In order to avoid working in a constrained hazardous environment, manual spray painting operation is gradually being replaced by automated robotic systems in many manufacturing industries. Application of spray painting robots ensures defect-free painting of dissimilar components with higher repeatability, flexibility, productivity, reduced cycle time and minimum wastage of paint. Due to availability of a large number of viable options in the market, selection of a spray painting robot suitable for a given application poses a great problem. Thus, this paper proposes the integrated application of step-wise weight assessment ratio analysis (SWARA) and combined compromise solution (CoCoSo) methods to identify the most apposite spray painting robot for an automobile industry based on seven evaluation criteria (payload, mass, speed, repeatability, reach, cost and power consumption). The SWARA method identifies cost as the most significant criterion based on a set preference order, whereas, Fanuc P-350iA/45 is selected as the best spray painting robot by CoCoSo method. The derived ranking results are also contrasted with other popular multi-criteria decision making (MCDM) techniques (TOPSIS, VIKOR, COPRAS, PROMETHEE and MOORA) and subjective criteria weighting methods (AHP, PIPRECIA, BWM and FUCOM). High degrees of similarity in the ranking patterns between the adopted approach and other MCDM techniques prove its effectiveness in solving complex industrial robot selection problems. This integrated approach is proved to be quite robust being almost unaffected by the changing values of the corresponding tuning parameter in low-dimensional MCDM problems.

Rare diseases (RDs) affect a small percentage of the population but collectively impact millions worldwide. Their diagnosis and treatment remain challenging due to clinical heterogeneity, limited research, and high costs. Advances in genomics, artificial intelligence, and orphan drug development have improved diagnostic precision and therapeutic options, yet access disparities persist. Patients face not only medical challenges but also significant psychosocial burdens. Addressing these issues requires interdisciplinary collaboration, increased research funding, and policy reforms to accelerate drug development and improve healthcare accessibility. Innovative solutions like telemedicine and data-sharing networks can bridge existing gaps. This article explores the current landscape of rare disease research, highlighting challenges and potential strategies to enhance diagnostics, treatment, and patient care. This review emphasizes the need for targeted interventions by highlighting recent advances such as CRISPR-Cas9 and AI-assisted diagnostics, which have shown transformative potential in improving early detection and treatment of rare diseases. This review further addresses the importance of real-world evidence (RWE), mental health integration, patient advocacy, and regulatory frameworks in shaping equitable and effective rare disease strategies. A conceptual framework is proposed to unify key challenges, stakeholders, solutions, and outcomes, offering a holistic path forward.

Purpose: The purpose of this paper is to exploit the fullest potential and capability of different non-traditional machining (NTM) processes, it is often recommended to operate them at their optimal parametric combinations. There are several mathematical tools and techniques that have been effectively deployed for identifying the optimal parametric mixes for the NTM processes. Amongst them, grey relational analysis (GRA) has become quite popular due to its sound mathematical basis, ease to implement and apprehensiveness for multi-objective optimization of NTM processes. Design/methodology/approach: In this paper, GRA is integrated with fuzzy logic to present an efficient technique for multi-objective optimization of three NTM processes (i.e. abrasive water-jet machining, electrochemical machining and ultrasonic machining) while identifying their best parametric settings for enhanced machining performance. Findings: The derived results are validated with respect to technique for order preference by similarity to ideal solution (TOPSIS), and analysis of variance is also performed so as to identify the most significant control parameters in the considered NTM processes. Practical implications: This grey-fuzzy logic approach provides better parametric combinations for all the three NTM processes with respect to the predicted grey-fuzzy relational grades (GFRG). The developed surface plots help the process engineers to investigate the effects of various NTM process parameters on the predicted GFRG values. Originality/value: The adopted approach can be applied to various machining (both conventional and non-conventional) processes for their parametric optimization for achieving better response values.

Electrical discharge machining (EDM) has recently been shown to be one of the most successful unconventional machining methods for meeting the requirements of today’s manufacturing sector by producing complicated curved geometries in a broad variety of contemporary engineering materials. The machining efficiency of an EDM process during hexagonal hole formation on pearlitic Spheroidal Graphite (SG) iron 450/12 grade material was examined in this study utilizing peak current (I), pulse-on time (T_on), and inter-electrode gap (IEG) as input parameters. The responses, on the other hand, were the material removal rate (MRR) and overcut. During the experimental trials, the peak current ranged from 32 to 44 A, the pulse-on duration ranged from 30–120 s, and the inter-electrode gap ranged from 0.011 to 0.014 mm. Grey relational analysis (GRA) was interwoven with a fuzzy logic method to optimize the multi-objective technique that was explored in this EDM process. The effect of changing EDM process parameter values on responses was further investigated and statistically analyzed. Additionally, a response graph and response table were produced to determine the best parametric setting based on the calculated grey-fuzzy reasoning grade (GFRG). Furthermore, predictor regression models for response characteristics and GFRG were constructed, and a confirmation test was performed using randomly chosen input parameters to validate the generated models.