Manufacturing systems need to meet I4.0 guidelines to deal with uncertainty in scenarios of turbulent demand for products. The engineering concepts to define the service’s resources to manufacture the products will be more flexible, ensuring the possibility of re-planning in operation. These can follow the engineering paradigm based on capabilities. The virtualization of industry components and assets achieves the RAMI 4.0 guidelines and (I4.0C), which describes the Asset Administration Shell (AAS). However, AAS are passive components that provide information about I4.0 assets. The proposal of specific paradigms is exposed for managing these components, as is the case of multi-agent systems (MAS) that attribute intelligence to objects. The implementation of resource coalitions with evolutionary architectures (EAS) applies cooperation and capabilities’ association. Therefore, this work focuses on designing a method for modeling the asset administration shell (AAS) as virtual elements orchestrating intelligent agents (MAS) that attribute cooperation and negotiation through contracts to coalitions based on the engineering capabilities concept. The systematic method suggested in this work is partitioned for the composition of objects, AAS elements, and activities that guarantee the relationship between entities. Finally, Production Flow Schema (PFS) refinements are applied to generate the final Petri net models (PN) and validate them with Snoopy simulations. The results achieved demonstrate the validation of the procedure, eliminating interlocking and enabling liveliness to integrate elements behavior.

Deep Learning (DL) oriented document processing is widely used in different fields for extraction, recognition, and classification processes from raw corpora of data. The article examines the application of deep learning approaches, based on different neural network methods, including Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN). The compared models were combined with two different word embedding techniques, namely: Bidirectional Encoder Representations from Transformers BERT and Gensim Word2Vec. The models are designed to evaluate the performance of architectures based on neural network techniques for the classification of CVs of Moroccan engineering students at ENSAK(National School of Applied Sciences of Kenitra, Ibn Tofail University). The used dataset included resumes collected from engineering students at ENSAK in 2023 for a project on the employability of Moroccan engineers in which new approaches were applied, especially machine learning, deep learning, and big data. Accordingly, 867 resumes were collected from five specialties of study (Electrical Engineering, Networks and Systems Telecommunications, Computer Engineering, Automotive Mechatronics Engineering, Industrial Engineering). The results revealed good performance of the proposed models based on the BERT embedding approach compared to models based on the Gensim Word2Vec embedding approach. Accordingly, the CNN-GRU/Bert model achieved slightly better accuracy with 0.9251 compared to other hybrid models.

This research investigates the potential application of generative language models, especially ChatGPT, in aviation safety analysis as a means to enhance the efficiency of safety analyses and accelerate the time it takes to process incident reports. In particular, ChatGPT was leveraged to generate incident synopses from narratives, which were subsequently compared with ground truth synopses from the Aviation Safety Reporting System (ASRS) dataset. The comparison was facilitated by using embeddings from Large Language Models (LLMs), with aeroBERT demonstrating the highest similarity due to its aerospace-specific fine-tuning. A positive correlation was observed between synopsis length and their cosine similarity. In a subsequent phase, human factor issues involved in incidents as identified by ChatGPT were compared to human factor issues identified by safety analysts. A concurrence rate of 61% was found, with ChatGPT demonstrating a cautious approach towards attributing human factor issues. Finally, the model was used to attribute incidents to relevant parties. As no dedicated ground truth column existed for this task, a manual evaluation was conducted. ChatGPT attributed the majority of incidents to the Flight Crew, ATC, Ground Personnel, and Maintenance. This study opens new avenues for leveraging AI in aviation safety analysis.

This paper presents a proof-of-concept study on the biocolonization of 3D-printed hydroxyapatite scaffolds with mesenchymal stem cells (MSCs). Three-dimensional (3D) printed biomimetic bone structure made of Calcium Deficient HydroxyApatite (CDHA) intended as future bone graft was made from newly developed composite material for FDM printing. The biopolymer polyvinyl alcohol serves in this material as a thermoplastic binder for 3D molding of the printed object with a passive function and is completely removed during sintering. The study presents the material, the process of fused deposition modeling (FDM) of CDHA scaffolds and its post-processing at three temperatures (1200, 1300, 1400 °C), as well it evaluates the cytotoxicity and biocompatibility of scaffolds with MTT and LDH release assays after 14 days. The study also includes a morphological evaluation of the cellular colonization with scanning electron microscopy (SEM) in two different filament orientations (rectilinear and gyroid). The results of the MTT assay showed that the tested material was not toxic, and cells were preserved in both orientations, with most cells present on the material fired at 1300°C. Results of the LDH release assay showed a slight increase in LDH leakage from all samples. Visual evaluation of SEM confirmed the ideal post-processing temperature of the 3D-printed FDM framework for samples fired at 1300°C and 1400°C, with a porosity of 0.3 mm between filaments. In conclusion, the presented fabrication and colonization of CDHA scaffolds have great potential to be used in the tissue engineering of bones.

Proteins having a variety of functions play many essential roles in maintaining various life activities in organisms. Various methods, by which new protein functions can be artificially produced, have progressed rapidly upon development in recombinant DNA technology and effective screening techniques. However, the obtainable scope of the new functions has been restricted in a narrow range, because only functions of presently existing proteins can be used. On the other hand, it has been considered that it would be impossible to create an entirely new protein, which does not show any meaningful homology with any other amino acid sequences of previously existing proteins. The reason is because one amino acid sequence for a protein cannot be selected out from an extraordinary large amino acid sequence diversity as ~10130. As a matter of course, it is impossible to design an amino acid sequence of a protein in advance and a gene encoding the protein cannot be also formed through random process. Nevertheless, extant organisms have generated a variety of entirely new proteins in some way to make full use of them. This means that extant organisms have equipped a mechanism with which entirely new proteins can be produced under the present core life system composed of protein, tRNA (genetic code) and gene. In this article, first I introduce the mechanism, with which entirely new proteins are created in extant organisms, and further propose a novel strategy for application of the mechanism to protein engineering through creation of entirely new proteins, which could contribute to development of various industries.

Ultrafast laser 3D lithography based on non-linear light-matter interactions, widely known as multi-photon lithography (MPL), offers unrivaled precision rapid prototyping and flexible additive manufacturing options. 3D printing equipment based on MPL are already commercially available, yet there is still no comprehensive understanding of factors determining spatial resolution, accuracy, fabrication throughput, repeatability, and standardized metrology methods for the accurate characterization of the produced 3D objects and their functionalities. The photoexcitation mechanisms, spatial-control or photo-modified volumes, and the variety of processable materials are topics actively investigated. The complexity of the research field is underlined by limited understanding and fragmented knowledge of light-excitation and material response. Research to date has only provided case-specific findings on photoexcitation, chemical modification, and material characterization of the experimental data. In this review, we aim to provide a consistent and comprehensive summary of the existing literature on photopolymerization mechanisms under highly confined spatial and temporal conditions, where, besides the excitation and cross-linking, parameters such as diffusion, temperature accumulation, and the finite amount of monomer molecules start to become of critical importance. Key parameters such as, photoexcitation, polymerization kinetics, and the properties of the additively manufactured materials at the nanoscale in 3D are examined, whereas, the perspectives for future research and as well as emerging applications are outlined.

Background/Objectives: Thyroid cancer, particularly well-differentiated thyroid cancer, is one of the most prevalent endocrine malignancies, with a rising incidence. Although it generally has a favorable prognosis, recurrence is common. Accurate prediction of recurrence is crucial for optimizing treatment plans and improving patient outcomes. This study aimed to advance the state-of-the-art in thyroid cancer recurrence prediction by refining feature engineering techniques and exploring a diverse ensemble of machine learning algorithms and an artificial neural network, using the differentiated thyroid cancer dataset from the UCI Machine Learning Repository; Methods: Various unsupervised data engineering methods, such as dimensionality reduction and clustering, were employed to enhance feature quality and mitigate noise, using stratified 10-fold cross-validation. The best-performing dimensionality reduction techniques were used to build classification model pipelines employing each of several machine learning models and an artificial neural network. The performance of these classification pipelines were assessed using metrics sensitive to class imbalance; Results: Principal Component Analysis and Truncated Singular Value Decomposition achieved superior clustering performance and moderate variances in their first principal components. Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbors, and Feedforward Neural Network models all achieved high performance, with Logistic Regression pipelines demonstrating balanced accuracy, F1 score, AUC, sensitivity, specificity, and precision—all greater than 0.93 on the test set and slightly lower in 10-fold cross-validation. Gradient Boosting classification pipelines performed the lowest, though still with respectable metrics; Conclusions: This study shows that employing feature engineering techniques like Principal Component Analysis or Truncated Singular Value Decomposition in Logistic Regression, Random Forest, Feedforward Neural Networks, Support Vector Machine, and K-Nearest Neighbors classification pipelines can improve thyroid cancer recurrence prediction accuracy and reliability, supporting more personalized treatment strategies in post-treatment patients.

This work aims to integrate the circular economy (CE) into learning experiences to enhance learning outcome development in Higher Education (HE). Building on existing active learning research, this work aligns CE initiatives—particularly waste reduction and elimination— with the Sustainable Development Goals (SDGs). The study proposes an experiential learning framework to cultivate citizenship commitment, sustainability impact, and engineering learning outcomes through realistic problem-solving in the supply chains of small and medium enterprises (SMEs). Using the ADDIE instructional design model and Kolb’s experiential learning cycle, the approach is illustrated in a case study within an undergraduate industrial engineering module. Based on student opinion surveys and achievement metrics, results indicate that the experience significantly enhanced student engagement and motivation. This research contributes to the broader dissem-ination of CE concepts in HE and supports the advancement of SDG #4 Quality Education and #12 Sustainable Consumption and Production. Future research will explore the application of this learning experience across diverse industries and assess its impact on student learning outcomes in various educational contexts.

Aristotle vs. Ringelmann was a discussion between two distinct research teams from the ETH Zürich who argued whether the productivity of Open Source software projects scales sublinear or superlinear with regard to its team size. This discussion evolved around two publications, which apparently used similar techniques by sampling projects on GitHub and running regression analyses to answer the question about superlinearity. Despite the similarity in their research methods, one team around Ingo Scholtes reached the conclusion that projects scale sublinear, while the other team around Didier Sornette ascertained a superlinear relationship between team size and productivity. In subsequent publications, the two authors argue that the opposite conclusions may be attributed to differences in project populations, since 81.7% of Sornette’s projects have less than 50 contributors. Scholtes, on the other hand, sampled specifically projects with more than 50 contributors. This publication compares the research from both authors by replicating their findings, thus allowing for an evaluation of how much project sampling actually accounted for the differences between Scholtes’ and Sornette’s results. Thereby, the discovery was made that sampling bias only partially explains the discrepancies between the two authors. Further analysis led to the detection of instrumentation biases that drove the regression coefficients in opposite directions. These findings were then consolidated into a quantitative analysis, indicating that instrumentation biases contributed more to the differences between Scholtes’ and Sornette’s work than the selection bias suggested by both authors.

This article reviews recent innovations in piezoelectric actuators, then indicates the future research targets. In the materials, the piezoelectric performances of relaxor-lead titanate (PZN-PT, PMN-PT) single crystals have been improved significantly by an AC electric poling technique. Also new loss characterization techniques facilitate developing high-power density piezoelectrics. The RMS vibration velocity reaches 0.6 m/s in the k₃₁ mode in lead zirconate titanate (PZT) nowadays, which corresponds to power density 30 W/cm³. Pb-free materials are urged due to the recent political regulation on toxic materials. In device designs, the original multilayer actuators (MLA) with Ag/Pd electrode have been switched to the ones with Cu or pure Ag, in order to suppress the heat generation, then to enhance the device power density. Piezoelectric micro-electro-mechanical-systems (MEMS’s) started to utilize the <1 0 0> oriented epitaxial films of rhombohedral PZT compositions, which exhibits enhanced piezoelectric properties. Also for higher-power applications, aero-sol deposition (AD) methods have been introduced to 10 -thick films. Regarding drive/control techniques in piezo-actuators, three developments are on-going, that is, “negative capacitance” power supplies for driving off-resonance (pseudo-DC) piezo-actuators, capacitive AC power supplies for efficiently driving resonance piezo-actuators, and DC/DC converters for harvesting electric energy from high-impedance piezoelectric devices under noise vibration. The applications of piezoelectric actuators in the 21^st century seem to be oriented toward “politico-engineering” with four keywords, “cooperation, protection, reduction, and continuation”. The sustainability technologies include the following piezoelectric items: (a) Pb-free piezoelectrics, (b) ultrasonic technology for decomposing hazardous materials, (c) reduction of contamination gas, (d) piezoelectric energy harvesting, and (e) device improvement with high energy-efficiency. On the other hand, the following crisis technologies have been developed with piezoelectrics: (a) detection and neutralization technologies of infectious/epidemic diseases such as anthrax, (b) autonomous unmanned underwater, aerial, and land vehicles with piezoelectric actuators, (c) pin-point target weapons, (d) high voltage supplies with piezoelectric transformers, and (e) compact energy sources for remote actuating/sensing systems.

Early reports demonstrated the presence of cells with stem-like properties in bone marrow, with these cells having both hematopoietic and mesenchymal lineages. Over the years, various investigations have purified and characterized mesenchymal stromal/stem cells (MSCs) from different human tissues as cells with multi-lineage differentiation potential under the appropriate conditions. Due to their appealing characteristics and potential, MSCs are leveraged in many applications including medicine, oncology, bioprinting and as recent as treatment of COVID-19. To date, reports indicate mesenchymal stromal/stem cells have varied differentiation capabilities into different cell types and demonstrate immunomodulating and anti-inflammatory properties. Reports indicate that different MSCs microenvironments or niche and the resulting heterogeneity may influence their behavior and differentiation capacity. The potential clinical applications of mesenchymal stromal/stem cells have led to an avalanche of research reports on their properties and hundreds of clinical trials being undertaken. The future looks bright and promising for mesenchymal stem cell research with many clinical trials under way to prove their utility in many applications and in the clinic. This report provides an update on the potential broader use of mesenchymal stromal/stem cells, review early observations of the presence of these cells in the bone marrow and their magnificent differentiation capabilities and immunomodulation.

Direct electron transfer (DET)-type bioelectrocatalysis, which couples electrode reactions and catalytic functions of redox enzymes without any redox mediator, is one of the most intriguing subjects studied over the past decades in the field of bioelectrochemistry. In order to realize the DET-type bioelectrocatalysis and to improve the performance, nanostructures of the electrode surface have to be carefully tuned for each enzyme. In addition, enzymes can also be tuned by protein engineering approach for the DET-type reaction. This review summarizes the resent progresses in this field of the research, while taking into consideration of the importance of nanostructure of electrodes as well as redox enzymes. Described are basic concepts and theoretical aspects of DET-type bioelectrocatalysis, significance of nanostructures as scaffolds for DET-type reactions, protein engineering approached for DET-type reactions, and concepts and facts of bidirectional DET-type reactions, from a cross-disciplinary viewpoint.

Integrated photovoltaic-fuel cell (IPVFC) systems have the potential to contribute to sustainable energy production for grid and off-grid applications. While there are studies focusing on fundamental science and designs for IPVFC systems, there are scarcely studies that have focused on the manufacturability of IPVFC systems, which is certainly the pathway for commercialisation of the systems. This study explores manufacturing strategies that can be considered for exploiting a family of eleven IPVFC systems. A survey and systems thinking were used to investigate the potential modularisation and supply chain management to achieve optimal lean and agile manufacturing strategy. Results show that the Photovoltaic-Thermal-Separate Converter/Inverter-Battery System received 25% of the responses. The optimal manufacturing strategy depends on the overall business strategy of the firm. Although 17% preference for System 1 is significant compared to four members of the family of IPVFC systems (Systems 2, 6, 8 and 9) that received only 2% of the responses, it indicates that there is a likelihood that the demand for System 1 will be among the top 42% of the total demand. Overall, this study provides new insights on how the family of IPVFC systems can contribute to realising greater access to clean energy, by extension contributing to the net-zero efforts using solar energy and solar hydrogen.

The authors undertook pilot studies to assess the accuracy of the reverse engineering process of worn nonstandard spur gears (non-standardized modulus value, unusual profile/pressure angle, high or low teeth), using conventional techniques and measuring instruments. Eight gears were tested, the module of which ranges from 1.020 to 4.98 mm and the number of teeth from 13 to 42. The key parameter, the basic pitch, was selected to estimate the accuracy of the process. The goal is to determine the value of the profile angle. Eleven models were proposed to estimate the nominal tolerance field, using various types of random data distribution. The tested gears were made in IT grade: 6, 7, 8 and 9 according to DIN 3961. Vernier disc micrometers with various measurement ranges were used for research. It has been shown that the nominal module does not have to be treated as a random variable in the population. EIG (Equation of Identity) was developed, allowing conversion of any gear with specific values of geometric parameters into an identical gear with alternative values of these parameters. The directions and degrees of shifts of the estimated tolerance fields relative to the nominal field were determined. The most effective estimating model was selected in the context of the percentage filling of the nominal tolerance field, taking into account the symmetric Student-Fisher distribution with a confidence level of 60%. Further directions of the research activities were described.

We introduce the Concept-Model-Graph-View Cycle (CMGVC). The CMGVC facilitates coherent architecture analysis, reasoning, insight, and decision-making based on conceptual models that are transformed into a generic, robust graph data structure (GDS). The GDS is then transformed into multiple views of the model, which inform stakeholders in various ways. This GDS-based approach decouples the view from the model and constitutes a powerful enhancement of model-based systems engineering (MBSE). The CMGVC applies the rigorous foundations of Category Theory, a mathematical framework of representations and transformations. We show that modeling languages are categories, drawing an analogy to programming languages. The CMGVC architecture is superior to direct transformations and language-coupled common representations. We demonstrate the CMGVC to transform a conceptual system architecture model built with the Object Process Modeling Language (OPM) into dual graphs and a stakeholder-informing matrix that stimulates system architecture insight.

Three-dimensional (3D) printing technologies are transforming the design and manufacture of components and products across a variety of disciplines, however their application in the construction industry is still limited. Material deposition processes can achieve infinite geometries and have advanced from rapid prototyping and model-scale markets to their application in fabricating functional products, large objects and the construction of full-scale buildings. Many international projects have recently been realized and the construction industry is beginning to utilise these dynamic technologies. The potential advantages for integrating 3D printing into house construction are significant, these include the capacity for mass customization of designs and parameters for functional and aesthetic purposes, reduction in construction waste from highly precise material placement, and the use of recycled waste products in layer deposition materials. With the ultimate goal of improving construction efficiency and decreasing building costs, applying Strand7 Finite Element Analysis software, a numerical model was designed specifically for 3D printing in a cement mix incorporated with recycled waste product High Density Polyethylene (HDPE) and found that construction of an arched truss-like roof was structurally feasible without the need for steel reinforcements. The lab sizes prototypes were manufactured based on the destined numerical model by using a 3D printing technology. Currently available 3D printing technologies can be adopted for building construction and this paper discusses the applications, advantages, limitations and future directions of 3D printing as an innovative and viable solution for affordable house construction.

Segmental bone defects caused by trauma, infection, tumor resection or osteoporotic fractures present significant surgical treatment challenges. Host bone autograft is considered the gold standard to restore function but comes with the cost of harvest site comorbidity. Allograft bone is a secondary option but has its own limitations in incorporation with host bone as well as cost. Therefore, developing new bone tissue engineering strategies to treat bone defects is critically needed. In the past three decades, the use of stem cells delivered with different scaffolds or growth factors for bone tissue engineering has made tremendous progress. Many varieties of stem cells have been isolated from different tissues for use in bone tissue engineering. This review summarizes the progress in using different postnatal stem cells including bone marrow mesenchymal stem cells, muscle-derived stem cells, adipose-derived stem cells, dental pulp stem cells/periodontal ligament stem cells, periosteum stem cells, umbilical cord-derived stem cells, peripheral blood stem cells and urine-derived stem cells for bone tissue engineering and repair. This review also summarizes the progress using exosomes or extracellular vesicles delivered with various scaffolds for bone repair. The advantages and disadvantages of each type of stem cell are also discussed and explained in detail. It is hopeful in the future that these preclinical results will translate into new therapies for bone defect repair.

Recombinant human collagen peptide (RCP) is a recombinantly created xeno-free biomaterial enriched in RGD (arginine-glycine-aspartic acid) sequences, with good processability that is being investigated for regenerative medicine applications. Recently, the biocompatibility and osteogenic ability of β-TCP/RCP (RCP granules combined with β-tricalcium phosphate (TCP) submicron particles) were demonstrated. In the present study, β-TCP/RCP was implanted into experimental periodontal tissue defects (three-walled bone defect) created in beagle dogs to investigate tissue responses and subsequent regenerative effects. Micro computed tomography image analysis at 8 weeks postoperatively showed that the amount of new bone after β-TCP/RCP graft was significantly greater (2.2 fold, P&lt;0.05) than that of the control (no graft) group. Histological findings showed that the transplanted β-TCP/RCP induced active bone-like tissue formation including TRAP-positive and OCN-positive cells as well as bioabsorbability. Ankylosis did not occur, and periostin-positive periodontal ligament-like tissue formation was observed. Histological measurements revealed that β-TCP/RCP implantation formed 1.7-fold more bone-like tissue and 2.1-fold more periodontal ligament-like tissue than the control, and significantly suppressed gingival recession and epithelial downgrowth (P&lt;0.05). These results suggest that β-TCP/RCP is effective as a periodontal tissue regenerative material.

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.

In the paper, by virtue of a Lévy–Khintchine representation and an alternative integral representation for the weighted geometric mean, the authors establish a Lévy–Khintchine representation and an alternative integral representation for the Toader–Qi mean. Moreover, the authors also collect an probabilistic interpretation and applications in engineering of the Toader–Qi mean.

This review paper delves into the multifaceted aspects of pineapple cultivation, focusing on morphology studies, harvesting challenges, and mechanization efforts. Pineapple, renowned for its commercial value and unique flavor, holds a prominent position in global agriculture, with India alone producing 19.64 lakh tonnes annually. However, the hilly terrains of regions like northeastern India pose significant hurdles to traditional harvesting methods, necessitating innovative solutions. Understanding the morphology of pineapple plants is crucial for optimizing cultivation practices and designing efficient harvesting equipment. Insights from various studies shed light on leaf structure, fruit maturity, and growth patterns, providing valuable information for engineering tailored solutions. Mechanization endeavors, including semi-mechanized harvesting machines and semi-manual harvesters, demonstrate promising results in improving efficiency and reducing labor-intensive practices. Despite challenges such as increased crop losses and operational costs, continued research and development in mechanization hold the key to sustainable pineapple cultivation. This paper underscores the importance of integrating morphology studies and mechanization efforts to address the unique challenges faced by pineapple farmers, particularly in hilly terrains, and highlights the need for ongoing innovation to ensure the long-term sustainability of pineapple cultivation.

COVID-19 is one of the deadliest respiratory diseases, and its emergence caught the pharmaceutical industry off guard. While vaccines have been rapidly developed, treatment options for infected people remain scarce, and COVID-19 poses a substantial global threat. This study presents a novel workflow to predict robust druggable targets against emerging RNA viruses using metabolic networks and information of the viral structure and its genome sequence. For this purpose, we implemented pymCADRE and PREDICATE to create tissue-specific metabolic models, construct viral biomass functions and predict host-based antiviral targets from more than one genome. We observed that pymCADRE reduces the computational time of flux variability analysis for internal optimizations. We applied these tools to create a new metabolic network of primary bronchial epithelial cells infected with SARS-CoV-2 and identified enzymatic reactions with inhibitory effects. The most promising reported targets were from the purine metabolism, while targeting the pyrimidine and carbohydrate metabolisms seemed to be promising approaches to enhance viral inhibition. Finally, we computationally tested the robustness of our targets in all known variants of concern, verifying our targets’ inhibitory effects. Since laboratory tests are time-consuming and involve complex readouts to track processes, our workflow focuses on metabolic fluxes within infected cells and is applicable for rapid hypothesis-driven identification of potentially exploitable antivirals concerning various viruses and host cell types.

Transition between the unfolded and native states of the ordered globular proteins is accompanied by accumulation of several intermediates, such as pre-molten globule, wet molten globule, and dry molten globule. Structurally equivalent conformations can serve as native functional states of intrinsically disordered proteins. This overview captures the characteristics and importance of these molten globules in both structured and intrinsically disordered proteins. It also discusses examples of engineered molten globules. The formation of these intermediates under the conditions of macromolecular crowding and their interactions with nanomaterials are also reviewed.

The Model-View-Controller (MVC) architecture, a cornerstone of modern software engineering, offers a robust framework for developing dynamic user interfaces. This paper conducts a broad analysis of MVC’s deployment across various sectors, emphasizing its critical role in technological advancement and market integration. By examining implementation strategies in web development, mobile applications, and enterprise solutions, the study illustrates how MVC enhances application manageability, promotes efficient code reuse, and facilitates parallel development. The paper explores the adoption of MVC principles within major technologies such as Java, .NET, and JavaScript frameworks, underlining its widespread acceptance. Additionally, the analysis covers the preferential use of MVC frameworks within diverse market sectors including technology enterprises, educational institutions, e-commerce, and financial services, which leverage MVC for superior data management and user experience. Emerging trends such as the shift towards the Model-View-ViewModel (MVVM) architecture and the integration of MVC with artificial intelligence and machine learning technologies are also discussed. This comprehensive review not only reaffirms MVC’s enduring relevance but also its adaptive evolution in response to emerging technological demands and market needs.

In the present work, a novel and the robust computational investigation is carried out to estimate solubility of different acids in supercritical carbon dioxide. Four different algorithms such as radial basis function artificial neural network, Multi-layer Perceptron (MLP) artificial neural network (ANN), Least squares support vector machine (LSSVM) and adaptive neuro-fuzzy inference system (ANFIS) are developed to predict the solubility of different acids in carbon dioxide based on the temperature, pressure, hydrogen number, carbon number, molecular weight, and acid dissociation constant of acid. In the purpose of best evaluation of proposed models, different graphical and statistical analyses and also a novel sensitivity analysis are carried out. The present study proposed the great manners for best acid solubility estimation in supercritical carbon dioxide, which can be helpful for engineers and chemists to predict operational conditions in industries.

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.

Emulsion stabilisation of base layers surfaced with chip seals often proves problematic with chips punching into the base and early distress. This can be aggravated by the use of modified binders that restricts the evaporation of moisture from pavement layers. The introduction of New-age (Nano) Modified Emulsion (NME) stabilisation has the advantage that water is chemically repelled from the stabilised layer resulting in an accelerated development of strength. A need was identified to evaluate the early life performance of selected chip seals, together with identified binders. Three different chip seal surfacings with unconventional modified binders were constructed and evaluated using Accelerated Pavement Testing (APT) with the MMLS3. The objectives of the experimental design and testing were to evaluate binder performance, early loss of chips before chip orientation at low temperatures, punching of the chips into the NME stabilised base, deformation characteristics of a Cape seal and the effect of the use of a standard normal modified binder. This paper contains details of the NME base layer, the binder and seal selection and the test results. It is shown that a cost-effective thin chip seal in combination with a suitable binder can be used on a NME stabilised base with confidence.

The use of naturally available materials not conforming to traditional specifications or standards, in the base and sub-base layers of road pavement structures, stabilised with New-age (Nano) Modified Emulsions (NME), have been tested, implemented and successfully verified through Accelerated Pavement Testing (APT) in South Africa. This was made possible through the development and use of a design procedure addressing fundamental principles and based on scientific concepts, which are universally applicable. The understanding of and incorporation of the chemical interaction between the mineralogy of the materials and a NME stabilising agent (compatibility between the chemistry of the reactive agents and material mineralogy) into the design approach is key to achieving the required engineering properties. Stabilised materials evaluation is done using tests indicative of the basic engineering properties (physics) of compressive strengths, tensile strengths and durability. This article describes the basic materials design approach developed to ensure that organofunctional nano-silane modified emulsions can successfully be used for pavement layer construction utilising naturally available materials, at a low risk. The enablement of the use of naturally available materials in all pavement layers can have a considerable impact on the unit cost and life-cycle costs of road transportation infrastructure. TRANSLATE with x English ArabicHebrewPolish BulgarianHindiPortuguese CatalanHmong DawRomanian Chinese SimplifiedHungarianRussian Chinese TraditionalIndonesianSlovak CzechItalianSlovenian DanishJapaneseSpanish DutchKlingonSwedish EnglishKoreanThai EstonianLatvianTurkish FinnishLithuanianUkrainian FrenchMalayUrdu GermanMalteseVietnamese GreekNorwegianWelsh Haitian CreolePersian TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster Portal Back

The article titled "Immunoglobulins and Its Applications" provides a comprehensive overview of the structure, function, and therapeutic significance of immunoglobulins, also known as antibodies. These vital proteins, produced by plasma cells in response to immunogens, are essential components of the immune system, playing a critical role in recognizing and neutralizing pathogens such as bacteria and viruses. The article elaborates on the five primary classes of immunoglobulins (IgA, IgD, IgE, IgG, and IgM), each distinguished by its unique structural and functional properties. It discusses the molecular composition of immunoglobulins, highlighting the significance of their variable and constant regions in antigen binding. The paper also explores the diverse immune functions mediated by these proteins, including precipitation, agglutination, and neutralization of antigens. Additionally, the article emphasizes the therapeutic applications of immunoglobulins, particularly in treating immune deficiencies and other diseases. Advances in monoclonal antibody technology have significantly expanded the utility of immunoglobulins in both research and medicine, leading to the development of innovative therapeutic approaches such as humanized monoclonal antibodies and bifunctional antibodies. This comprehensive analysis underscores the critical role of immunoglobulins in both natural immunity and therapeutic interventions.

SARS-CoV-2 Omicron variant spike RBD epitopes in complex with spike (S) -protein D614G mutations now predominate globally and increase infectivity by assembling more functional S proteins into the virion. On average, homeopathic medicine contains less than one molecule per dose on average. Single variable SphericalHarmonicY, LaguerreL, WhittakerM, SphericalBesselJ, LegendreP, LegendreQ, LaguerreL, ChebyshevT, and Hypergeometric1F1 Functions pFqarise in connection with the power series solution of the Schrodinger equation or in the summation of perturbation expansions in quantum mechanics. A quantum interpretation of the homeopathic method is presented here through a novel algebraic topology approach to supersymmetry and symmetry breaking in quantum fields and quantum gravity theory with the aim of developing a wide range of physical-based drug design applications in a Small Molecule Quantized Water Memory Network suggesting that the Hidden Black Hole Paradox of Information Loss might be solved under suitable conditions.