Performance Evaluation Method Research Articles

Exploring the Remote Work Revolution: A Managerial View of the Tech Sector’s Response to the New Normal

Abstract Background The global work landscape has undergone a significant transformation in the aftermath of the pandemic in 2019, resulting in the widespread adoption of remote working or working from home (WFH) practices. This paradigm shift has necessitated the adaptation of business strategies and the implementation of novel remote work policies by human resources (HR) and management departments within technology companies. Nevertheless, this rapid transition in the work model has introduced a range of benefits and drawbacks that warrant careful examination in terms of their impact on efficiency, challenges, recruitment processes, training, and psychological well-being. Research aims The primary objective of this study is to investigate and comprehend the impact of remote work applications within technology companies, specifically focusing on the changes experienced by management departments, challenges, recruitment practices, communication and engagement strategies, performance evaluation methods, and training programs. Methodology The study sample comprised of nine executives/managers who were currently employed in five distinct multinational technology companies. The research approach employed was qualitative in nature, utilizing a combination of interview, document review, and observation techniques. Findings The findings reveal varied impacts of remote work on organizational functioning in technology companies. Challenges in functions, responsibilities, and employee engagement were observed. Online evaluation systems and meetings were commonly adopted. The research supports the hypothesis and emphasizes the need for adaptation and tailored approaches in remote work environments.

A simple evaluation method of external Venetian blind shading performance for nearly zero energy buildings

A simple evaluation method of external Venetian blind shading performance for nearly zero energy buildings

Performance evaluation methods for chemical injection methods considering soil permeability and chemical rheology

Performance evaluation methods for chemical injection methods considering soil permeability and chemical rheology

The Resident or Community Partnership with Police in Addis Ababa City Administration

The future competition is anticipated to focus on Police Community Partnerships rather than physical material production in order to evaluate the Ethiopian community's participation in police member community partnership with police. This study was done to evaluate Police Community Partnerships procedures and issues specific to the Ethiopian population. The following particular goals were created: 432 police officers were chosen by the researchers using a scientific manner out of a total population of 13755 in order to evaluate the community partnership with police practices of police officers. The data was acquired using basic random sampling and purposeful sampling procedures, and structural equation modelling (SEM) was used to analyses both the quantitative and qualitative data. The main conclusions were that there were insufficient human resources, financial resources, and competent and accountable selectors. Additionally, there were insufficient background checks, physical and psychological examinations, assessments of applicants' potential for leadership, and the use of appropriate performance evaluation methods. According to the research team's recommendations, the Ethiopian community should work with all regional and local police commissioners, develop clear policies and standards, assign enough human resources, fulfil an adequate budget, and reject informal hiring practices. By working with accountable organizations, the Ethiopian community seeks to create a positive public impression of police officers, government support, and community perception to cooperate with all stakeholders during recruiting and selection.

Discordant pair analysis for sample efficient model evaluation

We present a new technique for assessing the effectiveness of a classification algorithm using discordant pair analysis. This method utilizes a known performance baseline algorithm and a large unlabeled dataset with an assumed class distribution to obtain overall performance estimates by only assessing the subset of examples that the algorithms classify discordantly. Our approach offers an efficient way to evaluate the performance of an algorithm that minimizes the human adjudications needed while also maintaining precision in the evaluation and in some cases improving the evaluation quality by reducing human adjudication errors. This approach is a computationally efficient alternative to the traditional exhaustive method of performance evaluation and has the potential to improve the accuracy of performance estimates. Simulation studies show that the discordant pair method reduces the number of adjudications by over 90%, while maintaining the same level of sensitivity and specificity.

Segmentation of hard exudate lesions in color fundus image using two-stage CNN-based methods

The presence of hard exudate (EX) lesions is an early clinical symptom of Diabetic Retinopathy (DR); its accurate segmentation is essential for diagnosis and treatment. Automatic segmentation of EX lesions is challenging because they have a wide range of sizes, shapes, and brightness, and can be confused with soft exudate lesions. In this paper, we present and assess an efficient segmentation method for interactively segmenting EX lesions in fundus images from DR patients. Our approach consisted of two main stages: (1) the first stage generates an automatic segmentation using a CNN-based method. (2) If the obtained segmentation is suboptimal, the second stage is used to refine each inaccurately segmented region using a CNN-based interactive segmentation method. To train and evaluate method performance, we used two public datasets (IDRiD and DDR) and two local datasets from two medical centers (EHos and EWT) containing a wide range of EX lesions. Several state-of-the art CNN-based models and the proposed method were trained and evaluated on IDRiD, DDR and EHos datasets using the area under the precision–recall curve (AUPR), the dice similarity coefficient (Dice) and the intersection over union (IoU). In addition, EWT dataset was used to evaluate the methods without retraining the CNN models. The evaluation results using IDRiD dataset showed that the proposed method achieved AUPR, mean Dice and mean IoU scores of 0.893, 76.6% and 62.4%, respectively, which are comparable to the state-of-the-art method on EX segmentation. When evaluated on the DDR dataset with large portion of small EX lesions, the proposed method achieved AUPR, mean Dice and mean IoU scores of 0.693, 60.8% and 45.5%, yielding state-of-the-art performance. Furthermore, with the main evaluation metric being AUPR, the evaluation results on two datasets, EHos and ETW, with scores of 0.689 and 0.667 demonstrated superiority of the proposed method over the other segmentation methods. The proposed two-stage CNN-based segmentation method is an useful approach for accurately segmenting EX lesions in color fundus images, enabling lesion quantification to aid experts in treating patients with DR. The source code, the trained CNN models and the data are publicly available at: https://github.com/dvquang2000/Interactive_HardExudates_Segmentation.

Assessing Arctic wetting: Performances of CMIP6 models and projections of precipitation changes

The Arctic region is experiencing a notable increase in precipitation, known as Arctic wetting, amidst the backdrop of Arctic warming. This phenomenon has implications for the Arctic hydrological cycle and numerous socio-ecological systems. However, the ability of climate models to accurately simulate changes in Arctic wetting has not been thoroughly assessed. In this study, we analyze total precipitation in the Arctic using station data, multiple reanalyses, and 35 models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). By employing the moisture budget equation and an evaluation method for model performance with ERA5 reanalysis as a reference, we evaluated the models' capability to reproduce past Arctic wetting patterns. Our findings indicate that most reanalyses and models are able to replicate Arctic wetting. However, the CMIP6 models generally exhibit an overestimation of Arctic wetting during the warm season and an underestimation during the cold season from 1979 to 2014 when compared to the ERA5 reanalysis. Further investigation reveals that the overestimation of wetting during the warm season is largest over the Arctic Ocean's northern part, specifically the Canadian Arctic Archipelago, and is associated with an overestimation of atmospheric moisture transport. Conversely, the models significantly underestimate wetting over the Barents-Kara Sea during the cold season, which can be attributed to an underestimation of evaporation resulting from the models' inadequate representation of sea ice reduction in that region. The models with the best performance in simulating historical Arctic wetting indicate a projected intensification of Arctic wetting, and optimal models significantly reduce uncertainties in future projections compared to the original models, particularly in the cold season and oceanic regions. Our study highlights significant biases in the CMIP6 models' simulation of Arctic precipitation, and improving the model's ability to simulate historical Arctic precipitation could reduce uncertainties in future projections.

An Engineering Alternative to Lockdown During COVID-19 and Other Airborne Infectious Disease Pandemics: Feasibility Study.

Now and in the future, airborne diseases such as COVID-19 could become uncontrollable and lead the world into lockdowns. Finding alternatives to lockdowns, which limit individual freedoms and cause enormous economic losses, is critical. The purpose of this study was to assess the feasibility of achieving a society or a nation that does not require lockdown during a pandemic due to airborne infectious diseases through the mass production and distribution of high-performance, low-cost, and comfortable powered air purifying respirators (PAPRs). The feasibility of a social system using PAPR as an alternative to lockdown was examined from the following perspectives: first, what PAPRs can do as an alternative to lockdown; second, how to operate a social system utilizing PAPR; third, directions of improvement of PAPR as an alternative to lockdown; and finally, balancing between efficiency of infection control and personal freedom through the use of Internet of Things (IoT). PAPR was shown to be a possible alternative to lockdown through the reduction of airborne and droplet transmissions and through a temporary reduction of infection probability per contact. A social system in which individual constraints imposed by lockdown are replaced by PAPRs was proposed, and an example of its operation is presented in this paper. For example, the government determines the type and intensity of the lockdown and activates it. At that time, the government will also indicate how PAPR can be substituted for the different activity and movement restrictions imposed during a lockdown, for example, a curfew order may be replaced with the permission to go outside if wearing a PAPR. The following 7 points were raised as directions for improvement of PAPR as an alternative method to lockdown: flow optimization, precise differential pressure control, design improvement, maintenance method, variation development such as booth type, information terminal function, and performance evaluation method. In order to achieve the effectiveness and efficiency in controlling the spread of infection and the individual freedom at a high level in a social system that uses PAPRs as an alternative to lockdown, it was considered effective to develop a PAPR wearing rate network management system utilizing IoT. This study shows that using PAPR with infection control ability and with less economic and social damage as an alternative to nationwide lockdown is possible during a pandemic due to airborne infectious diseases. Further, the efficiency of the government's infection control and each citizen's freedom can be balanced by using the PAPR wearing rate network management system utilizing an IoT system.

Enhancing channel estimation accuracy in polar-coded MIMO–OFDM systems via CNN with 5G channel models

In this paper, a novel channel estimation technique for multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO–OFDM) systems in fifth generation (5G) networks is proposed, which leverages the power of convolutional neural networks (CNNs) along with the application of polar coding. By employing polar encoding at the transmitter and polar decoding at the receiver, the proposed system enhances error correction performance, reliability, and data transmission rate in 5G networks. A novel optimized CNN-based channel estimation technique, named CNN-CENet, has been integrated into the polar-coded MIMO–OFDM receiver. This integration aims to address the interference and noise challenges in 5G systems. The proposed channel estimation method utilizes multiple layers of CNN to accurately characterize the properties of the real channel by incorporating least squares (LS) estimations. Hence, the proposed CNN-CENet provides an accurate estimation of the channel coefficients, enabling efficient signal detection at the receiver. The MATLAB simulation has been conducted using deep learning and 5G toolboxes to obtain results for various channel parameters. These results are then compared with the LS and minimum mean square error (MMSE) methods for performance evaluation. The proposed model surpasses the performance of the LS and MMSE methods, achieving a significant 95.6% reduction in mean square error (MSE) compared to LS and a substantial 59.7% reduction compared to MMSE. Additionally, it delivers notable bit error rate (BER) reductions of 59.8% and 53.2% for LS and MMSE, respectively, in low mobility scenarios, and 57.4% and 48.5% reductions in high mobility scenarios. This improvement leads to enhanced reliability and overall system efficiency.

Field testing and performance evaluation of AMD-controlled high-rise building structures with real-world validation

In recent decades, structural control of civil infrastructures, especially high-rise buildings, has been broadly investigated and developed to mitigate disaster-induced structural responses. The traditional tuned mass damper (TMD) has been exploited to the active mass damper (AMD) system, which leads to the necessary field testing and performance evaluation. Accordingly, a comprehensive dynamic testing and performance evaluation method is proposed in this paper for high-rise structures with AMD systems. Four testing scenarios are firstly adopted and investigated at the field testing stage, including ambient vibration, free vibration, forced vibration, and dynamic loading vibration. Correspondingly, a multi-level performance evaluation framework, sequentially containing direct response data analysis, modal characteristic identification, AMD excitation performance validation, and vibration control efficiency evaluation, is designed and presented for the control performance evaluation and confirmation. To illustrate the presented multi-scenario dynamic testing and multi-level performance evaluation framework, a field dynamic testing was systematically conducted on a real-world tall tower with an AMD system. In the first testing part, the tower's critical dynamic characteristics, such as natural frequency, damping ratio, and modal mass, are accurately identified. Subsequently, the control performance of the AMD system is evaluated by analysis of the excitation and actuation capacity and the structural vibration control efficiency. Furthermore, the amplitude dependency of the damping ratio is identified, and the potential mechanism is discussed.

Study on levitation control performance assessment of maglev train based on minimum entropy

In order to meet the strict requirements of the reliability and safety of the commercial operation of the maglev train, it is necessary to carry out the control performance evaluation of the suspension system. However, at present, variance is often used as the evaluation index of control system. This method cannot get accurate performance evaluation results when the system is affected by non-Gaussian disturbance. Therefore, aiming at the non-Gaussian control performance evaluation of maglev train suspension system, a control performance evaluation method of maglev train suspension system based on minimum entropy is proposed in this paper. In view of the non-Gaussian characteristics in the operation data of maglev train suspension system, this paper introduces the feedback invariant entropy of non-Gaussian system, and then realizes the evaluation and analysis of the control performance of maglev train suspension system based on the minimum entropy criterion. Finally, combined with the evaluation and analysis of the operation data of Changsha medium and low-speed maglev train, this paper verifies the effectiveness and accuracy of the proposed performance evaluation method for the control performance evaluation of maglev train suspension system.

An Angle Precision Evaluation Method of Rotary Laser Scanning Measurement Systems with a High-Precision Turntable

Rotary laser scanning measurement systems, such as the workshop measurement positioning system (wMPS), play critical roles in manufacturing industries. The wMPS realizes coordinate measurement through the intersection of multiple rotating fanned lasers. The measurement model of multi-laser plane intersection poses challenges in terms of accurately evaluating the system, making it difficult to establish a standardized evaluation method. The traditional evaluation method is based on horizontal and vertical angles derived from scanning angles, which are the direct observation of wMPS. However, the horizontal- and vertical-angle-based methods ignore the assembly errors of fanned laser devices and mechanical shafts. These errors introduce calculation errors and affect the accuracy of angle measurement evaluation. This work proposes a performance evaluation method for the scanning angle independent of the assembly errors above. The transmitter of the wMPS is installed on a high-precision turntable that provides the angle reference. The coordinates of enhanced reference points (ERP) distributed in the calibration space are measured by the laser tracker multilateration method. Then, the spatial relationship between the transmitter and the turntable is reconstructed based on the high-precision turntable and the good rotational repeatability of the transmitter. The simulation was carried out to validate the proposed method. We also studied the effect of fanned laser devices and shaft assembly errors on horizontal and vertical angles. Subsequently, the calibration results were validated by comparing the residuals with those derived from the space-resection method. Furthermore, the method was also validated by comparing the reference and scanning angles. The results show that the maximum angle measurement error was approximately 2.79″, while the average angle measurement error was approximately 1.26″. The uncertainty (k = 1) of the scanning angle was approximately 1.7″. Finally, the coordinate measurement test was carried out to verify the proposed method by laser tracker. The results show that the average re-scanning error was 2.17″.

The Critical Review to Evaluate Performance of Ready-Mix Concrete Production Plant

In Republic of Iraq, ready-mix concrete production plants have been adversely affected by the lack of modern and advanced technology to assess their performance in line with technological advancements. Current evaluation methods rely on traditional approaches and financial measures, yielding unrealistic performance results. To address this problem, there is a need to utilize modern models and methods for performance evaluation. The study's main objective This was achieved by employing a literature survey methodology and utilizing digital databases such as the Iraqi Scientific Journals website, virtual libraries, and scientific platforms like ScienceDirect, Springer, Google Scholar, and Gate Research between 2015 and 2023. The research study provided a comprehensive overview of performance evaluation, including its definitions, importance, and an introduction to modern models and evaluation methods. The study found that no previous studies have been conducted in Iraq to evaluate ready-mix concrete production plants. However, four studies were found in Egypt, Sudan, and India. The previous similar relevant studies discussed various topics and related studies. Firstly, they discussed the classification, advantages, and disadvantages of concrete mixing plants. Additionally, the previous studies analyzed the factors that most influence the performance of concrete production plants, including laboratory manager efficiency, work team efficiency, communication and relationships within work teams, plant operator, material transportation method, and time and courses. Furthermore, the previous research studies present a comprehensive analysis of all variable data simultaneously using the statistical package for Social Science (SPSS) input stage. The evaluation also extends to the evaluation of laboratories, encompassing plant arrangement, internal quality control systems, and final product quality. The overall evaluation results of previous studies. Indicate that 75% of the concrete production plants failed to meet the required criteria, while only 25% demonstrated satisfactory performance. The study proposed improvements to enhance the performance rate of ready-mix concrete production plants by leveraging the most influential variables, which will be considered in the study.

Power maximization control of small wind system using permanent magnet synchronous generator

This article describes modeling and simulations to determine a method for the power performance evaluation of autonomous wind turbine system with this simple load scheme. The method applies to small systems equipped with a permanent magnet generator in the wind turbine a diode rectifier, boost converter and batteries. In this work, the aerodynamic characteristics of wind turbines and the power conversion system topology are explained. The maximum power tracking of the wind turbine generator system using the Matlab software is presented and its results show, at least in principle, that the maximum power tracking algorithm developed is suitable for wind turbine generation systems.

Personal Wearable Thermal and Moisture Management Clothing: A Review on Its Recent Trends and Performance Evaluation Methods

Personal wearable systems designed to manage temperature and moisture are gaining popularity due to their potential to enhance human thermal comfort, safety, and energy efficiency, particularly in light of climate change and energy shortages. This article presents the mechanisms of thermal and moisture management, recent advances in wearable systems for human thermal and moisture management, and methods for their performance evaluation. It evaluates the pros and cons of various systems. The study finds that most wearable systems for thermal and moisture management are being examined as individual topics. However, human heat and moisture management have noteworthy interactions and impacts on human thermal comfort. There are certain limitations in the methods used for evaluating personal heat and moisture management in wearable systems. This review suggests future research directions for wearable systems to advance this field and overcome these limitations.

Waterproofing Performance Evaluation and Grading Methods for Lowest Level Floor Slabs and Positive-Side Walls of Residential Underground Structures

In Korea, large-scale apartment projects often give rise to disputes among residents, which have prompted implementation of the “Apartment Performance Rating System” by the Ministry of Land, Infrastructure, and Transport. In addition, disputes related to leakage defects in apartment structures are increasing, especially in underground spaces of joint residential complexes. This study aims to identify waterproofing materials and methods for specific underground structure components through experimental evaluations and to assign waterproofing performance ratings similar to existing apartment house grades. These performance ratings will serve as foundational data to prevent leakage in joint residential complexes. This study proposes composite and self-adhesive sheet waterproofing as effective methods, emphasizing the significance of sheet waterproofing materials for excellent performance. The need for improved waterproofing materials to address long-term permeability issues is also highlighted. This research provides essential data for future waterproofing performance ratings; therefore, contributing to construction quality and safety in joint residential complexes.

Comprehensive performance evaluation and sensitivity analysis method of a cutter-changing robot for a large-diameter shield machine

Cutter-changing robot is a new product to automatically replace the cutter in shield machine, whose performance is affected by multiple criteria with diversity, multiplicity and correlation, which is a complex multiple criteria decision-making problem with limited and fuzzy available information. This paper proposes a comprehensive performance evaluation model including the performance indicator hierarchy, weight determination, measurement and assessment methods of indicators and sensitivity analysis. Fuzzy Analytical Hierarchy Process (FAHP) is applied to identify the construction requirements from external shield machine and environment and design characteristics in the whole cutter-changing process, weight for 3 first-level indicators and 14 s-level indicators are determined. The objective evaluation indexes measured by instrumentation and subjective evaluation indexes quantified by fuzzy logic are normalized within the range of 0–1. The scores and proportion of assessment grade for each indicator at each level are obtained through Fuzzy Comprehensive Evaluation (FCE). Furthermore, the sensitivity of weight and evaluation data on performance evaluation result are deeply analyzed. The applicability and validity of the proposed approach were demonstrated by a case study of a cutter-changing robot made by China Railway Construction Heavy Industry Co., Ltd. According to the result, operational capacity is the most important criteria in the first level, and cutter grasping ability is the most important indicator in the second level. Path planning ability is the weakest indicators. Cutter grasping ability and time of replacement are the most critical affecting factors. The proposed method not only helps the designers capture the weak indicators accurately and improve the design quality, but also provides engineers with a holistic perspective to the cutter-changing robot before actual application.

Regional context-based recalibration network for cataract recognition in AS-OCT

Deep convolutional neural networks (CNNs) have been widely applied to cataract recognition tasks and have achieved promising results. However, most existing methods focused on designing data-driven CNN architectures, and failed to exploit asymmetric opacity distribution prior of cataract, which is significant for cataract diagnosis. To this end, this paper proposes a regional context-based recalibration (RCR) module, which fully leverages the clinical prior to recalibrate the feature maps with regional pooling, region-based context integration, and integrated context fusion. We stack these RCR modules to form an RCRNet based on anterior segment optical coherence tomography (AS-OCT) images for cataract recognition. Experiments on the AS-OCT-NC2 dataset and two publicly available medical datasets demonstrate that RCRNet achieves a better trade-off between performance and efficiency than state-of-the-art channel attention-based networks. We also explain the inherent behavior of RCRNet with the aid of the visual analysis. In addition, this paper is the first to study the effects of two performance evaluation methods on AS-OCT image-based cataract classification results: the single-image level and the single-eye level, suggesting that adopting the single-eye level to evaluate cataract classification performance according to clinical diagnosis requirement.

Performance design, development and verification of honeycomb aluminium barrier for vehicle side impact

SUVs are taken as the research object to develop the side deformation barrier with Chinese characteristics. By extracting characteristic parameters of typical SUVs, honeycomb aluminium barrier and trolley development objectives are defined and theoretical analysis are applied to guide the selection of barrier materials. Based on the dynamic load-cell wall test of barrier trolley, the evaluation method of honeycomb aluminium calibration performance is proposed. Through car-to-car crash test, a new verification method based on the deformation of the body structure and the height distribution of the crash load force is formatted. The results show that the new moving deformation barrier’s mechanical properties meet the design specifications, the prototype is stable in deformation modes and it can characterise the appearance and stiffness characteristics of SUV.

Systematic literature review of dermoscopic pigmented skin lesions classification using convolutional neural network (CNN)

The occurrence of pigmented skin lesions (PSL), including melanoma, are rising, and early detection is crucial for reducing mortality. To assist Pigmented skin lesions, including melanoma, are rising, and early detection is crucial in reducing mortality. To aid dermatologists in early detection, computational techniques have been developed. This research conducted a systematic literature review (SLR) to identify research goals, datasets, methodologies, and performance evaluation methods used in categorizing dermoscopic lesions. This review focuses on using convolutional neural networks (CNNs) in analyzing PSL. Based on specific inclusion and exclusion criteria, the review included 54 primary studies published on Scopus and PubMed between 2018 and 2022. The results showed that ResNet and self-developed CNN were used in 22% of the studies, followed by Ensemble at 20% and DenseNet at 9%. Public datasets such as ISIC 2019 were predominantly used, and 85% of the classifiers used were softmax. The findings suggest that the input, architecture, and output/feature modifications can enhance the model's performance, although improving sensitivity in multiclass classification remains a challenge. While there is no specific model approach to solve the problem in this area, we recommend simultaneously modifying the three clusters to improve the model's performance.