In daily life, sensorimotor integration processes are fundamental for many cognitive operations. The pursuit-tracking paradigm is an ecological and valid paradigm to examine sensorimotor integration processes in a mor...
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In daily life, sensorimotor integration processes are fundamental for many cognitive operations. The pursuit-tracking paradigm is an ecological and valid paradigm to examine sensorimotor integration processes in a more complex environment than many established tasks that assess simple motor responses. However, the analysis of pursuit-tracking performance is complicated, and parameters quantified to examine performance are sometimes ambiguous regarding their interpretation. We introduce an open-source algorithm (TRACK) to calculate a new tracking error metric, the spatial error, based on the identification of the intended target position for the respective cursor position. The identification is based on assigning cursor and target direction changes to each other as key events, based on the assumptions of similarity and proximity. By applying our algorithm to pursuit-tracking data, beyond replication of known effects such as learning or practice effects, we show a higher precision of the spatial tracking error, i.e., it fits our behavioral data better than the temporal tracking error and thus provides new insights and parameters for the investigation of pursuit-tracking behavior. Our work provides an important step towards fully utilizing the potential of pursuit-tracking tasks for research on sensorimotor integration processes.
This paper describes a novel electrostatically actuated microgripper with freeform geometries designed by a genetic *** new semiautomated design methodology is capable of designing near-optimal MEMS devices that are r...
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This paper describes a novel electrostatically actuated microgripper with freeform geometries designed by a genetic *** new semiautomated design methodology is capable of designing near-optimal MEMS devices that are robust to fabrication *** use of freeform geometries designed by a genetic algorithm significantly improves the performance of the *** experiment shows that the designed microgripper has a large displacement(91.5μm)with a low actuation voltage(47.5 V),which agrees well with the *** microgripper has a large actuation displacement and can handle micro-objects with a size from 10 to 100μm.A grasping experiment on human hair with a diameter of 77μm was performed to prove the functionality of the *** result confirmed the superior performance of the new design methodology enabling freeform *** design method can also be extended to the design of many other MEMS devices.
Aim: : The management of cardiorespiratory arrest in a diving bell presents multiple clinical, technical, and environmental considerations that standard resuscitation algorithms do not address, and no situation-specif...
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Aim: : The management of cardiorespiratory arrest in a diving bell presents multiple clinical, technical, and environmental considerations that standard resuscitation algorithms do not address, and no situation-specific algorithm exists. The development and testing of an algorithm to guide the management of cardiorespiratory arrest in a bell is described. Methods: : An iterative approach to algorithm development was used. Phase 1 involved a small multidisciplinary group and took place in a simulation centre and a decommissioned diving bell. The algorithm was then refined in a purpose-build simulation complex with repeated simulation by a group of divers, and with input from industry experts. ALS principles were followed unless contextual or technical factors necessitated deviation. Results: : Clinical and technical aspects of the resuscitation are addressed. Key priorities that conflict with standard ALS principles are: prioritisation of rescue breaths;use of mechanical CPR when available;and the provision of CPR with the casualty in a seated position where necessary. Conclusion: : This is the first algorithm to guide the delivery of resuscitation in a diving bell. It incorporates adapted ALS principles and available data concerning compression technique effectiveness, and was informed by industry and clinical expertise. It provides guiding principles that can be adapted to setting-specific needs, and we would encourage its industry-wide international adoption.
Introduction: Hidradenitis suppurativa is an occasionally devastating inflammatory skin disease. Currently, adalimumab is the only approved biological or small molecule medication for its treatment. Objectives: To eva...
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As the number of Internet of Things (IoT) devices being connected to the internet continues to rise, the security of these devices is becoming an increasingly pressing concern. With the increasing number of IoT device...
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ISBN:
(纸本)9798350349795;9798350349788
As the number of Internet of Things (IoT) devices being connected to the internet continues to rise, the security of these devices is becoming an increasingly pressing concern. With the increasing number of IoT devices in homes, businesses, and industrial settings, the potential for cyber-attacks targeting these devices has also increased. Although there are many methods of ensuring network security in IoT, such as using encryption, firewalls, and intrusion detection systems, the issue of security is still not been fully solved. Data encryption is one of the most implemented cybersecurity solutions as it provide data protection from being stolen by unauthorized parties. This paper presents the design, simulation, and performance measurement of Simon encryption algorithm in IoT environment. Simon algorithm is a lightweight encryption algorithm which suitable for resource constraint environment like IoT devices. The performance metric used to measure the performance are time duration for a complete process and CPU utilization. In future work, the algorithm can still be optimized to offer better performance. As we are transitioning into fully digital environment, a robust cybersecurity solution is demanded.
In this paper, we introduce an algorithm for determining the grammatical validity of a sentence in polynomial time. The algorithm works by encoding the English words based on word type which we call components. A sent...
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In this paper, we introduce an algorithm for determining the grammatical validity of a sentence in polynomial time. The algorithm works by encoding the English words based on word type which we call components. A sentence can be described both algebraically and geometrically. Our algorithm generates the geometric portion called underlinks from the generalized reductions of the algebraic portion. Underlinks uniquely determine the reduction of the components leading to the empty string. This is the mathematical basis for determining if a sentence is valid. We also provide a proof for the algorithm's time complexity of O(n(2)) along with a Python implementation. This paper is part of a bigger project where we explore the combination of a sentence's grammar and meaning. This is done by combining two compact closed categories;pregroups represent the grammar of a sentence, and finite dimensional vector spaces describe the meaning of a sentence. Together one compact closed category is created, representing both aspects of the sentence.
Soil organic matter (SOM) serves as a crucial indicator for assessing soil fertility and cultivated land quality. This study developed a SOM prediction method based on a back propagation neural network with genetic al...
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ISBN:
(纸本)9798350380613;9798350380606
Soil organic matter (SOM) serves as a crucial indicator for assessing soil fertility and cultivated land quality. This study developed a SOM prediction method based on a back propagation neural network with genetic algorithm variable selection. The experimental area was in Hailun City, a typical black soil region of Heilongjiang Province, China. A total of 157 sampling points were selected at a depth of 0-20 cm. The Landsat 8 OLI images from the bare soil period were used for calculating the spectral indexes. The Shuttle Radar Topography Mission (SRTM) data was utilized to extract topographic factors. Then the multiple stepwise regression (MSR) and genetic algorithm (GA) methods were used to screen the variables as input, respectively. Multiple linear regression (MLR) and back propagation neural network (BPNN) methods were utilized to build the SOM prediction model. The root mean square error (RMSE) and correlation coefficient (R2) were employed to evaluate the accuracy of SOM prediction. Finally, the optimal model was used to predict the SOM in the black soil area. The results indicated that the GA outperformed MSR in variable selection. The BP neural network (with GA-selected variables GA_BP) has the highest accuracy for the SOM prediction, with an R2 of 0.94 and an RMSE of 0.14, followed by the BP neural network with a stepwise regression-selected factor (MSR_BP) with an R2 of 0.91 and an RMSE of 0.24. The R2 of BPNN was 0.84 and the RMSE was 0.29, while MSR showed the lowest accuracy, with an R2 of 0.69 and an RMSE of 0.57. This study not only proved the advantages of GA in significantly improving model accuracy by decreasing the dimensionality of the inputsbut also provided effective methods for improving the precision of SOM remote sensing prediction, showing potential applications in predicting SOM in black soil areas.
In the recent past, the frequency distribution in wireless networks was known to be a major issue that resulted in the unfair utilization of wireless channels. The channel assignment becomes extremely complex due to t...
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A improved YOLOv5s (You Only Look Once 5s) algorithm is intended to dispose of the issues of low detection accuracy, low coverage, and large computational complexity of road object detection algorithms in complex envi...
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ISBN:
(纸本)9798350377859;9798350377842
A improved YOLOv5s (You Only Look Once 5s) algorithm is intended to dispose of the issues of low detection accuracy, low coverage, and large computational complexity of road object detection algorithms in complex environments. To improve the applicability of the algorithm in various complex scenarios, we draw the CBAM attention mechanism into the CBS module, which is the Backbone benchmark network to enhance the ability to obtain tiny target features and upgrade the recognition rate of tiny targets. By optimizing the CIoU loss function and the alpha IoU loss function, this paper use the alpha IoU loss function to substitute the traditional loss function of YOLOv5s model, this enhancement improves model training accuracy and hasten the convergence rate of the exercise process. The conclusion shows that the improved YOLOv5s algorithm has an accuracy of 94.06%, a recall rate is 92.33%, and an average accuracy is 95.38%. It is superior to other mainstream detection algorithms such as YOLOv5s and SSD, and can achieve road object detection in complex environments. The research results suggest that the improved YOLOv5s algorithm has improved practical efficiency, especially for complex scene object detection, which has important research significance.
BACKGROUND Multinucleated giant cells(MGCs)in bladder carcinomas are poorly *** To describe the function,morphogenesis,and origin of mononuclear and MGCs in urothelial carcinoma(UC)of the bladder in Bulgarian and Fren...
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BACKGROUND Multinucleated giant cells(MGCs)in bladder carcinomas are poorly *** To describe the function,morphogenesis,and origin of mononuclear and MGCs in urothelial carcinoma(UC)of the bladder in Bulgarian and French *** Urothelial bladder carcinomas(n=104)from 2016-2020 were analyzed retrospectively using immunohistochemical(IHC)and histochemical stain *** cells in the bladder stroma were found in 35.6%of cases,more often in *** We confirm that MGCs in the mucosa in UC of the bladder were positive for both mesenchymal and myofibroblast markers(vimentin,smooth muscle actin,Desmin,and CD34)and the macrophage marker ***,IHC studies revealed the following profile of these cells:Positive for p16;negative for epithelial(CK AE1/AE3 and GATA-3),vascular(CD31),neural(PS100 and CKIT),cambial,blastic(CD34-blasts and C-KIT),and immune markers(IG G,immunoglobulin G4,and PD-L1);no proliferative activity,possess no specific immune function,and cannot be used to calculate the Combined Positive Score *** In conclusion,the giant stromal cells in non-tumor and tumor bladder can be used as a characteristic and relatively constant,although nonspecific,histological marker for chronic bladder damage,reflecting the chronic irritation or ***,according to the morphological and IHC of the mono-and multinucleated giant cells in the bladder,they are most likely represent telocytes capable of adapting their morphology to the pathology of the organ.
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