Aditya Birla (India)

This study evaluates the effects of fused deposition modeling (FDM) parameters and annealing on the mechanical properties of 3D-printed nylon. Parameters examined include layer height, wall line count, nozzle temperature, and printing speed. Mechanical tests, tensile, impact, and flexural – were conducted on ASTM-standard specimens using a Taguchi L9 orthogonal array. Layer height had the most influence on tensile and flexural strength, while wall line counts impacted impact strength. The highest tensile strength before annealing was 25.42 MPa, and 25.139 MPa after annealing at 150°C for 91 minutes. Maximum flexural strength (71.91 MPa) and impact strength (16 J) were recorded at optimized parameter settings. Analysis of Variance (ANOVA) showed layer height (51.63%) as the most significant post-annealing, and printing speed (36.63%) before annealing. Scanning electron microscope (SEM) analysis revealed improved layer bonding and reduced voids in annealed samples. Unannealed samples showed brittle fractures and poor fusion. The study confirms the importance of process optimization and thermal treatment in improving nylon part performance.

The fast development of the applications of the Industrial Internet of Things (IIoT) requires the real-time object detection which has the capability to work effectively in energy-restrained edgefog conditions. Unlike the former YOLO-based and lightweight detectors and RL-based edgefog offloading mechanisms, the proposed RAEED framework combines lightweight detection and training-free adaptive inference offloading to optimize the accuracy, latency and energy consumption jointly in IIoT settings. The deep learning-based detectors are very accurate and capable of handling resource-intensive IIoT deployments, however, their systems are frequently computation- and powerintensive, which is not feasible in resource-constrained IIoT applications. In this effort, the present work suggests that a resource-conscious energy-efficient detector (RAEED) system is proposed which allocates inference resources between the edge and fog nodes dynamically projected by resource availability and network characteristics. The framework integrates a convolutional backbone which is lightweight together with adaptive offloading strategy to trade-off between detection accuracy, latency, and energy consumption. The applications to the COCO 2017 dataset under constrained deployment conditions indicate that RAEED can have 82.4% mAP, 83.1% precision and 81.7% recall, with a latency of 47.6 ms and with a consumption of 0.91 mJ/frame. These findings indicate that IIoT systems can greatly manage to find and identify objects with a much better energy and resource savings.

The full potential solar photovoltaic (PV) energy is not utilised by the mining industry due to heavy dust deposition and harsh operating condition. This study aims to quantify the seasonal impact of dust deposition on PV performance and determine the optimal cleaning frequency for optimize the performance of solar PV Panel in an operational mining condition. Additionally, an innovative automated dry dust cleaning system is designed, developed, and validated in the active mining field to evaluate its operational efficiency and durability. A comprehensive study of twenty-six week was conducted to monitor the variations in dust deposition density and output energy of PV panel across three distinct seasonal phases. Among these, pre-summer phase showed the highest dust deposition density of 5.98g/m2 and due to this maximum output power (Pmax) of dusty panel reduce by 63.50%. This phase experienced an intense mining activity and dry weather condition. Therefore, a cleaning schedule of 3 to 4 days is recommended to effectively utilise the potential of PV panel. The other two phases such as dry winter and early monsoon experienced comparatively lower average reduction of 35% to 40% in maximum output power. A moderate cleaning scheduled of 6 to 7day is recommended for these two phases Further, the developed dust cleaning system is validated in the mining field under three different environmental phases and an average recovery of 40% in Pmax is obtained across three phases. This presents the effectiveness of developed dust cleaning system under real operating condition. The outcome of this research highlights the need of dynamic cleaning schedule for effective utilisation of solar energy in mining industry. The integration of smart dust sensors and AI based predictive modelling of developed cleaning system for sustainable utilisation of solar energy in mining and allied industries.

This study examines the drilling characteristics of crab carapace-impregnated coir polyester composite material. The chemical structure of the crab carapace offers better mechanical properties; however, optimizing drilling performance in a full factorial design under various drilling conditions, with the parameters of drill bit diameter, feed rate, and spindle speed. A reduction in feed rate combined with a small drill diameter and high spindle speed creates the optimal conditions to minimize the delamination value of 1.05 while maintaining structural strength and producing a strong thrust of 100 N and torque effects of 5.0 N-m on the composite material. The ANN was used to predict the drilling parameters, and the ANN model provides a precise prediction of drilling parameters within the defined boundaries for improved operation efficiency of this composite material, with average absolute percentage error of less than 3%. The ANN-based methodology shows efficient modeling capabilities for drilling operations in complex structured composite materials.

Purpose This study investigates the relationship between informal employment (IE) and economic growth (EG) across countries at different stages of development. Specifically, it examines whether IE promotes or constrains EG, and whether institutional factors such as economic freedom (EF) and government expenditure (GE) moderate this relationship. Design/methodology/approach The study employs a balanced panel dataset of 161 countries from 2011 to 2019. Long-run econometric estimators including fixed effects, fully modified ordinary least squares and dynamic ordinary least squares are applied to address endogeneity, serial correlation and unobserved heterogeneity. IE is treated as the key independent variable, while per capita income, GE, EF and unemployment are included as control variables. A quadratic specification is incorporated to capture non-linear threshold effects. Findings The results confirm a statistically significant inverted U-shaped relationship between informality and EG. Moderate levels of IE initially support growth, but excessive informality inhibits gross domestic product (GDP) per capita growth. EF and GE exert significant positive effects on growth. Unemployment exhibits an indirect short-run positive effect by pushing workers into informal activities that sustain consumption and productivity. The positive growth effect of informality is strongest in emerging economies and weakest in high-income countries. Research limitations/implications This study reinforces the need to incorporate IE into mainstream economic development models rather than treating it as a residual labour category. The findings confirm that the relationship between informality and growth is non-linear and income-dependent, suggesting that future research should examine threshold levels of informality across different institutional settings and sectors. The results also highlight the importance of accounting for unpaid and informal work in national accounts, labour-market modelling, and development theory. Scholars may further extend this research by exploring how digitalisation, automation, and financial inclusion influence the transition from informal to formal employment. Practical implications The results show that the informal sector (IS) is a major source of jobs and economic stability, especially in emerging economies. Policies should not try to remove informality suddenly but should improve working conditions and support a gradual shift toward formality. Measures such as skills development, entrepreneurship support, financial inclusion, and digital access can increase income and productivity. Policy design should differ across income groups and sectors because the impact of informality on growth is not uniform. Social implications The IS plays a central role in reducing poverty by offering work to low income and vulnerable groups. Expanding access to microfinance, social protection, and skill building can improve income security and reduce inequality. Supporting informal workers can contribute to more inclusive growth where formal jobs are limited. Originality/value This study is one of the few that analyses the nonlinear link between IE and EG across many countries while combining dual sector theory, the Kuznets curve, and institutional perspectives. It offers evidence for designing development policies that support inclusive and sustainable growth.