Miracle-dz/UniFAR-FTUs huggingface dataset:
Significance of variation in wildlife red deer carcass yields

ABSTRACT Deer hunting has traditionally had great relevance due to the diversity of resources obtained from it, such as meat. Our aim has been to analyse the total post-mortem weight, dressed carcass weight and carcass yield, as well as their variations as a function of years, sex, age and hunting season (autumn vs. winter). Also, predictive equations for carcass weight from total weight were performed. A total of 947 red deer (Cervus elaphus hispanicus) of both sexes, hunted from 1989 to 1993 in the Quintos de Mora National Reserve, Spain, were analysed. Seasonal and interannual differences were detected in all weight measures and carcass yield, conditioned by sex, age and climatic factors such as temperature. In general, to obtain a higher meat yield deer extractions should be carried out in autumn. In addition, carcass weight variation (measured on cold carcasses dressed without head and feet, keeping skin, lateral diaphragm portions, fat deposits and legs) can be considered a good indicator of body condition that integrates seasonal nutritional gains and/or losses and long-term nutritional legacies. This index provides information on the animal's natural history and ecological environment conditions, making its standardized record an essential tool for monitoring wild game populations.
Estrogen Reduces Cardiac Injury and Expression of β1-Adrenoceptor upon Ischemic Insult in the Rat Heart

To test the hypothesis that estrogen confers cardioprotection by suppressing the expression of β-adrenoceptor (β-AR), we first correlated the infarct size in response to ischemic insult and β-AR stimulation with the expression of β1-AR in sham, ovariectomized (Ovx) and estrogen replaced (Ovx + E2) rats. When β-AR is being activated during ischemia, the infarct size was significantly greater in Ovx than in the sham and Ovx + E2 rats. There is a negative correlation between the infarct size and the expression level of β1-AR as revealed by Western blotting and supported by binding analysis. Incubation of ventricular myocytes from Ovx rats with estrogen at 10-9 M for 24 and 48 h, but not 12 h, significantly reduced lactate dehydrogenase release when the myocytes are subjected to simulated ischemia. The cardioprotective effect of 24 h estrogen incubation was accompanied by a reduction in the protein expression level of β1-AR, which is estrogen receptor-dependent, whereas the lack of protection of 12-h estrogen incubation was not accompanied by any alterations in the expression level of β1–AR. Together, the result from present study suggested that it is most likely that the cardioprotective effect of long-term estrogen replacement is due to suppressing the enhanced expression of cardiac β1-AR in the Ovx rats, which in turn reduces cardiac injury when β-AR is activated by sympathetic hyperactivity during ischemia. Therefore, suppression of the enhanced expression of cardiac β1-AR in Ovx rats represents a novel cardioprotective mechanism of estrogen replacement therapy.
Can EVI-derived land-surface phenology be used as a surrogate for phenology of canopy photosynthesis?

Canopy phenology plays a prominent role in determining the timing and magnitude of carbon uptake by many ecosystems. The Moderate Resolution Imaging Spectroradiometer (MODIS) Global Land Cover Dynamics product developed from the enhanced vegetation index (EVI) provides broad spatial and temporal coverage of land-surface phenology (LSP), and may serve as a useful proxy for the phenology of canopy photosynthesis. Here, we compare the MODIS growing season start and end dates (SOS and EOS) with the seasonal phenology of canopy photosynthesis estimated using the eddy covariance approach. Using 153 site-years obtained from the Ameriflux database, we calculated the SOS and EOS of gross primary production (GPP) and canopy photosynthesis capacity (CPC) for seven different boreal and temperate vegetation types. CPC is GPP at maximum radiation, estimated by fitting half-hourly GPP and radiation to a rectangular hyperbolic function. We found large mean absolute differences of up to 53 days, depending on vegetation type, between the phenology of canopy development and photosynthesis, indicating that remotely sensed LSP is not a robust surrogate of seasonal changes in GPP, particularly for evergreen needleleaf forests. This limited correspondence of ecosystem carbon uptake with the MODIS LSP product points to the need for improved remotely sensed proxies of GPP phenology.
PHYSICAL HYDROLOGY AND THE EFFECTS OF FOREST HARVESTING IN THE PACIFIC NORTHWEST: A REVIEW 1

The Pacific Northwest encompasses a range of hydrologic regimes that can be broadly characterized as either coastal (where rain and rain on snow are dominant) or interior (where snowmelt is dominant). Forest harvesting generally increases the fraction of precipitation that is available to become streamflow, increases rates of snowmelt, and modifies the runoff pathways by which water flows to the stream channel. Harvesting may potentially decrease the magnitude of hyporheic exchange flow through increases in fine sediment and clogging of bed materials and through changes in channel morphology, although the ecological consequences of these changes are unclear. In small headwater catchments, forest harvesting generally increases annual runoff and peak flows and reduces the severity of low flows, but exceptions have been observed for each effect. Low flows appear to be more sensitive to transpiration from vegetation in the riparian zone than in the rest of the catchment. Although it appears that harvesting increased only the more frequent, geomorphically benign peak flows in several studies, in others the treatment effect increased with return period. Recovery to pre‐harvest conditions appeared to occur within about 10 to 20 years in some coastal catchments but may take many decades in mountainous, snow dominated catchments.
Effects of Facial Expressions and Gestures on the Trustworthiness of a Person

Trust is a fundamental element in human relationships, playing a crucial role in decision-making processes. Despite its significance, numerous dimensions of perceived trustworthiness remain unexplored and warrant further investigation. Previous literature has highlighted the influence of emotions and sentiments on how individuals perceive trustworthiness, with visual, vocal, and behavioral cues serving as essential markers. This preliminary study aims to expand the existing knowledge in the field by investigating trustworthiness traits manifested through facial and gesture expressions in emotional videos across diverse cultural contexts. To address this objective, an annotation platform was developed to collect annotation data using the benchmarked One-Minute-Gradual Emotion (OMG) audiovisual corpus, enabling the annotation of actors’ perceived trustworthiness levels alongside other inquiries related to emotional state, gesture, activeness, comfort, and speech integrity. The findings of this study demonstrate a positive correlation between higher levels of speaker activity, faster gesturing, and a relaxed demeanor with increased levels of trust gained from audiences. The proposal presented in this paper holds potential for future studies focused on trustworthiness annotation, facilitating the measurement of trust-related features. Moreover, this research serves as a critical step towards understanding the foundations of trustworthiness in the development of synthetic agents that require perceived trustworthiness, particularly in domains involving negotiations or emergency situations where rapid data collection plays a pivotal role in saving lives.
Full-Polarization Analysis for Chang'E-4 Lunar Penetrating Radar Data Acquired During the Rover Turnings

The lunar penetrating radar(LPR) onboard the Chang E-4 (CE-4) probe provides a unique opportunity to map the shallow lunar structures on the far side of the Moon with high resolution. However, due to the timed-triggered acquisition mode, the LPR kept acquiring data when the rover was turning and stopped. In this study, we innovatively extracted the polarimetric scattering matrix from the data acquired at different orientations at the same location during rover turning. Subsequently, we quantify the regolith maturity within 24 m depth based on the Freeman decomposition results. The results provide new insights of the evolution of lunar regolith at landing site.
Economic Dispatching of Virtual Power Plant Considering the Shared Energy Storage

In the existing research on the economic dispatch of virtual power plants, there is little consideration of the cost of electricity on the user side, and in order to ensure its own benefits when interacting with the power grid, there will also be cases where the demand for peak-shaving and valley-filling on the power grid side is ignored. In order to solve the above problems, this paper proposes a new virtual power plant operation mode. Considering the advantages of shared energy storage such as good flexibility, good economic benefits, convenience for multi-party dispatching and the potential of residents’ demand-side response, a shared storage system with power grid, virtual power plant and users is established, and a multi-objective optimal dispatch model with users’ electricity costs and benefits of virtual power plant is constructed, the weight coefficient method is used to transform the multi-objective into a single-objective optimization problem, and proposes a new benefit distribution mechanism to minimize the user’s electricity cost while benefiting from the virtual power plant. Using the IBM solver Cplex to solve the problem, the simulation results show that the virtual power plant operation mode in which users participate in dispatching through shared energy storage can achieve a win-win situation for the energy supply side, energy transmission side, and energy consumption side, which is conducive to promoting the diversified development of energy storage and optimizing the structure of electric energy usage is instructive.
Highly Generalized Sleep Posture Recognition Using FMCW Radar

Identifying users’ sleep posture is significant in reducing sleep apnea events and avoiding postoperative pressure sores. Past studies have identified sleep posture by installing cameras, installing sensors on mattresses, or letting users wear wearable devices. However, the camera-based method is usually affected by light intensity or coverage and can invade users’ privacy. The method based on contact sensors will affect the comfort of users’ sleep. The use of radar can solve the problem of cameras and contact sensors, but previous studies need to collect data from new users for calibration to maintain high performance. In this work, we use FMCW radar to estimate the position image of the user in space. We propose a multi-task learning sleep posture recognition model based on a neural network, which uses the radar position image to estimate the user’s sleep posture. In addition, we use mix-up data enhancement to improve the model’s generalization. We collected data from 17 subjects to train and test our model. The proposed method can achieve 0.935 sleep posture recognition F1 score without collecting new user calibration data.
Understanding Privacy Risks versus Predictive Benefits in Wearable Sensor-Based Digital Phenotyping: A Quantitative Cost-Benefit Analysis

Wearable devices with embedded sensors can provide personalized healthcare and wellness benefits in digital phenotyping and adaptive interventions. However, the collection, storage, and transmission of biometric data (including processed features rather than raw signals) from these devices pose significant privacy concerns. This quantitative, data-driven study examines the privacy risks associated with wearable-based digital phenotyping practices, with a focus on user reidentification (ReID), which is the process of identifying participants’ IDs from deidentified digital phenotyping datasets. We propose a machine-learning-based computational pipeline to evaluate and quantify model outcomes under various configurations, such as modality inclusion, window length, and feature type and format, to investigate the factors influencing ReID risks and their predictive trade-offs. This pipeline leverages features extracted from three wearable sensors, resulting in up to 68.43% accuracy in ReID risk for a sample size of N=45 socially anxious participants based on only descriptive features of 10-second observations. Additionally, we explore the trade-offs between privacy risks and predictive benefits by adjusting various settings (e.g., the ways to process extracted features). Our findings highlight the importance of privacy in digital phenotyping and suggest potential future directions.
Manipulator Reinforcement Learning with Mask Processing Based on Residual Network

In the field of intelligent manufacturing, manipulators are expected to have a higher level of learning ability to master skills. In this paper, the method of visual support is adopted to enable the UR5 manipulator to learn the skills of grasping object blocks in a random environment. The deep reinforcement learning algorithm can effectively learn visual support mapping to guide the grasping action of the manipulator. To verify the designed algorithm, a manipulator grasping system and test scenario are built on the CoppeliaSim simulation platform. The simulation results show that the deep reinforcement learning algorithm combined with the residual network (ResNet) obtains good success rate for the manipulator to grasp the target block. The experimental results show that the method significantly improves the grasping efficiency of the manipulator.
MPC-Based Motion Planning and Tracking Control for Autonomous Underwater Vehicles

This paper proposes a motion planning and tracking control method based on model predictive control (MPC) for autonomous underwater vehicles (AUVs). Traditional planning algorithms do not smoothly search for paths on grid maps and fail to satisfy AUV’s kinematic constraints. To address this, a new objective function with an obstacle avoidance function is designed to reduce excessive avoidance in traditional algorithms. The motion planning controller is designed to find a kinematically feasible obstacle avoidance trajectory by solving a nonlinear optimization problem with constraints based on AUV state information, obstacle information, and reference target information. The MPC trajectory tracking controller combines the planned trajectory to track it. Simulation results validate the effectiveness of the proposed method.
Quality of life measurement: bibliographic study of patient assessed health outcome measures

Abstract Objectives: To assess the growth of quality of life measures and to examine the availability of measures across specialties. Design: Systematic searches of electronic databases to identify developmental and evaluative work relating to health outcome measures assessed by patients. Main outcome measures: Types of measures: disease or population specific, dimension specific, generic, individualised, and utility. Specialties in which measures have been developed and evaluated. Results: 3921 reports that described the development and evaluation of patient assessed measures met the inclusion criteria. Of those that were classifiable, 1819 (46%) were disease or population specific, 865 (22%) were generic, 690 (18%) were dimension specific, 409 (10%) were utility, and 62 (1%) were individualised measures. During 1990-9 the number of new reports of development and evaluation rose from 144 to 650 per year. Reports of disease specific measures rose exponentially. Over 30% of evaluations were in cancer, rheumatology and musculoskeletal disorders, and older people's health. The generic measures—SF-36, sickness impact profile, and Nottingham health profile—accounted for 612 (16%) reports. Conclusions: In some specialties there are numerous measures of quality of life and little standardisation. Primary research through the concurrent evaluation of measures and secondary research through structured reviews of measures are prerequisites for standardisation. Recommendations for the selection of patient assessed measures of health outcome are needed.
A Price-Based Bi-Level Scheduling Optimization Method for Power Systems and EA Enterprises

As a typical industrial load, electrolytic aluminum (EA) load is one of the most important demand-side resources. EA load participating in demand response (DR) is beneficial for EA industries to reduce energy bills and carbon emissions, and can also be coordinated with thermal units to cope with the uncertainties of renewable energy generations. Therefore, the research on the modeling and dispatch method of EA load for DR is of great significance to realize the economic operation of power systems. In this paper, a price-based bi-level scheduling optimization method for power systems and EA enterprises is proposed. The proposed model considers the power system and the EA enterprise as independent stakeholders, where the dynamic compensation prices are introduced to guide the DR of EA load. The formulated bi-level dispatch problem is addressed by the single-level reduction and the convexization techniques. Simulation results demonstrate the effectiveness of the proposed model. Corresponding to different aluminum profit per ton and carbon prices, the compensation price can be adjusted accordingly, which demonstrates the applicability of the proposed bi-level optimization model.
Optimization-Based Tracking Method for Active Navigation in Orthopedic Surgeries

Intraoperative optical tracking system (OTS) plays an essential role to measure the position and orientation of the surgical tools via passive tools during surgery operations. Due to the limited measurement volume (MV), the tracked tools might probably move out of the MV during the surgery, causing navigation failure. In addition, the accuracy of the OTS is influenced by several factors, i.e., working distance and the relative orientation of the tracked tool. If the surgical tool is near the border of the MV, the tracking accuracy may degrade and the risk of a missing tool also increases. To address this challenge, this paper proposes an active navigation system where a navigation robot is utilized to adjust the pose of the OTS such that the tracked tools are kept near the center of the MV, maintaining the tracking accuracy. A distance margin is proposed to quantify the degree of danger of losing the tracked tools. Subsequently, a real-time optimization tracking method is formulated to plan the next pose of the OTS until the distance margin is lower than the threshold. Ultimately, abundant simulations are performed on an active navigation system to demonstrate the effectiveness of the proposed optimization tracking method.
Towards Analysis-aware EEG Compression in Wearable Computing

EEG signals are known for their high temporal resolution and data volume, posing challenges in storage, transmission, and analysis. With the increasing prevalence of wearable mobile BCI devices replacing large medical-grade recording devices, the need for efficient on-device processing and wireless transmission has grown. We propose a novel analyzability-aware EEG compression scheme optimized for machine consumption, facilitating efficient, real-time wearable processing while allowing reconstruction for detailed analysis and human understanding. Our method employs a wavelet-based compression technique that considers the Compression Factor, reconstruction error, and the impact of compression on on-device processing and data transmission. Experimental results using publicly available datasets demonstrate that machine learning models trained on compressed data using our method achieve performance comparable to the winning solution of BCI Competition II. Moreover, our approach offers multiple compression configurations suitable for various wearable computing scenarios.
Voltage Recovery and Current Sharing of DC Microgrids With Uncertain Constant Power Loads Using Neural Networks

In this paper, we investigate distributed secondary control for DC microgrids with uncertain constant power load (CPL). First, a model of DC microgrids is established, where a controllable current source is introduced to be an control input and uncertain CPLs are regarded as unknown nonlinear dynamics. Based on the established model, an adaptive neural network (NN) controller is designed to achieve both voltage recovery and current sharing. The approximation property of radial basis function neural networks (RBFNNs) is used to neutralize the uncertain nonlinear dynamics in DC microgrids. It is proved that our proposed controller can guarantee the stability of DC microgrids. Simulations are performed to demonstrate the effectiveness and validity of the proposed method.
Revisiting PIC Method of Remote Respiratory Rate Measurement for Enhanced Robustness

Telehealth has revolutionized the provision of healthcare services over the past decade. In this context, various remote respiratory rate measurement methods were developed, including pixel intensity changes tracking. However, limited advancements in the intermediate signal processing stages of a widely adopted conventional approach have hindered its robust performance in real-life applications. This work proposes an innovative signal processing pipeline to the common method by introducing a new criterion for best motion signal selection based on normalized autocorrelation. By properly utilizing this and previous criteria, signals SNR was increased by 10%. Additionally, applied problems of signals grouping were investigated and solved. Evaluation results on COHFACE dataset showed 84.2% Success Rate 1, which is by 3.8% better than previous state-of-the-art method.
Control Infrastructure for Near-Term Long-Chain QCCD

Here, we propose a modular framework for representing and generating voltage solutions for near-term ion trap devices. Voltage graphs are separated into individual segments interconnected by shuttling paths through junctions managed by a Finite State Machine (FSM). This representation then maps onto a modular hardware architecture capable of driving up to 24 96-channel segments at 10MSPS with 10,000 voltage lines per channel. Multi-channel synchronous DACs denoted as “Arbitrary Path Generators” (APG) store the electrode voltages corresponding to each voltage line in a lookup table and execute path traversal sequences. A segment controller then directs the APG to execute a specific shuttling sequence as defined by the graph. Lastly, a trap controller is used to coordinate actions among multiple segments. We also explore the efficient generation and simulation of trap solutions for long-chain systems.
Vehicle Clustering Algorithm based on Reliable Node Screening

In order to solve the problem of unstable communication between high-speed moving vehicles in the Internet of Vehicles, a clustering algorithm based on reliable node screening was proposed in this paper. The algorithm screened the neighbor nodes according to the vehicle direction and the vehicle link survival time, and formed a list of reliable nodes. Furthermore, a cluster head election method was proposed based on node reliability degree. Through NS3 and Sumo co-simulation, it is found that the proposed algorithm has better cluster stability compared with the traditional algorithm in the environment of high speed and high vehicle density.