Multi-dimensional multiplexing optical secret sharing framework with cascaded liquid crystal holograms

Secret sharing is a promising technology for information encryption by splitting the secret information into different shares.However,the traditional scheme suffers from information leakage in decryption process since the amount of available information channels is limited.Herein,we propose and demonstrate an optical secret sharing framework based on the multi-dimensional multiplexing liquid crystal(LC)holograms.The LC holograms are used as spatially separated shares to carry secret images.The polarization of the incident light and the distance between different shares are served as secret keys,which can significantly improve the information security and capacity.Besides,the decryption condition is also restricted by the applied external voltage due to the variant diffraction efficiency,which further increases the information security.In implementation,an artificial neural network(ANN)model is developed to carefully design the phase distribution of each LC hologram.With the advantage of high security,high capacity and simple configuration,our optical secret sharing framework has great potentials in optical encryption and dynamic holographic display.

Envelope Method and More General New Global Structures of Solutions for Multi-dimensional Conservation Law

For the two-dimensional(2D)scalar conservation law,when the initial data contain two different constant states and the initial discontinuous curve is a general curve,then complex structures of wave interactions will be generated.In this paper,by proposing and investigating the plus envelope,the minus envelope,and the mixed envelope of 2D non-selfsimilar rarefaction wave surfaces,we obtain and the prove the new structures and classifications of interactions between the 2D non-selfsimilar shock wave and the rarefaction wave.For the cases of the plus envelope and the minus envelope,we get and prove the necessary and sufficient criterion to judge these two envelopes and correspondingly get more general new structures of 2D solutions.

Research and Practice on the Multi-dimensional Course Goal Achievement Evaluation of Software Engineering

According to the standards of engineering education accreditation,the achievement paths and evaluation criteria of course goals are presented,aimed at the objectives of software engineering courses and the characteristics of hybrid teaching in Kunming University of Science and Technology.Then a multi-dimensional evaluation system for course goal achievement of software engineering is proposed.The practice’s results show that the multi-dimensional course goal achievement evaluation is helpful to the continuous improvement of course teaching,which can effectively support the evaluation of graduation outcomes.

An Ordinal Multi-Dimensional Classification(OMDC)for Predictive Maintenance

Predictive Maintenance is a type of condition-based maintenance that assesses the equipment's states and estimates its failure probability and when maintenance should be performed.Although machine learning techniques have been frequently implemented in this area,the existing studies disregard to the nat-ural order between the target attribute values of the historical sensor data.Thus,these methods cause losing the inherent order of the data that positively affects the prediction performances.To deal with this problem,a novel approach,named Ordinal Multi-dimensional Classification(OMDC),is proposed for estimating the conditions of a hydraulic system's four components by taking into the natural order of class values.To demonstrate the prediction ability of the proposed approach,eleven different multi-dimensional classification algorithms(traditional Binary Relevance(BR),Classifier Chain(CC),Bayesian Classifier Chain(BCC),Monte Carlo Classifier Chain(MCC),Probabilistic Classifier Chain(PCC),Clas-sifier Dependency Network(CDN),Classifier Trellis(CT),Classifier Dependency Trellis(CDT),Label Powerset(LP),Pruned Sets(PS),and Random k-Labelsets(RAKEL))were implemented using the Ordinal Class Classifier(OCC)algorithm.Besides,seven different classification algorithms(Multilayer Perceptron(MLP),Support Vector Machine(SVM),k-Nearest Neighbour(kNN),Decision Tree(C4.5),Bagging,Random Forest(RF),and Adaptive Boosting(AdaBoost))were chosen as base learners for the OCC algorithm.The experimental results present that the proposed OMDC approach using binary relevance multi-dimensional classification methods predicts the conditions of a hydraulic system's multiple components with high accuracy.Also,it is clearly seen from the results that the OMDC models that utilize ensemble-based classification algorithms give more reliable prediction performances with an average Hamming score of 0.853 than the others that use traditional algorithms as base learners.

Data inversion of multi-dimensional magnetic resonance in porous media

Since its inception in the 1970s,multi-dimensional magnetic resonance(MR)has emerged as a powerful tool for non-invasive investigations of structures and molecular interactions.MR spectroscopy beyond one dimension allows the study of the correlation,exchange processes,and separation of overlapping spectral information.The multi-dimensional concept has been re-implemented over the last two decades to explore molecular motion and spin dynamics in porous media.Apart from Fourier transform,methods have been developed for processing the multi-dimensional time-domain data,identifying the fluid components,and estimating pore surface permeability via joint relaxation and diffusion spectra.Through the resolution of spectroscopic signals with spatial encoding gradients,multi-dimensional MR imaging has been widely used to investigate the microscopic environment of living tissues and distinguish diseases.Signals in each voxel are usually expressed as multi-exponential decay,representing microstructures or environments along multiple pore scales.The separation of contributions from different environments is a common ill-posed problem,which can be resolved numerically.Moreover,the inversion methods and experimental parameters determine the resolution of multi-dimensional spectra.This paper reviews the algorithms that have been proposed to process multidimensional MR datasets in different scenarios.Detailed information at the microscopic level,such as tissue components,fluid types and food structures in multi-disciplinary sciences,could be revealed through multi-dimensional MR.

Intelligent 6G Wireless Network with Multi-Dimensional Information Perception

Intelligence and perception are two operative technologies in 6G scenarios.The intelligent wireless network and information perception require a deep fusion of artificial intelligence(AI)and wireless communications in 6G systems.Therefore,fusion is becoming a typical feature and key challenge of 6G wireless communication systems.In this paper,we focus on the critical issues and propose three application scenarios in 6G wireless systems.Specifically,we first discuss the fusion of AI and 6G networks for the enhancement of 5G-advanced technology and future wireless communication systems.Then,we introduce the wireless AI technology architecture with 6G multidimensional information perception,which includes the physical layer technology of multi-dimensional feature information perception,full spectrum fusion technology,and intelligent wireless resource management.The discussion of key technologies for intelligent 6G wireless network networks is expected to provide a guideline for future research.

Pedestrian flow through exit:Study focused on evacuation pattern

Experiments are conducted on the evacuation rate of pedestrians through exits with queued evacuation pattern and random evacuation pattern. The experimental results show that the flow rate of pedestrians is larger with the random evacuation pattern than with the queued evacuation pattern. Therefore, the exit width calculated based on the minimum evacuation clear width for every 100 persons, which is on the assumption that the pedestrians pass through the exit in one queue or several queues, is conservative. The number of people crossing the exit simultaneously is greater in the random evacuation experiments than in the queued evacuation experiments, and the time interval between the front row and rear row of people is shortened in large-exit conditions when pedestrians evacuate randomly. The difference between the flow rate with a queued evacuation pattern and the flow rate with a random evacuation pattern is related to the surplus width of the exit, which is greater than the total width of all accommodated people streams. Two dimensionless quantities are defined to explore this relationship. It is found that the difference in flow rate between the two evacuation patterns is stable at a low level when the surplus width of the exit is no more than 45% of the width of a single pedestrian stream. There is a great difference between the flow rate with the queued evacuation pattern and the flow rate with the random evacuation pattern in a scenario with a larger surplus width of the exit. Meanwhile, the pedestrians crowd extraordinarily at the exit in these conditions as well, since the number of pedestrians who want to evacuate through exit simultaneously greatly exceeds the accommodated level. Therefore, the surplus width of exit should be limited especially in the narrow exit condition, and the relationship between the two dimensionless quantities mentioned above could provide the basis to some extent.

A divergent pattern in functional connectivity: a transdiagnostic perspective

Functional magnetic resonance imaging(fMRI)is a popular tool used to investigate not only how the brain responds to specific stimuli during sensorimotor or cognitive tasks,but also brain activity at rest.The physics beyond this approach is based on the analysis of the blood oxygenation level-dependent signal.

Pig Reproductive and Respiratory Syndrome Virus (PRRSV) Does Not Attach to Boar Sperm;It Affects Only the Velocity Pattern and the Mobility Pattern

The purpose of the study was to evaluate the sperm viability of semen infected with PRRSV viral particles, observing the effect of the Virus on the motility of boar sperm. The work was carried out at the FMVZ-BUAP Genetics and Reproduction Laboratory. 5 stallions were used. Each sample contained 1 × 106 sperm, the PRRS virus strain was ATCC-VR-2332 (0, 102, 104 and 106 copies of RNA/mL in triplicate), it was observed daily at the CASA;Hamilton Thorne®. Cells with MT (P < 0.05) on days 1, 3, 5, 7 and 10 of evaluation with 201 ± 7.3, 167 ± 10.1, 165 ± 14.6, 134 ± 8.2 and 120 ± 8.8, respectively. The % MP between control and virus concentrations (P ≥ 0.05). The LCV on day 1 and 7 PI at 10X2 and 10X6 (P < 0.05) vs control. In the Correlation Matrix, where it is observed that there is a correlation between VSL and VAP, VSL and VCL, VCL and ALH, VAP with ALH. There is a correlation of VSL and ALH, STR and ALH. In this study there were (P ≤ 0.01) in the VCL, in the concentrations (102) 162.81 ± 10.65 and (106) 177.12 ± 5.77 vs 193.04 ± 4.62 of control. This indicates that altering these parameters would be related to fertility and the PRRS virus affects the LCV. Regarding the VSL, it was observed that the sperm infected with viruses 102, 104 and 106 of 48.00 ± 3.38, 49.88 ± 1.83 and 50.55 ± 2.24 Vs. 56.66 ± 1.68 of control respectively, the control would have greater possibilities of fertilizing the oocyte. In this study, it was found (P ≤ 0.01) in the VAP with 102 of 77.26 ± 5.16, 104 with 83.35 ± 2.41 and 106 with 81.29 ± 3.14 vs the control with 90.56 ± 2.07. Regarding the ALH there is (P < 0.05) a 104 with 8.70 ± .26 and 106 with 9.64 ± 0.23 vs control 8.50 ± 0.27. The presence of different concentrations of PRRSV in boar semen induces changes in different types of sperm motility. Infection of ejaculates with the PRRS virus affects sperm motility on days 1, 3, 5, 7, and 10 post-infections.

A Portfolio Selection Method Based on Pattern Matching with Dual Information of Direction and Distance

Pattern matching method is one of the classic classifications of existing online portfolio selection strategies. This article aims to study the key aspects of this method—measurement of similarity and selection of similarity sets, and proposes a Portfolio Selection Method based on Pattern Matching with Dual Information of Direction and Distance (PMDI). By studying different combination methods of indicators such as Euclidean distance, Chebyshev distance, and correlation coefficient, important information such as direction and distance in stock historical price information is extracted, thereby filtering out the similarity set required for pattern matching based investment portfolio selection algorithms. A large number of experiments conducted on two datasets of real stock markets have shown that PMDI outperforms other algorithms in balancing income and risk. Therefore, it is suitable for the financial environment in the real world.

Pattern of Referral of Obstetric Patients at a Tertiary Care Hospital in Southern Nigeria

Background: Health challenges that are difficult to manage at primary health centres should be referred to secondary health facilities, and if untreated, to the tertiary hospitals. A good referral should include the patient’s biography, such as age, gender, tribe, religion, occupation, medical history, the reason for the referral, treatments received, and clinical diagnosis. Objectives: To evaluate the referral patterns, indications for referrals, and feto-maternal outcomes for obstetric patients who were referred to the University of Port Harcourt Teaching Hospital. Materials and Methods: A prospective study of patients admitted to the Obstetric unit from January 1, 2021, to December 31, 2022. Data was collected from patients while on admission or clinic visits and recorded in an excel spread sheet. Data was analyzed with the Statistical Package for Social Sciences (SPSS) version 25. Results: Of the 3469 patients were admitted to the obstetric unit, 1476 and 1993 were admitted in 2021 and 2022, respectively. Most (70.35%) of the patients were in the 20-34 years age group, parity 1-4 was the most frequent (66.49%), while 85.39% of patients were booked. 10.46% of the booked patients were referred from other facilities, whereas 89.54% of patients were booked at our facility from the onset. Most common indications of referrals were previous caesarean section (CS) at term (40.09%) and hypertensive disorders of pregnancy (17.59%). The outcome for 2021 indicated 17 maternal deaths, 132 fetal deaths and 1010 live births, giving maternal mortality ratio (MMR) as 1810.44 per 100,000 live births and perinatal mortality ratio (PMR) of 130.7 per 1000 births respectively. In 2022, there were 17 maternal deaths, 130 fetal deaths and 1297 deliveries, giving a MMR of 1399 per 100,000 live births and a PMR of 100.2 per 1000 births. Conclusion: The pattern of referral among obstetric patients in this study shows that a lot of the patients do not get adequate care at the lower cadre of the referral system, hence adequate facilities should be made available in primary and secondary health centres to tackle obstetric emergencies.

Tuning the diffusion constant to optimize the readout of positional information of spatial concentration patterns

Positional information encoded in spatial concentration patterns is crucial for the development of multicellular organisms.However,it is still unclear how such information is affected by the physically dissipative diffusion process.Here we study one-dimensional patterning systems with analytical derivation and numerical simulations.We find that the diffusion constant of the patterning molecules exhibits a nonmonotonic effect on the readout of the positional information from the concentration patterns.Specifically,there exists an optimal diffusion constant that maximizes the positional information.Moreover,we find that the energy dissipation due to the physical diffusion imposes a fundamental upper limit on the positional information.

Temporal and spatial evolution of global major grain trade patterns

The complex and volatile international landscape has significantly impacted global grain supply security. This study uses a complex network analysis model to examine the evolution and trends of the global major grain trade from 1990 to 2020, focusing on network topology, centrality ranking, and community structure. There are three major findings. First, the global major grain trade network has expanded in scale, with a growing emphasis on diversification and balance. During the study period, the United States, Canada, China, and Brazil were the core nodes of the network. Grain-exporting countries were mainly situated in Asia, the Americas, and Europe, and importing countries in Asia, Africa, and Europe. Second, a significant increase in the number of high centrality countries with high export capacity occurred, benefiting from natural advantages such as fertile land and favorable climates. Third, the main global grain trade network is divided into four communities, with the Americas-Europe community being the largest and most widespread. The formation of the community pattern was influenced by geographic proximity, driven by the core exporting countries. Therefore, the world needs to enhance the existing trade model, promote the multi-polarization of the grain trade network, and establish a global vision for the future community. Countries and regions should participate actively in global grain trade security governance and institutional reform, expand trade links with other countries, and optimize import and export policies to reduce trade risks.

Dissolvable temporary barrier:a novel paradigm for flexible hydrogel patterning in organ-on-a-chip models

A combination of hydrogels and microfluidics allows the construction of biomimetic three-dimensional(3D)tissue models in vitro,which are also known as organ-on-a-chipmodels.The hydrogel patterningwith awell-controlled spatial distribution is typically achieved by embedding sophisticated microstructures to act as a boundary.However,these physical barriers inevitably expose cells/tissues to a less physiologically relevant microenvironment than in vivo conditions.Herein,we present a novel dissolvable temporary barrier(DTB)strategy that allows robust and flexible hydrogel patterning with great freedom of design and desirable flow stimuli for cellular hydrogels.The key aspect of this approach is the patterning of a water-soluble rigid barrier as a guiding path for the hydrogel using stencil printing technology,followed by a barrier-free medium perfusion after the dissolution of the DTB.Single and multiple tissue compartments with different geometries can be established using either straight or curved DTB structures.The effectiveness of this strategy is further validated by generating a 3D vascular network through vasculogenesis and angiogenesis using a vascularized microtumor model.As a new proof-of-concept in vasculature-on-a-chip,DTB enables seamless contact between the hydrogel and the culture medium in closed microdevices,which is an improved protocol for the fabrication ofmultiorgan chips.Therefore,we expect it to serve as a promising paradigm for organ-on-a-chip devices for the development of tumor vascularization and drug evaluation in the future preclinical studies.

Recent Advances in Patterning Strategies for Full‑Color Perovskite Light‑Emitting Diodes

Metal halide perovskites have emerged as promising light-emitting materials for next-generation displays owing to their remarkable material characteristics including broad color tunability,pure color emission with remarkably narrow bandwidths,high quantum yield,and solution processability.Despite recent advances have pushed the luminance efficiency of monochromic perovskite light-emitting diodes(PeLEDs)to their theoretical limits,their current fabrication using the spincoating process poses limitations for fabrication of full-color displays.To integrate PeLEDs into full-color display panels,it is crucial to pattern red–green–blue(RGB)perovskite pixels,while mitigating issues such as cross-contamination and reductions in luminous efficiency.Herein,we present state-of-the-art patterning technologies for the development of full-color PeLEDs.First,we highlight recent advances in the development of efficient PeLEDs.Second,we discuss various patterning techniques of MPHs(i.e.,photolithography,inkjet printing,electron beam lithography and laserassisted lithography,electrohydrodynamic jet printing,thermal evaporation,and transfer printing)for fabrication of RGB pixelated displays.These patterning techniques can be classified into two distinct approaches:in situ crystallization patterning using perovskite precursors and patterning of colloidal perovskite nanocrystals.This review highlights advancements and limitations in patterning techniques for PeLEDs,paving the way for integrating PeLEDs into full-color panels.

Spatiotemporal pattern of climate change in the China-Myanmar Economic Corridor from 1901 to 2018

The China-Myanmar Economic Corridor(CMEC) is an important part of China's Belt and Road Initiative and an important area for global ecology and biodiversity. In this study, the annual and seasonal spatiotemporal patterns of temperature and precipitation in the CMEC over the past century were investigated using linear tendency estimation, the Mann-Kendall mutation test, the T-test, and wavelet analysis based on the monthly mean climatic data from 1901 to 2018 released by the Climatic Research Unit(CRU) of the University of East Anglia, UK. The results show that the CMEC demonstrated a trend of warming and drying over the past 100 years, and the rate of change in Myanmar was stronger than that in Yunnan Province of China. The warming rate was 0.039 ℃/10a. Precipitation decreased at a rate of -6.1 mm/10a. From the perspective of spatial distribution, temperature was high in the central and southern, low in the north of the CMEC, and the high-temperature centers were mainly distributed in the southern plain and river valley. Precipitation decreased from west to east and from south to north of the CMEC. From the perspective of the rate of change, warming was stronger in central and northern CMEC than in southern and northeastern CMEC. The rate of precipitation decline was stronger in the central and western regions than in the eastern region. This study provides a scientific reference for the CMEC to address climate change and ensure sustainable social and economic development and ecological security.

Migration Networks Pattern of China’s Floating Population from the Perspective of Complex Network

Since China’s reform and opening-up,the growing disparity between urban and rural areas and regions has led to massive migration.With China’s Rural Revitalization Strategy and the industrial transfer from the eastern coastal areas to the inland,the migration direction and pattern of the floating population have undergone certain changes.Using the 2017 China Migrants Dynamic Survey(CMDS),excluding Hong Kong,Macao,and Taiwan regions of China,organized by China’s National Health Commission,the relationship matrix of the floating population is constructed according to the inflow place of the interviewees and their outflow place(the location of the registered residence)in the questionnaire survey.We then apply the complex network model to analyze the migration direction and network pattern of China’s floating population from the city scale.The migration network shows an obvious hierarchical agglomeration.The first-,second-,third-and fourth-tier distribution cities are municipalities directly under the central government,provincial capital cities,major cities in the central and western regions and ordinary cities in all provinces,respectively.The migration trend is from the central and western regions to the eastern coastal areas.The migration network has‘small world’characteristics,forming nine communities.It shows that most node cities in the same community are closely linked and geographically close,indicating that the migration network of floating population is still affected by geographical proximity.Narrowing the urban-rural and regional differences will promote the rational distribution this population.It is necessary to strengthen the reform of the registered residence system,so that the floating population can enjoy urban public services comparable to other populations,and allow migrants to live and work in peace.

Dynamic Signature Verification Using Pattern Recognition

Dynamic signature is a biometric modality that recognizes an individual’s anatomic and behavioural characteristics when signing their name. The rampant case of signature falsification (Identity Theft) was the key motivating factor for embarking on this study. This study was necessitated by the damages and dangers posed by signature forgery coupled with the intractable nature of the problem. The aim and objectives of this study is to design a proactive and responsive system that could compare two signature samples and detect the correct signature against the forged one. Dynamic Signature verification is an important biometric technique that aims to detect whether a given signature is genuine or forged. In this research work, Convolutional Neural Networks (CNNsor ConvNet) which is a class of deep, feed forward artificial neural networks that has successfully been applied to analysing visual imagery was used to train the model. The signature images are stored in a file directory structure which the Keras Python library can work with. Then the CNN was implemented in python using the Keras with the TensorFlow backend to learn the patterns associated with the signature. The result showed that for the same CNNs-based network experimental result of average accuracy, the larger the training dataset, the higher the test accuracy. However, when the training dataset are insufficient, better results can be obtained. The paper concluded that by training datasets using CNNs network, 98% accuracy in the result was recorded, in the experimental part, the model achieved a high degree of accuracy in the classification of the biometric parameters used.

Plant-based dietary patterns and risk of esophageal cancer:A prospective cohort study spanning 17 years

Objective: Plant-based diets have multiple health benefits for cancers;however, little is known about the association between plant-based dietary patterns and esophageal cancer(EC).This study presents an investigation of the prospective associations among three predefined indices of plant-based dietary patterns and the risk of EC.Methods: We performed endoscopic screening for 15,709 participants aged 40-69 years from two high-risk areas of China from January 2005 to December 2009 and followed the cohort until December 31, 2022. The overall plant-based diet index(PDI), healthful plant-based diet index(h PDI), and unhealthful plant-based diet index(u PDI), were calculated using survey responses to assess dietary patterns. We applied Cox proportional hazard regression to estimate the multivariable hazard ratios(HRs) and 95% confidence intervals(95% CIs) of EC across 3plant-based diet indices and further stratified the analysis by subgroups.Results: The final study sample included 15,184 participants in the cohort. During a follow-up of 219,365person-years, 176 patients with EC were identified. When the highest quartile was compared with the lowest quartile, the pooled multivariable-adjusted HR of EC was 0.50(95% CI, 0.32-0.77) for h PDI. In addition, the HR per 10-point increase in the h PDI score was 0.42(95% CI, 0.27-0.66) for ECs. Conversely, u PDI was positively associated with the risk of EC, and the HR was 1.80(95% CI, 1.16-2.82). The HR per 10-point increase in the u PDI score was 1.90(95% CI, 1.26-2.88) for ECs. The associations between these scores and the risk of EC were consistent in most subgroups. These results remained robust in sensitivity analyses.Conclusions: A healthy plant-based dietary pattern was associated with a reduced risk of EC. Emphasizing the healthiness and quality of plant-based diets may be important for preventing the development of EC.

Clinical manifestation,lifestyle,and treatment patterns of chronic erosive gastritis:A multicenter real-world study in China

BACKGROUND Although chronic erosive gastritis(CEG)is common,its clinical characteristics have not been fully elucidated.The lack of consensus regarding its treatment has resulted in varied treatment regimens.AIM To explore the clinical characteristics,treatment patterns,and short-term outcomes in CEG patients in China.METHODS We recruited patients with chronic non-atrophic or mild-to-moderate atrophic gastritis with erosion based on endoscopy and pathology.Patients and treating physicians completed a questionnaire regarding history,endoscopic findings,and treatment plans as well as a follow-up questionnaire to investigate changes in symptoms after 4 wk of treatment.RESULTS Three thousand five hundred sixty-three patients from 42 centers across 24 cities in China were included.Epigastric pain(68.0%),abdominal distension(62.6%),and postprandial fullness(47.5%)were the most common presenting symptoms.Gastritis was classified as chronic non-atrophic in 69.9%of patients.Among those with erosive lesions,72.1%of patients had lesions in the antrum,51.0%had multiple lesions,and 67.3%had superficial flat lesions.In patients with epigastric pain,the combination of a mucosal protective agent(MPA)and proton pump inhibitor was more effective.For those with postprandial fullness,acid regurgitation,early satiety,or nausea,a MPA appeared more promising.CONCLUSION CEG is a multifactorial disease which is common in Asian patients and has non-specific symptoms.Gastroscopy may play a major role in its detection and diagnosis.Treatment should be individualized based on symptom profile.