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The recently discovered social place cells and grid cells in hippocampal formation are believed to be the neural basis underlying relative navigation of conspecifics. In this paper, we propose a new brain-inspired relative navigation model in a large-scale 3D environment for collective UAVs that translates the neurodynamics of the social place cell–grid cell circuit to robotic relative navigation algorithm for the first time. Our approach comprises three key parts: (1) a 3D isotropic Gaussian function-based cube social place cell network (cube-SPCNet), (2) a 3D continuous attractor neural network-based cube grid cell network (cube-GCNet), and (3) a population vector-based neural decoding module. The resulting brain-inspired relative navigation model incorporates the good relative information abstraction capabilities of the cube-SPCNet with the powerful temporal filtering capabilities of the cube-GCNet, yielding robustness and accuracy performance improvement for relative navigation. Experimental results show the new method can provide more robust and precise relative navigation results than its conventional counterpart, displaying a possible brain-inspired solution for relative navigation enhancement for collective UAVs.
Computer-based interactive items have become prevalent in recent educational assessments. In such items, detailed human–computer interactive process, known as response process, is recorded in a log file. The recorded response processes provide great opportunities to understand individuals’ problem solving processes. However, difficulties exist in analyzing these data as they are high-dimensional sequences in a nonstandard format. This paper aims at extracting useful information from response processes. In particular, we consider an exploratory analysis that extracts latent variables from process data through a multidimensional scaling framework. A dissimilarity measure is described to quantify the discrepancy between two response processes. The proposed method is applied to both simulated data and real process data from 14 PSTRE items in PIAAC 2012. A prediction procedure is used to examine the information contained in the extracted latent variables. We find that the extracted latent variables preserve a substantial amount of information in the process and have reasonable interpretability. We also empirically prove that process data contains more information than classic binary item responses in terms of out-of-sample prediction of many variables.
Accurate assessment of a student’s ability is the key task of a test. Assessments based on final responses are the standard. As the infrastructure advances, substantially more information is observed. One of such instances is the process data that is collected by computer-based interactive items and contain a student’s detailed interactive processes. In this paper, we show both theoretically and with simulated and empirical data that appropriately including such information in the assessment will substantially improve relevant assessment precision.
Process data refer to data recorded in log files of computer-based items. These data, represented as timestamped action sequences, keep track of respondents’ response problem-solving behaviors. Process data analysis aims at enhancing educational assessment accuracy and serving other assessment purposes by utilizing the rich information contained in response processes. The R package ProcData presented in this article is designed to provide tools for inspecting, processing, and analyzing process data. We define an S3 class ‘proc’ for organizing process data and extend generic methods summary and print for ‘proc’. Feature extraction methods for process data are implemented in the package for compressing information in the irregular response processes into regular numeric vectors. ProcData also provides functions for making predictions from neural-network-based sequence models. In addition, a real dataset of response processes from the climate control item in the 2012 Programme for International Student Assessment is included in the package.
Negative relationships between the parental age and offspring life history traits have been widely observed across diverse animal taxa. However, there is a lack of studies examining the influence of parental age on offspring performance using mites, particularly phytoseiid predators as subjects. This study explored the influence of maternal age on offspring life history traits in Amblyseius herbicolus (Chant) (Acari: Mesostigmata), a phytoseiid predatory mite reproducing through thelytokous parthenogenesis. We hypothesised that increased maternal age negatively impacts offspring traits, including developmental duration, body size, fecundity and lifespan. Amblyseius herbicolus was reared under controlled laboratory conditions, and the life history traits of offspring from mothers of varying ages were analysed using linear mixed-effect models. Our results showed that the increase in maternal age significantly reduced individual egg volume, but did not significantly affect offspring developmental duration, body size, fecundity or lifespan. These findings indicate that while older A. herbicolus females produced smaller eggs, the subsequent performance (i.e. body size, fecundity and lifespan) of offspring remained largely unaffected, suggesting possible compensatory mechanisms in the offspring or alternative maternal provisioning strategies. The results of this study offer useful insights into the reproductive strategies of phytoseiid predators and asexually reproducing species, enhancing our understanding of how maternal age affects offspring fitness. Further studies can examine how offspring of A. herbicolus from mothers of different ages perform under adverse environmental conditions.
The proton–boron ${}^{11}{\text{B}}\left( {p,\alpha } \right)2\alpha $ reaction (p-11B) is an interesting alternative to the D-T reaction ${\text{D}}\left( {{\text{T}},{\text{n}}} \right)\alpha $ for fusion energy, since the primary reaction channel is aneutronic and all reaction partners are stable isotopes. We measured the α production yield using protons in the 120–260 keV energy range impinging onto a hydrogen–boron-mixed target, and for the first time present experimental evidence of an increase of α-particle yield relative to a pure boron target. The measured enhancement factor is approximately 30%. The experiment results indicate a higher reactivity, and that may lower the condition for p-11B fusion ignition.
Depression is highly prevalent in haemodialysis patients, and diet might play an important role. Therefore, we conducted this cross-sectional study to determine the association between dietary fatty acids (FA) consumption and the prevalence of depression in maintenance haemodialysis (MHD) patients. Dietary intake was assessed using a validated FFQ between December 2021 and January 2022. The daily intake of dietary FA was categorised into three groups, and the lowest tertile was used as the reference category. Depression was assessed using the Patient Health Questionnaire-9. Logistic regression and restricted cubic spline (RCS) models were applied to assess the relationship between dietary FA intake and the prevalence of depression. As a result, after adjustment for potential confounders, a higher intake of total FA [odds ratio (OR)T3 vs. T1 = 1·59, 95 % confidence interval (CI) = 1·04, 2·46] and saturated fatty acids (SFA) (ORT3 vs. T1 = 1·83, 95 % CI = 1·19, 2·84) was associated with a higher prevalence of depressive symptoms. Significant positive linear trends were also observed (P < 0·05) except for SFA intake. Similarly, the prevalence of depression in MHD patients increased by 20% (OR = 1.20, 95% CI = 1.01–1.43) for each standard deviation increment in SFA intake. RCS analysis indicated an inverse U-shaped correlation between SFA and depression (Pnonlinear > 0·05). Additionally, the sensitivity analysis produced similar results. Furthermore, no statistically significant association was observed in the subgroup analysis with significant interaction. In conclusion, higher total dietary FA and SFA were positively associated with depressive symptoms among MHD patients. These findings inform future research exploring potential mechanism underlying the association between dietary FA and depressive symptoms in MHD patients.
Language is one of the most celebrated hallmarks of human cognition. With the continuous improvement of medical technology, functional MRI (fMRI) has been used in aphasia. Although many related studies have been carried out, most studies have not extensively focused on brain regions with reduced activation in aphasic patients. The aim of this study was to identify brain regions normally activated in healthy controls but with reduced activation in aphasic patients during fMRI language tasks.
Methods:
We collected all previous task-state fMRI studies of secondary aphasia. The brain regions showed normal activation in healthy controls and reduced activation in aphasic patients were conducted activation likelihood estimation (ALE) meta-analysis to obtain the brain regions with consistently reduced activation in aphasic patients.
Results:
The ALE meta-analysis revealed that the left inferior frontal gyrus, left middle temporal gyrus, left superior temporal gyrus, left fusiform gyrus, left lentiform nucleus and the culmen of the cerebellum were the brain regions with reduced activation in aphasic patients.
Discussion:
These findings from the ALE meta-analysis have significant implications for understanding the language network and the potential for recovery of language functions in individuals with aphasia.
As a member of the Scathophagidae family, Scathophaga stercoraria (S. stercoraria) is widely distributed globally and is closely associated with animal feces. It is also a species of great interest to many scientific studies. However, its phylogenetic relationships are poorly understood. In this study, S. stercoraria was found in plateau pikas for the first time. The potential cause of its presence in the plateau pikas was discussed and it was speculated that the presence of S. stercoraria was related to the yak feces. In addition, 2 nuclear genes (18SrDNA and 28SrDNA), 1 mitochondrial gene (COI), and the complete mitochondrial genome of S. stercoraria were sequenced. Phylogenetic trees constructed based on 13 Protein coding genes (13PCGs), 18S and 28S rDNA showed that S. stercoraria is closely related to the Calliphoridae family; phylogenetic results based on COI suggest that within the family Scathophagidae, S. stercoraria is more closely related to the genus Leptopa, Micropselapha, Parallelomma and Americina. Divergence times estimated using the COI gene suggest that the divergence formation of the genus Scathophaga is closely related to changes in biogeographic scenarios and potentially driven by a combination of uplift of the Qinghai-Tibetan Plateau (QTP) and dramatic climate changes. These results provide valuable information for further studies on the phylogeny and differentiation of the Scathophaga genus in the future.
Nontuberculous mycobacteria (NTM) is a large group of mycobacteria other than the Mycobacterium tuberculosis complex and Mycobacterium leprae. Epidemiological investigations have found that the incidence of NTM infections is increasing in China, and it is naturally resistant to many antibiotics. Therefore, studies of NTM species in clinical isolates are useful for understanding the epidemiology of NTM infections. The present study aimed to investigate the incidence of NTM infections and types of NTM species. Of the 420 samples collected, 285 were positive for M. tuberculosis, 62 samples were negative, and the remaining 73 samples contained NTM, including 35 (8.3%) only NTM and 38 (9%) mixed (M. tuberculosis and NTM). The most prevalent NTM species were Mycobacterium intracellulare (30.1%), followed by Mycobacterium abscessus (15%) and M. triviale (12%). M. gordonae infection was detected in 9.5% of total NTM-positive cases. Moreover, this study reports the presence of Mycobacterium nonchromogenicum infection and a high prevalence of M. triviale for the first time in Henan. M. intracellulare is the most prevalent, accompanied by some emerging NTM species, including M. nonchromogenicum and a high prevalence of M. triviale in Henan Province. Monitoring NTM transmission and epidemiology could enhance mycobacteriosis management in future.
This multi-method longitudinal study sought to investigate linkage in parental neuroendocrine functioning – indicated by cortisol – over two measurement occasions. In addition, we examined how parental cortisol linkage may operate as an intermediate factor in the cascade of contextual risks and parenting. Participants were 235 families with a young child (Mage = 33.56, 36.00 years for mothers and fathers respectively), who were followed for two annual measurement occasions. Parental cortisol linkage was measured around a laboratory conflict discussion task at both measurement occasions (i.e., pre-discussion, 20- and 40-minute post-discussion for each measurement occasion). Maternal and paternal parenting behavior was observed during a parent-child discipline discussion task. Findings indicated similar levels of cortisol linkage between parents over the two measurement occasions. Furthermore, cortisol linkage between parents operated as an intermediate factor between contextual risks and more compromised parenting behavior. That is, greater contextual risks, indicated by greater neighborhood risk and interparental conflict, were linked to greater cortisol linkage between parents over time, which was in turn linked to greater authoritarian parenting during parent-child interaction. Findings highlighted the importance of understanding physiological-linkage processes with respect to the impact of contextual risks on family functioning and may have crucial implications for clinical work.
In this chapter, differences between magnetic communication and electromagnetic wave-based communication are summarized and major advantages of magnetic communication are discussed, which provides a big picture of the applicable scenarios of magnetic communication. In addition, the physical circuit for magnetic communications is introduced. The fundamental performance metrics, such as path loss, bandwidth, capacity, and connectivity are discussed.
Magnetic communication is a novel physical communication technology. To connect a large number of magnetic communication devices, traditional networking protocols can be employed, but we need to make significant modifications on the physical layer to accommodate the special features of magnetic communication. For example, to access the communication medium, traditional carrier-sense multiple access or Zigbee can be adopted, but the magnetic communication has a short communication range, and the antennas of different devices may have substantial coupling, which can affect the communication performance. To address this issue, we need dedicated scheduling algorithms to reduce mutual couplings among coils and increase the network throughput. In this chapter, we first introduce a complete magnetic communication network stack. Then, we show the unique features of magnetic communication at the network level. After that, we introduce the scheduling algorithms for magnetic communication networks.
We study the connectivity of a large-scale ad hoc MI networks, whose nodes are randomly located with randomly deployed MI antennas. The pathloss model we use here considers the effect of MI noise via a signal-to-noise ratio threshold instead of magnetic signal strength. In addition, the effects of carrier frequency and eddy current both are considered for the determination of signal coverage. To study the MI coverage and connectivity under such assumptions, we develop a Lambert W-function-based integral method to evaluate the effective coverage space and the expected node degree of an MI node. The probability of having no isolated node in the network is further derived to estimate the required parameters for an almost surely connected network. Passive MI waveguide is not considered in this chapter. We also performed carrier frequency optimizations.
The so-called magnetic communication makes use of the time-varying magnetic field produced by the transmitting antenna, so that the receiving antenna receives the energy signal by mutual inductance. Research studies show that the penetrability of a magnetic communication system depends on the magnetic permeability of the medium. Because the magnetic permeability of the layer, rock, ice, soil, and ore bed is close to that of the air, channel conditions have less effects on magnetic transmission than electric transmission. Therefore, the communication network based on deep-penetrating MI can expand the perception ability and sensing range of information technology effectively, which can be applied to complex environments such as underground, underwater, tunnel, mountain, rock, ice, and forest. We conclude that the network construction of IoT based on magnetic communication is of great value and can be regarded as one of the reliable technologies to improve the connectivity of a wireless network.
One of the important peculiarities of MI communications is the use of magnetic antennas that can deliver information by using inductive coupling instead of radiation. The magnetic coupling is constrained in the near field, which is more secure since it is not easy to be detected. Moreover, magnetic fields have better penetration efficiency than electric fields. A magnetic antenna is more robust to environment changes than its electric counterpart. In this chapter, we first introduce the fundamentals of antennas to show the difference between magnetic antennas and the widely used electric antennas to gradually narrow our discussion from a big picture. We present the advantages of magnetic antennas and their applicable conditions. Also, we introduce the signal transmission techniques and channel characteristics for the single-input-single-output (SISO) system. Finally, we discuss the multiple-antenna MI system with different antenna placement strategies, which is more reliable and efficient than the SISO system.