Search Results (203)

Next-generation communication systems demand extreme spectral efficiency to handle ever-increasing data traffic. The combination of faster-than-Nyquist (FTN) signaling and reconfigurable intelligent surfaces (RISs) presents a promising solution to meet this demand. However, the aggressive time compression inherent to FTN signaling introduces severe and highly non-linear inter-symbol interference (ISI). This complex distortion is challenging for conventional linear equalizers and even for recurrent neural network (RNN)-based detectors, which can struggle to model long-range dependencies within the signal sequence. To overcome this limitation, this paper proposes a novel signal detection framework based on the transformer model. By leveraging its core multi-head self-attention mechanism, the transformer globally analyzes the entire received signal sequence at once. This enables it to effectively model and reverse complex ISI patterns by identifying the most significant interfering symbols, regardless of their position, leading to superior signal recovery. The simulation results validate the outstanding performance of the proposed approach. To achieve a target bit error rate (BER) of ${10}^{-4}$, the transformer-based detector shows a significant signal-to-noise ratio (SNR) gain of approximately 1.5 dB over a Bi-LSTM detector over 4 dB compared to the conventional FTN-RIS system, while maintaining a high spectral efficiency of nearly 2 bps/s/Hz. Full article

Urban rail transit (URT) systems are essential to ensuring efficient and sustainable urban mobility. However, the core components of operational planning, service frequency setting, train timetabling, and train allocation are often optimized separately, leading to fragmented decision-making and suboptimal system performance. This study addresses that gap by proposing an integrated optimization framework that simultaneously considers all three planning layers under time-dependent passenger demand conditions. The problem is formulated as a bi-objective Integer Nonlinear Programming (INLP) model, aiming to jointly minimize passenger waiting time and total operational cost. To solve this large-scale, combinatorial problem, a tailored Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is developed. The algorithm incorporates discrete variable handling, constraint-preserving mechanisms, and a customized encoding scheme that aligns with the structural characteristics of URT operations. The proposed framework is applied to real-world data from the Addis Ababa Light Rail Transit (AALRT) system. The results demonstrate that the MOPSO-based approach offers a more diverse and operationally feasible set of trade-off solutions compared to a widely used benchmark algorithm, NSGA-II. Specifically, it provides transit planners with a flexible decision-support tool capable of identifying schedules that balance service quality and cost, based on varying strategic or budgetary priorities. By integrating interdependent planning decisions into a unified model and leveraging the strengths of a customized metaheuristic algorithm, this study contributes a scalable, adaptable, and practically relevant methodology for improving the performance of urban rail systems. Full article

Background: The growing demand for fresh food leads to extensive use of cold chain logistics (CCL) that significantly contributes to greenhouse gas (GHG) emissions due to its dependence on energy-intensive transport refrigeration units (TRUs). Understanding the need to balance food preservation with environmental sustainability, this paper explores practical strategies for reducing GHG emissions in CCL, focusing on fresh food products. Methods: The quantitative and qualitative analyses are applied to analyse data from Transport for London and Transport Scotland. Emission data were assessed to evaluate the impact of alternative TRU technologies and route optimisation practices. Results: The findings reveal that electric and cryogenic TRUs, along with improved route planning and operational practices, can significantly reduce the emissions of carbon dioxide, nitrogen oxides and particulate matter. These results highlight the potential strategy for industry-led emission reductions without compromising food quality. Conclusions: This paper recommends the coordination of government policy and industry to support technological adaptation and infrastructure upgrades and to research into real-time monitoring and renewable energy integration in CCL systems. Full article

For a long time, the explanation of the various determinants of oil price fluctuations and their impact on economic activity has been based on the supply and demand mechanism. However, with various volatile changes in the international situation in recent years, such as threats to public health and an increase in regional conflicts, special attention has been paid to the geopolitical context as an additional driver of oil price fluctuations. This study examines the relationship between oil price changes and GDP growth and other macroeconomic variables from the perspective of the vulnerability of oil-importing and oil-exporting countries to unexpected oil price shocks, driven by tense geopolitical events, in three European countries (Norway, Germany, and Poland). We apply the Structural Vector Autoregressive (SVAR) model and orthogonalized impulse response functions, based on quarterly data, in regard to two samples: the first spans 1995Q1–2019Q4 (pre-2020 sample), with relatively gradual changes in oil prices, and the second spans 1995Q1–2024Q2 (whole sample), with sudden fluctuations in oil prices due to geopolitical developments. A key finding of this research is that vulnerability to unpredictable oil price shocks related to geopolitical tensions is higher than in regard to expected gradual changes in oil prices, both in oil-importing and oil-exporting countries. Different causality patterns and stronger responses in regard to GDP growth during the period, including in regard to tense geopolitical events in comparison to the pre-2020 sample, lead to the belief that economies are not more resilient to oil price shocks as has been suggested by some studies, which referred to periods that were not driven by geopolitical events. Our research also suggests that countries implementing policies to reduce oil dependency and promote investment in alternative energy sources are better equipped to mitigate the adverse effects of oil price shocks. Full article

With the growing global demand for clean energy, the accuracy of wind-power forecasting plays a vital role in ensuring the stable operation of power systems. However, wind-power generation is significantly influenced by meteorological conditions and is characterized by high uncertainty and multiscale fluctuations. Traditional recurrent neural network (RNN) and long short-term memory (LSTM) models, although capable of handling sequential data, struggle with modeling long-term temporal dependencies due to the vanishing gradient problem; thus, they are now rarely used. Recently, Transformer models have made notable progress in sequence modeling compared to RNNs and LSTM models. Nevertheless, when dealing with long wind-power sequences, their quadratic computational complexity (O(L²)) leads to low efficiency, and their global attention mechanism often fails to capture local periodic features accurately, tending to overemphasize redundant information while overlooking key temporal patterns. To address these challenges, this paper proposes a wind-power forecasting method based on dimension-transformed collaborative multidimensional multiscale attention (DTCMMA). This method first employs fast Fourier transform (FFT) to automatically identify the main periodic components in wind-power data, reconstructing the one-dimensional time series as a two-dimensional spatiotemporal representation, thereby explicitly encoding periodic features. Based on this, a collaborative multidimensional multiscale attention (CMMA) mechanism is designed, which hierarchically integrates channel, spatial, and pixel attention to adaptively capture complex spatiotemporal dependencies. Considering the geometric characteristics of the reconstructed data, asymmetric convolution kernels are adopted to enhance feature extraction efficiency. Experiments on multiple wind-farm datasets and energy-related datasets demonstrate that DTCMMA outperforms mainstream methods such as Transformer, iTransformer, and TimeMixer in long-sequence forecasting tasks, achieving improvements in MSE performance by 34.22%, 2.57%, and 0.51%, respectively. The model’s training speed also surpasses that of the fastest baseline by 300%, significantly improving both prediction accuracy and computational efficiency. This provides an efficient and accurate solution for wind-power forecasting and contributes to the further development and application of wind energy in the global energy mix. Full article

Navigating through doorways remains a daily challenge for wheelchair users, often leading to frustration, collisions, or dependence on assistance. These challenges highlight a pressing need for intelligent doorway detection algorithm for assistive wheelchairs that go beyond traditional object detection. This study presents the algorithmic development of a lightweight, vision-based doorway detection and alignment module with contextual awareness. It integrates channel and spatial attention, semantic feature fusion, unsupervised depth estimation, and doorway alignment that offers real-time navigational guidance to the wheelchairs control system. The model achieved a mean average precision of 95.8% and a F1 score of 93%, while maintaining low computational demands suitable for future deployment on embedded systems. By eliminating the need for depth sensors and enabling contextual awareness, this study offers a robust solution to improve indoor mobility and deliver actionable feedback to support safe and independent doorway traversal for wheelchair users. Full article

With the advancement of integrated energy systems (IES) and the increasing penetration of variable renewable energy, IES confronts complex uncertainties that necessitate enhanced flexibility. Therefore, this study focuses on improving IES flexibility. To this end, multi-dimensional flexibility evaluation indexes for the “Source–Structure–Demand” dimensions were established, and a multi-objective optimization model considering flexibility and source–demand side uncertainties was developed. The flexibility evaluation indexes include the Grid Dependency Level (GDL) for the source side, Insufficient Flexible Resource Probability (IFRP) for the structure side, and Loss of Load Probability (LOLP) for the demand side. Moreover, considering the distinct adjustment response times and inertia of different energy flows during IES operation, thermal and electrical energy are optimized on separate time scales. Thus, the multi-objective optimization constitutes a multi-time scale, high-dimensional, non-convex nonlinear model targeting economy, flexibility, security, and low carbon emissions. This paper employs single-economy objective, single-flexibility objective, and multi-objective optimization to analyze IES configuration, operation, risk, carbon emissions, and flexibility. The results indicate that poor flexibility leads to high operational risk, while excessive pursuit of flexibility incurs high costs and destabilizes operations. By implementing this multi-objective optimization, IES flexibility is enhanced while ensuring system economic performance. It also addresses the flexibility deficiency in traditional single-economy objective optimizations. Additionally, the system increases the renewable energy absorption rate by approximately 10%. Full article

The increasing discharge of nutrient and metal-laden effluents into saline environments demands sustainable remediation strategies. This study evaluated the phytoremediation potential of Salicornia brachiata, a halophytic plant, under hydroponic conditions using varying concentrations of three macronutrients—nitrate (NO₃⁻), phosphate (PO₄³⁻), and calcium (Ca²⁺)—and three heavy metals—lead (Pb²⁺), chromium (Cr⁶⁺), and copper (Cu²⁺). The plant exhibited high removal efficiencies across all treatments, with Pb²⁺ and Cr⁶⁺ reaching nearly 99% removal within two days, while macronutrient removal showed a steady, time-dependent increase over the 14-day period. Several biochemical parameters, including proline content and antioxidant enzyme activities (catalase, superoxide dismutase, peroxidase, polyphenol oxidase), were significantly affected by treatments, with most showing dose-dependent responses to heavy metal exposure, indicating strong biochemical resilience. Fourier transform infrared spectroscopy revealed pollutant-specific structural shifts and identified –OH, –NH, and –COO⁻ groups as key binding sites. The study quantifies the removal efficiency of S. brachiata for both nutrients and metals and provides mechanistic insight into its ionic stress response and binding pathways. These findings establish S. brachiata as a viable candidate for integrated phytoremediation in saline, contaminated water systems. Full article

In trans-border data (data transferred or accessed across national jurisdictions) exchange scenarios, identity authentication mechanisms serve as critical components for ensuring data security and privacy protection, with their effectiveness directly impacting the compliance and reliability of transnational operations. However, existing identity authentication systems face multiple challenges in trans-border contexts. Firstly, the transnational transfer of identity data struggles to meet the varying data-compliance requirements across different jurisdictions. Secondly, centralized authentication architectures exhibit vulnerabilities in trust chains, where single points of failure may lead to systemic risks. Thirdly, the inefficiency of certificate verification in traditional Public Key Infrastructure (PKI) systems fails to meet the real-time response demands of globalized business operations. These limitations severely constrain real-time identity verification in international business scenarios. To address these issues, this study proposes a trans-border distributed certificate-free identity authentication framework (STALE). The methodology adopts three key innovations. Firstly, it utilizes email addresses as unique user identifiers combined with a Certificateless Public Key Cryptography (CL-PKC) system for key distribution, eliminating both single-point dependency on traditional Certificate Authorities (CAs) and the key escrow issues inherent in Identity-Based Cryptography (IBC). Secondly, an enhanced Elliptic Curve Diffie–Hellman (ECDH) key-exchange protocol is introduced, employing forward-secure session key negotiation to significantly improve communication security in trans-border network environments. Finally, a distributed identity ledger is implemented, using the FISCO BCOS blockchain, enabling decentralized storage and verification of identity information while ensuring data immutability, full traceability, and General Data Protection Regulation (GDPR) compliance. Our experimental results demonstrate that the proposed method exhibits significant advantages in authentication efficiency, communication overhead, and computational cost compared to existing solutions. Full article

Background: Aneurysm-related subarachnoid hemorrhage is a life-threatening form of stroke. While medical image acquisition for aneurysm screening is limited to high-risk patients, advances in artificial intelligence (AI)-based image analysis suggest that AI-driven routine screening of imaging studies acquired for other clinical reasons could be valuable. Methods: A representative cohort of 1761 routine cranial magnetic resonance imaging scans [cMRIs] (with time-of-flight angiographies) from patients without previously known intracranial aneurysms was established by combining 854 general radiology 1.5T and 907 neuroradiology 3.0T cMRIs. TOF-MRAs were analyzed with a commercial AI algorithm for aneurysm detection. Neuroradiology consultants re-assessed cMRIs with AI results, providing Likert-based confidence scores (0–3) and work-up recommendations for suspicious findings. Original cMRI reports from more than 90 radiologists and neuroradiologists were reviewed, and patients with new findings were contacted for consultations including follow-up imaging (cMRI / catheter angiography [DSA]). Statistical analysis was conducted based on descriptive statistics, common diagnostic metrics, and the number needed to screen (NNS), defined as the number of cMRIs that must be analyzed with AI to achieve specific clinical endpoints. Results: Initial cMRI reporting by radiologists/neuroradiologists demonstrated a high risk of incidental aneurysm non-reporting (94.4% / 86.4%). A finding-based analysis revealed high AI algorithm sensitivities (100% [3T] / 94.1% [1.5T] for certain aneurysms of any size, well above 90% for any suspicious findings > 2 mm), associated with AI alerts triggered in 22% of cMRIs with PPVs of 7.5–25.2% (depending on the inclusion of inconclusive findings). The NNS to prompt further imaging work-/follow-up was 22, while the NNS to detect an aneurysm with a possible therapeutic impact was 221. Reference readings and patient consultations suggest that routine AI-driven cMRI screening would lead to additional imaging for 4–5% of patients, with 0.45% to 0.74% found to have previously undetected aneurysms with possibly therapeutic implications. Conclusions: AI-based second-reader screening substantially reduces incidental aneurysm non-reporting but may disproportionally increase follow-/work-up imaging demands also for minor or inconclusive findings with associated patient concern. Future research should focus on (subgroup-specific) AI optimization and cost-effectiveness analyses. Full article

The integration of renewable energy into residential microgrids presents significant challenges due to solar generation intermittency and variability in household electricity demand. Traditional forecasting methods, reliant on historical data, fail to adapt effectively in dynamic scenarios, leading to inefficient energy management. This paper introduces a novel adaptive energy management framework that integrates streaming machine learning (SML) with a hierarchical fractal microgrid architecture to deliver precise real-time electricity demand forecasts for a residential community. Leveraging incremental learning capabilities, the proposed model continuously updates, achieving robust predictive performance with mean absolute errors (MAE) across individual households and the community of less than 10% of typical hourly consumption values. Three battery-sizing scenarios are analytically evaluated: centralised battery, uniformly distributed batteries, and a hybrid model of uniformly distributed batteries plus an optimised central battery. Predictive adaptive management significantly reduced cumulative grid usage compared to traditional methods, with a 20% reduction in energy deficit events, and optimised battery cycling frequency extending battery lifecycle. Furthermore, the adaptive framework conceptually aligns with digital twin methodologies, facilitating real-time operational adjustments. The findings provide critical insights into sustainable, decentralised microgrid management, emphasising improved operational efficiency, enhanced battery longevity, reduced grid dependence, and robust renewable energy utilisation. Full article

Background: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that leads to severe functional decline and death, imposing significant physical, emotional, and ethical burdens on patients and healthcare providers. With no curative treatment, ALS care depends on the early and sustained integration of palliative care to address complex and evolving needs. Nurses play a pivotal role in this process, yet their lived experiences remain underexplored. This study aimed to synthesize qualitative evidence on nurses’ experiences in ALS care, with a focus on emotional, ethical, and palliative dimensions. Methods: A meta-synthesis of qualitative studies was conducted using Sandelowski and Barroso’s four-step method. A systematic search across five databases identified eight studies exploring nurses’ experiences with ALS care. Thematic synthesis was applied to extract overarching patterns. Results: Three core themes emerged: (1) Relational Dimension: From challenges to empathy and Trust and mistrust—emphasizing communication barriers and the value of relational trust; (2) Care Dimension: Competence, Palliative care needs, and Rewarding complexity—highlighting the emotional demands of care, the need for timely palliative integration, and the professional meaning derived from ALS care; (3) Ethical Dimension: Medical interventionism and Patient-centered values—exploring dilemmas around life-sustaining treatments, patient autonomy, and end-of-life decisions. Conclusion: Nurses in ALS care face complex emotional and ethical challenges that call for strong institutional support and palliative training. Enhancing palliative care integration from diagnosis, alongside targeted education and psychological support, is crucial to improving care quality and sustaining the well-being of both patients and nurses. Full article

The rapid growth of urban metro systems requires novel strategies to guarantee operational dependability and energy efficiency. This article describes a new way to use deep reinforcement learning (DRL) to help metro networks with predictive maintenance that adapts to changing conditions and energy optimization. We used real-world transit data from the General Transit Feed Specification (GTFS) to model the maintenance scheduling and energy management problem as a Markov Decision Process. This included important operational metrics like peak-hour demand, train arrival times, and station stop densities. A custom reinforcement learning environment mimics the changing conditions of metro operations. Deep Q-Networks (DQNs) and Proximal Policy Optimization (PPO) sophisticated deep reinforcement learning techniques were used to identify the optimal policies for decreasing energy consumption and downtime. The PPO hyperparameters were additionally optimized using Bayesian optimization by implementing Optuna, which produces a far greater performance than baseline DQNs and basic PPO. Comparative tests showed that our improved DRL-based method improves the accuracy of predictive maintenance and the efficiency of energy use, which lowers operational costs and raises the dependability of the service. These results show that advanced learning and optimization techniques could be added to public transportation systems in cities. This could lead to more sustainable and smart transportation management in big cities. Full article

Renewable energy sources (RESs) are increasingly combined into the power system due to market liberalization and environmental and economic benefits, but their weather-dependent variability causes power production and demand mismatches, leading to issues like frequency and regional power transmission fluctuations. To maintain synchronization in power systems, frequency must remain constant; disruptions in the proper balance of production and load might produce frequency variations, risking serious issues. Therefore, a mechanism known as load frequency control (LFC) or automated generation control (AGC) is needed to keep the frequency and tie-line power within predefined stable limits. In this study, advanced proportional–integral–derivative PID controllers such as fractional-order PID (FOPID), cascaded PI(PDN), and PI(1+DD) for LFC in a two-area power system integrated with RES are optimized using the catch fish optimization algorithm (CFA). The controllers’ optimal gains are attained through using the integral absolute error (IAE) and ITAE objective functions. The performance of LFC with CFA-tuned PID, PI, cascaded PI(PDN), and FOPID, PI(1+DD) controllers is compared to other optimization techniques, including sine cosine algorithm (SCA), particle swarm optimization (PSO), brown bear algorithm (BBA), and grey wolf optimization (GWO), in a two-area power system combined with RESs under various conditions. Additionally, by contrasting the performance of the PID, PI, cascaded PI(PDN), and FOPID, PI(1+DD) controllers, the efficiency of the CFA is confirmed. Additionally, a sensitivity analysis that considers simultaneous modifications of the frequency bias coefficient (B) and speed regulation (R) within a range of ±25% validates the efficacy and dependability of the suggested CFA-tuned PI(1+DD). In the complex dynamics of a two-area interconnected power system, the results show how robust the suggested CFA-tuned PI(1+DD) control strategy is and how well it can stabilize variations in load frequency and tie-line power with a noticeably shorter settling time. Finally, the results of the simulation show that CFA performs better than the GWO, BBA, SCA, and PSO strategies. Full article

Background: Foundational research on the gamma distribution and inventory control highlighted its flexibility and practicality for managing fast-moving finished goods. Nonetheless, concerns remain about conventional statistical approximations of lead-time demand (LTD) distributions. Real-world lead times often result in nonstandard LTD forms, and traditional methods may introduce parameter mismatches under nonclassical conditions. Despite these challenges, this research demonstrates that a gamma LTD approximation is an effective method for managing these goods. Methods: This study employs numerical experiments to assess accuracy at high service levels, focusing on errors in system cost and product availability. Three propositions are validated: (1) a standard distribution generally characterizes the demands of fast-moving items; (2) demand variability systematically modifies the form of nonstandard LTD distributions, enhancing accuracy; (3) nonclassical conditions generally improve the accuracy of properly parameterized gamma approximations. A purposive sample of disruptive lead-time distributions found in global maritime supply chains drives numerical experiments. Results: Externally validated evidence provides the following findings within our study context: (1) a nonstandard lead-time distribution does not necessarily result in a similar LTD distribution, as it also depends on demand variability; (2) demand variability positively affects the form of a nonstandard LTD distribution under conventional conditions, with nonclassical conditions enhancing this effect; (3) the shape transformations almost always improve the accuracy of a gamma approximation. Conclusions: A gamma LTD approximation can manage inventory for fast-moving finished goods effectively, even with disruptive lead times under both conventional and nonclassical conditions. Full article