Deepak Rajendra Unune

Resource conflicts constitute a major global issue in areas rich in natural resources. The modeling of factors influencing natural resource conflicts (NRCs), including environmental, health, socio-economic, political, and legal aspects, presents a significant challenge compounded by inadequate data. Quantitative research frequently emphasizes large-scale conflicts. This study presents a novel multilevel approach, SEFLAME-CM—Spatially Explicit Fuzzy Logic-Adapted Model for Conflict Management—for advancing understanding of the relationship between NRCs and drivers under territorial and rebel-based typologies at a community level. SEFLAME-CM is hypothesized to yield a more robust positive correlation between the risk of NRCs and the interacting conflict drivers, provided that the conflict drivers and input variables remain the same. Local knowledge from stakeholders is integrated into spatial decision-making tools to advance sustainable peace initiatives. We compared our model with spatial multi-criteria evaluation for conflict management (SMCE-CM) and spatial statistics. The results from the Moran’s I scatter plots of the overall conflicts of the SEFLAME-CM and SMCE-CM models exhibit substantial values of 0.99 and 0.98, respectively. Territorial resource violence due to environmental drivers increases coast-wards, more than that stemming from rebellion. Weighing fuzzy rules and conflict drivers enables equal comparison. Environmental variables, including proximity to arable land, mangrove ecosystems, polluted water, and oil infrastructures are key factors in NRCs. Conversely, socio-economic and political factors seem to be of lesser importance, contradicting prior research conclusions. In Third World nations, local communities emphasize food security and access to environmental services over local political matters amid competition for resources. The synergistic integration of fuzzy logic analysis and community perception to address sustainable peace while simultaneously connecting environmental and socio-economic factors is SEFLAME-CM’s contribution. This underscores the importance of a holistic approach to resource conflicts in communities and the dissemination of knowledge among specialists and local stakeholders in the sustainable management of resource disputes. The findings can inform national policies and international efforts in addressing the intricate underlying challenges while emphasizing the knowledge and needs of impacted communities. SEFLAME-CM, with improvements, proficiently illustrates the capacity to model intricate real-world issues.

This study investigates the energy profile of a regional hospital in North America using an ASHRAE Level 2 Energy Audit. The audit aimed to assess the efficiency of existing systems, identify areas for improvement, and recommend cost-effective measures to reduce energy consumption and environmental impact. The analysis revealed a well-maintained heating system with efficient boilers. However, a critical concern emerged regarding operational risks due to the lack of redundancy in the cooling system. To address this vulnerability, the study recommends installing an air-cooled chiller for increased operational resilience. The economic feasibility of proposed improvements was evaluated, highlighting the balance between initial investment and long-term savings in energy costs and reduced greenhouse gas emissions. The findings contribute to the discourse on sustainable healthcare practices, emphasizing the importance of energy audits, strategic planning, and redundancy measures for optimized performance and environmental responsibility in hospitals.

Nickel–Chromium (Ni-Cr) foam is a highly versatile material, combining excellent high-temperature and corrosion resistance with lightweight and energy absorption properties, making it a popular choice for a wide range of applications. This study explores the development of Ni-Cr foam using Pulse electrodeposition techniques, specifically ultrasonic-assisted pulse electroplating of nickel (UAPEPN) and ultrasonic-assisted pulse electroplating of chromium (UAPEPCr). The study evaluates the surface morphology, coating thickness and minimum mass gain of Ni-Cr foam after each process. A dedicated test rig was designed to measure the pressure drop across the foam, revealing a pressure drop of 0.29 bar at an input pressure of 70 PSI and a flow rate of 17.53 LPM of oil. The porosity percentage and permeability of the foam were evaluated at each developmental stage, with values noted as 91% and 1.0032 × 10 ⁻⁸ m ² , respectively, after the UAPEPCr stage. Uniaxial tensile and compression tests were conducted to measure the foam’s maximum strength and energy absorption per unit volume, yielding valuable insights into the material’s performance characteristics.

The surface quality of manufactured components is a primary concern for the current industries, as poor surface finishes can lead to various issues during the component’s operation and lifespan. Nonconventional finishing technologies, with or without magnetic field assistance, play a crucial role in attaining micron-level surface finishes. The present research focuses on the fabrication of a magnetic-assisted vacuum abrasive finishing (MAVAF) setup, aimed at improving the surface finish of automobile engine coupler components. A novel magnetic-assisted self-deformable abrasive finishing (MASDAF) media was synthesized, utilizing marble waste as a base material, paraffin oil and grease as additives, and silicon carbide and iron powder as abrasive particles. The MASDAF media was characterized for its viscosity and rheological behavior and the best composition of MASDAF was selected for use in the MAVAF process. The results reveal that the percentage improvement in the change of average surface roughness value ([ ]Ra) decreased with an increase in abrasive mesh size. However, the impact of abrasive concentration on [ ]Ra showed mixed effects, with [ ]Ra initially increasing with increasing abrasive concentration, before declining with further increases in concentration.

Resource conflicts represent a significant global challenge in regions abundant with natural re-sources. Modelling the myriad factors driving natural resource-based conflicts (NRBCs), spanning environmental, health, socio-economic, and political dimensions, is a complex endeavor exacer-bated by data scarcity. Furthermore, existing quantitative studies often focus solely on large-scale conflicts. This article introduces a novel algorithm, the Spatially Explicit Fuzzy Logic-Adapted Model for Conflict Management (SEFLAME-CM), which integrates the local knowledge of stakeholders into spatial decision-making technologies to support sustainable peace efforts. The results are validated with spatial multi-criteria evaluation (SMCE) using spatial statistics. The Moran’s I scatter plots for the overall conflicts reveal significant values of 0.99 and 0.98 for both the SEFLAME-CM and SMCE, respectively, with significant spatial autocorrelation. While there re-mains room for improvement in enhancing the model's quality, SEFLAME-CM demonstrates its capacity to transparently model complex real-world problems. The findings underscore the im-perative for a holistic approach to addressing environmental degradation, socio-economic, and political drivers of resource conflicts at the community level. Our paper demonstrates the signif-icance of spatial information technologies and knowledge exchange between experts and local stakeholders in effectively managing resource conflicts. These insights should inform national policies and international interventions, ensuring that the complex underlying issues are ad-dressed while prioritizing the knowledge and needs of affected communities.

Conventional machining of super alloy Monel 400 is difficult due to its high strength, low heat conductivity, and gummy nature. In this work, the performance of the electrical discharge diamond face grinding (EDDFG) while processing supper alloy Monel 400 is assessed using the super abrasive diamond-coated grinding wheels of three different grit numbers. Preliminary experiments highlighted the superior performance of the EDDFG as compared to that of electrical discharge machining (EDM) and electrical discharge face grinding (EDFG); in terms of material removal rate (MRR), average surface roughness (R_a), and microscopic images of surfaces using scanning electron microscope (SEM). This work aimed to assess the influence of the diamond grit number (DG _n ) of the grinding wheel along with other process parameters, viz. grinding wheel speed (GWS), peak current (I _p ), and pulse-on-time (T _on ) on R_a and MRR. The analysis of variance (ANOVA) confirmed that DG _n and GWS significantly affect the response variables. Finally, the multi-objective genetic algorithm (GA) optimization approach was used to determine the optimal parametric settings of the EDDFG process.