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While end users typically perceive rail transport as safer than other forms of transportation, it still confronts substantial threats and risks that demand meticulous management. One of the most crucial challenges in rail transport is the management of dense railway traffic on limited infrastructure. The effectiveness of this management is critical to ensuring safety and reliability. To address these challenges, integrating and adapting Railway Intelligent Transportation Systems (RITS) into railway transport systems has become essential for creating a safer and more reliable railway system. A railway system that is poorly structured and does not use advanced technology appropriately struggles to manage these risks effectively. Therefore, the integration of RITS is crucial. Decision-makers must carefully evaluate and select the most suitable RITS to ensure safety and reliability. However, since many conflicting criteria and decision factors affect the evaluation process, selecting the most appropriate RITS is a complex decision problem. This study proposes a new decision-making model by considering these requirements. In this context, the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method, enhanced with Intuitionistic Fuzzy Sets and reinforced by integrating Schweizer–Sklar Hamy Mean Operators, was developed as a practical solution to address the decision-making problem. According to the research results, reliability and the use of the most advanced technology are the effective criteria that influence the selection of appropriate RITSs. In addition, A3 Aselsan, one of the key players in the intelligent transport system manufacturing industry, has been determined to be the most suitable alternative for railway transportation systems. Ultimately, extensive reality tests involving sensitivity and comparative analysis were conducted to check the robustness of the model. The analysis proves the model's soundness and practicality.

Socio-economic development (SED) remains a critical priority for policymakers aiming to foster inclusive growth and drive national progress. This study presents a comprehensive multi-criteria assessment of regional SED across 16 Indian states, focusing on the influence of innovation (INV) performance and foreign direct investment (FDI) on achieving sustainable development goals (SDGs). A new multi-criteria decision-making (MCDM) method, called Preference using Root Value based on Aggregated Normalisations (PROVAN), is introduced in this paper to enhance decision accuracy by integrating five different normalization techniques. Criteria weights are determined using an extended version of Weights by ENvelope and SLOpe (WENSLO) method, which incorporates multiple normalization strategies to improve robustness. The evaluation considers nine SED and seven INV criteria derived from secondary data sources. The causal relationships are statistically analyzed using Somer's δ test, and the model's reliability is confirmed through comparative and sensitivity analyses. Results reveal that Maharashtra emerges as the top-performing state in both SED (1.5572) and INV (1.5473), followed by Tamil Nadu and Karnataka, indicating strong performance across socio-economic and innovation indicators. The findings highlight significant inter-state disparities and confirm that states with stronger innovation capabilities tend to achieve better socio-economic outcomes. FDI is shown to positively influence sustainable economic development, reinforcing the strategic importance of attracting capital to advance SDGs. The proposed PROVAN-WENSLO framework offers a robust and adaptable tool for regional development planning and policy formulation.

Hydrogen use as fuel in internal combustion engines is limited by abnormal combustion known as knock. Knock is highly undesirable, because it accelerates engine wear, may cause severe damage, increases heat losses, and lowers engine efficiency. Novelty of the work is correlations for the hydrogen knock onset and its intensity vs. in-cylinder temperature at spark timing, hereafter referred to as the ignition temperature. Methodology was based on comparative analysis from tests conducted on two single-cylinder spark-ignition engines – one water-cooled and one air-cooled. Knock intensity was quantified as the intensity of in-cylinder combustion pressure oscillations. Tests were performed at a constant engine speed, equivalence ratio, spark timing, and hydrogen injection dose. Auto-ignition-driven knock was found to be very likely when the ignition temperature exceeded approximately 740K. Finally, scenarios illustrating the transition from deflagration combustion to knock, as well as potential strategies to mitigate severe knock in real applications, are discussed.

Context It is widely accepted that a systematic literature review (SLR) is an effective, comprehensive, repeatable, less biased, and transparent method for gathering and condensing knowledge from existing scientific publications. The SLR method facilitates the identification of gaps for new research opportunities, fostering decision-making based on evidence. At the same time, SLR is a time and effort-consuming task that is threatened by the increasing volume of publications. However, there is a lack of comprehension about which factors directly impact manually conducted SLR performance, limiting researchers to better plan and optimize their processes. Objective To enhance the understanding of the attributes that directly influence the SLR process in terms of time consumption. Methods We performed a tertiary study that (i) identified 138 secondary studies, (ii) mapped the possible influential attributes for SLR performance, (iii) extracted data from SLR reports and metadata, synthesized and analysed their influence, providing an overview of core trends related to those attributes over time. Results Our SLR mapped four main attributes influencing the performance time of the SLR process – number of authors, number of initially retrieved papers from databases, number of included papers for data synthesis, and usage of the snowballing techniques. We noticed a trend for smaller research groups (2–5 persons) using 4–6 different databases and processing to process a large number of studies, and an increasing adoption of the snowballing technique. Conclusion This paper reveals a bottleneck in manually conducted SLR, reinforcing the need for evolving automation. Mapping the attributes is only the first step to making the SLR process more measurable regarding its resource consumption. We contribute by providing recommendations to assist scientists and practitioners in planning their future SLRs and IT projects, including SLRs, particularly in the evolving landscape of digital transformation and innovations.

Research purpose. The purpose of this paper is to analyse the challenges and transformation directions of digital transformation in higher education institutions (HEIs), with a direct focus on management processes. The paper study applies a systems-based managerial perspective to identify and systematise the main and supplementary processes directly affected by digital transformation, and to evaluate their implications for governance, institutional competitiveness, and academic performance. Design / Methodology / Approach. The study is based on secondary sources, including legislation and policy documents, and publicly available studies. A qualitative case study and thematic content analysis were applied to examine strategic and operational documents of nine Lithuanian HEIs. Document selection followed the defined inclusion criteria of relevance, validity, and institutional scope. Coding was performed using both deductive and inductive categorisation. The study adopts a systems approach to the digitalisation of higher education, focusing on aspects such as the transformation of higher education, process transformation, and the governance of digitalisation of higher education processes. Findings. Digitalisation is transforming the main functions of HEIs, reshaping traditional activities into new formats. At the same time, findings reveal that digitalisation is unevenly implemented across different institutional processes. Core academic functions such as study organisation, assessment, and research are more advanced, while supplementary areas like student support, administrative modernisation, and digital communication remain underdeveloped. The study identifies challenges in managing digital transformation and highlights areas where effective governance solutions are lacking to ensure a smooth and sustainable transition. Key governance challenges identified include the lack of strategic alignment, limited system capacity, and insufficient digital competencies among staff. Originality / Value / Practical implications. This research contributes to the understanding of digital transformation in higher education by offering insights into the challenges and opportunities associated with digital transformation. It provides a systems-oriented contribution by combining managerial decisions with technological change, emphasising governance, leadership, and organisational culture. Practical recommendations are offered for HEIs, particularly for institutions with limited resources, on how to prioritise actions, enhance digital capabilities, and implement digital strategies.

Viruses pose significant threats to global health, and the development of effective antiviral therapies is challenging because of the high costs and long timelines associated with drug development. Drug repurposing has emerged as a promising approach to accelerate treatment discovery. In this study, we introduce a novel framework for predicting virus–drug associations, termed Preprocessing–enhanced Self-Paced Learning and HyperGraph regularized Nonnegative Matrix Factorization (Pre–SPLHGNMF). The model integrates linear neighborhood similarity to enhance similarity fusion, and Gaussian processes to reduce the impact of false negatives. It employs hypergraph regularization to capture higher-order relationships in virus–drug, virus–virus, and drug–drug associations, improving the model’s ability to make reliable predictions. Self-paced learning further optimizes the model by iteratively introducing samples from simple to complex, thus enhancing robustness against noisy data, ensuring more accurate association predictions. The experimental results show that Pre-SPLHGNMF outperforms the other methods in 10-fold cross-validation, achieving AUCs of 0.8526 and 0.8629 on the HDVD and VDA datasets, respectively. These findings highlight the potential of Pre-SPLHGNMF to advance virus–drug association prediction and support the development of effective antiviral strategies.

This paper studies the feasibility of green hydrogen production using monofacial and bifacial photovoltaic (PV) power systems in Indian context. The recent developments in the hydrogen electrolyzer and fuel cell technology facilitates the generation and utilization of green hydrogen. In this study an alkaline electrolyzer (AEL) and a proton exchange membrane electrolyzer (PEMEL) is employed along with the PV panels for the green hydrogen generation. Then the levelized cost of hydrogen (LCOH) is evaluated and presented for all locations of India. The results show that the alkaline electrolyzer can generate an average of 0.0531 kg/m2 of green hydrogen using bifacial PV and 0.0438 kg/m2 using monofacial PV system. Further, the hydrogen generated using PEMEL is 2.2% higher when compared to the AEL system. The levelized cost of hydrogen is found to be 4.47 USD/kg using bifacial photovoltaic system and 5.07 USD/kg using monofacial PV system, and the LCOH obtained using the PEMEL is 1.84 USD/kg higher than that of AEL. These results will contribute to the feasibility and economic viability studies and policy making for the green hydrogen production and utilization in India.

Plant based fuels have been incessantly researched as a substitute of diesel. However, their use in compression ignition engine tends to deteriorate engine performance and increase the engine emissions. Thereby, offsetting the advantage of the fuel being carbon neutral. Hydrogen induction in intake manifold with the direct injection of biofuel enhances the engine performance and simultaneously reduce the emissions. This way diesel can be completely substituted with carbon neutral fuels. In the present study to replace diesel, honge oil was transesterified to its methyl ester and used as the direct injected fuel and the flow rate of hydrogen was varied. The results indicate an improvement in engine performance with higher in-cylinder pressure and heat release rate with 30 L per minute (lpm) flow rate of hydrogen. The brake specific energy consumption (BSEC) of the engine was reduced to 12.2kJ/kWh with the highest flow rate of hydrogen at full load condition. The unburnt hydrocarbon emission, carbon monoxide emission and smoke opacity reduced from 30 ppm, 0.8% & 59% to 19 ppm, 0.48% & 47%, respectively with maximum flow rate of hydrogen. However, due to improvement in combustion, the oxides of nitrogen emission increased from 1224 ppm to 1450 ppm with hydrogen premixing. For the same engine, if fuel is varied then extensive experimental study is required for analysing the engine performance which is costly, time consuming and may itself be a source of pollution. If the exhaust emissions can be accurately predicted with variation in fuels, then the previously mentioned issues can be resolved. In this regard novel features namely percentage of carbon, hydrogen and oxygen present in the fuel were used along with hydrogen flow rate. Various algorithms were compared for predicting the emissions. The results show that the lowest mean absolute percentage error (MAPE) and root mean square error (RMSE) of 0.78, 0.00647, 0.074, 0.00391, 0.155 and 0.013 was observed with support vector regression (SVR) for CO, HC and smoke emissions, respectively. While gradient process regression (GPR) algorithm resulted in lowest error of MAPE (0.22) and RMSE (0.65) for NO emission.

To date, many exoplanets have been discovered which exhibit distinct characteristics not observed within our own Solar System, raising numerous unresolved questions regarding their compositions, atmospheres, formation processes, and evolutionary pathways. Several missions have been dedicated to enhance the understanding of the exoplanets like James Webb and Hubble Space Telescopes. However, they have a limited spectral range and resolution to allow for a complete characterisation of atmospheric dynamics. The Aetheras mission proposal was developed at the Summer School Alpbach 2023 and presents a satellite mission to overcome these limitations to better understand the formation, evolution and characteristics of exoplanets. This mission aims to unravel key enigmas in contemporary exoplanetary research by investigating atmospheric escape mechanisms and measuring proxies of magnetic fields’ influence on atmospheric loss. Focusing on objects in the Radius Valley and the Hot Neptune desert, the mission seeks to discover their origins. By defining mission needs and designing a potential instrument based on derived requirements, a space mission architecture is envisioned to fulfil the proposed mission objectives. A spacecraft design has been made with top down systems engineering approach. Employing transit spectroscopy in the near-infrared range (1070nm to 1090nm) and ultraviolet range (115nm to 285nm) outside the geocoronal influence, the mission gains valuable insights to planetary formation and evolution. The mission architecture comprises a 1302kg spacecraft equipped with a 1.5m main mirror to observe the sky over a mission lifetime of three years.

Lithium-ion cells are essential in daily life, but accurately assessing their state of health (SOH) is challenging because deep learning models need large, consistent cycling datasets that are often impractical to obtain. Moreover, the real-world data can be incomplete or inconsistent. In this study, the SOH was predicted online utilizing fusion models of convolutional neural network (CNN), long short-term memory (LSTM) convolution block attention mechanism (CBAM), and multi-head attention mechanism (MHA) based architecture. Datasets prepared by the reaserchers at Massachussets Institute of Technology (MIT), and Sandia National Laboratory (SNL) were combined for training various models. Bayesian optimization algorithm was used to optimize the hyperparameters. The results reveal that the CNN-LSTM fusion model enhances the accuracy of capacity estimation. Oxford University dataset was used to test the efficacy of the models. Highest accuracy in prediction was found with the CNN-LSTM model having lowest root mean squared error (RMSE) of 0.0136. Ablation experiments were carried out and the performance of the fusion models was found better than the base models. The trained models were also retrained for another voltage range. CALCE dataset, provided by the University of Maryland, was utilized for the experiments. 2 and 7 cells dataset were separately used for the training. The CNN-CBAM framework with 5 CNN layers was found to be the best model. The models generated in this study can be used for predicting the SOH online for cells having different kind of form, chemistry, charged/discharged at different rates and varying temperatures demonstrating its practicality and generalization ability.