Quantitative phase microscopy (QPM), a technique combining phase imaging and microscopy, enables visualization of the 3-D topography in reflective samples as well as the inner structure or refractive index distribution of transparent and translucent samples. However, as in conventional optical microscopy, QPM provides either a large field of view (FOV) or a high resolution but not both. Many approaches such as oblique illumination, structured illumination and speckle illumination have been proposed to improve the spatial resolution of phase microscopy by restricting other degrees of freedom (mostly time). Therefore, the space bandwidth product (SBP) of QPM becomes enlarged. This paper aims to provide an up-to-date review on the resolution enhancement approaches of QPM, discussing the pros and cons of each technique as well as the confusion on resolution definition claim on QPM and other coherent microscopy.

1) Background: A population-based cross-sectional study was conducted to understand how green cover and accessibility of common public open spaces in compact urban areas affect physical activeness of resident. 2) Methods: A total of 554 residents completed a structured questionnaire on quality-of-life, physical activity level, and healthy eating practice. 3) Results: The sampled population lived with green cover averaged 10.11 ± 7.95% (ranged 1.56–9.88), whereas majority (90%) of the residents performed physical activities at medium and high levels. Metabolic Equivalent of Task (MET)-minutes/week was associated with the green cover percentage (Pearson r = 0.092; p < 0.05). Irrespective of age and physical activity level, active residents commonly used public open spaces within district for performing exercise, especially parks and promenade were mostly used by older residents while sports facilities by the younger groups. 4) Conclusions: Current findings suggested promotion of exercise could be achieved by the design or redesign of built environment to include more parks accessible to the residents with the increase of vegetation.

A stable driver state is essential in the process of manually transition control, which inevitably occurs in Level 3 automated driving situations. To this end, this paper proposed a CNN algorithm-based driver state monitoring system that uses multi-sensor data such as driver's face image, biometric information, and vehicle behavior information as input. This system calculates the probability of drowsiness for each of the four time periods using a convolutional neural network (CNN) based on ToF camera-based eye blinking, ECG information (pulse rate) embedded in the steering wheel, and vehicle information (steering angle data). In order to build a reliable and high-quality training dataset (Ground Truth) for the CNN algorithm, a baseline was established by matching the driver's face image with the electrocardiogram (ECG) and electroencephalogram (EEG) changes in the drowsy and normal states. In a simulation test of the proposed CNN algorithm using more than 20,000 driver image data acquired using a driving simulator, the TNR was 94.8% and the accuracy was 94.2%. Our proposed method is expected to minimize human errors that may occur when switching control by monitoring inappropriate driver state (drowsiness) in real time.

The paper presents an analysis of the method of recording the magnetic component of the Earth’s natural pulse electromagnetic field in an urban environment. This method of recording has already proved itself to be a method that allocates the stressed sections of rock mass at mining and, therefore, authors suppose its effectiveness for allocating active tectonic disturbances and forecasting accidents at underground utilities, what will help reduce the potential environmental hazard of these objects.

This research aims to study the ultimate limit state (ULS) behaviour of stiffened panel under longitudinal compression by non-linear finite element method (NLFEM). There are different types of stiffeners being used in shipbuilding i.e. T-bar, flat-bar and angle-bar. However, this research focuses on the ultimate compressive strength behaviour of flat-bar stiffened panel. A total of 420 of reliable scenarios of flat-bar stiffened panel are selected for numerical simulation by ANSYS NLFEM. The ultimate strength behaviours obtained were used as data for the development of closed form shape empirical formulation. Recently, Kim et al. [1] proposed for advanced empirical formulation for T-bar stiffened panel and the applicability of the proposed formulation to flat-bar stiffened panel will be confirmed by this study. The accuracy of the empirical formulation obtained for flat-bar stiffened panel has been validated by FE simulation results of statistical analysis (R2 = 0.9435). The outcome obtained will be useful for ship structural designers in predicting the ultimate strength performance of flat-bar type stiffened panel under longitudinal compression.

Often, in biology, we are faced with the problem of exploring relevant unknown biological hypotheses in the form of myriads of combination of factors that might be affecting the pathway under certain conditions. Currently, a major persisting problem is to cherry pick the combinations based on expert advice, literature survey or guesses for investigation. This entails investment in time, energy and expenses at various levels of research. To address these issues, a search engine design was recently been developed, which showed promise by revealing existing confirmatory published wet lab results. Additionally and of import, the engine mined up a range of unexplored/untested/unknown combinations of genetic factors in the Wnt pathway that were affected by ETC-1922159 enantiomer, a PORCN-WNT inhibitor, after the colorectal cancer cells were treated with the inhibitor drug. As an example, MYC is known to upregulate PRC2 complex. PRC2 complex contains EZH2, which suppresses tumor suppressor genes via epigenetic modifications. MYC and HOXB8 are up regulated in colorectal cancer, however, the dual working mechanism of the same is not known. The in silico engine showed positioning which correctly approximates and assigns to this 3rd order combination of MYC-HOXB8-EZH2, pointing to the in vitro/in vivo down regulation by ETC-1922159. If the protein interaction of MYC-HOXB8 can be established and a study be done apropos EZH2, it will establish at in vitro/in vivo level, the in silico ranking also. The potential of this engine is immense given the problem faced in biology and other fields. Here we elucidate the R code to understand the mechanics of the search engine in a fluid manner for systems biologists. Though the search engine is in the developmental stage, we share the detailed mechanism of the working principles of the same as it can be generalized to problems in other fields.

We report on ground-based measurements of the atmospheric electric field (Ez= -Potential Gradient (PG)) and current density (Jz) that were conducted at two locations in Israel. One is the Emilio Segre cosmic ray station located on Mt. Hermon (34.45 N, 2020 m AMSL) located in northern Israel near the Syrian-Lebanon border and at the other at the Wise astronomical observatory in the Negev desert highland plateau of southern Israel (31.18 N, 870 m AMSL). We searched for possible effects of strong, short-term solar events on the potential gradient and the vertical current density, as disruption to the Global Electric Circuit are often observed following strong solar events. The first case study (St. Patrick Day, 17 March 2015) was classified as the strongest event of 2015. The second case study (8 Sep 2017) was categorized as the strongest event of 2017 and one of the twenty strongest events on record to date. The results show that the electrical parameters measured at ground level at both stations were not affected during the two massive proton events and the ensuing geomagnetic storms. The magnetospheric shielding in lower latitudes is strong enough to shield against flux of energetic particles from solar events, obscuring any impact that may be noticeable above the local daily variations induced by local meteorological conditions (aerosol concentrations, clouds, high humidity, and wind speed) which were investigated as well.

Recurrent neural networks (RNNs) transmit information over time through recurrent connections. In contrast, biological neural networks use many other temporal processing mechanisms. One of these mechanisms is the inter-neuron delays caused by varying axon properties. Recently, this feature was implemented in echo state networks (ESNs), a type of RNN, by assigning spatial locations to neurons and introducing distance-dependent inter-neuron delays. These delays were shown to significantly improve ESN task performance. However, thus far it is still unclear why distance-based delay networks (DDNs) perform bettern than ESNs. In this paper, we show that by optimizing inter-node delays, the memory capacity of the network matches the memory requirements of the task. As such, networks concentrate their memory capabilities to the points in the past which contain the most information for the task at hand. Moreover, we show that DDNs have more total linear memory capacity, with the same amount of non-linear processing power.

The rate constants of elementary processes in the atom-diatom system O(³P)+H₂(v), including the processes of vibrational relaxation and dissociation, were studied using the quasi-classical trajectory method. All calculations were carried out along the ground potential energy surface (PES) ³A′′ that was approximated by a neural network. Approximation data were obtained using ab initio quantum chemistry methods at the extended multi-configuration quasi-degenerate second-order perturbation theory XMCQDPT2 in a basis set limit. The calculated cross sections of the reaction channels are in good agreement with the literature data. A complete set of state-to-state rate constants was obtained for the metathesis reaction, the dissociation and VT relaxation of the H₂ molecule upon collision with an O atom. According to this data Arrhenius approximations over a wide temperature range were obtained for the thermal rate constants of considered processes. Data obtained on the dissociation constants and VT relaxation of vibrationally excited H₂ molecules can be used in constructing kinetic models describing the oxidation of hydrogen at high temperatures or highly nonequilibrium conditions.

The robust fusion estimation problem of a multi-sensor system with communication constraints, random packet drops, d-step state delays, and deterministic control inputs is investigated. The state augmentation method is used to transform the time delay system into a non-time delay system, and then a robust fusion estimation algorithm based on sensitivity penalty is proposed and the pseudo-cross-covariance matrix is updated. An event-triggered robust fusion estimator with state delay and deterministic control inputs is considered to make the model closer to reality while effectively reducing network congestion and lowering communication costs. Conditions are given to ensure that the estimation errors of the robust fusion estimator are uniform boundedness. Two sets of numerical simulations are provided to illustrate the effectiveness of the derived fusion estimator.

With the urgent need of precise positioning faced by internet of things (IoT) applications, the universality and cost of indoor positioning devices become key factors. Since the 5G network has been widely deployed, new opportunity is brought by tightly fusing the traditional low-cost sensors, i.e., the magnetometer. In this study, using 5G channel state information (CSI) and geomagnetic data, a multi-input convolutional neural network (CNN) localization system was proposed. First, to generate a data tensor that is easy for CNN processing, the raw data was reconstructed individually. Then, to comprehensively incorporate the features of 5G CSI and geomagnetic strength data, the ReLU function was chosen as the activation function of the convolutional layer. After that, a multi-input CNN was trained using the incorporated geomagnetic strength and CSI amplitude in the off-line side, and the trained CNN was recorded as a location fingerprint, which can be used for the user position prediction. Finally, in the online side and using a multi-input CNN, the 2-D coordinates are estimated and tested indoors in a typical conference room scenario. The results showed that longer sampling time of fingerprint data result in better uniqueness of the reference point, while the data collection time of locating points does not need to be long. Taking the positioning efficiency and accuracy into consideration, a sampling time of 3s at the reference point and 0.2s at the locating point are recommended. The positioning accuracy using the proposed method was 1.41 m, with an improvement of 22.9% compared with the 5G positioning, and an improvement of 18.0% compared with the 5G and geomagnetic fusing positioning using single CNN.

Accurate rainfall measurement is vital when investigating spatial and temporal precipitation variability at different scales. However, there are many regions around the world, such as North Darfur State in Sudan, where ground-based observations are few. Satellite-based precipitation products can fill such regions' spatial and temporal rainfall data gaps. Six satellite rainfall prod-ucts, namely the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), African Rainfall Climatology Version 2 (ARC2.0), Climate Hazards Group In-frared Precipitation with Station Data (CHIRPS2.0), the Integrated Multi-satellitE Retrievals for Global Precipitation Measurements (GPM) Final Run v 6 (GPM IMERG6), Precipitation Estima-tion from Remote Sensing Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and the Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT) v3.1 were evaluated to assess their accuracy in estimating rainfall amounts variability trends in the study area. The global-based satellite rainfall products were assessed at monthly and annual time scales by applying a point-to-pixel comparison with ground-based rain gauge data for the period 2000–2019. Based on the overall statistical results at monthly and temporal yearly scales, five satellite precipitation products (TMPA, CHIRPS, GPM IMERG6, PERSIANN-CDR, and TAMSATv3.1) overestimated rainfall amounts by values ranging from 1.49% to 82.69%. In contrast, the ARC2 product underestimated rainfall amounts by values ranging from-16.9% to-20.25%. The TAMSATv3.1, CHIRPS, and TMPA performed relatively better, showing stronger correlations and higher values of Nash-Sutcliffe efficiency. This study showed that the TAMSATv3.1 and CHIRPS products could reasonably estimate rainfall amounts in the North Darfur State.

Music-domain visual programming languages (VPLs) have shown to be Turing complete. However, the common lack of built-in flow control structures can obstruct using VPLs implementing general-purpose algorithms, which harms the direct use of algorithms and algorithm theory in art creation processes using VPLs. In this article, we show how to systematically implement general-purpose algorithms in music-domain visual languages by using the Finite State Machines with Datapath computation model. The results expose a finite state machine and a set of internal state variables that walk paths whose speed can be controlled using a metronome ticks and whose path depends on the initial conditions of the algorithm. These elements can be further mapped to music elements according to the musician's intentions. We demonstrate this technique by implementing Euclid's Greatest Common Divider algorithm and using it to control high-level music elements in an implementation of Terry Riley's In C, and to control audio synthesis parameters in a FM synthesizer.

This review article aims at improving understanding of the theoretical discussion on technonationalism. Reviewing conventional works of the literature proposes protectionist, innovationist, and strategic industrial approaches toward high-tech industries. The findings show that technonationalism is a concept linking technology and national security, seeking geoeconomic interests in the integrated global market to exert state power for economic statecraft. The nature of industry highly relies on the global value chain, and technological innovation in the high-tech sectors occurs mainly in the private sector; thus, this article points out the issue of embracing business interests into the state’s strategic addenda as a critical criterion of technonationalism. Also, technonationalism is a narrowly defined concept with state actors involved in national security affairs and high-tech business actors.

Abstract: Coal bed methane (CBM) reservoirs are complex systems whose properties differ from those of conventional reservoirs. Coal seams are dual-porosity systems that comprise the porosities of the matrix and cleat system. Gas in the coal seams can be stored as free gas in the cleat system and as adsorbed gas in the porous medium. The flow mechanisms of the natural gas through the formation include desorption, diffusion, and Darcy’s flow regimes. The permeability of CBM reservoirs is more sensitive to pressure variations than conventional gas reservoirs. To study the flow behavior of CBM reservoirs it is mandatory to use a model that considers their unique characteristics. The objective of this study was to propose a physical and mathematical model of production performance for horizontal wells in CBM reservoirs whose permeability is dependent on pressure. A solution for the model was obtained by applying Pedrosa´s transformation, perturbation theory, Laplace transformation, the point source method, and Sthefest´s algorithm. The solution to this problem was validated with previous work thoroughly. The type curves of the model were built and the pressure transient behavior of the model was analyzed and discussed. The effects of several parameters on pressure behavior were also discussed.

In cosmology, the cosmic curvature $K$ and the cosmological constant $\Lambda$ are two most important parameters, whose values have strong influence on the behavior of the universe. By analyzing the energy-momentum tensor and equations of state of ideal gas, scalar, spinor and vector potential in detail, we find that the total mass density of all matter is always positive, and the initial total pressure is negative. Under these conditions, by qualitatively analyzing the global behavior of the dynamical equation of cosmological model, we get the following results: (i) $K= 1$, namely, the global spatial structure of the universe should be a 3-dimensional sphere $S^3$. (ii) $0\le\Lambda < 10 ^ {-24} {\rm ly} ^ {-2}$, the cosmological constant should be zero or an infinitesimal. (iii) $a(t)>0$, the initial singularity of the universe is unreachable, and the evolution of universe should be cyclic in time. This means that the initial Big Bang is impossible at all. Since the matter components considered are quite complete and the proof is very elementary and strict, these logical conclusions should be quite reliable. Obviously, these conclusions will be much helpful to correct some popular misconceptions and bring great convenience to further research other problems in cosmology such as property of dark matter and dark energy.

A recent study demonstrated that noninvasive measurements of cortical hemodynamics and metabolism in the resting human prefrontal cortex can facilitate quantitative metrics of unilateral mitochondrial-hemodynamic coupling and bilateral connectivity in infraslow oscillation (ISO) frequencies in young adults (YA). The ISO includes three distinct vasomotions with endogenic (E), neurogenic (N), and myogenic (M) frequency bands. The goal of this study was to prove the hypothesis that there are significant differences between young and older adults (OA) in the unilateral coupling (uCOP) and bilateral connectivity (bCON) in the prefrontal cortex. Accordingly, we performed measurements from 24 OA (67.2 ± 5.9 years of age) using the same 2-channel broadband near-infrared spectroscopy (bbNIRS) setup and resting-state experimental protocol as those in the recent study (Shahdadian et al, Cerebral Cortex Communications, 2022). After quantification of uCOP and bCON in three E/N/M frequencies and statistical analysis, we demonstrated that OA had significantly weaker bilateral hemodynamic connectivity but significantly stronger bilateral metabolic connectivity than YA in the M band. Furthermore, OA exhibited significantly stronger unilateral coupling on both prefrontal sides in all E/N/M bands, particularly with a very large effect size in the M band (&gt; 1.9). These age-related results clearly support our hypothesis and were well interpreted following neurophysiological principles. The key finding of this paper is that the neurophysiological metrics of uCOP and bCON are highly associated with age and may have the potential to become meaningful features for human brain health and be translatable for future clinical applications, such as early detection of Alzheimer’s disease.

Recent progress in deep learning has led to accurate and efficient generic object detection networks. Training of highly reliable models depends on large datasets with highly textured and rich images. However, in real-world scenarios, the performance of the generic object detection system decreases when (i) occlusions hide the objects, (ii) objects are present in low-light images, or (iii) they are merged with background information. In this paper, we refer to all these situations as challenging environments. With the recent rapid development in generic object detection algorithms, notable progress has been observed in the field of object detection in challenging environments. However, there is no consolidated reference to cover state-of-the-art in this domain. To the best of our knowledge, this paper presents the first comprehensive overview, covering recent approaches that have tackled the problem of object detection in challenging environments. Furthermore, we present the quantitative and qualitative performance analysis of these approaches and discuss the currently available challenging datasets. Moreover, this paper investigates the performance of current state-of-the-art generic object detection algorithms by benchmarking results on the three well-known challenging datasets. Finally, we highlight several current shortcomings and outline future directions.

Purpose: This research compared the dissociated phoria at near and distance fixation in free space using three techniques: the Howell test, alternate Cover test, and Thorington test. Methods: 220 healthy Mexican children (age 5.06-17.11) participated in this study. Phorias were quantified at both distances using each test, from the least to the most disruptive. The stereopsis degree and near point of convergence (break/recovery) were analyzed to understand their role on the visual system's sensorimotor balance. Results: For the exophoria/esophoria at near, the alternate Cover and Thorington tests showed similar values. The exophoria at distance, was similar with the alternate Cover and Howell tests, while esophoria, with the alternate Cover and Thorington tests. The break value and near exophoria relates to each other and affect the stereopsis degree, whereas age is associated with the stereopsis degree and break value. Conclusions: The analysis revealed that alternate Cover and Thorington tests can substitute one another, but not with the Howell test. The test's specific dissociation method may explain these results. Analyzing the binocular visual system provides information about its sensorimotor balance and fragility when faced to the technique’s degree of dissociation.

This paper presents a discrete compartmental Susceptible-Exposed-Infected-Recovered/Dead (SEIR/D) model to address the expansion of Covid-19 pandemic. When time passes, the status of the cells is determined by binary rules that update following both a neighbourhood and a delay pattern. The model assumes the environmental parameters have a crucial impact on the expansion of the disease so a grid is assigned to each parameter to model the single effect caused by this parameter. The expansion is then the weighted sum of all the grids. This proposal shows how the grid architecture, along with an update rule and a neighbourhood pattern is a valuable tool to model the pandemic expansion. This model has already been analyzed in previous works and compared with the corresponding continuous models solved by ordinary differential equations (ODE), coming to find the homologous parameters between both approaches. Thus, it has been possible to prove that the combination neighborhood-update rule is responsible for the rate of expansion and recovering/death of the illness. The delays (between Susceptible and Asymptomatic, Asymptomatic and Infected, Infected and Recovered/Dead) may have a crucial impact on both the peak of Infected and the Recovery/Death rate. This theoretical model has been successfully tested in the case of the dissemination of information through mobile social networks and in the case of plant pests.

A relationship between the functional Schr\"odinger representation and the precanonical quantization of a nonlinear scalar field theory is extended to arbitrary curved space-times. The canonical functional derivative Schr\"odinger equation is derived from the manifestly covariant precanonical Schr\"odinger equation in a singular limiting case when the ultraviolet parameter $\varkappa$ introduced by precanonical quantization is identified with the invariant delta-function at equal spatial points. In the same limiting case, the Schr\"odinger wave functional is expressed as the trace of the multidimensional product integral of Clifford-algebra-valued precanonical wave functions restricted to a certain field configuration. Thus the standard QFT in curved space-time in functional Schr\"odinger representation emerges from the precanonical formulation of quantum fields as a singular limiting case.

Naturally occurring small molecules include a large variety of natural products from different sources that have confirmed activity against epigenetic targets. In this work we review chemoinformatic, molecular modeling and other computational approaches that have been used to uncover natural products as inhibitors of DNA metiltransferases, a major family of epigenetic targets with significant potential for the treatment of cancer and several other diseases. Examples of these computational approaches include docking, similarity-based virtual screening, and pharmacophore modeling. It is also commented the chemoinformatic-based exploration of the chemical space of naturally occurring compounds as epigenetic modulators which may have significant implications in epigenetic drug discovery and nutriepigenetics.

The use of acoustic and magnetic methods of non-destructive testing to detect zones of stable localization of deformation in order to assess and predict the performance of long-term equipment is of scientific and practical interest at present. A structural-mechanical criterion has been developed, that reflects the revealed relationships between the structural and substructural states, internal stress fields and stable localization of deformations with the characteristics of non-destructive tests in the metal of long-term equipment made of structural 0.2 С steel and heat-resistant 0,12C-1Cr-1Mo-1V steel. The values of the structural-mechanical criteria Ks.-m for structural 0.2 С steel and for heat-resistant 0,12C-1Cr-1Mo-1V steel, corresponding to the moment of stable localization of deformation, are established. At the same time, it is recommended to replace the checked equipment nodes due to the exhaustion of the resource. The proposed and justified approach to assessing and predicting the performance and residual life of long-term power equipment, based on the identified relationships between the structural and substructural states, internal stress fields and stable localization of deformations with the characteristics of non-destructive tests and the calculation of the structural-mechanical criterion, was applied at a number of power plants in the Kemerovo region – Kuzbass. A methodology has been developed for evaluating the residual life, based on the identification and use of relationships between structural and substructural states, internal stress fields and stable localization of deformations with the characteristics of non-destructive tests and the calculation of a structural-mechanical criterion.

To follow the growth and development of each tree (N = 1200) Pinus sylvestris L., var. Negorelskaya starting from the seed and continuing at the juvenile (or even generative) stage is the goal of an ambitious project to create a plant passport if possible. Five datasets of replenished empirical (tab-ular and file) and systematic data reflect the early results of a unique ongoing experiment conducted with families moved along a climatic gradient from the collection area (1731 degree days, 722 mm) to the experimental area (2326 degree days; 786 mm). Datasets of morphometric, VIS-spectrometric, and germination seed data, combined with the first results of biometric data (including the Dickson quality index) of seedlings produced from these seeds, will allow scientists in the future to conduct correlation and other statistical analysis, or train a neural network to determine the presence of connections between a seed and a plant. These data contribute to the early non-destructive diag-nosis and grading of seeds according to VIS-spectrometric properties. This dataset structure can complement the FLR-Library. In the future, by tracing seedlings in their early growth (for example, over three growing seasons at the site being restored), it will be possible to determine the degree of intensification of FRM production depending on the initial properties of the seed.

The integration of Unmanned Aircraft Systems (UAS) into the current airspace poses significant challenges in terms of safety, security and operability. As an example, the European Union has defined in 2019 a set of rules to support the digitalization of UAS Traffic Management (UTM) systems and services, namely the U-Space regulations. Current propositions opted for a centralized and private model, concentrated around governmental authorities (e.g., AlphaTango provides the Registration service and depends on the French government). In this paper, we advocate in favor of a more decentralized and transparent model in order to improve safety, security and operability among UTM stakeholders. As such, we propose DFly, a Publicly Auditable and Privacy-preserving UAS Traffic Management system on Blockchain, and developed two initial services: Registration and Flight Authorization. We demonstrate that the use of a blockchain guarantees the public auditability of the two services and corresponding service providers’ actions. In addition, it facilitates the comprehensive and distributed monitoring of airspace occupation and the integration of additional functionalities (e.g., the creation of a live UAS tracker). The combination with zero knowledge proofs enable the deployment of an automated, distributed, transparent and privacy-preserving flight authorization service, performed on-chain thanks to the blockchain logic. In addition to its construction, this paper details the instantiation of the proposed UTM system with the Ethereum Sepolia’s testnet and the Groth16 ZK-SNARK protocol. On-chain (gas cost) and off-chain (execution time) performance analyses confirm that the proprosed solution is a viable and efficient alternative in the spirit of digitalization and offers additional security guarantees.

Theory says that the first things to exist in the universe and the current moment were driven by compelling necessity; that is, physical spaces influence the existence of elements and facts based on the numerous observations in physical spaces in relation to elements and facts.The existence of obligatory elements in the space is motivated by the characteristics of the spaces that offer conditions for such obligation. The theory states that the universe did not come about by chance; that is, the universe came about by determination influenced by what surrounds it. The purpose of the study is to help explain the elements and facts of the universe from the origin to the present.

Space instable target simulator (SITS) is a vital actuator for ground verification of on-orbital capture technology, the motion performance of which directly affects simulation credibility. Different delays reduce the stability of SITS and ultimately lead to its divergence. In order to achieve high fidelity simulation, the impact of force measurement delay, discrete control cycle, and simulator response delay on stability is analyzed firstly. Then, the dynamic equation and transfer function identification model of hybrid simulator is constructed, and the necessary and sufficient conditions of its stability and convergence are obtained using the Routh criterion. After that, a switching compensator with variable gain is proposed, the compensation principle diagram of which is build, and its mathematical model including the energy observer and tracking differentiator is also established. Finally, three sets of numerical simulations are conducted to validate the correctness of the stability analysis and effectiveness of proposed compensation method. Simulation results show that all three types of delay can cause SITS to lose stability under critical stable motion state, and the delay in force measurement has the greatest impact, followed by the influence of control cycle. Compared with the force applied to simulated target, velocity and its recovery coefficient of space instable target using fixed gain and linear gain compensation, the proposed compensator has significantly better performance.

Few assessments and indicators on cultural sustainability have been applied to national and local development policies in Indonesia. Said policies have had the notion of space solely defined through its physical aspect. This study aims to establish the cultural aspect of space as a workable indicator for sustainability in development planning. The study grounds itself in the coastal area of Kalibaru Subdistrict, North Jakarta, a region characterized by dense and culturally diverse residential areas located in between heavy industrial development. Primary data are gathered from 10 informants of varying occupations with residence in the area for more than 20 years. In-depth interviews and Spatial Cognitive Mapping were conducted to articulate the cultural spaces of their everyday activities. Prevalent themes of spatial practice were identified and described in 3 categories of 1. core cultural spaces, 2. tactical cultural spaces, and 3. spaces of cultural conflict. The participatory procedures to identify cultural spaces as described in the study were concluded to be operable within existing indicator or index frameworks for sustainability policy. The availability of cultural spaces and its enumeration may be incorporated into national surveys for effective data collection and use.

As a rapidly growing coastal megacity, Shanghai is continuously threatened with land subsidence issues since 1920s. Land subsidence was controlled in 1960s, however in 1990s, unconscious and dangerous urban underground space (UUS) exploration and tunneling development are causing further land subsidence. It is imperative to study previous relations towards future adaptive and resilient scenario modelling and planning. There are multiple cause-effect factors determined in the urban built environment of Shanghai megacity. This paper presents the current evidence based on the relations of the multifactor of the spectrum. Methods consist of understanding the cause-effect relations and spatiotemporal from the crucial period of 1960-2020. Data are collected secondarily from multiple open sourced databases. The results determine Shanghai are highly influenced by the UUS development induced-subsidence, tunneling leakage and weak spatial modelling. Spatiotemporal pattern has shown a mixed positive-negative impact: population, land subsidence is growing in parallel distribution (positive) with tunneling leakage, construction of tunneling, metro system, UUS development, building price, reconstruction area, GDP growth, land price, arable land decrease and further tunnel settlement in Urban City Centre, Pudong New Area, Minhang, Baoshan and Songjiang districts. These results are useful for further adaptive and resilient scenario modelling and spatial planning.

The COVID-19 pandemic has brought about unprecedented changes to human lifestyles across the world. The virus and associated social restriction measures have been linked to an increase in mental health conditions. A considerable body of evidence shows that spending time in and engaging with nature can improve human health and wellbeing. Our study explores nature’s role in supporting health during the COVID-19 pandemic. We created web-based questionnaires with validated health instruments and conducted spatial analyses in a geographic information system (GIS). We collected data on people’s patterns of nature exposure, associated health and wellbeing responses, and potential socioecological drivers such as relative deprivation, access to greenspaces, and land-cover greenness. We applied a range of statistical analyses including bootstrap resampled correlations and binomial regression models, adjusting for several potential confounding factors. We found that respondents significantly changed their patterns of visiting nature as a result of the COVID-19 pandemic. People spent more time in nature and visited nature more often during the pandemic. People generally visited nature for a health and wellbeing benefit and felt that nature helped them cope during the pandemic. Greater land-cover greenness within a 250 m radius around a respondent’s postcode was important in predicting higher levels of mental wellbeing. There were significantly more food-growing allotments within 100 m and 250 m of respondents with high mental wellbeing scores. The need for a mutually-advantageous relationship between humans and the wider biotic community has never been more important. We must conserve, restore and design nature-centric environments to maintain resilient societies and planetary health.

A crewed mission to and from Mars may include an exciting array of enabling biotechnologies that leverage inherent mass, power, and volume advantages over traditional abiotic approaches. In this perspective, we articulate the scientific and engineering goals and constraints, along with example systems, that guide the design of a surface biomanufactory. Extending past arguments for exploiting stand-alone elements of biology, we argue for an integrated biomanufacturing plant replete with modules for microbial \textit{in situ} resource utilization, production, and recycling of food, pharmaceuticals, and biomaterials required for sustaining future intrepid astronauts. We also discuss aspirational technology trends in each of these target areas in the context of human and robotic exploration missions in the coming century.

By defining a constant probabilistic orbit of and iterated functions, the stability dynamics of these functions in possible interactions through connectivity provides the formation of a dynamic fixed point as a metric space between both iterated functions. The presence of a dynamic fixed point identifies qualitatively phases of iteration time lengths and interaction orbits of the event. Qualitative results show that the greater the average distance from one of the functions to the fixed point of the other (all possible solutions), the higher the iteration expression on time (false asymptotic effect) of one of the functions and in the opposite hand, the lower the average distance, the higher orbit’s interactions proximity between iterated functions (stability). This feature reveals asymptotic (well-defined) behavior between functions f and g within a well-defined Lyapunov stability.

A rigorous model for the electron is presented by generalizing the Coulomb's Law or Gauss's Law of electrostatics, using a unified theory of electricity and gravity. The permittivity of the free-space is allowed to be variable, dependent on the energy density associated with the electric field at a given location, employing generalized concepts of gravity and mass/energy density. The electric field becomes a non-linear function of the source charge, where concept of the energy density needs to be properly defined. Stable solutions are derived for a spherically symmetric, surface-charge distribution of an elementary charge. This is implemented by assuming that the gravitational field and its equivalent permittivity function is proportional to the energy density, as a simple first-order approximation, with the constant of proportionality referred to as the Unifield Electro-Gravity (UEG) constant. The stable solution with the lowest mass/energy is assumed to represent a ``static'' electron without any spin. Further, assuming that the mass/energy of a static electron is half of the total mass/energy of an electron including its spin contribution, the required UEG constant is estimated. More fundamentally, the lowest stable mass of a static elementary charged particle, its associated classical radius, and the UEG constant are related to each other by a dimensionless constant, independent of any specific value of the charge or mass of the particle. This dimensionless constant is numerologically found to be closely related to the the fine structure constant. This possible origin of the fine structure constant is further strengthened by applying the proposed theory to successfully model the Casimir effect, from which approximately the same above relationship between the UEG constant, electron's mass and classical radius, and the fine structure constant, emerges.

Robotic path planning is a field of research which is gaining traction given the broad domains of interest to which path planning is an important systemic requirement; the aim being to optimise the efficacy of robotic movements in a defined operational environment. For example, robots have been employed in many domains including: cleaning robots (such as vacuum cleaners), automated paint spraying robots, window cleaning robots, forest monitoring robots, agricultural robots (often driven using satellite and geostationary positional satellite data). Additionally, mobile robotic systems have been utilised in disaster areas and locations hazardous to humans (such as war zones in mine clearance). The coverage path planning problem describes an approach which is designed to determine the path that traverses all points in a defined operational environment while avoiding static and dynamic (moving) obstacles. In this paper we present our proposed Smooth-STC model, the aim of the model being to identify an optimal path, avoid all obstacles, prevent (or at least minimise) backtracking, and maximise the coverage in any defined operational environment. The experimental results in a simulation show that, in uncertain environments, our proposed smooth STC method achieves an almost absolute coverage rate and demonstrates improvement when measured against alternative conventional algorithms.

Software defect prediction is an important means to guarantee software quality. Because there are no sufficient historical data within a project to train the classifier, cross-project defect prediction (CPDP) has been recognized as a fundamental approach. However, traditional defect prediction methods using feature attributes to represent samples, which can not avoid negative transferring, may result in poor performance model in CPDP. This paper proposes a multi-source cross-project defect prediction method based on dissimilarity space ( DM-CPDP). This method first uses the density-based clustering method to construct the prototype set with the cluster center of samples in the target set. Then, the arc-cosine kernel is used to form the dissimilarity space, and in this space the training set is obtained with the earth mover’s distance (EMD) method. For the unlabeled samples converted from the target set, the KNN algorithm is used to label those samples. Finally, we use TrAdaBoost method to establish the prediction model. The experimental results show that our approach has better performance than other traditional CPDP methods.

This article is the first part of a scientific project under the general title "Geometrized Vacuum Physics". This study is based on two main postulates: 1) independence of the propagation velocity of electromagnetic waves in vacuum from their frequency; 2) the constancy of the averaged zero vacuum balance, associated with the assertion that only mutually opposite formations are born from vacuum, so that on average they completely compensate for each other's manifestations. In this part of the "Geometrized Vacuum Physics" the foundations of the Algebra of Stignatures are laid, which is the mathematical and logical foundation of the entire project. In the next articles of this project it will be shown that the Algebra of Stignatures can be used for the development of "zero" (vacuum) technologies, algebraic genetics, vacuum cosmology, the vacuum standard model of "elementary particles", vacuum gravity and levitation, vacuum energy, ethics and aesthetics and many other branches of knowledge. At the end of this article, one of the many possible applications of the Algebra of Stignatures is given, in particular, the basics of the stignature-spectral analysis are outlined, with the help of which the possibilities of communication channels can be significantly expanded.

Tumorigenesis is a complex biological phenomenon that includes extensive genetic and phenotypic heterogeneities and complicated regulatory mechanisms. In the recent few years, our studies demonstrate that tumor-initiating cells are similar to neural stem/progenitor cells in regulatory networks, tumorigenicity and pluripotent differentiation potential. In the review, I will make further discussion on these observations and propose a rule of cell biology by integrating these findings with evidence from developmental biology, tumor biology and evolution, which suggests that neural stemness underlies two coupled cell properties, tumorigenicity and pluripotent differentiation potential. Tumorigenicity and phenotypic heterogeneity in tumor is a result of acquirement of neural stemness in cells. The neural stemness property of tumor-initiating cells can hopefully integrate different concepts/hypotheses underlying tumorigenesis. Neural stem cells/neural progenitors and tumor-initiating cells share regulatory networks; both exhibit neural stemness, tumorigenicity and differentiation potential; both are dependent on expression or activation of ancestral genes (the atavistic effect); both rely primarily on aerobic glycolytic metabolism; both can differentiate into various cells or tissues that are derived from three germ layers, resembling severely disorganized or more severely degenerated process of embryonic development; both are enriched in long genes with more splice variants that provide more plastic scaffolds for cell differentiation, etc. The property of neural stemness might be a key point to understand tumorigenesis and pluripotent differentiation potential, and possibly explain certain pathological observations in tumors that have been inexplicable. Therefore, behind the complexity of tumorigenesis might be a general rule of cell biology, i.e., neural stemness represents the ground state of cell tumorigenicity and pluripotent differentiation potential.

We present a model of space that considers it to be a quantized dynamical entity which is a component of the universe along with matter and radiation. The theory is used together with thermodynamic data to provide a new view of cosmic evolution and an insight into the nature of dark energy and dark matter. Space is made up of energy quanta. The universe started from an atomic size volume at very high temperature and pressure near the Planck epoch. Upon expansion and cooling, phase transitions occurred resulting in the formation of radiation, fundamental particles, and matter. These nucleate and grow into stars, galaxies, and clusters. From a phase diagram of cosmic composition, we obtained a correlation between dark energy and the energy of space. Using the Friedmann equations, data from WMAP studies of the composition of the universe at 3.0 x 10⁵ (a=5.25 x 10^-2) years and at present (a=1), are well fitted by our model with an equation of state parameter, w= -0.7. The accelerated expansion of the universe, starting at about 7 billion years, determined by BOSS measurements, also correlates well with the dominance of dark energy at 7.25 x 10⁹ years ( a= 0.65). The expansion can be attributed to Quintessence with a space force arising from a quantum space field. From our phase diagram, we also find a correlation suggesting that dark matter is a plasma form of matter similar to that which existed during the photon epoch immediately prior to recombination. Our Quantum Space Model leads to a universe which is homogeneous and isotropic without the need for inflation. The thermodynamics of expansion is consistent with BOSS data that show the process to be adiabatic and the rate of expansion decelerating during the first 6 billion years after the Big Bang. However, it became non-adiabatic and accelerating thereafter. This implies an influx of energy from a source outside the universe; it warrants consideration as a possible factor in the accelerated expansion of the universe.

The article is devoted to the preliminary concept of the Future Planetary Defense System (FPDS), emphasizing Astroballistics. This paper is intended to support international efforts to improve the planetary security of the Earth. The work covers three areas of knowledge: Astronautics, Astrodynamics, and Astroballistics. The most important part of the presented article is dynamic, contact combat modeling against small, deformable celestial bodies. For these purposes, the original, proprietary hydrocode of the Free Particle Method (HEFPM-G) with gravity was used. The main aim of combat is to redirect the Potentially Hazardous Objects (PHOs) to orbits safe for the Earth or destroy them. This concept's first task is to find, prepare and use dynamic three-dimensional models of celestial bodies' motion and spacecraft or human-crewed spaceships in the Solar System's relativistic frame. The second task is to prepare the FPDS' architecture and computer simulation space missions' initial concepts in the internal part of the solar system. The third and main task covers simulating, using hydrocodes, selected methods of fighting 100 m and 140 m diameter asteroids. The order of the article is as follows. The first part of the article presents an architecture and FPDS' modus operandi. Preliminary design and development of FPDS' space missions, including navigation, mission dynamics simulation, is prepared using an open-source space mission analysis and design tool. E.g., Asterank and Trajectory Browser or GMAT are presented in the second part. The third part of the article is devoted to computer hydrocodes (HEFPM-G) and the modeling and simulation of asteroid–asteroid collision, laser radiation effects on an asteroid, and FPDS spacecraft's warhead contact interaction on the small celestial body. The authors formulated the Main Planetary Defense Problem (MPDP) in this paper. The proposition of this problem solving has been realized by preparing its concepts, architecture, and modus operandi of the FPDS mission. Finally, a series of realistic simulations were made using hydrocode to deflect or destroy dangerous asteroids. The summary and conclusions can be found in the fourth part of the article.

The purpose of this article is to disclose the strenuous efforts of Laszlo Hudec in China and Antonin Raymond in Japan and India to create a modern architectural stance by heralding an incipient space syntax. In the turn of the 19th Century, for dynastic, politic and economic reasons, Eastern Asia had very little modern Architecture. It is a surprising fact that, out of happenstance, two European architects Antonin Raymond and Laszlo Hudec, had to intervene to remedy this situation, to the point of becoming 20th Century icons in Japan and China. Their fruitful careers spanned over thirty years and included locations, like Tamil Nadu or the Philippines. The Oriental territories were not an easy ground for the bold architectural achievements that they produced. Despite of faraway strangeness and uncountable personal losses, in revolutions and wars, which eventually forced them both to leave for the United States of America and never to return, they were successful in the manner of establishing a broad avenue for Modern Asian Architecture which is still recognisable today thanks to their systematic approach. However, theirs is an endangered heritage and the intention of this article is to be a just remembrance of in which way such actions could be performed, how they predated by many years a syntactic approach to architectural composition and why their legacy should be preserved.

Harnessing Quantum Energy States for Unprecedented Computing Capabilities In this proposal, we advocate for the use of energy states as fundamental units of quantum computer programming. Each unit of programming is conceptualized as an energy state, manifested as an essence-unit. An essence-unit, defined as the minimal form encapsulating complete uniqueness or specificity, serves as the cornerstone of this paradigm. This approach enables the recording of various forms of matter on quantum computers in essence-units, portraying their energy states. The recording of energy states is achieved through the creation of four distinct coherent potentials, facilitated by quantum dots or crystals. Crucially, these energy states embodied in essence-units resist subdivision. The study explores the intricate relationship among similarity, fractals, and uniqueness in quantum dot operations, revealing their profound implications for information transfer efficiency. Normalized entropy characterizes charge localized in quantum dot impurities in systems that have distortions. Through the utilization of N-level recording and entropy-fractal dimension equivalence, the paper elucidates the potential of quantum dots in reducing transmission time and modeling complex systems. The proposed methodology signifies a paradigm shift in quantum computing, presenting unparalleled possibilities for tackling hitherto insurmountable challenges.

Recently, a layer-stacked ESN model named deep echo state Network (DeepESN) has been established. As an interactional model of recurrent neural network and deep neural network, investigations of DeepESN are of significant importance in both areas. Optimizing the structure of neural networks remains a common task in artificial neural networks, and the question of how many neurons should be used in each layer of DeepESN must be stressed. In this paper, our aim is to solve the problem of choosing the optimized size of DeepESN. Inspired by the sensitive iterative pruning algorithm, a neuronal similarity-based iterative pruning merging algorithm (NS-IPMA) is proposed to iteratively prune or merge the most similar neurons in DeepESN. Two chaotic time series prediction tasks are applied to demonstrate the effectiveness of NS-IPMA. The results show that the DeepESN pruned by NS-IPMA outperforms unpruned DeepESN with the same network size, and NS-IPMA is a feasible and superior approach to improving the generalization performance of DeepESN.

For crystal periodic structure prediction, a new and concise approach based on the principles of statistical physics was employed to derive a new form of the equation to determine their period vectors (cell edge vectors), under general external stress. Then the new form is applicable to both classical physics and quantum physics. It also turned out to be the equation of state and the mechanical equilibrium condition for crystals under external stress and temperature. It should be helpful in piezoelectric and piezomagnetic studies, as the period vectors were changed by the external stress. For linear elastic crystals, it is actually also the microscopic but temperature-dependent form of the generalized Hooke's law, then can be used to calculate the corresponding elastic constants of the law, for given temperatures.

Considering the safety issues of Li ion batteries, all-solid-state polymer electrolyte has been one of the promising solutions. In this point, achieving a Li ion conductivity in the solid state electrolytes comparable to liquid electrolytes (>1 mS/cm) is particularly challenging. Employment of polyethylene oxide (PEO) solid electrolyte has not been not enough in this point due to high crystallinity. In this study, hybrid solid electrolyte (HSE) systems are designed with Li_1.3Al_0.3Ti_0.7(PO₄)₃(LATP), PEO and Lithium hexafluorophosphate (LiPF₆) or Lithium bis(trifluoromethanesulfonyl)imide (LiTFSI). Hybrid solid cathode (HSC) is also designed using LATP, PEO and lithium cobalt oxide (LiCoO₂, LCO)—lithium manganese oxide (LiMn₂O₄, LMO). The designed HSE system displays 3.0 × 10⁻⁴ S/cm (55 ℃) and 1.8 × 10⁻³ S/cm (23 ℃) with an electrochemical stability as of 6.0 V without any separation layer introduction. Li metal (anode)/HSE/HSC cell in this study displays initial charge capacity as of 123.4/102.7 mAh/g (55 ℃) and 73/57 mAh/g (25 °C). To these systems, Succinonitrile (SN) has been incorporated as a plasticizer for practical secondary Li ion battery system development to enhance ionic conductivity. The incorporated SN effectively increases the ionic conductivity without any leakage and short-circuits even under broken cell condition. The developed system also overcomes the typical disadvantages of internal resistance induced by Ti ion reduction. In this study, optimized ionic conductivity and low internal resistance inside the Li ion battery cell have been obtained, which suggests a new possibility in the secondary Li ion battery development.

Developing particles of different anisotropic shapes are the hot topic since decades as they guarantee some special features of properties not possible through other means. Again, controlling atoms to develop certain size and shape particle is a quite challenging job. In this study, gold particles of different shapes are developed via pulse-based electronphoton-solution interface process. Gold atoms of certain transition state develop monolayer assembly at solution surface around the light glow (known in argon plasma) being generated at bottom of copper capillary (known in cathode). The rate of uplifting gold atoms to solution surface is being controlled by forcing energy (travelling photons) pursuing electrons and high energy photons (in high density) entering to solution. Gold atoms dissociated from the precursor under dissipating heat energy into the solution supplied under propagating photons characteristic current through immersed graphite rod (known in anode). Placing packets of nano shape energy of tuned pulse protocol over compact monolayer assembly comprising transition state atoms develop tiny-sized particles of formed shape. On separation of joint tiny particles into two equilateral triangular-shaped tiny particles, exerting forces of surface format elongate atoms of one-dimensional arrays converting them into structures of smooth elements. Due to immersing level of force, such tiny-shaped particles pack from different zones at centre of light glow where they assembled structures of smooth elements for developing mono-layers of different shapes of particles. Developing one-dimensional particles deal assembling of structures of smooth elements of packing tiny-shaped particles from nearly rearward zones of reflection of north-south poles, whereas, developing multi-dimensional particles deal assembling of structures of smooth elements of packing tiny-shaped particles from the east-west poles and near regions. Depending on the number of assembled structures of smooth elements at point of nucleation, packing of tiny-shaped particles from different zones develop different shapes of the anisotropic particles. At fixed precursor concentration, increasing the process time results into develop particles of low aspect ratio. Under tuned parameters, developing mechanisms of particles exhibiting unprecedented features are discussed.

It is often said in human society that it all starts from the mind, and this is true. The answer to the question “Why is it that pets require much fewer years to fully develop than humans?” could be headed to the Theory of Relativity and the perceived time according to the levels of instinct and rationality. It is perhaps no wonder that the French language translates the word “mind” as “l’esprit”, which also means “spirit” in the English language. Given that the Theory of Relativity applies to all physical matter, the theory applies to chemistry, biology and psychology as well. The level of instinct is proportional with the perceived speed of time and implicitly, to the speed of time itself for the whole organism, given that the nervous system coordinates all bodily functions. A pet may experience a few years as humans experience over a decade, making their perceived lifetime to be approximately equal to the human’s. The same applies to the perception of space and its components. This aspect would emphasise upon the fact that all life on Earth shows the same degree of value with regards to uniqueness. Nevertheless, the values represented by the hierarchy of competences among living organisms at the same time differs based on species. Honour of uniqueness and hierarchy of competences represent two scientific concepts that never overcross. As a result, there is no evidence to suggest that scientific facts as such constitute a form of contradiction to philosophical and religious ideas, given that religion refers to animals as companions of humans. With regards to the clock of the living organism; there are three kinds of clocks in a living organism: the emotional clock, the cognitive clock and the molecular clock. These three categories actually constitute the temporal layers of our dimension, so they are in a relationship of interdependence. When one has a feeling that time is slower or quicker, they are not fully experiencing that change in the time speed, but only partially. And one can profoundly experience the change of perceived time speed when they experience profound feelings, which sweep them away from reality. Complete, deep mental focus can bring about this profound change in perception, which will make the individual feel as if they are in a different form of reality. When one is more instinctive, the person in cause perceives time as slower and sees more opportunities within the same frame of time and space. When one experiences the “Fight or flight” mode, the person in the cause finds themselves to be in danger and has two options; to try to prevail over the danger or to flee it. Whilst experiencing such a mode, people are much more alert and therefore, time passes more slowly and the physical space dilates. When people are relaxed, however, time and space become smaller, and when they are in a deep state of resting, they become very small. And where there is a smaller dimension, there is a greater speed, just as the circulating air gains speed when it crosses narrow passages.

The magnetic field of the earth plays an important role in the ecosystem, and the magnetic field of celestial bodies is also important in the formation of cosmic large-scale structures, but the origin and evolution of the celestial magnetic field is still an unresolved mystery. Many hypotheses to explain the origin have been proposed, but there are some insurmountable difficulties for each one. At present, the theory widely accepted in scientific society is the dynamo model, it says that, the movement of magnetofluid inside celestial bodies, which can overcome the Ohmic dissipative effect and generate persistent weak electric current and macroscopic magnetic field. However, this model needs an initial seed magnetic field, and the true values of many physical parameters inside the celestial body are difficult to obtain, and there is no stable solution to the large range of fluid motion. These are all difficulties for the dynamo model. Furthermore, it is difficult for the dynamo to explain the correlation between the dipole magnetic field and angular momentum of a celestial body. In this paper, by calculating the interaction between spin of particles and gravity of celestial body according to Clifford algebra, we find that a rotational celestial body provides a field $\Omega^\alpha$ for spins, which is similar to the magnetic field of a dipole, and the spins of charged particles within the celestial body are arranged along the flux line of $\Omega^\alpha$, then a macroscopic magnetic field is induced. The calculation shows that the strength of $\Omega^\alpha$ is proportional to the angular momentum of the celestial body, which explains the correlation between the magnetic intensity and angular momentum. The results of this paper suggest that further study of the effects of internal variables such as density, velocity, pressure and temperature of a celestial body on $\Omega^\alpha$ may provide some new insights into the origin and evolution of celestial magnetic field.

We present a new interpretation of the Higgs field as a composite particle made up of a positive, with, a negative mass Planck particle. According to the Winterberg hypothesis, space, i.e., the vacuum, consists of both positive and negative physical massive particles, which he called planckions, interacting through strong superfluid forces. In our composite model for the Higgs boson, there is an intrinsic length scale associated with the vacuum, different from the one introduced by Winterberg, where, when the vacuum is in a perfectly balanced state, the number density of positive Planck particles equals the number density of negative Planck particles. Due to the mass compensating effect, the vacuum thus appears massless, charge-less, without pressure, energy density, or entropy. However, a situation can arise where there is an effective mass density imbalance due to the two species of Planck particle not matching in terms of populations, within their respective excited energy states. This does not require the physical addition or removal of either positive or negative Planck particles, within a given region of space, as originally thought. Ordinary matter, dark matter, and dark energy can thus be given a new interpretation as residual vacuum energies within the context of a greater vacuum, where the populations of the positive and negative energy states exactly balance. In the present epoch, it is estimated that the dark energy number density imbalance amounts to, , per cubic meter, when cosmic distance scales in excess of, , are considered. Compared to a strictly balanced vacuum, where we estimate that the positive, and the negative Planck number density, is of the order, particles per cubic meter, the above is a very small perturbation. This slight imbalance, we argue, would dramatically alleviate, if not altogether eliminate, the long standing cosmological constant problem.

Nano-drugs and nano-delivery systems are rapidly evolving, with new strategies emerging in the current practices. The evolution of these technologies began with modifying the chemical structure, progressing to supramolecular ionic complexes, and culminating in elegant ad hoc delivery systems. Nanoparticles have numerous benefits as a carrier system for delivering therapeutic agents to intra-arterial sites. These benefits include their subcellular size, targeted surfaces, good suspensibility, and uniform dispersity, making them an ideal choice for catheter-based delivery. Despite the advancements made in the field of nano-drugs and nano-delivery systems, there are still some hurdles to overcome in terms of their commercial availability.The current review presents an updated summary of recent advancements in nano-drugs and nano-delivery systems, including their commercial availability. We aim to discuss the present challenges and prospects of commercially available nano-drugs and nano-delivery systems. Here, we provide a precise and informative overview of the current state of these technologies and underscore the potential they hold for future developments. Further, we have categorized commercially available modifications, name, parent company and their main applications in nano-drugs.

Despite huge effort has been devoted to the design of the initiators and reaction conditions, it remains challenging to synthesize high molecular weight polypeptides with conventional solution phase synthesis. In this work, surface-initiated vapor deposition polymerization (SI-VDP) was utilized to graft synthetic polypeptides poly (γ-benzyl L-glutamate) (PBLG) from polystyrene (PS) resin beads by ring-opening polymerization of N-carboxyanhydrides (NCAs). It was demonstrated for the first time that high molecular weight bulk PBLG (> 500,000) could be readily obtained within one hour via solvent-free synthetic method which paves the way for the synthesis of copolypeptides with high molecular weight.

Fundamental principles of classical (Newtonian) physics are employed to probe the cosmological lambda Λ; it yields the values ρ_vac = 2.61 x 10^-39 g cm^-3 and Λ = 4.87 x 10^-66 cm^-2. It is revealed that Λ is a fundamental physical constant defined by vacuum density-light speed ρ_vacc² correlation. However, the constant accelerates along the groups and periods of a universal spatial periodicity equivalent to the chemical periodicity. Previous results are cited to show that chemical elements are quantum harmonic (periodic) oscillators QHOs and their waveform oscillations exclusively define the vacuum field. The cosmological periodic unit CPU is introduced, it relies on the cosmological principle to argue that a relative physical quantity evaluated for the QHO applies to constituents of corresponding cosmological spatial quanta. Compelling evidences, backed with relevant data and quantitative expressions, are then presented to argue that: there was never a big bang, it is a Linde-universe sans “chaotic”; nature posts no singularity; mass does not curve spacetime, neither does metric space curvature trace directly to gravitation nor particle creation, gravity is classical, not quantum; reality is quadri-phasic not mono-phasic with a clear distinction between the atomic waveform defined with absolute atomic mass and condensed matter defined with relative atomic mass; every chemical element exists in three particle-generations thus, dark matter is invisible form (conjugate) of the visible element, its waveform manifests dark energy, it is not implicated in metric space expansion; Planck scale does not exist, radioactivity constrains fundamental length to atomic radius of the heaviest element.

With the rapid advancements in remote sensing technology, the spectral information from hyperspectral remote sensing images has become increasingly rich, facilitating detailed spectral analysis of the Earth's surface objects. However, this abundance of spectral data poses significant challenges in data processing, such as the curse of dimensionality leading to the “Hughes” phenomenon, "strong correlation" due to high resolution, and "non-linear characteristics" caused by varied surface reflectance rates. Therefore, dimensionality reduction of hyperspectral data has become a crucial task. This paper, grounded in manifold theory and manifold learning techniques, and considering the non-linear structures and features in hyperspectral remote sensing data, elucidates the principles and processes of dimensionality reduction in hyperspectral remote sensing images using manifold learning, with a formalized expression of the process. This article introduces spectral information divergence (SID) into the nearest neighbor graph computation of manifold learning algorithms. The principles and computational processes of nearest neighbor graph algorithms based on Euclidean distance (ED), spectral angle mapping (SAM), and SID are studied, and a comparative analysis of the dimensionality reduction effects under these three metrics in hyperspectral data is conducted. Experiments on feature extraction under different metrics were performed using the publicly available Indian Pines hyperspectral dataset. The intrinsic features obtained post-dimensionality reduction were used as inputs for classification algorithms in ground objects classification experiments, with algorithm runtime, overall accuracy, and Kappa coefficient as evaluation metrics for dimensionality reduction quality. The results demonstrate that nearest neighbor graph computation based on SAM and SID outperforms traditional ED methods; SAM-based computation has the lowest time complexity, while SID-based manifold learning yields the highest accuracy in ground objects classification. Thus, manifold learning based on SAM and SID metrics proves to be an effective method for feature extraction in hyperspectral remote sensing data, underscoring the potential of manifold learning techniques in the dimensionality reduction of hyperspectral remote sensing images.

Sodium-phosphate-based glass-ceramics (GCs) are promising materials for a wide range of applications, including solid-state sodium-ion batteries, microelectronic packaging substrates, and humidity sensors. This study investigates the impact of 24-hour heat-treatments at varying temperatures on Na-Ge-P glass, with a focus on (micro)structural, electrical, and dielectric properties of prepared GCs. Various techniques such as powder X-ray diffraction (PXRD), infrared spectroscopy-attenuated total reflection (IR-ATR), and scanning electron microscopy-energy dispersive spectroscopy (SEM-EDS) are employed. With the elevation of heat-treatment temperature, crystallinity progressively rises; at 450 °C, the microstructure retains amorphous traits featuring nanometric grains, whereas at 550 °C, heat-treatment results in fully crystallized structures characterized by square-shaped micron-scale grains of NaPO3. The insight into the evaluation of electrical and dielectric properties is provided by Solid-State Impedance Spectroscopy (SS-IS), revealing a strong correlation with the conditions of controlled crystallization and observed (micro)structure. The glass-ceramic heat-treated at 450 °C achieves the highest DC conductivity of 2.30×10–8 Ω–1 cm–1 at 393 K. As the heat-treatment temperature rises, the crystallization of the NaPO3 phase depletes the glass matrix of mobile Na+ ions, resulting in a reduction in DC conductivity. Dielectric parameters also decrease with rising heat-treatment temperature, with the glass-ceramic heat-treated at 550 °C displaying the lowest permittivity of 19.37 and the dielectric loss of 0.008. This research uncovers the intricate relationship between heat-treatment conditions and material properties, emphasizing that controlled crystallization allows for precise modifications to microstructure and phase composition within remaining glassy phase, ultimately facilitating fine-tuning of material properties.

The Internet-of-Things (IoT) enables enterprises to obtain profits from data but triggers data protection questions and new types of cyber risk. Cyber risk regulations for the IoT however do not exist. The IoT risk is not included in the cyber security assessment standards, hence, often not visible to cyber security experts. This is concerning, because companies integrating IoT devices and services need to perform a self-assessment of its IoT cyber security posture. The outcome of such self-assessment needs to define a current and target state, prior to creating a transformation roadmap outlining tasks to achieve the stated target state. In this article, a comparative empirical analysis is performed of multiple cyber risk assessment approaches, to define a high-level potential target state for company integrating IoT devices and/or services. Defining a high-level potential target state represent is followed by a high-level transformation roadmap, describing how company can achieve their target state, based on their current state. The transformation roadmap is used to adapt IoT risk impact assessment with a Goal-Oriented Approach and the Internet of Things Micro Mart model.

Lewis Carroll, both logician and writer, suggested a logical paradox containing furthermore two connotations (connotations or metaphors are inherent in literature rather than in mathematics or logics). The paradox itself refers to implication demonstrating that an intermediate implication can be always inserted in an implication therefore postponing its ultimate conclusion for the next step and those insertions can be iteratively and indefinitely added ad lib, as if ad infinitum. Both connotations clear up links due to the shared formal structure with other well-known mathematical observations: (1) the paradox of Achilles and the Turtle; (2) the transitivity of the relation of equality. Analogically to (1), one can juxtapose the paradox of the Liar (for Lewis Carroll’s paradox) and that of the arrow (for “Achilles and the Turtle”), i.e. a logical paradox, on the one hand, and an aporia of motion, on the other hand, suggesting a shared formal structure of both, which can be called “ontological”, on which basis “motion” studied by physics and “conclusion” studied by logic can be unified being able to bridge logic and physics philosophically in a Hegelian manner: even more, the bridge can be continued to mathematics in virtue of (2), which forces the equality (for its property of transitivity) of any two quantities to be postponed analogically ad lib and ad infinitum. The paper shows that Hilbert arithmetic underlies naturally Lewis Carroll’s paradox admitting at least three interpretations linked to each other by it: mathematical, physical and logical. Thus, it can be considered as both generalization and solution of his paradox therefore naturally unifying the completeness of quantum mechanics (i.e. the absence of hidden variables) and eventual completeness of mathematics as the same and isomorphic to the completeness of propositional logic in relation to set theory as a first-order logic (in the sense of Gödel (1930)’s completeness theorems).

Adequate public spaces and urban green areas are key criteria for urban development and infrastructure implementation of healthy cities. Latterly, there has been an increasing number of research methods using artificial intelligence (AI) to monitor, quantify, and control the state of these spaces with an aim towards pioneering research in Urban Studies. However, in informal areas, open-data access tends to lack adequate and updated information, making it difficult to use AI methods. Hence, we propose a methodology for restricted open data collection and preparation for future use in machine learning or spatial data science models for similar areas. To that extent, we examine two peripheral and low-income neighborhoods in Quito, Ecuador, La Bota and Toctiuco, to analyze their public spaces, urban green areas, points of interest, and road networks, and how they address healthy cities criteria. We develop an original methodological approach that combines an index of proximity, accessibility, quantity, and quality of these spaces with geospatial and network analysis techniques. Results indicate that the connectivity and structure of these spaces are centralized and nodal, representing exclusion and segregation. This work provides insights on potential healthy spaces, and information to urban planners and policymakers in decision-making for healthy urban infrastructure.

Brine-dependent recovery process has seen much global research efforts in the past two decades because of their benefits over other oil recovery methods. The process involves the tweaking of the ionic composition and strength of the injected water to improve oil production. In recent years, several studies ranging from laboratory coreflood experiments by many researchers to field trials by several companies admit to the potential of recovering additional oil in sandstone and carbonate reservoirs. Sandstone and carbonate rocks are composed of completely different minerals, with varying degree of complexity and heterogeneity, but wettability alteration has been widely considered as the consequence rather than the cause of brine-dependent recovery. However, there is no consensus on the cause as several mechanisms have been proposed to relate the wettability changes to the improved recovery. This review paper aims to provide a state-of-the-art development in published research and various efforts of the industry. This review outlines an overview of laboratory and field observations, descriptions of underlying mechanisms and their validity, the complexity of the oil-brine-rock interactions, modelling works, and comparison between sandstone and carbonate rocks. The provided information is intended to provide the reader with up-to-date information, point to relevant studies for those who are new and those implementing either laboratory- or field-scale projects to speed up the process of further investigations in this research area. Overall, the outcome of this review would potentially be of immense benefit to the oil industry.

The theory of special relativity has reexamined and reconstructed the concepts of space and time, which are indispensable for describing physical laws, consequently instigating a major revolution in physics in general. Although the correctness of the special theory of relativity has been con-firmed by many observations and experiments, it is unclear why the "principle of relativity" and the "principle of the constant speed of light," which form the basis of the idea, are valid. Moreo-ver, although special relativity has advanced our understanding of time, our understanding of the fundamental question, "What is time?" is still limited. The theory of special relativity regards space as a four-dimensional construct consisting of one-dimensional time and three-dimensional space. This paper proposes an alternative framework, wherein real space is uniform and isotropic four-dimensional space, and elementary particles are waves that propagate through this four-dimensional space at a constant speed. This new perspective provides evidence to support two of Einstein’s key principles—the "principle of relativity" and the "principle of the constant speed of light." Furthermore, this model clarifies what time is and why time flows at a constant rate from the past to the future.

We address weighted composition operators between Bers-type spaces on generalized Hua-Cartan-Hartogs domains and provide necessary and sufficient conditions for their boundedness and compactness. We then apply our results to study the boundedness and the compactness of weighted composition operators between Bers-type spaces on five different domains: generalized Hua domains, Cartan-Hartogs domains, generalized Cartan-Hartogs domains, generalized Cartan-Hartogs domains over different Cartan domains and generalized ellipsoidal-type domains.

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.

Unlike plants which have special gravity-sensing cells, such special cells in animals are yet to be discovered. However, microgravity, the condition of apparent weightlessness, causes bone, muscular and immune system dysfunctions in astronauts following spaceflights. Decades of investigations show correlations between these organ and system-level dysfunctions with changes induced at the cellular level both by simulated microgravity as well as microgravity conditions in outer space. Changes in single bone, muscle and immune cells include morphological abnormalities, altered gene expression, protein expression, metabolic pathways and signaling pathways. These suggest that human cells mount some response to microgravity. However, the implications of such adjustments on many cellular functions and responses are not clear. Here, we addressed the question whether microgravity induces alterations to drug response in cancer cells. We used both adherent cancer cells (T98G) and cancer cells in suspension (K562) to confirm known effects of microgravity and then treated the K562 cells with common cancer drugs (hydroxyurea and paclitaxel) following 48 hours of exposure to microgravity via a NASA-developed rotary cell culture system. Fluorescence-guided morphometry revealed microgravity-induced loss of the significant reduction (p &lt; 0.0l) to the nuclear to cytoplasm ratio of cancer cells treated with hydroxyurea. Our results call for more studies on impact of microgravity on cellular drug-response, in view of the growing need for space medicine, as space exploration grows.

In this paper, we prove that the sub convexlikeness introduced by V. Jeyakumar [1], and the subconvexlikeness defined in V. Jeyakumar [2] are equivalent in loccallly convex topological spaces. And then, we work with set-valued vector optimization problems and obtain some vector saddle-point theorems and vector Lagrangian theorems.

An accurate flux measurement of low-energy charged particles trapped in the magnetosphere is necessary for space weather characterization and to study the coupling between the lithosphere and magnetosphere, which allows for the investigation of the correlations between seismic events and particle precipitation from Van Allen belts. In this work, the project of a CubeSat space spectrometer, the Low-Energy Module (LEM), is shown. The detector will be able to perform an event-based measurement of the energy, arrival direction, and composition of low-energy charged particles down to 0.1 MeV. Moreover, thanks to a CdZnTe mini-calorimeter, the LEM spectrometer also allows for photon detection in the sub-MeV range, joining the quest for the investigation of the nature of Gamma-ray bursts. The particle identification of the LEM relies on the ΔE−E technique performed by thin silicon detectors. This multipurpose spectrometer will fit within a 10 × 10 × 10 cm3 CubeSat frame, and it will be constructed as a joint project between the University of Trento, FBK, and INFN-TIFPA. To fulfil the size and mass requirements, an innovative approach, based on active particle collimation, was designed for the LEM; this avoids the heavy/bulky passive collimators of previous space detectors. In this paper, we will present the LEM geometry, its detection concept, the results from the developed GEANT4 simulation, and some characterisations of a candidate silicon detector for the instrument payload.

Exact sequential state estimation of orbiting objects in space can be found by predicting the state using Fokker Planck Kolmogorov Equation (FPKE) and measurement correction using Bayes’ conditional/posterior Probability Density Function (PDF). Posterior PDF can be expressed as Gaussian density expanded in terms of Hermite polynomials named as Gram Charlier Series (GCS). This research is an extension of an earlier work on second order linearized solution to nonlinear Bayesian filtering using Taylor series and third order GCS expansion of posterior PDF. In this new extension, Bayes’ posterior PDF is approximated by a mixture of GCS functions for which the parameters are propagated using linear propagation theory. The update of weights of different components of GCS mixture model uses the FPKE error as feedback to adapt for the amplitude of different GCS components while solving a quadratic programming problem earlier used for Gaussian Mixture Model (GMM) PDF. Proposed filtering method is applied on tracking of space debris. The simulation results for the filter shows performance is moderately better than single GCS filter, Extended Kalman Filter (EKF) and Gaussian Sum Filter (GSF) under space debris’ highly uncertain initial conditions and sparse measurement availability.

We introduce the notion of p-adic magic contraction on p-adic Hilbert space. We derive p-adic Halmos dilation, p-adic Egervary N-dilation, p-adic von Neumann inequality and p-adic Sz.-Nagy dilation for p-adic magic contraction.

Recently in various theoretical works, path-breaking progress has been made in recovering the well-known Page Curve of an evaporating black hole with Quantum Extremal Islands, proposed to solve the long-standing black hole information loss problem related to the unitarity issue. Motivated by this concept, in this paper, we study cosmological circuit complexity in the presence (or absence) of Quantum Extremal Islands in negative (or positive) Cosmological Constant with radiation in the background of Friedmann-Lemaî tre-Robertson-Walker (FLRW) space-time. Without using any explicit details of any gravity model, we study the behaviour of the circuit complexity function with respect to the dynamical cosmological solution for the scale factors for the above mentioned two situations in FLRW space-time using squeezed state formalism. By studying the cosmological circuit complexity, Out-of-Time Ordered Correlators, and entanglement entropy of the modes of the squeezed state, in different parameter space, we conclude the non-universality of these measures. Their remarkably different features in the different parameter space suggests their dependence on the parameters of the model under consideration.

We emphasize the point that, standard model of cosmology is basically a model of classical general relativity and it seems inevitable to have a revision with reference to quantum model of cosmology. Utmost important point to be noted is that, ‘Spin’ is a basic property of quantum mechanics and ‘rotation’ is a very common experience. In this context, we propose five assumptions in line with Planck mass as the baby universe. We appeal that, 1) Universe can be modeled as a time-reversed black hole (a white hole) with rotation and light speed expansion, and 2) ‘Light speed expanding cosmic space’ can be called as ‘Flat space’. With reference to light speed expansion, if one is willing to re-define cosmic red shift as [z/(1+z)], without considering Lambda cosmology model of matter density fractions, light travel distances can be reproduced with a marginal error of +8.6% at z =1.2, (i.e. traditional light travel distance is 8.6% higher than our estimate), 0% at z = 11.5 and -5.5% at z = 1200.( i.e. traditional light travel distance is 5.5% lower than our estimate). Advantages of our model are, 1) A quantum model of cosmology can be developed with unification of general theory of relativity and quantum mechanics. 2) Tension in estimating the current Hubble parameter can be eliminated via scaled Hawking’s black hole temperature formula with great confidence. 3) Galactic dark matter and visible matter can be studied in a unified manner. 4) Galactic light travel distances can be estimated very easily without matter density fractions. 5) Big Bang and Inflation like non-general relativistic concepts can be relinquished with further study.

We explore several ways, in order to include fermionic structures naturally in a physical theory in C⁴. We begin with the standard Dirac formalism and we proceed by using Cartan's property of triality as a second option. Afterwards, we suggest a new approach (in a preliminary basis), by introducing an 1-linear form, as the "square root of the geometry" derived by the usual 2-linear forms (quadratic forms). Keeping this way, we introduce n-linear forms, in order to formulate a new geometric structure, which could be suitable for the formulation of a pure geometric uni ed theory.

We explore the possibility to nd the usual quantum theories, within the formulation of a classic theory of mechanics in C⁴. Speci cally, by releasing the end-point of the integral of the action derived in C⁴, we derive the dynamic path length of the geodesic equation in C⁴. In the at case, the derived Hamilton-Jacobi equations, were identi ed as the usual Klein-Gordon equation, where the complex functional action S(z_i), is identi ed as the usual complex scalar field φ. Afterwards, we study the energy-momentum 4-d complex vector, in order to re-establish the usual covariant derivative of gauge theories.

In Europe we can see a change in the social structure in the last period. Average life expectancy has increased dramatically over the last 50 years. Because of the improved life situation and advanced level of health care, older people are slower. With advancing age, the likelihood of experiencing a variety of constraints such as visual impairment, reduced hearing or physical ability increases. In such a life stage tenants are often forced to leave their long-term living space because these homes can not serve "new" individual needs and the resulting personal protection goal. This transition from the privacy of their home to the new environment often appears to be a painful change. They will take their familiar and well-known surroundings, because their homes can not be adapted to serve new needs. It must be the policy’s role to create a new inclusive social space and the requirement for architects and designers to create new goals for the design of an adaptable environment. This is a comprehensive approach to the design of the outer and inner space that could serve people even if there is an unexpected situation and changes in movement and physiological limitations of older people. The contribution shows the results of the survey conducted in Germany and Slovakia. In the survey respondents expressed their opinion on what they considered important in creating an adaptive environment. Results are processed graphically with explanation. The results were mainly for designers and developers of the indoor environment. Based on the results of the questionnaire survey, studies of possible modifications in the interior of the flats were then prepared. The contribution yielded these results in three age groups of respondents; i. people aged 35, 50 and over 50.

Computation of Common Middle Point seismic sections and their subsequent time migration and diffraction imaging provides very important knowledge about the internal structure of 3D heterogeneous geological media and are key elements for successive geological interpretation. Full-scale numerical simulation, that computes all single shot seismograms, provides a full understanding of how the features of the image reflect the properties of the subsurface prototype. Unfortunately, this kind of simulations of 3D seismic surveys for realistic geological media needs huge computer resources, especially for simulation of seismic waves’ propagation through multiscale media like cavernous fractured reservoirs. In order to significantly reduce the query of computer resources we propose to model these 3D seismic cubes directly rather than the shot-by-shot simulation with subsequent CMP stacking. To do that we modify the well known "exploding reflectors principle" for 3D heterogeneous multiscale media by use of the finite-difference technique on the base of grids locally refined in time and space. To be able to simulate realistic models and acquisition we develop scalable parallel software, which needs reasonable computational costs. Numerical results for simulation of Common Middle Points sections and their time migration are presented and discussed.

The world is currently experiencing the fourth industrial revolution driven by the newest wave of digitisation in the manufacturing sector. The term Industry 4.0 (I4.0) represents at the same time: a paradigm shift in industrial production, a generic designation for sets of strategic initiatives to boost national industries, a technical term to relate to new emerging business assets, processes and services, and a brand to mark a very particular historical and social period. I4.0 is also referred to as Industrie 4.0 the New Industrial France, the Industrial Internet, the Fourth Industrial Revolution and the digital economy. These terms are used interchangeably in this text. The aim of this article is to discuss major developments in this space in relation to the integration of new developments of IoT and cyber physical systems in the digital economy, to better understand cyber risks and economic value and risk impact. The objective of the paper is to map the current evolution and its associated cyber risks for the digital economy sector and to discuss the future developments in the Industrial Internet of Things and Industry 4.0.

In this paper, spatial pulse position modulation (SPPM) is used as a case study to investigate the performance of the optical spatial modulation (SM) technique in outdoor atmospheric turbulence (AT). A closed-form expression for the upper bound on the asymptotic symbol error rate (SER) of SPPM in AT is derived and validated by closely-matching simulation results. The error performance is evaluated in weak to strong AT conditions. As the AT strength increases from the weak to strong, the channel fading coefficients become more dispersed and differentiable. Thus, a better error performance is observed under moderate-to-strong AT compared to weak AT. The performance in weak AT can be improved by applying unequal power allocation to make FSO links more distinguishable at the receiver. Receive diversity is considered to mitigate irradiance fluctuation and improve the robustness of the system to turbulence-induced channel fading. The diversity order is computed as half of the number of detectors. Performance comparisons, in terms of energy and spectral efficiencies, are drawn between the SPPM scheme and conventional MIMO schemes such as repetition coding and spatial multiplexing.

As the application of cyber-physical systems (CPSs) becomes more and more widespread, its security is becoming a focus of attention. Currently, there has been much research on the security defense of the physical layer of the CPS. However, most of the research only focuses on one of the aspects, for example, attack detection, security state estimation or recovery control. Obviously, the effectiveness of security defense targeting only one aspect is limited. Therefore, in this paper, a set of security defense processes is proposed for the case that a CPS containing multiple sensors is subject to three kinds of stealthy attacks (i.e., zero-dynamics attack, covert attack, and replay attack). Firstly, the existing attack detection method based on improved residuals is used to detect stealthy attacks. Secondly, based on the detection results, an optimal state estimation method based on improved Kalman filtering is proposed to estimate the actual state of the system. Then, based on the optimal state, internal model control (IMC) is introduced to complete the recovery control of the system. Finally, the proposed methods are integrated to give a complete security defense process, and the simulation is verified for three kinds of stealthy attacks. The simulation results show that the proposed methods are effective.

Quantum computing arrives. Preceded by Mādhava of Sangamagrāma and other Hindu mathematicians, around 1,500 CE, and our previous work, we show at least 4 quantum properties that are indeed universal in some number systems; here we use them to enable quantum computing. Using the sets B={0,1}, B^n, N, Z, Q, and Q*, one has +4 quantum properties that one can trust, as archetypes of easy communication to friends and foes, to ignoranti or cognoscenti, and for quantum consciousness. Therefore, this work applies to computer calculations, apps in cell phones, algebra, atoms, mathematics, physics, and the human mind.

We present a new vision on the double-slit experiment (DSE), that does not use any waves or the so-called wave-particle duality. It is based on particles, viewed as localized vibrations. Rather than being illogical, or mysterious, the approach reproduces logically the interference patterns observed externally, using the well-known ``amplification of stimulated emission'' (ASE). The conservation of particle number is not observed. Different particles can present ASE with in-phase or counter-phase behavior, making the patterns observed for electrons, other particles, even atoms. The Law of the Excluded Middle (LEM) is not broken illogically in two-states macroscopically, but broken naturally in three or more states, microscopically, using ASE. Any particle supporting ASE, in-phase or counter-phase, can enter a DSE, and provide an external interference pattern. Frequency plays a role, while amplitude is not considered. This work is important in quantum computing, possibly saving cost, time, and welcoming more participants.

Ensuring a protection scheme in DC distribution is more difficult to achieve against pole-to-ground fault than in AC distribution system because of the absence of zero crossing points and low line impedance. To complement the major obstacle of limiting the fault current, several compositions have been proposed related to mechanical switching and solid-state switching. Among them, solid-state circuit breakers(SSCBs) are considered a possible solution to limit fast fault current. However, they may cause problems in circuit complexity, reliability and cost-related troubles due to the use of multiple power semiconductor devices and additional circuit configuration to commutate current. This paper proposes the SSCB with a coupled inductor(SSCB-CI) which has symmetrical configuration. The circuit is comprised of passive components like commutation capacitors, a CI and damping resistors. Thus, proposed SSCB-CI offers the advantages of simple circuit configuration and fewer utilized power semiconductor devices than another typical SSCBs in LVDC microgrid. For analysis, six operation states are described for the voltage across main switches and fault current. The effectiveness of the SSCB-CI against a short-circuit fault is proved via simulation and experimental results in a lab-scale prototype.

Abstract: Ultrasonic vocalizations (USVs) are one of the evolutionarily oldest forms of animal communication. In order to study the communication architecture in an aversive social situation, we used a behavioral model in which one animal, the observer, is witnessing as his cagemate, the demonstrator, is experiencing a series of mild electrical foot-shocks (aversive stimuli). We studied the effect of foot-shocks experience in the observer and the influence of a warning sound (emit-ted shortly before the shock is applied) on USVs communication. These experiments revealed that such a warning seems to increase the arousal level, which differentiates the responses depending on previous experience. It can be identified by the emission of characteristic, short 22-kHz calls, of a duration below 100 ms. Furthermore, by analyzing temporally overlapping USVs, we found that in ‘Warned’ pairs with a naive observer, 22-kHz were mixed with 50-kHz calls. This fact, combined with a high fraction of very high-pitched 50-kHz calls (over 75-kHz), suggests the presence of the phenomenon of social buffering. On the other hand, in ‘Warned’ pairs with an experienced observer, pure 22-kHz overlaps were mostly found, signifying possible fear contagion with dis-tress sharing. Hence the importance of differentiating 22-kHz calls to long and short.

Bioenergetics, genetic coding, and catalysis are all difficult to imagine emerging without pre-existing historical context. That context is often posed as a “Chicken and Egg” problem; its resolution is concisely described by de Grasse Tyson: “the egg was laid by a bird that was not a chicken”. The concision and generality of that answer furnish no details—only an appropriate framework from which to examine detailed paradigms that might illuminate paradoxes underlying these three life-defining biomolecular processes. We examine experimental aspects here of five examples that all conform to the same paradigm. The paradox in each example is resolved by coupling if, and only if, conditions for two related transitions between levels. One drives, and each restricts fluxes through, or “gates” the other. That reciprocally-coupled gating, in which two gated processes constrain one another, maps onto the formal structure of “strange loops”. That mapping may help unite the axiomatic foundations of genetics, bioenergetics, and catalysis. As a physical analog for Gödel’s logic, biomolecular strange-loops provide a natural metaphor around which to organize these data, linking biology to the physics of information, free energy, and the second law of thermodynamics.

In a recent paper, we extended previous studies on solar-terrestrial relationships by investigating the origin of the March 2012 heatwave in northeast USA. In the present study we check the possible relationship of solar activity with the early March 2012 bad weather in northeast Thrace, Greece. To this end, we examined data from various remote sensing instrumentation monitoring the Sun (SDO satellite), the Interplanetary Space (ACE satellite), the Earth’s magnetosphere (Earth based measurements, NOAA-19 satellite), the top of the clouds (TERRA and AQUA satellites) and the near ground atmosphere. Our comparative data analysis suggests that: (i) the winter-like weather (rainfall, fast winds, decreased temperature) in Thrace started on 6 March 2012, on the same day as the heatwave starting in USA, (ii) during the March 2012 winter-like event in Thrace (6-15 March), the ACE satellite recorded enhanced fluxes of solar energetic particles (SEPs), while SOHO and PAMELA recorded solar protons at very high energies (> 500 MeV), (iii) Thrace experienced particularly intense cyclonic circulation, during periods of magnetic storms on 8 - 10 and 12-13 March, which occurred after the arrival at ACE of two interplanetary shock waves, on March 8 and March 11, respectively, (iv) at the beginning of the two above mentioned periods large atmospheric electric fields were recorded, with values ranging between ~-2000V/m - ~1800V/m on 8 March, (v) the winter-like 8-10 March 2012 weather occurred after the detection of the main SEP event related with a coronal mass ejection released in the interplanetary space as a result of intense solar flare activity observed by SDO on 7 March 2012, (vi) the 8-10 March weather was related with a deep drop of ~630C in the cloud top temperature measured by MODIS/TERRA, which favors strong precipitation. Finally, we analyzed data from the electric power network in Thrace (~410N) and we found, for the first time sudden voltage changes of ~3.5kV in the electric grid in Greece, during the decay phase of the March 2012 superstorm. We discuss the winter-like March 2012 event in Thrace in terms of solar cosmic ray influence on the low troposphere mediated by positive NAO. Finally, we infer that the novel finding of the geomagnetic effects on the electric power grid in Thrace may open a new window into space weather applications research.

“You should know the words by the company they keep!” has been one of the most famous slogans attributed to John Rubert Firth, 1957. This has ignited a whole school in linguistic research known as the British empiricist contextualism. Sixty years later, many un- or semi-supervised machine learning algorithms have been successfully designed and implemented aiming at extracting word meaning from within the context of a text corpus. These algorithms treat words, more or less, as vectors of real numbers representing frequencies of word occurrences within context and word meaning as positions of words in a high-dimensional vector space model. Word associations, in turn, are treated as calculated distances among them. With the rise of Deep Learning (DL) and other artificial neural networks based architectures, learning the positioning of words and extracting word associations as measured by their distances has further improved. In this paper, however, we revisited the main stream of algorithmic approaches and set the stage for a partly cross-disciplinary evaluation framework to judge about the nature of the extracted word associations by state-of-the-art machine learning algorithms. Our preliminary results are based on word associations extracted from the application of DL framework on a Google News text corpus, as well as on comparisons with human created word association lists such as word collocation dictionaries and psycholinguistic experiments. The results and conclusions provide some insights into the inherited limitations in interpreting the type of word associations and underpinning relations between words with inevitable consequences in other areas, such as extraction of knowledge graphs or image understanding.

Recent brain imaging findings by using different methods (e.g., fMRI and PET) have suggested that social anxiety disorder (SAD) is correlated with alterations in regional or network-level brain function. However, due to many limitations associated with these methods, such as poor temporal resolution and limited number of samples per second, neuroscientists could not quantify the fast dynamic connectivity of causal information networks in SAD. In this study, SAD-related changes in brain connections within the default mode network (DMN) were investigated using eight electroencephalographic (EEG) regions of interest. Partial directed coherence (PDC) was used to assess the causal influences of DMN regions on each other and indicate the changes in the DMN effective network related to SAD severity. The DMN is a large-scale brain network basically composed of the mesial prefrontal cortex (mPFC), posterior cingulate cortex (PCC)/precuneus, and lateral parietal cortex (LPC). The EEG data were collected from 88 subjects (22 control, 22 mild, 22 moderate, 22 severe) and used to estimate the effective connectivity between DMN regions at different frequency bands: delta (1–3 Hz), theta (4–8 Hz), alpha (8–12 Hz), low beta (13–21 Hz), and high beta (22–30 Hz). Among the healthy control (HC) and the three considered levels of severity of SAD, the results indicated a higher level of causal interactions for the mild and moderate SAD groups than for the severe and HC groups. Between the control and the severe SAD groups, the results indicated a higher level of causal connections for the control throughout all the DMN regions. We found significant increases in the mean PDC in the delta (p = 0.009) and alpha (p = 0.001) bands between the SAD groups. Among the DMN regions, the precuneus exhibited a higher level of causal influence than other regions. Therefore, it was suggested to be a major source hub that contributes to the mental exploration and emotional content of SAD. In contrast to the severe group, HC exhibited higher resting-state connectivity at the mPFC, providing evidence for mPFC dysfunction in the severe SAD group. Furthermore, the total Social Interaction Anxiety Scale (SIAS) was positively correlated with the mean values of the PDC of the severe SAD group, r (22) = 0.576, p = 0.006 and negatively correlated with those of the HC group, r (22) = −0.689, p = 0.001. The reported results may facilitate greater comprehension of the underlying potential SAD neural biomarkers and can be used to characterize possible targets for further medication.

The aim of the present work is to explore a different non-traditional approach to higher dimensional spacetime which differs from the usual approaches such as large extra space dimensions, warped extra space dimensions and multiple extra time dimensions. This approach will be referred to as "Dynamic" higher dimensional spacetime. By this term will be introduced a new kind of higher dimensional spacetime (more than the usual 3+1 dimensions), which behaves dynamically in the sense that time is not a fixed dimension but is the extra plus one dimension to the spatial dimensions of the phenomenon we are studying. For a higher dimensional phenomenon, the lower dimensional time will behave as space and the new time will be a plus one dimension, meaning if we study a 3-dimensional phenomenon then time is the usual 4th dimension but if we are studying a 4-dimensional phenomenon then its time will be an extra 5th dimension and for this phenomenon the 4th dimension will behave as space. All spatial dimensions are indistinguishable from each other and behave in the exact same way, which means that the important parameter is not the dimension we are studying (for example the 4th or the 5th) but the number of dimensions in which a phenomenon can interact and how higher dimensional phenomena interact with lower ones. This non-traditional approach to higher dimensional spacetime may appear confusing at first but will become clearer in the rest of the paper. For this reason, we will first associate the time dimension with propagation relative to space dimensions and change orthogonal to them in order to give the reader a better intuition. Following that, we will describe how higher - dimensional phenomena would appear to us, the 3 - dimensional observer and try to set some guidelines on how we should mathematically model higher - dimensional phenomena in order for us to make accurate predictions of their behaviour. Then we will express the general framework of a "Dynamic" higher - dimensional spacetime and turn our gaze to 4+1 – dimensional phenomena. We will explore how a mass term arises for 4 – dimensional waves by generalizing the frequency – speed – wavelength relation of 3D waves and give a basic description on a possible way to describe a 4+1 – dimensional disturbance in a scalar Energy Field in order to make accurate predictions on our 3+1 dimensional plane. In this framework we will derive the Klein – Gordon equation by generalizing the wave equation and ignoring all lower than 4+1 - dimensional disturbances and compere our mass term findings with the Klein – Gordon equation mass term and with results from gravity – induced quantum interference experiments. Lastly, we will describe how a "Dynamic" 5 - dimensional spacetime would appear in a 3D observer and examine whether our findings are compatible with quantum mechanics and the special theory of relativity.

The advanced industrial systems are characterized by complex event-driven processes. The specification and computer analysis are illustrated by the common problem of a servo drive with the PWM controller. A mathematical model from the class of Cauchy problems with non-liner control logic is presented to resolve the task discussed. The designed model is performed in two stages: the mathematical model specification (modeling) and implementation (simulation). Two approaches to make a model specification are considered, the first one is the traditional structural technique, and the advanced method is based on the finite state machine paradigm. Structural and finite state software models are built in progressive simulation tools. Computer implementation of the software models of hybrid systems is performed using platform built-in libraries with numerical methods and algorithms for correct event detection. The results of computational experiments for transient processes of phase variables are qualitatively identical, that proves the adequacy of the developed models. The algorithm for asymptotic event detection is considered in detail. This technique demonstrated the best results by the one-sidedness criterion, which is explained by using the author’s algorithm for the integration step determining and the event function dynamics monitoring in the vicinity of the event limits.

Helmholtz energy of ice VII-X is determined in a pressure regime extending to 450 GPa at 300 K using local basis functions in the form of b splines. The new representation for the equation of state is embedded in a physics-based inverse theory framework of parameter estimation. Selected pressures as a function of volume from 14 prior experimental studies and two theoretical studies constrain the behavior of Helmholtz energy. Separately measured bulk moduli, not used to construct the representation, are accurately replicated below about 20 GPa and above 60 GPa. In the intermediate range of pressure, the experimentally determined moduli are larger and have greater scatter than values predicted using the Helmholtz representation. Although systematic experimental error in the experimental elastic moduli is possible and likely, the alternative hypothesis is a slow relaxation time associated with changes in proton mobility or the ice VII to X transition. A correlation is observed between anomalies in the pressure derivative of the predicted bulk modulus and previously suggested higher-order phase transitions. Improved determinations of elastic properties at high pressure would allow refinement of the current equation of state. More generally, the current method of data assimilation is broadly applicable to other materials in high-pressure studies.

Human Face and facial parts are the most significant parts as it reveals a person’s true identity. It plays an important role in various biometric applications like crowd analysis, human tracking, photography, cosmetic surgery, etc. There are many techniques are available to detect a facial image. Among them, skin detection is the most popular one. The aim of this paper is to detect first the person's identity from facial image and finally check any spot present the the detected person. The first step is to detect the maximum skin region based on a combination method of RGB and HSV color space model. Next it is to verify the skin areas of human through machine learning approach. The Aggregated Channel Features (ACF) detector is used to identify the different facial parts like eye pairs, nose, and mouth. Bootstrap aggregation decision tree classifier is applied to classify the person’s identity based on Histogram Oriented Gradient (HOG) features value. The experimental results show that the proposed method gives the average 97% accuracy.

In this paper, new types of M-fractional wave solutions of mathematical physics model named as truncated M-fractional (1+1)-dimensional non-linear simplified Modified Camassa-Holm model are achieved by applying the modified simplest equation (MSE), Sardar sub-equation and generalized Kudryashov techniques. The gained solutions containing dark, bright, periodic and mixed wave solitons. Effect of fractional order derivative is also discussed. Achieved wave solitons are verified by Mathematica tool. Few of the gained wave solitons are also described through 2-dimensional, 3-dimensional and contour graphs through Mathematica tool. The gained solutions are helpful for the further development of concerned model. Finally, these techniques are simple, fruitful and effective to deal with non-linear FPDEs.

We prove a version of Ekeland Variational Principle (EkVP) in a weighted graph $G$ and its equivalence to Caristi fixed point theorem and to Takahashi minimization principle. The usual completeness and topological notions are replaced with some weaker versions expressed in terms of the graph $G$. The main tool used in the proof is the OSC property for sequences in a graph. Converse results, meaning the completeness of graphs for which one of these principles holds is also considered.

The time symmetry of T_c = CT is redefined based on the 4-D Euclidean space. The time inversion symmetry of T_c = CT changes the signs of the charge (q) and absolute time (ct) of the particle. The P symmetry changes the signs of the space momenta and handedness of the particle. The T_c symmetry changes the signs of the time momentum (P_t=E/c) and charges. The handedness are classified as the left-handedness and right-handedness on the 3-D space. The charges (|q| = cDt) and energies (E= cDtDV) of the particles are originated from the upward warping (q>0) and downward warping (q<0) of the particles along the time axis. The evolution of our matter universe with P_t > 0 since the big bang can be interpreted based on the CTP symmetry with the partner antimatter universe with p_t < 0. The photon space is called as the vacuum space. Then the E and M waves are the space fluctuations, and the gravitational G wave is the time fluctuations. And the E, M and G fields are newly explained. The zero E and M fields of the photon space indicates that the photon space and gamma ray are the 2EM waves. The rest mass energy of the particle is the 4-D space volume of the warped photon space. In the present work, the quantum wave function is considered as the 4-D vector with the space wave function and time axis wave function in the 4-D Euclidean space. The warped photon space corresponds to the squared wave function which is the probability density. It is concluded that, in the quantum mechanics, the imaginary number concept is introduced in the quantum wave functions instead of the time axis component of the 4-D wave function vector. The left-handed neutrino puzzle is explained from the handedness partner relation with the right-handed dark matters. Finally, the modified Lorentz transformations derived in the 4-D Euclidean space are compared with the Lorentz transformations of the special and general relativity theories in the 4-D Minkowski space.

We prove a quasi-pseudometric version of the strong Ekeland principle proved by P. Georgiev and T. Suzuki.

In this paper, we derive Jeffreys divergence, generalized Fisher divergence and corresponding De Bruijn identities on space-time random field. First, we determine the relation between Fisher information on the space-time random field in one of the space-time points and the ratio of Jeffreys divergence on a space-time random field at distinct space-time positions to the square of coordinate difference. In addition, we also find identities between the partial derivative of the Jeffreys divergence and the generalized Fisher divergence with respect to space-time variables, i.e. the De Bruijn identities, between two space-time random fields obtained by different parameters under the same Fokker-Planck equations. At the end of this paper, we present three examples of the Fokker-Planck equations on space-time random fields, identify their density functions, and derive the Jeffreys divergence, generalized Fisher information, generalized Fisher divergence, and accompanying De Bruijn identities.

The ubiquitous connectivity for the space-air-ground integrated network (SAGIN) of the beyond fifth generation of communication and sixth generation of communication (B5G/6G) is envisaged to meet the needs for the demanded quality of service (QoS), green communication, and "dual carbon" target. However, the offloading and computation of massive latency-sensitive tasks dramatically increases the energy consumption of the network. Furthermore, the traditional power supply technology of the network base stations (BSs) enhances the carbon emission. To address these issues, we first propose a SAGIN architecture with energy harvesting devices, where the BS is powered by both renewable energy (RE) and the conventional grid. The BS explores wireless power transfer (WPT) technology to power the unmanned aerial vehicle (UAV) for stable network operation. RE sharing between neighbouring BSs is designed to fully utilize RE for reduce carbon emission. Secondly, on the basis of task offloading decision, UAV trajectory, and RE sharing ratio, we construct cost functions with joint latency-oriented, energy consumption, and carbon emission. Then, we develop a twin delayed deep deterministic policy gradient (TD3PG) algorithm based on deep reinforcement learning to minimize the cost function. Finally, simulation results demonstrate that the proposed algorithm outperforms the benchmark algorithm in terms of reducing latency, energy saving, and lower carbon emission.

As an emerging network paradigm, Space-Air-Ground integrated networks (SAGIN) has attracted the attentions from academia and industry. That is because SAGIN can implement seamless global coverage and connections among electronic devices in space, air, and ground spaces. Additionally, the shortages of computing and storage resources in mobile devices greatly affect the quality of experiences for intelligent applications. Hence, we devise to integrate SAGIN as an abundant resource pool into mobile edge computing environments (MEC). To facilitate efficient processing, we need to solve the optimal task offloading decisions. Different from the existing MEC task offloading solutions, we have to face some new challenges, such as the fluctuation of processing capability for an edge computing node, the uncertainty of the transmission latency caused by the heterogeneous network protocols, the uncertain amount of uploaded tasks during a period, and so on. In this paper, we firstly describe a task offloading decision problem in new challenge environments. But, we cannot use the standard robust optimization and stochastic optimization methods to obtain the optimal result under the uncertain network environments. In this paper, we propose the condition value at risk-aware distributionally robust optimization algorithm, named as CVAR-DRO, to solve the task offloading decision problem. The proposed CVAR-DRO method combines the distributionally robust optimization and the condition value at risk model for solving the optimal result. And then, We have evaluated our approach in simulation SAGIN environments with the confidence interval, the number of mobile task-offloading and the various parameters. We compare our proposed CVAR-DRO algorithm with the state-of-the-art algorithms, such as the standard robust optimization algorithm, the stochastic optimization algorithm, the DRO algorithm, and the brute algorithm. The experimental results show that CVAR-DRO can get a sub-optimal mobile task-offloading decision. Overall, CVAR-DRO is more robust than others to the new challenges mentioned above in SAGIN.

Compared to wide-field telescopes, small-field detection systems have higher spatial resolution, resulting in stronger detection capabilities and higher positioning accuracy. When detecting by small-field in synchronous orbit, both space debris and fixed stars are imaged as point targets, making it difficult to distinguish them. In addition, with the improvement of detection capabilities, the number of stars in the background rapidly increases, which puts higher requirements on recognition algorithms. Therefore, star detection is indispensable for identifying and locating space debris in complex backgrounds. To address these difficulties, this paper proposes a real-time star extraction method based on adaptive filtering and multi frame projection. We use bad point repair and background suppression algorithms to preprocess star image. Afterwards, we analyze and enhance the target signal-to-noise ratio(SNR). Then, we use multi frame projection to fuse information. Subsequently, adaptive filtering, adaptive morphology, and adaptive median filtering algorithms are proposed to detect trajectories. Finally, the projection is released to locate the target. Our recognition algorithm has been verified by real star images, and the images were captured using small-field telescope. The experimental results demonstrate the effectiveness of the algorithm proposed in this paper. We successfully extracted hip-27066 star, which has magnitudes about 12 and SNR about 1.5. Compared with existing methods, our algorithm has advantages in both recognition rate and false-alarm rate, and can be used as real time target recognition algorithm for space-based synchronous orbit detection payload.

A class of nearly Sasakian manifolds is considered. We discussion geometric effects of some symmetries on such manifolds, and show, under a certain condition, that the class of Ricci-symmetric nearly Sasakian manifolds is a subclass of Einstein manifolds. We prove that a nearly Sasakian space form with Ricci tensor satisfying the Codazzi equation is either a Sasakian manifold with a constant $\phi$-holomorphic sectional curvature $\mathcal{H}=1$ or a $5$-dimensional proper nearly Sasakian manifold with a constant $\phi$-holomorphic sectional curvature $\mathcal{H}>1$. We also prove that the spectrum of the operator $H^{2}$ generated by the nearly Sasakian manifold is a set of simple eigenvalue $0$ and an eigenvalue of multiplicity $4$. We show that there exist integrable distributions on the same manifolds with totally geodesic leaves, and prove that there are no proper nearly Sasakian space forms with constant sectional curvature.

Aiming at the problem of low charging efficiency caused by the scattered sensor nodes in traditional wireless rechargeable sensor networks (WRSNs), a UAV-assisted WRSN Online Charging Strategy Based on Dynamic Queue and Improved K-means (UOCS) is proposed. The scheme assumes that the energy consumption of nodes is unpredictable, and only generates charging requests when the energy is lower than a threshold, and performs on-demand responses to nodes that issue charging requests. The scheme combines the characteristics of one-to-one charging of UAVs, the selection and allocation timing of waiting queues and the number of UAVs, and the improved K-means partitioning based on space-time coordination(SPKM), which simplifies the problem of coordinated charging of multiple UAVs and maximizes energy. Using the efficiency and charging success rate, the optimal charging trajectory can be found under the constraint that the node will not starve to death due to power shortage. Finally, a simulation comparison experiment is carried out with the existing UAV charging scheduling strategy. UOCS achieves the optimal node survival rate with low algorithm complexity.

Here we studied Bianchi type-III string cosmological models generated by means of a cloud of strings with particles connected to them in the framework of Lyra geometry considering five-dimensional space-time. To obtain the exact solutions of field equations we consider that the shear scalar and the scalar expansion are proportional, σ∞θ which leads to D=cn and secondly we adopt the assumption considering the Reddy String Condition, ρ+λ=0. From the two different cases obtained here, first case leads to the Bianchi type-III string cosmological model in Lyra geometry in five-dimensional space-time and the second case leads to the five-dimensional vacuum universe in general relativity. The Geometrical and physical properties of the model universe are studied comparing with the present day’s observational findings. The model universe obtained here starts with the big bang and as time progresses both particle density ρp and energy density ρ decreases with expansion of our Universe.

Firstly, a new set of Quasi-Cubic Trigonometric Bernstein basis with two tension shape parameters is constructed, and we prove that it is an optimal normalized totally basis in the framework of Quasi Extended Chebyshev space. And the Quasi-Cubic Trigonometric Bézier curve is generated by the basis function and the cutting algorithm of the curve are given, the shape features (cusp, inflection point, loop and convexity) of the Quasi-Cubic Trigonometric Bézier curve are analyzed by using envelope theory and topological mapping; Next we construct the non-uniform Quasi-Cubic Trigonometric B-spline basis by assuming the linear combination of the optimal normalized totally positive basis have partition of unity and continuity, and its expression is obtained. And the non-uniform B-spline basis is proved to have totally positive and high-order continuity. Finally, the non-uniform Quasi Cubic Trigonometric B-spline curve and surface are defined, the shape features of the non-uniform Quasi-Cubic Trigonometric B-spline curve are discussed, and the curve and surface are proved to be continuous.

The processing of cloud free geo-referenced imagery is one of the preliminary processing step of any land application. This letter describe the methodology developed to obtain a seamless cloud free composite of Africa for 2016 using Sentinel-2A data at 10 meters resolution freely available from the European Space Agency. The method is based on an hybrid method resulting from the merging of the two most robust time series methods namely the "darkest pixel" and the "maximum NDVI" previously developed with AVHRR time series.

Foundational and state-of-the-art anomaly detection methods through power system state estimation are reviewed. The traditional components for bad data detection such as chi-square testing, residual-based methods, and hypothesis testing are discussed to explain the motivations for recent anomaly detection methods given the increasing complexity of power grids, energy management systems, and cyber-threats. In particular, state estimation anomaly detection based on data-driven quickest change detection and artificial intelligence are discussed and directions for research are suggested with particular emphasis on considerations of the future smart grid.