This contribution is an attempt to reconstruct the favorable atmosphere that prevailed in Lau-sanne in 1986-87 and provided the backdrop of our invention of two-dimensional ion cyclotron resonance mass spectroscopy (2D ICR-MS). To avoid a self-centered histoire d’ancien combattant, we shall try to emphasize the context, the contributions of key players within our nascent research group at UNIL and the established group of Tino Gäumann at EPFL, the role of external speakers, and the open atmosphere that was not yet polluted by bibliometrics, obsessive concern with im-pact factors, and top-down management of research. We shall also explain why the idea of 2D ICR-MS has been ignored for many years and still has a limited impact: different scientific cul-tures in the ICR and NMR communities, different concerns with fundamental vs. applied research, different status of theory and numerical simulations, different levels of commitment of instrument manufacturers, not to mention many theoretical problems that appear to be at least as challenging in ICR as in NMR.

This article deals with the conformable double Laplace transforms and their some properties with examples and also the existence Condition for the conformable double Laplace transform is studied. Finally, in order to obtain the solution of nonlinear fractional problems, we present a modified conformable double Laplace that we call conformable double Laplace decomposition methods (CDLDM). Then, we apply it to solve, Regular and singular conformable fractional coupled burgers equation illustrate the effectiveness of our method some examples are given.

Exact solutions to conformable time fractional (3+1)- dimensional equations are derived by using the modified form of the Kudryashov method. The compatible wave transformation reduces the equations to an ODE with integer orders. The predicted solution of the finite series of a rational exponential function is substituted into this ODE. The resultant polynomial equation is solved by using algebraic operations. The method works for the Jimbo-Miwa, the Zakharov-Kuznetsov and the modified Zakharov-Kuznetsov equations in conformable time fractional forms. All the solutions are expressed in explicit forms.

An abdominal aortic aneurysm (AAA) is usually asymptomatic until rupture, which is associated with extremely high mortality. Consequently, early detection of AAAs is of paramount importance in reducing mortality; however, most AAAs are detected by medical imaging incidentally. The aim of this study was to investigate the feasibility of machine learning-based pulse wave (PW) analysis for the early detection of AAAs using a database of in silico PWs. PWs in the large systemic arteries were simulated using one-dimensional blood flow modelling. A database of in silico PWs representative of subjects (aged 55, 65 and 75 years) with different AAA sizes was created by varying the AAA-related parameters with major impacts on PWs – identified by parameter sensitivity analysis – in an existing database of in silico PWs representative of subjects without AAA. Then, a machine learning architecture for early detection of AAAs was proposed, which was trained and tested using the new in silico PW database. The parameter sensitivity analysis revealed that the AAA maximum diameter and stiffness of the large systemic arteries were the dominant AAA-related biophysical properties that significantly influence the PW. The simulated PW indexes extracted from the database showed that the PW was not only influenced by the presence of an AAA but was also significantly affected by multiple cardiovascular parameters that compromised the detection of AAAs by using individual PW indexes. Alternatively, the trained machine learning model performed well in classifying normal and AAA conditions using digital photoplethysmogram PWs from the database. These findings suggest that machine learning-based PW analysis is a promising approach for AAA screening using PW signals acquired by wearable devices.

The compound, C27H23N3 (1), crystallizes in the triclinic system of the P-1 space group. The unit cell comprises a dimer of 1, in which the monomers are linked by two complementary hydrogen bonds between N1 and H1-C1 of another molecule. The dimers form chains along the a-axis through intermolecular interactions between the N2 acceptor atoms and C17 donors from molecules in the nearest neighbouring dimer. These interactions form extended sheets of the dimers of 1, along the ab plane. The quinolinylmeth-2-yl groups of 1 lie in almost orthogonal planes and their N1/2(q) donor atoms being away from the apical amino N3 atom.

In this paper, some new properties of the fundamental solution to the multi-dimensional space- and time-fractional diffusion-wave equation are deduced. We start with the Mellin-Barnes representation of the fundamental solution that was derived in the previous publications of the author. The Mellin-Barnes integral is used to get two new representations of the fundamental solution in form of the Mellin convolution of the special functions of the Wright type. Moreover, some new closed form formulas for particular cases of the fundamental solution are derived. In particular, we solve an open problem of representation of the fundamental solution to the two-dimensional neutral-fractional diffusion-wave equation in terms of the known special functions.

We describe a new approach for integrating multiple views of data into a common latent space using non-negative tensor factorization (NTF). This approach, which we refer to as the "Integrated Sources Model" (ISM), consists of two main steps: embedding and analysis. In the embedding step, each view is transformed into a matrix with common non-negative components. In the analysis step, the transformed views are combined into a tensor and decomposed using NTF. Noteworthy, ISM can be extended to process multi-view data sets with missing views. We illustrate the new approach using two examples: the UCI digit dataset and a public cell type gene signatures dataset, to show that multi-view clustering of digits or marker genes by their respective cell type is better achieved with ISM than with other latent space approaches. We also show how the non-negativity and sparsity of the ISM model components enable straightforward interpretations, in contrast to latent factors of mixed signs. Finally, we present potential applications to single-cell multi-omics and spatial mapping, including spatial imaging and spatial transcriptomics, and computational biology, which are currently under evaluation. ISM relies on state-of-the-art algorithms invoked via a simple workflow implemented in a Jupyter Python notebook.

The current methods for lightning risk warnings that are based on atmospheric electric field (AEF) data have a tendency to rely on single features, which results in low robustness and efficiency. Additionally, there is a lack of research on cancelling warning signals, contributing to the high false alarm rate (FAR) of these methods. To overcome these limitations, this study proposes a lightning risk warning method that incorporates enhanced empirical Wavelet transform-Adaptive Savitzky Gorey filter (EEWT-ASG) and one-dimensional morphology, using time-frequency domain features obtained through the Wavelet transform (WT). The proposed method achieved a probability of detection (POD) of 77.11%, miss alarm rate (MAR) of 22.89%, FAR of 40.19%, and critical success index (CSI) of 0.51, as evaluated on 83 lightning processes. This method can issue a warning signal up to 22 minutes in advance for lightning processes.

The effect of elastic constants, cij, on the nature (easy or difficult) of a cleavage system in YBa2Cu3O7−δ is investigated, by employing a novel three-dimensional eigenfunction expansion technique, based in part on separation of the thickness-variable and partly a modified Frobenius type series expansion technique in conjunction with the Eshelby-Stroh formalism. Out of the available three complete sets of elastic constants, the first constitutes an estimate, while the second assumes tetragonal symmetry. This leaves only the experimental measurements by resonant ultrasound spectroscopy, despite reported values of c12 and to a lesser extent, c66, being excessively high. The present investigation considers six through-thickness crack systems weakening orthorhombic YBCO mon-crystalline plates. More important, the present approach predicts whether a crack would propagate in its original plane/direction or deflect to a different one. This fracture mechanics criterion is then employed for accurate determination of the full set of elastic constants of mono-crystalline YBCO. Finally, generally unavailable results, pertaining to the through-thickness variations of stress intensity factors and energy release rates for a crack corresponding to symmetric and skew-symmetric hyperbolic cosine loads that also satisfy the boundary conditions on the bounding surfaces of an orthorhombic monocrystalline plate bridge a longstanding gap in the field.

proposal for a fundamental theory is described in which classical and quantum physics as a representation of the universe as a gigantic dendrogram are unified. The latter is the explicate order structure corresponding to the purely number-theoretical implicate order structure given by p-adic numbers. This number field was zero-dimensional, totally disconnected, and disordered. Physical systems (such as electrons, photons) are sub-dendrograms of the universal dendrogram. Measurement process is described as interactions among dendrograms; in particular, quantum measurement problems can be resolved using this process. The theory is realistic, but realism is expressed via the the Leibnitz principle of the Identity of Indiscernible. The classical-quantum interplay is based on the degree of indistinguishability between dendrograms (in which the ergodicity assumption is removed). Depending on this degree, some physical quantities behave more or less in a quantum manner (versus classic manner). Ideologically, our theory is very close to Smolin’s dynamics of difference and Rovelli’s relational quantum mechanics. The presence of classical behavior in nature implies a finiteness of the Universe-dendrogram. (Infinite Universe is considered to be purely quantum.) Reconstruction of events in a four-dimensional space type is based on the holographic principle. Our model reproduces Bell-type correlations in the dendrogramic framework. By adjusting dendrogram complexity, violation of the Bell inequality can be made larger or smaller.

Tumor growth, progression, and therapy resistance are crucial factors in the prognosis of cancer. Properties of three-dimensional tumor-like organoids (tumoroids) more closely resemble in vivo tumors compared to two-dimensionally cultured cells and are therefore effectively used for assays and drug screening. We here established a repurposed drug for novel anticancer research and therapeutics using a tumoroid-based screening system. We screened 6 pharmacologically active compounds by using an original tumoroid-based multiplex phenotypic screening system with matrix metalloproteinase 9 (MMP9) promoter-driven fluorescence reporter for the evaluation of both tumoroid formation and progression. The effects of one of the hit compounds were examined on tumor formation and progression in vitro and in vivo. Antiparkinson drug benztropine was the most effective compound uncovered by the screen. Benztropine significantly inhibited in vitro tumoroid formation, cancer cell survival, and MMP9 promoter activity. Benztropine also reduced the activity of oncogenic signaling transducers and trans-activators for MMP9, including STAT3, NF-κB, and β-catenin, and properties of cancer stem cells / cancer-initiating cells. Benztropine and GBR-12935 directly targeted the dopamine transporter DAT/SLC6A3, whose genetic alterations such as amplification were correlated with poor prognosis for cancer patients. Benztropine also inhibited tumor growth, circulating tumor cell (CTC) number, and rate of metastasis in a tumor allograft model in mice. In conclusion, we propose the repurposing of benztropine for anticancer research and therapeutics that can suppress tumor progression, CTC, and metastasis of aggressive cancers by reducing key pro-tumorigenic factors.

The city-port involves a decisive reality for the economic development of the territories and nations, capable of significantly influencing the conditions of well-being and quality of life, and of making the Circular City Model operational, preserving and enhancing seas and marine resources in a sustainable way, through the construction of appropriate production and consumption models, with attention to relations with the urban and territorial system. The Circular Economy paradigm identifies the ideal context in the city-port to rethink traditional development models and make ports driver areas for the regeneration of the city and metropolitan territories, in compliance with the EU Directive 2014/89 which considers maritime spatial planning as a tool for public authorities and stakeholders to achieve an integrated approach, promoting the development of maritime and coastal economies and the sustainable use of resources. The paper, starting from these assumptions, presents an adaptive decision-making process for the strategies development of the Naples (Italy) commercial port, aimed at re-establishing a sustainable city-port relationship and making operative Circular Economy principles.

This paper identifies a criterion for choosing the largest set of rejected hypotheses in high-dimensional data analysis where Multiple Hypothesis testing is used in exploratory research to identify significant associations among many variables. The method neither requires predetermined thresholds for level of significance, nor uses presumed thresholds for false discovery rate. The upper limit for number of rejected hypotheses is determined by finding maximum difference between expected true hypotheses and false hypotheses among all possible sets of rejected hypotheses. Methods of choosing a reasonable number of rejected hypotheses and application to non-parametric analysis of ordinal survey data are presented.

The spin magnetic moments and spin g factors (g_s = -2) of electron, muon and tau are explained based on the electric charges (EC) and lepton charges (LC) in terms of the three-dimensional quantized space model. The spin g factors of electron, muon and tau are g_s = -2 which is the sum of the EC g factor (g_EC = -1) and the LC g factor (g_LC = -1). The spin g factor (g_s = -2) of the electron is predicted by the Dirac’s equation. The orbit g factors of electron, muon and tau are g_L = g_EC = -1 from the EC g factor (g_EC = -1) without the contribution of the LC g factor (g_LC = -1). The spin g factors of the elementary fermions are calculated from the equation of g_s = g_EC + g_LC + g_CC where g_EC = EC/|EC|, g_LC = LC/|LC| and g_CC = CC/|CC|. For example, the spin g factors of the neutrinos and dark matters are g_s = -1. The spin g factors of the u and d quarks are g_s = 0 and g_s = -2, respectively. The g factor problem of neutrinos with the non-zero LC charges are solved by the LC Coulomb force of F_c(LC) ≈0. It is, for the first time, proposed that the binary motion (fluctuations) of the m_EC and m_LC masses for the electron, muon and tau leptons make the anomalous g factor. This binary motion could be originated from the virtual particle processes including the photons. Also, the weak force (beta) decay is closely related to the binary motion of the m_EC and m_LC for the electron, muon and tau leptons.

In this paper, several two-dimensional clustering scenarios are given. In those scenarios, soft partitioning clustering algorithms (Fuzzy C-means (FCM) and Possibilistic c-means (PCM)) are applied. Afterward, VAT is used to investigate the clustering tendency visually, and then in order of checking cluster validation, three types of indices (e.g., PC, DI, and DBI) were used. After observing the clustering algorithms, it was evident that each of them has its limitations; however, PCM is more robust to noise than FCM as in case of FCM a noise point has to be considered as a member of any of the cluster.

Ontological premises have profound theoretical implications. This analysis posits a single, universal structure composed of two ontic entities: discrete, quantized units of space ("Planck Spheres") and a State of Absolute Nothingness (the "SOAN"). Planck Spheres and the SOAN form a physical, ultra-high (3 x N) dimensional space ("Planck Space") and a discrete 3D space (or "4D spacetime"). Together, Planck Space and 4D spacetime form a tightly integrated ((3 x N) + 3) hyperspace (the "Dual Ontology"). Critically, a one-to-one mapping and identity exists between the Planck Spheres comprising 4D spacetime and Planck Space. The Dual Ontology conjecture supports radically simplified physical explanations for three distinct areas of theoretical physics: First, the conjecture reconciles fundamental issues in Special Relativity and quantum mechanics by explaining why single and N-body quantum states (a) dynamically evolve in 4D spacetime subject to the speed of light and (b) instantaneously collapse in a physical Planck Space that is not subject to Special Relativity. Second, the temporal and physical asymmetry between the dynamic evolution of quantum states in 4D spacetime subject to Special Relativity and the instantaneous collapse of quantum states in a Planck Space where Special Relativity is inapplicable explains quantum path irreversibility and the arrow of time. Third, at or near the heat death of 4D spacetime, the instantaneous collapse of the energy content of Planck Space and the simultaneous transition of 4D spacetime's extremely widely dispersed energy content to a non-singular, generally localized volume at t = 0 explains 4D spacetime's isotropy, homogeneity, extremely high energy, pressure, and temperature, flatness, and low gravitational entropy. It also explains the horizon and fine-tuning problems.

Alzheimer’s Disease (AD) has no cure. Earlier, we showed that partial inhibition of mitochondrial complex I (MCI) with small molecule CP2 induces adaptive stress response activating multiple neuroprotective mechanisms. Chronic treatment reduced inflammation, improved synaptic and mitochondrial functions, and blocked neurodegeneration in symptomatic APP/PS1 mice, a translational model of AD. Here, using serial block-face scanning electron microscopy (SBFSEM) and three-dimensional (3D) EM reconstructions combined with Western blot analysis and next-generation RNA sequencing, we demonstrate that CP2 treatment also restores mitochondrial morphology and mitochondria-endoplasmic reticulum (ER) communication in the APP/PS1 mouse brain. Using 3D EM volume reconstructions, we show that mitochondria in AD dendrites exist primarily as mitochondria-on-a-string (MOAS). Compared to other morphological phenotypes, MOAS are extensively enveloped in the ER membranes forming multiple mitochondria-ER contact sites (MERCS) known to contribute to abnormal lipid and calcium homeostasis. CP2 treatment specifically reduced MOAS formation, consistent with improved energy homeostasis in the brain, with concomitant reduction in MERCS, ER stress, and improved lipid homeostasis. These data provide novel information on the role MOAS play in AD and additional support for further development of partial MCI inhibitors as disease modifying strategy for AD.

Congenital breast asymmetry represents a particular challenge to the classical techniques of plastic surgery due to a young group of patients. This study compares traditional breast augmentation using silicone implants to the more innovative lipograft technique regarding long-term results. To achieve this, we not only captured subjective parameters like satisfaction with outcome and symmetry, but also objective parameters such as breast volume and anthropometric measurements. Objective examination was performed manually and by using the Vectra® H2 photogrammetry scanning system. Patients who underwent implant augmentation and lipograft both showed no significant differences in patient´s satisfaction with surgical outcome (p = 0.55) and symmetry (p = 0.69). Furthermore, a breast symmetry of 93 % in both groups was reported. Likewise, no statistically significant volume difference between left and right breast was observed in both groups (p<0.41). However, on average, lipograft patients needed 1.3 procedures more until the desired result was achieved. In contrast, patients treated with implant-based breast augmentation usually need several implant changes during their life. In conclusion, both methods should be considered for patients with congenital breast asymmetry.

The Combined Theoretical Signal (CTS) model, with a 90% or higher validity rate, stands as the first validated universal model integrating gravity and quantum physics. This manuscript presents the final validation and enhancement of the CTS model using recent pulsar-sourced gravitational wave data (J2322+2057, J2234+0611, and J1012+5307) and strain data from GW150914 and GW151226. Our findings demonstrate a significant alignment between theoretical predictions and observational data, positioning CTS as a groundbreaking framework with profound implications for theoretical physics and cosmology. The CTS model not only bridges the gap between gravity and quantum mechanics but also offers a new perspective on the formation of massive black holes, the Quantum Cosmic Fabric (QCF), and the structure of the universe as an interconnected hierarchical tree of universes.

A method for generating fluoroscopic (time-varying) volumetric images using patient-specific motion models derived from 4-dimensional cone-beam CT (4D-CBCT) images is developed. 4D-CBCT images acquired immediately prior to treatment have the potential to accurately represent patient anatomy and respiration during treatment. Fluoroscopic 3D image estimation is done in two steps: 1) deriving motion models and 2) optimization. To derive motion models, every phase in a 4D-CBCT set is registered to a reference phase chosen from the same set using deformable image registration (DIR). Principal components analysis (PCA) is used to reduce the dimensionality of the displacement vector fields (DVFs) resulting from DIR into a few vectors representing organ motion found in the DVFs. The PCA motion models are optimized iteratively by comparing a cone-beam CT (CBCT) projection to a simulated projection computed from both the motion model and a reference 4D-CBCT phase, resulting in a sequence of fluoroscopic 3D images. Patient datasets were used to evaluate the method by estimating the tumor location in the generated images compared to manually defined ground truth positions. Experimental results showed that the average tumor mean absolute error (MAE) along the superior-inferior (SI) direction and the 95th percentile in two patient datasets were (2.29 mm and 5.79 mm) for patient 1 and (1.89 mm and 4.82 mm) for patient 2. This study has demonstrated the feasibility of deriving 4D-CBCT-based PCA motion models that have the potential to account for the 3D non-rigid patient motion and localize tumors and other patient anatomical structures on the day of treatment.

We apply principles of Gibbs phase plane chemistry to and across the entire ocean-atmospheric interface. Surface tension increments support a two dimensional, tangential pressure well known to determine rates of bulk gas, bubble, salt, spray and momentum transfer plus both sensible and latent heat fluxes. Hence it is worth asking whether tension mapping follows from current understanding of two dimensional composition. A history is provided dating back centuries and demonstrating that detrital organic macromolecules are central; subtle surfactant functional variation creates a microforcing field which dissipates turbulent energy at the sub-meter scale. Since we have just distributed major biopolymeric classes emitted as primary organic aerosol, further climate links can be established by considering full planar thermochemistry. Organic microlayer behaviors are reviewed with attention to confined, analog phase transitions among two dimensional “solid, liquid, (and) gaseous” states serving as elasticity indicators. We also discuss surfactant properties of general marine dissolved organic carbon, demonstrating that only proteins and lipids are capable of occupying significant local micro-area. The literature often suggests albumin and stearic acid as best proxies, and so we distribute their concentrations through multilevel global ecodynamic simulations. Consensus distributions are obtained in order to control adsorptive equilibria. Working from conservation of planar free energy, a parametric equation of state is devised relating excess coverage to the surface pressure-modulus. Constant settings for the proxy pair are drawn from laboratory study, and they successfully reproduce frequencies for surfactant solid-to-gas occurrence in ambient compression experiments. Functionally resolved organic measurements are rare and so we group them into super-ecological province tables showing that our bulk concentration estimates are reasonable. Outputs are then fed into a coverage-tension-elasticity code. Resulting contours traverse the critical range for piston velocity, bubble-spray and damping effects on either a regional or seasonal basis. There is also a possibility for widespread microlayer crystallization in polar seas. The concepts are a direct extension of our organic aerosol work, and the two approaches could be inserted into Earth System Models in tandem. Uncertainties in the logic are enumerated and include kinetic and thermochemical factors at multiple scales. But the problems are reducible through molecular modeling coupled to renewed laboratory and field study. Connections to marine colloids-gels, microlayer iron chelation, and linings of the ice channel network are discussed additionally.

The unresolved territories are privileged places for the proliferation of degradation phenomena that affect the environment and human well-being. The impacts of their critical conditions go beyond the limits of the damaged urban fragments, involving the built environment, society, economy, culture and conditioning quality of life. This paper proposes a methodological approach to landscape design supported by an evaluation framework to orient strategic design planning with specific attention to unresolved territories consistent with circular economy perspective. The circular city principles are applied to spatial planning of landscape, by operationalising Ecosystem Services, Landscape Services, and Ecosystem Disservices, as interpretative categories for multi-dimensional regenerative strategies. Starting from the theoretical framework, the objective of the analysis is to implement an approach to the regenerative design of landscapes of waste, defined wastescapes. The industrial area of East Naples is the case study where an incremental evaluative approach has been defined to design scenarios to provide services and values, aimed to drive the conversion in a regenerativescape. A multi-criteria analysis through PROMETHEE-GAIA method has been implemented to compare the base case scenario with two incremental new scenarios and identify situated sustainable priorities.

This study presents a method for the rapid and accurate generation of two-dimensional (2D) orthomosaic maps using selected image data collected by drone-captured video. The focus is on developing a real-time method capable of creating maps more quickly than image selection. The scale-invariant feature transform (SIFT) algorithm is applied to drone images to extract features in various scale regions. For feature point matching, a matching method based on the fast library for approximate nearest neighbors (FLANN) was adopted. A comparison of the computational speed of the FLANN with that of the k-nearest neighbors (KNN) and brute force matcher during the matching process revealed FLANN's superior capability for real-time data processing. The random sample consensus (RANSAC) algorithm was employed to enhance the accuracy of the matching by removing outliers, effectively identifying and eliminating mismatches, and reinforcing the reliability of feature point matching. The combination of SIFT, FLANN, and RANSAC algorithms demonstrates the capacity to process drone-captured image data in real time, facilitating the generation of precise 2D orthomosaic maps. The proposed method was assessed and validated using imagery obtained as the drone executed curvilinear and straight flight paths, confirming its accuracy and operational efficiency concurrently with the image capture process.

Tea (Camellia sinensis) is one of the most popular beverages worldwide. Many types of tea prod-ucts continuously emerge in an endless stream; so, the classification or verification of tea be-comes more difficult. Aroma is a vital indicator of tea quality. The present study deals with the identification of aroma compounds in 18 different kinds of tea belonging to three typical tea va-rieties, including green tea, oolong tea, and black tea, using GC-IMS and GC×GC-O-MS. More-over, the clustering of all 18 tea samples and the in-depth correlation analysis between sensory evaluation and instrumental data were performed using the PCA and OPLS-DA. The results re-vealed that in all 18 kinds of tea, a total of 85 aroma compounds were detected by GC-IMS, whereas 318 were detected by GC×GC-O-MS. The PCA result revealed that green tea, oolong tea, and black tea could be clearly separated based on their peak areas. The OPLS-DA result showed that a total of 49 aroma compounds with VIP value > 1.0 could be considered as the potential in-dicators to quickly classify or verify tea types. This study not only compared the aroma differ-ences across different types of teas, but also provided ideas for the rapid monitoring of tea qual-ity and variety.

The paper briefly reviews the Clifford algebras of space Cl(3,0) and anti-space Cl(0,3) with a particular focus on the paravector representation, emphasizing the fact that both algebras have an isomorphic center represented just by two coordinates. Since the paravector representation allows assigning the scalar element of grade 0 to the time coordinate, we consider the relativity in such two-dimensional spacetime for a uniformly accelerated frame with the constant acceleration 3Hc. Using the Rindler coordinate transformations in two-dimensional spacetime and then applying it to Minkowski coordinates, we obtain the FLRW metric, which in the case of the Clifford algebra of space Cl(3,0) corresponds to the anti-de Sitter (AdS) flat (k=0) case, the negative cosmological term and an oscillating model of the universe. The approach with anti-Euclidean Clifford algebra Cl(0,3) leads to the de Sitter model with the positive cosmological term and the exact form of the scale factor used in modern cosmology.

Recent advances in nanomaterials have been heavily influenced by low-dimensional nanocarbon allotropes. In particular, carbyne has attracted attention for its potential as a true one-dimensional carbon chain with sp1 hybridization. To maximize the capabilities of this material, we employ a focused data-driven inverse design approach based on the carbon nanomaterials genome concept. This involves using deep learning neural network models to identify key descriptors tied to desired properties, enabling property prediction and reverse engineering of nanocarbons. Our iterative approach entails: (i) gathering growth/property data on nanostructures; (ii) identifying informative numerical/categorical predictors; (iii) developing deep learning models mapping descriptors to properties; (iv) refining models with new insights; (v) determining required descriptors/conditions for target properties via inverse mapping; (vi) validating models by synthesizing predicted nanostructures; and (vii) enhancing models with validation data. This allows uncovering hidden growth-property connections, precisely tuning nanocarbons for desired attributes. We introduce new methodologies including exciting synergistic effects, synchronizing atomic vibrations, active screen plasma, energy-driven transformations, surface acoustic micro/nano-manipulation, doping and directed self-assembly to expose relationships and integrate insights into the inverse design flow. This research promises to accelerate discovery of next-generation low-dimensional nanocarbons with exceptional properties and applications.

The human antibody response to influenza virus infection or vaccination is as complicated as it is essential for protection against flu. The constant antigenic changes of the virus to escape human herd immunity hinder the yearly selection of vaccine strains since it is hard to predict which virus strains will circulate for the coming flu season. A "universal" influenza vaccine that could induce broad cross-influenza subtype protection would help to alleviate this burden. However, the human antibody response is intricate and often obscure, with factors like antigenic seniority or original antigenic sin "OAS", and back-boosting ensuring that each person mounts a unique immune response to infection or vaccination with any new influenza virus strain. Notably, the effects of existing antibodies on cross-protective immunity after repeated vaccinations are unclear. More research is needed to characterize the mechanisms at play, but traditional assays such as hemagglutinin inhibition (HAI) and microneutralization (MN) are excessively limited in scope and too resource-intensive to effectively meet this challenge. In the past ten years, new multiple dimensional assays (MDAs) have been developed to help overcome these problems by simultaneously measuring antibodies against a large panel of influenza hemagglutinin (HA) proteins with a minimal amount of sample in a high throughput way. MDAs will likely be a powerful tool for accelerating the study of the humoral immune response to influenza vaccination and the development of a universal influenza vaccine.

Using multi-color flow cytometry analysis, we studied the immunophenotypical differences between leukemic blasts/LSCs from patients with AML/MDS and hematopoietic stem and progenitor cells (HSPCs) from patients in complete remission (CR) following their successful treatment. The panel of markers included CD34, CD38, CD45RA, CD123 as representatives for a hierarchical hematopoietic stem and progenitor cell (HSPC) classification as well as programmed death ligand 1 (PD-L1). Rather than restricting the evaluation on a 2- or 3-dimensional analysis, we applied a t-distributed stochastic neighbor embedding (t-SNE) approach to get a deeper insight and segregation between leukemic and normal HPSCs. For that purpose, we created a t-SNE map, which resulted in the visualization of 27 cell clusters based on their similarity concerning the composition and intensity of antigen expression. Two of these clusters were “leukemia-related” containing a great proportion of CD34⁺/CD38^- hematopoietic stem cells (HSCs) or CD34⁺ cells with a strong coexpression of CD45RA/CD123, respectively. CD34⁺ cells within the latter cluster were also highly positive for PD-L1 reflecting their immunosuppressive capacity. Beyond this proof of principle study, the inclusion of additional markers will be helpful to refine the differentiation between normal HSPCs and leukemic cells, particularly in the context of minimal disease detection and antigen-targeted therapeutic interventions. Furthermore, we suggest a protocol for the assignment of new cell ensembles in quantitative terms, via a numerical value, the Pearson coefficient, based on a similarity comparison of the t-SNE pattern with a reference.

Three-dimensional quantized space model is newly introduced. Quantum mechanics and relativity theory are explained in terms of the warped three-dimensional quantized spaces with the quantum time width (Dt=t_q). The energy is newly defined as the 4-dimensional space-time volume of E = cDtDV in the present work. It is shown that the wave function of the quantum mechanics is closely related to the warped quantized space shape with the space time-volume. The quantum entanglement and quantum wave function collapse are explained additionally. The special relativity theory is separated into the energy transition associated with the space-time shape transition of the matter and the momentum transition associated with the space-time location transition. Then, the quantum mechanics and the general relativity theory are about the 4-dimensional space-time volume and the 4-dimensional space-time distance, respectively.

The swift progress in low-dimensional nanocarbons, specifically in the realm of the authentic one-dimensional carbon chain featuring sp¹ hybridization, has unveiled exciting prospects for integrating them into cutting-edge technologies across diverse industries. To fully harness their potential, precise control over the growth process is necessary to obtain desired nanostructure and functionality. However, optimizing properties through traditional approaches has been challenging due to complex interactions. To address this, we implement a focused data-driven strategy for nanocarbon inverse design by leveraging a state-of-the-art data-driven nanocarbon genome approach (NCGA). By uncovering relationships between growth parameters and resultant traits, this serves as an expedient catalyst in engineering nanostructures with tailored attributes. We introduce an extensive array of technological approaches aimed at precisely controlling the growth process. These methods encompass stimulating and precisely adjusting synergistic effects, coordinating atomic vibrations at a collective level through the implementation of multilayer nano-interfaces, harnessing the potential of active screen plasma surface engineering, utilizing nano-patterning and allotropic phase transformations, incorporating heteroatom doping, and effectively directing self-assembly. These aim to unlock nanomaterials' latent potential and reveal novel neural pathways within the data-driven NCGA, enhancing its predictive capabilities. Specifically, triggering self-organization during growth can potentially unlock previously unexplored neural pathways. The data-driven NCGA offers a paradigm shift, accelerating discovery and design of nanocarbons with optimized, application-specific properties.

This paper explores the Wilsonian renormalization group of probe-D-branes, focusing on the Dirac-Born-Infeld (DBI) action. The analysis involves deriving the constant of motion and formulating the Wilsonian renormalization group equation. The study extends to the infrared regime, considering both zero and non-zero temperatures. Additionally, thermodynamic aspects related to the functional form of the grand potential are discussed. The investigation provides insights into the renormalization of probe-D-branes and their thermodynamic properties.

Spinors are used for the computation of probability in quantum mechanics. They are treated as elements of a complex vector space in contrast to a real vector space. Spinor theory is abstract mathematics with an ambiguous interpretation. The overall phase of spinors does not affect the computation of probability in quantum mechanics. If two spinors have an overall phase of imaginary number i, then they can be treated like vectors in a real vector space. The square of the magnitude of the number, which is the sum of dot product and the term having basis vector i of cross product of two such vectors, is equal to the probability computed using the corresponding spinors. Therefore, the geometry of such spinors can be easily depicted in a three-dimensional space like vectors in a real vector space. Spinors are not isotropic vectors in Hilbert space. The sum of dot product and cross product of two complex numbers is equivalent to the quotient of the division of one complex number by another. Similarly, the sum of dot product and cross product of two vectors is the quotient of the division of one vector by another. Using the rules of division of vectors, we can find the rules of multiplication of vectors. The rules of multiplication and division of basis vector i match the rules of multiplication and division of imaginary number i. Therefore, basis vector i is nothing but imaginary number i. Multiplication of dot and cross product of two vectors to the second vector will give us the components of the first vector that are parallel and orthogonal to the second vector. Vectors are also made up of complex numbers like spinors. The reason for finding dot product of complex numbers in the process of computing the probability is to ignore the overall phase in contrast to the phase difference. This is misconstrued as complex vector space in quantum mechanics. 3-D number which is nothing but a spinor with new notation is an extension of vector algebra. It can have a real number as a term in addition to the terms i, j and k. In all other respects, it is a vector. 3-D numbers are part of the number system like real numbers and complex numbers. The real numbers are one dimensional numbers, the complex numbers are two-dimensional numbers and the 3-D numbers as well as vectors and spinors are three-dimensional numbers. As polarisation and spin are one and the same, we can straight away apply 3-D numbers to polarisation of light. 3-D numbers will greatly simplify the study of areas of science where three-dimensional space is involved.