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OFDM-structure based waveform designs for integrated sensing and communication

science China(Information sciences) 2025年第5期68卷 267-284页

作者： Jiaqi LIU Ping YANG Ke JIANG Jiarong ZHAO Wei XIANG Jianping WEI Ruixiang DUAN Saviour ZAMMIT Tony Q.S.QUEK National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China School of Computing Engineering and Mathematical Sciences La Trobe University Meituan Academy of Robotics Department of Communications and Computer Engineering University of Malta Information Systems Technology and Design Pillar Singapore University of Technology and Design

The future sixth-generation(6G) paradigm aims to seamlessly integrate communication and environmental sensing capabilities into a single radio signal, promising improved efficiency and cost-effectiveness through simultaneous data communications and environmental perception. At the core of this evolution, orthogonal frequency division multiplexing(OFDM) and its advanced waveforms emerge as pivotal for integrated sensing and communications(ISAC). This study introduces a concise and unified ISAC waveform design framework based on orthogonal multicarriers. This framework supports versatile applications of OFDM and its derivative waveforms within a generalized ISAC system, marking a significant leap in integrating communication and sensing capabilities. A distinguishing feature of this framework is its adaptability,allowing users to intelligently select modulation strategies based on their specific environmental needs. This adaptability optimizes performance across diverse scenarios. Central to our innovations is the proposal of discrete Fourier transformspread OFDM with index modulation(DFT-S-OFDM-IM). This framework is paired with newly proposed signal processing methods for single-input single-output and multiple-input multiple-output(MIMO) systems. Extensive evaluations highlight DFT-S-OFDM-IM's superiority, including dramatically reduced peak-to-average power ratios(PAPRs), competitive communication performance, and exceptional sensing capabilities, striking an elegant balance between communication capacity and environmental sensing precision.

关键词： DFT-S-OFDM-IM index modulation integrated sensing and communication OFDM

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A Review of Machine Learning Algorithms in Diabetes Management 4

A Review of Machine Learning Algorithms in Diabetes Manageme...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Gayaki, Unnati Daronde, Subodh Tale, Abhay Barhate, Aditya Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha India Faculty of Engineering and Technology Department of Biomedical Engineering Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha India

ISBN: (纸本)9798331523923

Diabetes is a prevalent and chronic disease affecting millions worldwide, posing significant challenges in its management and treatment. This review article aims to explore the current and potential future roles of machine learning (ML) in diabetes management, focusing on progress, applications, and challenges. It discusses various ML techniques, including support vector machines (SVM), logistic regression, decision trees, ensemble methods, and advanced deep learning approaches such as long short-term memory (LSTM) networks and convolutional neural networks (CNN). These methods have demonstrated benefits in analytics, patient monitoring, decision support systems, personalised treatment, and patient engagement. However, significant challenges persist, such as computational complexity, data quality, limited interoperability of systems, and ethical concerns regarding AI use in healthcare. The article addresses these challenges by fostering collaboration between AI implementers and health care professionals. This collaboration is essential to develop long-term, legally compliant, and solution-oriented frameworks that enhance patient care and diabetes management and pave the way for more efficient and ethical AI applications in healthcare. © 2025 IEEE.

关键词： Support vector machines

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Optimization of 6G resource allocation using CyberTwin function-based service enhancement scheme

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Eurasip Journal on Wireless Communications and Networking 2025年第1期2025卷 1-27页

作者： Aldrees, Asma Min, Hong Daradkeh, Yousef Ibrahim Dutta, Ashit Kumar Anjum, Mohd Department of Informatics and Computer Systems College of Computer Science King Khalid University Abha61421 Saudi Arabia School of Computing Gachon University Seongnam13120 Korea Republic of Department of Computer Engineering and Information College of Engineering in Wadi Alddawasir Prince Sattam Bin Abdulaziz University Al-Kharj16273 Saudi Arabia Department of Computer Science and Information Systems College of Applied Sciences Almaarefa University Ad Diriyah Riyadh13713 Saudi Arabia Department of Computer Engineering Aligarh Muslim University Aligarh202002 India

The most innovative Service-Optimized Logging for Resource Allocation (SOL-RA) is in situations that support 6G connection. The CyberTwin architecture optimizes the quality of service provided to end users in 6G communications by using the dependability of terahertz connections and interactions of a machine-type nature. Elastically sharing elastic resources among users is made possible by the proposed resource allocation strategy, which makes use of proprietary logger functions. This ensures that efficient allocation is achieved without overlapping or lengthy wait periods. Within the framework of SOL-RA, a categorization mechanism is implemented for dense requests, which differentiates them as either stationary or priority services. Resource allocations are dynamically done based on this categorization, which enhances the organization's responsiveness to different needs. Through a tree classifier learning mechanism, CyberTwin logs play an essential part in the processing of requests and the preparation of resources. This helps to ensure that resources are distributed without inefficiency. Requests that are generating a backlog are identified and processed in parallel by the system, which significantly reduces the amount of time that is wasted waiting. In order to guarantee a specialized distribution for the categorized outputs, resource allocations are directed by CyberTwin data logs. An examination of the performance of the SOL-RA scheme takes into account important metrics such service latency, service backlog, resource usage, and request-to-response ratio. This research offers insights into the efficacy of the scheme in maximizing service quality and resource consumption in 6G settings. © The Author(s) 2025.

关键词： Resource allocation

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Diabetes Prediction Using Hybrid 1D CNN and PeeHole Long Short-Term Memory Algorithm 5

Diabetes Prediction Using Hybrid 1D CNN and PeeHole Long Sho...

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5th International Conference on Emerging systems and Intelligent Computing, ESIC 2025

作者： Behera, Mandakini Mishra, Debahuti Sarangi, Archana Sarangi, Shubhendu Deemed to be University School of Computer Engineering Bhubaneswar India University Department of Computer Science & Engineering Bhubaneswar India University Department of Electronics and Telecommunications Bhubaneswar India University Faculty of Engineering Bhubaneswar India

ISBN: (纸本)9798331522100

According to the survey of International Diabetes Federation (IDF) the number of persons with diabetes is continuously increasing. There are currently 3820 million diabetics worldwide, and during the next 15 years, this figure might double. Diabetes can create several life-threatening diseases, including heart attack, renal failure, and stroke. To sustain a healthy life, early diabetes identification is crucial. machine learning (ML) is a approach which automatically learns from experience to improve performance and produce more accurate predictions, that can be used to forecast diabetes. Deep learning is a branch of machine learning, and the human brain inspires both its architecture and the way it *** manuscript presents a hybrid method that combines peephole LSTM and 1D CNN to improve the accuracy of diabetes prediction. Performance is evaluated based on F1 score, accuracy, specificity, sensitivity, and epoch-wise accuracy and *** Pima Indian diabetes dataset is utilized for experimental validation. Experimental results demonstrate high prediction accuracy of 96.74% on training and 75.32% on testing for diabetes prediction. © 2025 IEEE.

关键词： Deep learning

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IoT Based Monitoring System for Health During Menstruation 4

IoT Based Monitoring System for Health During Menstruation

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Khadse, Chinmaya Hajare, Shital Sheikh, Nishad Dahule, Roshni Kate, Priya Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Sawangi Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Sawangi Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Sawani Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

The integration of IoT and ML brought forth new prospective solutions in menstrual health management, pertaining to continuous monitoring of physiological signals and personalization of insights for women. This article discusses the usage of IoT, smartphones, and cloud-based systems for tracking vital signs, temperature, pulse rate, and flow of menstruation that will enable pro-active management of the cycle. The use of ML algorithms, including supervised models-like Random Forest and Logistic Regression-and complex techniques like RNNs and LSTM networks-offers added advantages for predicting menstrual cycles and ovulation. These technologies offer personalized health recommendations, early diagnosis of menstrual disorders, and help in communicating with healthcare providers in a very short span of time. While offering huge potential to improve women's health, concerns around data privacy, ethical considerations, and equitable access to these digital health solutions are highlighted. The existing systems showed improvements yet they encountered problems because of insufficient data diversity and health system integration as well as insufficient personalization and imprecise cycle predictions. Data privacy issues and security threats that stem from ML models along with constant bias concerns prevent broad acceptance of ML systems in general use. This research study analyzes IoT and ML applications in menstrual health today and introduces their advantages as well as the barriers that require resolution before mass acceptance can occur. The proposal introduces an approach to create menstrual tracking software through a combination of rule-based systems with ML algorithms that generates precise forecasts alongside tailored information for women who have PCOS or PCOD. © 2025 IEEE.

关键词： Electronic health record

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Ladon: High-Performance Multi-BFT Consensus via Dynamic Global Ordering 25

Ladon: High-Performance Multi-BFT Consensus via Dynamic Glob...

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20th European Conference on computer systems, EuroSys 2025, co-located 30th ACM International Conference on Architectural Support for Programming Languages and Operating systems, ASPLOS 2025

作者： Lyu, Hanzheng Xie, Shaokang Niu, Jianyu Feng, Chen Zhang, Yinqian Beschastnikh, Ivan Canada Southern University of Science and Technology China Canada Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering SUSTech China

ISBN: (纸本)9798400711961

Multi-BFT consensus runs multiple leader-based consensus instances in parallel, circumventing the leader bottleneck of a single instance. However, it contains an Achilles’ heel: the need to globally order output blocks across instances. Deriving this global ordering is challenging because it must cope with different rates at which blocks are produced by instances. Prior Multi-BFT designs assign each block a global index before creation, leading to poor performance. We propose Ladon, a high-performance Multi-BFT protocol that allows varying instance block rates. Our key idea is to order blocks across instances dynamically, which eliminates blocking on slow instances. We achieve dynamic global ordering by assigning monotonic ranks to blocks. We pipeline rank coordination with the consensus process to reduce protocol overhead and combine aggregate signatures with rank information to reduce message complexity. Ladon’s dynamic ordering enables blocks to be globally ordered according to their generation, which respects inter-block causality. We implemented and evaluated Ladon by integrating it with both PBFT and HotStuff protocols. Our evaluation shows that Ladon-PBFT (resp., Ladon-HotStuff) improves the peak throughput of the prior art by ≈ 8× (resp., 2×) and reduces latency by ≈ 62% (resp., 23%), when deployed with one straggling replica (out of 128 replicas) in a WAN setting. © 2025 Copyright held by the owner/author(s).

关键词： Network protocols

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Physicochemical Properties and Cost-Benefit of Supplementing Signal Grass (Brachiaria decumbens) in Sasso Broilers Production

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BioResources 2025年第2期20卷 3176-3194页

作者： Seng, Kelly Wong Kai Zheng, Alvin Lim Teik Ong, Yee Lyn Lease, Jacqueline Andou, Yoshito Jesse, Faez Firdaus Abdullah Dunshea, Frank R. Chung, Eric Lim Teik Department of Agribusiness and Bioresource Economics Faculty of Agriculture Universiti Putra Malaysia UPM Selangor Serdang43400 Malaysia Department of Science and Technology Faculty of Humanities Management and Science Universiti Putra Malaysia Bintulu Campus Sarawak Bintulu97008 Malaysia Department of Animal Science Faculty of Agriculture Universiti Putra Malaysia UPM Selangor Serdang43400 Malaysia Department of Life Science and Systems Engineering Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology Fukuoka808-0196 Japan Department of Veterinary Clinical Studies Faculty of Veterinary Medicine Universiti Putra Malaysia UPM Selangor Serdang43400 Malaysia School of Agriculture Food and Ecosystem Faculty of Science The University of Melbourne ParkvilleVIC3010 Australia Faculty of Biological Science The University of Leeds LeedsLS2 9JT United Kingdom

There has been a growing interest in using natural alternatives to synthetic additives in animal feed. This study aimed to examine the physicochemical properties of signal grass (Brachiaria decumbens) and its cost-benefit application in broiler production. The characterization was performed using Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), thermogravimetric/derivative thermogravimetry (TG/DTG), and scanning electron microscopy (SEM). A feeding trial involving 216 Sasso broiler chicks was conducted to assess the economic value of including B. decumbens grass meal in their diets. The chicks were divided into six groups, with Treatments 1 and 2 serving as controls (without antibiotics and with oxytetracycline, respectively). Treatments 3 to 6 received diets supplemented with 1.25, 2.50, 3.75, and 5.00 g/kg of B. decumbens grass meal without antibiotics. Body weight and feed intake were monitored over eight weeks to determine growth performance and feed conversion ratio. Broilers in Treatment 6, which received 5.00 g/kg of the grass meal, showed significantly improved growth (p © 2025, North Carolina State University. All rights reserved.

关键词： Fourier transform infrared spectroscopy

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Stability and bifurcation analysis of delayed tri-trophic food chain model in poisoned environment with fear effect and additional food

Stability and bifurcation analysis of delayed tri-trophic fo...

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作者： Ramasamy, Sivasamy Banjerdpongchai, David Park, PooGyeon Center of Excellence in Intelligent Control Automation of Process Systems Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand Department of Electrical Engineering Pohang University of Science and Technology Pohang Korea Republic of

One of the most important goals of theoretical ecologists is to find a strategy for controlling the chaos in ecological models to maintain healthy ecosystems. We investigate the influence of fear and the supply of additional food to predators in a delayed tri-trophic food chain model, where interference among the species is determined by Holling type-II functional response. The proposed model incorporates the fact that the growth rates of prey and middle predators are decreased due to the fear of middle and special predators, respectively. The additional food is supplied to both predators by either nature or external agencies. Furthermore, the poisoned prey in the predator's environment is taken into account. In the proposed model, we use two time delays: one for the growth term of the middle predator and another for the gestation delay of the special predator. We determine the conditions for the existence of ecologically feasible equilibrium points and their local and global stability. In addition, we establish the conditions for the existence of Hopf bifurcation around interior equilibrium to seek periodic behavior in nondelayed and delayed models. Numerical investigations are performed to justify the proposed theoretical findings through phase portraits, time series of solutions, and bifurcation diagrams. We observe that the chaotic dynamics of a tri-trophic food chain model can be controlled by the proper choice of the fear effect and by supplying additional food parameters. As a result, our findings provide more ecological insights into the dynamics of the delayed tri-trophic food chain model with the fear effect and additional food supply. © 2023 John Wiley & Sons, Ltd.

关键词： Hopf bifurcation

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Sub-zero soil CO2 respiration in biostimulated hydrocarbon-contaminated cold-climate soil can be linked to the soil-freezing characteristic curve

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Environmental science and Pollution Research 2025年第4期32卷 1783-1804页

作者： Nayeema, Tasnim Lee, Aslan Hwanhwi Richter, Amy Ng, Kelvin Tsun Wai Chang, Wonjae Department of Civil Geological and Environmental Engineering College of Engineering University of Saskatchewan 57 Campus Drive Engineering Building SaskatoonSKS7N 5A9 Canada Faculty of Engineering and Applied Science Environmental Systems Engineering University of Regina 3737 Wascana Parkway ReginaSKS4S 0A2 Canada

Extending unfrozen water availability is critical for stress-tolerant bioremediation of contaminated soils in cold climates. This study employs the soil-freezing characteristic curves (SFCCs) of biostimulated, hydrocarbon-contaminated cold-climate soils to efficiently address the coupled effects of unfrozen water retention and freezing soil temperature on sub-zero soil respiration activity. Freezing-induced soil respiration experiments were conducted under the site-relevant freezing regime, programmed from 4 to − 10 °C at a seasonal soil-freezing rate of − 1 °C/day. The effects of unfrozen water retention on extending soil respiration activity emerged at the onset of soil-freezing. The unfrozen water effect became significant below 0 °C (correlation r = 0.83–0.94) and comparable to the temperature effect (correlation r = 0.82–0.90), successfully demonstrating the coupled effects on sub-zero respiration activity. Soil CO2 respiration modelling based on the temperature dependency only (Arrhenius and Q10 models) did not accurately describe sub-zero respiration activity associated with increased unfrozen water retention in treated contaminated soils. The shifted SFCCs of the treated soils, expressed as a function of soil temperature (T) and unfrozen water content (θ), served as a key framework for efficiently developing the sub-zero respiration model (SFCC-RESP). The developed SFCC-RESP model closely approximated the changes in soil respiration rates influenced by T and θ in the treated soils (R2 = 0.94–0.98) and described the abrupt decrease and subsequent stabilization in CO2 production during the transition to the deeply frozen soil phase. The SFCC-RESP model integrated with soil thermal models (TEMP/W) can be used to produce spatial distributions of T, θ, and CO2 production in the treated soil matrix, providing a tool to approximate the abundance of unfrozen habitable niches when developing cold-tolerant bioremediation strategies. © The Author(s), under exclusive li

关键词： Soil pollution

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Optimal coordination of Production, Logistics, and Maintenance 4.0: A framework for Multi-Site Stamping Plants 6

Optimal coordination of Production, Logistics, and Maintenan...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Zerrad, Raoua Diaz, Kamar Mechkouri, Meriem Hayani Sefiani, Naoufal Department of Mechanical Engineering Research Laboratory in Engineering Innovation and Management of Industrial Systems Faculty of Sciences and Techniques Abdelmalek Essaadi University Tangier Morocco Department of Industrial Engineering and Maintenance Computer Engineering Production and Maintenance Laboratory University of Lorraine Metz France

This paper presents a comprehensive framework for optimal coordination of production, logistics, and maintenance in multi-site stamping plants within the context of Industry 4.0. By integrating key technologies such as the Internet of Things, cloud computing and big data analytics, we developed a system that would enable real-time data sharing, predictive analytics, and intelligent decision-making. This framework aims to enhance operational efficiency, reduce downtime, and improve service rate. Additionally, our approach aims to optimize spare parts inventory management across different sites, minimizing inventory costs and lead times by ensuring timely availability and efficient sourcing of spare parts. © 2024 The Authors. Published by Elsevier B.V.

关键词： Inventory control

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