Digital infrastructure is the basic guarantee for digital transformation, it is of practical significance to discuss how to effectively use digital infrastructure construction to promote the digital transformation of tourism enterprises. This paper examines the impact of digital infrastructure construction on tourism enterprises based on the "Broadband China" policy as a quasi-natural experiment. A-share tourism listed enterprises from 2010 to 2021 are studied using the double difference method. Findings indicate that digital infrastructure construction positively and significantly influences the digital transformation of tourism enterprises, driven by digital leadership and innovation capabilities. State-owned, small-scale, and high-growth tourism enterprises benefit more from this transformation. The results suggested that the state should develop differentiated digital infrastructure plans based on the specific conditions of different tourism enterprises. Additionally, tourism enterprises should enhance digital leadership and innovation capabilities through strategic changes and digital applications to amplify the impact of digital infrastructure on their transformation.

A La2FeCrO6 (LFCO) double perovskite powders were synthesized by hydrothermal method, which crystallized in an orthorhombic (Pnma) structure and exhibited a spherical morphology with average particle size of 900 nm. Fourier transformation infrared spectroscopy confirmed the octahedral coordination of Fe and Cr ions in the powders. XPS spectra revealed dual oxide states of Fe (Fe2+/Fe3+) and Cr (Cr3+/Cr4+) elements, and oxygen element appeared as lattice oxygen and defect oxygen, respectively. The LFCO powders exhibit ferromagnetic behavior at 5 K with saturation magnetization of 0.31 μB/f.u., coercive field of 8.0 kOe, and Curie temperature of 200 K. Griffiths phase appeared in the powders between 200 K and 223 K. The LFCO ceramics displayed a butterfly-like magnetoresistance (MR)-magnetic field (H) curve at 5 K with MR (5 K, 6 T) value of -4.07%. Temperature dependence of resistivity of the LFCO ceramics demonstrated their semiconducting nature. Electrical transport data were fitted by different conduction models. Strong frequency dispersion dielectric behavior was observed in the LFCO ceramics. A dielectric abnormality observed around 260 K, was ascribed to the jumping of electrons trapped in shallow levels created by oxygen vacancies. The dielectric relaxation behavior exhibited by dielectric loss between 160 K and 260 K, was contributed to the singly-ionized oxygen vacancies.

Based on the elasticity theory, this paper deals with the static analysis of a cracked double beam system in presence of a Winkler medium. The double-beam system is also supposed to be constrained at both ends by elastically flexible springs, with transverse and rotational stiffness. Using a variational formulation, the static governing equations are derived and solved by using analytical and numerical approaches. In the first approach, the closed-form solutions for the displacements functions are obtained based on the Euler-Bernoulli beam theory. In the second approach, the Cell-Discretization Method (CDM) is performed, according to which the two beams are reduced to a set of rigid bars linked together by means of elastic constraints, where the bending stiffness of the bars is concentrated. The resulting stiffness matrix is easily deduced, and the governing equations of the static problem can be immediately solved. A comparative analysis is performed in order to verify the accuracy and validity of the proposed method. This study focuses on the effect of various parameters including the crack depth and position, boundary conditions, elastic medium and slenderness. The validity of the proposed analysis is confirmed by comparing the present results with those obtained from the other approach. In particular, the results obtained by closed-form solution and Cell-discretization method (CDM) are compared by Finite element method (FEM). Accuracy of the results has been evaluated by making comparisons with the results in literature and reported in bibliography. It is demonstrated that the proposed algorithm provides a simple and powerful tool in dealing with the static analysis of a double-beam system. Finally, some concluding remarks are made.

When using meta-heuristic optimization approaches for optimization, a large number of samples are required. In particular, when generating subgeneration, the utilization of existing samples is low and the number of individuals is high. Therefore, surrogate-based optimization has been developed, which greatly reduces the number of individuals in the subgeneration and the cost of optimization. In complex air supply scenarios, single-objective optimization results may not be comprehensive; therefore, this paper developed a double-objective air supply optimization method based on the Kriging surrogate model and Non-dominated Sorting Genetic Algorithms-II. And proposed the infill criteria based on clustering to advance the Pareto Frontier. The method was validated by an inverse prediction case, and in particular, the problems when based on 3D steady-state simulations were analyzed. The results showed that the method can quickly achieve an approximate prediction of the boundary conditions (when prediction were made based on experimental data, the number of simulations was 82 and the average error was 6.8%). Finally, the method was used to optimize the air supply parameters of a dual-aisle, single-row cabin. The Pareto set suggested that an airflow organization with dual circulation may be optimal.

Diffusion-controlled cyclic voltammograms at fast scan rates show peak shifts as well as decreases in the peak currents from the predicted diffusion-controlled currents especially when currents are large in low concentration of supporting electrolyte. This has been conventionally recognized as an IR-drop effect by solution resistance on the peaks as well as heterogeneous kinetics. It is also brought about by negatively capacitive currents associated with charge transfer reactions. The reaction product generates dipoles with counterions to yield a capacitance, of which current flows oppositely to that of the double layer capacitance. The three effects are specified here in the oxidation of a ferrocenyl derivative by fast scan voltammetry. The expression for voltammograms complicated with IR-drop is derived analytically, and yields deformed voltammograms. The peak shift is approximately linear with the IR-term, but exhibits a convex variation. Dependence of some parameters on the peaks by the IR-drop is compared with those by the negative capacitance. The latter is more conspicuous than the former under conventional conditions. The two effects cannot be distinguished specifically except for variations of conductance of the solution.

With technological transformations such as big data, blockchain, artificial intelligence, and cloud computing, digital techniques are penetrating the field of finance. Digital finance is a resource-saving and environmentally friendly innovative financial service. It shows great green attributes and can drive the flow of financial resources towards environmentally friendly enterprises, thereby promoting green and low-carbon development. This paper investigates the effects of digital finance on green and low-carbon cyclic development and the mechanism. First, the level of green and low carbon cyclic development in China is estimated from multiple aspects based on the panel data of 31 provinces by using the spatio-temporal range entropy weight method. Then, the working mechanism of digital finance on green and low carbon cyclic development is revealed by performing empirical tests based on panel regression models, mediating effect models, and threshold models. Finally, the effects of digital finance and conventional variables on green and low-carbon cyclic development are investigated by using a random forest model and a CatBoost model in based on the machine learning. The results show that digital finance has significant positive effects on green and low-carbon cyclic development, and technological innovation plays a key role in the effects of digital finance on green and low-carbon cyclic development; meanwhile, the driving effect of digital finance on green and low carbon cyclic development shows nonlinear features with increasing “marginal effect”; besides, both digital finance and conventional factors have significant impacts on green and low carbon cyclic development. This study provides an empirical basis and path reference for digital finance to achieve “carbon peak, carbon neutralization” in China.

As the global aging trend continues to intensify, there will inevitably be more complex and diverse aging problems in the future. There is no doubt that designers have the responsibility to explore the possibilities of the future and solve the problems that will be faced in the future. Based on the Future Cone, the Double Diamond Model and the IDEO Method Cards, this study proposes a new model to guide the design practice of future aging issues in the context of aging. With the aim of validating and refining the framework, an ageing designer workshop was held where participants were asked to imagine, explore and express ideas about future ageing issues. The workshop was used to refine the proposed model. More specifically, the model includes a future concept, a design guidance process based on the Double Diamond Model, and tools that can be applied at all stages of design, which can help designers generate ideas and solutions for future aging problems, and collectively lead society to a more desirable future. Moreover, this study also explores broader directions for the development of the model and provides a reference for continued research on this topic in the future.

Relative contribution of different repair pathways to the radiation-induced DNA damage responses remains a challenging issue of studying the radiation injury endpoints. Comparative manifestation of homologous recombination (HR) after application of different radiation doses greatly determines an overall effectiveness of recovery in dividing cell after irradiation, since HR is an error-free mechanism intended for repair of DNA double strand breaks (DSB) during S/G2 phases of cell cycle. In this article, we present an experimentally observed evidence of dose-dependent shift in relative contribution of HR in human fibroblasts after X-ray exposure at doses 20-1000 mGy, which is also supported by quantitative modeling of DNA DSB repair. Our findings indicate that the radiation dose increase leads to dose-dependent decrease in relative contribution of HR into the entire repair process.

New hybrid composites based on the inclusion of a vegetal extract, i.e., Boswellia Serrata extract (BSE), rich in boswellic acids, well known as a potent anti-inflammatory natural drug, in a lamellar solid layered double hydroxide (LDH), i.e. a Magnesium Aluminum Carbonate, were developed and characterized in the present work. The adopted LDH exhibits a layered crystal structure, comprised of positively charged hydroxide layers and interlayers composed of carbonate anions and water molecules, thus it was expected to embed negatively charged boswellic acids. In the present case, a calcination process was also adopted on LDH to increase organic acids loading, based on the replacement of original inorganic anions. An accurate investigation was carried out by TGA, PXRD, FT-IR/ATR, XPS and LC-MS to ascertain the nature, interaction, and quantifica-tion of the active molecules of the vegetal extract loaded in the developed hybrid materials. As a result, a significant disruption of the original layered structure was observed in LDH subjected to calcination (LDHc), and this material was able to include a higher amount of organic acids when its composite with BSE was prepared. However, in vitro tests on composites bioactivity, expressed in terms of antimicrobial and anti-inflammatory activity, evidenced LDH-BSE as a better material compared to BSE and to LDHc-BSE, thus suggesting that, though the embedded organic acids amount was lower, they could be more available since not firmly bound to the clay. The composite was able to significantly decrease the number of viable pathogens such as Escherichia Coli and Staphylococcus aureus, as well as the internalization of toxic active species into human cells imposed to oxidative stress in comparison to the BSE.

The G-NUMEN array is the future gamma spectrometer of the NUMEN experiment (Nuclear Matrix Element for the Neutrinoless double beta decay), to be installed around the object point of the MAGNEX magnetic spectrometer at the INFN-LNS laboratory.. This project aims at exploring Double Charge Exchange (DCE) reactions in order to obtain crucial information about the neutrinoless double beta decay (0νββ). The primary objective of the G-NUMEN array is to detect the gamma rays emitted from the de-excitation of the excited states populated via DCE reactions with good energy resolution and detection efficiency, amidst a background composed of transitions from competingreaction channels with far higher cross-sections. To achieve this, the G-NUMEN signalswill be processed in coincidence with those generated by the detection of the reaction ejectiles in the MAGNEX Focal Plane Detector(FPD). Under the expected experimental conditions, G-NUMEN detectors will operate at high counting rates, of the order of hundreds of kHz per detector, while maintaining excellent energy and timing resolutions. The complete array will consist of over 100 LaBr3(Ce) scintillators. Initial tests have been conducted on the first detectors of the array, allowing for the determination of their performance at high rates.

The crystal structure of quintinite, Mg4Al2(OH)12(CO3)·3H2O, from Jacupiranga alkaline complex (Cajati, São Paulo, Brazil) has been refined for two samples (91002 and C7029) using single-crystal X-ray diffraction data. The mineral crystallizes in the P-3c1 space group, a = 5.246/5.298, c = 15.110/15.199 Å for the samples 91002/C7029. The crystal structure consists of octahedral sheets with Mg and Al ordering according to the 3 × 3 superstructure. The Mg and Al atoms are coordinated by six hydroxylated oxygen atoms each with the average <Mg–O> and <Al–O> bond distances being in the ranges 2.022–2.053 Å and 1.974–1.978 Å, respectively. The interlayer structures are identical (in contradiction to the previous assumptions) and compose from disordered (CO3)2- groups and (H2O)0 molecules. The samples from Jacupiranga can be identified as quintinite-2T that is the second find of the polytype after the Kovdor alkaline complex (Kola peninsula, Russia). The powder X-ray diffraction pattern of quintinite-2T contains weak superstructure reflection at 4.57 Å (010) indicative of Mg and Al ordering. An important crystal-chemical criterion of quintinite is the interlayer distance (d00n-value) of ~ 7.56 Å, which is steady for natural specimens from various findings worldwide.

Minimal rotating thermodynamic systems are addressed. Particle m placed into the rotating symmetrical double-well potential (bowl), providing binary logical system is considered. The condition providing the transfer of the particle from one frictionless half-well to another, and in this way possibility to record 1 bit of information is derived. The procedure of recording turns out to be irreversible; it is impossible to return the particle to its initial state under rotation about the same axis. The same rotating double-well system exerted to the thermal noise is considered. Minimal rotating thermal engine built of the rotating chamber, movable partition and the particle confined within the chamber is treated. Rotation of the system displaces the partition; thus, enabling erasing of one bit information. Erasing of 1 bit of information is due to the inertia (centrifugal force) acting on the partition. Isothermal expansion of the “minimal gas” expectedly gives rise to the Landauer bound. Compression of the “gas” with the rotation around the same axis is impossible and demands the additional axis of rotation. The interrelation between the possibility of recording/erasing information and the symmetry of the system is considered.

To understand the enhanced protection mechanism of CoCrNiAlY-YSZ-LaMgAl11O19 dou-ble-layer ceramic coating with aluminum plating, a finite element simulation method was used to simulate the distribution of thermal stress in the coating in all directions. The results show that in the air exposure of the un-aluminized coating, high temperature causes a large radial thermal stress on the surface of the LaMgAl11O19 (LMA) layer, and it increases with the increase in temperature, which is the main reason for the initiation of axial cracks. After arc aluminum plating, the aluminum plating layer effectively inhibited the volume shrinkage of the coating through good adhesion to the coating and internal diffusion, the thermal stress of the coating was considerably reduced, and the CoCrNiAlY-YSZ-LMA coating had an effective enhancement and protection effect; however, there was still a certain amount of shear thermal stress inside the LMA layer, the top of the crack, and the bottom of the crack. This thermal stress caused the initi-ation of radial microcracks in the LMA layer, which also becomes a risk point for the failure of the aluminum coating.

Urinary and fecal incontinence as well as skin pressure injury are common healthcare problems in nursing home; however, these prevalence and related risk factors were not well understood in the Japanese special elderly nursing home settings. We surveyed the prevalence of urinary, fecal and double incontinence, and skin pressure injury among the elderly living in special elderly nursing home in Japan. A nationwide cross-sectional epidemiological survey was conducted with a total of 4,881 residents. The prevalence of urinary, fecal and double incontinence was 82.9%, 68.9% and 64.9%, respectively. Skin pressure injury was found in 283 residents (283/4881, 5.8%). Age, Care-Needs level, loss of voiding desire, and fecal incontinence were significant risk factors for urinary incontinence. Residential period, Care-Needs level, loss of voiding and defecation desires, and urinary incontinence were significant risk factors for fecal incontinence. Only male sex was a significant risk factor for skin pressure injury. Our study revealed continence status and the prevalence of pressure skin injury among older adult residents who receive an end-of-life care in special nursing homes in Japan. Further studies should be conducted to examine whether recovery of urinary and fecal sensations improves continence status.

Phytophthora. infestans is a well known late blight causing oomycetes pathogen. It evolves and adapts to the host background and new fungicide molecules rapidly within a few years of their release, may be due to the predominance of transposable elements in its genome. Frequent and huge applications of fungicides cause environmental concerns. Here we developed target specific RNA interference based molecules, that are capable of effectively reducing the late blight infection. cDNA microarray expression data was used for the selection of genes involved in the early stage of infection process, sporulation etc. The in vitro synthesis of double stranded RNA molecule, targeting SDH, EF-1α, GPI-HAM344, PLD-3 and HSP-90 encoding genes revealed the reduction in growth, sporulation and symptom expression, which were subsequently assessed by culture bioassay, detached leaf assay and topical application methods. The multiple genes targeted dsRNA nano clay sprayed plants showed enhanced disease resistance (4% disease severity) and least sporulation (<1x103), compared to naked dsRNA spray. Use of nano clay was assumed to be involved in the effective delivery, protection and boosting the action of RNAi in potato plants. A significant difference in the growth, sporulation count, disease severity and reduced expression of the genes and confocal microscopy imaging authenticates the effects of SIGS on late blight disease progression. Our research demonstrated that topical dsRNA nano clay spray under the open-air environment could be an alternative to chemical fungicides and transgenic approaches as a novel plant protection strategy for late blight in an environmentally friendly manner.

Acceptance of SNAP benefits has increased in farmers’ markets (FM) over the years. However, getting SNAP recipients to take up the opportunity of extra dollars for healthy food options remains a challenge. Thus, we tracked the use of the Double-up Food Buck (DUFB) program among SNAP recipients who visit participating FMs in Utah to understand the unique characteristics of the program users. Our results identified varying types of program users (multiple and single market shoppers, more frequent and less frequent shoppers). While our findings indicate a higher DUFB transaction in token value among frequent users, larger markets are more likely to have higher DUFB transactions with a higher number of market visits compared to smaller ones. In conclusion, while all types of users may add to the total revenue generated by participating FM, additional initiatives are required to encourage frequent visits and uptake of the program for better access to healthy foods.

The study explored the contribution of ICT-based information sources to market participation among smallholder livestock farmers. Use of ICTs is considered paramount for providing smallholder farmers with required market information, in order to reduce market asymmetries. A Double Hurdle regression was utilized to analyze data collected from 150 smallholder livestock farmers in the study area. The results show that while use of ICT-based market information sources significantly influenced market participation, the effect of using ICT-based information sources on intensity of market participation was not significant. Other variables shown to influence both market participation and the intensity of market participation were age, additional income and membership of farmer cooperatives. This suggests the need to also consider other associated factors in the application of interventions which utilize ICT-based information sources in achieving planned market interventions.

A double-stranded RNA (dsRNA) virus was isolated and characterized from a strain EW220 of the phytopathogenic fungus Botryosphaeria dothidea. The full-length cDNAs of dsRNAs were 6,434 bp and 5,986 bp in size, respectively. The largest dsRNA of BdBRV1 encodes a cap-pol fusion protein that contains part coat protein gene and a RNA-dependent RNA polymerase (RdRp) domain, and the second dsRNA encodes a hypothetical protein. The dsRNA virus was named Botryosphaeria dothidea botybirnavirus 1 (BdBRV1). BdBRV1 contains spherical virions that are 37 nm in diameter consisting of two segments. The structural proteins of BdBRV1 virus particles were 110 kDa, 90 kDa, and 80 kDa, which encoded by dsRNA1 and 2-ORFs. The analysis of genome sequences revealed that the sequences of BdBRV1 shared 99% identity with Bipolaris maydis botybirnavirus 1(BmBRV1). Phylogenetic reconstruction indicated that BdBRV1 and BmBRV1 are phylogenetically related to the genus Botybirnavirus. Importantly, BdBRV1 influences the growth of B. dothidea and confers hypovirulence to the fungal host. BdBRV1 and BmBRV1 were two strains of the same virus. To our knowledge, this is the first report of a botybirnavirus in B. dothidea and our result might be the first funding in the different fungi infected by the same virus.

Previous high throughput screening studies indicated trans-eQTLs tend to be tissue specific. In this study, I probed if this feature can be used to identify tissue-specific gene regulatory networks. eQTL data (P<5e-8) for 16,259 genes were identified and their summary association statistics were obtained from the eQTLGene database. Next, eQTLs that display both cis and trans regulatory effects were selected and the association between their corresponding genes were examined using Mendelian randomization. A total of 169 genes that exerted trans-regulatory impacts on 692 genes were identified. 90% of the genes (N=749) aggregated into a gene regulatory network significantly enriched in hemo-immune processes. The robustness of finding was confirmed through simulation. The identified network displayed the scale-free topology. This provided the reason to examine the association of the network’s hub genes with the phenome. The outcome of analyses revealed GSDMB and ORMDL3 impact several disorders of immune origin and ALDH2 overexpression contributes to obesity. This study reports trans-eQTLs can be used to identify tissue-specific gene regulatory networks and describes a workflow to achieve this purpose. The network identified in this study showed scale-free topology indicating the hub genes of a GRN could be targeted to prevent disease outcomes.

Several effective online and offline inference tools offer an interactive platform for inference, visualization, and analysis of gene regulatory networks. However, a tool currently needs to offer a scalable, lightweight, integrated online platform for carrying out inference, visualization, benchmarking, and extensive network analysis in an integrated manner. We introduce NetRA (Network Reconstruction and Analysis for Gene Regulatory Network), a comprehensive web tool for network analysis, visualization, and inference of large expression networks. Additionally, a platform for benchmarking and evaluation is provided. In order to deliver a highly scalable, lightweight, and rich user interface, the tool is created using the most recent technologies. We incorporate the original code of eleven (11) inference algorithms (not limited to) from Bioconductor in the current version. We also report a qualitative comparison of 11 candidate methods regarding prediction accuracy and network characteristics which will shed light on their performance against 4000 sizes synthetic DREAM network. We believe the tool will significantly aid biologists' downstream system biology research. NetRA tool can be extensible to different kinds of complex network analyses. Availability: On request (sroy01@cus.ac.in).

Keywords: Convolutional Neural Network, Deep Learning, Skin cancer, Generative Adversarial Network

Scientific social networking sites like Researchgate or Academia.edu have become part of the work practice of academic researchers. These digital platforms have been designed precisely to encourage the exchange of knowledge between scholars and to help the expansion of collaborative networks among them. Even if studies on this topic have multiplied in recent years, there is a dearth of research on the actual impact of these platforms on scientific production. The goal of this study is to investigate the relation between the attitude of researchers towards scientific social networks and the use of knowledge in their scientific work. Data from users of the scientific social networking site Researchgate were collected. A total of 143 valid responses were received and structural equation modeling was applied for data analysis. Findings of this study confirmed that researchers use knowledge obtained from scientific social networking sites both incorporating it within their research products and to acquire new competences. In particular approaching the platform as a scientific community with a shared language and a shared vision was found to have a positive impact on knowledge use. To the best knowledge of the authors this is the first study investigating the actual use of knowledge from scientific social networking sites by academic researchers. Results help to understand the impact of these platforms on the work practice of a strategic sector like scientific research.

Animal-focused research has been crucial for scientific advancement however, in this matter, rodents are still taking a starring role. Coming out from merely being supportive of evidence found in rodents, the use of fish models has slowly taken a more central role and expanded its overall contributions in areas such as social sciences, evolution, physiology, and recently in translational medical research. In neurosciences, zebrafish has been widely adopted, contributing to our understanding of the genetic control of brain processes, and the effects of pharmacological manipulations. However, discussion continues regarding the paradox of function versus structure, when fish and mammals are compared, and on the potentially evolutionarily conserved nature of behaviour across fish species. From the behavioural stand point we explored aversive/stress and social behaviour in selected fish models, and refer to the extensive contributions of stress and monoaminergic systems. We suggest that, in spite of marked neuroanatomical differences between fish and mammals, stress and sociality are conserved at the behavioural and molecular levels. We also suggest that stress and sociality are mediated by monoamines in predictable and non-trivial ways, and that monoamines could “bridge” the relationship between stress and social behaviour. To reconcile the level of divergence with the level of similarity, we need neuroanatomical, pharmacological, behavioural, and ecological studies conducted in the laboratory and in nature. These areas need to add to each other to enhance our understanding of fish behaviour and ultimately how this all may translate to better model systems for translational studies.

Speech Emotion Recognition (SER) is an interesting and difficult problem to handle. In this paper, we deal with it through the implementation of deep learning networks. We have designed and implemented six different deep learning networks, a Deep Belief Network (DBN), a simple deep neural network (SDNN), a LSTM network (LSTM), a LSTM network with the addition of an attention mechanism (LSTM-ATN), a Convolutional neural network (CNN), and a Convolutional neural network with the addition of an attention mechanism (CNN-ATN), having in mind, apart from solving the SER problem, to test the impact of attention mechanism to the results. Dropout and Batch Normalization techniques are also used to improve the generalization ability (prevention of overfitting) of the models as well as to speed up the training process. The Surrey Audio-Visual Expressed Emotion database (SAVEE), and the Ryerson Audio-Visual Database (RAVDESS) database were used for training and evaluation of our models. The results showed that networks with the addition of the attention mechanism did better than the others. Furthermore, they showed that CNN-ATN was the best among tested networks, achieving an accuracy of 74% for the SAVEE and 77% for the RAVDESS dataset, and exceeded existing state-of-the-art systems for the same datasets.

Speech recognition, or automatic speech recognition (ASR), is a technology designed to convert spoken language into text using software. However, conventional ASR methods involve several distinct components, including language, acoustic, and pronunciation models with dictionaries. This modular approach can be time-consuming and may influence performance. In this study, we propose a method that streamlines the speech recognition process by incorporating a unified recurrent neural network (RNN) architecture. Our architecture integrates a convolutional neural network (CNN) with an RNN and employs a connectionist temporal classification (CTC) loss function. Key experiments were carried out using a dataset comprising 576,656 valid sentences, using erosion techniques. Evaluation of the model performance, measured by the word error rate (WER) metric, demonstrated remarkable results, achieving a WER of 2%. This approach has significant implications for the realm of speech recognition, as it alleviates the need for labor-intensive dictionary creation, enhancing the efficiency and accuracy of ASR systems, and making them more applicable to real-world scenarios. For future enhancements, we recommend the inclusion of dialectal and spontaneous data in the dataset to broaden the model's adaptability. Additionally, fine-tuning the model for specific tasks can optimize its performance for targeted objectives or domains, further enhancing its effectiveness in those areas.

This study examined the tourism spatial distribution of nine cities in the Fujian province and assessed the impacts of COVID-19. The modified gravity model found that it was widely dispersed, with uneven and relatively independent tourism development in different cities. The social network analysis showed that tourism connections across cities were significantly reduced after the pandemic. The impacts of brand awareness and transport accessibility on spatial networks were positive in pre-pandemic but became negative during the pandemic. In contrast, tourist volume had negative impacts on spatial networks in pre-pandemic but had positive ones during the pandemic. Tourism resources and market performance had significantly positive impacts in the post-pandemic era. These findings provide advice on tourism recovery and destination management in coping with future critical events. It also contributes to the theoretical gravity framework in tourism and the research scope of the social networks analysis at the city level.

Computer-aided image diagnostics (CAD) have been used in many fields of diagnostic medicine. It relies heavily on classical computer vision and artificial intelligence. Quantum neural network (QNN) has been introduced by many researchers around the world and presented recently by research corporations such as Microsoft, Google, and IBM. In this paper, the investigation of the validity of using the QNN algorithm for machine-based breast cancer detection was performed. To validate the learnability of the QNN, a series of learnability tests were performed alongside with classical convolutional neural network (CCNN). QNN is built using the Cirq library to perform the assimilation of quantum computation on classical computers. Series of investigations were performed to study the learnability characteristics of QNN and CCNN under the same computational conditions. The comparison was performed for real Mammogram data sets. The investigations showed success in terms of recognizing the data and training. Our work shows better performance of QNN in terms of successfully training and producing a valid model for smaller data set compared to CCNN.

Recently, with the rapid advancement of the 4th Industrial Revolution, there has been a continuous outlook that Data (D), Network (N), and Artificial Intelligence (A) should be heavily utilized as resources in light of new value. South Korea is preparing policy training plans the likes of 5G network enhancement, AI manpower training, and data openings, etc., recognizing DNA technology as the key driving force behind the 4th Industrial Revolution and 5G network enhancement, AI manpower training, and data opening, etc. However, there is a criticism that the DNA industry remains in an early stage. Despite policy support within South Korea, the DNA industry has not been revitalized due to various causes, and in particular, not being concentrated on limited resources selectively is also pointed to as an important cause. this study would quantitatively draw the priority of policy measures as well as propose policy implications for the revitalization of the DNA industry by means of the ISM-ANP model. It is judged that South Korea’s experience in this way will be an important lesson for other countries which would politically promote the DNA industry as a new growth engine within the 4th Industrial Revolution environment.

Wireless Sensor Networks (WSNs) is considered to be a promising research area since it entails sensing capabilities. WSNs are confronted by fabricated explosion injection attacks and in return results into draining of vitality on sensor nodes routing mainly because of increased capabilities in a broad range of applications where sensor nodes use a battery as the power source. In this paper, we reveal and claim a state-of-the-art model to minimize vitality depletion of nodes within the network in return increases network period to more realistic stretch. It is revealed that the main cause is vitality consumption unbalances in different parts of the network direct resulting from having a static sink. More still, the network nodes neighboring the sink insistently partake in progressing data of other nodes to the sink. Consequently, the network nodes are supplementarily subjected to untimely vitality exhaustion. This paper mainly provides a novel movable (mobile) fuzzy logic sink approach and an intelligent system like expect systems that are able to scrutinize network situation, and make the superlative choice for spreading the network period robotically.

The Double Random Phase Encoding (DRPE) image encryption method has garnered significant attention in color image processing and optical encryption, thanks to its parallel encryption of R, G, B. However, DRPE-based color image encryption faces two challenges. Firstly, it disregards the correlation of R, G, and B, compromising the encrypted image's robustness. Secondly, DRPE schemes relying on Discrete Fourier Transform (DFT) and Discrete Fractional Fourier Transform (DFRFT) are vulnerable to linear attacks, such as Known Plaintext Attack (KPA) and Chosen Plaintext Attack (CPA). Quantum walk stands out as a remarkable tool for designing modern cryptographic mechanisms, offering resistance to potential attacks from both classical and quantum computers.Therefore, this study presents an optical color image encryption algorithm that combines two-dimensional quantum walk with 24-bit plane permutation, dubbed OCT. This approach employs pseudo-random numbers generated by Two-Dimensional Quantum Walking (TDQW) for phase modulation in DRPE and scrambles the encrypted image's real and imaginary parts using the Generalized Arnold Transform. The 24-bit plane permutation helps reduce correlation of the R, G, B, while the Generalized Arnold Transform bolsters DRPE's resistance to linear attacks. By incorporating TDQW, the key space is significantly expanded. Experimental results validate the effectiveness and security of proposed method.

Plant cells' ability to withstand abiotic stress is strongly linked to modifications in their mechanical characteristics. Nevertheless, the lack of a workable method for consistently tracking plant cell mechanical properties severely restricts our comprehension of the mechanical alterations in plant cells under stress. With Polyethylene Glycol (PEG6000), we created a drought-like environment, and we used the Double Resonator Piezoelectric Cytometry (DRPC) method to dynamically and non-invasively track changes in the stress (ΔS) created and viscoelasticity (storage modulus G' and loss modulus G") of protoplasts and suspension cells of rice during drought stress. The findings demonstrate that, rice suspension cells and protoplasts react mechanically differently to 5%–15% PEG6000 stress, implying distinct resistance mechanisms. However, neither of them can withstand 25% PEG6000 stress, they respond mechanically similarly to 25% PEG6000 stress. The results of DRPC are further corroborated by the morphological alterations of rice cells and protoplasts observed under an optical microscope. To sum up, the DRPC technique functions as a precise cellular mechanical sensor and offers novel research tools for the evaluation of plant cell adversity and differentiating between the mechanical reactions of cells and protoplasts under abiotic stress.

Light can interfere like a wave in a multi-path experiment, but it also has particle properties when interacting with matter. In a closer look, the interference pattern is created by many individual detections ("clicks"), which do not all occur simultaneously. An established assumption is therefore that a single light particle is capable of interfering with itself by superimposing all possibilities to get from the source to the detector. This is in stark contrast to our experience with macroscopic objects, which always "decide" in favour of only one of the alternative paths.In 2010, Jin et al. (Ref. 15) described an event-based model which allowed the authors to show that an interference pattern consisting of clicks can arise in a multi-path experiment even if photons, like macroscopic objects, only follow one path. To do this, photons must be able to transmit the information about their travelling duration to the detector and the detector must have a basic memory. A threshold function uses the message of several light quanta to decide whether a single detection is initiated, which is not the evidence for a single photon. In this "proof of principle", the authors do not claim that the detector function is a realistic one.In the present paper, this detector is replaced by a material consisting of ionizable molecules whose interaction with light takes place according to a modified version of Einstein's radiation laws. Based on this, a photon pair interaction mediated by matter takes place, which in a multi-path experiment generates an interference pattern of clicks at the detector, even if an arbitrary amount of time elapses between the individual detections. In the present model an interference pattern with minima that have less than half the intensity of the maxima is the result of a peculiar behaviour of the detector: In this case, the entirety of its electrons must be described as a unit (a “field”) with many equidistant energy levels. The results of a simulation are presented which enable the model to be tested in experiments.

Light can interfere like a wave in a multi-path experiment, but it also has particle properties when interacting with matter. In a closer look, the interference pattern is created by many individual detections ("clicks"), which do not all occur simultaneously. An established assumption is therefore that a single light particle is capable of interfering with itself by superimposing all possibilities to get from the source to the detector. This is in stark contrast to our experience with macroscopic objects, which always "decide" in favour of only one of the alternative paths.In 2010, Jin et al. (Ref. 15) described an event-based model which allowed the authors to show that an interference pattern consisting of clicks can arise in a multi-path experiment even if photons, like macroscopic objects, only follow one path. To do this, photons must be able to transmit the information about their travelling duration to the detector and the detector must have a basic memory. A threshold function uses the message of several light quanta to decide whether a single detection is initiated, which is not the evidence for a single photon. In this "proof of principle", the authors do not claim that the detector function is a realistic one.In the present paper, this detector is replaced by a material consisting of ionizable molecules whose interaction with light takes place according to a modified version of Einstein's radiation laws. Based on this, a photon pair interaction mediated by matter takes place, which in a multi-path experiment generates an interference pattern of clicks at the detector, even if an arbitrary amount of time elapses between the individual detections. In the present model an interference pattern with minima that have less than half the intensity of the maxima is the result of a peculiar behaviour of the detector: In this case, the entirety of its electrons must be described as a unit (a “field”) with many equidistant energy levels. The results of a simulation are presented which enable the model to be tested in experiments.

This paper proposes an analytical model of the double-stator spoke-type permanent-magnet vernier machine (DSSTVM) using the subdomain model (SDM), which can be used to calculate the mag-netic field distribution and corresponding electromagnetic parameters of the DSSTVM. The whole field domain is divided into several subdomains according to the magnetic characteristics of each region, within which Laplace's and Poisson's equations are solved accordingly in terms of magnetic vector potential (MVP). Then, the magnetic flux density distribution, back electromotive force (EMF), and electromagnetic torque of the DSSTVM can be obtained correspondingly. Ultimately, finite element analysis (FEA) is adopted to validate the proposed analytical model's effectiveness for quickly predicting the no-load and on-load performances of the DSSTVM.

Oxygen evolution reaction (OER) is a key half-reaction in electrocatalytic water splitting. Large-scale water electrolysis is hampered by the commercial precious metal-based OER electrocatalysts owing to their high cost and slow kinetics. To address these issues, we present a facile one-pot room-temperature coprecipitation method to quickly synthesize carbon nanotubes-interconnected amorphous NiFe-layered double hydroxides (NiFe-LDH@CNT) as low-cost, efficient and stable OER electrocatalyst. NiFe-LDH@CNT hybrid catalyst delivers outstanding OER performance with a low onset overpotential of 255 mV and a small Tafel slope of 51.2 mV dec-1, as well as outstanding long-term stability. The terrific catalytic capability of NiFe-LDH@CNT can be associated with the synergistic effects of its room-temperature synthesized amorphous structure, bi-metallic modulation, and conductive CNT skeleton. The room-temperature synthesis can not only offer economic feasibility, but also obtain amorphous NiFe-LDH without crystalline boundaries, facilitating long-term stability during OER process. The bi-metallic nature of NiFe-LDH guarantees a modified electronic structure, providing additional catalytic sites. Simultaneously, the highly conductive CNT network fosters a nanoporous structure, facilitating electron transfer, O2 release and enriching catalytic sites. This study presents a strategy to purposefully design nanoarchitecture and facilely synthesize amorphous transition metal-based OER catalysts, ensuring the cost effectiveness, production efficiency, and long-term stability.

Bone metastasis detection and quantification on bone scintigraphy is challenging and clinical important for treatment and patient’s life quality. To develop a CNN based diagnostic system for automated segmentation on bone metastasis regions is non-trivial, especially for a small dataset. A dataset in house comprising 100 breast cancer patients and 100 prostate cancer patients is utilized for this research. The Double U-Net model is adapted through the integration of background removal, adding negative samples, and transfer learning methods for bone metastasis detection. The performance is investigated via 10-fold cross-validation and computed in pixel-wise scale. The best model we achieved has precision of 63.08%, sensitivity of 70.82%, and F1-score of 66.72%. The developed system has the potential to provide pre-diagnostic reports for physicians in final decisions and the calculation of the bone scan index (BSI) with the combination with bone skeleton segmentation.

Channel estimation is an important module to enhance the performance of orthogonal frequency division multiplexing (OFDM) systems. However, the presence of a large amount of noise in multipath fading time-varying channels significantly affects the channel estimation accuracy and thus the recovery quality of the received signals. Therefore, this paper proposes a double-threshold (DT) channel estimation method based on adaptive frame statistics (AFS). The method first adaptively determines the number of statistical frames based on the temporal correlation of the received signals, and preliminarily detects the channel structure by analyzing the distribution characteristics of multipath sampling points and noise sampling points during adjacent frames. Subsequently, a multi-frame averaging technique is used to expand the distinction between multipath and noise sampling points. Finally, the DT is designed to better recover the channel based on the preliminary detection results. Simulation results show that the proposed adaptive frame statistics-double-threshold (AFS-DT) channel estimation method is effective and has better performance compared with many existing channel estimation methods.

Environmental and occupational exposure to hexavalent chromium, Cr(VI), causes female reproductive failures and infertility. Cr(VI) is used in more than 50 industries and is a group-A carcinogen, mutagenic and teratogenic, and a male and female reproductive toxicant. Our previous findings indicate that Cr(VI) causes follicular atresia, trophoblast cell apoptosis, and mitochondrial dysfunction in metaphase II (MII) oocytes. However, the integrated molecular mechanism of Cr(VI)-induced oocyte defects are not understood. The current study investigates the mechanism of Cr(VI) in causing meiotic disruption of MII oocytes, leading to infertility in superovulated rats. Postnatal day (PND)-22 rats were treated with potassium dichromate (1 and 5 ppm) in drinking water from PND 22-29 and superovulated. MII oocytes were analyzed by immunofluorescence, and images were captured by confocal microscopy and quantified by Image Pro Plus software. Our data showed that Cr(VI) increased microtubule misalignment, missegregation of chromosomes, a bulged and folded actin cap, oxidative DNA and protein damage, increased DNA double-strand break, and DNA repair protein RAD51. Cr(VI) also induced incomplete cytokinesis and delayed polar body extrusion. Our study indicates that exposure to environmentally relevant doses of Cr(VI) caused severe DNA damage, distorted oocyte cytoskeletal proteins, and caused oxidative DNA and protein damage leading to infertility.

In order to solve the interference of narrowband communication system to ultra-wideband (UWB) system and meet the design requirements of high isolation of multiple-input multiple-output (MIMO) antenna, two MIMO antennas with double notch structures are designed. Firstly, a two-element antenna was placed symmetrically in parallel, and two rectangular branches were loaded to improve the isolation. U-shaped slots and inverted U-shaped slots are etched on the radiating patch and feeder to achieve notch characteristics in WIMAX and ITU band, respectively. Then, in order to meet more application scenarios, a four-element MIMO antenna is designed, where each element is placed orthogonally to each other, and the isolation is improved by loading a cross-shaped branches in the middle of elements. Both antenna samples are tested to verify the design. Measured results show that the working bandwidth of the two-element UWB MIMO antenna is 2.45-14.88 GHz, the isolation is greater than 17 dB, the peak gain is 5.7 dB, the maximum radiation efficiency can reach 96%, the envelope correlation coefficient (ECC)is less than 0.02. Meanwhile, the working bandwidth of the four-element antenna is 2.14-14.95 GHz, the isolation is greater than 20 dB, the peak gain is 5.9 dB, the maximum radiation efficiency can reach 95%, the ECC is lower than 0.05. They both are with very good omnidirectional characteristic, and meet the requirements of the UWB application.

Particles and photons appear to be total opposites; the former has rest mass which requires space to exist; the latter has kinetic energy which requires time to occur (oscillate). But they do share certain properties (e.g., quantization) that remain invariant when one is transformed (swapped) for the other. This gauge invariance is developed in some detail.The symmetry between particle and photon turns out to be one of inversion. It is the equalities of special relativity that support this inversion and the accompanying invariances: mass transformed to energy; space transformed to time. The great advantage of these symmetries (inversions) is that they provide guidance for an object little understood (the photon) based upon an object well understood (the particle). On this basis, progress can be made in the understanding of some long-standing issues: wave-particle duality, time’s arrow, the constant speed of light and nonlocality.

This study estimates the effects of double long memory and structural breaks on the persistence level of six major cryptocurrency markets. We apply the Bai and Perron’s structural break test, Inclán and Tiao’s iterated cumulative sum of squares (ICSS) algorithm, and the fractionally integrated generalized autoregressive conditional heteroscedasticity (FIGARCH) model with different distributions. The results show that long memory and structural breaks characterize the conditional volatility of cryptocurrency markets and confirm our hypothesis that ignoring structural breaks leads to an underestimation of the persistence of volatility modelling. The ARFIMA-FIGARCH model with structural breaks and a skewed Student–t distribution fits the cryptocurrency market’s price dynamics well.

We investigated experimentally, analytically and numerically the formation process of double emulsion formations under dripping regime in a tri-axial co-flow capillary device. The results show that mismatches of core and shell droplets under a given flow condition can be captured both experimentally and numerically. We propose a semi-analytical model using the match ratio between the pinch-off length of the shell droplet and the product of the core growth rate and its pinch-off time. The mismatch issue can be avoided if the match ratio is lower than unity. We considered a model with the wall effect to predict the size of the matched double emulsion. The model shows slight deviations with experimental data if the Reynolds number of continuous phase is lower than 0.06, but asymptotically approaches to good agreement if the Reynolds number increases from 0.06 to 0.14. The numerical simulation generally agrees with the experiments under various flow conditions.

We construct a microscopic model to study discrete randomness in bistable systems coupled to an environment comprising many degrees of freedom. A quartic double well is bilinearly coupled to a finite number N of harmonic oscillators. Solving the time-reversal invariant Hamiltonian equations of motion numerically, we show that for N=1, the system exhibits a transition with increasing coupling strength from integrable to chaotic motion, following the KAM scenario. Raising N to values of the order of 10 and higher, the dynamics crosses over to a quasi-relaxation, approaching either one of the stable equilibria at the two minima of the potential. We corroborate the irreversibility of this relaxation on other characteristic timescales of the system by recording the time dependences of autocorrelation, partial entropy, and the frequency of jumps between the wells as functions of N and other parameters. Preparing the central system in the unstable equilibrium at the top of the barrier and the bath in a random initial state drawn from a Gaussian distribution, symmetric under spatial reflection, we demonstrate that the decision whether to relax into the left or the right well is determined reproducibly by residual asymmetries in the initial positions and momenta of the bath oscillators. This result reconciles the randomness and spontaneous symmetry breaking of the asymptotic state with the conservation of entropy under canonical transformations and the manifest symmetry of potential and initial condition of the bistable system.

DNA double-strand breaks (DSBs) are deleterious lesions that are generated in response to ionizing radiation or replication fork collapse that can lead to genomic instability and cancer. Eukaryotes have evolved two major pathways, namely homologous recombination (HR) and non-homologous end joining (NHEJ) to repair DSBs. Whereas the roles of protein-DNA interactions in HR and NHEJ have been fairly well defined, the functions of small and long non-coding RNAs and RNA-DNA hybrids in the DNA damage response is just beginning to be elucidated. This review summarizes recent discoveries on the identification of non-coding RNAs and RNA-mediated regulation of DSB repair

In the current study, the failure behavior of retrofitted steel structures was studied experimentally and theoretically with steel/CFRP double strap joints (DSJs) under quasi-static tensile loading. A series of DSJs with different bonding lengths are also considered and examined to experimentally assess the effective bond length. To predict the failure load values of the tested specimens, a new stress-based criterion, namely the point stress (PS) criterion is proposed. Although some theoretical predictive modelling for the strength between steel/CFRP joints under various loading conditions has been presented, in this work by using the new proposed approach, one can calculate rapidly and conveniently the failure loads of the steel/CFRP specimens. Furthermore, to assess the validity of the new proposed criterion, further experimental data on steel/CFRP DSJs available in the open literature are predicted using the PS criterion. Finally, it was found that a good agreement exists between the experimental results and the theoretical predictions based on the PS criterion.

With the emergence of Advance Logistics, the logistics industry has witnessed significant advancements, leading to a greater reliance on intelligent technologies. These technologies play a crucial role in gathering and transmitting logistics data, but they also introduce security and privacy risks to logistics management systems. The sharing of customer-sensitive information among stakeholders for efficient operations becomes vulnerable to unauthorized access, thus compromising privacy. This, amongst others, is a critical problem that needs to be addressed. The unique features of a cryptographic hash function, including immutability, efficient verification, and anonymity, make it a transformative technology that has the potential to address the mentioned challenges in various sectors. This disruptive technology offers solutions by ensuring data integrity, enabling quick verification, and preserving privacy. The findings suggest that employing Cryptographic Hash Functions to share customer data among logistics partners offers robust protection against cyber-attacks. The proposed system guarantees asset security, and as demonstrated by the case study evaluation, exhibits strong resilience in safeguarding against various security and privacy threats. Although this study is significant, it does have limitations. One of these limitations is the considerable overlap between logistics research and supply chain management (SCM). While our focus was on logistics tasks, we did not extensively delve into the broader context of SCM

Optical artificial neural networks (ONNs) — analog computing hardware tailored for machine learning [1,2] — have significant potential for ultra-high computing speed and energy efficiency [3]. We propose a new approach to architectures for ONNs based on integrated Kerr micro-comb sources [4] that is programmable, highly scalable and capable of reaching ultra-high speeds. We experimentally demonstrate the building block of the ONN — a single neuron perceptron — by mapping synapses onto 49 wavelengths of a micro-comb to achieve a high single-unit throughput of 11.9 Giga-FLOPS at 8 bits per FLOP, corresponding to 95.2 Gbps. We test the perceptron on simple standard benchmark datasets — handwritten-digit recognition and cancer-cell detection — achieving over 90% and 85% accuracy, respectively. This performance is a direct result of the record small wavelength spacing (49GHz) for a coherent integrated microcomb source, which results in an unprecedented number of wavelengths for neuromorphic optics. Finally, we propose an approach to scaling the perceptron to a deep learning network using the same single micro-comb device and standard off-the-shelf telecommunications technology, for high-throughput operation involving full matrix multiplication for applications such as real-time massive data processing for unmanned vehicle and aircraft tracking.

The application of artificial intelligence to point-of-care testing (POCT) disease detection has become a hot research field, in which breath detection which detects the patient's exhaled VOCs combined with sensor arrays of Convolutional Neural Network (CNN) algorithms as a new lung cancer detection is attracting more researchers' attention. However, due to low accuracy, high complexity computation and large number of parameters make the CNN algorithms difficult transplant to the embedded system of POCT devices. A lightweight neural network (LTNet) in this work is proposed to deal with this problem, and meanwhile, achieve high-precision classification of acetone and ethanol gases, which are respiratory markers for lung cancer patients. Compared to currently popular lightweight CNN models such as EfficientNet, LTNet has fewer parameters (32K) and its training weight size is only 0.155 MB. LTNet achieved an overall classification accuracy of 99.06% and 99.14% in the own mixed gas dataset and the University of California (UCI) dataset, which are both higher than that of the five existing models’, and it also offers the shortest training (844.38s and 584.67s) and inference time (23s and 14s) in the same validation set. Compared to the existing CNN models, LTNet is more suitable for resource-limited POCT devices.

Next-generation cancer and oncology research needs to take full advantage of the multi-modal structured, or graph, information, with the graph datatypes ranging from molecular structures to spatially resolved imaging and digital pathology to biological networks to knowledge graphs. Graph Neural Networks (GNNs) efficiently combine the graph structure representations with the high predictive performance of deep learning, especially on the large multi-modal datasets. In this review article, we survey the landscape of recent (2020-present) GNN applications in the context of cancer and oncology research, and delineate six currently predominant research areas. Subsequently, we identify the most promising directions for future research. We compare GNNs with graphical models and "non-structured" deep learning, and devise the guidelines for cancer and oncology researchers or physician-scientists asking the question of whether they should adopt the GNN methodology in their research pipelines.

Relay protection service is the most crucial service carried by the electric power communication network, which is directly linked to the safe operation of the power grid. In the route planning of power services, a route planning architecture for the power communication network is constructed in this paper, considering the operation and maintenance requirements, such as relay protection service overload (RPSO) and dual route. To minimize network risk variance, the problem of single-service route allocation is formulated by taking the factors into account such as service delay, link bandwidth utilization, and RPSO constraints. Based on $k$ shortest paths algorithm, a route planning scheme for the power services is proposed, which considers RPSO and risk balance, and a strategy for implementing multi-service route planning of the communication network is studied. The simulation results show that the proposed route planning strategy can effectively avoid relay protection service overload, reduce network risk variance, and balance network risk.

In the trend of transforming existing systems and services into non-face-to-face models, the concert industry is also showing movements toward transitioning to virtual formats. Physical concerts in the real world require venues that can accommodate hundreds to tens of thousands of spectators, but non-face-to-face methods that can accommodate large audiences face various limitations. Moreover, to elevate the satisfaction level of virtual concert attendees to that of real-world concerts, it's important to implement interaction between performers and audiences. Modern metaverse platforms apply cutting-edge network technologies to accommodate numerous users within a single channel. Many researchers are adopting network technologies such as SDN (Software Defined Networking) and CDN (Content Delivery Network) to set up a virtual concert that can accommodate large audiences. In this paper, we propose a network framework to be designed for the composition of virtual concerts. In particular, we separate a channel dedicated to interaction in order to provide an immersive experience of exchanging interactions between performers and audiences. As massive audiences transmitting interaction data to the performer in a 1:N format can lead to problems with acceptance and latency, this study introduces a concept of a channel form called 'Zone' and proposes an interaction data channel network framework that does not compromise immersion. The proposed framework supports tasks for effectively transmitting interaction data using network technologies for metaverse platforms like XDN and clustering algorithms like fuzzy c-means. We also suggest a CDN-based architecture that can ensure low latency for performers to transmit interaction data to the audience.

Coastal marine ecosystems worldwide, like Florida Bay, are increasingly affected by tide alteration and anthropogenic disturbances which affect water quality imbalance leading to algal blooms. Increased bloom persistence is a serious threat due to the long-lasting impact on processes like carbon cycle and services like species presence. However, exploring eco-environmental feedback patterns of algal blooms remains challenging and poorly investigated, also due to the paucity of data. Florida Bay, taken as an epitome, has long experienced algal blooms in its central and western regions, and in 2006 an unprecedented bloom occurred in the eastern habitats. We analyzed the occurrence of blooms from three perspectives: (1) the spatial spreading networks of chlorophyll-a (CHLa) that pinpoints source and unbalanced habitats; (2) the fluctuations of water quality factors pre- and post-bloom outbreaks to in assess environmental impacts of ecological dysbiosis and target prevention and control of algal of blooms; and (3) the biogeochemical-spreading network topological co-evolution to quantify ecosystemic stability and ecological shift likelihood in long-term. Here, we propose Transfer Entropy (TE) to quantify dynamical interactions between spatial areas and biogeochemical factors (ecosystem connectome) underpinning bloom emergence and spreading, as well as environmental effects; the Pareto principle is defined for identifying the salient eco-environmental interactions of CHLa. We quantified the spatial dynamics of algal blooms, and thus obtained areas in need for ecological monitoring and potential bloom control. Results show that algal blooms are increasingly persistent over space with long-term negative effects on water quality factor. A dichotomy is reported between spatial ecological corridors of spreading and biogeochemical networks as well as divergence from the optimal eco-organization: randomization of the former due to nutrients' overload and temperature increase leads to scale-free CHLa spreading and extreme outbreaks a posteriori. Subsequently this increases bloom persistence, turbidity and salinity with potentially strong ecological effects on highly biodiverse and vulnerable areas such as tidal flats and marshes. Algal blooms are important ecosystem regulators of nutrient cycles; however, beyond limit chlorophyll-a outbreaks cause aquatic species mortality due to their effect on water turbidity, nutrient balance (nitrogen and phosphorus in particular), salinity and temperature. Beyond compromising local environmental quality, socio-ecological services are also compromised at large scales, yet ecological assessment models like the one presented are in need of application in subtropical and tropical bays worldwide.

With the development of the city, a large number of water networks in the built-up areas of Bangkok have been filled and hardened, resulting in poor urban flooding and aggravating flooding, causing loss of life and property of citizens. In this paper, on the basis of combing the current water networks and open space potential flood storage points in the built-up areas of Bangkok, the complex network diagram of the water system in the built-up areas of Bangkok is constructed by combining the theory of complex networks and analyzing the attribute parameters of the network and the characteristic parameters of the open space storage nodes and water system paths, and finding that the water system network in the built-up area of Bangkok has complex network characteristics such as robustness, clustering and hierarchy. By exploring the key storage points and water system connection paths, the researchers initially constructed a flood safety pattern in the built-up area of Bangkok with 145 key nodes and 127 river paths as the backbone, and conceptualized the development study of the flood safety pattern in both horizontal and vertical directions. The urban flood safety pattern based on complex network theory proposed in this paper provides a case reference and methodological ideas to scientifically solve the game conflict between the demand for construction land for urban development and the construction area of urban open space storage points and water storage network under the increasingly severe flooding situation.

Forest fire identification is important for forest resource protection. Effective monitoring of forest fires requires the deployment of multiple monitors with different viewpoints, while most traditional recognition models can only recognize images from a single source. By ignoring the information from images with different viewpoints, these models produce high rates of missed and false alarms. In this paper, we propose a graph neural network model based on the similarity of dynamic features of multi-view images to improve the accuracy of forest fire recognition. The input features of the nodes on the graph are converted into relational features of different gallery pairs by establishing pairs (nodes) representing different viewpoint images and gallery images. The new feature library relationship is used to update the image gallery with dynamic features in order to achieve the estimation of similarity between images and improve the image recognition rate of the model. In addition, to reduce the complexity of image pre-processing process and extract key features in images effectively, this paper also proposes a dynamic feature extraction method for fire regions based on image segment ability. By setting the threshold value of HSV color space, the fire region is segmented from the image, and the dynamic features of successive frames of the fire region are extracted. The experimental results show that, compared with the baseline method Resnet, this paper's method is more effective in identifying forest fires, and its recognition accuracy is improved by 2%. And the scheme of this paper can adapt to different forest fire scenes, with better generalization ability and anti-interference ability.

Over the past decade, wireless communication technologies have developed significantly for intelligent applications in road transport. This paper provides an overview of telecommunications-based intelligent transport systems with a focus on ensuring system safety and resilience. In vehicle-to-everything, these problems are extremely acute due to the specifics of the operation of transport networks, which requires the use of special protection mechanisms. In this regard, it was decided to use blockchain as a system platform to support the needs of transport systems for secure information exchange. This paper describes the technological aspects of implementing blockchain technology in vehicle-to-network; the features of such technology are presented, as well as the features of their interaction.

One of the most serious concerns about mega event-related changes to small cities is how to effectively utilize newly developed public attractions after the Olympic Games. Making connections with existing local amenities and forming attraction networks can be effective strategies for continuing a city’s revitalization. However, despite the expected benefits, attraction network research shows that these benefits often fail to materialize. With the case of Gangneung, a 2018 winter Olympic hosting city, this study investigated visitation patterns to nineteen selected attractions using network analysis. The results indicate that the most influential nodes are located on the northern coast, the eastern coast, and in the south downtown area, those nodes being the central locations where the strongest of connections are made. New attractions such as the Olympic Park and Walwha Linear Park were rather isolated. While seasonal and periodic variations, visitors’ residences, and destination choice attitudes had a significant effect on visitation patterns, the attraction networks, modes of transportation, proximity to region, and type similarity were not significant factors in the forming of visitation patterns. The results make a methodological contribution to tourist behavior and network research. In addition, beyond individual attraction development, the results provide practical implications in regard to networking and cooperation between multiple attractions using temporal and spatial strategies such as management/investment prioritizing, travel route development, and program scheduling.

Alzheimer’s Disease (AD) is a neurodegenerative condition that currently has no known cure. The principles of the expanding field of network medicine (NM) have recently been applied to AD research. The main principle of NM proposes that diseases are much more complicated than one mutation in one gene, and incorporate different genes, connections between genes, and pathways that may include multiple diseases to create full scale disease networks. AD research findings as a result of the application of NM principles have suggested that functional network connectivity, myelination, myeloid cells, and genes and pathways may play an integral role in AD progression, and may be integral to the search for a cure. Different aspects of the AD pathology could be potential targets for drug therapy to slow down or stop the disease from advancing, but more research is needed to reach definitive conclusions. Additionally, the holistic approaches of network pharmacology in traditional Chinese medicine (TCM) research may be viable options for the AD treatment, and may lead to an effective cure for AD in the future.

Linear state estimation (SE) formulation under a rectangular coordinate system has been proved to be applicable for real-time distribution network management. Micro phasor measurements’ model can be accommodated into this kind of SE easily. However, voltage magnitude, active power and reactive power measurements are transformed to linear measurements with large node voltage error. To cope with this issue, a linear state estimation under a polar coordinate system is adopted at the first stage to obtain accurate enough complex node voltage, and then nonlinear measurements are transformed to be linear with complex node voltage. At the second stage, linear SE under a rectangular coordinate system can be adopted to satisfy more strict network constraints. The proposed two-stage linear SE is validated on balanced 14, 33, 70,84, 119, 135 nodes network and IEEE 13, 34, 37,123 unbalanced test feeders.

A hypothesis that the generally accepted value of the LiPON conductivity should be attributed to the absorption and displacement currents is substantiated. The reason is a small contribution of the drift current due to field screening by the electric double layer. The basis for this assumption is the measurement of the LiPON absorption capacitance, according to which its dielectric constant is about 106. An alternative equivalent circuit containing a non-ideal absorption element is proposed and its impedance is calculated. It is shown that the Bode diagrams of the alternative circuit well approximate the experimental curves. Parameters and the magnitude of electric field screening are calculated basing on proposed model of a double electric layer. Considering the screening effect, the drift conductivity of LiPON is obtained, which value is in good agreement with the data on lithium concentration and mobility.

The Double U-pipe ground heat exchanger, recognized for its straightforward process, cost-effectiveness, high heat exchange efficiency, and low thermal resistance, stands as the predominant ground heat exchanger type in today's shallow geothermal energy development and utilization. Recent years have seen significant research into the heat transfer influencing factors and heat exchange performance of Double U-pipe ground heat ex-changers via experimental testing methods. Yet, studies integrating numerical simulation with in-situ testing have been less frequent. Drawing on the cylindrical heat source model theory and the outcomes of regional in-situ thermal response tests, this paper develops a Double U-pipe ground heat transfer model by establishing physical, mathematical, and heat transfer geometric models. It assesses the impact of varying inlet temperatures, flow rates, and initial ground temperatures on heat exchange efficiency under heating conditions. The results validate the precision of the double U-pipe ground heat exchanger model based on in-situ testing. They show that enhancing the temperature differential between the inlet and initial temperatures, elevating the initial ground temperature, and moderately increasing the flow rate can boost the system's heat exchange efficiency.

Elastic yarns are the key component of high-performance compression garments. However, it remains a challenge to fabricate anti-fatigue yarns with high mechanical force and long elongation for generating compression garments with prolonged wear. In this paper, we report the development of anti-fatigue double-wrapped yarns with excellent mechanical properties by wrapping high-denier Spandex with nylon filaments in opposite twists. In particular, 560D high-denier Spandex as the core was untwisted which can maximumly reduce the interaction between the core and wrapping filaments, enabling high elongation of the double-wrapped yarns. In addition, we chose 70D nylon filaments with a tensile force of 387.40 ± 8.82 cN as the wrapping materials to provide sufficient force for double-wrapped yarns. Notably, opposite twists were induced for the inner and outer wrapping filaments to achieve a balanced stable yarn structure. By systematically optimizing manufacturing parameters including inner wrapping density, outer wrapping density, taking-up ratio, and drafting ratio, we obtained double-wrapped yarn with excellent tensile force (952.00 ± 24.03 cN) and elongation (357.28% ± 9.10%). Notably, the stress decay rate of optimized yarns was only 12.0% ± 2.2%. In addition, the optimized yarn was used as the weft-lining yarn for generating weft-lined fabrics, the elastic recovery rate of the obtained fabric was only 2.6% after cyclic stretching, much lower than the control fabric. Our design of anti-fatigue double-wrapped yarns could be widely used for fabricating high-performance compression garments.

As the world’s population age, Cognitive Frailty (CF) is becoming one of the most serious health problems and elucidating its biological mechanisms along with prevention and treatments becomes increasingly important also considering the associated health costs. This pilot study aims to investigate the potential cognitive and neuropsychological effects of a 5-week treatment with a therapy based on the regenerative properties of ozone (O3) in a cohort of patients stratified according to CF. We also study the potential effects of O3 on blood-based biomarkers indicative of specific biological systems that may be altered in CF. Seventy-five older persons have been recruited and randomly assigned to receive the active treatment (150cc of oxygen-O2-O3 mixture at the concentration of 30ug of O3 per cc of O2) or O2, or the placebo (air) for 5 weeks. The primary endpoint is a change in the scores of clinical scales from baseline (T0) to weeks 3 (T3), 9 (T9), 15 (T15) after treatment. The secondary endpoint is a change in biomarkers levels resulting from transcriptomics, proteomics and metabolomic patterns at the same times. This study will evaluate whether an intervention with the O2-O3 therapy could have potential cognitive, neuropsychological and biological effects on CF.

Significant changes to the new National College Entrance Examination (NCEE) and the implementation of the “Double First-Class” (DFC) initiative in China are coincidentally in the national spotlight and fall within the same policy window. However, few studies have investigated the effects of the new NCEE on the quality of students in DFC universities. By leveraging the 2018-2022 college admission data from Shandong Province, these effects are examined via the interrupted time-series technique. Weighted rankings for liberal arts and science disciplines and the admission ranking coefficient are adopted to ensure comparability before and after the reform. The findings reveal that after the reform, the quality of students in both world-class universities and first-class discipline universities improved, with greater improvements seen in world-class universities. Focusing on urban differences, the quality of students in DFC universities located in second-tier cities is found to have experienced a higher increase as compared with students in the first-tier and new first-tier cities. In addition, the reform has had different impacts on the quality of students in different majors. Furthermore, when selecting postsecondary institutions, high-scoring students prioritize the quality of universities and majors over urban factors. Finally, the implications of the new NCEE in relation to a sustainable development of DFC universities for research, policy, and practice are further discussed.

Weak interaction processes continue to be hot topics in fundamental physics research. In this paper, we briefly review some recent advances in the theoretical study of beta and double-beta decays that include both the nuclear and atomic part of these processes. On the nuclear side, we present a statistical approach for the computation of the nuclear matrix elements (NME) for neutrinoless double-beta (0νββ). A range of NME values, the most probable value for NME, and the associated theoretical uncertainty are given. Correlations with other related observables are shown as well. On the atomic side, we first briefly review the methods used to get the electrons’ wave functions. Further, we use them for the computation of some relevant kinematic quantities such as Fermi functions, electron spectra and angular correlation between the emitted electrons. Then, we present applications of these calculations to the experimental data analysis related to the search of the Lorentz invariance violation in two-neutrino double-beta (2νββ) decay and description of the decay rates and decay rate ratios for allowed and unique forbidden electron capture (EC) processes.

Steel damper columns (SDCs) are energy-dissipating members that are suitable for reinforced concrete (RC) buildings and are often used for multistory housing. The evaluation of the peak deformation and hysteretic dissipated energy of such building structures is essential for the rational seismic design of RC buildings with SDCs. In a previous study, the authors proposed an energy-based prediction procedure for the peak and cumulative response of an RC frame building with SDCs. In this procedure, the accuracy of the equivalent velocity of the maximum momentary input energy (V_ΔE1^*)–peak equivalent displacement (D₁^*_max) relationship is essential for high quality predictions. In this article, the V_ΔE1^*–D₁^*_max relationships of RC moment-resisting frames with and without SDCs are invested using a critical pseudo-double impulse (PDI) analysis based on a study by Takewaki and coauthors. The results show that the V_ΔE1^*–D₁^*_max relationship obtained from the critical PDI analysis agrees well with that calculated from the equations proposed in the previous study.

A new three-dimensional numerical model of a polymer electrolyte fuel cell (PEFC) with a single straight channel has been developed to primarily investigate the important impact of the double-sided microporous layer (MPL) coating on the overall performance of the fuel cell and the distribution of the current and the oxygen concentration within the cathode gas diffusion layers (GDLs). Realistic experimentally estimated interfacial contact resistance values between the gas diffusion layer and each of the bipolar plates and the catalyst layer values were incorporated into the model, and parametric studies were performed. The results showed that the double-sided MPL coating could significantly improve the fuel cell performance by up to 30%. Also, it was shown that the neglect of the contact resistance between the MPL and the catalyst layer could overestimate the fuel cell performance by up to 6%. In addition, the results showed that the fuel cell performance and the distribution of the current and oxygen are more sensitive to the porosity of the MPL facing the bipolar plate than the porosity of the MPL facing the catalyst layer. All the above results are presented and critically discussed in detail.

The thoracic surgery has increased drastically in recent years, especially in the light of the severe outbreak of 2019 novel coronavirus disease (COVID-19). Routine “passive” chest computed tomography (CT) screening of inpatients detects some pulmonary diseases requiring thoracic surgeries timely. As an essential device for thoracic anesthesia, the double-lumen tube (DLT) is particularly important for anesthesia and surgery. With the continuous upgrading of the DLTs and the widespread use of the fiberoptic bronchoscopy (FOB), the position of DLT in thoracic surgery is gradually becoming more stable and easier to observe or adjust. However, the DLT malposition still occurs during transferring patients from supine to lateral position in thoracic surgery, which leads to lung isolation failure and hypoxemia during one-lung ventilation (OLV). Recently some innovative DLTs or improved intervention methods have shown good results in reducing the incidence of DLT malposition. This review aims to summarize the recent studies of the incidence of left-sided DLT malposition, the reasons and effects of malposition, and summarize current methods for reducing DLT malposition and prospects for possible approaches. Meanwhile, we use bibliometric analysis to summarize the research trends and hot spots of the DLT research.

Archie model is the basis of calculating oil saturation, but there are some limitations when using this model to calculate oil saturation in water-flooded layer. The main reason is that the main parameters, such as rock resistivity and formation water resistivity, are constantly changing dynamic parameters in the underground with the influence of injected water. Considering that water-flooded layers changes with injection waters, influence factors of rock resistivity and formation water resistivity of primary parameters are analyzed. Considering the dynamic data of water cut is the most reactive underground fluid characteristics of real information, combined with dynamic and static, and the “double ratio” model of later development by the same sedimentary layer is established, which realizes the inversion of rock resistivity and formation water resistivity, then modified Archie model relating to water-flooded layers of the same sedimentary explaining formation. The explanation of actual data indicates that the “double ratio” model well considers the dynamic variation of production data, which makes the inversion of the flooded rock resistivity relatively accurate, besides, the modified Archie model can accurately calculate the oil saturation of water-flooded layers with a reasonable result, which offers scientific basis for the predicting of remaining oil distribution rules.

We study the connection between the phase and the amplitude of the wave function and the conditions under which this relationship exists. For this we use model of particle in a box. We have shown that the amplitude can be calculated from the phase and vice versa if the log Analytical uncertainty relations are satisfied.

In today’s digital media data communication over the internet increasing day by day. Therefore the data security becomes the most important issue over the internet. With the increase of data transmission, the number of intruders also increases. That’s the reason it is needed to transmit the data over the internet very securely. Steganography is a popular method in this field. This method hides the secret data with a cover medium in a way so that the intruders cannot predict the existence of the data. Here a steganography method is proposed which uses a video file as a cover medium. This method has five main steps. First, convert the video file into video frames. Then a particular frame is selected for embedded the secret data. Second, the Least Significant Bit (LSB) Coding technique is used with the double key security technique. Third, an 8 characters password verification process. Fourth, reverse the encrypted video. Fifth, signature verification process to verify the encryption and decryption process. These five steps are followed by both the encrypting and decrypting processes.

Neonatal palliative care (NPC) is an integrated and holistic approach that is an integral part of the contemporary neonatal treatment delivery paradigm. It is the highest fulfillment of the notion of beneficence (doing or creating 'good') that has otherwise been neglected/underestimated by the focus of modern medicine on technology and instrumental treatments by its commitment to alleviating patient pain and quality of life. For decades, the Double-Effect Doctrine (DDE) has been used to consider and address a range of ethically questionable circumstances, often at the end of life, including euthanasia, termination of pregnancy to save maternal life, and morally justified warfare. The theory has continued to be mired in controversy as ethicists, legal scholars, theologians, and philosophers discuss the abstract concepts of moral reasoning, purpose, foresight, and other underlying moral theories. In this paper, the moral theory of DDE is discussed in its clinical application to the ethical decision-making process in neonatal palliative and end-of-life treatment, specifically 1) the administration of opioids as required for symptom control and 2) the use of palliative sedation for intractable symptom care.

Management of multiple primary cancers, a not so infrequent event in oncology practice, is a critical issue due to the lack of literature . In this study, we reported the case of a patient with metachronous double metastatic non small cell lung cancer (NSCLC) and pancreatic ductal adenocarcinoma (PDAC) who received gefitinib in combination with gemcitabine plus nab-paclitaxel and with mFOLFOX6 in first and second line, respectively. She achieved a progression free survival and an overall survival (OS) of 28 months for NSCLC and PFS-1 and OS of 20 and 13 months, respectively for PDAC. Moreover, the combination of gefitinib and chemotherapies treatments displayed a good safety profile. Given the insignificant frequency of this case, we performed a molecular characterization of both neoplasms with the aim to investigate the existence of particular activated pathways and/or similar immunological mutations. it is interesting to note that two neoplasms shared a commune mutation of B7-H3 gene, with the consecutive impairment of its expressed protein. In both PDAC and NSCLC, the expression of this protein was associated with a worse survival. Since B7-H3 is an anti-apoptotic protein, the reduction of its expression or function should justify a pro-apoptotic activity with a putative justification of the long survival of the patient considered in this report.

This paper introduces a novel architecture—bidirectional optical neural network (BONN)—for providing backward connections alongside forward connections in artificial neural networks (ANNs). BONN incorporates laser diodes and photodiodes and exploits the properties of Köhler illumination to establish optical channels for backward directions. Thus, it has bidirectional functionality that is crucial for algorithms such as the backpropagation algorithm. BONN has a scaling limit of 96 × 96 for input and output arrays, and a throughput of 8.5 × 1015 MAC/s. While BONN's throughput may rise with additional layers for continuous input, limitations emerge in the backpropagation algorithm as its throughput doesn't scale with layer count. Successful BONN-based implementation of the backpropagation algorithm requires the development of fast spatial light modulator to accommodate frequent data flow changes. A two-mirror-like BONN and its cascaded extension are alternatives for multilayer emulation, and they help save hardware space and increase the parallel throughput for inference. An investigation into the application of the clustering technique to BONN revealed its potential to help overcome scaling limits and to provide full interconnections for backward directions between doubled input and output ports. BONN's bidirectional nature holds promise for enhancing supervised learning in ANNs and increasing hardware compactness.

Artificial Intelligence (AI) made its first appearance in 1956, and since then it has progressively introduced itself in healthcare system and patients’ information and care. AI functions can be grouped under the following headings: Machine Learning (ML), Deep Learning (DL), Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Computer Vision (CV). Chronic limb-threatening ischemia (CLTI) represents the last stage of peripheral artery disease (PAD), and has increased over recent years, together with the rise in prevalence of diabetes and population ageing. Nowadays AI grants the possibility of developing new diagnostic and treatment solutions in the vascular field, given the possibility of accessing clinical, biological, and imaging data. By assessing the vascular anatomy in every patient, as well as the burden of atherosclerosis, and classifying the level and degree of disease, sizing and planning the best endovascular treatment, defining the perioperative complications risk, integrating experiences and resources between different specialties, identifying latent PAD, thus offering evidence-based solutions and guiding surgeons in the choice of the best surgical technique, AI challenges the role of the physician’s experience in PAD treatment.

Investigating the network response to node removal and the efficacy of the node removal strategies are related and fundamental questions in network science. Research studies proposed many node centralities based on the network structure ranking node to remove. The random walk (RW) on networks describes a stochastic process in which a walker travels among nodes. RW can be a model of transport, diffusion, and search on networks, and an essential tool for studying the importance of network nodes. In this manuscript, we propose four new measures of node centrality based on RW. Then, we compare the efficacy of the new RW node removal strategies to network dismantle with effective node removal strategies from the literature, such as betweenness and closeness node removal over synthetic and real-world networks. We evaluate the network dismantle along node removal using the size of the largest connected component (LCC). We find that, hence the betweenness nodes attack is the best strategy overall, the new node removal strategies based on RW show the highest efficacy in peculiar network topology. Specifically, RW strategies based on covering times emerge as the most effective strategy on a synthetic lattice network and two real-world road networks. Our results may be useful in selecting the best node attack strategies in a specific class of networks and in building more robust network structures.

Human activity recognition (HAR) using wearable sensors is gaining popularity in the healthcare domain, enabling continuous monitoring and timely interventions. The conventional HAR systems are centralised and need data sharing with a server for processing and model training. However, this data sharing leads to various challenges, such as privacy concerns, increased communication and storage costs, inefficient computational algorithms, data scarcity due to unwillingness to share, and difficulty achieving high accuracy and personalised models simultaneously. To overcome these challenges, this paper introduces a privacy and energy-aware federated learning (FL) framework for model training without data sharing. The proposed scheme incorporates spiking neural networks (SNNs), which are particularly suited for resource-constrained devices due to their event-driven information processing. The key idea is to synergise the strengths of SNNs and long short-term memory (LSTM) to create a hybrid model for robust HAR. The proposed hybrid spiking LSTM (S-LSTM) model uses the LSTM as an input layer to capture temporal dependencies efficiently, followed by spiking layers that replace the typical activation using leaky integrate-and-fire neurons. We employ the combination of surrogate gradient learning and backpropagation through time (BPTT) to update the weights of SNN layers in a supervised manner. The performance of the proposed model is evaluated using two publicly available datasets for both indoor and outdoor scenarios. The results show that the proposed S-LSTM performs much better than spike convolutional neural networks (S-CNN) and simple CNN, achieving the accuracy of 97% and 87% for indoor and outdoor datasets, respectively. Personalisation, also known as fine-tuning the global model using local data, is crucial in HAR. It allows the model to adapt to individual behaviour and preferences, making predictions more accurate. The results also show that personalisation improves the performance of participants up to 9% on average. Additionally, analysis with 50% random client participation reduces communication costs by 50% with minimal impact on accuracy.

A scalable optical convolutional neural network (SOCNN) based on free-space optics and Koehler illumination was proposed to address the limitations of the previous 4f correlator system. Unlike Abbe illumination, Koehler illumination provides more uniform illumination and reduces crosstalk. SOCNN allows for scaling up of the input array and the use of incoherent light sources. Hence, the problems associated with 4f correlator systems can be avoided. We analyzed the limitations in scaling the kernel size and parallel throughput and found that SOCNN can offer a multilayer convolutional neural network with massive optical parallelism.

The root system plays an irreplaceable role in plant growth. Its improvement can increase crop productivity. However, such system is still mysterious for us. The underlying mechanism has not been fully uncovered. The investigation on proteins related to the root system is an important means to complete this task. In the previous time, lack of root-related proteins makes it impossible to adopt machine learning methods for designing efficient models for the discovery of novel root-related proteins. Recently, a public database on root-related proteins was set up and machine learning methods can be applied in this field. In this study, we proposed a machine learning based model, named Graph-Root, for identification of root-related proteins. The features derived from protein sequences and one network were extracted, where the former features were processed by graph convolutional neural network and multi-head attention, and the later features abstracted the linkage between proteins. These features were fed into the fully connected layer to make prediction. The 5-fold cross-validation and independent tests suggested its good performance. It also outperformed the only one previous model, SVM-Root. Furthermore, the importance of each feature type and component in the proposed model was investigated.

With the rapid development of Internet of Things (IoT)-based near-earth remote sensing technology, the problem of network intrusion for near-earth remote sensing systems has become more complex and large-scale. Therefore, it is essential to seek an intelligent, automated, and robust network intrusion detection method. In recent years, network intrusion detection methods based on graph neural networks (GNNs) have been proposed. However, there are still some practical issues with these methods. For example, they have not taken into consideration the characteristics of near-earth remote sensing systems, the state of the nodes, and the temporal features. Therefore, this article analyzes the characteristics of existing near-earth remote sensing systems and proposes a spatio-temporal graph attention network (N-STGAT) that considers the state of nodes. The proposed network applies spatiotemporal graph neural networks to the network intrusion detection of near-earth remote sensing systems and validates the effectiveness of the proposed method on the latest flow-based dataset.

With the development of deep learning technology, various structures and research methods for super-resolution restoration of natural images and document images have been introduced. In particular, a number of recent studies have been conducted and developed in image restoration using generative adversarial network. Super-resolution restoration is ill-posed problem because of some complex restraints such as a lot of high-resolution images being restored for the same low-resolution image and also difficulty in restoring noises like edges, light smudging, and blurring. In this study, we utilized the spatially adaptive de-normalization (SPADE) structure for document image restoration to solve previous problems such as edge unclearness, hardness to catch features of texts, and the image color transition. Consequently, it can be confirmed that the edge of the character and the ambiguous stroke are restored more clearly when contrasting with the other previously suggested methods. Also, the proposed method’s PSNR and SSIM scores are geting 8% and 15% higher, respectively, compared to the previous methods.

Many complex networks have a connectivity that might be only partially detected or that tends to grow over time, hence the prediction of non-observed links is a fundamental problem in network science. The aim of topological link prediction is to forecast these non-observed links by only exploiting features intrinsic to the network topology. It has a wide range of real applications, like suggesting friendships in social networks or predicting interactions in biological networks.The Cannistraci-Hebb theory is a recent achievement in network science that includes a theoretical framework to understand local-based link prediction on paths of length n. In this study we introduce two innovations: theory of modelling (science) and theory of realization (engineering). For the theory of modelling we first recall a definition of network automata as a general framework for modelling the growth of connectivity in complex networks. We then show that several deterministic models previously developed fall within this framework and we introduce novel network automata following the Cannistraci-Hebb rule. For the theory of realization, we present how to build adaptive network automata for link prediction, which incorporate multiple deterministic models of self-organization and automatically choose the rule that better explains the patterns of connectivity in the network under investigation. We compare Cannistraci-Hebb adaptive (CHA) network automaton against state-of-the-art link prediction methods such as structural perturbation method (SPM), stochastic block models (SBM) and artificial intelligence algorithms for graph embedding. CHA displays an overall higher link prediction performance across different evaluation frameworks on 1386 networks. Finally, we highlight that CHA offers the key advantage to explicitly explain the mechanistic rule of self-organization which leads to the link prediction performance, whereas SPM and graph embedding not. In comparison to CHA, SBM unfortunately shows irrelevant and unsatisfactory performance demonstrating that SBM modelling is not adequate for link prediction in real networks.

Unmanned Aerial Vehicles (UAVs) in particular multirotors are becoming the {\it de facto} tool for aerial sensing and remote inspection. In large industrial facilities, a UAV can transmit an online video stream to inspect difficult to access structures, such chimneys, deposits and towers. However, the communication range is limited, constraining the UAV operation range. This limitation can be overcome with relaying UAVs placed between the source UAV and the control station, creating a line of communication links. In this work we assume the use of a digital data packet network technology, namely WiFi, and tackle the problem of defining the exact placement for the relaying UAVs that creates an end-to-end channel with maximal delivery of data packets. We consider asymmetric communication links and we show an increase as large as $15$\% in end-to-end packet delivery ratio when compared to an equidistant placement. We also discuss the deployment of such a network and propose a fully distributed method that converges to the global optimal relay positions taking, on average, 1.4 the time taken by a centralized method.

Corynebacterium glutamicum is a Gram-positive bacterium found in soil where the condition changes demand plasticity of the regulatory interactions, which study at the global scale has been challenged by the lack of data integration. Here, we update the manually-curated C. glutamicum transcriptional regulatory network, now including protein-protein interactions having a direct effect on gene transcription. The network model with regulations supported by any experimental evidence increased by 557 interactions regarding the previous (2018) version. 73 interactions supported by directed experiments were also included in a second model. We included 545 sRNA-mediated regulations in a third model with a total of 5164 interactions. We deposited the three network models in Abasy Atlas v2.4. We study the C. glutamicum regulatory structure by comparing it against the networks for more than 40 species, finding it to contrast in several global structural properties. We analyze the system-level components of the networks, finding that the inclusion of the sRNAs regulations changes their proportions, transferring part of the basal machinery to the modular class and increasing the number of modules while decreasing their size. Finally, we use strong networks of three model organisms to provide insights in future directions of the C. glutamicum network characterization.

The proliferation of mobile and IoT devices, coupled with the advances in the wireless communication capabilities of these devices, have urged the need for novel communication paradigms for such heterogeneous hybrid networks. Researchers have proposed opportunistic routing as a means to leverage the potentials offered by such heterogeneous networks. While several proposals for multiple opportunistic routing protocols exist, only a few have explored fuzzy logic to evaluate wireless links status in the network to construct stable and faster paths towards the destinations. We propose FQ-AGO, a novel Fuzzy Logic Q-learning Based Asymmetric Link Aware and Geographic Opportunistic Routing scheme that leverages the presence of long-range transmission links to assign forwarding candidates towards a given destination. The proposed routing scheme utilizes fuzzy logic to evaluate whether a wireless link is useful or not by capturing multiple network metrics, the available bandwidth, link quality, node transmission power, and distance progress. Based on the fuzzy logic evaluation, the proposed routing scheme employs a Q-learning algorithm to select the best candidate set toward the destination. We implement FQ-AGO on the NS-3 simulator and compare the performance of the proposed routing scheme with three other relevant protocols: AODV, DSDV, and GOR. For precise analysis, we consider various network metrics to compare the performance of the routing protocols. Our simulation result validates our analysis and demonstrates remarkable performance improvements in terms of total network throughput, packet delivery ration, and end-to-end delay.

Speech recognition, also known as automatic speech recognition (ASR), is a technology that enables software to transcribe spoken language into text. However, existing Amharic ASR methods require multiple separate blocks, such as language, acoustic, and pronunciation models with dictionaries, which can be time-consuming and influence performance. This study proposes an approach that replaces much of the speech pipeline with a single recurrent neural network (RNN) architecture. Our proposed architecture is based on a hybrid approach that combines a convolutional neural network (CNN) with a recurrent neural network (RNN) and a connectionist temporal classification (CTC) loss function. We conducted several experiments with noisy audio data that contain 20,000 valid sentences. The model was evaluated using the word error rate (WER) metric, achieving impressive results of 7% WER on noisy data. This approach has significant implications for the field of speech recognition, as it reduces the human effort required to create dictionaries and improves the efficiency and accuracy of ASR systems, making them more practical for real-world applications.

A novel quasi-twisted miniaturized wideband Branch Line Coupler (BLC) is proposed. The design is based on bisecting the conventional microstrip line BLC transversely and folding bisected sections on double-layered substrates with a common ground plane in between. The pair of quarter wavelength horizontal parallel arms is converted to a Z-shape meandered microstrip line in the designed structure. On the other hand, the pair of quarter wavelength vertical arms are cut by half into two lines and converted to a periodically loaded slow wave structure. The bisected parts of BLC are placed on the opposite side of the doubled-layer substrate and connected through four vias passing through the common ground plane. This technique enabled a compact BLC size of 6.4×18 mm2, which corresponds to a surface area miniaturization by ~50% as compared to the classical BLC size of 10×23 mm2 at 6GHz, Moreover, the attained relative bandwidth is 73.9% (4.6-10 GHz) for S11, and 75.8% (4.5-10 GHz) for S33 as compared to 41% for the ordinary BLC at the same resonant frequency. The circuit is constructed on a double-layered low-cost FR4 substrate with a relative permittivity of 4.3, and a loss tangent of 0.025. An isolation of -13dB was realized in both S13 and S31 demonstrating an excellent performance. The transmission coefficients between input/output ports S21, S41, S23, and S43 are between -3.1dB to -3.5dB at the frequency of 6GHz. Finally, the proposed BLC provides phase differences between output ports of 90.5o and 94.8o at the frequency of 6GHz when the input ports 1 and 3 are excited, respectively. The presented design offers the potential of being utilized as a unit cell for building a Butler-matrix (BM) for sub-6GHz 5G beamforming networks.

B and T lymphocytes demonstrate important alterations in patients with Systemic Lupus Erythematous (SLE), with significant up-regulation of Double Negative (DN) B cells. Aim of this study was to evaluate correlation of T cell immunity changes with the distinct B cell-pattern SLE. In the present study, Flow cytometry was performed in 30 patients with SLE, on remission, and 31 healthy controls, to detect DN B cells (CD19+IgD-CD27-) and a wide range of T lymphocyte subpopulations, based on the presence of CD45RA, CCR7, CD31, CD28, CD57, defined as naive, memory and advanced differentiated/senescent T cells. Both B and T lymphocytes were significantly reduced in SLE patients. However, the percentage of DN B cells were increased, compared to HC, 12.9(2.3-74.2) vs. 8(1.7-35), p=0.04. Distribution of CD4 and CD8 lymphocytes demonstrated a shift to advanced differentiated subsets. The population of DN B cells had significant positive correlation with most of early differentiated T lymphocytes, CD4CD31+, CD4CD45RA+CD28+, CD4CD45RA+CD57-, CD4CD45RA-CD57-, CD4CD28+CD57-, CD4CD28+CD57+, CD4 CM, CD8 CD31+, CD8 NAÏVE, CD8CD45RA-CD57-, CD8CD28+CD57-, CD8CD28+CD57+. Multiple Regression analysis revealed CD4CD31+, CD8CD45RA-CD57- and CD8CD28+CD57- cells as independent parameters contributing to DN B cells, adjusted R2=0.534, p<0.0001. The predominance of DN B cells in patients with SLE is closely associated with early differentiated T lymphocyte subsets, indicating a potential causality role of DN B cells in T lymphocyte activation.

In recent years, the need to make the built environment more resilient and adaptable to climate change has become increasingly evident. In Europe, this aspect concerns the vast majority of existing buildings, which present several deficiencies from the energy-efficiency point of view, considering they were designed before the introduction of modern energy codes. Nowadays, it is possible to retrofit existing buildings using advanced and high-efficient technologies such as Double Skin Façades (DSFs). The research aims to evaluate the use of properly designed DSFs for the energy restoration of existing buildings. In detail, various DSF configurations are applied to a residential building located in central Italy and investigated under present and future climate conditions, estimated through regional climate models. The results underline that all the analysed Double Skin Façades confirm to be a useful option, and, in particular, the Multi-Storey typology allows drastic energy needs decrement. Moreover, the general increase in temperatures and solar radiations could affect the building energy performance, and the insertion of DSF can mitigate the climate change effects, reducing the predicted energy consumption and ensuring better behaviours than the building in its original state.

This short review is aimed at giving an overview of catalytic carbonylative double cyclization reactions, which are processes in which suitable organic substrates and carbon monoxide are sequentially activated by a promoting catalyst to afford the formation of two new cycles with concomitant incorporation of carbon monoxide as a carbonyl function into the final product. Paradigmatic examples of this powerful synthetic methodology, which allows the one-step synthesis of complex molecular architectures from simple building blocks using the simplest and readily available C-1 unit (CO) are illustrated and discussed. The review is divided into five sections: 1) Introduction; 2) Functionalized Olefinic Substrates; 3) Functionalized Acetylenic Substrates; 4) Functionalized Halides; 5) Conclusions and Future Perspectives.

DNA double-strand breakage is the most lethal damage to chromosomal DNA. It activates a series of cellular DNA damage response pathways, including DNA damage sensing, control of cell cycle arrest and apoptosis, and DNA repair. DNA damage response pathways are regulated by complex signaling machineries. Of the intracellular signaling cascades, diacylglycerol kinase (DGK) phosphorylates diacylglycerol (DG) to generate phosphatidic acid (PA). Because both DG and PA serve as second messengers, DGK activity induces a shift of signaling pathways from DG-mediated to PA-mediated cascades, thereby implicating DGK in the regulation of widely various functions. Reportedly, one member of the DGK family, DGKζ, is intimately involved in the regulation of stress responses through p53 and NF-κB. Stresses such as ischemia and infarction cause DGKζ downregulation. Experimental DGKζ depletion renders cells and mice vulnerable to various stressors such as chemotherapeutic agents and ionizing irradiation. Nevertheless, how DGKζ is involved in DNA repair, a critical event of DNA damage response for survival remains unknown. For this study, we examined how DGKζ depletion affects DNA repair mechanisms. We demonstrated that DGKζ depletion causes attenuation of Akt activation and DNA-PK protein expression upon DNA damage, which might engender downregulated BRCA1 protein synthesis and stability. Results suggest that DGKζ depletion attenuates BRCA1-mediated DNA repair machinery, thereby conferring vulnerability to DNA damage.

In the post antimicrobial era, increasing attention is paid towards using bacteriophage (phage in short) therapy to control antibiotic-resistant bacteria. The first step in phage therapy applications is isolating highly efficient lytic phages or phage cocktails from various sources. When a double-layer- agar with around 0.7% agar in top agar is employed, it results in a low number of phage isolation with a poor resolution, and in many cases, you miss the phage. To address this problem, a low concentration of agar in top agar is examined for better phage isolation. Here, our results proved the efficiency of isolating phage upon formulating a double-layer agar with 0.3% agar in top agar. A sewage sample was collected then phages were isolated, purified, and spotted on a top layer agar with 0.3% agar. The results showed the possibility of isolating a higher number of phages on 0.3% top agar than 0.7%. The finding advocates using 0.3% top agar for the double-layer agar, as it will provide fast, better, and easy phage screening and isolation.

The boundary layer height (BLH) distinguishes the interface between the lower and free atmosphere, which is a key variable in numerical simulation, aerosol, and environmental pollution studies. Thus, the goal of this work is to propose a novel method conjuncting with numerical regularization to analyze climate characteristics of the marine boundary layer height (MBLH) using 2007-2011 GPS-RO data from the COSMIC mission. While traditionally, the difference method has been used to achieve this aim, herein, we propose an innovative method, in which the bending angle profile gradient was calculated using the numerical regulation method where the regulation parameters are determined by the double-parameter model function method. Then, the MBLH was determined by employing the maximum gradient method to ascertain the height corresponding to the smallest gradient. The results show a correlation between currents and the MBLH—a relationship that has not been previously demonstrated. A low MBLH is associated with seasons and regions where cold ocean currents are prevailing; whereas a high MBLH is observed in the seasons and places where warm currents are prevailing. This correlation was validated by comparing the obtained results with different occultation data—i.e., atmprf and echprf—which also established that atmprf is more sensitive to convective cloud top capture. In seas with active convection, the armprf calculated bending angle is higher than that from echprf. Subsequently, the standard deviation was used to express the MBLH confidence level. The results show that the MBLH standard deviation is highest in low latitudes and lowest in the middle and high latitudes. Furthermore, we analyzed the interannual MBLH variation trend, which displayed a seasonal variation and spatial distribution corresponding with the current and subsolar point. Finally, we conducted a case study in the South China Sea, and identified a distinctive seasonal change and downward trend.

Conjugated singlet ground state diradicals have received remarkable attention owing to their potential applications in optoelectronic devices. A distinctive character of these systems is the location of the double exciton state, a low lying excited state dominated by the doubly excited H,H→L,L configuration, which may influence optical and other photophysical properties. In this contribution we investigate this specific excited state, for a series of recently synthesized conjugated diradicals, employing time dependent density functional theory based on the unrestricted parallel spin reference configuration in the spin-flip formulation (SF-TDDFT) and standard TD calculations based on the unrestricted antiparallel spin reference configuration (TDUDFT). The quality of the computed results is assessed considering diradical and multiradical descriptors and the excited state wavefunction composition.

In the pursuit of energy efficiency and reduced environmental impact, adequate ventilation in enclosed spaces is essential. This study presents a hybrid neural network model designed for real-time monitoring and prediction of environmental variables. The system comprises two phases: An IoT hardware-software platform for data acquisition and decision-making, and a hybrid model combining short-term memory and convolutional recurrent structures. The results are promising and hold potential for integration into parallel processing AI architectures.

By framing the problem of determining the state of Schrodinger’s cat as a problem of searching for Hidden Markov Model (HMM), this work shows that Schrodinger’s cat is dead with stationary probabilities P(Cat = Dead) = 1 and P(Cat = Alive) = 0 consistently in various observation-based modelling of the HMM.

In the landscape of network management, software-defined networking (SDN) technology emerges as a dynamic approach, facilitating efficient network configuration for improved performance and monitoring, akin to the agility of cloud computing rather than traditional methods. However, its centralized structure exposes SDN to various attack vectors. Distributed Denial of Service (DDoS) attacks, particularly, pose a significant threat to SDN. This study employs machine learning algorithms and Network Traffic Classification Analysis (NCA) to classify SDN traffic as potential attacks. Notably, Decision Trees (DT) out shine other algorithms with a flawless 100% classification success, spotlighting their supremacy. Through this research, a robust path toward fortified SDN security takes form, where technological prowess and strategic intelligence unite for enhanced defense.

We study the synchronous dynamics of three diffusively coupled erbium-doped fiber lasers (EDLFs) in the unidirectional ring configuration without external pump modulation. The dynamical behavior of the system is analyzed using time series, Fourier spectra, Poincaré sections, bifurcation diagrams, and Lyapunov exponents for different values of the coupling strength. For weak coupling, we observe a well-known route to chaos from a stable equilibrium through a Hopf bifurcation and a series of torus bifurcations as the coupling strength is increased. An interesting result is found for large values of the coupling strength, where the phase locking is close to zero. This allows a significant increase in the peak energy of the EDFLs pulses, i.e., above the coupling strength the lasers switch to a Q-switching mode with large-amplitude short pulses. This result allows us to propose a new method for increasing the laser pulse energy based on the control of the bistability by the rotating wave in the array of three unidirectionally ring-coupled EDFLs as a function of the coupling strength. In our system, we were able to increase the peak laser power by almost 20 times more than a continuous single EDFL.

In the fast-paced internet world that we live in today, internet technologies and tools that enable us to connect online are rapidly changing and improving. To adapt to the changing environment, many game developers are changing their business models to adjust to this online environment by developing games that are centred around online gaming. As such, the internet demands of consumers such as gamers are increasing because of the need for quality internet connection for optimal gameplay. There are too many factors that can affect online gameplay. Moreover, not all gamers are equipped with the network knowledge and skills to diagnose and troubleshoot any internet issues that may arise during their gameplay session. The main contribution of this research was to gather insights from Malaysian broadband internet consumers, mainly from gamers and general users regarding their opinions of their ISP service experience in gaming, general use and service quality. A 1-click solution is presented as a smart autonomous network monitoring tool that can autonomously monitor, diagnose, and troubleshoot network problems with minimal input from the user. This research was conducted using a mixed-mode research methodology where the participants were asked to fill up a questionnaire regarding the user experience of their Internet Service Provider (ISP), and their perception of the 1 click-automated networking tool for their personal use. It is shown that there is a market demand for a 1 click-automated networking tool as gamers and general users both agree that such a tool would be beneficial to them by improving the internet quality of a network.

In the age of social media your personal branding is critical to find a job. Information is highly available and handy for everyone looking for a job as well as for people trying to recruit employees at all levels. Currently, there is a first e-impression that encompasses employee education, qualifications, and relationships. This e-impression is given by your social media, specifically those websites that were built to facilitate interactions between employee and employer during the job-hunting process. There are different websites that enable you to establish a social network. For example, meet, Xing, Bark, Opportunity, Lunchmeet, and LinkedIn among others. This article is written by a group of young undergraduate and graduate researchers, who have noticed the importance of social networks to apply for graduate school and industry jobs. This manuscript offers ten simple rules that include resources to facilitate the journey on LinkedIn-like social networks, hoping that you have a successful career description in the most popular professional social networking site.

Background: Disruption of alternative splicing (AS) is frequently observed in cancer and it might represent an important signature for tumor progression and therapy. Exon skipping (ES) represents one of the most frequent AS events and in non-small cell lung cancer (NSCLC) MET exon 14 skipping was shown to be targetable. Methods: We constructed a neural network (NN) specifically designed to detect MET exon 14 skipping events using RNAseq data. Furthermore, for discovery purpose we also developed a sparsely connected autoencoder to identify uncharacterized MET isoforms. Results: The NN had 100% Met exon 14 skipping detection rate, when tested on a manually curated set of 690 TCGA bronchus and lung samples. When globally applied to 2605 TCGA samples, we observed that the majority of false positives was characterized by a blurry coverage of exon 14, but interesting they share a common coverage peak in the second intron and we speculate that this event could be the transcription signature of a LINE1-MET fusion. Conclusions: Taken together our results indicate that neural networks can be an effective tool to provide a quick classification of pathological transcription events and sparsely connected autoencoders could represent the basis for the development of an effective discovery tool.