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The purpose of this paper is to discuss the performance of efficient cold-formed steel (CFS) sections in building a truss system. A comparative study was performed comparing trusses built with cold-formed and hot-rolled sections.

Medium-scale specimens were fabricated and tested under monotonic loading. Closed CFS sections (tubular sections) were adopted as compression members of the truss, against the open sections (angle sections) in the hot-rolled steel truss. While as open sections (angle sections) were adopted as tension members in both these cases, the performance assessment was made on the basis of the peak loads carried by the trusses, the vertical deflections and the failure modes exhibited.

The results of this study indicated that the overall strength, strength-to-weight ratio and overall convenience in terms of cost and fabrication, in the CFS truss was better than that of the hot-rolled one. Also, the judicious utilization of steel which has limited reserves can be achieved.

Cold-formed and hot-rolled sections are widely used in the steel structures. There are advantages and disadvantages in using each of these configurations, discussed in this work. The advantages are widely known by the scientific community; however, few studies are developed with the purpose of quantifying the gains of each solution. Thus, this work emerges with great innovation, with regard to the experimental evaluation of the trusses' behavior composed of different structural sections.

Artificial Intelligence (AI) is one of the newest technology that is quickly advancing and can be utilised to improve human resource competence in the age of rapid digital transformation. The present competitive scenario demands accurate data that need to be collected and analysed for organisational growth.

Purpose: The research examines the applications and usage of AI in performance management and further analyses the future of PM from the perspectives of AI.

Methodology: The study is conceptual and relies on secondary data from research papers, publications, HR blogs, survey reports and other sources. Employee performance and attitudes were monitored using digital technologies, big data analytics and AI. The quality of employee performance continues to increase with the integration of AI, enabling predictive analytics to increase employee performance.

Research Implication: In employee performance appraisal, a digital performance management system leads to openness and honesty with time, effort and sincerity. It is based on the performance management system’s practical usefulness.

Theoretical Implication: The study’s findings provide HR managers, academics, IT professionals and practitioners with an understanding of how AI may be used for performance management and its consequences on their operations. In addition, the connection between the HR devolution theory on performance management and AI is discussed.

In the wake of the COVID-19 pandemic, India's tourism industry has the opportunity to further grow and expand through the development and implementation of sustainable policies. The diversity of India's geography is observed in its weather which is variable both spatially and temporally throughout the year. Seasonal changes in weather influence the number of foreign tourists arrivals in India. Consequently, significant reductions in visitor numbers are observed during the monsoon season. In future decades, the changing climate has the potential to shape tourism patterns. Warmer temperatures and an increased frequency of high-intensity rainfall are the two most common predictions concerning future climate in India. It will result in a shorter winter tourism season in the northern states where the cold weather enables winter sports activities such as skiing and snowboarding. Coastal tourism along India's stretched coastline may become less attractive to tourists due to damage and disruption to coral reefs and marine wildlife. Sea-level rise and coastal erosion may push beach tourists to more desirable and scenic destinations. India's transport infrastructure is key to enabling the safe and efficient movement of tourists around the country. The current weather is already impacting the air, road and rail networks and, further challenges are highly likely due to a changing climate. There is still an opportunity for India's tourism industry to adapt through physical and policy developments. It would make India a more competitive and sustainable tourism destination.

The purpose of the paper is to study the effect of color stability on introducing chenodeoxycholic acid (CDCA) into a colored liquid extract from saffron and determine the color quality of the extract over a nine-month period.

Six colored liquid samples with different CDCA contents ranging from 0 to 45 Wt.% have been successfully prepared. Chromaticity (C*), color saturation (s), UV-Vis spectroscopy and coloring strength studies have been assessed to determine how CDCA influences the color properties and to study the color quality over time. The color quality was analyzed using the Commission Internationale de l’Eclairage (CIE) system.

All results obtained revealed that the addition of CDCA significantly influenced the overall color performance of the saffron extraction. However, the most pronounced improvement was recorded with the use of 45 Wt.% CDCA. The sample exhibited the highest color quality at the end of nine months of storage with highest absorbance: C* value = 91.38, color saturation = 0.96 and coloring strength = 687.

This preliminary study offers significant findings for further research focused on stability of natural colorants extracted from Spanish saffron that can provide benefits for future applications especially in coating industry, food, agriculture, medicine and others.

The values of this work can be observed from the information and evidence provided by CIE color stability in terms of chromaticity and saturation, as well as UV-Vis spectrophotometric measurement. It showed that the addition of CDCA additive can help to prolong and enhance the natural colorant properties from Spanish saffron (Crocus sativus L.) for nine month of storage. This proved that by adding additives such as CDCA the saffron colorant can be maintained. To the best of the authors’ concern, this is the first time CDCA is used to prevent color degradation of natural colorant from saffron.

The purpose of this paper is to estimate the degree of technical efficiency, determinants of technical inefficiencies and driving forces behind the production growth for a panel data set collected during the 1998/1999 and 2004/2006 Kharif cropping season, from 452 small-scale rice farming households in the Giridih and Purulia districts of Eastern India.

The estimations of technical efficiency utilize stochastic frontier production function with a sub-model of inefficiency effects at both aggregated farm level and disaggregated plot level where traditional varieties (TVs) and high-yielding varieties (HYVs) are differentiated. The output growth decomposition analysis identifies the main contributor to the total rice production growth.

The results indicate that the sampled farms are operated at moderate levels of technical efficiency. The production of HYV rice is associated with higher technical efficiency compared to TV rice. Farming experience, education attainment, landholding size, the share of non-agricultural income and the share of land in the lower terraces account for the differences in technical inefficiencies across the sampled farms. The decomposition analysis suggests that as technical efficiency decreased, technical change is the main source of production growth during the survey period.

The small sample size applied in the analysis will result in an insufficient representativeness of the study area.

This paper fills the literature gap as estimations of technical efficiency that account for subtle differences in adopted rice varieties are still rare in India.

Plastic has been a very useful material which is very cheap, easy to carry and is resilient to biodegradation. That is why plastic has been used, sometimes reused, and overused due to the reasons mentioned above. As a result, landfills and oceans are full of plastic. But if we consider all the negative health effects, environmental / ecological effects it has in present times, we can understand that it is environmentally very expensive to use plastic. Bangladesh is a relatively young country with dense population and limited resource. Proper management of plastic remains an issue with the country. Considering these, this chapter focuses on how plastic is used, how it is treated as waste and what can be possible solutions in reducing the amount of plastic in Bangladesh.

This study aims to explore the dynamic interplay of key resources (i.e. research and development (R&D), advertising and exports) in affecting the performance of USA manufacturing firms. Specifically, the authors examine the dynamic impact of joint resources and predict differential effect scales contingent on firm capabilities.

This study presents a combined multiple regression analysis (MRA)-multilayer perceptron (MLP) neural network modeling and investigates the complex interlinkage of capabilities, resources and performance. As an innovative approach, the MRA-MLP model investigates the effect of capabilities under the combinatory deployment of joint resources.

This study finds that the impact of joint resources and synergistic rents is not uniform but rather distinctive according to the combinatory conditions and that the pattern is further shaped by firm capabilities. Accordingly, besides signifying the contingent aspect of capabilities across a range of resource combinations, the result also shows that managerial sophistication in adaptive resource control is more than a managerial ethos.

The proposed analytic process provides scientific decision support tools with control mechanisms with respect to deploying multiple resources and setting actionable goals, thereby presenting pragmatic benchmarking options to industry managers.

Using the theoretical underpinnings of the resource-based view (RBV) and resource orchestration, this study advances knowledge about the complex interaction of key resources by presenting a salient analytic process. The empirical design, which portrays holistic interaction patterns, adds to the uniqueness of this study of the complex interlinkages between capabilities, resources and shareholder value.

This study aims to explore the strategic impact of R&D and export activity on the diverse dimensions of US manufacturing firms’ performance. It also explores, using a predictive analytic model, the interactive synergistic effect that R&D and exports have on firm performance.

This study presents an innovative two-stage regression-neural network approach. Complementing conventional statistical analysis, the predictive backpropagation neural network explores the relative impact of R&D and exports and their synergistic effect on firm performance.

This study demonstrates the significant and positive effect of R&D and export strategy/activity on the economic performance of leading US manufacturing firms, particularly on their market-based performance (i.e. sustained growth rate or SGR). Furthermore, this study finds that the synergistic effect of R&D and exports on short-term performance (i.e. return on investment) is positive in high-tech firms but negative in low-tech firms. However, the synergistic effect on SGR is increasingly positive regardless of the level of technology.

In addition to traditional statistical analysis, this study uniquely investigates the relative importance of selected strategic variables, along with R&D and export activity and their differential synergistic effects, for firms’ economic performance in contrasting industry settings (high-tech vs low-tech).