Global climate change poses a serious threat to <i>Torreya grandis</i>, a rare and economically important tree species, making the accurate mapping of its spatial distribution essential for forest resource management. However, extracting forest-growing areas remains challenging due to the limited spatial and temporal resolution of remote sensing data and the insufficient classification capability of traditional algorithms for complex land cover types. This study utilized monthly Sentinel-2 imagery from 2023 to extract multitemporal spectral bands, vegetation indices, and texture features. Following minimum redundancy maximum relevance (mRMR) feature selection, a spatial–spectral fused attention network (SSFAN) was developed to extract the distribution of <i>T. grandis</i> in the Kuaiji Mountain area and to analyze the influence of topographic factors. Compared with traditional deep learning models such as 2D-CNN, 3D-CNN, and HybridSN, the SSFAN model achieved superior performance, with an overall accuracy of 99.1% and a Kappa coefficient of 0.961. The results indicate that <i>T. grandis</i> is primarily distributed on the western, southern, and southwestern slopes, with higher occurrence at elevations above 500–600 m and on slopes steeper than 20°. The SSFAN model effectively integrates spectral–spatial information and leverages a self-attention mechanism to enhance classification accuracy. Furthermore, this study highlights the joint influence of natural factors and human land-use decisions on the distribution pattern of <i>T. grandis.</i> These findings aid precision planting and resource management while advancing methods for identifying tree species.
Ha-Nam Nguyen, Hong-Lam Le, Ngo-Thi-Thu-Trang
et al.
Research and development of highly accurate falling detection systems (FDSs) for individuals with medical conditions or the elderly are crucial for mitigating the risks associated with falls. These systems are of great significance for real-world applications in healthcare and elderly care. However, improving the accuracy of the FDS is a significant challenge, particularly due to the difficulty in collecting comprehensive and accurate fall data which causes imbalance in datasets. In this paper, we introduce a Genetic Algorithm (GA) based Random Forest (RF) method, named GA4RF, to enhance the accuracy of fall detection models by optimizing their hyperparameters. Specifically, we propose a fitness function based on the Matthews Correlation Coefficient (MCC) to enable the GA to identify the optimal hyperparameter set for the RF classifier. This targeted optimization ensures that the algorithm prioritizes balanced accuracy across both fall and non-fall events, mitigating the bias inherent in imbalanced datasets. Additionally, we select a chromosome structure focusing on three hyperparameters to effectively narrow the search space, reducing computational complexity while maintaining high detection performance. Experimental evaluations on the MobiAct v2.0 and UP-Fall detection datasets demonstrate that GA4RF achieves high human activity recognition accuracy, especially a significant improvement of fall detection performance. Compared to the conventional RF method, the F1-score increases from 95.23% to 96.91% on the MobiAct v2.0 dataset and from 96.16% to 97.67% on the UP-Fall dataset. These results indicate that GA4RF is a promising approach for improving FDS, especially when dealing with highly imbalanced data and meeting performance requirements in real-world applications.
Abstract The use of limestone powder as a partial replacement for cement in concrete has gained significant attention due to its potential to enhance compressive strength and promote sustainability. This study investigates the mechanical behavior of limestone-modified concrete, focusing on strength development over various curing periods. Advanced machine learning techniques—Gradient Boosting (GB) and K-Nearest Neighbors (KNN)—are employed to optimize mix proportions and accurately predict compressive strength. The GB model achieved a high predictive accuracy with an R² value of 0.98, effectively capturing the complex nonlinear relationships between cement content, limestone dosage, and curing time. Meanwhile, the KNN model demonstrated strong performance with an R² of 0.965 by leveraging pattern similarities in experimental data. Both regression models align closely with experimental results, validating limestone’s positive impact on long-term concrete performance. This data-driven approach enhances mix design decisions, ensuring structural reliability and sustainability while reducing cement usage and its associated environmental footprint.
Yi-Hsuan Lin, Lalitphat Khongsomchit, Sakdirat Kaewunruen
et al.
IntroductionBuilding Information Modelling (BIM) has emerged as a multidisciplinary methodology that integrates information-rich data with virtual representations to support the management of built assets throughout their lifecycle. While BIM is increasingly adopted in architecture, engineering, and construction (AEC) industries and demonstrates significant value in infrastructure projects; however, its application in the railway sector remains limited. The complexity of railway networks, combined with the growing demand for transit projects, presents unique challenges that hinder effective implementation.MethodsThis study investigates the barriers of BIM adoption within the railway industry through a structured questionnaire distributed to professionals and a subsequent detailed analysis of responses.ResultsThis study identifies critical gaps in current BIM practices and highlights several severe obstacles that require urgent attention. Feedback reveals key challenges across four main areas: (1) Technology, (2) Market, (3) Socio-cultural factors, and (4) Policy.DiscussionBy outlining these barriers and suggesting potential solutions, the study provides valuable insights for stakeholders and identifies future research directions to advance BIM integration in railway projects.
Engineering (General). Civil engineering (General), City planning
We introduce for non-uniform messages a novel hybrid universal network coding cryptosystem (NU-HUNCC) in the finite blocklength regime that provides Post-Quantum (PQ) security at high communication rates. Recently, hybrid cryptosystems offered PQ security by premixing the data using secure linear coding schemes and encrypting only a small portion of it. The data is assumed to be uniformly distributed, an assumption that is often challenging to enforce. Standard fixed-length lossless source coding and compression schemes guarantee a uniform output in normalized divergence. Yet, this is not sufficient to guarantee security. We consider an efficient compression scheme uniform in non-normalized variational distance for the proposed hybrid cryptosystem, that by utilizing a uniform sub-linear shared seed, guarantees PQ security. Specifically, for the proposed PQ cryptosystem, first, we provide an end-to-end practical coding scheme, NU-HUNCC, for non-uniform messages. Second, we show that NU-HUNCC is information-theoretic individually secured (IS) against an eavesdropper with access to any subset of the links and provide a converse proof against such an eavesdropper. Third, we introduce a modified security definition, individual semantic security under a chosen ciphertext attack (ISS-CCA1), and show that against an all-observing eavesdropper, NU-HUNCC satisfies its conditions. Finally, we provide an analysis of NU-HUNCC's high data rate, low computational complexity, and the negligibility of the shared seed size.
Nitrogen plays a crucial role in cotton growth, making the precise diagnosis of its nutrition levels vital for the scientific and rational application of fertilizers. Addressing this need, our study introduced an EMRDFC-based diagnosis model specifically for cotton nitrogen nutrition levels. In our field experiments, cotton was subjected to five different nitrogen application rates. To enhance the diagnostic capabilities of our model, we employed ResNet101, MobileNetV2, and DenseNet121 as base models and integrated the CBAM (Convolutional Block Attention Module) into each to improve their ability to differentiate among various nitrogen levels. Additionally, the Focal loss function was introduced to address issues of data imbalance. The model’s effectiveness was further augmented by employing integration strategies such as relative majority voting, simple averaging, and weighted averaging. Our experimental results indicated significant accuracy improvements in the enhanced ResNet101, MobileNetV2, and DenseNet121 models by 2.3%, 2.91%, and 2.93%, respectively. Notably, the integration of these models consistently improved accuracy, with gains of 0.87% and 1.73% compared to the highest-performing single model, DenseNet121FC. The optimal ensemble model, which utilized the weighted average method, demonstrated superior learning and generalization capabilities. The proposed EMRDFC model shows great promise in precisely identifying cotton nitrogen status, offering critical insights into the diagnosis of crop nutrient status. This research contributes significantly to the field of agricultural technology by providing a reliable tool for nitrogen-level assessment in cotton cultivation.
Branching processes as a mathematical concept has applications in various fields, including information technology. In information technology, branching processes can be used to model and analyze various scenarios, such as the propagation of data or information in a network, the growth of computer viruses, the spread of software bugs, and more. Branching processes are particularly useful for understanding the dynamics of systems where events can lead to multiple new events in a probabilistic manner. Overall, branching processes provide a valuable mathematical framework for modeling and analyzing various aspects of information technology, helping to make informed decisions and optimize IT systems and networks. We have studied transient phenomena for branching processes with an infinite number of types close to critical. The analytical apparatus for this study is Markov renewal theorems. Branched processes were used to evaluate the performance of IT systems and predict their behavior under different conditions. This is important for capacity planning and resource allocation.
Ana Sofia Teixeira, Maria Joana Campos, Carla Sílvia Fernandes
et al.
Background & Aim: This scoping review aims to identify and summarize how Technology can help in the prevention of work-related Musculoskeletal Injuries of healthcare professionals.
Methods & Materials: We conducted a scoping review following the steps provided by the Joanna Briggs Institute. The PRISMA® - Preferred Reporting Items for Systematic Reviews and Meta-Analyses model was used to organize the information, following the recommendations described in PRISMA-ScR (PRISMA Extension for Scoping Reviews) for the article presentation. A search of PubMed, Scopus, and CINAHL databases was conducted for all articles in December 2023.
Results: Of the 964 initial articles identified, 7 met the inclusion criteria. The reviewed studies highlight the effectiveness of various technological interventions in reducing musculoskeletal injuries among healthcare professionals. Wearable technologies, such as inertial measurement units, have been effective in promoting correct posture and reducing the risk of musculoskeletal disorders. However, the studies also identified significant challenges, including the generalizability of findings, the need for more robust empirical evidence, and issues related to the long-term sustainability and cost-effectiveness of these technologies.
Conclusion: The conclusion of this analysis highlights the need for scalable, effective, and customized therapies and calls for more study and development in gamification, wearable technologies, and tailored mobile applications.
This paper introduces an extendable modular system that compiles a range of music feature extraction models to aid music information retrieval research. The features include musical elements like key, downbeats, and genre, as well as audio characteristics like instrument recognition, vocals/instrumental classification, and vocals gender detection. The integrated models are state-of-the-art or latest open-source. The features can be extracted as latent or post-processed labels, enabling integration into music applications such as generative music, recommendation, and playlist generation. The modular design allows easy integration of newly developed systems, making it a good benchmarking and comparison tool. This versatile toolkit supports the research community in developing innovative solutions by providing concrete musical features.
In today's data-driven world, the proliferation of publicly available information raises security concerns due to the information leakage (IL) problem. IL involves unintentionally exposing sensitive information to unauthorized parties via observable system information. Conventional statistical approaches rely on estimating mutual information (MI) between observable and secret information for detecting ILs, face challenges of the curse of dimensionality, convergence, computational complexity, and MI misestimation. Though effective, emerging supervised machine learning based approaches to detect ILs are limited to binary system sensitive information and lack a comprehensive framework. To address these limitations, we establish a theoretical framework using statistical learning theory and information theory to quantify and detect IL accurately. Using automated machine learning, we demonstrate that MI can be accurately estimated by approximating the typically unknown Bayes predictor's log-loss and accuracy. Based on this, we show how MI can effectively be estimated to detect ILs. Our method performs superior to state-of-the-art baselines in an empirical study considering synthetic and real-world OpenSSL TLS server datasets.
During the 2020/2021 winter season, three nationwide cold waves took place from 28 to 31 December 2020, as well as from 5 to 8 January and 14 to 17 January 2021. These cold waves resulted in extreme cold weather in northern and eastern China. In this study, the common features of these cold waves were analyzed, and the key factors contributing to cold waves were illustrated, and the performance of the CMA-GEPS numerical model was evaluated in predicting the cooling effect of the cold waves, and its predictability source was discussed. The results indicated that the cold waves were caused by synergistic effects in the mid- to high-latitude atmospheric circulation of both the upper and lower atmosphere, including polar vortex splitting, enhancement of blocking high, and increased meridional circulation anomaly in the Siberian high area. During the time of cold waves, the mid- to high-latitude atmospheric circulation was undergoing low-frequency adjustment, with the Arctic oscillation continuously weakening, while the blocking high and Siberian high gradually increased to historically high-intensity states. The outbreaks of the three cold waves occurred at the peak and declining points of the blocking high and Siberian high, respectively, acting as short- to medium-term forecast factors. The CMA-GEPS model demonstrated high forecasting ability for the cooling of the cold waves due to its ability to accurately predict the evolution of the Siberian high and blocking high prior to and after the cold wave with a long lead time. Predictability analysis suggested the strong variability of key factors (such as the Siberian high and blocking) in cold wave events may benefit the model’s prediction of cold wave events. These findings contribute to the understanding of the physical mechanisms behind cold waves and the potential for improved forecasting of extreme cold weather events.
Karrouchi Mohammed, Rhiat Mohammed, Nasri Ismail
et al.
The majority of modern vehicles have electronic control units (ECUs) in charge of controlling their functions. These ECUs communicate with one another using the CAN (Controller Area Network) communication protocol. This practical bus offers great transfer of data quality by enabling wide propagation that quickly reaches all sections of a vehicle. Unfortunately, this specific protocol places little focus on security, making the CAN bus control system susceptible. This is owing to its ease of physical or remote access and lack of confidentiality. This vulnerability makes it feasible to take control of the vehicle and endanger the safety of the passengers. The main objective of this work is to present the current existing vulnerabilities of the CAN Bus, to discuss a practical demonstration of hacking as well as to propose a technique to fight against these malicious actions, and all this by practical demonstrations on a DACIA Lodgy and Sandero 2014 vehicles.
For a multi-user integrated sensing and communication system in the network of vehicles, a robust sensing-assisted communication massive multiple-input multiple-output (MIMO) orthogonal time frequency space (OTFS) transmission scheme was proposed.Due to the limited sensing accuracy of the radar, errors existed in the channel state information (CSI) reconstructed based on sensing parameters.The transmission performance decreased as a result.To address this issue, the CSI in the delay doppler domain was reconstructed based on the sensing parameters by the transmitter firstly.And a robust beam forming scheme was designed considering the CSI error.Secondly, the channel estimation error and inter user interference were perceived by receivers based on sensing parameters.Then the robust receiver was designed by incorporating the perceived interference errors into the signal detector in an analytical way.Finally, numerical simulation results show that the proposed method effectively reduces the system bit error rate and increases the data reception rate of users.The proposed method improves the overall system performance in this situation.
Information technology, Management information systems
Anmol Agarwal, Shrey Gupta, Vamshi Bonagiri
et al.
Information Disguise (ID), a part of computational ethics in Natural Language Processing (NLP), is concerned with best practices of textual paraphrasing to prevent the non-consensual use of authors' posts on the Internet. Research on ID becomes important when authors' written online communication pertains to sensitive domains, e.g., mental health. Over time, researchers have utilized AI-based automated word spinners (e.g., SpinRewriter, WordAI) for paraphrasing content. However, these tools fail to satisfy the purpose of ID as their paraphrased content still leads to the source when queried on search engines. There is limited prior work on judging the effectiveness of paraphrasing methods for ID on search engines or their proxies, neural retriever (NeurIR) models. We propose a framework where, for a given sentence from an author's post, we perform iterative perturbation on the sentence in the direction of paraphrasing with an attempt to confuse the search mechanism of a NeurIR system when the sentence is queried on it. Our experiments involve the subreddit 'r/AmItheAsshole' as the source of public content and Dense Passage Retriever as a NeurIR system-based proxy for search engines. Our work introduces a novel method of phrase-importance rankings using perplexity scores and involves multi-level phrase substitutions via beam search. Our multi-phrase substitution scheme succeeds in disguising sentences 82% of the time and hence takes an essential step towards enabling researchers to disguise sensitive content effectively before making it public. We also release the code of our approach.
A major roadblock to scalable quantum computing is phase decoherence and energy relaxation caused by qubits interacting with defect-related two-level systems (TLS). Native oxides present on the surfaces of superconducting metals used in quantum devices are acknowledged to be a source of TLS that decrease qubit coherence times. Reducing microwave loss by surface engineering (i.e., replacing uncontrolled native oxide of superconducting metals with a thin, stable surface with predictable characteristics) can be a key enabler for pushing performance forward with devices of higher quality factor. In this work, we present a novel approach to replace the native oxide of niobium (typically formed in an uncontrolled fashion when its pristine surface is exposed to air) with an engineered oxide, using a room-temperature process that leverages Accelerated Neutral Atom Beam (ANAB) technology at 300 mm wafer scale. This ANAB beam is composed of a mixture of argon and oxygen, with tunable energy per atom, which is rastered across the wafer surface. The ANAB-engineered Nb-oxide thickness was found to vary from 2 nm to 6 nm depending on ANAB process parameters. Modeling of variable-energy XPS data confirm thickness and compositional control of the Nb surface oxide by the ANAB process. These results correlate well with those from transmission electron microscopy and X-ray reflectometry. Since ANAB is broadly applicable to material surfaces, the present study indicates its promise for modification of the surfaces of superconducting quantum circuits to achieve longer coherence times.