Daily Archives: August 5, 2026

Authors: Adilov Sobit Uktamovich, Gulomov Gofir Komil o‘g‘li, Umirzoqov Jamshid Mirzayevich, Umirov Ismoil Eshonqulovich

Abstract: This study presents a hydrochemical characterization of groundwater from four wells (Chanishbay yakka quduq, Kupet turar joy, Nissan uy oldi, and Beknazar quduq) within the Central Kyzylkum desert ecosystem, Uzbekistan, focusing on major cations (Ca²⁺, Mg²⁺, Na⁺, K⁺), a set of regulated trace elements (Cu, Cr, Fe, Cd), and the Sodium Adsorption Ratio (SAR). Samples were collected on 30 April 2026 and analysed by Flame Atomic Absorption Spectrometry (FAAS) at an accredited laboratory (Workshop No. 081, order No. 23764). Results were compared against World Health Organization (WHO) drinking-water guidelines and the United States Salinity Laboratory (USSL) SAR classification. The hydrochemical facies at all sites were sodium-dominant (Na⁺ > Ca²⁺ > Mg²⁺ > K⁺), and sodium concentrations exceeded the WHO palatability threshold (200 mg/L) at every site. Calculated SAR values ranged from 4.9 (low hazard, S1) to 55.8 (very high hazard, S4), indicating a substantial sodium/salinity hazard should these waters be used for irrigation. Concentrations of the regulated heavy metals (Cu, Cr, Cd) were below the detection limit at all sites. The findings underline the need for expanded hydrochemical monitoring of groundwater in arid desert ecosystems such as Central Kyzylkum.

DOI: https://doi.org/10.5281/zenodo.21805869

Published by: vikaspatanker

Authors: Miss Anukula Roja, Athili Venkat Raju

Abstract: Surface defect detection in strip steel is a critical quality assurance task in modern steel manufacturing, as defects such as scratches, inclusions, patches, and rolled-in scales can significantly degrade product quality, mechanical performance, and production efficiency. Conventional manual inspection methods are labour-intensive, subjective, and incapable of satisfying the speed and accuracy requirements of automated manufacturing environments. This paper presents an intelligent hybrid framework for automated strip steel surface defect detection by integrating traditional machine learning and deep learning techniques. The proposed approach incorporates mean filtering for noise reduction and adaptive threshold-based segmentation to accurately extract defect regions from strip steel images. To improve classification performance, an ensemble model combining Random Forest (RF) and ResNet50 is developed, where ResNet50 extracts rich hierarchical visual features and Random Forest effectively classifies discriminative statistical features. The proposed framework is evaluated using a multi-class strip steel surface defect dataset comprising various defect categories. Experimental results demonstrate that the hybrid RF–ResNet50 model outperforms individual machine learning and deep learning models in terms of classification accuracy, robustness, and generalization capability. The complementary learning characteristics of both models enable effective representation of low-level texture information and high-level semantic features, resulting in reliable defect identification under diverse surface conditions. Furthermore, the proposed framework is computationally efficient and scalable for real-time industrial deployment, reducing reliance on manual inspection while enhancing product quality and manufacturing productivity. These findings highlight the potential of hybrid artificial intelligence techniques as an effective solution for next-generation intelligent surface inspection and quality control systems in smart manufacturing.

DOI: http://doi.org/http://doi.org/10.61137/ijsret.vol.12.issue4.146

Published by: vikaspatanker

Authors: Arpit Darbari

Abstract: The accelerating impacts of climate change, resource depletion, and increasing stakeholder expectations have fundamentally transformed the way organizations approach sustainability. Manufacturing industries, which account for a significant proportion of global greenhouse gas emissions, water consumption, and resource utilization, are under growing pressure to move beyond voluntary sustainability initiatives toward structured, measurable, and transparent Environmental, Social, and Governance (ESG) management. Regulatory developments such as the European Union's Corporate Sustainability Reporting Directive (CSRD), the European Sustainability Reporting Standards (ESRS), and internationally accepted frameworks including the Greenhouse Gas (GHG) Protocol and the United Nations Sustainable Development Goals (UN SDGs) have collectively established a comprehensive sustainability reporting landscape. However, organizations continue to face considerable challenges in translating these independent frameworks into a cohesive operational strategy. This paper proposes a Unified Digital ESG Framework (UDEF) designed specifically for manufacturing industries. Unlike existing studies that examine ESG reporting, carbon accounting, or sustainability regulations independently, the proposed framework integrates CSRD compliance, ESRS disclosure requirements, GHG Protocol-based emissions accounting, and UN SDG alignment into a single implementation architecture supported by digital technologies. The framework incorporates smart metering, Industrial Internet of Things (IIoT) devices, cloud-based Energy Management Systems (EMS), real-time KPI monitoring, and artificial intelligence-assisted decision support to facilitate continuous ESG performance measurement. The proposed framework establishes a systematic methodology for identifying material sustainability issues, collecting operational data, quantifying environmental impacts, mapping ESG indicators to regulatory disclosure requirements, and supporting strategic decision-making through digital dashboards. A manufacturing-oriented implementation model demonstrates how energy, water, fuel, waste, emissions, and governance indicators can be consolidated into an integrated ESG management ecosystem. The paper further introduces a Digital ESG Maturity Model to evaluate organizational readiness and monitor continuous improvement. The study contributes to existing sustainability literature by presenting an implementation-oriented framework that bridges the gap between regulatory compliance and operational sustainability management. It offers practical guidance for manufacturing organizations seeking to improve ESG performance, strengthen corporate governance, enhance investor confidence, and accelerate progress toward net-zero and circular economy objectives. The framework also provides a foundation for future research on digital sustainability, AI-enabled ESG reporting, and Industry 5.0-driven environmental governance.

Published by: vikaspatanker

Authors: Ashvin Kulkarni

Abstract: Parcel sortation to postal sacks is one of the most labor-intensive, error-prone, and operationally costly processes in ecommerce fulfillment. This paper documents the multi-site rollout of an AI-driven robotic pick-and-sort system across a major 3PL logistics network. Deployed under a Robotics-as-a-Service (RaaS) model, the technology replaced manual sortation operations at three facilities and achieved a 75% increase in parcels per hour relative to fully manual operations, while reducing required headcount per sortation pod by 75%. Over a 4-year contract term, the projected total savings across the network reach approximately $15 million. Beyond the headline economics, this case surfaces a subtler argument: in a labor market where wages are volatile and worker turnover in sortation operations is chronic, the fixed monthly cost of robotics is not just cheaper—it is structurally more predictable. That predictability turns out to matter as much as the savings themselves.

DOI: https://doi.org/10.5281/zenodo.21802715

Published by: vikaspatanker

Authors: Ms. Achal A. Koyale, Ms. Shravani S. Golegaonkar, Ms. Maithili V. Mangalagiri

Abstract: In recent years, the increasing number of incidents related to missing children and child safety has created a strong need for reliable and intelligent monitoring systems. This paper proposes a smart child tracking and safety system that uses Internet of Things (IoT) technology integrated with Global Positioning System (GPS) and Global System for Mobile Communication (GSM) for real-time location monitoring and emergency communication. The proposed system enables parents or guardians to continuously track the child’s location through a mobile application and receive instant notifications whenever the child moves outside a predefined safe zone. The system also includes an emergency alert feature that allows immediate communication during critical situations. The proposed model is designed with a focus on accuracy, low power consumption, affordability, and ease of use, making it suitable for practical day-to-day applications. The integration of geofencing and real-time data transmission improves the efficiency and reliability of child monitoring in crowded places such as schools, malls, parks, and public transport areas. Experimental analysis shows that the system provides fast response time, reliable location tracking, and improved child security compared to conventional monitoring methods.This research contributes toward the development of a cost-effective and user-friendly child safety solution capable of reducing risks associated with child loss, kidnapping, and unauthorized movement. The proposed system demonstrates how modern wireless communication and IoT technologies can be effectively utilized to enhance child protection and parental confidence in real-world environments

DOI: http://doi.org/10.5281/zenodo.21802462

Published by: vikaspatanker

Authors: Jackson Matsanga

Abstract: The development and achievement of sustainable success among individuals, organisations, and institutions increasingly depends on the integration of strategic planning and sustained determination. Although these concepts have been extensively examined within strategic management, psychology, leadership, and organisational studies, existing scholarship has largely considered them independently or within discipline-specific frameworks. Consequently, limited philosophical attention has been devoted to explaining how strategic planning and determination function together to promote self-reliance and dependable achievement. This study developed the Strategic Wolverine Theory (TSWT), an original philosophical framework inspired by the documented behavioural characteristics of the wolverine (Gulo gulo). The study adopted a philosophical research design and employed conceptual analysis, literature synthesis, observational behavioural analysis, theory development procedures, and inductive reasoning to formulate the theory. The findings revealed that the wolverine consistently demonstrates strategic food caching, prudent resource management, determination, resilience, adaptability, self-reliance, and dependable survival under harsh environmental conditions. These behavioural characteristics were systematically interpreted and synthesised into a coherent philosophical framework. The study established that strategic planning and determination are the fundamental pillars of self-reliance and dependable achievement. Strategic planning provides purposeful direction through anticipation, preparation, and prudent resource utilisation, while determination sustains disciplined implementation despite adversity. Their interaction strengthens self-reliance, resulting in consistent and sustainable achievement across diverse contexts. The theory further identifies leadership effectiveness and resource availability as moderating factors that may influence these relationships. The Strategic Wolverine Theory contributes to contemporary scholarship by integrating strategic planning, determination, self-reliance, and dependable achievement into a unified philosophical framework grounded in documented behavioural evidence. The theory provides a conceptual foundation for future empirical research and offers practical guidance for leadership, education, organisational management, entrepreneurship, governance, public administration, and community development. It therefore represents an original contribution to nature-inspired philosophical theory development and the broader understanding of sustainable success.

DOI: https://doi.org/10.5281/zenodo.21802083

Published by: vikaspatanker