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The ideology of this article is to study the performance concerns of SDN Controllers, with the help of developed SRGM and thereby obtain its optimal testing duration. The effect of undetected uncertainty in the parameter values have also been catered in the proposal.

These uncertainties in the parameter values are studied as the risk of not meeting desired set of requirements, whose removal causes additional cost. Considering these two constructs as attributes of MAUT, the controller's optimal testing duration is obtained.

The article focuses towards obtaining the optimal duration for which the SDN Controllers must be tested. It was observed that the inculcation of risk-attribute has provided the higher utility value as compared to any other existing scenarios.

Plenty of SRGM have been proposed in the literature which talks about the testing stop time determination problems. But, none of them have considered the impact of risk of not meeting the requirements (reliability) along with cost to obtain its testing stop time. Further, validation of the proposed model in presented with the help of two releases versions of SDN controller platform, ONOS, entitled as “Kingfisher” and “Loon” and has acquired promising results.

The present work focuses on evaluating the physical and mechanical characteristics of geopolymer concrete (GPC) by replacing the sodium silicate waste (SSW) in place of traditional river sand. The aim is to create eco-friendly concrete that mitigates the depletion of conventional river sand and conserves natural resources. Additionally, the study seeks to explore how the moisture content of filler materials affects the performance of GPC.

SSW obtained from the sodium silicate industry was used as filler material in the production of GPC, which was cured at ambient temperature. Instead of the typical conventional river sand, SSW was substituted at 25 and 50% of its weight. Three distinct moisture conditions were applied to both river sand and SSW. These conditions were classified as oven dry (OD), air dry (AD) and saturated surface dry (SSD).

As the proportion of SSW increased, there was a decrease in the slump of the GPC. The setting time was significantly affected by the higher percentage of SSW. The presence of angular-shaped SSW particles notably improved the compressive strength of GPC when replacing a portion of the river sand with SSW. When exposed to elevated temperatures, the performance of the GPC with SSW exhibited similar behavior to that of the mix containing conventional river sand, but it demonstrated a lower residual strength following exposure to elevated temperatures.

Exploring the possible utilization of SSW as a substitute for river sand in GPC, and its effects on the performance of the proposed mix. Analyzing, how varying moisture conditions affect the performance of GPC containing SSW. Evaluating the response of the GPC with SSW exposed to elevated temperatures in contrast to conventional river sand.

The capability of steel columns to support their design loads is highly affected by the time of exposure and temperature magnitude, which causes deterioration of mechanical properties of steel under fire conditions. It is known that structural steel loses strength and stiffness as temperature increases, particularly above 400 °C. The duration of time in which steel is exposed to high temperatures also has an impact on how much strength it loses. The time-dependent response of steel is critical when estimating load carrying capacity of steel columns exposed to fire. Thus, investigating the structural response of cold-formed steel (CFS) columns is gaining more interest due to the nature of such structural elements.

In this study, experiments were conducted on two CFS configurations: back-to-back (B-B) channel and toe-to-toe (T-T) channel sections. All CFS column specimens were exposed to different temperatures following the standard fire curve and cooled by air or water. A total of 14 tests were conducted to evaluate the capacity of the CFS sections. The axial resistance and yield deformation were noted for both section types at elevated temperatures. The CFS column sections were modelled to simulate the section's behaviour under various temperature exposures using the general-purpose finite element (FE) program ABAQUS. The results from FE modelling agreed well with the experimental results. Ultimate load of experiment and finite element model (FEM) are compared with each other. The difference in percentage and ratio between both are presented.

The results showed that B-B configuration showed better performance for all the investigated parameters than T-T sections. A noticeable loss in the ultimate strength of 34.5 and 65.6% was observed at 90 min (986℃) for B-B specimens cooled using air and water, respectively. However, the reduction was 29.9 and 46% in the T-T configuration, respectively.

This research paper focusses on assessing the buckling strength of heated CFS sections to analyse the mode of failure of CFS sections with B-B and T-T design configurations under the effect of elevated temperature.

This study aims to discover the key performance indicators (KPIs) of the agricultural cold supply chain (ACSC) and analyze their consequences on the performance of ACSC within the bounds of Indian topography.

The KPIs have been explored based on the literature review both in global and Indian context and domain expert's opinions. The interdependency characteristics and cause–effect relationship among the KPIs have been analyzed using a fuzzy decision-making trial and evaluation laboratory (f-DEMATEL) approach.

The findings extracted from the empirical assessment of the problem find strong compliance with the notions of theoretical model assessment. The results highlight that the cost of product waste and operating and performance costs are the two most important performance indicators of an Indian ACSC. Furthermore, governmental policies and regulations and the effectiveness of cold chain (CC) equipment also have a high degree of influencing characteristics on ACSC performance.

To connect the study with practicalities, the assessment of the KPIs is allied with real-time practices by clustering the beliefs of Indian professionals. Therefore, the decision-making behavior of the experts might be influenced by geographical constraints. However, the key findings provide advantages to the ACSC players, a bright hope for future food security and a significant profit for farmers.

The presented paper encompasses various aspects of the ACSC, including theoretical and empirical perspectives exercised to contemplate the system dynamics, which inculcates the essence of the associated practicalities. Thus, this study has various practical contributions relevant to managerial and societal perspectives.

Due to the importance of quality to customers, this study considers criteria of quality and profit and optimizes both in a multi-echelon cold chain of perishable agricultural products whose quality immediately begins to deteriorate after harvest. The two objectives of the proposed cold chain are to maximize profit and quality. Since postharvest quality loss in the supply chain depends on various decisions and factors, in addition to strategic decisions, the authors consider the temperature setting in refrigerated facilities and transportation vehicles due to the unfixed shelf life of the products which is related to the temperature found by Arrhenius formula.

The authors use bi-objective mixed-integer nonlinear programming to design a four-echelon supply chain. The authors integrate the supply chain echelons to detect the sources and factors of quality loss. The four echelons include supply, processing, storage and customer. The decisions, including facility location, assigning nodes of each echelon to corresponding nodes from the adjacent echelon, allocation of vehicles to transport the products from farms to wholesalers, processing selection, and temperature setting in refrigerated facilities, are made in an integrated way. Model verification and validation in the case study are done based on three perishable herbal plants.

The model obtains a 29% profit against a total cost of 71 and 93% of original quality of the crops is maintained, indicating a 7% quality loss. The final quality of 93% is the result of making a US$6m investment in the supply chain, including the procurement of high-quality raw materials; facility establishment; high-speed, high-capacity vehicles; location assignment; processing selection and refrigeration equipment in the storage and transportation systems, helping to maximize both the final quality of the products and the total profit.

The proposed supply chain model should help managers with modeling decisions, especially when it comes to cold chains for agricultural products. The model yields these results – optimal location-allocation decisions for the facilities to minimize distances between the network nodes, which save time and maintain the majority of the products’ original quality; choosing the most appropriate processing method, which reduces the perishability rate; providing high-capacity, high-speed vehicles in the logistics system, which minimizes transportation costs and maximizes the quality; and setting the right temperature in the refrigerated facilities, which mitigates the postharvest decay reaction rate of the products.

Comparison of the results of the present research with those of the traditional chain (obtained through experts) shows that since the designed chain increases the profit as well as the final quality, it has benefits for the main chain stakeholders, which are customers of agricultural products. This study model is expected to have a positive impact on the environment by placing strong emphasis on quality and preventing excessive waste generation and air pollution by imposing a financial penalty on extra demand production.

Since profit and quality of the final product are two important factors in all cultures and communities, the proposed supply chain model can be used in any food industry around the world. Applying the proposed model induces growth in local industries and promotes the culture of prioritizing quality in societies.

To the best of the authors’ knowledge, this is the first research on a bi-objective four-echelon (supply, processing, storage and customer) postharvest supply chain for agricultural products including that integrates transportation logistics and considers the deterioration rate of products as a time-dependent variable at different levels of decision-making.

Due to the recent disruptions caused by COVID-19, global supply chains are stress tested. The affected supply chains have interfered with market tonnage prices for the yield of perishable products like mangoes that are highly dependent on their quality. This research, through empirical findings, thus determines and comprehends the factors influencing mango quality (size).

A framework is developed for finding the potential factors of quality building on the previous literature and studies on the available topic. The data collection included face-to-face interviews comprising 240 farmers, hired managers and preharvest contractors in India's Jangaon, Rangareddy and Yadadri Bhuvanagiri districts of Telangana state. The data analysis is done using multiple regression, and the outcomes form the basis of the design of the experiments model.

The empirical insights support that the quality of mango is affected by factors such as the number of picking cycles, the cost of fertilizer, the variety of fertilizers used, the variety of pesticides used and pesticide application frequency. The direct implications are the benefit to farmers in improving mango quality and maximizing profit per yield cycle.

To the best of the authors’ knowledge, the first research that has specifically focused on holistically improving the quality(size) of mangoes.

The findings contribute to the perishable supply chain literature, specifically to the mango study, to comprehensively showcase the factors impacting the quality of mangoes and provide guidance to farmers regarding orchard practices.

The purpose of this article is to develop relationships between many major issues relevant to the agriculture supply chain.

With the purpose of gaining an all-encompassing understanding of the agriculture supply chain, this work uses 233 filtered research articles and three bibliometric analysis tools, namely VOSviewer, term frequency-inverse document frequency (TF-IDF) and Person correlation. The collected research publications were also catalogued using Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA).

Using analytic techniques, a total of 12 keywords were obtained. The study found that agri-products are in dire need of digitisation via Internet of things (IoT) and blockchain due to the usage of economic variables and comprehensive management of total food waste throughout transportation, anchoring quality and the predominant variable.

The study was limited to the Scopus and Web of Science (WoS) indexing in order to assess the viability of the linked idea and problem.

This study aims to generate vital knowledge in the field of horticulture-focused agriculture supply chain based on previous justification and relationship formation.

The relationship between the various existing smell taxonomies and the smell impacting factors has been established. The ideology is to identify the most critical smell influencing factors in the vicinity of various software development environments.

To fulfill the said task, the utilization of the amalgamation of two multicriteria decision-making techniques, namely, Entropy method and CODAS method, is presented.

Through this article, the most critical smell impacting criteria with respect to the smell taxonomies is identified. Furthermore, the behaviour of 4 software development principles was then analysed, and their working state has been successfully assessed.

The ideology to study design-related smells in the software system has been studied by a lot of researchers. Some of them have worked upon their detection and the corresponding refactoration process with the help of several algorithms like machine learning and artificial intelligence. But how and to what extent these design-related smells impact the software development environment has remained out of the limelight till now. Through this article, this research gap has been identified, and an attempt to fill it has been made.

Positive psychology helps us understand the knowledge required to contribute to adolescents' societal development and adjustability. Adolescence is the crucial stage to work on for a balanced personality. The present study concerned adolescents' self-strength, happiness, and help-seeking behaviour. The authors aimed to explore the relationship between adolescents' self-strength and happiness and investigate the mediational effect of adolescents' help-seeking behaviour on their self-strength and happiness.

The research design of the current quantitative study was correlational, and 809 adolescents from China and Pakistan participated in the present study. Data were personally collected from participants through self-developed scales.

We deployed Pearson correlation and simple mediation using SPSS software and found a linear, positive, strong (r = 0.654, n = 809, p = 0.000 < 0.01) and statistically significant correlation between adolescents' self-strength and happiness. The authors also found a significant indirect effect of help-seeking on adolescents' self-strength and happiness at (β = 0.373, t(907) = 7.01).

Using self-reported scales to gather information was one of the study's limitations. Adolescents may have misunderstood the notion or construct narrated in words or responded biasedly despite the bilingual scales.

This study offers social and practical implications for educators, parents, and school administrators to address the development of adolescents' personalities using a positive psychology lens.

The findings are of significant importance for teachers working in the elementary schools. They may work on adolescents' self-strength, happiness, and help-seeking to develop balanced personalities.

This study empirically demonstrates a contradiction between pillar 3 of Basel norms III and the designation of Systemically Important Banks (SIBs), also known as Too Big to Fail (TBTF). The objective of this study is threefold, which has been approached in a phased manner. The first is to determine the systemic importance of the banks under study; second, to examine if market discipline exists at different levels of systemic importance of banks and lastly, to examine if the strength of market discipline varies at different levels of systemic importance.

This study is based on all the public and private sector banks operating in the Indian banking sector. The Gaussian Mixture Model algorithm has been utilized to classify banks into distinct levels of systemic importance. Thereafter, market discipline has been observed by analyzing depositors' sentiments toward banks' risk (CAMEL indicators). The analysis has been performed by employing the system Generalized Method of Moments (GMM) to estimate models with different dependent variables.

The findings affirm the existence of market discipline across all levels of systemic importance. However, the strength of market discipline varies with the systemic importance of the banks, with weak market discipline being a negative externality of the SIBs designation.

By employing the Gaussian Mixture Model algorithm to develop a framework for categorizing banks on the basis of their systemic importance, this study is the first to go beyond the conventional method as outlined by the Reserve Bank of India (RBI).