Search Results (163)

Deep neural networks (DNNs) require efficient hardware accelerators due to the high cost of vector–matrix multiplication operations. Computing-in-memory (CIM) architectures address this challenge by performing computations directly within memory arrays, reducing data movement and improving energy efficiency. This paper introduces a novel analog-domain CIM architecture that enables flexible organization of weights across both rows and columns of the CIM array. A pipelining scheme is also proposed to decouple the multiply-and-accumulate and analog-to-digital conversion operations, thereby enhancing throughput. The proposed architecture is compared with existing approaches in terms of latency, area, energy consumption, and utilization. The comparison emphasizes architectural principles while deliberately avoiding implementation-specific details. Full article

This paper proposes a distributed model predictive control (DMPC) framework integrating an event-triggered mechanism and an adaptive alternating direction method of multipliers (ADMM) to address the challenges of constrained computational resources and stringent real-time requirements in distributed vehicle platoon control systems. Firstly, the longitudinal dynamic model and communication topology of the vehicle platoon are established. Secondly, under the DMPC framework, a controller integrating residual-based adaptive ADMM and an event-triggered mechanism is designed. The adaptive ADMM dynamically adjusts the penalty parameter by leveraging residual information, which significantly accelerates the solving of the quadratic programming (QP) subproblems of DMPC and ensures the real-time performance of the control system. In order to reduce unnecessary solver invocations, the event-triggered mechanism is employed. Finally, numerical simulations verify that the proposed control strategy significantly reduces both the computation time per optimization and the cumulative optimization instances throughout the process. The proposed approach effectively alleviates the computational burden on onboard resources and enhances the real-time performance of vehicle platoon control. Full article

Background/Objectives: Rheumatoid arthritis (RA) is a chronic inflammatory disease characterized by inflammation of the synovium. The inflammation accelerates the development and progression of atherosclerosis, a key phenomenon in the onset of cardiovascular diseases. The aim of this review was to synthetize the traditional and RA-specific cardiovascular risk (CVR) factors and the CVR assessment guidelines in RA patients. Methods: We performed a PubMed search using specific keywords. We synthetized the main findings. Results: Although the risk factors are the traditional ones, with certain particularities, the mechanisms that lead to cardiovascular disease are distinguished. In RA, the “lipid paradox” occurs: low levels of total cholesterol and low-density lipoprotein (LDL)-cholesterol, and high levels of high-density lipoprotein (HDL)-cholesterol. Despite this phenomenon, patients have an elevated risk of cardiovascular events. This is due to inflammation, which increases cholesterol catabolism and interferes with the anti-oxidant properties of HDL-cholesterol. There is a significant association between serum C-reactive protein (CRP) value and cardiovascular risk: each 20 mg/L increase in CRP causes a 1% increase in cardiovascular risk. The evaluation of the CVR through standard matrices undervalues the risk in patients with RA. Various approaches have been suggested to improve the accuracy of cardiovascular risk appraisal: from multiplying standard scores, including specific biomarkers, to modifying the impact of certain parameters in risk calculation. Conclusions: RA inflammatory and autoimmune mechanisms increase the cardiovascular morbidity and mortality in this group of patients. Therefore, this category of patients requires a proper cardiovascular (CV) evaluation. Carotid ultrasound ensures a better classification of RA patients, especially women, in the cardiovascular risk categories. Full article

In resource-constrained edge environments—such as mobile devices, IoT systems, and electric vehicles—energy-efficient Convolution Neural Network (CNN) accelerators on mobile Field Programmable Gate Arrays (FPGAs) are gaining significant attention for real-time object detection tasks. This paper presents a low-power implementation of the Tiny YOLOv4 object detection model on the Xilinx ZCU104 FPGA platform by using Register Transfer Level (RTL) optimization techniques. We proposed three RTL techniques in the paper: (i) Local Explicit Clock Enable (LECE), (ii) operand isolation, and (iii) Enhanced Clock Gating (ECG). A novel low-power design of Multiply-Accumulate (MAC) operations, which is one of the main components in the AI algorithm, was proposed to eliminate redundant signal switching activities. The Tiny YOLOv4 model, trained on the COCO dataset, was quantized and compiled using the Tensil tool-chain for fixed-point inference deployment. Post-implementation evaluation using Vivado 2022.2 demonstrates around 29.4% reduction in total on-chip power. Our design supports real-time detection throughput while maintaining high accuracy, making it ideal for deployment in battery-constrained environments such as drones, surveillance systems, and autonomous vehicles. These results highlight the effectiveness of RTL-level power optimization for scalable and sustainable edge AI deployment. Full article

In the context of the “Dual Carbon” goals and ecological civilization development, enhancing forestry ecological total factor productivity (FETFP) has become vital for advancing green development and environmental governance. Confronted with tightening resource constraints and pressure to transform traditional growth models, whether digital intelligence integration can effectively empower improvements in FETFP requires in-depth empirical validation. Based on publicly available panel data from 30 Chinese provinces spanning 2012 to 2022, this study constructs an index system for measuring digital intelligence integration and FETFP. Using the Double Machine Learning (DML) framework, the study empirically identifies the impact of digital intelligence development on FETFP and explores its internal mechanisms. The key results show that (1) digital intelligence integration significantly enhances FETFP. For every unit increase in digital and intelligent integration, FETFP rises by an average of 19.97%; (2) mechanism analysis reveals that digital intelligence improves FETFP by optimizing the forestry industrial structure, promoting green technological innovation, and amplifying the synergistic effects of fiscal support; (3) and heterogeneity analysis suggests that the positive impact of digital intelligence integration is more pronounced in regions with higher environmental expenditures and stronger green finance support. Accordingly, this study proposes several policy recommendations, including accelerating digital infrastructure development, strengthening foundational digital intelligence capabilities, enhancing support for green innovation, leveraging the ecological multiplier effects of digital transformation, tailoring digital strategies to local conditions, and improving the precision of regional environmental governance. The findings provide robust empirical evidence for improving FETFP in developing and developed economies. Full article

Spectrum map fusion has emerged as an effective technique to enhance the accuracy of spectrum map construction. However, many existing fusion methods fail to address the strong correlation between spectrum data, resulting in sub-optimal performance. In this paper, we propose a new multi-unmanned aerial vehicle (UAV) spectrum map fusion method based on differential ridge regression. We first construct spectrum maps of UAVs by using differential features of spectrum data. Next, we present a spectrum map fusion model by leveraging the spatial distribution characteristic of spectrum data. To reduce the sensitivity of the fusion model to the strongly correlated data, a new map fusion regularization term is designed, which introduces $l_2$-norm to constrain the fusion regularization parameters and compress the ridge regression coefficient sizes. As a result, accurate spectrum maps can be constructed for the environments with highly correlated spectrum data. We then formulate a model-driven solution to the spectrum map fusion problem and derive its lower bound. By combining the propagation characteristics of the spectrum signal with the developed Lagrange duality, we can guarantee the convergence of map fusion processing while enhancing the convergence rate. Finally, we propose an accelerated maximally split alternating directions method of multipliers (AMS-ADMM) to reduce the computational complexity of spectrum map construction. Simulation results demonstrate that our proposed method can effectively eliminate external noise interference and outliers, and achieve an accuracy improvement of more than 27% compared to state-of-the-art fusion methods in spectrum map construction with low complexity. Full article

This study investigates the dynamic behavior of an articulated boom offshore crane under various load application laws. The following steps were taken to perform numerical simulations using the finite-element method (FEM): Definition of the model’s geometry, materials, and boundary conditions. The modal analyses reveal significant resonance frequencies in the direction of load application (payload). The crane’s displacement, velocity, and acceleration responses are closely related to load application laws, specifically the time required to reach the structure’s full payload (epsilon). It is highly correlated with the dynamic factor (maximum acceleration multiplied by payload), which has a wide range of effects on the structure, including the effects of overstress, overturning, buckling, and so on. The main findings reveal a very strong exponential correlation, allowing the dynamic effect to be estimated as a function of epsilon time. This is a useful tool for increasing the safety and reliability of offshore lifting operations. Full article

For the splitting iterative scheme to solve the system of linear equations, an equivalent form in terms of descent and residual vectors is formulated. We propose an extrapolation technique using the new formulation, such that a new splitting iterative scheme (NSIS) can be simply generated from the original one by inserting an acceleration parameter preceding the descent vector. The spectral radius of the NSIS is proven to be smaller than the original one, and so has a faster convergence speed. The orthogonality of consecutive residual vectors is coined into the second NSIS, from which a stepwise varying orthogonalization factor can be derived explicitly. Multiplying the descent vector by the factor, the second NSIS is proven to be absolutely convergent. The modification is based on the maximal reduction of residual vector norm. Two-parameter and three-parameter NSIS are investigated, wherein the optimal value of one parameter is obtained by using the maximization technique. The splitting iterative schemes are unified to have the same iterative form, but endowed with different governing equations for the descent vector. Some examples are examined to exhibit the performance of the proposed extrapolation techniques used in the NSIS. Full article

While the interest and participation in general endurance training and recreational sports competitions have continuously increased in recent decades, the number of recreational master-level endurance athletes has additionally multiplied. Athletes, active men and women older than 40 years of age, who participate in competitive athletics are usually referred to by the term master athletes (MAs). Previous research revealed the significant benefits of regular moderate physical activity, i.e., its positive influence on cardiovascular risk factors and cardiovascular health; however, recent data have raised concerns that long-term endurance exercise participation is associated with cardiac remodeling and potential adverse cardiovascular outcomes. Previous research also indicated potential structural, functional, and electrical remodeling in MAs due to prolonged and repeated exposure to high-intensity endurance exercise—a condition known as athlete’s heart. In this review, we focus on the association between extreme levels of endurance exercise and potential cardiovascular controversies, such as arrhythmogenesis due to new-onset atrial fibrillation, accelerated coronary artery atherosclerosis, and exercise-induced cardiac remodeling. Additionally, the exercise-dependent modulation of immunological response, such as proteomic response and cytokine alterations, is discussed. Furthermore, we discuss the impact of nutritional supplements in MAs and their potential benefits and harmful interactions. We aim to provide sports medicine practitioners with knowledge of these contemporary longevity controversies in sports cardiology and to highlight the importance of shared decision making in situations of clinical uncertainty. Full article

For data-intensive applications like edge AI and image processing, we present a new reconfigurable 8T SRAM-based in-memory computing (IMC) macro designed for high-performance and energy-efficient operation. This architecture mitigates von Neumann limitations through numerous major breakthroughs. We built a new architecture with an adjustable capacitance array to substantially increase the multiply-and-accumulate (MAC) engine’s accuracy. It achieves 10–20 TOPS/W and >95% accuracy for 4–10-bit operations and is robust across PVT changes. By supporting binary and ternary neural networks (BNN/TNN) with XNOR-and-accumulate logic, a dual-mode inference engine further expands capabilities. With sub-5 ns mode switching, it can achieve up to 30 TOPS/W efficiency and >97% accuracy. In-memory Hamming error correction is implemented directly using integrated XOR circuitry. This technique eliminates off-chip ECC with >99% error correction and >98% MAC accuracy. Machine learning-aided co-optimization ensures sense amplifier dependability. To ensure CMOS compatibility, the macro may perform Boolean logic operations using normal 8T SRAM cells. Comparative circuit-level simulations show a 31.54% energy efficiency boost and a 74.81% delay reduction over other SRAM-based IMC solutions. These improvements make our macro ideal for real-time AI acceleration, cryptography, and next-generation edge computing, enabling advanced compute-in-memory systems. Full article

With the widespread application of combined heat and power (CHP) units, the coupling between electricity and heat systems has become increasingly close. In response to the problem of low operational efficiency of electricity–heat integrated energy systems (EH-IESs) with buildings in uncertain environments, this paper proposes a chance-constrained programming (CCP)-based decentralized optimization method for EH-IESs with buildings. First, based on the thermal storage capacity of building envelopes and considering the operational constraints of an electrical system (ES) and thermal system (TS), a mathematical model of EH-IESs, accounting for building thermal inertia, was constructed. Considering the uncertainty of sunlight intensity and outdoor temperature, a CCP-based optimal scheduling strategy for EH-IESs is proposed to achieve a moderate trade-off between the optimal objective function and constraints. To address the disadvantages of high computational complexity and poor information privacy in centralized optimization, an accelerated asynchronous decentralized alternating direction method of multipliers (A-AD-ADMM) algorithm is proposed, which decomposes the original optimization problem into sub-problems of ES and TS for distributed solving, significantly improving solution efficiency. Finally, numerical simulations prove that the proposed strategy can fully utilize the thermal storage characteristics of building envelopes, improve the operational economics of the EH-IES under uncertain environments, and ensure both user temperature comfort and the information privacy of each subject. Full article

The objective of our study is to monitor the stability of Extra Virgin Olive Oil (EVOO) flavoured with cloves. Two flavouring processes were tested, namely the maceration of cloves in olive oil and the grinding of cloves with olives. The analysis of the obtained oils showed that the process of the simultaneous grinding of the cloves with the olives produced a better oil quality than the maceration process in terms of richness in total phenols. The co-crushing method increased the total phenols in the olive oil by 34.24% and 73.37%, compared to the maceration method with an increase of only 17.1% and 52.35%, respectively, for the 2 and 4% of cloves addition. Fluorescence spectroscopy analysis of the oils supplied useful and complementary results. The aromatized olive oil developed by simultaneous grinding was subjected to ageing acceleration at 60 °C in the dark for 165 days. Results indicated that the acidity and the value of the specific extinction coefficient K₂₃₂ of the control EVOO followed the standards of the International Olive Oil Council. During accelerated storage, the degradation of total phenols was marked as less for the flavoured EVOOs than for the control samples. After 165 days of storage, the colour of all olive oil samples was modified, with this change being the most apparent for unflavoured oil with a 45.6% and 46.4% decrease in L and b* vs. 38.8% and 22.4% for C1, and 45.5% and 37.2% for C2 respectively. After 165 days of storage, all the oil samples were darker and red. Flavouring EVOO with cloves offered a better stability to the oil. A consumer survey involving 224 participants revealed that despite the fact that only 30% were familiar with flavoured oils, 83.9% expressed a willingness to purchase clove-flavoured olive oil if it became available on the market. Flavoured oils offer a good alternative to multiply olive oil-based products and thus offer additional opportunities for the marketing of olive oils. Full article

This study examined the dynamic relationship between China’s carbon pricing (CP) and green technology innovation (GTI) using monthly data from August 2013 to February 2025 through sub-sample rolling-window Granger causality tests. The results revealed a time-varying bidirectional relationship where CP significantly promotes GTI during periods when innovation offset effects dominate (such as from July to October 2021 and October 2023 to March 2024), but inhibits GTI when compliance cost effects prevail (as observed from February to June 2022). Conversely, GTI alternately suppressed CP from June to November 2017 and enhanced it from February to July 2024. These patterns demonstrate that the interaction between CP and GTI is critically shaped by three key factors: policy synergy between carbon markets and complementary environmental regulations, international competitive pressures from carbon border mechanisms, and financial market capacity to support green investments. Based on these findings, we propose a comprehensive policy framework that includes expanding emissions trading to heavy industries, implementing dynamic CP stabilization mechanisms, introducing innovation-linked quota incentives with 1.1 to 1.5 multipliers, and developing integrated green financial instruments. This framework can effectively align CP with GTI to accelerate China’s low-carbon transition while maintaining industrial competitiveness. Full article

In this paper, we propose a novel alternating direction method of multipliers based on acceleration technique involving two symmetrical inertial terms for a class of nonconvex optimization problems with a two-block structure. To address the nonconvex subproblem, we introduce a proximal term to reduce the difficulty of solving this subproblem. For the smooth subproblem, we employ a gradient descent method on the augmented Lagrangian function, which significantly reduces the computational complexity. Under appropriate assumptions, we prove subsequential convergence of the algorithm. Moreover, when the generated sequence is bounded and the auxiliary function satisfies Kurdyka–Łojasiewicz property, we establish global convergence of the algorithm. Finally, effectiveness and superior performance of the proposed algorithm are validated through numerical experiments in signal processing and smoothly clipped absolute deviation penalty problems. Full article

The global energy transition is accelerating, bringing new challenges to power systems. A high penetration of renewable energy increases grid volatility. Virtual power plants (VPPs) address this by dynamically responding to market signals. They integrate renewables, energy storage, and flexible loads. Additionally, they participate in multi-tier markets, including energy, ancillary services, and capacity trading. This study proposes a load factor-based VPP pre-dispatch model for optimal resource allocation. It incorporates the coupling effects of electricity–carbon markets. A Nash negotiation strategy is developed for multi-VPP cooperation. The model uses an accelerated adaptive alternating-direction multiplier method (AA-ADMM) for efficient demand response. The approach balances computational efficiency with privacy protection. Revenue is allocated fairly based on individual contributions. The study uses data from a VPP dispatch center in Shanxi Province. Shanxi has abundant wind and solar resources, necessitating advanced scheduling methods. Cooperative operation boosts profits for three VPPs by CNY 1101, 260, and 823, respectively. The alliance’s total profit rises by CNY 2184. Carbon emissions drop by 31.3% to 8.113 tons, with a CNY 926 gain over independent operation. Post-cooperation, VPP1 and VPP2 see slight emission increases, while VPP3 achieves major reductions. This leads to significant low-carbon benefits. This method proves effective in cutting costs and emissions. It also balances economic and environmental gains while ensuring fair profit distribution. Full article