Machinelike or Humanlike? A Literature Review of Anthropomorphism in AI-Enabled Technology

Due to the recent proliferation of AI-enabled technology (AIET), the concept of anthropomorphism, human likeness in technology, has increasingly attracted researchers’ attention. Researchers have examined how anthropomorphism influences users’ perception, adoption, and continued use of AIET. However, researchers have yet to agree on how to conceptualize and operationalize anthropomorphism in AIET, which has resulted in inconsistent findings. A comprehensive understanding is thus needed of the current state of research on anthropomorphism in AIET contexts. To conduct an in-depth analysis of the literature on anthropomorphism, we reviewed 35 empirical studies focusing on conceptualizing and operationalizing AIET anthropomorphism, and its antecedents and consequences. Based on our analysis, we discuss potential research gaps and offer directions for future research

Selecting and Testing of Cement-Bonded Magnetite and Chalcopyrite as Oxygen Carrier for Chemical-Looping Combustion

Combining iron and copper ores can generate an oxygen carrier that has a synergic effect of high temperature resistance and high reactivity. In this work, typical cements available in the market were studied as binders to bind magnetite and chalcopyrite to develop a suitable oxygen carrier for chemical-looping combustion (CLC). A first selection step suggested that an aluminate cement, namely CA70, could favor the generation of oxygen carrier particles having good crushing strength, good particle yield, and high reactivity. The CA70-bonded oxygen carrier was then subjected to cyclic tests with CH4, CO, and H-2 in reduction and in air oxidation at temperatures of 850, 900, and 950 degrees C with gas concentrations of 5, 10, 15, and 20% in a batch-fluidized bed reactor. The increase in temperature promoted the fuel conversion. At 950 degrees C, the conversions of CH4 and CO reached up to 80.4% and 99.2%, respectively. During more than 30 cycles, the oxygen carrier kept a similar reactivity to the fresh carrier and maintained its composition and physical properties. The oxygen transport capacity was maintained at 21-23%, and the phases were CuO, Fe2O3, Al2O3, and minor CaS. In the used sample, some grains were observed, but the morphology was not greatly changed. Agglomeration was absent during all the cycles, except for the deep reduction with H-2

Effects of gap size, gap age, and bamboo Fargesia denudata on Abies faxoniana recruitment in South-western China

Aim of study: to study the effects of gap size, gap age and bamboo Fargesia denudata on natural regeneration of Abies faxoniana, both of which are the ubiquitous dominants in our research area.Area of study: subalpine coniferous forests in Wanglang Natural Reserve in Southwestern China.Material and Methods: 10 transect belts were randomly established, and a total of 97 gaps were recorded and used.Main results: (1) the number of bamboos with coverage of <17% significantly increased with increases of gap size and age, but the latter had little influence on the numbers of F. denudata with coverage of >17%. (2) F. denudata strongly inhibited A. faxoniana seedlings and saplings in small, young and old gaps, where the amount of A. faxoniana recruitment was relatively abundant, than in other types of gap. (3) The numbers of A. faxoniana seedlings in A-gaps, significantly decreased with the increases in gap size. However, in gaps where F. denudate was also present, A. faxoniana seedlings and saplings were insensitive to gap size or age. Research highlights: thick F. denudata would not be influenced by gap size or age. Because of the low occurrences of A. faxoniana seedlings and saplings, the negative effect of gap size, gap age and F. denudata on A. faxoniana recruitment was unclear.Key words: Abies faxoniana; Fargesia denudata; gap age; gap size; regeneration

Structural and functional connectivity of the whole brain and subnetworks in individuals with mild traumatic brain injury:Predictors of patient prognosis

Patients with mild traumatic brain injury have a diverse clinical presentation, and the underlying pathophysiology remains poorly understood. Magnetic resonance imaging is a non-invasive technique that has been widely utilized to investigate neurobiological markers after mild traumatic brain injury. This approach has emerged as a promising tool for investigating the pathogenesis of mild traumatic brain injury. Graph theory is a quantitative method of analyzing complex networks that has been widely used to study changes in brain structure and function. However, most previous mild traumatic brain injury studies using graph theory have focused on specific populations, with limited exploration of simultaneous abnormalities in structural and functional connectivity. Given that mild traumatic brain injury is the most common type of traumatic brain injury encountered in clinical practice, further investigation of the patient characteristics and evolution of structural and functional connectivity is critical. In the present study, we explored whether abnormal structural and functional connectivity in the acute phase could serve as indicators of longitudinal changes in imaging data and cognitive function in patients with mild traumatic brain injury. In this longitudinal study, we enrolled 46 patients with mild traumatic brain injury who were assessed within 2 weeks of injury, as well as 36 healthy controls. Resting-state functional magnetic resonance imaging and diffusion-weighted imaging data were acquired for graph theoretical network analysis. In the acute phase, patients with mild traumatic brain injury demonstrated reduced structural connectivity in the dorsal attention network. More than 3 months of followup data revealed signs of recovery in structural and functional connectivity, as well as cognitive function, in 22 out of the 46 patients. Furthermore, better cognitive function was associated with more efficient networks. Finally, our data indicated that small-worldness in the acute stage could serve as a predictor of longitudinal changes in connectivity in patients with mild traumatic brain injury. These findings highlight the importance of integrating structural and functional connectivity in understanding the occurrence and evolution of mild traumatic brain injury. Additionally, exploratory analysis based on subnetworks could serve a predictive function in the prognosis of patients with mild traumatic brain injury.</p

Pregabalin alleviates postherpetic neuralgia by downregulating spinal TRPV1 channel protein

Purpose: To determine the mechanism involved in pregabalin-induced alleviation of postherpetic neuralgia in a rat model.Methods: Ninety-sixty healthy Sprague-Dawley (SD) rats were assigned to sham, model andpregabalin groups (32 rats per group). A model of postherpetic neuralgia (PN) was established. The expressions of IL-1β and TNF-α in spinal cord tissue were determined 7 days after administration of treatments. The proportions of fluorescence areas in astrocytes in the dorsal horn, prefrontal lobe and hippocampus, and level of spinal cord TRPV1 channel protein in each group were evaluated.Results: Relative to model rats, IL-1β and TNF-α in spinal cord of pregabalin rats were significantly reduced (p &lt; 0.05). The areas of fluorescence in astrocytes in dorsal horn of spinal cord, prefrontal lobe and hippocampus of model group were significantly increased, relative to sham, but were decreased in rats in pregabalin group (p &lt; 0.05).Conclusion: Pregabalin significantly alleviates postherpetic neuralgia via mechanisms which may be related to the inflammatory response of spinal dorsal horn and downregulation of TRPV1 channel protein expression. This finding may be useful in developing new drugs for alleviating postherpetic neuralgia

Attenuation by a Human Body and Trees as well as Material Penetration Loss in 26 and 39 GHz Millimeter Wave Bands

This paper investigates the attenuation by a human body and trees as well as material penetration loss at 26 and 39 GHz by measurements and theoretical modeling work. The measurements were carried out at a large restaurant and a university campus by using a time domain channel sounder. Meanwhile, the knife-edge (KE) model and one-cylinder and two-cylinder models based on uniform theory of diffraction (UTD) are applied to model the shape of a human body and predict its attenuation in theory. The ITU (International Telecommunication Union) and its modified models are used to predict the attenuation by trees. The results show that the upper bound of the KE model is better to predict the attenuation by a human body compared with UTD one-cylinder and two-cylinder models at both 26 and 39 GHz. ITU model overestimates the attenuation by willow trees, and a modified attenuation model by trees is proposed based on our measurements at 26 GHz. Penetration loss for materials such as wood and glass with different types and thicknesses is measured as well. The measurement and modeling results in this paper are significant and necessary for simulation and planning of fifth-generation (5G) mm-wave radio systems in ITU recommended frequency bands at 26 and 39 GHz

Nomogram based on computed tomography images and clinical data for distinguishing between primary intestinal lymphoma and Crohn’s disease: a retrospective multicenter study

BackgroundDifferential diagnosis of primary intestinal lymphoma (PIL) and Crohn’s disease (CD) is a challenge in clinical diagnosis.AimsTo investigate the validity of the nomogram based on clinical and computed tomography (CT) features to identify PIL and CD.MethodsThis study retrospectively analyzed laboratory parameters, demographic characteristics, clinical manifestations, and CT imaging features of PIL and CD patients from two centers. Univariate logistic analysis was performed for each variable, and laboratory parameter model, clinical model and imaging features model were developed separately. Finally, a nomogram was established. All models were evaluated using the area under the curve (AUC), accuracy, sensitivity, specificity, and decision curve analysis (DCA).ResultsThis study collected data from 121 patients (PIL = 69, CD = 52) from Center 1. Data from 43 patients (PIL = 24, CD = 19) were collected at Center 2 as an external validation cohort to validate the robustness of the model. Three models and a nomogram were developed to distinguish PIL from CD. Most models performed well from the external validation cohort. The nomogram showed the best performance with an AUC of 0.921 (95% CI: 0.838–1.000) and sensitivities, specificities, and accuracies of 0.945, 0.792, and 0.860, respectively.ConclusionA nomogram combining clinical data and imaging features was constructed, which can effectively distinguish PIL from CD

Enhanced YOLOv5s + DeepSORT method for highway vehicle speed detection and multi-sensor verification

Addressing the need for vehicle speed measurement in traffic surveillance, this study introduces an enhanced scheme combining YOLOv5s detection with Deep SORT tracking. Tailored to the characteristics of highway traffic and vehicle features, the dataset data augmentation process was initially optimized. To improve the detector’s recognition capabilities, the Swin Transformer Block module was incorporated, enhancing the model’s ability to capture local regions of interest. CIoU loss was employed as the loss function for the vehicle detection network, accelerating model convergence and achieving higher regression accuracy. The Mish activation function was utilized to reduce computational overhead and enhance convergence speed. The structure of the Deep SORT appearance feature extraction network was modified, and it was retrained on a vehicle re-identification dataset to mitigate identity switches due to obstructions. Subsequently, using known references in the image such as lane markers and contour labels, the transformation from image pixel coordinates to actual coordinates was accomplished. Finally, vehicle speed was measured by computing the average of instantaneous speeds across multiple frames. Through radar and video Multi-Sensor Verification, the experimental results show that the mean Average Precision (mAP) for target detection consistently exceeds 90%. The effective measurement distance for speed measurement is around 140 m, with the absolute speed error generally within 1–8 km/h, meeting the accuracy requirements for speed measurement. The proposed model is reliable and fully applicable to highway scenarios