Clinical and morphological phenotypes in intrathoracic sarcoidosis

Aim. To study clinical and morphological phenotypes in different variants of the course of intrathoracic sarcoidosis and isolate new phenotypes.Materials and methods. The study included 121 patients with intrathoracic sarcoidosis aged 21–66 years (50.4% were men, 49.6% were women, the average age at the time of the disease onset was 38 years) over the period 2007– 2019. During the examination, patients’ complaints were studied thoroughly, and the diagnosis was histologically verified in all cases. During an extended histological examination, the quantitative and qualitative composition of biopsy specimens was investigated. The number of granulomas in the field of vision and the content of giant cells, macrophages, lymphocytes, neutrophils, and eosinophils in them were studied. Qualitative parameters were assessed for the presence of hyalinosis, Schaumann bodies, necrosis, stamping, calcification, fibrosis, and vasculitis. All patients were retrospectively divided into two clinical groups depending on the outcomes of the disease: group 1 included patients with a favorable course of sarcoidosis, proceeding without relapses and signs of progression; group 2 encompassed patients with an unfavorable course of the disease with relapses and progression, requiring long-term administration of systemic glucocorticoids.Results. The analysis showed that among all general clinical manifestations, only the presence of dyspnea, skin manifestations, and weight loss occurred significantly more often in the patients with an unfavorable course of intrathoracic sarcoidosis (р = 0.04; 0.02; and 0.01, respectively). Among morphological parameters, a large number of macrophages was significantly more frequent in the biopsy specimens in this group of patients (р < 0.01).

Drone-Based Water Level Detection in Flood Disasters

Japan was hit by typhoon Hagibis, which came with torrential rains submerging almost eight-thousand buildings. For fast alleviation of and recovery from flood damage, a quick, broad, and accurate assessment of the damage situation is required. Image analysis provides a much more feasible alternative than on-site sensors due to their installation and maintenance costs. Nevertheless, most state-of-art research relies on only ground-level images that are inevitably limited in their field of vision. This paper presents a water level detection system based on aerial drone-based image recognition. The system applies the R-CNN learning model together with a novel labeling method on the reference objects, including houses and cars. The proposed system tackles the challenges of the limited and wild data set of flood images from the top view with data augmentation and transfer-learning overlaying Mask R-CNN for the object recognition model. Additionally, the VGG16 network is employed for water level detection purposes. We evaluated the proposed system on realistic images captured at disaster time. Preliminary results show that the system can achieve a detection accuracy of submerged objects of 73.42% with as low as only 21.43 cm error in estimating the water level.

Ikaros Proteins in Tumor: Current Perspectives and New Developments

Ikaros is a zinc finger transcription factor (TF) of the Krüppel family member, which significantly regulates normal lymphopoiesis and tumorigenesis. Ikaros can directly initiate or suppress tumor suppressors or oncogenes, consequently regulating the survival and proliferation of cancer cells. Over recent decades, a series of studies have been devoted to exploring and clarifying the relationship between Ikaros and associated tumors. Therapeutic strategies targeting Ikaros have shown promising therapeutic effects in both pre-clinical and clinical trials. Nevertheless, the increasingly prominent problem of drug resistance targeted to Ikaros and its analog is gradually appearing in our field of vision. This article reviews the role of Ikaros in tumorigenesis, the mechanism of drug resistance, the progress of targeting Ikaros in both pre-clinical and clinical trials, and the potential use of associated therapy in cancer therapy.

Does Useful Field of Vision Predict Attention While Driving Between Young and Older Adults?

The Insurance Information Institute (2017) reports drivers aged 65 and up are involved in the second highest rate of fatal car crashes. It is important that there is a fair and standardized assessment to test driving fitness. The prime objective was to assess the utility of the Useful Field of View (UFOV) across young and old groups to predict performance on a simulated driving exercise. Community-dwelling adults aged 65 and older (n=48) and students (n=48) recruited from an undergraduate research pool served as participants. They completed a series of demographic, health and cognitive measures, besides a Useful Field of Vison (UFOV) task and a driving simulation exercise. Results showed that collision avoidance and braking varied between age groups, with older adults appearing to be less likely to avoid collision (Older M = 12.46, SD = 10.25, Younger (M = 7.96, SD =4.92; n = 47), but quicker to brake (Older M = 3.64, SD = 3.41, Younger M = 9.79, SD =7.91). There were group differences for driving simulator performance, predicted by cognitive measures (Young; R2 = .099, p = 0.005; Old; R2 = 0.094, p = 0.038). UFOV scores did not predict group differences in driving simulator performance (Young; R2 = 0.009, p = 0.664; β = 0.089, p = 0.437) , (Older; R2 = 0.061, p = 0.522; UFOV-DA β = -0.074, p = 0.555; UFOV_SA β = 0.289, p = 0.194). These findings have implication for the development of pragmatic capacity to drive assessments.

OBSERVER’S TRAJECTORY TRACKING OBJECT BYPASSING OBSTACLE ON THE SHORTEST CURVE

Motion of some object is considered. The object t moves from the initial point t∗ to the final one t ∗ . But since absent of the direct path, he should bypass an obstacle a connected bodily set G. It is supposed that t moves by the most short trajectory T = Tt , and the trajectory T is a convex curve. The observer’s f task is to find the trajectory Tf that provides tracking the object on the most part of the object’s motion and, if possible, the lesser object’s stealth of motion along the trajectory T . The latency is defined by the distance that the observer must pass to see the object in the field of vision. The object and observer start at the same initial instant, and their velocities are equal. In the paper, examples of the trajectories Tf in R 2 are presented, on which the observer can see the object’s trajectory T ; also, the value of the object’s latency is shown for the invisible parts of the trajectory T . The variant of Tf in R 3 is shown.

Translation Accuracy Evaluation of English Complex Long Sentences Based on Multi-Label Clustering Algorithms

In the traditional sense, the translation evaluation of English complex long sentences is often limited to the idea of whether or how to realize the semantic transformation of the original text, so many phenomena that have nothing to do with language but directly affect the translation evaluation are not included in the field of vision and can be interpreted. In order to solve the above problems, a multi-label clustering algorithm is proposed to evaluate the translation accuracy of English complex long sentences. The multi-label clustering algorithm is introduced into the translation evaluation activities to carry out the translation and detection parameters of complex long sentences. The comprehensive description, the accuracy of generalization and the rationality of interpretation lay a solid foundation for English translation activities.

Research on Environmental Pollution regulation in China’s Construction Industry

China has proposed the notion of sustainable development in the framework of a hazardous society in response to global environmental challenges. Research on regulatory concerns is critical for ensuring the application of the concept of sustainable development in the building industry. China’s regulation, on the other hand, suffers from a lack of supply and an ambiguous value orientation. The former is a symptom of the current regulatory structure, whereas the latter is the root of the problem. To overcome the value-oriented dilemma, some studies plan to conduct research using the logic of the norm. This has the disadvantage of having a limited field of vision. The interaction between man and nature should be considered while examining value orientation. When anthropocentrism, ecocentrism, and anthropocentrism in ecology are compared to the environmental legislation in my nation, it is clear that anthropocentrism in ecology is the value orientation that should be followed at the moment. Three challenges that require attention are presented based on this value orientation, as well as the uniqueness of the construction industry and risk society theory.

Electromagnetic Wave Absorption and Mechanical Properties of CNTs@GN@Fe3O4/PU Multilayer Composite Foam

With the development of intelligent communications and stealth technology in the military field, electromagnetic wave pollution cannot be ignored, and absorbing materials have entered people’s field of vision and gradually become a research hotspot. The ideal absorbing material should have the characteristics of “strong, wide, thin, and light”, but a single absorbing material often cannot meet the above conditions. At present, absorbing metal powder combined with two-dimensional carbon nanomaterials (such as carbon nanotubes, graphene, etc.) has became a trend. This article focus on a three-layer composite of Fe3O4, Carbon nanotubes@ Fe3O4, Carbon nanotubes@Graphene nano-platelets@ Fe3O4, which was synthesized by solvothermal method. The results show that the electromagnetic wave absorption performance of the three-layer foam at a thickness of 3.0 mm is more excellent. The minimum of RL can reach −67.0 dB, and the effective bandwidth is above 5.0 GHz. All this is due to the synergy of dielectric and magnetic loss between Fe3O4, CNTs, and GN, the increase of interface polarization and the path of electromagnetic wave reflection and scattering by three-layer foam.

Path Optimization of Joint Delivery Mode of Trucks and UAVs

With the development of e-commerce and information technology, new modes of distribution are emerging. A new type of distribution tool, UAV (unmanned aerial vehicle), has entered into the public’s field of vision. In the background of growing e-commerce, this paper proposes a new delivery mode of joint delivery of trucks and UAVs which particularly has been popular in recent years, with the advantages of prompt delivery, low cost, and independence from terrain restrictions, while traditional transportation tools such as trucks have more advantages in terms of flight distance and load capacity. Therefore, the joint delivery mode of trucks and UAVs proposed in this paper can well realize the complementary advantages of trucks and UAVs in the distribution process and consequently optimize the distribution process. Moreover, the growing e-commerce promotes customers’ higher needs for delivery efficiency and the integrity of the delivered goods which urges companies to pay more attention to customers’ satisfaction. This paper analyzes the joint delivery mode of trucks and UAVs, aims to minimize total delivery cost and maximize customer satisfaction, and builds a multiobjective optimization model for joint delivery. Furthermore, an improved ant colony algorithm is proposed in order to solve the mode in this paper. In order to effectively avoid prematurity of the ant colony algorithm, the limited pheromone concentration and the classification idea of the artificial bee colony algorithm are introduced to improve the ant colony algorithm. Finally, some experiments are simulated by MATLAB software, and the comparison shows that the joint delivery of trucks and UAVs has more advantages, and the improved ant colony algorithm is more efficient than the traditional ant colony.

Joint intent detection and slot filling with wheel-graph attention networks

Intent detection and slot filling are recognized as two very important tasks in a spoken language understanding (SLU) system. In order to model these two tasks at the same time, many joint models based on deep neural networks have been proposed recently and archived excellent results. In addition, graph neural network has made good achievements in the field of vision. Therefore, we combine these two advantages and propose a new joint model with a wheel-graph attention network (Wheel-GAT), which is able to model interrelated connections directly for single intent detection and slot filling. To construct a graph structure for utterances, we create intent nodes, slot nodes, and directed edges. Intent nodes can provide utterance-level semantic information for slot filling, while slot nodes can also provide local keyword information for intent detection. The two tasks promote each other and carry out end-to-end training at the same time. Experiments show that our proposed approach is superior to multiple baselines on ATIS and SNIPS datasets. Besides, we also demonstrate that using bi-directional encoder representation from transformer (BERT) model further boosts the performance of the SLU task.