Miracle-dz/UniFAR-FTUs huggingface dataset:
Chemical composition and biomedical effects of essential oil from Commiphora foliacea Sprague stem

Abstract Commiphora foliacea Sprague and related species are renowned for the expensive perfumes, cosmetics, and aromatherapy due to its pleasant smell of essential oils used to promote relaxation, reduce stress, and improve memory loss. The chemical ingredients from the stem of C. foliacea Sprague essential oil (EO) were evaluated for GC-MS analysis, in vitro (antioxidant, antimicrobial, antidiabetic, and carbonic anhydrase-II (CA-II)) and in vivo (anti-analgesic and anti-inflammatory) studies. The EO encompasses 56 compounds contributed 93.58% of the total with leading compounds of camphene (14.02%) followed by eremophilene (11.46%). Maximum activity was depicted via DPPH and ABTS protocols compared to the standards. The EO was found effective against B. subtilis (gram-positive bacteria), while maximum antifungal inhibition was observed against A. parasiticus. The substantial antidiabetic and CA-II potential was noted compared with standards. The EO presented considerable potential against breast cancer (MDA-MB-231) and normal (MCF-10A) cell lines with an IC50 of 108.4 and 300 μg/mL, correspondingly and it has been assumed that the essential oil did not deliver much damage to the normal cell lines. Moreover, among the in vivo, the EO revealed the substantial anti-inflammatory potential to reduce paw edema with 63.81% inhibition in comparison to the standard Diclofenac. EO also depicted a significant reduction in analgesic potential (62.05%) compared to the standard aspirin. Furthermore, up to the best of our knowledge, this is the first report on the GC-MS analysis, in vitro, anti-analgesic, and anti-inflammatory potential of the EO isolated from the stem of C. foliacea.
Dinoflagellate cyst stratigraphy and palaeoecology of the Pliocene in northern Belgium, southern North Sea Basin

Dinoflagellate cysts and other palynomorphs from the Pliocene Kattendijk and Lillo formations, exposed in two temporary outcrops in northern Belgium, provide new information on the biostratigraphic position and sequence stratigraphic interpretation of these units. Dinoflagellate cysts from the Kattendijk Formation indicate an age between about 5.0 Ma and 4.7–4.4 Ma (early Early Pliocene) in our sections, confirming a correlation with standard sequence 3.4 and implying a slightly greater age than the Ramsholt Member of the Coralline Crag Formation of eastern England. The unconformity at the base of the Kattendijk Formation was not seen, but presumably correlates with sequence boundary Me2 at 5.73 Ma. The overlying Lillo Formation is late Early Pliocene or early Late Pliocene (c. 4.2–2.6 Ma) in age, and the unconformity at its base may be correlated with sequence boundary Za2 at 4.04 Ma or Pia1 at 3.21 Ma. The Oorderen Sands and superjacent Kruisschans Sands members (Lillo Formation) are both part of the same depositional cycle. They were probably deposited before 2.74 Ma, and certainly before the onset of Northern Hemisphere cooling at c. 2.6 Ma. Evidence from dinoflagellate cysts indicates that both a shelly unit at the base of the Lillo Formation and the lower part of the overlying Oorderen Sands were deposited during a conspicuously cool climatic phase, with warmer temperatures returning during later deposition of the Oorderen Sands and Kruisschans Sands members. Many dinoflagellate cyst and acritarch species are reported here for the first time from the southern North Sea Basin. Selenopemphix conspicua (de Verteuil & Norris, 1992) stat. nov. is proposed.
Physical activity as a moderator for OSA and cardiometabolic risk in EPISONO study

Obstructive sleep apnea (OSA) is positively associated with cardiometabolic diseases; however, high levels of physical activity (PA) could decrease the incidence of OSA and associated comorbidities. In this study we aimed to examine the incidence of OSA in relation to PA, and its role as a protective factor in individuals with OSA on the incidence of cardiometabolic diseases, in an 8-9 year follow up study. We analyzed data of 658 volunteers from Sao Paulo Epidemiologic Sleep Study, a cohort study of individuals between 20-80 years of age, collected through polysomnography, the international physical activity questionnaire and an assessment of cardiometabolic profile. Active subjects had a lower risk of developing OSA compared to non-active subjects (RR=0.877, 95%CI=0.296-0.855), and there was a reduced risk of developing type 2 diabetes mellitus (T2DM) in active/apneic subjects (RR=0.493, 95%CI=0.252-0.961), compared to non-active subjects. Metabolic equivalent was negatively associated to cardiometabolic markers, such as C-reactive protein (β=0.72, P=0.001), interleukin-6 (β=0.991, P=0.03), insulin (β=0.982, P=0.03), triglycerides (β=0.997, P<0.001), HOMA-IR (β=0.946, P<0.02), QUICKI (β=0.992, P<0.001) and mean arterial pressure (β=0.987, P=0.001). PA was a protective factor against T2DM in apneic individuals, moreover, being active reduced the risk of developing OSA and was associated with a better cardiometabolic profile.
Sport Supplements and the Athlete’s Gut: A Review

A well-functioning gut is a key, albeit sometimes overlooked, contributor to athlete performance and health. Indeed, increases in gastrointestinal (GI) symptoms correspond with worsened performance [1–3], and certain GI symptoms such as nausea, intestinal cramping, and loose stools can even cause athletes to drop out of competition or be unable to complete an exercise task [4, 5]. Hoffman and Fogard [4], for example, found that Western States 100mile Endurance Run participants reported nausea and/or vomiting as the leading reason for withdraw from the race. This contrasts with other symptoms (e. g. flatulence, belching) which are unlikely to affect performance or lead to competition withdrawal. Dietary supplements are frequently used to improve athletic performance, training, and recovery, particularly in elite athletes [6]. Certain supplements (e. g. probiotics, glutamine, bovine colostrum) have been studied as gut function enhancers in the context of exercise [3, 7, 8], and various manufacturers are marketing these and other supplements to exercisers/athletes to improve performance and GI function. Conversely, other sports nutrition supplements have known GI-related side effects that can impair performance or interfere with training in some situations. These include supplements with a long track record of study and use (carbohydrate, caffeine, sodium bicarbonate) as well as more novel products (exogenous ketones). Athletes who decide to use these supplements may need to implement mitigation strategies to minimize GI-related adverse effects and maximize these supplements’ ergogenic properties. Given the widespread use of dietary supplements and their potential impacts on the gut, the aims of this narrative review were twofold: 1) to address the potential of certain dietary supplements to enhance gut function and reduce exercise-associated GI sympSport Supplements and the Athlete’s Gut: A Review
Periodic Sampled-Data Consensus for Leader-Following Nonholonomic Mobile Robots

In this paper, a sampled-data controller is designed for the exact discrete-time model of nonholonomic mobile robots, which solves the leader-following consensus problem. We describe the uniform global asymptotic stability of closed-loop systems with time-varying and periodic characteristics through the generalized Krasovskii-LaSalle Theorem together with the stability theorem of cascaded system. A numerical simulation is used to illustrate our conclusions.
Handling Orientation and Aspect Ratio of Modules in Electrostatics-Based Large Scale Fixed-Outline Floorplanning

In this paper, we present an improved electrostatics-based analytical method for fixed-outline floorplanning, which incorporates module rotation and sizing driven by wirelength. To accurately compute the density function after module rotation, we propose a novel density calculation algorithm based on line drawing and polygon clipping algorithms commonly used in computer graphics. By using this algorithm, we are able to accurately compute the density function after module rotation without adding any complexity. Moreover, we propose a module legalization algorithm by adding module sizing and module rotation after the existing constraint graph adjustment step. Furthermore, we adopt a linear programming to minimize wirelength to improve the quality of the results. Experimental results demonstrate that our floorplanning algorithm achieves at least 5.9% and 11% reduction in half-perimeter wirelength on the HB+ and ami49_x benchmarks, respectively, compared to state-of-the-art floorplanners.
Trajectory Tracking for Discrete-Time UAVs Formation Using Improved Model Predictive Control with Packet Dropout

This paper studies a model predictive tracking control scheme under packet dropout for multiple unmanned aerial vehicles (UAVs). To solve the tracking control problem of multiple UAVs cooperatives with packet dropout, we propose a dynamic tracking control strategy combined with an improved model predictive control (MPC) method. Firstly, a time-varying leader-follower cooperative tracking control model is established. Secondly, a tracking control scheme of the UAV using an improved MPC method with kinematics constraints is proposed, and a control input adjust term is designed to restrain the jump phenomenon of the control input. Finally, simulation results show the effectiveness of the proposed control strategy.
An Effective Task Node Selection and Energy Pre-allocation Algorithm in Wireless Rechargeable Sensor Networks

Wireless rechargeable sensor networks provide a feasible alternative for breaking through energy bottlenecks in wireless sensor networks. It adopts the limited applicable energy carried by the mobile charger to replenish energy for the task sensor nodes that are running out of energy. Therefore, how to select the task sensor nodes for charging and implement the energy allocation is a key issue to be addressed. To fully exploit the energy budget for wireless charging, a kind of novel task node selection and energy pre-allocation algorithm is proposed in this paper. It defines charging rewards as the charging urgency with a joint metric of temporal and spatial attributes of the individual task node. By the discrete binary particle swarm optimization, the charging rewards of the task nodes are maximized. Extensive simulation demonstrates that the proposed algorithm achieves better performance on charging scheduling.
An Moblie Edge Compting Based Train Control System With LQG Control

Nowadays, the construction and development of urban rail transit network is facing many challenges. With the increasing demand for transportation capacity, the shortage of on-board computing capacity has become one of the important problems. In recent years, edge computing (EC) has become a new solution. Through edge computing, complex on-board control computing tasks can be unloaded to the trackside computing server, and the computational power of on-board equipment can be liberated. The fifth generation (5G) mobile communication has the advantages of high capacity and high reliability, and combining 5G with edge intelligence (EI) can show lower delay and higher stability in train control systems. In this paper, we propose an autonomous train control system with edge intelligence and 5G.The system is based on Google Kubernetes high reliability edge computing platform to implement automatic train control algorithm, which is divided into infrastructure layer, edge computing layer and cloud service layer in function. The linear quadratic Gaussian control (LQG) algorithm is used to model the train autonomous control process, and the cloud safety computer is used to ensure the high reliability of the whole system without being affected by local faults. Extensive experimental results show that the train control system proposed by us based on 5G and EI has high operational reliability and data security, as well as low delay performance in the communication packet interval. Using this train control system can greatly improve the efficiency and safety of train operation, so as to improve the quality of train operation.
Convex quadratic bilevel programming problem based on the redesigned BAS algorithm and application to supply chain

In this paper, convex quadratic bilevel programming problem is studied based on the redesigned beetle antennae search algorithm and applied to the supply chain. Beforhand, the beetle antennae search algorithm is introduced and the convex quadratic bilevel programming problem is proposed. Subsequntly, in order to solve the convex quadratic bilevel programming problem, a redesigned beetle antennae search algorithm is designed. Then, an example is given to show the effectiveness of the redesigned beetle antennae search algorithm. Finally, our method is applied to a supply chain model and the effectiveness of our method is shown as the simulation results.
IIBLAST: Speeding Up Commercial FPGA Routing by Decoupling and Mitigating the Intra-CLB Bottleneck

We identified that in modern commercial FPGAs, routing signals from the general interconnect to the configurable logic blocks (CLBs) through a very sparse input interconnect block (IIB) represents a significant runtime bottleneck. This is despite academic research usually altogether neglecting the runtime of last-mile routing through the IIB. To alleviate this bottleneck, we combine computer-aided design (CAD) and FPGA architecture enhancements. We propose a multi-stage FPGA routing approach, based on the premise that once the signals are legally routed in general interconnect-only reaching the inputs of the IIB, but not the final targets-the remaining last-mile routing through the IIB can be completed efficiently and independently for each FPGA tile. Then, the final routing solution can simply be built by joining the previously obtained partial solutions. However, we observe that some properties of modern IIB architectures limit the success rate of the intra-CLB routing, creating the need for revisiting the routing in the general interconnect and inevitably impairing the multi-stage routing runtime gains. We show that an enhanced IIB architecture mitigates the issue at a minimal cost. With ISPD16 benchmarks and an FPGA architecture model closely resembling AMD UltraScale FPGAs, we demonstrate the dominant contribution of last-mile routing to the router's runtime. After applying our multi-stage routing approach and the proposed enhancements, we show that the observed bottleneck can be mitigated, resulting in 4.94× faster routing on average.
Furnace status recognition based on involution network and decoupling learning

With the continuous development of smart manufacturing, the use of machine learning methods in industrial processes has increased, and as a key part of hot rolling production, the heating furnace needs to be accurately judged in terms of its operating status. The sample characteristics of the different statuses of the furnace are too similar and the data are unbalanced, which makes it difficult to distinguish. In this paper, we propose a model that uses both involution and convolution, so that the model is able to improve the accuracy of recognition and the parameters can be reduced significantly. Moreover, we use decoupling learning to use different strategies in the data sampling and classifier parts to improve the model's ability to identify long-tail data. The experimental results show that the status-of-the-art performance can be achieved on the furnace dataset using the base network modified by involution under the decoupling learning strategy.
Using Network Analysis to Examine the Relationship between Specific Panic Symptoms and Heart Rate Variability

The purpose of this study is to examine the relationship between individual panic symptoms and heart rate variability (HRV). Participants were recruited from a psychiatric outpatient clinic. HRV indices were derived from electrocardiogram (ECG) records. Panic symptoms and anxiety were measured using the panic disorder distress scale (PDSS) and state-trait anxiety inventory (STAI). With these data, network analysis was conducted and the strongest node in the network was ‘PDSS 3 (anticipatory anxiety)’ and mean RR intervals. There were both positive and negative edges between HRV variables and psychological variables in the network. The relationship between variables revealed from the analysis is discussed.Clinical Relevance—The result of this study cast light on the specific mechanism of how panic symptoms are related to HRV measures which is an important indicator of autonomic nervous system activity
A Distributed Algorithm for Nonsmooth Resource Allocation Problems with Directed Networks

This paper studies a nonsmooth convex resource allocation problem subjects to local inequality constrains and global equality constraints. A distributed continuous-time algorithm is designed to solve the problem where each agent only knows the local non-smooth objective function and linear or nonlinear local constraints over a weight-balanced directed network. Through nonsmooth analysis and set-valued LaSalle invariance principle, the global convergence to the optimal solution is analyzed. Meanwhile, the distributed algorithm can converge to the optimal solution from any initial condition. To demonstrate the effectiveness of the proposed distributed algorithm, a numerical example is presented to demonstrate the effectiveness of the distributed algorithm.
Defect detection of automotive leather based on Nanodet-Plus

During the processing of automotive leather, various defects such as folding and oil stains can occur for several reasons. Therefore, it is essential to detect such defects before leather manufacturing to prevent them and enhance the quality of leather products. However, the current defect detection methods are not only expensive but also inefficient, indicating a need for a more efficient alternative. To address this problem, we constructed an Automotive-leather-defect dataset for vehicles based on collected leather defect samples. Subsequently, we proposed a defect detection method using the NanoDet-Plus neural network model, which can enhance the accuracy and speed of the detection process.
Multi-Objective Architecture Search and Optimization for Heterogeneous Neuromorphic Architecture

Neuro-inspired in-memory computing offers a promising solution to overcome the limitations of traditional von Neumann architectures by emulating brain activities. This approach takes advantage of parallel processing while minimizing power consumption and area overhead. However, maximizing the performance of neuromorphic hardware, particularly for increasingly deep and complex NN models, is a challenging task. Existing design space exploration methods focus primarily on layer placement and resource allocation, ignoring hardware-level configurations that directly influence performance, power, and area (PPA) trade-offs. Additionally, current tiled neuromorphic architectures lack support for size heterogeneity, making optimal resource utilization difficult. To address these challenges, we propose a multi-objective architecture search and optimization mechanism for neuromorphic architectures. Our approach introduces heterogeneous architectures with multiple tile/processing element/synaptic array sizes and provides a comprehensive end-to-end design automation tool to support them. We define a heterogeneous neuromorphic architecture, as exemplified by a “big-tile, little-tile” architecture with mesh interconnects. Our search mechanism expands the search space by considering candidates for both homogeneous and heterogeneous architectures and performs Pareto-front searches guided by user-defined weights or constraints on performance metrics. We also implement a hierarchical beam search technique to explore the vast search space of heterogeneous architecture candidates more effectively. Our mechanism identifies numerous heterogeneous architectures that outperform the baseline for different convolutional neural network (CNN) models. For EfficientNetB0, we achieve PPA improvements of 40.1%, 19.3%, and 4.4% over the baseline. Our tool is available at https://github.com/wntjd9805/hetero-neurosim-search.
Fractional Order Active Disturbance Rejection Controller for First Order Plus Time Delay Systems

This paper proposes a modified fractional order active disturbance rejection controller (MFOADRC) for the first order plus time delay systems. To improve the disturbance rejection ability, the model-based extended state observer with model informations is structured. The mathematical derivation and frequency-domain analysis verify that the proposed control system satisfies the separation principle. The foundational of separation principle and Bodes ideal transfer function plus time delay generate the fractional order feedback controller, which consists of a fractional order operator and a PI controller. A frequency-domain tuning method based on gain crossover frequency and phase margin is given to calculate the fractional order controller (FOC). A systematic and complete frequency-domain analysis method is introduced to reveal the disturbance rejection and noise suppression abilities of MESO, open-loop frequency characteristic, closed-loop frequency characteristic, disturbance rejection and noise suppression abilities of closed-loop system, and robustness to plant gain variations. Simulation illustrations for lag-dominant plant, lag-delay balanced plant and delay-dominant plant, using the proposed MFOADRC are presented to demonstrate the significant performance improvement, especially for ramp load disturbances rejection ability, over timedelay active disturbance rejection controller and fractional filter fractional order PID.
VTMonitor: Tidal Volume Estimation Using Earbuds

Tidal volume (VT) is defined as the volume of inhaled and exhaled air during normal breath, which is crucial for maintaining respiratory function, such as adequate air exchange in and out of the body. However, existing estimation methods either require complex setups or involve inconvenient and expensive devices, such as spirometer and chestband. Thus, leveraging the advanced artificial intelligence (AI) and wearable devices, we aim to develop a novel, accessible and convenient approach to estimate tidal volume. In this study, we propose the VTMonitor system, which utilizes consumer earbuds’ motion sensor data to estimate the tidal volume. We conducted two experiments, collecting data either in lab or at home. After analyzing the data, our VTMonitor system is effective in measuring the tidal volume.