It has been more than a century since Erlang published his paper in 1909. The classical queueing system for the M/D/c queue has been investigated by many authors. The key is to find the roots of an equation using explicit or numerical methods. We present an alternate form of explicit solution(s) of this/these root(s) using the Lambert-W function in this paper. In addition, the unsaturated probabilities of the number of customers in the system is derived explicitly in terms of this/these root(s). Finally, the mean number of customers in the system and the mean queueing delay are obtained from these probabilities. The exact queueing delay formula for the M/D/c queue is expressed in closed-form in terms of the root(s). Numerical results show that they are in excellent agreement with the numerical results published by Seelen, Tijms and Van Hoorn.

Abstract: In order to accelerate deployment of distributed recycling by providing low-cost feed stocks of granulated post-consumer waste plastic, this study analyzes an open source waste plastic granulator system. It is designed, built and tested for its ability to convert post-consumer waste, 3-D printed products and waste into polymer feedstock for recyclebots of fused particle/granule printers. The technical specifications of the device are quantified in terms of power consumption (380 to 404W for PET and PLA, respectively) and particle size distribution. The open source device can be fabricated for less than USD$2000 in materials. The experimentally-measured power use is only a minor contribution to the overall embodied energy of distributed recycling of waste plastic. The resultant plastic particle size distributions were found to be appropriate for use in both recyclebots and direct material extrusion 3-D printers. Simple retrofits are shown to reduce sound levels during operation by 4dB-5dB for the vacuum. These results indicate that the open source waste plastic granulator is an appropriate technology for community, library, makespace, fab lab or small business-based distributed recycling.

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.

Nutrition, diabetes, and mental disorders are interconnected and significantly impact an individual's overall health and well-being. This comprehensive review aims to explore the complex interplay between nutrition, diabetes, and mental disorders, highlighting the latest research findings in this field. While the influence of nutrition on the development and management of both diabetes and mental disorders is widely recognized, there remains a gap in understanding the intricate interplay between nutrition, mental health, and diabetes. Diabetes is associated with an increased risk of mental disorders, including depression, anxiety, and cognitive decline. Mental disorders can also contribute to the development of diabetes through various mechanisms including increased stress, poor self-care behaviors, and adverse effects on glucose metabolism. Thus, the mechanisms linking nutrition, diabetes, and mental disorders are complex and multi-factorial. Inflammation, oxidative stress, gut microbiota alterations, and neuroendocrine dysregulation have emerged as potential pathways that may mediate the relationship between nutrition, diabetes, and mental disorders. Additionally, deficiencies in essential nutrients such as omega-3 fatty acids, vitamin D, B vitamins, zinc, chromium, magnesium, and selenium have been implicated in the pathogenesis of both diabetes and mental disorders. Our findings indicate that the use of personalized dietary interventions and targeted nutrient supplementation can improve metabolic and mental health outcomes in patients with type 2 diabetes.

Space-time evolution is briefly explained by using the 3-dimensional quantized space model (TQSM) based on the 4-dimensional (4-D) Euclidean space. The energy (E=cDtDV), charges (|q|= cDt) and absolute time (ct) are newly defined based on the 4-D Euclidean space. The big bang is understood by the space-time evolution of the 4-D Euclidean space but not by the sudden 4-D Minkowski space-time creation. The big bang process created the matter universe with the positive energy and the partner anti-matter universe with the negative energy from the CPT symmetry. Our universe is the matter universe with the negative charges of electric charge (EC), lepton charge (LC) and color charge (CC). This first universe is made of three dark matter -, lepton -, and quark - primary black holes with the huge negative charges which cause the Coulomb repulsive forces much bigger than the gravitational forces. The huge Coulomb forces induce the inflation of the primary black holes, that decay to the super-massive black holes. The dark matter super-massive black holes surrounded by the normal matters and dark matters make the galaxies and galaxy clusters. The spiral arms of galaxies are closely related to the decay of the 3-D charged normal matter black holes to the 1-D charged normal matter black holes. The elementary leptons and quarks are created by the decay of the normal matter charged black holes, that is caused by the Coulomb forces much stronger than the gravitational forces. The Coulomb forces are very weak with the very small Coulomb constants (k₁(EC) = k_dd(EC) ) for the dark matters and very strong with the very big Coulomb constants (k₂(EC) = k_nn(EC)) for the normal matters because of the non-communication of the photons between the dark matters and normal matters. The photons are charge dependent and mass independent. But the dark matters and normal matters have the similar and very weak gravitational forces because of the communication of the gravitons between the dark matters and normal matters. The gravitons are charge independent and mass dependent. Note that the three kinds of charges (EC, LC and CC) and one kind of mass (m) exist in our matter universe. The dark matters, leptons and quarks have the charge configurations of (EC), (EC,LC) and (EC,LC,CC), respectively. Partial masses of elementary fermions are calculated, and the proton spin crisis is explained. The charged black holes are not the singularities.

Solid tumor cells have an altered metabolism that can protect them from cytotoxic lymphocytes. The antidiabetic drug metformin modifies tumor cell metabolism and several clinical trials are testing its effectiveness for the treatment of solid cancers. The use of metformin in hematologic cancers has received much less attention, although allogeneic cytotoxic lymphocytes are very effective against these tumors. We show here that metformin induces expression of Natural Killer G2-D (NKG2D) ligands (NKG2DL) and intercellular adhesion molecule-1 (ICAM-1), a ligand of the lymphocyte function-associated antigen 1 (LFA-1). This leads to enhance sensitivity to cytotoxic lymphocytes. Overexpression of antiapoptotic Bcl-2 family members decrease both metformin effects. The sensitization to activated cytotoxic lymphocytes is mainly mediated by the increase on ICAM-1 levels, which favors cytotoxic lymphocytes binding to tumor cells. Finally, metformin decreases the growth of human hematological tumor cells in xenograft models, mainly in presence of monoclonal antibodies that recognize tumor antigens. Our results suggest that metformin could improve cytotoxic lymphocyte-mediated therapy.

For Visually impaired People (VIPs), the ability to convert text to sound can mean a new level of independence or the simple joy of a good book. With significant advances in Optical Character Recognition (OCR) in recent years, a number of reading aids are appearing on the market. These reading aids convert images captured by a camera to text which can then be read aloud. However, all of these reading aids suffer from a key issue – the user must be able to visually target the text and capture an image of sufficient quality for the OCR algorithm to function – no small task for VIPs. In this work, a Sound-Emitting Document Image Quality Assessment metric (SEDIQA) is proposed which allows the user to hear the quality of the text image and automatically captures the best image for OCR accuracy. This work also includes testing of OCR performance against image degradations, to identify the most significant contributors to accuracy reduction. The proposed No-Reference Image Quality Assessor (NR-IQA) is validated alongside established NR-IQAs and this work includes insights into the performance of these NR-IQAs on document images.

C-type lectin CD209/DC-SIGN and CD209L/L-SIGN proteins are distinct cell adhesion and pathogen recognition receptors that mediate cellular interactions and recognize a wide range of pathogens including, viruses such as SARS, SARS-CoV-2, bacteria, fungi and parasites. Pathogens exploit CD209L family proteins to promote infection and evade the immune recognition system. CD209L and CD209 are widely expressed in SARS-CoV-2 target organs and can contribute to infection and pathogenesis. CD209L family receptors are highly susceptible to alternative splicing and genomic polymorphism, which may influence virus tropism and transmission in vivo. The carbohydrate-recognition domain (CRD) and the neck/repeat region represent the key features of CD209L family proteins that are also central to facilitating cellular ligand interactions and pathogen recognition. While, the neck/repeat region is involved in oligomeric dimerization, the CRD recognizes the mannose containing structures present on specific glycoproteins such as those found on the SARS-CoV-2 spike protein. Considering the role of CD209L and related proteins in diverse pathogen recognition, this review article discusses the recent advances in the cellular and biochemical characterization of CD209 and CD209L and their roles in viral uptake, which has important implications in understanding the host-pathogen interaction, the viral pathobiology and driving vaccine development of SARS-CoV-2.

Centrifuges are commonly required devices in medical diagnostics facilities as well as scientific laboratories. Although there are commercial and open source centrifuges, costs of the former and required electricity to operate the latter, limit accessibility in resource-constrained settings. There is a need for low-cost, human-powered, verified and reliable lab-scale centrifuge. This study provides the designs for a low-cost 100% 3-D printed centrifuge, which can be fabricated on any low-cost RepRap-class fused filament fabrication (FFF) or fused particle fabrication (FPF)-based 3-D printer. In addition, validation procedures are provided using a web camera and free and open source software. This paper provides the complete open source plans including instructions for fabrication and operation for a hand-powered centrifuge. This study successfully tested and validated the instrument, which can be operated anywhere in the world with no electricity inputs obtaining a radial velocity of over 1750rpm and over 50N of relative centrifugal force. Using commercial filament the instrument costs about US$25, which is less than half of all commercially available systems; however, the costs can be dropped further using recycled plastics on open source systems for over 99% savings. The results are discussed in the contexts of resource-constrained medical and scientific facilities.

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.

In this paper, we study the effect of parasitic channel on electrical characteristics and power consumptions of vertically stacked gate-all-around (GAA) silicon nanosheet (NS) MOSFETs of the bulk and silicon-on-insulator (SOI) substrates. Through deliberate control of the bottom parasitic channel coverage, we systematically assess its influence on DC, AC, and RF characteristics including power consumption. The observed findings highlight a significant differentiation among the bottom parasitic channels: GAA (i.e., control sample with 100% coverage), omega-shaped gate NS (70% and 80% coverages), and FinFET (60% coverage). Compared with the device with a 60% coverage channel, the device with a 100% coverage channel possesses 3520% and 8030% normalized leakage current improvement for both NFET and PFET under the bulk substrate, respectively. Compared with bulk-substrate devices, the SOI-substrate devices further significantly improve the leakage current. Due to enhanced off-state current leakages in the 100% covered bulk-substrate device, the static power demonstrates a notable improvement (about 98.1%), compared to 60% covered devices. Hence, the utilization of a 60% covered channel will result in a degradation of both device and circuit performance. The observation of GAA NS MOSFET indicates that an increase in the bottom parasitic channel coverage ratio leads to a positive impact on power consumption and leakage currents. The examination presented here is useful in the fabrication of stacked GAA NS MOSFETs.

The influence and importance of research and development (R&D) for business sustainability have gained increasing interests, especially in the high-tech sector. However, the efforts of R&D might cause complex and mixed impacts on the financial results considering the associated expenses. Thus, this study aims to examine how R&D efforts may influence business to improve its financial performance considering the dual objectives: the gross and the net profitability. This research integrated a rough-set-based soft computing technique and multiple criteria decision-making (MCDM) methods to explore this complex and yet valuable issue. A group of public listed companies from Taiwan, all in the semiconductor sector, was analyzed as a case study. Initially, more than 30 variables were considered, and the adopted soft computing technique retrieved 14 core attributes—for the dual profitability objectives—to form the evaluation model. The importance of R&D for pursuing superior financial prospects is confirmed, and the empirical case demonstrates how to guide an individual company to plan for improvements to achieve its long-term sustainability by this hybrid approach.

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.

Short-range Internet of Things (IoT) sensor nodes operating at 2.4 GHz need to provide ubiquitous wireless sensor networks (WSNs) with energy-efficient, wide-range output power (POUT) and fully integrated on a single chip for wireless body area networks (WBANs) and wireless personal area networks (WPANs) using low-power Bluetooth (BLE) and Zigbee standards. The proposed fully integrated transmitter (TX) utilizes a digitally controllable current-mode class-D (CMCD) power amplifier (PA) with a second harmonic distortion (HD2) suppression to reduce VCO pulling in an integrated system while meeting harmonic limit regulations. The CMCD PA is divided into 7-bit slices that can be reconfigured between differential and single-ended topologies. Duty cycle distortion compensation is performed for HD2 suppression, and an HD2 rejection filter and a modified C-L-C low-pass filter (LPF) reduce HD2 further. Implemented in a 28 nm CMOS process, the TX achieves a wide POUT range of from 12.1 to -31 dBm and provides a maximum efficiency of 39.8% while consuming 41.1 mW at 12.1 dBm POUT. The calibrated HD2 level is -82.2 dBc at 9.93 dBm POUT, resulting in a transmitter figure of merit (TX_FoM) of -97.52 dB. Higher order harmonic levels remain below -41.2 dBm even at 12.1 dBm POUT, meeting regulatory requirements.

This study describes the development of an automated bag valve mask (BVM) compression system, which, during acute shortages and supply chain disruptions can serve as a temporary emergency ventilator. The resuscitation system is based on the Arduino controller with a real-time operating system installed on a largely RepRap 3-D printable parametric component-based structure. The cost of the system is under $170, which makes it affordable for replication by makers around the world. The device provides a controlled breathing mode with tidal volumes from 100 to 800 milliliters, breathing rates from 5 to 40 breaths/minute, and inspiratory-to-expiratory ratio from 1:1 to 1:4. The system is designed for reliability and scalability of measurement circuits through the use of the serial peripheral interface and has the ability to connect additional hardware due to the object-oriented algorithmic approach. Experimental results demonstrate repeatability and accuracy exceeding human capabilities in BVM-based manual ventilation. Future work is necessary to further develop and test the system to make it acceptable for deployment outside of emergencies in clinical environments, however, the nature of the design is such that desired features are relatively easy to add with the test using protocols and parametric design files provided.

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.

Pediatric onset multiple sclerosis (POMS), characterized by the onset of multiple sclerosis before the age of 18, is gaining increased recognition. Approximately 5 percent of MS cases manifest before the age of 18, with less than 1 percent occurring before the age of 10. Despite its rarity, pediatric MS exhibits distinct characteristics, with an association between younger age at onset and a comparatively slower disease progression. Despite this slower progression, individuals with POMS historically reach disability milestones at earlier ages than those with adult-onset multiple sclerosis. While various immunomodulatory agents demonstrate significant benefits in MS treatment, such as reduced relapse rates and slower accumulation of brain lesions on magnetic resonance imaging (MRI), the majority of disease-modifying therapies (DMTs) commonly used in adult MS lack evaluation through pediatric clinical trials. Current evidence is predominantly derived from observational studies. This comprehensive review aims to consolidate existing knowledge on the mechanisms of action, efficacy, safety profiles, and recommended dosages of available DMTs specifically in the context of pediatric MS. Furthermore, the review outlines recent advancements and explores potential medications still in developmental stages, providing a thorough overview of the current landscape and future prospects for treating POMS.

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.

The affinity constant, also known as the equilibrium constant, binding constant, equilibrium association constant, or the reciprocal value, the equilibrium dissociation constant (K_d), can be considered as one of the most important characteristics for any antibody-antigen pair. Many methods based on different technologies have been proposed and used to determine this value. However, since a very large number of publications and commercial datasheets do not include this information, significant obstacles in performing such measurements seem to exist. In other cases where such data are reported, the results have often proved to be unreliable. This situation may indicate that most of the technologies available today require a high level of expertise that does not seem to be available in many laboratories. In this paper, we present a simple approach based on standard immunoassay technology that is easy and quick to perform. It relies on the effect that the molar IC₅₀ approaches the K_d value in the case of infinitely small concentrations of the reagent concentrations. A two-dimensional dilution of the reagents leads to an asymptotic convergence to K_d. The approach has some similarity to the well-known checkerboard titration used for the optimization of immunoassays. A well-known antibody against the FLAG peptide, clone M2, was used as a model system and the results were compared with other methods. This approach could be used in any case where a competitive assay is available or can be developed. The determination of an affinity constant should belong to the crucial parameters in any quality control of antibody-related products and assays and should be mandatory in papers using immunochemical protocols.

The use of chimeric antigen receptors (CAR-T cells) for the treatment of patients with malignant haematological diseases has become a well-established application, for conditions such as refractory or relapsed B cell acute lymphoblastic leukaemia (B-ALL), B cell lymphomas (BCL), and multiple myeloma (MM). Nearly 35,000 patients have received autologous CAR-T cells for the treatment of these conditions only in the USA. Since their approval by the Food and Drug Administration (FDA) in 2017, over 1200 clinical trials have been initiated globally and there are at least 10 different CAR-T cells with approval by different regulatory agencies around the globe. In the USA the Food and Drug Administration (FDA) has approved 6 commercial CAR-T cells that are widely distributed worldwide. At the time of this writing, several clinical trials have been performed in patients with solid tumours such as glioblastoma, renal and pancreatic cancer, as well as in patients with autoimmune conditions such as systemic lupus erythematosus (SLE), idiopathic inflammatory myositis (IIM), and systemic sclerosis (SS). There are also several studies showing the potential benefit of CAR-T cells for other non-malignant diseases such as asthma and even fungal infections. In this review, without pretending to cover all the current areas of treatments with CAR-T cells, we offer a brief summary of some of the most relevant aspects of the use of CAR-T cells for some of these conditions.

The Hyper-Torus Universe Model (HTUM) proposes a novel framework to unify quantum mechanics, cosmology, and consciousness. This paper introduces HTUM's core concept: a higher-dimensional hyper-torus encompassing all possible states of existence. HTUM suggests that the universe functions as a quantum system where all outcomes are inherently connected, with consciousness playing a significant role in actualizing reality. The model introduces several key innovations, including the universe's 4-dimensional toroidal structure (4DTS), providing a new geometric interpretation of spacetime. HTUM presents dark matter and dark energy as nonlinear probabilistic phenomena within this toroidal framework, offering a unique perspective on their nature and interactions. The model proposes a mechanism for wave function collapse through universal self-observation, bridging quantum mechanics and gravity. It introduces a Topological Vacuum Energy Modulator (TVEM) function, offering a potential resolution to the cosmological constant problem. Furthermore, HTUM integrates consciousness as a fundamental aspect of the universe, influencing quantum state actualization and potentially resolving the hard problem of consciousness. The model explores the implications of a timeless singularity and the emergence of time as a product of causal relationships within the toroidal structure. This framework addresses critical challenges in modern physics, including the nature of quantum entanglement, the origin and fate of the universe, and the relationship between mind and matter. The paper discusses the mathematical formulation of HTUM, its implications for quantum mechanics and cosmology, and its potential to bridge the gap between science and philosophy. We present detailed predictions for the cosmic microwave background (CMB) power spectrum, gravitational wave signatures, and large-scale structure formation, demonstrating how HTUM differs from the standard ΛCDM model. Our analysis reveals distinctive oscillatory patterns in the relative difference between HTUM and ΛCDM predictions, with deviations reaching up to ±20% at certain scales. The paper also explores the unification of fundamental forces within the HTUM framework, proposing novel approaches to particle physics and quantum field theory. It introduces the concept of unified mathematical operations, suggesting a more holistic approach to mathematical thinking that aligns with the model's interconnected view of the universe. Additional testable predictions and proposed experimental approaches are discussed, including advanced space-based tests and laboratory experiments probing quantum gravity effects. These specific, quantifiable predictions offer multiple avenues for empirical validation, highlighting HTUM's potential to be distinguished from ΛCDM and other alternative cosmological models. HTUM represents a significant new perspective in our understanding of the universe, inviting further research and exploration into the nature of reality, consciousness, and the fundamental structure of the cosmos. By providing a comprehensive framework that unifies various aspects of physics, cosmology, and consciousness studies, HTUM offers a promising path toward a more complete understanding of our universe.