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 S., Soren A.A., and Sridevi P. 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.
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 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.
This work aims to evaluate the introduction of biomass flame weeding (FW) as a carbon-neutral technology for weed management in agricultural enterprises. The case study focuses on a biodynamic farm in the Molise region (Italy) introducing FW as a complementary technology alongside the traditional mechanical methods. In the project layout FW is applied exclusively around the trunks of olive and vine crops. The case study foresees the generation of thermal energy required for FW using a gasifier powered directly by the woody biomass waste produced by the farm. The production of biochar as a solid by-product of the gasification process was also examined. The economic analysis was conducted by structuring a simulation based on the Monte Carlo method, applied to the Net Present Value (NPV). Three output parameters were taken into consideration: NPV at the final year of investment, Internal Rate of Return and the payback period. For each parameter, a corresponding probability distribution was established. The results indicate that the average NPV can range from €6,342.27 to €9,796.06. Furthermore, the probability that the payback period is between zero and fifteen years can vary between 78.2% and 83.9%, suggesting a strong capacity for the project to be self-sustaining.
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 advent of the current Digital Era is intricately linked to the evolution of Financial Technology (Fintech), with recent events such as the COVID-19 pandemic significantly accelerating this progress. This rapid advancement has resulted in a widespread enhancement of digital literacy among individuals; nevertheless, there is a discernible increase in the significance of financial literacy. In Jordan, there remains a notable deficiency in Financial Literacy, accompanied by a substantial gap between Financial Literacy and Financial Inclusion. The primary objective of this research is to determine the role of financial literacy and digital literacy in influencing millennials’ Fintech usage, while considering the moderating effect of gender. The research adopts a quantitative cross-sectional design. To collect the required data for this research, a survey has been conducted via a questionnaire to investigate Jordanian citizens’ (Millennials) perceptions regarding the research model constructs. A sample of 463 completed the questionnaire based on their awareness of Fintech services and their ability to participate in the study. This study adopted a structural equation modelling (SEM) technique with partial least squares (PLS) as an analysis method. Findings revealed that both financial and digital literacy jointly determine the use of Fintech services. Also, the results indicated that gender did not moderate the relationships between digital literacy and financial literacy with the use of Fintech. The study contributes some recommendations and future work.
Convection heat transfer enhancement techniques play a vital role in many industrial thermal processing applications, including food thermal processing, and the pharmaceutical, and chemical manufacturing industries. These techniques contribute to reducing the size and cost of heat exchangers, conserving energy, improving product quality, and enhancing both energy efficiency and thermal performance. Among passive solutions, corrugated wall tubes are widely adopted in heat exchangers for such applications. This study applies the inverse heat conduction problem (IHCP) method combined with infrared thermography data to estimate the local temperature and convective heat transfer coefficient distributions for forced convection in a transversally corrugated wall tube with high viscosity fluid flow under laminar conditions. The IHCP is solved within the corrugated wall domain using measured external wall temperatures as input. Thermal performance was evaluated over a Reynolds number range of 290–1200. The findings showed that at Re $<$ 350, irregular local temperature and convective heat transfer distributions led to reduced thermal efficiency, unreliable sterilization, and increased microbial risk, whereas for 650 $<$ Re $<$ 1200, thermal efficiency improved significantly. These findings support the development of more efficient heat exchanger designs, offering significant benefits to industries requiring precise thermal management.
Traffic congestion in urban commercial districts presents a critical challenge to sustainable mobility, particularly in developing cities. This study addresses this issue by developing and calibrating a mesoscopic simulation model to optimize traffic performance parameters in the commercial district of Ayacucho, Peru. The methodology was based on extensive fieldwork to gather traffic volume, travel time, and parking data. Using this data, a PTV Vissim model was developed and rigorously calibrated, with its accuracy validated through the Geoffrey E. Havers (GEH) statistic. Various traffic management strategies, including signal timing adjustments and parking supply regulation, were simulated and evaluated. The results indicate a substantial improvement in network performance: Average intersection delay was reduced from 10.72 seconds to 7.40 seconds, and a significant decrease in queue lengths was observed. The findings confirm that calibrated mesoscopic simulation serves as a robust and effective tool for quantitatively assessing traffic interventions, thereby providing municipal authorities with reliable data for evidence-based urban planning.
Overurbanization poses environmental challenges that threaten human health and biodiversity. Nature-Based Solutions (NBS) enhance urban livability, restore biodiversity, and provide vital Ecosystem Services (ES), such as mitigating the Urban Heat Island (UHI) effect. This study evaluates environmental monitoring at Marco Biagi Park (Reggio Emilia, Italy) as part of the Life City AdapT3 project. Following the introduction of micro-forests, rural edges, tree rows, and a wetland, data were collected to assess local climate mitigation and carbon storage. Microclimatic effects were analyzed using satellite images (Landsat 8) and on-site measurements. Between 2021-2024, summer Land Surface Temperature (LST) decreased in post-intervention period by 2.1℃. Air temperature in urban forest areas averaged 1.2℃ lower, while humidity increased by 10% compared to built-up areas. Using the i-Tree model, it was estimated that Marco Biagi Park stored 332.20 kg of carbon in 2024 and 825.20 kg in 2025—representing a 148.4% increase in just one year. Species of the Quercus genus, Prunus avium and Tilia platyphyllos contributed 58.26% to this carbon storage in 2025. Findings highlight NBS effectiveness in improving urban microclimates and carbon sequestration, reinforcing their role in sustainable city planning.
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.
Urban air pollution remains a persistent challenge in the Global South, where rapid urbanization, limited monitoring infrastructure, and weak regulatory frameworks hinder effective environmental governance. In Lima, Peru—one of the most polluted capitals in Latin America—elevated PM2.5 and PM10 concentrations continue to pose serious threats to public health and sustainable urban development. Traditional Air Quality Index (AQIs), such as the U.S. EPA standard, often struggle to account for data uncertainty, pollutant interactions, and spatial heterogeneity. To address these gaps, this study introduces a novel AQI based on grey systems theory, applying a grey clustering framework enhanced with center-point triangular whitenization weight functions (CTWF). The model was specifically designed to handle ambiguous data and overlapping pollution categories. It was applied to daily PM2.5 and PM10 data from nine monitoring stations across metropolitan Lima, with validation conducted against both Peru’s national air quality standards and the U.S. EPA AQI. Results showed that the proposed index outperformed conventional methods under uncertain conditions, revealing critical spatial disparities often missed by traditional models. Beyond diagnostic accuracy, the index offers a scalable and transferable tool for urban planners and decision-makers to support targeted interventions, inform policy development, and advance Sustainable Development Goals—specifically SDG 3 (Good Health and Well-Being) and SDG 11 (Sustainable Cities and Communities).
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