Energy Ratio Research Articles

Numerical investigation and optimization of the ammonia/diesel dual fuel engine combustion under high ammonia substitution ratio

This study investigated the effects of initial temperature, equivalence ratio, and diesel injection timing on engine combustion and emission characteristics at high ammonia substitution ratios. Increased compression temperature and pressure significantly reduce ignition delay, enhance combustion speed and efficiency, and decrease N2O and unburned NH3 emissions. A strong correlation exists between the amount of N2O produced and the mass of unburned NH3 when ammonia combustion efficiency is high. The N2O distribution is concentrated near the cylinder walls and the bottom surface of the piston platform, in areas with high concentrations of unburned NH3. As the equivalence ratio increases from 0.6 to 0.75, flame propagation speed and indicated thermal efficiency (ITE) increase, while NOx, N2O, and unburned NH3 emissions decrease. The combustion performance and emissions were optimized by advancing the diesel injection timing and increasing the equivalence ratio to accelerate the combustion speed. This adjustment increases ITE to 47.6% at an 80% ammonia energy ratio. Post-optimization results show a reduction in unburned NH3 emissions from 51.7 g/kW·h to 5.9 g/kW·h and a decrease in N2O emissions from 0.930 g/kW·h to 0.370 g/kW·h, culminating in a 60.4% reduction in greenhouse gas (GHG) emissions.

A Methodology for Detecting Loss of Support Under PCC Pavement Based on Real-Time Structural Acceleration Response

Loss of support is a concealed distress in PCC pavements, weakening the support condition under PCC slabs and causing other distresses such as cracks, which accelerate the pavement deterioration. Currently, the traditional loss of support detection method uses the falling weight deflectometer (FWD) to obtain maximum deflection response, which requires traffic closure and makes it impossible to conduct real-time monitoring. To realize real-time dynamic health monitoring of PCC pavement, this study proposes a methodology for detecting loss of support under PCC pavement based on real-time structural acceleration response. Specifically, this study constructed PCC pavements without loss of support, with loss of support at the slab edge, and with loss of support at the slab center. The slab corner and center were loaded and monitored, where loss of support easily occurs. Acceleration signals were collected using self-developed MEMS acceleration sensors suitable for monitoring PCC pavements. Analysis of the time-domain signals and frequency spectrums shows that both the maximum acceleration response and the main frequency at the loss of support region would significantly increase when the loss of support region is loaded. Two critical values are proposed, including the amplitude ratio of acceleration signals at the slab corner and center [Formula: see text] and the ratio of signal energy within the high-frequency range to that within the low-frequency range (ER). The results indicate that by deploying sensors at the slab corner and center, the loss of support condition can be evaluated based on [Formula: see text] and ER when the slab corner is loaded. Compared to traditional deflection detection methods, the proposed methodology offers a better integral structural monitoring for detecting loss of support. The findings can be used for future detection of support loss under PCC pavements subjected to traffic load, enabling real-time monitoring and maintenance guidance for PCC pavements that have been open to traffic.

Priority/Demand-Based Resource Management with Intelligent O-RAN for Energy-Aware Industrial Internet of Things

The last decade has witnessed the explosive growth of the internet of things (IoT), demonstrating the utilization of ubiquitous sensing and computation services. Hence, the industrial IoT (IIoT) is integrated into IoT devices. IIoT is concerned with the limitation of computation and battery life. Therefore, mobile edge computing (MEC) is a paradigm that enables the proliferation of resource computing and reduces network communication latency to realize the IIoT perspective. Furthermore, an open radio access network (O-RAN) is a new architecture that adopts a MEC server to offer a provisioning framework to address energy efficiency and reduce the congestion window of IIoT. However, dynamic resource computation and continuity of task generation by IIoT lead to challenges in management and orchestration (MANO) and energy efficiency. In this article, we aim to investigate the dynamic and priority of resource management on demand. Additionally, to minimize the long-term average delay and computation resource-intensive tasks, the Markov decision problem (MDP) is conducted to solve this problem. Hence, deep reinforcement learning (DRL) is conducted to address the optimal handling policy for MEC-enabled O-RAN architectures. In this study, MDP-assisted deep q-network-based priority/demanding resource management, namely DQG-PD, has been investigated in optimizing resource management. The DQG-PD algorithm aims to solve resource management and energy efficiency in IIoT devices, which demonstrates that exploiting the deep Q-network (DQN) jointly optimizes computation and resource utilization of energy for each service request. Hence, DQN is divided into online and target networks to better adapt to a dynamic IIoT environment. Finally, our experiment shows that our work can outperform reference schemes in terms of resources, cost, energy, reliability, and average service completion ratio.

Energetics of particle-size segregation

We introduce a continuum framework for the energetics of particle-size segregation in bidisperse granular flows. Building on continuum segregation equations and a recent segregation flux model, the proposed framework offers general analytical expressions to study the physics of granular flows from a mechanical energy perspective. We demonstrate the framework's applicability by examining the energetics of shear-driven granular flows. Numerical experiments with varying frictional coefficients and particle-size ratios reveal two distinct phases in the energetics, marked by the separate onset of particle segregation and diffusive remixing. Furthermore, our numerical simulations alongside previous experimental results show that the bulk Richardson number $Ri$ , defined as the potential energy to kinetic energy ratio at steady state, follows the scaling relationship $Ri\equiv \hat {E}^{(s)}_{gp} / \hat {E}^{(s)}_{k} \propto Pe^{-1/2}_{sr}$ for $0.1 \leq Ri\leq ~10^{3}$ and $10^{-4} \leq Pe_{sr} \leq ~300$ , the segregation–rheology Péclet number. Finally, we present a Péclet-number-dependent theoretical expression for the degree of mixing (or segregation), validated by the compiled numerical and experimental dataset. Our findings hint that the bulk segregation–mixing state can be predicted and controlled using the segregation Péclet number $Pe$ and $Pe_{sr}$ , both determined from known system parameters, providing an instrumental tool for engineering and geophysical applications.

Breakage dynamics and scaling of wet aggregates of rigid particles

The mechanical behaviour of wet particle aggregates is crucial in many granular processes such as wet granulation and soil degradation. However, the interplay of capillary and viscous forces for aggregate stability and breakage have remained elusive due to the complexity of granular dynamics. We use particle dynamics simulations to analyse the deformation and breakage of wet aggregates colliding with a flat wall. The aggregates are composed of spherical particles and the effect of liquid bonds is modelled through capillary and lubrication forces acting between particles. We perform an extensive parametric study by varying surface tension, impact velocity and liquid viscosity in a broad range of values. We show that when lubrication force is neglected, aggregate breakage is fully controlled by the reduced kinetic energy $\xi$ , defined as the ratio of incident kinetic energy to the initial capillary energy. At low values of $\xi$ , the aggregate deforms without breakage due to inelastic energy loss induced by rearrangements and loss of capillary bonds, whereas above a critical value of $\xi$ it breaks into smaller aggregates due to the transfer of kinetic energy from aggregate to fragments. In the presence of lubrication forces, the crossover from capillary to viscous regime is controlled by the capillary number, defined as the ratio of viscous dissipation to capillary energy. We find that the critical value of $\xi$ for aggregate breakage in the viscous regime increases as a power law with capillary number while the effective restitution coefficient follows the same trend as in the capillary regime.

Underwater acoustic multi-target recognition based on channel attention mechanism

The recognition of ship-radiated noise presents a critical challenge in underwater acoustic target recognition, primarily due to interference from multiple vessels in real-world settings. This interference produces complex signals that contain multiple and overlapping targets. Despite the practical relevance of multi-target recognition, most existing research in underwater acoustic target recognition has concentrated on single-target recognition. To address the multi-target recognition problem, this paper proposes a deep learning (DL) framework that combines residual neural network (ResNet) architecture with channel attention modules. These modules excel in selectively filtering channel information within feature maps, amplifying informative features while mitigating noise interference. Thus, it enables accurate recognize both the quantity and types of targets within single-channel acoustic signals. In addition, this paper explores the effects of signal duration and energy ratios between different targets in mixed signals on recognition performance. Experimental results reveal that shorter signal durations hinder the extraction of discriminative features, reducing recognition accuracy. Notably, for signal durations of 5 s, the proposed model achieves a recognition accuracy of 97.16% on the dataset with energy ratios between −5 dB and 5 dB, closely approaching the accuracy of 98.09% obtained at a dataset with energy ratio of 0 dB.

Visualization of ammonia-methanol solution combustion under spark and passive jet ignition mode in an optically-accessible engine

Ammonia-methanol (A-M) solution is a promising carbon-neutral fuel for internal combustion engines. In this paper, the in-cylinder flame imaging and measurements of A-M solution under spark ignition (SI) and passive jet ignition (JI) mode were conducted, based on an optically-accessible engine. For 7 mol/L A-M solution combustion, the flame shows brown to purple color under stoichiometric condition and orange color under lean conditions. An increased burning rate is obtained under JI mode, with higher flame speed, shorter ignition delay and combustion duration at different excess air ratios (λ = 1∼1.6) than that in SI mode. It also allows a retarded spark timing for higher power output and thermal efficiency. As ammonia energy ratio increases from 13.7 % to 50 %, the flame development becomes slower under SI mode, while JI mode shows limited improvement of combustion. For pre-chamber nozzle designs, an area-to-volume ratio larger than 0.025 cm−1 is conducive to ignition and combustion for 7 mol/L A-M solution. Besides, the effects of slot, unequal and jet-crossing nozzle were also evaluated. Overall, this work provides the basic insights of flame characteristics and demonstrates the combustion accelerating effect of JI method for ammonia-methanol fueled engines.

Optimizing spectral phase transfer in four-wave mixing with gas-filled capillaries

Four-wave mixing (FWM) in gas-filled hollow-core capillaries, a nonlinear optical process that mixes signal and pump photon frequencies to generate idler frequency photons, offers a method for precise spectral phase transfer from signal to idler at ultrashort timescales and extreme powers. However, this regime is challenged by competing linear and nonlinear dynamics, leading to significant trade-offs between spectral phase transfer and conversion efficiency. Our computational investigation focuses on the upconversion of femtosecond pulses from the infrared (IR) to the ultraviolet (UV), a range notoriously difficult to manipulate. We explore an intermediate energy regime that strikes an optimal balance between FWM-mediated phase-transfer fidelity and nonlinear conversion efficiency. By adjusting the energy ratios and spectral phase profiles of the input signal, we achieve conversion efficiencies of approximately 5-15% while maintaining an effective quasi-linear spectral phase transfer. These findings will contribute to establishing first-principles and scaling laws essential for applications such as high-precision imaging, spectroscopy, quantum transduction, and distributed entangled interconnects, facilitating advanced control of ultrafast photonic and electronic wavepackets in quantum materials with unprecedented spatial and temporal precision.

Testing of a multi-energy complementary absorption heat pump prototype for combined cooling and heating with large temperature range from − 20 to 90 °C

In the process of carbon neutrality, the comprehensive utilization of clean and renewable energy, including solar, geothermal and biomass, is a potential solution to the low-carbon cooling and heating in distributed areas with weak power grids, such as countryside and suburban. In this paper, an absorption heat pump with multi-energy complementary was built to provide combined cooling and heating. Solar energy was collected through an evacuated tube collector using heat conduction oil, and a gas boiler was adopted to further heat the oil and balance the solar thermal fluctuations. Heat collected in the oil circulation was used to drive an ammonia-water absorption heat pump. A control strategy was proposed to achieve stable energy supply under different weather condition, and efficient operation in wide temperature zone. Environmental test of the prototype was performed in Jinan. The results showed that the prototype ran stably to provide 5-15 kW cooling at −20 to 10 °C, and 20-35 kW heating at 40 to 90 °C, with solar thermal ratio of 20–35 % in different weather conditions, and the renewable energy ratio in heating mode could exceed 55 % through further recovery of ambient heat. Furthermore, the COP for cooling reached 0.30–0.43 at −20 °C cold supply, and 0.70–0.78 at 7 °C cold supply, with cooling water temperatures varied from 30 to 20 °C; and the COP of heating reached 1.40–1.90 at 45 °C heat supply, and 1.35–1.56 at 80 °C heat supply, with evaporation temperature varied from −15 to 20 °C. Results demonstrated that the proposed prototype has significant energy and carbon reduction potential, and is a solution for combined cooling and heating in distributed areas.

A light-temperature neuron and its adaptive regulation

Abstract The appropriate firing modes for a neuron can be excited under the external stimulus. From the viewpoint of physical, the intrinsic biophysical effects, functional encoding, and the mechanism for the transcription of external signals play an extremely important role in building reliable neuron models. In this paper, a light-temperature neuron model is proposed by connecting a phototube and a thermistor into a nonlinear circuit for investigating the information encoding and responses of neurons under the external optical signals and temperature signals. In this neuron model, a phototube is used to encode external light signals, similar to artificial eyes, and a thermistor can encode temperature intensity. Furthermore, the Hamilton energy (HE) function of neurons is calculated based on the Helmholtz’s theorem, and a self-regulation method is designed by applying the ratio of electric field energy to magnetic field energy to estimate the self-regulation of neurons. The results show that the proposed neuron can reproduce the main characteristics of biological neurons by adjusting the external stimulus. The double coherence resonance is induced under noise temperature. These results could be helpful for researching the collective behaviors in functional neural networks.

Investigating the impacts of charge composition and temperature on ammonia/hydrogen combustion in a heavy-duty spark-ignition engine

Ammonia, as a hydrogen carrier with mature production technology and convenient storage, has become one of the most promising zero carbon fuels in recent years. The use of ammonia/hydrogen mixture fuel in spark ignition (SI) engines has drawn significant attentions since it solves the problems of low flame speed, high ignition energy requirement and narrow flammable range of pure ammonia. In this study, the combustion and emission processes of an ammonia/hydrogen port fuel injection (PFI) engine at high load operation are numerically analyzed to investigate the effects of intake hydrogen energy ratio (HER), equivalence ratio (φ), intake temperature and combustion chamber wall temperature on energy distribution and pollutants. The results indicate that under the same HER of 25%, the lean-burned mode provides favorable thermal efficiency compared to stoichiometric mode due to reduced combustion and wall heat loss. However, lower cylinder temperature at lean condition inhibits the participation of NH3 in the reduction reactions and the consumption of N2O, increasing the residuals of both pollutants. The NOx emission is promoted by excessive O radicals at lean conditions, and the pathways of fuel NOx and thermal NOx are also discussed using an isotope labeling method. At stoichiometric mode, increasing fuel HER (10%–25%) only has minor impacts on improving thermal efficiency, but can promote the consumption of NH3 and N2O by increasing H radicals and cylinder temperature. The study also shows that optimizing the intake and wall temperatures can effectively reduce NH3 and N2O emissions by 87.5% and 71.7%, respectively, while slightly reducing NOx.

Energy Budgeting of Different Cropping Sequences in the Indian Upper Gangetic Plains

Background: This study is meant to examine the energy requirement and energy input- output of different cropping sequences .The crucial objective to conduct experiment to understand energy efficiency using inputs and outputs which were disbursed in cropping sequences. Hence, it is need of the hour to identify the most remunerative and cost effective cropping sequence with high energy efficient for UGP of India. Methods: This study was carried out at the research farm of ICAR-Indian Institute of Farming Systems Research, Modipuram, during 2017-2021.The divergent cropping sequences viz. sugarcane-ratoon-wheat (CS1); rice-wheat-dhaincha (CS2); pigeonpea +maize-chickpea-okra (CS3); maize-berseem-black gram (CS4); sorghum-mustard-green gram (CS5) and Napier+cowpea/berseem (CS6) were compared in reference to curtail higher energy inputs through selected alternate cropping sequences. The obtained energy values were calculated by multiplying the amount of inputs and outputs by using energy conversion factors. Result: Maximum inputs energy consumed by sugarcane crop alone (33.14´103 MJ ha-1). Results shown that irrigation, seed, fertilizers and diesel required higher energy for the completion of cultural operations. However, higher inputs energy was used in irrigation followed by seed and fertilizers, respectively. In regard to per cent energy intake through inputs, the highest energy spent was for irrigation (35.30 MJ ha-1) and fertilizer (23.80 MJ ha-1).The wheat equivalent yield was higher in sugarcane-ratoon-wheat (125.58 t ha-1). Maximum output energy was with above system (596.70×103 MJ ha-1). Highest net energy returns was counted with sugarcane-ratoon-wheat (549.37´103 MJ ha-1), energy ratio (12.60) and energy profitability (11.60). Indeed, energy efficiency was highest in same system (1657.50) followed by maize-berseem-black gram (1421.96).

Development of wind power plants to increase the fulfilment ratio of renewable energy in Taiwan using system dynamics modelling

ABSTRACT This research aims to improve wind energy development by addressing environmental challenges and market dynamics. To achieve this, a system dynamics model was used to analyze the complex relationships between various factors, including wind energy production, electricity demand, costs, and government policies. The study focuses on increasing the fulfilment ratio and reducing the price of wind energy. By simulating different scenarios, researchers explored the potential impact of expanding wind farm capacity through the addition of onshore and offshore turbines. The research also investigates the role of government incentives, particularly the feed-in tariff (FIT), in stimulating investment in wind energy. The findings suggest that a higher FIT can significantly boost profitability, attract more investment, and accelerate the expansion of wind farm capacity. The optimal FIT level of 5.5 NTD/KWh is projected to lead to a 6.7% annual growth rate and substantial profits by 2040.

Association of Low Protein-to-Carbohydrate Energy Ratio with Cognitive Impairment in Elderly Type 2 Diabetes Patients.

The relationship between macronutrient intake and cognitive decline in older adults with type 2 diabetes mellitus (T2DM) remains underexplored. This cross-sectional study aimed to evaluate the association between the protein-to-carbohydrate energy ratio (%E:P) and cognitive impairment among 192 elderly T2DM patients. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA) and the Self-Administered Gerocognitive Exam (SAGE), while dietary intake data, including (%E:P), was gathered using a validated semi-quantitative food frequency questionnaire. Participants had a mean age of 71 ± 6 years, 46.4% were female, and the median BMI was 30 ± 4 kg/m2. After adjusting for confounding variables, patients in the highest (%E:P) tertile showed significantly higher MoCA and SAGE scores compared to those in the lowest tertile (p < 0.005). We identified an optimal (%E:P) threshold of 0.375 for predicting cognitive impairment, with a sensitivity of 53% and specificity of 64%. These findings suggest that a lower (%E:P) ratio may be a risk factor for cognitive impairment in elderly T2DM patients. Monitoring this ratio may serve as an early detection tool for cognitive deterioration. Moreover, current protein intake recommendations for older adults with T2DM may be insufficient to prevent cognitive impairment. Further research is needed to establish optimal dietary guidelines for this population.

Internal gravity waves in flow past a bluff body under different levels of stratification

The flow field of a bluff body, a circular disk, that moves horizontally in a stratified environment is studied using large-eddy simulations. Five levels of stratification (body Froude numbers of ${{Fr}} = 0.5, 1, 1.5, 2$ and $5$ ) are simulated at Reynolds number of ${{Re}} = 5000$ and Prandtl number of $Pr =1$ . A higher ${{Re}} = 50\,000$ database at ${{Fr}} = 2, 10$ and $Pr =1$ is also examined for comparison. The wavelengths and amplitudes of steady lee waves are compared with a linear-theory analysis. Excellent agreement is found over the entire range of ${{Fr}}$ if an ‘equivalent body’ that includes the separation region is employed for the linear theory. For asymptotically large distances, the velocity amplitude varies theoretically as ${{Fr}}^{-1}$ but a correction owing to the dependence of the separation zone on ${{Fr}}$ is needed. The wake waves propagate in a narrow band of angles with the vertical, and have a wavelength that increases with increasing ${{Fr}}$ . The envelope of wake waves, demarcated using buoyancy variance, exhibits self-similar behaviour. The higher ${{Re}}$ results are consistent with the buoyancy effects exhibited at the lower ${{Re}}$ . The wake wave energy is larger at ${{Re}} = 50\,000$ . Nevertheless, independent of ${{Fr}}$ and ${{Re}}$ , the ratio of the wake wave potential energy to the wake turbulent energy increases to approximately 0.6–0.7 in the non-equilibrium stage showing their energetic importance besides suggesting universality in this statistic. There is a crossover of energetic dominance of lee waves at ${{Fr}} &lt;2$ to wake-wave dominance at ${{Fr}} \approx 5$ .

Abstract 4135434: Effect of early nutritional guidance for patients with acute myocardial infarction: A single center retrospective cohort study

Introduction/Background: Acute myocardial infarction (AMI) stands as a major global cause of mortality. Various intervention strategies, such as pre-hospital care, reperfusion therapies, pharmacological interventions, dietary modifications, and exercise therapies, have been researched to improve patient prognosis. Research Question: There remains a lack of evidence regarding the impact of early nutritionist-led dietary guidance on the prognosis of AMI patients. Aim: This study aimed to elucidate whether nutritional guidance by nutritionists who have national qualifications would improve the mortality and the primary composite endpoint (including acute decompensated heart failure, stroke, and acute coronary syndrome) of patients following an acute myocardial infarction. Methods/Approach: A retrospective cohort study enrolling 409 AMI patients who underwent emergency percutaneous coronary interventions between September 1, 2015, and October 31, 2023. Nutritionists guided 223 patients (54.5%) during hospitalization in this study. The nutritional guidance used in our study was also based on the definition of adequate caloric intake of 25–30 kcal per ideal body weight (kg). Under an appropriate total energy intake, a fat energy ratio of 20–25% and a carbohydrate energy ratio of 50–60% are recommended, with protein and lipid restrictions depending on the disease state. Results/Data: During a median follow-up of 39 months, 24 cases of all-cause mortality (5.9%) and 6 cases of cardiovascular mortality (1.5%) were recorded. Kaplan–Meier curves demonstrated significantly higher incidence rates of death and primary composite endpoints among patients without nutritional guidance during hospitalization. The multivariate Cox regression model revealed early nutritional guidance as a significant predictor for long-term mortality but not for primary composite endpoints. Furthermore, undernourished patients, defined as those with a geriatric nutritional risk index of 98 or less, showed a similar prognosis to those with good nutritional status after receiving nutritional guidance, indicating that nutritional guidance improves prognosis. Conclusion: Nutritionist-led dietary guidance for patients with AMI can potentially improve total mortality and is expected to be more effective in undernourished patients. Further research is warranted to explore the physiological impacts of improved diets resulting from such guidance and their effects on enhancing prognosis.

Fault detection of RV reducer cycloidal gear under robot working conditions based on optimization improved envelope spectrum sideband energy ratio

Abstract Under industrial robot operating conditions, the cycloidal gear in the rotary vector(RV) reducer in the robot joint runs in a compound rotation motion with a low-speed and incomplete cycle, which complicates the fault detection for the cycloidal gear. Until now, no effective detection method for the cycloidal gear tooth fault under industrial robot operating conditions has been reported. Consequently, a method based on optimization improved envelope spectrum sideband energy ratio is proposed in this paper. Firstly, encoder signals are acquired and used to estimate the instantaneous angular speed (IAS) signals, which are then separated into two sets of data based on rotational directions. Then, the data corresponding to the approximately stable speed is truncated and connected to form a constructed signal. Next, the coherence of the constructed signal is calculated using the fast spectral coherence algorithm. Subsequently, the improved envelope spectrum sideband energy ratio is used as the objective value, and a global optimization algorithm is employed to determine the integration frequency band of the improved envelope spectrum (IES). Finally, the IES of this frequency band is calculated to obtain the fault-relatedorder characteristics. An RV reducer test rig was used for the experiment, and the analysis results from the proposed approach, cyclic spectral coherence (CSCoh), and improved envelope spectrum via feature optimization-gram (IESFOgram) were contrasted. Experiment results show that the proposed method is valid for the detection of tooth faults in the cycloidal gear of the RV reducer in robot operating conditions.&amp;#xD;

Complementarity and competitive trade-offs enhance forage productivity, nutritive balance, land and water use, and economics in legume-grass intercropping

Legume-grass intercropping is proposed as a globally sustainable approach to enhance forage crop productivity and quality while supporting agropastoral ecosystem functioning. However, the mechanism involved in interspecific complementarity and competition driven forage productivity, quality, resource utilization, and economic benefits across different proportions of intercrops remains unclear, particularly under interannual climate variability. To address this, a 3-year field experiment was conducted in the agropastoral area of the Qinghai-Tibet Plateau (QTP) to assess the effects of different legume proportions (five legume-grass intercropping and their respective monocultures) and growing seasons on the productive, biological and economic viability. The results showed that legume proportions of 40 % and 50 % achieved the highest forage yield, system productivity (SP), water use efficiency (WUE), land equivalent ratio (LER), net profit (NP), return on investment (ROI), biodiversity effect (NE), and complementarity effect (CE) compared to other intercropping and monocultures (P < 0.05). As the legume proportion increased, yield stability, selection effect, crude protein (CP) and ash contents, grass aggressivity and competitive ratio significantly increased (P < 0.05), while ether extract (EE), crude fiber (CF), nitrogen-free extract (NFE), gross energy (GE), legume aggressivity and competitive ratio significantly decreased (P < 0.05). Additionally, the lowest forage yield, SP, NP, ROI, CP, EE, NFE, GE, and the highest WUE, LER, NE, CE, CF and ash contents were observed during the dry season (P < 0.05). Overall, our results suggested that the optimal legume proportions of 40–50 % increased forage productivity, land and water use efficiency, and economic benefits in intercropping system by improving complementarity and competitive trade-offs, which helps the intercropping systems better adapt to climatic droughts in the semiarid regions of the QTP.

Identifying and Estimating Frailty Phenotypes by Vocal Biomarkers: Cross-Sectional Study.

Researchers have developed a variety of indices to assess frailty. Recent research indicates that the human voice reflects frailty status. Frailty phenotypes are seldom discussed in the literature on the aging voice. This study aims to examine potential phenotypes of frail older adults and determine their correlation with vocal biomarkers. Participants aged ≥60 years who visited the geriatric outpatient clinic of a teaching hospital in central Taiwan between 2020 and 2021 were recruited. We identified 4 frailty phenotypes: energy-based frailty, sarcopenia-based frailty, hybrid-based frailty-energy, and hybrid-based frailty-sarcopenia. Participants were asked to pronounce a sustained vowel "/a/" for approximately 1 second. The speech signals were digitized and analyzed. Four voice parameters-the average number of zero crossings (A1), variations in local peaks and valleys (A2), variations in first and second formant frequencies (A3), and spectral energy ratio (A4)-were used for analyzing changes in voice. Logistic regression was used to elucidate the prediction model. Among 277 older adults, an increase in A1 values was associated with a lower likelihood of energy-based frailty (odds ratio [OR] 0.81, 95% CI 0.68-0.96), whereas an increase in A2 values resulted in a higher likelihood of sarcopenia-based frailty (OR 1.34, 95% CI 1.18-1.52). Respondents with larger A3 and A4 values had a higher likelihood of hybrid-based frailty-sarcopenia (OR 1.03, 95% CI 1.002-1.06) and hybrid-based frailty-energy (OR 1.43, 95% CI 1.02-2.01), respectively. Vocal biomarkers might be potentially useful in estimating frailty phenotypes. Clinicians can use 2 crucial acoustic parameters, namely A1 and A2, to diagnose a frailty phenotype that is associated with insufficient energy or reduced muscle function. The assessment of A3 and A4 involves a complex frailty phenotype.

Research on Airborne Ground-Penetrating Radar Imaging Technology in Complex Terrain

The integration of ground-penetrating radar (GPR) with unmanned aerial vehicles (UAVs) enables efficient non-contact detection, performing exceptionally well in complex terrains and extreme environments. However, challenges in data processing and interpretation remain significant obstacles to fully utilizing this technology. To mitigate the effects of numerical dispersion, this paper develops a high-order finite-difference time-domain (FDTD(2,4)) three-dimensional code suitable for airborne GPR numerical simulations. The simulation results are compared with traditional FDTD methods, validating the accuracy of the proposed approach. Additionally, a Kirchhoff migration algorithm that considers the influence of the air layer is developed for airborne GPR. Different processing strategies are applied to flat and undulating terrain models, significantly improving the identification of shallowly buried targets. Particularly under undulating terrain conditions, the energy ratio method is introduced, effectively suppressing the interference of surface reflections caused by terrain variations. This innovative approach offers a new technical pathway for efficient GPR data processing in complex terrains. The study provides new insights and methods for the practical application of airborne GPR.