IoT and Wearable Devices-Enhanced Information Provision of AR Glasses: A Multi-Modal Analysis in Aviation Industry

While Augmented Reality (AR) glasses are now instrumental in industries for delivering work-related information, the current one-size-fits-all information provision of AR glasses fails to cater to diverse workers’ needs and environmental conditions. We propose a framework for harnessing Internet of thing (IoT) and wearable technology to improve the adaptability and customization of information provision by AR. As a preliminary exploration, this short paper develops a multi-modal data processing system for work performance classification in the aviation industry. Using machine learning algorithms for multi-modal feature extraction and classifier construction, this framework provides a more objective and consistent evaluation of work performance compared to single-modal approaches. The proposed analytics architecture can provide valuable insights for other industries struggling to implement IoT and mixed reality

Genome-wide characterization of the biggest grass, bamboo, based on 10,608 putative full-length cDNA sequences

<p>Abstract</p> <p>Background</p> <p>With the availability of rice and sorghum genome sequences and ongoing efforts to sequence genomes of other cereal and energy crops, the grass family (Poaceae) has become a model system for comparative genomics and for better understanding gene and genome evolution that underlies phenotypic and ecological divergence of plants. While the genomic resources have accumulated rapidly for almost all major lineages of grasses, bamboo remains the only large subfamily of Poaceae with little genomic information available in databases, which seriously hampers our ability to take a full advantage of the wealth of grass genomic data for effective comparative studies.</p> <p>Results</p> <p>Here we report the cloning and sequencing of 10,608 putative full length cDNAs (FL-cDNAs) primarily from Moso bamboo, <it>Phyllostachys heterocycla </it>cv. <it>pubescens</it>, a large woody bamboo with the highest ecological and economic values of all bamboos. This represents the third largest FL-cDNA collection to date of all plant species, and provides the first insight into the gene and genome structures of bamboos. We developed a Moso bamboo genomic resource database that so far contained the sequences of 10,608 putative FL-cDNAs and nearly 38,000 expressed sequence tags (ESTs) generated in this study.</p> <p>Conclusion</p> <p>Analysis of FL-cDNA sequences show that bamboo diverged from its close relatives such as rice, wheat, and barley through an adaptive radiation. A comparative analysis of the lignin biosynthesis pathway between bamboo and rice suggested that genes encoding caffeoyl-CoA O-methyltransferase may serve as targets for genetic manipulation of lignin content to reduce pollutants generated from bamboo pulping.</p

Terahertz-driven irreversible topological phase transition in two-dimensional MoTe2_{2}

Recent discoveries of broad classes of quantum materials have spurred fundamental study of what quantum phases can be reached and stabilized, and have suggested intriguing practical applications based on control over transitions between quantum phases with different electrical, magnetic, and$/$or optical properties. Tabletop generation of strong terahertz (THz) light fields has set the stage for dramatic advances in our ability to drive quantum materials into novel states that do not exist as equilibrium phases. However, THz-driven irreversible phase transitions are still unexplored. Large and doping-tunable energy barriers between multiple phases in two-dimensional transition metal dichalcogenides (2D TMDs) provide a testbed for THz polymorph engineering. Here we report experimental demonstration of an irreversible phase transition in 2D MoTe$_{2}$ from a semiconducting hexagonal phase (2H) to a predicted topological insulator distorted octahedral ($1T^{'}$) phase induced by field-enhanced terahertz pulses. This is achieved by THz field-induced carrier liberation and multiplication processes that result in a transient high carrier density that favors the $1T^{'}$ phase. Single-shot time-resolved second harmonic generation (SHG) measurements following THz excitation reveal that the transition out of the 2H phase occurs within 10 ns. This observation opens up new possibilities of THz-based phase patterning and has implications for ultrafast THz control over quantum phases in two-dimensional materials

The aging lung: microenvironment, mechanisms, and diseases

With the development of global social economy and the deepening of the aging population, diseases related to aging have received increasing attention. The pathogenesis of many respiratory diseases remains unclear, and lung aging is an independent risk factor for respiratory diseases. The aging mechanism of the lung may be involved in the occurrence and development of respiratory diseases. Aging-induced immune, oxidative stress, inflammation, and telomere changes can directly induce and promote the occurrence and development of lung aging. Meanwhile, the occurrence of lung aging also further aggravates the immune stress and inflammatory response of respiratory diseases; the two mutually affect each other and promote the development of respiratory diseases. Explaining the mechanism and treatment direction of these respiratory diseases from the perspective of lung aging will be a new idea and research field. This review summarizes the changes in pulmonary microenvironment, metabolic mechanisms, and the progression of respiratory diseases associated with aging

Dynamic Inertia Weight Binary Bat Algorithm with Neighborhood Search

Binary bat algorithm (BBA) is a binary version of the bat algorithm (BA). It has been proven that BBA is competitive compared to other binary heuristic algorithms. Since the update processes of velocity in the algorithm are consistent with BA, in some cases, this algorithm also faces the premature convergence problem. This paper proposes an improved binary bat algorithm (IBBA) to solve this problem. To evaluate the performance of IBBA, standard benchmark functions and zero-one knapsack problems have been employed. The numeric results obtained by benchmark functions experiment prove that the proposed approach greatly outperforms the original BBA and binary particle swarm optimization (BPSO). Compared with several other heuristic algorithms on zero-one knapsack problems, it also verifies that the proposed algorithm is more able to avoid local minima

A Blockchain-Based Decentralized Public Key Infrastructure for Information-Centric Networks

How to achieve secure content distribution and accountability in information-centric networking (ICN) is a crucial problem. Subscribers need to verify whether the data came from a reliable source, rather than from a spoofing adversary. Public key cryptography was introduced to achieve a method of authentication that binds the data packet to its owner. In existing prototypes, PKIs, identity-based signatures (IBSs) and recommendation networks are the common schemes used to ensure the authenticity and availability of public keys. However, CA-based PKIs and KGC-based IBSs have been proven to be weak when it comes to resisting security attacks, with recommendation networks being too complex to deploy. In this respect, we designed a novel distributed authentication model as a secure scheme to support public key cryptography. Our model establishes a decentralized public key infrastructure by combining the smart contracts of blockchain and optimized zero-knowledge proof-verifiable presentations by utilizing the DID project, which realizes the management of public key certificates through blockchain and ensures the authenticity and availability of public keys in decentralized infrastructure. Our scheme fundamentally solves the issues of security and feasibility in existing schemes and provides a more scalable solution with respect to authenticating data sources. An experiment demonstrated that our proposal is 20% faster than the original zero knowledge proof scheme in registration

Reduced-order Koopman modeling and predictive control of nonlinear processes

In this paper, we propose an efficient data-driven predictive control approach for general nonlinear processes based on a reduced-order Koopman operator. A Kalman-based sparse identification of nonlinear dynamics method is employed to select lifting functions for Koopman identification. The selected lifting functions are used to project the original nonlinear state–space into a higher-dimensional linear function space, in which Koopman-based linear models can be constructed for the underlying nonlinear process. To curb the significant increase in the dimensionality of the resulting full-order Koopman models caused by the use of lifting functions, we propose a reduced-order Koopman modeling approach based on proper orthogonal decomposition. A computationally efficient linear robust predictive control scheme is established based on the reduced-order Koopman model. A case study on a benchmark chemical process is conducted to illustrate the effectiveness of the proposed method. Comprehensive comparisons are conducted to demonstrate the advantage of the proposed method.Ministry of Education (MOE)Submitted/Accepted versionThis research is supported by Ministry of Education, Singapore, under its Academic Research Fund Tier 1 (RS15/21)

Global attractors for a tropical climate model

summary:This paper is devoted to the global attractors of the tropical climate model. We first establish the global well-posedness of the system. Then by studying the existence of bounded absorbing sets, the global attractor is constructed. The estimates of the Hausdorff dimension and of the fractal dimension of the global attractor are obtained in the end

The effectiveness of theory-based smoking cessation interventions in patients with chronic obstructive pulmonary disease: a meta-analysis

Abstract Background Smoking cessation can effectively reduce the risk of death, alleviate respiratory symptoms, and decrease the frequency of acute exacerbations in patients with chronic obstructive pulmonary disease (COPD). Effective smoking cessation strategies are crucial for the prevention and treatment of COPD. Currently, clinical interventions based on theoretical frameworks are being increasingly used to help patients quit smoking and have shown promising results. However, theory-guided smoking cessation interventions have not been systematically evaluated or meta-analyzed for their effectiveness in COPD patients. To improve smoking cessation rates, this study sought to examine the effects of theory-based smoking cessation interventions on COPD patients. Methods We adhered to the PRISMA guidelines for our systematic review and meta-analysis. The Cochrane Library, Web of Science, PubMed, Embase, Wanfang, CNKI, VIP Information Services Platform, and China Biomedical Literature Service System were searched from the establishment of the database to April 20, 2023. The study quality was assessed using the Cochrane Collaboration's risk assessment tool for bias. The revman5.4 software was used for meta-analysis. The I 2 test was used for the heterogeneity test, the random effect model and fixed effect model were used for meta-analysis, and sensitivity analysis was performed by excluding individual studies. Results A total of 11 RCTs involving 3,830 patients were included in the meta-analysis. Results showed that theory-based smoking cessation interventions improved smoking cessation rates, quality of life, and lung function in COPD patients compared to conventional nursing. However, these interventions did not significantly affect the level of nicotine dependence in patients. Conclusion Theory-based smoking cessation intervention as a non-pharmacologically assisted smoking cessation strategy has a positive impact on motivating COPD patients to quit smoking and improving their lung function and quality of life. Trial registration PROSPERO registration Number: CRD42023434357