehensive. However, the correlation between the experts’ MIE-CAT and GOALS scores indicates video clip assessments (instead of full length video assessments) could be helpful to shorten assessment time.