Volume 7 Issue 11 2025

This conceptual paper discusses how digital innovation and sustainability converge and how organizations may introduce sustainability interests within the framework of bias-resilient Artificial Intelligence (AI) to create a value to the business, society, and the environment. To reduce the risks of bias in AI, it is proposed that a bias-resilient AI is a system that reduces algorithmic bias to the maximum, enhances fairness, and supports ethical decision-making. Rather than a quantitative method, the paper relies on a qualitative, exploratory research approach using case studies to examine the role of the primary digital technologies of AI, Internet of Things (IoT), Blockchain, Cloud Computing, and Big Data Analytics in sustainable development and operational excellence. The examples of industry leaders Tesla, Unilever, Walmart, and Siemens demonstrate the implementations that achieve efficiency of resources, lessen carbon emission, and enhance visibility of supply chain. There is a special section on the reasons why algorithms can be biased, a description of countermeasures, and putting them into the broader context of ethical transformation of digital processes. Some of the best challenges that are also discussed in the paper include the importance of technological limits, regulatory complexity, cost barriers, and talent gap, and the strategic solutions include open innovation, circular economy, and collaborative digital ecosystems. Lastly, the paper also provides practical recommendations on how to connect business strategy to the United Nations Sustainable Development Goals and develop inclusive and resilient digital infrastructures. Keywords Digital Innovation, Bias-Resilient Artificial Intelligence, Sustainable Development Goals, Supply Chain Transparency, Ethical AI, Operational Efficiency.

Cyber security is the protection of internet-connected systems such as hardware, software and data from cyber threats. The practice is used by individuals, also by enterprises to protectagainst unauthorized access to data centres also with other computerized systems in supply chain. Cyber security is considered as a set of practices that is liable to protect the information systems of the suppliers”, manufacturers, distributors, wholesalers, involved in supply chain, having the entire affect on the network, having network collaboration in supply chain. Relationship with suppliers is to develop, a strong flow of information system, during Cyber Security adopted systems, audits, from suppliers, improvement of practices, encryption access, continuous intrusion detection systems, monitor vendor access network in supply chain. Relationship with vendors, suppliers’ training employees, on Cyber security risk, establishing security standards, protect protocols automations, reduce human errors, to an extended possibility, protecting interconnected network involved in the distribution of goods, does become an important activity in supply chain. On the Intellectual Property Protection where sourcing, planning, scheduling execution controlling, monitoring flow of goods, services information with the concept of integration, needs protection of Cyber Security, on the various Cyber crimes promulgated in supply chain

Bio-inspired optimization methods are potent for addressing high-dimensional and complex design spaces. The research proposes a Paillier Homomorphic Encryption Optimization (PHEO) algorithm using the Hybrid Adaptive Greylag Goose– Crayfish Optimization (A-GGCO), designed to enhance Paillier Homomorphic Encryption (PHE) parameters for secure and scalable cloud applications. The algorithm incorporates a diversity-driven switching mechanism to balance global exploration and local exploitation, drawing on the migratory behavior of geese and the adaptive movements of crayfish. An experimental evaluation was conducted on leading optimizers, including PHE (baseline), GGO, CO, HO, JSO, and CSO-MA. Results demonstrate that PHEO achieves faster convergence, higher accuracy, and robustness, supported by statistical validation (Wilcoxon test, ANOVA, and effect-size analysis) and confirmed significant reductions in key generation, encryption, and decryption times, with practical benefits for IoT healthcare and latency-sensitive cloud environments.

Objective: This study aimed to evaluate Ferric Carboxymaltose (FCM) effectiveness in postpartumiron deficiency anemia (PP-IDA) treatment. Methods: One hundred fifteen (115) women with PP-IDA (serum ferritin <15 μg/L, hemoglobin <11 g/dL after delivery or at 1st postpartum week, and <12 g/dL at 8th postpartum week) were included in the current comparative study following STROBE Checklist. Studied women received FCM (Ferinject®) infusion to treat their PP-IDA. Studied women mean levels of serum ferritin, hemoglobin, and mean RBCs-indices were compared both before (pre-treatment), and 8-weeks after (post-treatment) receiving Ferinject® to determine the effectiveness of Ferinject® in PP-IDA treatment. Results: Studied women mean level of serum ferritin statistically elevated from 10.02 ± 2.4 before (pre-treatment) Ferinject® to 140.7 ± 8.9 ug/L after (post-treatment) Ferinject® (p<0.0001). Studied women mean hemoglobin level statistically elevated from 8.03 ± 0.6 before (pre-treatment) Ferinject® to 14.06 ± 0.45 g/dL after (post-treatment) Ferinject® (p<0.0001). Studied women mean RBCs-volume statistically elevated from 71.9 ± 3.5 before (pre-treatment) Ferinject® to 90.3 ± 2.6 fL after (post-treatment) Ferinject® (p<0.0001). Studied women mean RBCshemoglobin statistically elevated from 24.2 ± 1.8 before (pre-treatment) Ferinject® to 30.03 ± 1.6 pg after (post-treatment) Ferinject® (p<0.0001). Conclusion: Studied women mean levels of serum ferritin, hemoglobin and mean RBCs-indices (volume and hemoglobin) were statistically elevated after Ferinject®. This study suggests rapid correction of postpartum-IDA using Ferinject® to improve postpartum maternal physical performance, and to enhance mothers’ ability to take care of their babies.

This research aims to examine outdoor EM field levels, residential indoor exposures, and floor-specific exposures near mobile base stations. The study examines the electrical fields' intensity and strength at various distances and times, specifically during sunny weather, by analyzing data collected from multiple base stations. Individuals' contributions were also calculated. Antenna masts. All EM field measurements were compared against national and international standards, such as those set by the Department of Telecommunications (DoT) and the International Commission on Non-Ionizing Radiation Protection (ICNIRP). Outdoor electromagnetic fields in crowded places measured lower than recommended by ICNIRP standards but higher than those set by Austria. Indoor residential EM areas were evaluated in Kolhapur, a western Maharashtra district, examining potential health risks due to nearby MBS antenna radiation. In the study, efforts were made across multiple areas within the homes, such as the hall, kitchen, bedroom, and roof, to ensure that the power density and strength of the electric field did not exceed the safety standards set by ICNIRP. The results showed that these levels remained well below the permitted limits. The EM field intensity changed depending on where the antenna mast was placed and how many antennas were used. Further, floor-wise EM field exposure was analysed in multi-storey residential buildings. Measurements were taken at various levels, including the ground floor, first floor, second floor, higher floors, and rooftop terraces, using a three-axis EM field meter (Model KM-195). The results revealed that floor exposure levels were also well within standard safety limits. It was found that indoor EM field strength in dwellings depends on the location of the building, the number and height of antennas on nearby MBS, while floor-wise exposure is further influenced by the tilt angle of the antennas.

This study conceptualizes the utilization of plastic waste into multipurpose boards through the principles of the circular economy. The research aims to reduce the volume of plastic waste that negatively impacts the environment and to create new economic value by transforming waste into valuable products, while simultaneously conserving natural resources by reducing the use of virgin raw materials. The method employed involves creative recycling or upcycling processes that integrate the principles of Reduce, Reuse, and Recycle. The process includes collecting, sorting, shredding, and molding processed plastics into new products such as durable multipurpose boards, thereby reducing waste and generating new economic value. The results demonstrate the creation of high-value products, such as boards that can be used to make items like storage boxes, reading tables, and even paving blocks, alongside associated economic and environmental benefits. The economic benefits include the creation of new job opportunities in the recycling sector, while the environmental benefits encompass the reduction of plastic waste and the conservation of natural resources by decreasing dependence on new raw materials.

This review paper presents a extensive examination of the latest developments in deep learning-driven exercise pose estimation. It highlights the key methodologies, datasets and evaluation metrics employed in this domain. The paper discusses the significant transformation in the domain of human posture and positioning evaluation driven by the advent of deep learning Methods such as deep neural network model like RNN and CNN. These data-driven models have shown higher performance compared to traditional feature-engineering approaches leveraging the inherent hierarchical feature representation and powerful learning capabilities of deep neural networks. However, the task of exercise posture estimation remains a complex task due to factors such as depth uncertainties, self-occlusions, small scale varied and uncontrolled datasets. The review paper examines the various deep learning-based approaches explored by researchers in addressing these challenges surrounding assessment and analysis of the human form posture and positioning from simpler 2D methods to more intricate 3D techniques.