The rapid growth of global trade has heightened the importance of efficient container handling, environmentally responsible operations, and high-performing equipment selection in sustaining the competitiveness of modern supply chains. Container Freight Stations (CFS) serve as critical operational hubs where loading, unloading, inspection, and temporary storage activities are conducted, thereby requiring equipment capable of safely and efficiently handling heavy-tonnage cargo while aligning with green port transformation goals. Forklifts, which constitute one of the core equipment groups in CFS yards, differ significantly in terms of lifting capacity, power systems, maneuverability, hydraulic performance, ergonomics, and environmental impact, transforming forklift selection into a complex, multi-dimensional decision problem shaped by both technical and Environmental, Social, and Governance (ESG)-oriented considerations. Incorrect equipment choices may lead to operational downtime, energy inefficiency, equipment failures, and occupational safety risks, particularly in operations involving loads exceeding 25 tons. To address these challenges, this study proposes a hybrid decision-making framework that integrates expert-driven fuzzy assessments with sustainability-based evaluation using the FF-Hamacher-MEREC-ARLON methodology. In the first stage, expert weights and criterion importance values were calculated through the FF-MEREC approach, while alternative forklifts were ranked using the FF-ARLON method in the second stage. Two sensitivity analysis scenarios were applied: one by modifying the tradeoff ratio within ARLON and the other by sequentially removing each criterion. In both scenarios, the fourth alternative consistently emerged as the most suitable option. Furthermore, comparative analyses using eight established MCDM techniques; ALWAS, AROMAN, ARTASI, MABAC, MARCOS, RAM, SAW, and WASPAS; demonstrated complete agreement with the proposed model, confirming the fourth alternative as the top-ranked choice. The findings highlight the robustness, reliability, and sustainability alignment of the proposed framework for high-stakes heavy-duty equipment selection in port-based logistics operations.

Global research trends on Over-The-Top (OTT) media services were examined through a bibliometric and systematic review of 176 peer-reviewed documents published between 2010 and 2024, sourced from 117 journals and books. The analysis conducted using Bibliometrix, VOSviewer, and the PRISMA methodology identified a compound annual growth rate of approximately 34% in scientific output from 2010 to 2023, indicating rapid expansion in OTT research. The most prolific contributors by publication count are Chakraborty, Soren, and Sridevi. The leading countries in this field are India, South Korea, and the United States. Key research themes include platform dominance, consumer behaviour, and the impact of COVID-19 on OTT adoption, with “OTT” and “Netflix” as dominant topics in scholarly discussions. Although the data were sourced exclusively from Scopus, the findings offer a comprehensive overview of the evolution of themes, principal research networks, and prevailing trends in the OTT research domain.

Perovskite solar cells (PSCs) continue to advance toward higher efficiencies, yet the geometrical design of functional layers remains a critical bottleneck for device optimization and manufacturability. This work establishes a hybrid physics-data framework that integrates three-dimensional finite-element modeling with machine-learningbased surrogate prediction to accelerate PSC thickness optimization. A full 3D COMSOL Multiphysics model was developed to resolve charge-transport behavior, spatial electric fields, and recombination profiles within TiO₂/MAPbI₃/Spiro-OMeTAD architectures. Systematic variations in electron transport layer (ETL), perovskite absorber, and hole transport layer (HTL) thicknesses reveal that device power conversion efficiency (PCE) is governed by a trade-off between optical absorption, interface recombination, and resistive losses. A multi-layer perceptron regressor was trained using simulation data and achieved strong predictive fidelity (R2 ≈ 0.98) with a mean absolute error below 0.3%. The resulting surrogate model rapidly identifies optimal structural configurations without requiring additional high-cost simulations, demonstrating a reduction of design time by more than an order of magnitude. The proposed workflow provides a transferable route toward digital-twin-driven photovoltaic design and offers practical guidance for high-performance PSC engineering with reduced material consumption and enhanced computational efficiency.

The Load Haul Dumper (LHD) is essential machinery utilized for moving ore in the underground mining industry, in order to fulfil production targets. In this connection, the efficiency of the equipment should be maintained at an ideal standard, to be accomplished by reducing unexpected failure of components or subsystems in this intricate system. Downtime analysis helped identify faulty components and subsystems, which require the development of complementary maintenance plans to facilitate the replacement or fixing of parts. Proper practices of maintenance management improve the performance of the equipment. In this research, the efficiency of the LHD machine was assessed through reliability methods. Initially, the assumption of independent and identical distribution (IID) for the data sets was validated using trend and serial correlation analyses. The statistical tests indicated that the data sets adhered to the IID assumption. Therefore, a renewal process method was utilized for additional examination. The Kolmogorov-Smirnov (K-S) test was utilized to identify the most suitable distribution for the data sets. The theoretical probability distributions were estimated parametrically using the Maximum Likelihood Estimate (MLE) approach. The dependability of each separate subsystem was determined using the optimal fit distribution. Based on the reliability outcomes, preventive maintenance (PM) time plans were created to reach the targeted 90% reliability. Different maintenance strategies, in addition, were suggested to the maintenance team to extend the lifespan of the machine.

This study proposed a novel pin-level dynamic compensation strategy to combat the critical challenge of springback in the three-dimensional numerically controlled bending of ship hull plates. A collaborative prediction model combining convolutional and bidirectional recurrent networks (CNN-BiLSTM) was optimized using an improved metaheuristic algorithm, the Modified Sparrow Search Algorithm (MCSSA), to achieve millimeter-level precision in springback compensation. Based on the 225-pin independent control architecture, the system enabled real-time compensation with millisecond-level response ($\leq$ 50 ms) on standard industrial computing hardware, to overcome the limitations of conventionally fixed compensation methods. The optimized algorithm enhanced global search capability, population diversity, and convergence efficiency, hence yielding a prediction accuracy of RMSE = 4.41 $\times$ $10^{-5}$ mm. The integrated spatiotemporal learning framework effectively combined feature extraction, sequential modeling, and critical region emphasis, to achieve a test-set $R^2$ of 0.969. Industrial validation of the SKWB-1600 system demonstrated significant improvements in traditional stepwise approximation methods: (i) Post-compensation forming errors were reduced to 0.13–0.26 mm with a 47–62% improvement; and (ii) Curvature errors in high-stress zones were maintained within $\pm$ 0.02 mm, thus forming iterations decreased by 42% and energy consumption reduced by 35%. This certified pin-level dynamic compensation solution provides a new methodology for forming precision of complex curved ship hull plates under industrial conditions and establishes a technical paradigm for manufacturing related components requiring high precision and efficiency.

Organizational commitment (OC) has gained popularity in recent times. It’s a very crucial determinant of employee retention, productivity, and a contributor to organizational growth and prosperity. The role of a faculty is very important in shaping the careers of students and in the overall growth of higher education institutions. The current study emphasizes how job-related factors like designation and demographic profiles like age, gender, education qualification, marital status, and area of belongingness affect OC of the faculty working in the HEIs of Uttarakhand, India. A simple random technique was used to determine the sample of the study; the sample of the study was 235 faculty members engaged in the higher education institutions (HEIs). 15 items were adapted from the questionnaire on OC developed by Mowday. To collect the data, an online questionnaire was sent to the faculty engaged in the HEIs through email and the WhatsApp application. To check the internal consistency, Cronbach's Alpha test was applied. The data were distributed normally, hence a parametric test was used. The study reveals that age, gender, experience, and marital status influence OC, and designation and area of belongingness have no impact on the OC. The policymakers need to develop strategies and policies keeping in mind both demographic and job-related factors to embrace and foster commitment amongst the employees.