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A major key success factor regarding proficient Bayes threshold denoising refers to noise variance estimation. This paper focuses on assessing different noise variance estimations in three Bayes threshold models on two different characteristic brain lesions/tumor magnetic resonance imaging (MRIs).

Here, three Bayes threshold denoising models based on different noise variance estimations under the stationary wavelet transforms (SWT) domain are mainly assessed, compared to state-of-the-art non-local means (NLMs). Each of those three models, namely D1, GB and DR models, respectively, depends on the most detail wavelet subband at the first resolution level, on the entirely global detail subbands and on the detail subband in each direction/resolution. Explicit and implicit denoising performance are consecutively assessed by threshold denoising and segmentation identification results.

Implicit performance assessment points the first–second best accuracy, 0.9181 and 0.9048 Dice similarity coefficient (Dice), sequentially yielded by GB and DR; reliability is indicated by 45.66% Dice dropping of DR, compared against 53.38, 61.03 and 35.48% of D1 GB and NLMs, when increasing 0.2 to 0.9 noise level on brain lesions MRI. For brain tumor MRI under 0.2 noise level, it denotes the best accuracy of 0.9592 Dice, resulted by DR; however, 8.09% Dice dropping of DR, relative to 6.72%, 8.85 and 39.36% of D1, GB and NLMs is denoted. The lowest explicit and implicit denoising performances of NLMs are obviously pointed.

A future improvement of denoising performance possibly refers to creating a semi-supervised denoising conjunction model. Such model utilizes the denoised MRIs, resulted by DR and D1 thresholding model as uncorrupted image version along with the noisy MRIs, representing corrupted version ones during autoencoder training phase, to reconstruct the original clean image.

This paper should be of interest to readers in the areas of technologies of computing and information science, including data science and applications, computational health informatics, especially applied as a decision support tool for medical image processing.

In most cases, DR and D1 provide the first–second best implicit performances in terms of accuracy and reliability on both simulated, low-detail small-size region-of-interest (ROI) brain lesions and realistic, high-detail large-size ROI brain tumor MRIs.

In order to pursue men's suit wear comfort, the basic data on an accurate men's suit comfort analysis with clothing pressure is required. Therefore, in such investigation, it is difficult to find out ideal persons as test subjects. In the measuring experiments, we used dummies designed for resuscitation practice to obtain the clothing pressure with both normal standing posture and movement patterns and compared these data with subjects' to give the relationship of clothing pressure between the dummy and subject. The correlation between the measurements with dummies and the subjects' are shown. It turns out that the dummy D1 and D6 are mainly as intended in pressure measurement with normal standing posture. But the dummy D2 and D3 are not. It turns out that the dummy D1 can imitate the human shoulder's movement patterns well, and the dummy D4 and D6 are mainly as intended. But the dummy D2 is not. It turns out that all the dummies have a certain limitation to be placed in clothing pressure measurements. It also shows that instead of subject to use dummy to investigate clothing pressure with both normal standing posture and movement pattern, not only dummy's features such as compression hardness, form and size, but measuring postures are also needed to take into consideration.

COVID-19 would have a far-reaching impact on the international health-care industry and the patients. For COVID-19, there is a need for unique screening tests to reliably and rapidly determine who is infected. Medical COVID images protection is critical when data pertaining to computer images are being transmitted through public networks in health information systems.

Medical images such as computed tomography (CT) play key role in the diagnosis of COVID-19 patients. Neural networks-based methods are designed to detect COVID patients using chest CT scan images. And CT images are transmitted securely in health information systems.

The authors hereby examine neural networks-based COVID diagnosis methods using chest CT scan images and secure transmission of CT images for health information systems. For screening patients infected with COVID-19, a new approach using convolutional neural networks is proposed, and its output is simulated.

The required patient’s chest CT scan images have been taken from online databases such as GitHub. The experiments show that neural networks-based methods are effective in the diagnosis of COVID-19 patients using chest CT scan images.

Reactive rules are used for programming rule-based Web agents, which have the ability to detect events and respond to them automatically and can have complex structure and unpredictable behavior. The aim of this paper is to provide an appropriate formal framework for analyzing such rules.

To achieve this goal, the authors give two alternative semantics for the basic reactive rules’ families which allow us to specify reactive rule-based agents and verify their intended behavior. The first approach expresses the functionality of production and event condition action rules in terms of equations, whereas the second methodology is based in the formalism of rewriting logic. Both semantics can be expressed within the framework of CafeOBJ algebraic specification language, which then offers the verification support and have their advantages and downsides.

The authors report on experiences gained by applying those methodologies in a reactive rule-based system and compare the two methodologies.

Finally, the authors demonstrate a tool that translates a set of reactive rules into CafeOBJ rewrite rules, thus making the verification of reactive rules possible for inexperienced users.

In this paper, we develop and evaluate an approach to assessing the content quality in a location‐based service (LBS). The proposed approach, instead of assessing the quality in absolute terms such as completeness or accuracy, measures the effect that the imperfection of the content is having on the reliability of that specific LBS. We apply the basic ideas from Software Reliability Engineering (SRE), but develop a modification of SRE, 2‐Branch, in order to separate content quality from other factors, such as positioning imprecision, and to reduce the measurement error. In our experimental study, we first compare 2‐Branch to the standard SRE, after which we experimentally analyze some properties of SRE methodology as such in the context of an LBS. The experiments indicate that 2‐Branch has in most cases a lower measurement error than the standard SRE. A corollary to that is that 2‐Branch can achieve, therefore, as low an error level as the standard SRE, using a worse and thus cheaper oracle. Getting a good oracle is probably the main cost factor in evaluating the quality of an information service, thus being able to use a cheaper one may result in significant savings.

In order to allow flexibility in the enforcement of the tax law, the language used is often intentionally vague and ambiguous. This enables the government to implement the intent of the lawmakers in administering that law. However, interpreting these vague and ambiguous laws requires tax professionals to face planning situations that are complex and uncertain. Due to an increase in civil litigation, the importance of tax professionals making defensible decisions has been magnified in recent years. Carnes, et al. (1994) report that tax partners with Big‐Six accounting firms spend about 30 to 45 percent of their time resolving ambiguous tax questions. Therefore, tax professionals could benefit from models or systems (i.e., decision support systems, expert systems, artificial intelligence) that provide decision direction when facing ambiguous tax situations. One such area in which tax professionals must assist their clients is the determination of what levels of compensation are reasonable for owner‐employees of closely‐held corporations (Hagan, et al. 1995).

The purpose of this paper was to synthesize novel antibacterial reactive dyes for dyeing cotton fabrics.

Four synthetic novel antibacterial reactive dyes based on sulfonamide (D1-D4) have been synthesized by the coupling reaction of sulfonamide diazonium salt with sulfonamido-cyanurated 7-amino-4-hydroxynaphthalene-2-sulfonic acid “j-acid”. The chemical structure of the synthesized dyes was secured by their spectral data [infra red (IR) and proton Nuclear magnetic Resonance (1HNMR)].

The prepared reactive dyes (D1-D4) were applied to cotton fabrics. Optimum conditions of the dying samples at sodium sulfate 100 g/l, liquor ratio (L.R.) 1:10, sodium carbonate 20 g/l at 80°C (D1, D2 and D4), 60°C (D3 for 60 min) were investigated. The fastness properties toward washing, perspiration, rubbing and light were evaluated. Dyed fabrics showed good light fastness property and good to very good washing and perspiration fastness properties according to the gray scale. Antimicrobial activities for synthesized dyes showed excellent activity against gram-negative organisms such as Pseudomonas aeruginosa and Proteus mirabilis faecalis, whereas very good activity against gram-positive organisms such as Staphylococcus aureus and Enterococcus faecalis with respect to the standard drugs ampicillin and chloramphenicol.

The principle advantages in this study were that the synthesis of novel synthesized dyes by introducing bisulfonamide-based moieties to increase the antimicrobial activity of the cellulose fabrics could be used as a medical textile, short reaction time and reaction procedure conducted in few steps, the work up is convenient and thus the starting material can be easily prepared.

Constructing porous wind barriers is one of the most effective approaches to increase the running safety of trains on viaducts in crosswinds. This paper aims to further improve the wind-sheltering performance of the porous wind barriers.

Improved delayed detached eddy simulations based on the k-ω turbulence model were carried out, and the results were validated with wind tunnel tests. The effects of the hole diameter on the flow characteristics and wind-sheltering performance were studied by comparing the wind barriers with the porosity of 21.6% and the hole diameters of 60 mm–360 mm. The flow characteristics above the windward and leeward tracks were analyzed, and the wind-sheltering performance of the wind barriers was assessed using the wind speed reduction coefficients.

The hole diameters affected the jet behind the wind barriers and the recirculation region above the tracks. Below the top of the wind barriers, the time-averaged velocity first decreased and then increased with the increase in the hole diameter. The wind barrier with the hole diameter of 120 mm had the best wind-sheltering performance for the windward track, but such barrier might lead to overprotection on the leeward track. The wind-sheltering performance of the wind barriers with the hole diameters of 240 mm and 360 mm was significantly degraded, especially above the windward track.

The effects of the hole diameters on the wake and wind-sheltering performance of the wind barriers were studied, by which the theoretical basis is provided for a better design of the porous wind barrier.

The purpose of this paper is to evaluate logistics service quality using the Kano’s service quality model in the logistics-triadic context.

This study undertakes a survey research in semiconductor manufacturing equipment (SME) industry. Logistics service quality is categorized using Kano’s classification and a comparison is made of the perception of three sample groups who are SME firms, semiconductor manufacturers (SMs) and third-party logistics (TPL) providers.

Several attributes of logistics service quality are evaluated as “Must-be” quality which does not contribute to customer satisfaction, indicating existence of the non-linear relationship between some logistics service quality attributes and customers’ satisfaction. The group comparison reveals that TPL firms perceive most quality attributes as “Attractive” and “One-dimensional” whereas SMs and SME firms perceive more attributes as “Must-be.”

Logistics service quality elements should not be used in the same way assuming the same effect of logistics service elements on customer satisfaction. It should be also noted that logistics service quality attributes have different effect on satisfaction according to the positions in the logistics triad.

This paper provides SME firms with useful information on how to manage their logistics service quality to improve customer satisfaction. SME firms and TPL providers need to arrange a collaborative format where logistics service quality is regularly monitored and three members discuss solutions to improve their quality performance.

This study is a rare empirical investigation to actually assess and compare perception on logistics service quality among the entities in a logistics triad. This study applies the Kano model to the triadic relationship in logistics service provision, which may be the first attempt to the authors’ best knowledge.

The paper aims to identify, prioritize and rank lean practices in the context of an Indian automotive component manufacturing organization using interpretive ranking process (IRP) and interpretive structural modeling (ISM) approaches.

Lean practices are identified from the literature. Then, two hierarchical models were are developed using two distinct modeling approaches – ISM and IRP with expert opinions from an Indian automotive component manufacturing organization to analyze the contextual relationships among the various lean practices and to prioritize and rank them with respect to performance dimensions.

In the study, the hierarchical structural models are developed using ISM and IRP approaches for an Indian automotive component manufacturing organization. In ISM-based modeling, lean practices can be categorized into five levels. Top priority should be given to the motivators followed by value chain, system/technology and organization centric practices. IRP model shows the dominance relationship among the various lean practices with respect to performance dimensions.

The models are constructed from the organizational standpoint to evaluate their impact to the implementation of lean manufacturing. The study leverages the organizations to prioritize limited resources as per the hierarchy. Managers get the inter-linkages and ranking of various lean practices, which leads to a better perspective for the effective implementation of lean. The structural models also assist management to assign proper roles to employees/departments for effective lean implementation.

There is hardly any structural model of lean practices in the literature for clustering, prioritizing and ranking of lean practices. The study fills this gap and develops the hierarchical models of lean practices through IRP and ISM approaches for an Indian automotive component manufacturing organization. The results from both approaches are compared for illustrating the benefits of one over the other.