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E-commerce companies use dark patterns to manipulate customer decisions to survive in the crowded online market and make profit. Although some online customers are aware of the dark patterns, they cannot overcome such manipulations. Therefore, the purpose of this study is to identify and model the barriers to overcoming dark patterns using total interpretive structural modeling (TISM).

Barriers to overcoming dark patterns were identified from the extant literature and were validated by a panel of 18 domain experts. In the modeling phase, TISM technique was used to identify the relationships between the barriers and assign priority to the barriers. Finally, the barriers were plotted and classified into three categories.

User unawareness, trust in brands and normalization of aggressive marketing were found to be the highest priority barriers. Whereas, designer bias, user fatigue, short-term user benefits and design complexity were identified as the most challenging barriers because they have least dependence over the other barriers.

Because TISM results are based on the opinion of domain experts, other statistical techniques could be applied for validation.

This study would educate online customers, while assisting online user communities and regulatory bodies to devise strategies to overcome dark patterns. Additionally, business managers could use the study’s findings to encourage designers to embrace ethical design methods as a competitive advantage.

This study contributes to the research as it is first of its kind to examine the link between dark pattern barriers.

Accurate and automatic clothing pattern making is very important in personalized clothing customization and virtual fitting room applications. Clothing pattern generating as well as virtual clothing simulation is an attractive research issue both in clothing industry and computer graphics.

Physics-based method is an effective way to model dynamic process and generate realistic clothing animation. Due to conceptual simplicity and computational speed, mass-spring model is frequently used to simulate deformable and soft objects follow the natural physical rules. We present a physics-based clothing pattern generating framework by using scanned human body model. After giving a scanned human body model, first, we extract feature points, planes and curves on the 3D model by geometric analysis, and then, we construct a remeshed surface which has been formatted to connected quad meshes. Second, for each clothing piece in 3D, we construct a mass-spring model with same topological structures, and conduct a typical time integration algorithm to the mass-spring model. Finally, we get the convergent clothing pieces in 2D of all clothing parts, and we reconnected parts which are adjacent on 3D model to generate the basic clothing pattern.

The results show that the presented method is a feasible way for clothing pattern generating by use of scanned human body model.

The main contribution of this work is twofold: one is the geometric algorithm to scanned human body model, which is specially conducted for clothing pattern design to extract feature points, planes and curves. This is the crucial base for suit clothing pattern generating. Another is the physics-based pattern generating algorithm which flattens the 3D shape to 2D shape of cloth pattern pieces.

Due to the increase in the prevalence of other risk factors of heart diseases, the age of heart disease has decreased and it has led to premature heart disease. One of the main risk factors of this disease is metabolic syndrome (MetS). One of the key ways to control MetS is dietary modification. The purpose of this study is to investigate the relationship between dietary patterns and MetS in patients with premature heart disease.

This study was conducted on 409 people with premature heart disease. The diagnosis of MetS was made based on the ATP III criteria. Dietary intake for the past year was collected using the validated semi-quantitative food frequency questionnaire. Dietary patterns were determined by factor analysis with principal components approach.

Three dietary patterns were identified, including the healthy, western and traditional patterns. The findings showed that people who followed the traditional pattern more than those who followed less had a lower risk of MetS. (OR:0.23; 95% CI: 0.11–0.52). But, no relation was observed between healthy (OR:1.45; 95% CI:0.64–3.25) and western (OR:1.04; 95% CI:0.51–2.13) patterns with MetS.

The findings of this study showed that following a traditional dietary pattern based on high consumption of whole grains, red meat, viscera, fish, eggs, high-fat dairy products, soft drinks, mayonnaise and solid oil was associated with a lower risk of MetS.

E-commerce companies often manipulate customer decisions through dark patterns to meet their interests. Therefore, this study aims to identify, model and rank the enablers behind dark patterns usage in e-commerce companies.

Dark pattern enablers were identified from existing literature and validated by industry experts. Total interpretive structural modeling (TISM) was used to model the enablers. In addition, “matriced impacts croisés multiplication appliquée á un classement” (MICMAC) analysis categorized and ranked the enablers into four groups.

Partial human command over cognitive biases, fighting market competition and partial human command over emotional triggers were ranked as the most influential enablers of dark patterns in e-commerce companies. At the same time, meeting long-term economic goals was identified as the most challenging enabler of dark patterns, which has the lowest dependency and impact over the other enablers.

TISM results are reliant on the opinion of industry experts. Therefore, alternative statistical approaches could be used for validation.

The insights of this study could be used by business managers to eliminate dark patterns from their platforms and meet the motivations of the enablers of dark patterns with alternate strategies. Furthermore, this research would aid legal agencies and online communities in developing methods to combat dark patterns.

Although a few studies have developed taxonomies and classified dark patterns, to the best of the authors’ knowledge, no study has identified the enablers behind the use of dark patterns by e-commerce organizations. The study further models the enablers and explains the mutual relationships.

This study aims to explore the connection between demographic traits and the choice of attraction patterns among international repeat tourists.

The study employed a questionnaire survey to collect data from 1550 international repeat tourists who visited Tanzania between November 2022 and July 2023. Convenient sampling was employed as tourists were selected from the three international airports of Tanzania, namely Kilimanjaro International Airport, Julius Nyerere International Airport, and Abeid Aman Karume International Airport. A multinomial logistic regression model was used to examine the impact of socio-demographic characteristics on the selection of attraction patterns among international repeat tourists.

The study revealed that demographic factors, including age, marital status, income level, occupation, and education level, exhibit statistically significant correlations with preferences for distinct attraction patterns. This significance was established through a p-value of less than 0.05 for all the aforementioned variables.

This study is primarily focused on international repeat tourists, thereby limiting insights into the preferences of domestic tourists. To better inform strategies aimed at attracting a larger domestic tourist base, future research may prioritize the investigation of choice of attractions patterns among domestic tourists in relation to their demographic characteristics.

This study contributes to the nuanced understanding of international tourist behavior by unraveling the extent to which demographic traits impact tourists’ choices of attraction patterns, thereby providing insights crucial for effective marketing strategies, improved visitor experiences, and sustainable tourism development strategies.

This study establishes the principles and process steps of a new basic trousers pattern using measurements obtained according to the rules of the anthropometric measurement system. The newly developed pattern-making system in this study will be called the “Anthropometric Measurements Based Pattern Making System” (AnMePa). It is aimed at producing trousers that are more fitting to the body, thanks to this pattern-making system.

In this research, four pattern-making systems used in many parts of the world were compared with the “Anthropometric Measurements Based Pattern Making System” (AnMePa) with regard to the overall appearance and body fit of trousers prepared according to these systems. 10 virtual mannequins (VM) with different adult female body measurements were created, and trousers patterns were prepared for these mannequins. The trousers’ patterns were made and dressed on the mannequins in a 3D virtual dressing system. The body fit of the virtual garments was evaluated by five experts. The scores given by the experts were evaluated using the fuzzy logic method.

According to the results, it is seen that the new basic trousers pattern developed by utilizing the anthropometric measurement system, AnMePa, provides the best body fit among the basic trousers patterns created according to the other examined pattern-making systems. The combination of 3D virtual dressing and fuzzy logic in the evaluation of garment body fit is considered an innovative method for the future of fashion design and production.

In the developed AnMePa, unlike the existing pattern-making systems, values that can be associated with the body measurements of individuals in a way that could be suitable for each community were used instead of constant values in the pattern-making process. Furthermore, the integration of 3D virtual fitting and fuzzy logic in assessing garment fit is considered a pioneering approach with significant implications for the future landscape of fashion design and production.

A comprehensive apparel CAD system was developed to perform automatic garment pattern drafting and the prediction of the final drape shape of designed garment putting on the human body. Three dimensional apparel CAD system starts with a flat garment pattern drafting system. A computerized pattern design script language has been created based on the traditional patterner’s principles to develop an automatic draft system of performing basic garment pattern drafting as well as grading rule generation. A pattern modification system was also developed considering functions required in apparel CAD such as auxiliary pattern generation, seam line creation, and dart manipulation to generate engineering patterns which can be used in the three dimensional garment shape prediction system presented later in part II of this paper.

The purpose of this article is to link the associative learning process of the human brain to the relationship and emergence of really significant ideas on the global horizon.

First, learning is explored from the viewpoint of the brain/mind, with a focus on the creation of patterns and their relationships to our personal frames of reference. Second, the associations of three really significant ideas are explored, and a pattern of patterns is surfaced.

The paper finds that in concert with the functioning of the brain, significant ideas emerge in relationship with other ideas that have personal historical significance, i.e. external patterns from the environment are detected, recognized, made sense of and have meaning in relationship with our internal patterns of significance.

The paper creates an appreciation of the role of patterns in thinking and learning.

Researchers in the past have used Fourier transformation method to determine the in‐plane displacement components from moiré fringes generated by a pair of overlapping circular gratings. In this approach it is necessary to assume that the transmittance is sinusoidal. The purpose of this paper is to propose a graphical method for determining the 2D displacement components from the moiré patterns more easily instead of the complex Fourier transformation method.

The moiré patterns were spatially transformed from Cartesian‐to‐polar coordinate system. The morphological grayscale dilation operation was used to eliminate the residual gratings in the transformed pattern while preserving the moiré fringes. The center line of the moiré fringe was fitted with a sine curve and the in‐plane displacement values were determined directly from the peak‐to‐valley height and the position of the peak in the fitted curve.

Experimental results showed that the proposed moiré pattern analysis method is able to give in‐plane displacement accuracies of 0.002 mm in the x‐direction and 0.01 in the y‐direction without the need for complex computation.

Resolution of the proposed method is limited only by the resolution of the imaging system.

The proposed graphical method for determining 2D displacement components from the moiré patterns can be applied to low‐frequency circular gratings whose transmittance is not sinusoidal.

The graphical analysis method is novel and allows the displacements components to be determined more easily.

Apparel pattern making creates a set of pattern pieces of fabric which are sewn into the desired garment. The pattern pieces are developed through fashion analysis, pattern design and pattern drafting. Seeks to build an object‐oriented model of the apparel pattern‐making process through these subprocesses. Defines the model in terms of a requirements specification and subsequently uses it in the development of a computerized pattern‐making system. Uses object behaviour analysis, which is derived from object‐oriented technology, as the method for defining the model.