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This paper aims to improve the delimitation of plant growth stages in the context of weather index insurance design. We propose a data-driven phase division that minimizes estimation errors in the weather-yield relationship and investigate whether it can substitute an expert-based determination of plant growth phases. We combine this procedure with various statistical and machine learning estimation methods and compare their performance.

Using the example of winter barley, we divide the complete growth cycle into four sub-phases based on phenology reports and expert instructions and evaluate all combinations of start and end points of the various growth stages by their estimation errors of the respective yield models. Some of the most commonly used statistical and machine learning methods are employed to model the weather-yield relationship with each selected method we applied.

Our results confirm that the fit of crop-yield models can be improved by disaggregation of the vegetation period. Moreover, we find that the data-driven approach leads to similar division points as the expert-based approach. Regarding the statistical model, in terms of yield model prediction accuracy, Support Vector Machine ranks first and Polynomial Regression last; however, the performance across different methods exhibits only minor differences.

This research addresses the challenge of separating plant growth stages when phenology information is unavailable. Moreover, it evaluates the performance of statistical and machine learning methods in the context of crop yield prediction. The suggested phase-division in conjunction with advanced statistical methods offers promising avenues for improving weather index insurance design.

This paper aims to focus on the selection of an appropriate nature-inspired texture pattern for cutting tool tribological surface. The selection process uses the recognized skin textures of different snakes scrolling on highly rough and projected surface conditions to analyze suitability of texture based on the texture geometry and machining conditions. The work also aims to propose a texture pattern selection process to incorporate on cutting tool tribological surface.

The selection of alternative nature-inspired texture patterns based on the texture pattern geometry and machining properties leads to a multi-criteria decision-making problem. Thirteen criteria are considered for selecting an appropriate texture pattern among 14 alternatives, i.e. nature-inspired texture patterns. In the present work, an integrated analytical hierarchy process (AHP)-TOPSIS, AHP-multi-objective optimization on the basis of ratio analysis (MOORA) and AHP-Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) approaches have been proposed for the selection of an appropriate nature-inspired texture pattern. AHP is used for the formulation of decision-making matrix and criteria weight calculations and ranking of alternatives is done by three methods. Spearman’s correlation compared and found positive relations between rank assigned by methods. Experimental validation is done in Lathe for selected texture effects.

The texture parameters C-1 (Width of texture) and C-2 (Depth of texture) are found significant, while T-2 (Blended Krait) and T-6 (Banded Racer-1) texture is found optimal to generate on cutting tool surface.

Only some nature-inspired texture patterns have been recognized before the selection; an infinite number of textures are available in nature. The size of the texture pattern is difficult to identify by the selection process because each texture pattern may have different effects on tribological surfaces.

The proposed selection methodology of nature-inspired texture patterns will help identify optimal texture geometry for specific tribological applications. The nature-inspired texture patterned tool has a significant impact on the cutting force and temperature due to its tribological effect on the cutting tool surface; it decreases the power required for machining. The machining characteristics like roughness are found to decrease by using nature-inspired texture patterned tools.

Various nature-inspire texture studies to generate specific effects on the tribological surfaces may be started study for the surface of aircraft, ships, bearings, etc. Small and big fabrication industries may benefit by decreasing the cost of machining using nature-inspired texture-patterned tools. Research society will pay attention to nature’s inspiration.

Novel snake-skin-inspired texture patterns are recognized and hybrid MCDM methods are proposed to select optimal texture pattern. Proposed method used single time normalization to effectively rank the alternatives. The insights gained from this research can be extrapolated to address similar challenges in selecting nature-inspired textures for various applications.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0163/

This paper reviews and analyses renewable energy options, namely underground thermal, solar, wind and marine wave energy, in seaport cargo terminal operations.

Four renewable energy options that are deployed or tested in different ports around the world are qualitatively examined for their overall implementation potential and characteristics and their cost and benefits. An application to the port of Singapore is discussed.

Geophysical conditions are key criteria in assessing renewable energy options. In the case of Singapore, solar power is the only suitable renewable energy option.

Being a capital-intensive establishment with high intensities of cargo operations, seaports usually involve a high level of energy consumption. The study of renewable energy options contributes to seaport sustainability.

A key recommendation is to implement a smart energy management system that enables the mixed use of renewable energy to match energy demand and supply optimally and achieve higher energy efficiency.

The use of renewable energy as an eco-friendlier energy source is underway in various ports. However, there is almost no literature that analyses and compares various renewable energy options potentially suitable for cargo terminal operations in ports. This paper narrows the knowledge gaps.

Several chaotic system-based encryption techniques have been presented in recent years to protect digital images using cryptography. The challenges of key distribution and administration make symmetric encryption difficult. The purpose of this paper is to address these concerns, the novel hybrid partial differential elliptical Rubik’s cube algorithm is developed in this study as an asymmetric image encryption approach. This novel algorithm generates a random weighted matrix, and uses the masking method on image pixels with Rubik’s cube principle. Security analysis has been conducted, it enhances and increases the reliability of the proposed algorithm against a variety of attacks including statistical and differential attacks.

In this light, a differential elliptical model is designed with two phases for image encryption and decryption. A modified image is achieved by rotating and mixing intensities of rows and columns with a masking matrix derived from the key generation technique using a unique approach based on the elliptic curve and Rubik’s cube principle.

To evaluate the security level, the proposed algorithm is tested with statistical and differential attacks on a different set of test images with peak signal-to-noise ratio, unified average changed intensity and number of pixel change rate performance metrics. These results proved that the proposed image encryption method is completely reliable and enhances image security during transmission.

The elliptic curve–based encryption is hard to break by hackers and adding a Rubik’s cube principle makes it even more complex and nearly impossible to decode. The proposed method provides reduced key size.

Try to find a way to treat wastewater and achieve its purification from suspended waste, which was removed by examining the magneto-Williamson fluid on a horizontal cylindrical tube while taking advantage of solar radiation and nanotechnology.

The effect of Cattaneo–Christoph law of heat transfer, solar radiation, oblique magnetic field, porosity and internal heat generation on the flow was studied. The control system was solved by the numerical technique of Chebyshev pseudospectrum (CPS) with the help of the program MATHEMATICA 12. The tables comparing the published data results with the existing numerical calculation match exactly.

The tables comparing the published data results with the existing numerical calculation match exactly. The current simulation results indicate that when using variable viscosity, the Nusselt number and surface friction decrease significantly compared to their value in the case of constant viscosity, and variable viscosity has a significant effect on flow, which reduces speed. Curves and increasing temperature profiles.

Developing a theoretical framework for the problem of sewage turbidity in a healthier and less costly way, by studying the flow of Williamson fluid with variable viscosity (to describe the intensity of sewage turbidity) on a horizontal cylindrical tube, and taking advantage of nanotechnology, solar radiation, Christoph’s thermal law and internal heat generation to reach water free of impurities. Inclined magnetic force and porous force were used, both of which played an effective role in the purification process.

The emergence of consumerism has led to regulatory measures being integrated into business practices, but the influence of consumers in developing countries remains limited, resulting in businesses being less responsive. The digital retail landscape is undergoing a transformative revolution, driven by Industrial Revolution (IR) 4.0 technological advancements such as artificial intelligence (AI), wearables, virtual reality (VR), augmented reality (AR), and blockchain technology. This development focuses on convenience, personalisation, and emotional connections. Companies are adapting to modern consumer behaviour through various strategies, including online shopping, mobile commerce, data analytics, technology integration, user reviews, and contactless payments. The COVID-19 pandemic has accelerated this seismic shift in the retail industry, and online retail is expected to continue to grow post-pandemic, driven by these technologies. AI enhances the customer experience, wearables provide interactive engagement, VR offers immersive shopping, AR merges online and physical shopping, and blockchain ensures secure transactions in the emerging metaverse. As retail converges with the metaverse, the potential for borderless and personalised shopping experiences is enormous. Advances in VR technology could lead to interconnected virtual spaces that seamlessly connect physical and digital retail, providing immersive and personalised shopping experiences. However, challenges such as cost, learning curves, digital security, legal ambiguity, data privacy, financial risk, and ethical considerations need to be addressed through vigilant and informed consumer engagement in this evolving digital landscape.

While ChatGPT is gaining popularity, its potential role in supply chains (SCs) remains unexplored. This study explores the potential applications, benefits and challenges of using ChatGPT as a tool in SCs.

The data were gathered through an online survey involving 116 respondents from the academic and industrial sectors who have knowledge of ChatGPT and SC management. These participants were affiliated with the Decision Science Institute (DSI) in the USA and contributed to the published DSI conference proceedings from 2019 to 2022. The survey is structured in three main sections: (1) general information (5 background questions), (2) ChatGPT's potential applications and benefits in SCs (15 pre-determined questions) and (3) potential challenges with using ChatGPT in SCs (5 pre-determined questions). The collected data underwent analysis using IBM SPSS Statistics software.

ChatGPT can potentially benefit SC operations in 15 areas. Eight potential benefits received more support than the rest, including enhanced process efficiency, cost reduction, providing sustainability reports, better demand forecasting, improved data analysis, streamlined supplier communication, streamlined customer communication, supported promotional activities and enhanced customer satisfaction, but all were supported. Also, the study identified some challenges and hurdles currently impacting the use of ChatGPT in the SC, including that ChatGPT cannot replace experts, it is not an immediate game changer, its uses may lack accuracy, and ChatGPT may take time to reach maturity.

The study is the first to offer empirically grounded evidence of ChatGPT's potential in SCs. The research enhances academic literature by deepening our comprehension of the potential applications of ChatGPT within SCs. Therefore, the study makes an invaluable contribution to the extant literature on ChatGPT in SCs. It can benefit manufacturers, suppliers, logistics providers and other types of businesses through more efficient procurement practices, supplier management, operations and inventory management, logistics practices and customer relationships. Future research may explore how and why ChatGPT is used in SCs.

This research focuses on the controlling irreversibilities in a radiative, chemically reactive electromagnetohydrodynamics (EMHD) flow of a nanofluid toward a stagnation point. Key considerations include the presence of Ohmic dissipation, linear thermal radiation, second-order chemical reaction with the multiple slips. With these factors, this study aims to provide insights for practical applications where thermal management and energy efficiency are paramount.

Lie group transformation is used to revert the leading partial differential equations into nonlinear ODE form. Hence, the solutions are attained analytically through differential transformation method-Padé and numerically using the Runge–Kutta–Fehlberg method with shooting procedure, to ensure the precise and reliable determination of the solution. This dual approach highlights the robustness and versatility of the methods.

The system’s entropy generation is enhanced by incrementing the magnetic field parameter (M), while the electric field (E) and velocity slip parameters (ξ) control its growth. Mass transportation irreversibility and the Bejan number (Be) are significantly increased by the chemical reaction rate (C_r). In addition, there is a boost in the rate of heat transportation by 3.66% while 0.05⩽ξ⩽0.2; meanwhile for 0.2⩽ξ⩽1.1, the rate of mass transportation gets enhanced by 12.87%.

This paper presents a novel approach to analyzing the entropy optimization in a radiative, chemically reactive EMHD nanofluid flow near a stagnation point. Moreover, this research represents a significant advancement in the application of analytical techniques, complemented by numerical approaches to study boundary layer equations.

In this study, the authors sought to investigate how the implicit social ties of both project owners and potential backers are associated with crowdfunding project success.

Drawing on social ties theory and factors that affect crowdfunding success, in this research, the authors developed a model to study how project owners' and potential backers' implicit social ties are associated with crowdfunding projects' degrees of success. The proposed model was empirically tested with crowdfunding data collected from Kickstarter and social media data collected from Twitter. The authors performed the test using an ordinary least squares (OLS) regression model with fixed effects.

The authors found that project owners' implicit social ties (specifically, their social media activities, degree centrality and betweenness centrality) are significantly and positively associated with crowdfunding projects' degrees of success. Meanwhile, potential project backers' implicit social ties (their social media activities and degree centrality) are negatively associated with crowdfunding projects' degrees of success. The authors also found that project size moderates the effects of project owners' social media activities on projects' degrees of success.

This work contributes to the literature on crowdfunding by investigating how the implicit social ties of both potential backers and project owners on social media are associated with crowdfunding project success. This study extends the previous research on social ties' roles in explaining crowdfunding project success by including implicit social ties, while the literature explored only explicit social ties.