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For the accumulation of pollutants and high-temperature environment in the kitchen, this paper comprehensively considers kitchen pollution emissions, air supply, and thermal comfort, innovatively introduces the concepts of arithmetic superposition index P and human respiratory zone thermal sensitivity weighting, and proposes a multi-objective optimization of kitchen comfort based on the P index. This study adopts a combination of orthogonal experiments and computational fluid dynamics (CFD) simulations to study the exhaust characteristics of range hoods under different structural parameters and compares and analyzes the rationality and effectiveness of different auxiliary ventilation methods in kitchen ventilation systems to propose the optimal solution. The research findings indicate that adjusting the range hood barrier angle significantly reduces kitchen pollution, and workstation air supply markedly improves kitchen and breathing zone comfort. Optimizing range hood parameters (178°angle, 1.544m height, 20.57 Pa pressure) increased PM10 capture efficiency by 25.68 % and reduced PM10 intake fraction by 72.55 %. This also decreased the breathing zone's weighted PMV by 37.17 % and increased kitchen ADPI by 27.7 %. Compared to no auxiliary air supply, workstation air supply decreased kitchen PMV by 53.08 % and breathing zone thermal weighted PMV by 96.99 %. Additionally, it lowered the thermal weighted air age in the breathing zone by 11.98 % and improved ventilation efficiency by 12.55 %, greatly enhancing kitchen comfort.

In order to analyze the motion of the human lower limb, a comprehensive process was created in this paper. Firstly, an exoskeleton robot platform was leveraged and a dynamic model of human-exoskeleton lower limb was created for identifying human parameters. Then the sEMG signal was utilized to extract information about muscle activity through multi-group experiments, and a backpropagation neural network (BPNN) was built to forecast joint torque. An inverse dynamics analysis combining the human motion data with the dynamic model can not only verify the reliability of prediction result by this BPNN but also the correctness of identified results before. Moreover, the mean absolute error (MAE), root mean square error (RMSE), and Pearson correlation coefficient (PCC) were used as evaluation index of both identification and prediction results. The proposed protocol can give accurate identified parameters for subject and estimated joint torque from sEMG during swing motion. We believe it can be extended to various types of human motion movement and potentially applied to complete human motion analysis.

Abstract Exoskeletons generally require accurate dynamic models to design the model-based controller conveniently under the human-robot interaction condition. However, due to unknown model parameters such as the mass, moment of inertia and mechanical size, the dynamic model of exoskeletons is difficult to construct. Hence, an enhanced whale optimization algorithm (EWOA) is proposed to identify the exoskeleton model parameters. Meanwhile, the periodic excitation trajectories are designed by finite Fourier series to input the desired position demand of exoskeletons with mechanical physical constraints. Then a backstepping controller based on the identified model is adopted to improve the human-robot wearable comfortable performance under cooperative motion. Finally, the proposed Model parameters identification and control are verified by a two-DOF exoskeletons platform. The knee joint motion achieves a steady-state response after 0.5 s. Meanwhile, the position error of hip joint response is less than 0.03 rad after 0.9 s. In addition, the steady-state human-robot interaction torque of the two joints is constrained within 15 $${\text{N}\cdot\text{m}}$$ N · m . This research proposes a whale optimization algorithm to optimize the excitation trajectory and identify model parameters. Furthermore, an enhanced mutation strategy is adopted to avoid whale evolution's unsatisfactory local optimal value.

The precise detection of Chinese bayberry locations using object detection technology is a crucial step to achieve unmanned harvesting of these berries. Because of the small size and easy occlusion of bayberry fruit, the existing detection algorithms have low recognition accuracy for such objects. In order to realize the fast and accurate recognition of bayberry in fruit trees, and then guide the robotic arm to carry out accurate fruit harvesting, this paper proposes a detection algorithm based on an improved YOLOv7-tiny model. The model introduces partial convolution (PConv), a SimAM attention mechanism and SIoU into YOLOv7-tiny, which enables the model to improve the feature extraction capability of the target without adding extra parameters. Experimental results on a self-built Chinese bayberry dataset demonstrate that the improved algorithm achieved a recall rate of 97.6% and a model size of only 9.0 MB. Meanwhile, the precision of the improved model is 88.1%, which is 26%, 2.7%, 4.7%, 6.5%, and 4.7% higher than that of Faster R-CNN, YOLOv3-tiny, YOLOv5-m, YOLOv6-n, and YOLOv7-tiny, respectively. In addition, the proposed model was tested under natural conditions with the five models mentioned above, and the results showed that the proposed model can more effectively reduce the rates of misdetections and omissions in bayberry recognition. Finally, the improved algorithm was deployed on a mobile harvesting robot for field harvesting experiments, and the practicability of the algorithm was further verified.

Abstract Dysfunction of parvalbumin (PV) neurons is closely involved in depression, however, the detailed mechanism remains unclear. Based on the previous finding that multiple endocrine neoplasia type 1 (Protein: Menin; Gene: Men1) mutation (G503D) is associated with a higher risk of depression, a Menin‐G503D mouse model is generated that exhibits heritable depressive‐like phenotypes and increases PV expression in brain. This study generates and screens a serial of neuronal specific Men1 deletion mice, and found that PV interneuron Men1 deletion mice (PcKO) exhibit increased cortical PV levels and depressive‐like behaviors. Restoration of Menin, knockdown PV expression or inhibition of PV neuronal activity in PV neurons all can ameliorate the depressive‐like behaviors of PcKO mice. This study next found that ketamine stabilizes Menin by inhibiting protein kinase A (PKA) activity, which mediates the anti‐depressant function of ketamine. These results demonstrate a critical role for Menin in depression, and prove that Menin is key to the antidepressant function of ketamine.

The accurate prediction of joint torque is required in various applications. Some traditional methods, such as the inverse dynamics model and the electromyography (EMG)-driven neuromusculoskeletal (NMS) model, depend on ground reaction force (GRF) measurements and involve complex optimization solution processes, respectively. Recently, machine learning methods have been popularly used to predict joint torque with surface electromyography (sEMG) signals and kinematic information as inputs. This study aims to predict lower limb joint torque in the sagittal plane during walking, using a long short-term memory (LSTM) model and Gaussian process regression (GPR) model, respectively, with seven characteristics extracted from the sEMG signals of five muscles and three joint angles as inputs. The majority of the normalized root mean squared error (NRMSE) values in both models are below 15%, most Pearson correlation coefficient (R) values exceed 0.85, and most decisive factor (R2) values surpass 0.75. These results indicate that the joint prediction of torque is feasible using machine learning methods with sEMG signals and joint angles as inputs.

Aging is a systemic process, which is a risk factor for impaired physiological functions, and finally death. The molecular mechanisms driving aging process and the associated cognitive decline are not fully understood. The hypothalamus acts as the arbiter that orchestrates systemic aging through neuroinflammatory signaling. Our recent findings revealed that Menin plays important roles in neuroinflammation and brain development. Here, we found that the hypothalamic Menin signaling diminished in aged mice, which correlates with systemic aging and cognitive deficits. Restoring Menin expression in ventromedial nucleus of hypothalamus (VMH) of aged mice extended lifespan, improved learning and memory, and ameliorated aging biomarkers, while inhibiting Menin in VMH of middle-aged mice induced premature aging and accelerated cognitive decline. We further found that Menin epigenetically regulates neuroinflammatory and metabolic pathways, including D-serine metabolism. Aging-associated Menin reduction led to impaired D-serine release by VMH-hippocampus neural circuit, while D-serine supplement rescued cognitive decline in aged mice. Collectively, VMH Menin serves as a key regulator of systemic aging and aging-related cognitive decline.

The cooking oil fume particulate (COFP) produced by indoor cooking can harm human health seriously, and therefore requires urgent monitoring and optimization. In this paper, the kitchen cooking simulation process was established by using computational fluid dynamics (CFD) based on the fluid dynamics theory. Combined with the user defined function (UDF), the spatial and temporal distributions of COFP and carbon dioxide (CO2) during the cooking process were simulated and analyzed, respectively. Both simulation results were verified using experimental data. Moreover, this paper introduces a COFP concentration correlation function that utilizes the spatiotemporal correlation between COFP and CO2 concentrations during the cooking process. The function is based on the spatiotemporal distribution of CO2 concentration. By comparing it with traditional calculations, the proposed function is shown to achieve a remarkable 70% improvement in efficiency and maintain an accuracy rate exceeding 90%. This enables the rapid analysis and control of COFP concentration through monitoring and analyzing CO2 levels in the kitchen.

Abstract The model identification and uncertain parameters estimation are common control problems in multi‐DOF manipulator system, since both model parametric uncertainties and unknown external disturbance often degrade the output performance and the close‐loop system stability. In this study, by using the joint angle and the torque information, a neighborhood field optimization is adopted to identify the Lagrange model parameters of two‐DOF hydraulic manipulator. The fitness function of the neighborhood field optimization is designed to optimize the minimal squared error of the torque estimation and to obtain the lumped estimated parameters with high accuracy. Then based on the identification model, a parametric adaptive controller is designed to address uncertain electro‐hydraulic parameters and external disturbances. Furthermore, a new stable control variable is redesigned to avoid the redundant input saturation of electro‐hydraulic actuator. The effectiveness of the proposed controller is verified by the comparative experimental results with the general backstepping controller.

Abstract In this study, a humanoid prototype of 2-DOF (degrees of freedom) lower limb exoskeleton is introduced to evaluate the wearable comfortable effect between person and exoskeleton. To improve the detection accuracy of the human-robot interaction torque, a BPNN (backpropagation neural networks) is proposed to estimate this interaction force and to compensate for the measurement error of the 3D-force/torque sensor. Meanwhile, the backstepping controller is designed to realize the exoskeleton's passive position control, which means that the person passively adapts to the exoskeleton. On the other hand, a variable admittance controller is used to implement the exoskeleton's active follow-up control, which means that the person's motion is motivated by his/her intention and the exoskeleton control tries best to improve the human-robot wearable comfortable performance. To improve the wearable comfortable effect, serval regular gait tasks with different admittance parameters and step frequencies are statistically performed to obtain the optimal admittance control parameters. Finally, the BPNN compensation algorithm and two controllers are verified by the experimental exoskeleton prototype with human-robot cooperative motion.

Repulsive guidance molecule a (RGMa) plays a vital role in the progression of numerous inflammatory diseases. However, whether it participates in atherosclerosis development is not known. Here, we explored the influence of RGMa in atherogenesis by investigating whether an association exists between functional polymorphisms in the RGMa promoter and cerebrovascular atherosclerosis burden (CAB) in Chinese Han patients diagnosed with acute ischemic cerebrovascular accident. To this end, we conducted a genetic association study on 201 patients with prior diagnoses of acute ischemic stroke or transient ischemic attack recruited from our hospital. After admission, we conducted three targeted single-nucleotide polymorphisms (SNPs) genotyping and evaluated CAB by computed tomography angiography. We used logistic regression modeling to analyze genetic associations. Functional polymorphism analysis indicated an independent association between the rs725458 T allele and increased CAB in patients with acute ischemic cerebrovascular accident [adjusted odds ratio (OR) = 1.66, 95% confidence interval (CI) = 1.01–2.74, P = 0.046]. In contrast, an association between the rs4778099 AA genotype and decreased CAB (adjusted OR = 0.10, 95% CI = 0.01–0.77, P = 0.027) was found. Our Gene Expression Omnibus analysis revealed lower RGMa levels in the atherosclerotic aortas and in the macrophages isolated from plaques than that in the normal aortas and macrophages from normal tissue, respectively. In conclusion, the relationship between RGMa and cerebrovascular atherosclerosis suggests that RGMa has a potential vasoprotective effect. The two identified functional SNPs (rs725458 and rs4778099) we identified in the RGMa promoter are associated with CAB in patients diagnosed with acute ischemic cerebrovascular accident. These findings offer a promising research direction for RGMa-related translational studies on atherosclerosis.

It is necessary to improve the safety of the underwater acoustic sensor networks (UASNs) since it is mostly used in the military industry. Specific emitter identification is the process of identifying different transmitters based on the radio frequency fingerprint extracted from the received signal. The sonar transmitter is a typical low-frequency radiation source and is an important part of the UASNs. Class D power amplifier, a typical nonlinear amplifier, is usually used in sonar transmitters. The inherent nonlinearity of power amplifiers provides fingerprint features that can be distinguished without transmitters for specific emitter recognition. First, the nonlinearity of the sonar transmitter is studied in-depth, and the nonlinearity of the power amplifier is modeled and its nonlinearity characteristics are analyzed. After obtaining the nonlinear model of an amplifier, a similar amplifier in practical application is obtained by changing its model parameters as the research object. The output signals are collected by giving the same input of different models, and, then, the output signals are extracted and classified. In this paper, the memory polynomial model is used to model the amplifier. The power spectrum features of the output signals are extracted as fingerprint features. Then, the dimensionality of the high-dimensional features is reduced. Finally, the classifier is used to recognize the amplifier. The experimental results show that the individual sonar transmitter can be well identified by using the nonlinear characteristics of the signal. By this way, this method can enhance the communication safety of the UASNs.

In this paper, a combination of simulation and testing is used to quantitatively analyze the influence of a fresh air system of indoor range hood on kitchen air quality. The evaluation criteria of kitchen air quality are established based on the air age and air exchange efficiency. The results show that, when the fresh air system is switched to on from off, the indoor mean air age is reduced to 94.7 s from 468.6 s, and the air exchange efficiency is improved to 82.4% from 16.7%. The air quality is upgraded to fresh from moderate pollution. The presented simulation analysis process can provide a guideline for in-depth study of the kitchen air quality and quantitatively assess the kitchen fresh air system.

The Acinetobacter calcoaceticus-Acinetobacter baumannii (ACB) complex has emerged as a high priority among hospital-acquired pathogens in intensive care units (ICUs), posing a challenge to infection management practices. In this study, the clinical characteristics, antimicrobial susceptibility patterns, and patients outcome among genospecies were retrospectively compared. Samples were taken from the tracheal secretions of 143 patients in the ICU. Genospecies of the ACB complex were discriminated by analysis of the 16S-23S rRNA gene intergenic spacer (ITS) sequence. Univariate and multiple variable logistic regression analyses were performed to identify risk factors for infection and mortality. Three genospecies were isolated: A. baumannii (73, 51.0%), A. nosocomialis (29, 20.3%), and A. pittii (41, 28.7%). The results showed that the distribution of infection and colonization among the three genospecies were the same, while A. baumannii was more resistant to common antibiotics than A. nosocomialis and A. pittii. Advanced age, a long stay in the ICU, acute physiology and chronic health evaluation (APACHE) II score, the use of a mechanical ventilator, and previous antibiotic use were risk factors for patient infection. The APACHE II score was a risk factor for mortality in patients with ACB complex isolated from tracheal secretions. Poor outcome of patients with ACB complex isolated from tracheal secretion appears to be related to the APACHE II score rather than genospecies.