Search Results (1,701)

Gene regulatory networks (GRN) govern cellular identity and function through precise control of gene transcription. Single-cell technologies have provided powerful means to dissect regulatory mechanisms within specific cellular states. However, existing computational approaches for modeling single-cell RNA sequencing (scRNA-seq) data often infer local regulatory interactions independently, which limits their ability to resolve regulatory mechanisms from a global perspective. Here, we propose a deep learning framework (Planet) based on diffusion models for constructing cell-specific GRN, thereby providing a systems-level view of how protein regulators orchestrate transcriptional programs. Planet jointly optimizes local network structures in conjunction with gene expression profiles, thereby enhancing the structural consistency of the resulting networks at the global level. Specifically, Planet decomposes GRN generation into a series of Markovian evolution steps and introduces a Triple Hybrid-Attention Transformer to capture long-range regulatory dependencies across diffusion time-steps. Benchmarks on multiple scRNA-seq datasets demonstrate that Planet achieves competitive performance against state-of-the-art methods and yields only a slight improvement over DigNet under comparable conditions. Compared with conventional diffusion models that rely on fixed sampling schedules, Planet employs a fast-sampling strategy that accelerates inference with only minimal accuracy trade-off. When applied to mouse-lung Cd8⁺Gzmk⁺ T cells, Planet successfully reconstructs a cell-type-specific GRN, recovers both established and previously uncharacterized regulators, and delineates the dynamic immunoregulatory changes that accompany ageing. Overall, Planet provides a practical framework for constructing cell-specific GRNs with improved global consistency, offering a complementary perspective to existing methods and new insights into regulatory dynamics in health and disease. Full article

Renewable energy sources can be of significant help to rural communities with inadequate electricity access. This study presents a comprehensive techno-economic assessment of a 500 kWp solar Photovoltaic (PV) energy system designed for Ibadan, Nigeria. A novel hybrid modeling framework was developed in which technical performance analysis was employed using PVSyst, whereas economic and optimization analysis was carried out using HOMER. Simulation outputs from PVSyst were integrated as inputs into HOMER, enabling a more accurate and consistent cross-platform assessment. Nigeria’s enduring energy crisis, marked by persistent grid unreliability and limited electricity access, necessitates need for exploration of sustainable alternatives. Among these, solar photovoltaic (PV) technology offers significant promise given the country’s abundant solar irradiation. The proposed system was evaluated using meteorological and load demand data. PVSyst simulations projected an annual energy yield of 714,188 kWh, with a 25-year lifespan yielding a performance ratio between 77% and 78%, demonstrating high operational efficiency. Complementary HOMER Pro analysis revealed a competitive levelized cost of energy (LCOE) of USD 0.079/kWh—substantially lower than the baseline grid-only cost of USD 0.724/kWh, and a Net Present Cost (NPC) of USD 6.1 million, reflecting considerable long-term financial savings. Furthermore, the system achieved compelling environmental outcomes, including an annual reduction of approximately 160,508 kg of CO₂ emissions. Sensitivity analysis indicated that increasing the feed-in tariff (FiT) from USD 0.10 to USD 0.20/kWh improved the project’s financial viability, shortening payback periods to just 5.2 years and enhancing return on investment. Overall, the findings highlight the technical robustness, economic competitiveness, and environmental significance of deploying solar-based energy solutions, while reinforcing the urgent need for supportive energy policies to incentivize large-scale adoption. Full article

This study proposes and empirically assesses a bio-inspired conceptual framework, termed Digital DNA, for modeling Industry 5.0 transformation as a complementary extension of established Industry 4.0 principles with an explicit focus on human-centricity, sustainability, and resilience. Rather than positing a new industrial revolution, our positioning follows the European Commission’s view that Industry 5.0 complements Industry 4.0 by emphasizing stakeholder value and human-technology symbiosis. We encode organizational capabilities (genotype) into four gene groups, Adaptability, Technology, Governance, and Culture, and link them to five human-centric outcomes (phenotype). Twenty capability genes and ten outcome measures were scored, normalized (0–100 scale), and analyzed using correlations, K-means clustering, and mutation/drift tracking to capture both static maturity levels and dynamic change patterns. Results show that high Industry 5.0 readiness is consistently associated with elevated Governance and Culture scores. Three transformation archetypes were identified: Alpha, representing holistic socio-technical integration; Beta, with strong technical capacity but weaker cultural alignment; and Gamma, with fragmented capabilities and elevated vulnerability. The Digital DNA framework offers a replicable diagnostic tool for linking socio-technical capabilities to human-centric outcomes, enabling readiness assessment and guiding adaptive, ethical manufacturing strategies. Full article

The Artificial Intelligence of Things (AIoT) is rapidly evolving from basic connectivity to intelligent perception, reasoning, and decision making across domains such as healthcare, manufacturing, transportation, and smart cities. Multimodal generative AI (GAI) and digital twins (DTs) provide complementary solutions. DTs deliver high-fidelity virtual replicas for real-time monitoring, simulation, and optimization with GAI enhancing cognition, cross-modal understanding, and the generation of synthetic data. This survey presents a comprehensive overview of DT–GAI integration in the AIoT. We review the foundations of DTs and multimodal GAI and highlight their complementary roles. We further introduce the Sense–Map–Generate–Act (SMGA) framework, illustrating their interaction through the SMGA loop. We discuss key enabling technologies, including multimodal data fusion, dynamic DT evolution, and cloud–edge–end collaboration. Representative application scenarios, including smart manufacturing, smart cities, autonomous driving, and healthcare, are examined to demonstrate their practical impact. Finally, we outline open challenges, including efficiency, reliability, privacy, and standardization, and we provide directions for future research toward sustainable, trustworthy, and intelligent AIoT systems. Full article

In the post-Moore era, carbon nanotube field-effect transistors (CNTFETs) are a promising alternative to complementary metal-oxide-semiconductor (CMOS) technology at and below the 5 nm node. Compact models bridge circuit design and device physics, yet the electrostatic discharge (ESD) behavior of CNTFETs remains insufficiently captured. Focusing on the local bottom-gate (LBG) CNTFET structure, which offers enhanced gate control due to its bottom-gate configuration, this paper investigates three dominant ESD-triggering mechanisms—thermionic current, tunneling leakage current, and thermal failure breakdown. Then, a hybrid compact–behavioral ESD model for CNTFETs is established. After theoretical derivation and comparison with test results, the model parameters are optimized through fitting. The simulation results exhibit excellent agreement with CNTFET measurements, particularly capturing the Human Body Model (HBM) pre-charge threshold phenomenon at 72 V and accurately predicting the subsequent voltage collapse behavior. This validates the accuracy and effectiveness of the model, laying a theoretical and experimental foundation for further construction of carbon-based standard-cell and I/O libraries. Full article

In the realm of Industry 5.0, the incorporation of Artificial Intelligence (AI) in overseeing workers, machinery, and industrial systems is essential for fostering a human-centric, sustainable, and resilient industry. Despite technological advancements, the construction industry remains largely labor intensive, with site management and interventions predominantly reliant on manual judgments, leading to inefficiencies and various challenges. This research emphasizes identifying unsafe behaviors and risks within construction environments by employing AI. Given the continuous emergence of unsafe behaviors that requires certain caution, it is imperative to adapt to these novel categories while retaining the knowledge of existing ones. Although deep convolutional neural networks have shown excellent performance in behavior recognition, they traditionally function as predefined multi-way classifiers, which exhibit limited flexibility in accommodating emerging unsafe behavior classes. Addressing this issue, this study proposes a versatile and efficient recognition model capable of expanding the range of unsafe behaviors while maintaining the recognition of both new and existing categories. Adhering to the continual learning paradigm, this method integrates two types of complementary prompts into the pre-trained model: task-invariant prompts that encode knowledge shared across tasks, and task-specific prompts that adapt the model to individual tasks. These prompts are injected into specific layers of the frozen backbone to guide learning without requiring a rehearsal buffer, enabling effective recognition of both new and previously learned unsafe behaviors. Additionally, this paper introduces a benchmark dataset, Split-UBR, specifically constructed for continual unsafe behavior recognition on construction sites. To rigorously evaluate the proposed model, we conducted comparative experiments using average accuracy and forgetting as metrics, and benchmarked against state-of-the-art continual learning baselines. Results on the Split-UBR dataset demonstrate that our method achieves superior performance in terms of both accuracy and reduced forgetting across all tasks, highlighting its effectiveness in dynamic industrial environments. Full article

This article examines the growth threshold effect on renewable energy transition in eight Southeast Asian countries from 2000 to 2023. Utilizing panel data and threshold regression analysis, the study confirms the following established findings: (1) There is evidence of a significant impact of the moderating effect of growth/FDI on the nexus between access to clean energy and the renewable energy transition in Southeast Asian countries. (2) There is evidence of a significant impact of the moderating effect of growth/trade on the nexus between access to clean energy and the renewable energy transition in Southeast Asian countries. (3) There is evidence of a significant effect of the moderating effect of growth/R&D on the nexus between access to clean energy and the renewable energy transition in Southeast Asian countries. (4) Lastly, the complementary growth threshold of 1.68% is to be checked for access to clean energy and technologies and the renewable energy transition in Southeast Asian countries. Therefore, policies should promote sustained growth while ensuring investments in research and development, trade, and foreign direct investment (FDI), which are expected to benefit the region in the long term. In the short term, it may be necessary to reassess current policies to prevent misallocation of resources, ensuring progress towards SDG-7 before the 2030 deadline. Future research should investigate additional factors that could facilitate a sustained transition to renewable energy and examine the complex relationship between economic growth, access to clean energy, and renewable energy transition in Southeast Asian countries. Full article

Background/Objectives: Ultrasound-guided transvaginal follicular aspiration is a central procedure in in vitro fertilisation (IVF), aiming to collect oocytes necessary for the success of assisted reproduction treatments. Follicular flushing, proposed in the absence of cumulo-oocyte complex (COC) at initial aspiration, remains controversial regarding its real impact on oocyte quality and pregnancy rates. Methods: In this controlled study, conducted in 274 patients, we evaluated the effects of systematic follicular flushing up to 10 washes with a standardised medium (pH 7.3 ± 0.1; 37.2 ± 0.2 °C) on oocyte yield, oocyte morphology, embryo kinetics and clinical outcomes. Results: Flushing resulted in an additional 38% recovery of COCs, mostly between the second and fifth flush, with no significant increase in oocyte dysmorphisms or major embryonic abnormalities. A slight increase in slow cleavages was observed (27% vs. 23%, p = 0.04), as well as a lower oocyte maturation rate when ovulation was triggered by Ovitrelle alone. Clinically, pregnancy rates per transfer were comparable between groups (33.27% without flushing vs. 32.86% with flushing; p = 0.67), as were miscarriage rates (9.11% vs. 8.69%; p = 0.81). Conclusions: These results indicate that follicular flushing, when applied according to a standardised protocol, significantly increases oocyte yield without compromising oocyte morphological quality or embryonic development potential. Although the observed clinical benefits remain modest, this approach could constitute a relevant complementary strategy, particularly in patients with poor ovarian response or in the context of poor initial recovery. However, the controlled but non-randomised nature of this study requires cautious interpretation of the findings. Larger randomised trials, integrating dynamic assessment technologies, such as time-lapse imaging or oocyte transcriptomic analysis, are needed to refine the clinical indications of this technique and explore its underlying biological mechanisms. Full article

Compaction quality is a critical factor in ensuring the long-term performance of subgrade structures; however, traditional testing methods are limited by their destructive nature and delayed feedback. To address these shortcomings, this study proposes a dynamic identification method for subgrade compaction based on embedded attitude sensors. A customized sensor unit integrated with an inertial measurement module was embedded in soil samples to record triaxial acceleration and attitude angles during the compaction process. Signal processing techniques, including an improved wavelet-based denoising strategy, were employed to separate long-term compaction trends from transient impact disturbances. Attitude features such as cumulative angular change, angular velocity, root mean square values, and a comprehensive inclination index were extracted as predictive variables. Ridge regression, random forest, and XGBoost models were constructed to establish the mapping relationship between attitude features and compaction degree. Experimental results on clay, loam, and sand samples indicate that the yaw angle is most sensitive to vertical settlement, while pitch and roll angles provide complementary information on lateral and rotational behaviors. Comparative analysis of filtering methods shows that the transient masking interpolation (TMI) approach outperforms the traditional asymmetric wavelet thresholding (AWT) method in effectively preserving baseline trends. Among the regression models, XGBoost demonstrated the best predictive performance, achieving an R² exceeding 0.995 at high compaction levels. The proposed method has been experimentally demonstrated as a laboratory-scale proof of concept, showing strong potential for future real-time field application, offering a novel technological pathway for intelligent quality control in road construction. Full article

This article examines the asymmetric effects of renewable energy on sustainable economic growth across six South Asian countries from 2000 to 2023, employing panel data and threshold regression analysis. The findings indicate that CO₂ emissions must remain below a threshold of 2.38% to support the integration of renewable energy with sustainable growth. Furthermore, access to clean energy and technologies should exceed 3.38%, and urbanization must be managed at a complementary threshold of 3.21%. These results are consistent with various studies investigating the renewable energy transition’s economic impacts globally. It is recommended that South Asia focus on reducing CO₂ emissions below the identified threshold, enhancing clean energy access and innovation above the designated thresholds, and supporting urban growth as part of its policy initiatives. Such actions are essential for fostering economic growth and ensuring the sustainability of the region. The study recommends that the South Asian region take decisive steps to reduce CO₂ emissions and enhance access to clean energy while accommodating urban population growth. It highlights the importance of transitioning to renewable energy to stimulate economic growth and maintain trade and foreign direct investment (FDI) as a viable part of the gross domestic product. The study suggests that investments in Gross Capital Formation (GCF), trade, and FDI will yield long-term benefits, although short-term policy adjustments may disrupt resource allocation and hinder economic and renewable energy development. Future research should explore the complex interactions between CO₂ emissions, clean energy access, FDI, and trade, particularly in light of recent trade policies, including U.S. tariffs. Investigating these relationships through advanced methodologies, such as machine learning, could provide valuable insights into drivers of renewable energy transition and economic outcomes. Full article

Multi-sensor fire detection technology has been widely adopted in practical applications; however, existing methods still suffer from high false alarm rates and inadequate adaptability in complex environments due to their limited capacity to capture deep time-series dependencies in sensor data. To enhance robustness and accuracy, this paper proposes a novel model named BiLSTM-LN-SA, which integrates a Bidirectional Long Short-Term Memory (BiLSTM) network with Layer Normalization (LN) and a Self-Attention (SA) mechanism. The BiLSTM module extracts intricate time-series features and long-term dependencies. The incorporation of Layer Normalization mitigates feature distribution shifts across different environments, thereby improving the model’s adaptability to cross-scenario data and its generalization capability. Simultaneously, the Self-Attention mechanism dynamically recalibrates the importance of features at different time steps, adaptively enhancing fire-critical information and enabling deeper, process-aware feature fusion. Extensive evaluation on a real-world dataset demonstrates the superiority of the BiLSTM-LN-SA model, which achieves a test accuracy of 98.38%, an F1-score of 0.98, and an AUC of 0.99, significantly outperforming existing methods including EIF-LSTM, rTPNN, and MLP. Notably, the model also maintains low false positive and false negative rates of 1.50% and 1.85%, respectively. Ablation studies further elucidate the complementary roles of each component: the self-attention mechanism is pivotal for dynamic feature weighting, while layer normalization is key to stabilizing the learning process. This validated design confirms the model’s strong generalization capability and practical reliability across varied environmental scenarios. Full article

In this paper, the rail mean vertical alignment is estimated through double integration of wheel–rail contact forces measured using dynamometric wheelsets on a dedicated track recording vehicle (TRV). A simplified three degrees of freedom (DOF) linear model of half a train coach has been developed for this purpose. The model’s ability to simulate the average left and right longitudinal level has been tested using vertical contact force recordings from a constant speed track section, as measured by the TRV. The results are compared with available track geometry (TG) data, recorded by the optical system of the same vehicle, used for condition monitoring of the Italian railway infrastructure. Model parameters, such as masses, stiffness, and damping of the suspensive system have been optimized. An error analysis has been conducted on results. A good agreement is found between simulated and recorded vertical alignment at the D1 level, suggesting the feasibility of using contact forces measured with instrumented wheelsets for railway TG condition monitoring. This computationally efficient approach highlights the potential of strain gauges and instrumented wheelsets as alternative or complementary technologies to the widely adopted accelerometers, rate gyros, and optical devices for railway condition monitoring. Given its low computational cost, embedded and real-time TG estimation could be further investigated. Full article

In the context of increasingly strong digital transformation and production automation, choosing the right palletizing robot plays a key role in optimizing operational efficiency in industrial chains. However, the wide variety of robot types and specifications complicates decision-making and increases the risk of biased judgments. To overcome this challenge, this study develops an objective multi-criteria decision-making (MCDM) framework that integrates two complementary methods for selecting the optimal industrial pal-letizing robot in the context of modern manufacturing that is increasingly dependent on intelligent automation solutions. Specifically, an improved CRITIC approach is employed to determine objective criteria weights by refining the measurement of contrast intensity and inter-criteria conflict, while normalization ensures comparability of heterogeneous robot parameters. CRADIS is then applied to rank the alternatives based on their relative closeness to the ideal solution. The contributions of this study are twofold: methodological, enhancing the objectivity and robustness of weighting through refined CRITIC and normalization, and practical, offering a reproducible evaluation framework for managers when choosing industrial robots. Application to eight palletizing robots demonstrates that “repeatability” and “power consumption” significantly influence rankings. Sensitivity analysis further confirms the model’s stability and reliability. These findings not only support evidence-based investment decisions but also provide a foundation for extending the method to other industrial technology selection problems. Full article

The sustainable preservation of cultural heritage, as articulated in Sustainable Development Goal (SDG) 11.4, requires strategies that not only safeguard tangible and intangible assets but also enhance their long-term cultural, social, and economic value. Artificial intelligence (AI) and digital technologies are increasingly applied in heritage conservation. However, most research emphasizes technical applications, such as improving data accuracy and increasing efficiency, while neglecting their integration into a broader framework of cultural sustainability and heritage tourism. This study addresses this gap by developing a set of practical guidelines for the sustainable use of AI in cultural heritage preservation. The guidelines highlight six dimensions: inclusive data governance, data authenticity protection, leveraging AI as a complementary tool, balancing innovation with cultural values, ensuring copyright and ethical compliance, long-term technical maintenance, and collaborative governance. To illustrate the feasibility of these guidelines, the paper analyses three representative case studies: AI-driven 3D reconstruction of the Old Summer Palace, educational dissemination via Google Arts & Culture, and intelligent restoration at E-Dunhuang. By situating AI-driven practices within the framework of cultural sustainability, this study makes both theoretical and practical contributions to heritage governance, to enhance cultural sustainability commitments and align digital innovation with the enduring preservation of humanity’s shared heritage, providing actionable insights for policymakers, institutions, and the tourism industry in designing resilient and culturally respectful heritage strategies. Full article

Background: Digital polymerase chain reaction (dPCR) is a highly sensitive molecular method that allows rapid detection of bacterial DNA and resistance genes, requiring only a small blood volume. Although not a new technology, its application in pediatric patients with suspected catheter-related bloodstream infection (CRBSI) remains limited. Case presentation: A 16-year-old female, diagnosed with recurrent acute myelogenous leukemia, received re-induction chemotherapy through a peripherally inserted central venous catheter (PICC). The patient developed a fever, and the blood culture (BC) drawn from the PICC was positive for methicillin-resistant S. epidermidis, leading to suspicion of CRBSI. Several antibiotics were used, and the PICC was replaced. Eventually, the fever subsided, and the BC was negative after PICC removal. The levels of S. epidermidis-specific DNA sequences and mecA genes were correlated with the results of the BC and clinical course. Turnaround time was significantly shorter in dPCR (3.5 h) than in the BC (14–21 h); dPCR was performed using only 400 µL of blood. Conclusions: This case highlights the potential of dPCR as a complementary tool to conventional BCs in the management of pediatric CRBSI. dPCR may support rapid decision-making and monitoring of the treatment response, particularly when sample volumes are limited. Full article