This study investigated the efficiency of scale-propellers, typically used on small drones. A scale-propeller is accepted as having a diameter of 7 to 21 inches. The Ukraine War has demonstrated the utility of relatively small, low-cost first-person view (FPV) drones, which are attritable. This investigation outlines the development of a MATLAB optimisation code, based on minimum induced loss propeller theory, which calculates the optimal chord and twist distribution for a chosen propeller operating in known flight conditions. The MATLAB code includes a minimum Reynolds number functionality, which provides the option to alter the chord distribution to ensure the entire propeller is operating above a set threshold value of Reynolds (>100,000), as this has been found to be a transition point between low and high section lift-to-drag ratios. Additional functions allow plotting of torque and thrust distributions along the blade. The results have been validated on experimental data taken from an APC ‘Thin Electric’ 10” x 7” propeller, where it was found that both the chord and twist distributions were accurately modelled. The MATLAB code resulted in a 16% increase in the maximum propulsive efficiency. Further work will investigate a direct interface to SolidWorks to aid rapid propeller manufacturing capability.

Proteins are the main active molecules of Life. While natural proteins play many roles, as enzymes or antibodies for example, there is a need to go beyond the repertoire of natural proteins to produce engineered proteins that precisely meet application requirements, in terms of function, stability, activity or other protein capacities. Computational Protein Design aims at designing new proteins from first principles, using full-atom molecular models. However, the size and complexity of proteins require approximations to make them amenable to energetic optimization queries. These approximations make the design process less reliable and a provable optimal solution may fail. In practice, expensive libraries of solutions are therefore generated and tested. In this paper, we explore the idea of generating libraries of provably diverse low energy solutions by extending Cost Function Network algorithms with dedicated automaton-based diversity constraints on a large set of realistic full protein redesign problems. We observe that it is possible to generate provably diverse libraries in reasonable time and that the produced libraries do enhance the Native Sequence Recovery, a traditional measure of design methods reliability.

The recent outbreak of the new virus in Wuhan city, China from the sea food market has led to the identification of a new strain called the corona virus and named as novel corona virus (2019-nCoV) belonging to Coronaviridae family. This has created major havoc and concern due to the mortality of 250 persons and affecting more than 10,000 people. This virus causes sudden fever, pneumonia and also kidney failure. In this study a computational approach is proposed for drug and vaccine design. The spike protein sequences were collected from a protein database and analysed with various bioinformatics tools to identify suitable natural inhibitors for the N-terminal receptor binding domain of spike protein. Also, it is attempted to identify suitable vaccine candidates by identifying B-Cell and T-cell epitopes. In the drug design, the tanshinone Iia and methyl Tanshinonate were identified as natural inhibitors based on the docking score. In the vaccine design, B-cell epitope VLLPLVSSQCVNLTTRTQLPPAYTN was found to have the highest antigenicity. FVFLVLLPL of MHC class-I allele and FVFLVLLPL of MHC class-II allele were identified as best peptides based on a number of alleles and antigencity scores. The present study identifies natural inhibitors and putative antigenic epitopes which may be useful as effective drug and vaccine candidates for the eradication of novel corona virus.

Due to the vast conformational space proteins can adopt, accurate and efficient prediction of protein structure remains still a challenging task, coupled with the intricacies of interatomic interactions and the limitations of current computational models in effectively navigating this complex molecular landscape. Additionally, the lack of comprehensive experimental data for all protein structures further exacerbates the difficulty in reliable machine learning-based prediction of the three-dimensional conformations of the proteios building block of life. Geometrically, Cartesian coordinate system (CCS, X, Y and Z) and spherical coordinate system (SCS, ρ, θ and φ) are two interconvertible coordinate systems, and are like two sides of one coin. Since the beginning of Protein Data Bank (PDB) in 1971, CCS has been the default approach to specify atomic positions with X, Y and Z in PDB. In this manuscript, therefore, I present a novel method for the reversible spherical geometric conversion of protein backbone structure coordinate matrices to three independent vectors: ρ, θ and φ. This reversible conversion facilitates lossless extraction of essential structural features from protein backbone structural data, enabling the development of advanced novel algorithms for protein structure prediction in future. In short, this inter-atomic SCS approach offers a comprehensive yet efficient means of representing protein backbone geometry, leveraging spherical coordinates to capture spatial relationships in a compact and intuitive inter-atomic manner, and to provide a robust framework for reversible feature extraction for the ongoing efforts in advancing the field of protein structure prediction, the holy grail of computational structural biology.

In the domain of drug discovery and design, the acquisition of precise structural and intermolecular Kd data for biological molecules is paramount. However, conventional methods for obtaining such data are often beset by challenges including high costs, time intensiveness, and inherent limitations in scalability and diversity. This article puts forward a high-throughput approach for in silico generation of structural and intermolecular binding affinity (Kd) data, which keeps exploring the uncharted territories of drug discovery & design by harnessing computational methodologies to synthetically generate diverse datasets. Through a meticulously designed workflow encompassing molecular structural modeling, structural biophysics-based calculations of intermolecular binding affinity (Kd), this innovative methodology transcends the constraints of traditional experimentation, offering a cost-effective, scalable, and efficient alternative. By simulating molecular interactions and binding interfaces and predicting binding affinities with reasonable accuracy, this approach not only expedites the drug development process but also enables the exploration of vast molecular space, thereby facilitating the discovery of novel therapeutics beyond conventional drug modality as a form factor. Moreover, the versatility of synthetic data extends beyond virtual screening and lead optimization, encompassing applications such as dataset augmentation, model validation, and benchmarking against experimental data. This article elucidates the conceptual underpinnings, methodological intricacies, validation strategies, and potential ramifications of in silico generated data, heralding a paradigm shift in drug discovery paradigms. By fostering synergy between computational and experimental research domains, this innovative approach promises to accelerate the pace of drug discovery, enhance the robustness of predictive models, and pave the way for transformative advancements in the entire pharmaceutical industry.

Phosphodiesterase type 5 (PDE5) inhibitors are widely used in the treatment of various conditions, including erectile dysfunction and pulmonary hypertension. Despite their clinical efficacy, these drugs quite often cause visual disturbances due to off-target effects on Phosphodiesterase type 5 (PDE6) in the retina. This review explores the application of a general intermolecular binding affinity calculator (GIBAC)-based binding selectivity strategy in the design of PDE5 inhibitors, aiming to enhance binding selectivity and minimize visual side effects. This GIBAC strategy integrates computational structural biological approaches to iteratively refine drug-target binding affinities, thereby improving target specificity and the structural biophysical limit for the efficacy-safety balance of PDE5 inhibitors. Through detailed analysis of PDE5’s and PDE6’s biological role and the molecular mechanisms underlying visual disturbances, this article underscores the necessity of target binding selectivity in PDE5 inhibitor design in future. Additionally, this article discusses the practical applications of GIBAC in computational drug discovery and design, along with the future directions and the potential for GIBAC to transform PDE5 inhibitor development, ultimately enhancing therapeutic outcomes and patient safety. Finally, this article calls for a GIBAC-based paradigm shift in computational drug discovery and design towards the continued development of the pharmaceutical industry.

Currently, there is a high interest in smart sensors and integrated composite materials in various industries such as construction, aviation, automobile, medical, information technology, communication, and manufacturing. Here, a new conceptual design for a force and temperature sensor system is developed by integrating fiber optic Bragg grating sensors embedded within composite materials, and a mathematical model is proposed that allows one to estimate strain and temperature based on signals obtained from optical Bragg gratings. This is important for understanding the behaviors of sensors under different conditions and for creating effective monitoring systems. Describing the strain gradient distribution, especially considering different materials with different values of Young's modulus, provides insight into how different materials respond to applied forces and temperature changes. The shape of the strain gradient distribution was obtained, which is a quadratic function with a maximum value of 1500 µ, with a maximum value at the center of the lattice and a symmetrically decreasing strain value with distance from the central part of the fiber Bragg grating. With axial strain at the installation site of the Bragg grating sensor under applied force values ranging from 10 to 11 N, the change in strain was linear. As a result of theoretical research, it was found that the developed system with fiber-optic sensors based on Bragg gratings embedded in composite materials is resistant to external influences and temperature changes.

Light is a crucial environmental factor that influences the phenotypic development of plants. Despite extensive studies on the physiological, biochemical, and molecular mechanisms of the impact of light on phenotypes, genetic investigations regarding light-induced transgenerational plasticity in Arabidopsis thaliana remain incomplete. In this study, we used Arabidopsis thaliana as the material, then gathered phenotypic data regarding leaf number and plant height under high- and low-light conditions from two generations. In addition to the developed genotype data, a functional mapping model was used to locate a series of significant SNPs. Under low-light conditions, a noticeable adaptive change in the phenotype of leaf number in the second generation suggests the presence of transgenerational genetic effects in Arabidopsis thaliana under environmental stress. Under different lighting treatments, 33 and 13 significant genes associated with transgenerational inheritance were identified, respectively. These genes are largely involved in signal transduction, technical hormone pathways, light responses, and the regulation of organ development. Notably, genes identified under high-light conditions more significantly influence plant development, whereas those identified under low-light conditions focus more on responding to external environmental stimuli.

The apicomplexan parasite, Sarcocystis neurona causes the degenerative neurological equine protozoal myeloencephalitis (EPM) disease of horses. Due to its host range expansion, S. neurona is an emerging threat that requires close monitoring. In apicomplexans, protein kinases (PKs) have been implicated in a myriad of critical functions such as host cell invasion, cell cycle progression and host immune responses evasion. Here, we used various bioinformatics methods to define the kinome of S. neurona and phylogenetic relatedness of its PKs to other apicomplexans. Further, three-dimensional (3D) homology models for selected S. neurona putative PKs were constructed and evaluated for inhibitor docking. We identified 92 putative PKs clustering within the AGC, CAMK, CK1, CMGC, STE, TKL, aPK and OPK groups. Although containing the universally conserved PKA (AGC group), S. neurona kinome was devoid of PKB and PKC, but contained the six apicomplexan conserved CDPKs (CAMK group). The OPK group was represented by ROPKs 19A, 27, 30, 33, 35 and 37, but was devoid of the virulence-associated ROPKs 5, 6, 18 and 38. Two out of the three S. neurona CK1 enzymes had high sequence similarities to T. gondii TgCK1-α and TgCK1-β and the Plasmodium PfCK1. Docking of four inhibitors onto homology models of putative ROP27 and PKA indicated that inhibition of S. neurona PKs is feasible, but needs to be experimentally tested. The essentiality of apicomplexan PKs makes the elucidation of S. neurona kinome a key milestone for development of novel therapeutics for EPM.

Background: Symmetry is a special kind of balance. This study aims to systematically explore and apply the role of balanced composition in aesthetic judgments by focusing on balanced composition features and employing research methods from computational aesthetics and Neuroaesthetics. Methods: First, experimental materials were classified by quantifying balanced composition using several indices, including symmetry, center of gravity, and negative space. An EEG experiment was conducted with 18 participants, who were asked to respond dichotomously to the same stimuli under different judgment tasks (balance and aesthetics), with both behavioral and EEG data being recorded and analyzed. Subsequently, participants' data were combined with balanced composition indices to construct and analyze various SVM classification models. Results: Participants largely used balanced composition as a criterion for aesthetic evaluation. ERP data indicated that from 300-500ms post-stimulus, brain activation was more significant in the aesthetic task, with unbeautiful and imbalanced stimuli eliciting larger frontal negative waves and occipital positive waves. From 600-1000ms, beautiful stimuli caused smaller negative waves in the PZ channel. The results of the SVM models indicated that the model incorporating aesthetic subject data (ACC=0.9989) outperforms the model using only balanced composition parameters of the aesthetic object (ACC=0.7074). Conclusions: Balanced composition is a crucial indicator in aesthetics, with similar early processing stages in both balance and aesthetic judgments. Multimodal data models validated the advantage of including human factors in aesthetic evaluation systems. This interdisciplinary approach not only enhances our understanding of the cognitive and emotional processes involved in aesthetic judgments but also enables the construction of more reasonable machine learning models to simulate and predict human aesthetic preferences.

Background: In recent years, computational aesthetics and neuroaesthetics have provided novel insights into understanding beauty. Building upon the findings of traditional aesthetics, this study aims to combine these two research methods to explore an interdisciplinary approach to studying aesthetics; Method: Abstract artworks were used as experimental materials. Based on traditional aesthetics and in combination, features of composition, tone, and texture were selected. Computational aesthetic methods were then employed to correspond these features to physical quantities: blank space, gray histogram, GLCM, LBP, and Gabor filters. An EEG experiment was carried out, in which participants conducted aesthetic evaluations of the experimental materials in different contexts (genuine, fake), and their EEG data was recorded to analyze the impact of various feature classes in the aesthetic evaluation process. Finally, a SVM was utilized to model the feature data, EEG data, context data, and subjective aesthetic evaluation data; Result: Behavioral data revealed that higher aesthetic ratings in the genuine context. EEG data indicated that genuine contexts elicited more negative deflections in the prefrontal lobes between 200-1000ms. Class II compositions demonstrated more positive deflections in the parietal lobes at 50-120ms, while Class I tones evoked more positive amplitudes in the occipital lobes at 200-300ms. Gabor features showed significant variations in the parieto-occipital area at an early stage. Class II LBP elicited a prefrontal negative wave with a larger amplitude. The results of the SVM models indicated that the model incorporating aesthetic subject and context data (ACC=0.76866) outperforms the model using only parameters of the aesthetic object (ACC=0.68657); Conclusion: A positive context tends to provide participants with a more positive aesthetic experience, but abstract artworks may not respond to this positivity. During aesthetic evaluation, the ERP data activated by different features show a trend from global to local. The SVM model based on multimodal data fusion effectively predicts aesthetics, further demonstrating the feasibility of the combined research approach of computational aesthetics and neuroaesthetics.

Predicting the outcome of a future game between two National Basketball Association (NBA) teams poses a challenging problem of interest to statistical scientists as well as the general public. In this article, we formalize the problem of predicting the game results as a classification problem and apply the principle of maximum entropy to construct NBA maximum entropy (NBAME) model that fits to discrete statistics for NBA games, and then predict the outcomes of NBA playoffs by the NBAME model. The best NBAME model is able to correctly predict the winning team 74.4 percent of the time as compared to some other machine learning algorithms which is correct 69.3 percent of the time.

Huperzine A is an herbal reversible inhibitor of Acetylcholinesterase (AChE). A molecular docking analysis on Huperzine A molecule has been carried out to understand its structure, conformational flexibility, intermolecular interaction and the binding affinity in the active site of AChE enzyme. Further, the charge density distribution of huperzine A molecule (lifted from the active site of AChE) was determined from the high level quantum chemical calculations coupled with charge density analysis. The binding affinity of Huperzine A towards AChE was calculated from the molecular docking; the lowest docked energy is -8.46 kcal/mol. In the active site, huperzine A molecule interacts with acyl binding pocket-Phe330 of AChE, that is, the bicyclo ring group of huperzine A forms an intermolecular interaction with the oxygen atom of main chain of the amino acid residue Phe330 at the distances 3.02 and 3.25 Å respectively. On the other hand, a gas phase study on huperzine A molecule also performed using HF and DFT (B3LYP) methods with the basis set 6-311G**. The molecular structure, conformation, and the charge density distribution of huperzine A molecule in the gas phase have determined using quantum chemical calculations and the charge density analysis. The comparative studies between the gas phase and the active site forms of huperzine A molecule, explicitly reveals the degree of conformational modification and the charge density redistribution of huperzine A when present in the active site. The dipole moment of the molecule in the active site is 6.85 D, which is slightly higher than its gas phase value (5.91 D). The electrostatic potential (ESP) surface of active site molecule clearly shows the strong electronegative and positive ESP regions of the molecule, which are the expected strong reactive locations of the molecule.

Semaglutide is a potent GLP-1 receptor agonist used in the treatment of type 2 diabetes mellitus due to its ability to regulate blood glucose levels and promote weight loss. On July 26, 2021, with a manually defined set of computational structural and biophysical analysis, a simple Val27-Arg28 exchange was for the first time introduced in the backbone of semaglutide to strengthen the semaglutide-GLP-1R binding affinity. In this article, a comprehensive structural and biophysical analysis approach is for the first time proposed towards an exhaustive exploration of the semaglutide-GLP-1R sequence space for the design of semaglutide analogues with elevated binding affinity to GLP-1R, thereby potentially enhancing therapeutic efficacy of structurally conceivable semaglutide analogues. Through structure biophysics-based rational design and computational modeling, this article puts forward a set of semaglutide analogues and calculated their binding affinities to GLP-1R, with one particular semaglutide analogue-GLP-1R structural model reaching a K_d of 3.0 × 10^-8 M, while the K_d is 3.4 × 10^-6 M for the binding of native semaglutide to GLP-1. Overall, the computationally designed semaglutide analogues here constitute a hopeful approach for developing GLP-1 receptor agonists with improved efficacy for the treatment of diabetes and weight management in future.

This study provides a comprehensive analysis of the conformational dynamics of pseudo-endocannabinoid receptors (p-CBRs) CB₁ and CB₂ in response to ligand binding. The ligands studied include N-arachidonoyl dopamine (NADA), Cannabidiol (CBD), known agonists (FUB-MDMB and E3R), and inverse agonists (Taranabant and AM630). Using homology models based on crystal structures (PDB ID: 5U09 for CB₁ and 5ZTY for CB₂), molecular docking, and molecular dynamics (MD) simulations, the research validates the structural integrity and dynamic behaviours of these pseudo-receptors. Induced-fit docking revealed NADA as having the highest binding affinity for both receptors, with docking scores of −13.92 kcal/mol for CB₁ and −13.32 kcal/mol for CB₂, facilitated by key hydrogen bond interactions. MD simulations over 250 ns at ~298 K showed successful equilibration, indicating the most stable interaction for NADA, thereby further validating the models. CB₁ and CB₂ exhibited distinct conformational responses to ligands, with Cα RMSD and Cα RMSF analyses suggesting a more rigid binding pocket for CB₁ and a flexible binding pocket for CB₂. These findings highlight the significance of receptor behaviour and dynamics in the development of cannabinoid-based therapeutics, supporting the potential of CBD and NADA as modulators for CB₁ and CB₂, respectively.

The emergence of prosumers, who actively participate in designing and producing goods, has generated interest in domestically manufactured products. Factors such as innovation, design methods, component reuse, prototyping, and digital fabrication empower prosumer designers to realize their ideas and enhance products to meet their specific design requirements. This article presents a case that combines bioinspired design methods with prosumer characteristics from the perspective of a designer who uses biological research results acting as a prosumer. By analyzing prosumer needs, bioinspired design, and domestic fabrication requirements, designers define the methods that best suit their possibilities. As a result of using biological information, the geometry of Samara seeds, propeller sizing, wind tunnel tests, and subsequent improvements, the custom design of a bioinspired microgenerator has been refined to the final design of a bicycle flashlight. The aim is to popularize a working approach that facilitates the interaction of design methods and reinforces domestic manufacturing processes. Implementing bioinspired design methods within the prosumer context offers a methodological alternative to transform the current economic model, promoting systemic sustainability and the development of proprietary technologies.

The Chikungunya virus (CHIKV) poses a significant global public health concern, especially in Africa. Since its first isolation in Tanzania in 1953, CHIKV has caused recurrent outbreaks, challenging healthcare systems in low-resource settings. Recent outbreaks in Africa highlight the dynamic nature of CHIKV transmission and the challenges of underreporting and underdiagnosis. Here, we review literature and analyze publicly available cases, outbreaks, and genomic data providing insights into the epidemiology, genetic diversity, and transmission dynamics of CHIKV in Africa. Our analyses reveal the circulation of geographically distinct CHIKV genotypes, with certain regions experiencing a disproportionate burden of disease. Phylogenetic analysis of sporadic outbreaks in West Africa suggests repeated emergence of the virus through enzootic spillover, which is markedly different from inferred transmission dynamics in East Africa, where the virus is often introduced from Asian outbreaks, including the recent reintroduction of the Indian Ocean lineage from the Indian subcontinent to east Africa. Furthermore, there is limited evidence of viral movement between these two regions. Understanding the history and transmission dynamics of outbreaks is crucial for public health planning. Despite advances in surveillance and research, diagnostic and surveillance challenges persist. This review and secondary analysis highlight the importance of ongoing surveillance, research, and collaboration to mitigate the burden of CHIKV in Africa and improve public health outcomes.

Long COVID, also referred to as post-COVID-19 condition, describes the lingering after-effects of COVID-19 infection. This complex syndrome encompasses a range of symptoms affecting physical well-being, cognitive function, and mental health. The widespread impact of long COVID extends beyond individual sufferers, placing significant strain on healthcare systems and causing far-reaching socioeconomic consequences. The scale of this multifaceted health crisis is unparalleled in recent history, affecting millions globally and challenging our understanding of post-viral conditions. This study aimed to explore the possibility of targeting G protein-coupled receptors (GPCRs) signaling pathway-related genes as therapeutics for long COVID using computational analysis. Targeting GPCRs and their signaling pathways holds significant promise for long COVID therapeutics, as these receptors are involved in crucial physiological processes including immune regulation, inflammation control, and neurotransmission. All of these are implicated in the diverse and persistent symptoms of long COVID, potentially offering a versatile approach to addressing multiple aspects of the condition simultaneously. This study identified 21 genes associated with GPCRs for further exploration as potential targets. Notably, GPCR genes such as HRAS, KRAS, GNAQ, and GNA11 were determined to be possible therapeutic targets for the pathophysiological mechanisms of long COVID. FDA-approved drugs targeting these genes were identified, including Binimetinib, Cabozantinib, Selumetinib, Panitumumab, Cetuximab, Adagrasib, Tipifarnib, and Sotorasib. Additionally, Naltrexone and Tipifarnib have emerged as potential new treatment options for long COVID. SARS-CoV-2 may hijack GPCR signaling pathways to dysregulate lung ion and fluid transport, contributing to the pathophysiology of pulmonary edema in patients with COVID-19. Further research is required to translate the findings into the development of therapeutics and to investigate the relationship between viral infection and GPCR signalling.

In the last decades, the impacts of urbanization on the hydrological cycle have led to an increase in the frequency and magnitude of urban flooding events, also amplified by the effects of climate change. Sustainable Drainage Systems (SuDS) provide a revolutionary change in this field, improving the sustainability and resilience of cities. This research explores the integration of different SuDS with the aim of significantly reducing both flow volume and celerity of floods in a residual urban catchment area of metropolitan city of Querétaro (Mèxico), where extreme rainfall frequently occurs. This catchment is a representative suburb of urban pressure and environmental degradation problems. Currently, managing storm water under climate uncertainty through a multi-disciplinary approach is a major concern in this urban area. A 1D-2D coupling model of shallow water equations, finite volume method, unstructured meshing method, and hybrid parallel computing application defined the optimal configuration of SuDS at catchment scale to reduce the flood vulnerability in Querétaro. Specifically, in this paper we explore the management issues of the proposed SuDS configuration that acts as a water resource system with multiple purposes. A generic simulation model called MODSIM was applied to simulate the designed urban drainage system under a balanced IPCC future climate scenario in terms of reliability, resilience and vulnerability against water scarcity.

Ming-style round-backed armchairs as a classic Chinese chair design are renowned for the blend of ergonomics, practicality, and elegance. Although the Chinese armchair furniture industry has made strides in sustainability, there is still a need for further improvements in material selection, manufacturing processes, and supply chain management to fully support the development of sustainable furniture. Laminated bamboo lumber as new sustainable materials are becoming a popular option for furniture designers in the era of "sustainable design". This research aims to investigate the feasibility and application of laminated bamboo lumber in the design of Chinese armchair furniture and proposes innovative ideas for optimizing the armchair's design. First, an empirical study was conducted to comprehensively understand the classification of Ming-style round-backed armchairs’ structure to develop a 3D model. Secondly, an experimental research method was then employed to explore the parameters of laminated bamboo lumber for future studies. Additionally, a validation experiment was conducted to compare real-world scenarios with simulations using finite element analysis in Ansys. Finally, finite element analysis was used to test the feasibility and application of laminated bamboo lumber in furniture design, focusing on the material’s mechanical properties. Results indicated that laminated bamboo lumber has excellent mechanical properties as furniture design materials, subsequently, using the optimized results, the use of 0.6319 kg of steel was successfully reduced by 23.68% to ensure the armchair’s stability. This provides a reference for the future compter-assist and standardized innovative design of Chinese armchair. By incorporating compter-assist design and lightweight optimization, it is possible to save materials and make more efficient use of resources, thereby contributing to sustainable development goals in the field of furniture design.

Aloe vera is well known for its biological properties as a bioflavonoid-anti-inflammatory, antibacterial agent. It has been used frequently in the food sector as food coating for its hygroscopic properties and as an ingredient in the lucrative industry. Studies have also included Aloe vera as an eco-friendly green solution based on these properties. The current research focuses on the use of Aloe vera gel in printing pastes as an alternative sustainable solution to synthetic thickeners, evaluating its wet performance and ease of fabric stitching, and has been inspired by studies that similarly used this substance measuring its effect on the fabric's coefficient of friction and antimicrobial action. In the current study, printing pastes with natural colourants and Aloe vera gel thickener derived from natural leaves from Crete increased the fabric's mechanical resistance to abrasion compared to the untreated pastes. The measured performance did not differ substantially from prints with traditional synthetic pastes, hence tolerating the substitution with the non-contaminant variant. The enhanced resistance to abrasion and wear prolongates the fabric's serviceable life and resulting garments, decreasing the need for high industry processing volumes and, as a result, reducing pollution. The resistance to wear was evaluated using the dominant method in textile testing of the Martindale apparatus, which measured the cycles to failure, weight loss, and general appearance deterioration using the official photographic standards.

Additive manufacturing processes offer various options to fabricate metal parts, which show numerous ecological and technological advantages compared to formative and subtractive manufacturing methods. Due to quick availability and high accuracy, they are particularly suitable to produce complex prototypes. Metal fused deposition modeling is a relatively new material extrusion additive manufacturing technique based on the widely used fused filament deposition process. This study focuses on the Inconel 625 metal filament from Markforged Inc., it consists of a mixture of metal powder and a binder system, the latter being removed during washing and sintering. In this article, the mechanical properties of printed and sintered Inconel were investigated using tensile testing specimens with three different infill patterns, solid, triangular, and gyroid, printed in flatwise and upright direction. In addition, investigations into the dimensional accuracy of the printed specimens and the sintering shrinkage were carried out. The results showed that neither the dimensional accuracy nor the sintering shrinkage was significantly influenced by the printing orientation. In the context of lightweight engineering, the structures proofed beneficial especially within the elastic region. Generally, triangular infill patterns resulted in higher stiffness, while gyroids led to more ductile specimens. A mass-related evaluation of tensile testing elucidates that with aid of the infill structures weight savings of 40 % resulted in mechanical performance decreased by only 20 % on average.

A proposed low-temperature forging method aims to enhance stainless steel bearings by creating a martensitic subsurface layer, significantly boosting bearing fatigue life due to increased surface hardness. This technique induces beneficial residual stresses, particularly in axial bearings, streamlining their construction and improving machine elements. Challenges persist, especially with radial bearings, but simplicity in axial bearing forging promotes compact, resource-efficient facility construction. Future research will focus on applying this technique to axial bearing washers, potentially replicating success in other bearing components. Despite the energy ex-penditure for cooling during forging, the substantial increase in bearing fatigue life offsets this, enhancing overall longevity and reliability of critical machine components. Integration of this forging technique into bearing fabrication appears seamless, offering a promising trade-off be-tween energy use and enhanced performance.

Tremor is a prevalent neurological disorder characterized by involuntary shaking or trembling of body parts. This condition impairs fine motor skills and hand coordination to varying degrees and can even affect overall body mobility. As a result, tremors severely disrupt the daily lives and work of those affected, significantly limiting their physical activity space. This study developed an innovative Spatial Augmented Reality (SAR) system aimed at assisting individuals with tremor disorders to overcome their physical limitations and expand their range of activities. The system integrates eye-tracking and Internet of Things (IoT) technologies, enabling users to smoothly control objects in the real world through eye movements. It employs a virtual stabilization algorithm for stable interaction with objects in the real environment. The study comprehensively evaluated the system's performance through three experiments: 1) assessing the effectiveness of the virtual stabilization algorithm in enhancing the system's ability to assist individuals with tremors in stable and efficient interaction with remote objects; 2) evaluating the system's fluidity and stability in performing complex interactive tasks; 3) investigating the precision and efficiency of the system in remote interactions within complex physical environments. The results demonstrate that the system significantly improves the stability and efficiency of interactions between individuals with tremor and remote objects, reduces operational errors, and enhances the accuracy and communication efficiency of interactions.

In the global carbon cycle, atmospheric carbon emissions, both ‘natural’ and anthropogenic, are balanced by carbon uptake (i.e. sequestration) that mostly occurs via photosynthesis, plus a much smaller proportion via geological processes. Since the formation of the Earth about 4.54 billion years ago the ratio between emitted and sequestered carbon has varied considerably with atmospheric CO₂ levels ranging from 100,000 ppm to a mere 100 ppm. Over this time, a huge amount of carbon has been sequestered due to photosynthesis and essentially removed from the cycle, being buried as fossil deposits of coal, oil and gas. Relatively low atmospheric CO₂ levels were the norm for the past 10 million years, and during the past million years they averaged about 220 ppm. More recently, the Holocene epoch, starting ~11,700 years ago, has been a period of unusual climatic stability with relatively warm, moist conditions and low atmospheric CO₂ levels of between 260 and 280 ppm. During the Holocene, stable conditions facilitated a social revolution with domestication of crops and livestock leading to urbanisation and development of complex technologies. As part of the latter process, immense quantities of sequestered fossil carbon have recently been used as energy sources, resulting in a particularly rapid increase in CO₂ emissions after 1950 CE to the current value of 424 ppm, with further rises to >800 ppm predicted by 2100. This is already perturbing the previously stable Holocene climate and threatening future food production and social stability. Today, the global carbon cycle has been shifted such that carbon sequestration is no longer keeping up with recent anthropogenic emissions. In order to address this imbalance it is important to understand the roles of high-capacity carbon sequestration systems, such as natural forests and tropical croplands, and to devise strategies to facilitate net CO₂ uptake.

Optimal control is a critical tool for mechanical robotic systems, facilitating precise manipulation of dynamic processes. These processes are described through differential equations governed by a control function, addressing a time-optimal problem with bilinear characteristics. Our study utilizes the classical approach complemented by Pontryagin's Maximum Principle (PMP) to explore this inverse optimal problem. The objective is to develop an exact piecewise control function that effectively manages trajectory control while considering the effects of viscous friction. Our simulations demonstrate that the proposed control law markedly diminishes oscillations induced by boundary conditions. This research not only aims to delineate the reachability set but also strives to determine the minimal time required for the process. The findings include an exact analytical solution for the stated control problem.

The multiphase motors due to their reliability find new areas of application in the industry and transportation. Developing of any system with multiphase motors at first requires elaborate model of the system, define control mode and controllers. The model of control system for the six-phase drive is based on its conventional d-q mathematical model and indirect field-oriented control. Simulink model of six-phase drive with indirect field-oriented control and simulated with two types PID and PID fuzzy controllers. The simulation results, reflecting step response and provided speed reference law as well as load application at constant speed, are presented and analyzed. Fuzzy controller with 49 rules is considered and applied. Six-phase electric drive with field-oriented control at well-tuned PID controller has good step response specifications: settling time 0.13 s at starting without load, step response has no overshoot, small about 1 % short time decrease of speed at load of 5 Nm application. The system employing PID fuzzy controller shows slightly better results in response to torque application: the decrease in speed 1 % is eliminated in shorter time. Simulation results indicate the robustness of both systems with six-phase drive to disturbance and provide high quality transient specifications at determined reference speed.

Nanoporous membranes promise energy-efficient water desalination. Hexagonal boron nitride (h-BN), like graphene, exhibits outstanding physical and chemical properties, making it a promising candidate for water treatment. We employed Car–Parrinello molecular dynamics simulations to establish accurate modeling of Na+ and Cl- permeation through hydrogen passivated nanopores in graphene and h-BN membranes. In these complex systems the classical potentials cannot properly account for the physics at play. We demonstrate that ion separation works well for the h-BN by imposing a barrier of 0.13 eV and 0.24 eV for Na+ and Cl- permeation respectively, compared to the graphene where the Cl- ion faces a negative barrier of 0.68 eV and is prone toward blockade and Na+ permeation is associated with a slightly negative barrier of 0.03 eV. From the change in the solvation shell, we get the following trend: Na/h-BN > Na/G > Cl/G > Cl/h-BN. We argue that the trend in the permeation barrier changes to Cl-h-BN > Na-h-BN > Na-G > Cl-G due to a combination of several interactions, including the distortion of the water network induced by the ions, the ion-water interaction, and the ion-nanopore interaction. Overall, the desalination performance of h-BN surpasses that of their graphene counterparts.

This paper presents a methodology based on deep learning models and metaheuristic algorithms for the optimization of airfoils for the design of aircraft wings with large endurance. The use of AZTLI-NN (a neural network with an architecture composed of a multilayer perceptron and a variational autoencoder) is implemented for the prediction of graphs of the aerodynamic coefficients of the airfoil as a function of the angle of attack. This neural network presents good predictions of the aerodynamic coefficients, similar to the coefficients obtained with computational fluid dynamics simulations. AZTLI-NN in combination of metaheuristic algorithms and the CST profile parameterization method show excellent performance in single-objective and multi-objective profile optimization tasks.

Accurate diagnosis for the 400 million people with rare diseases is critical for healthcare decisions, prognosis, understanding disease mechanisms, and identification of treatments. Despite advances in genome sequencing, barriers such as high interpretation costs, diagnostic expertise, throughput associated delays, and uncertain variant classifications persist, with demand exceeding capacity. Many variant classification methods focus narrowly on specific consequences, leading to the use of complex integrative pipelines that often overlook transcript variability and lack prediction transparency. To overcome these limitations, we introduce DITTO. This transparent, transcript-aware machine-learning method demonstrates superior overall performance in accuracy, recall, and precision when compared to existing tools. DITTO is publicly available at https://github.com/uab-cgds-worthey/DITTO

Mobile inspections conducted by intelligent tunnel robots are instrumental in broadening the inspection reach, economizing on inspection expenditures, and augmenting the operational efficiency of inspections. Despite differences from fixed surveillance, mobile-captured traffic videos have complex backgrounds and device conditions that interfere with accurate traffic event identification, warranting more research. This paper proposes an improved algorithm based on YOLOv9 and DeepSORT for intelligent event detection in edge computing mobile device using the intelligent tunnel robot. The enhancements comprise the integration of the Temporal Shift Module to boost temporal feature recognition and the establishment of logical rules for identifying diverse traffic incidents in mobile video imagery. Experimental results show that our fused algorithm achieves a 93.25% accuracy rate, an inprovement of 1.75% over the baseline. The algorithm is also applicable to inspection vehicles, drones, and autonomous vehicles, effectively enhancing the detection of traffic events and improving traffic safety.

The development of ballistic protection vests (BPVs) has become increasingly significant, particularly in the design of soft panels, which are critical to the overall size and configuration of these vests. Despite their importance, a standardized design process for soft panels is lacking in the literature. This research addresses this gap by creating soft panels tailored to the body types of regional soldiers while adhering to relevant standards. We propose a comprehensive design algorithm and evaluate the applicability of the National Institute of Justice (NIJ) standard 0101.06 test templates for configuring these panels. This study aims to design soft ballistic panels for BPVs that cater to the body type requirements of regional soldiers and comply with NIJ 0101.06 standards. We have developed an algorithm for designing soft panels and analyzed the feasibility of using NIJ test templates for their configuration. The research encompasses a detailed literature review, analysis of existing BPV soft panels, and comparison with NIJ test templates. The panels were designed and scaled using computer-aided design (CAD) systems and evaluated through by physical fitting on mannequins and virtual fitting using the Clo3D program. Our findings indicate that the NIJ standard test templates are only partially applicable to the design of soft panels. The developed methodology includes an algorithm and size specifications tailored to regional covers. A coefficient (K) was identified to calculate the soft panel surface area prior to design, thereby saving time and resources.

Nowadays, microfluidics has become an anarchist interdisciplinary topic with considerable attention in a wide range of biotechnology applications. In this research work, numerical investigation of microfluidic micro-mixer is carried out using hybrid actuation approach with different micropillar shapes and gaps. For this purpose, COMSOL Multiphysics is used with three different physics such as thermoviscous acoustic to solve acoustic governing equations, laminar physics is used to solve fluid flow governing equations and diluted transport species is used to solve mixing governing equations. The simulations were carried out with flow velocity of 200 µm/s at both inlets with an oscillation frequency of 15kHz. The outcomes were in the form of acoustic characteristics such as acoustic pressure, acoustic velocity, acoustic streaming and mixing index. The results revealed that the inclusion of micropillars enhanced the mixing performance and strength of acoustic field resulting in improvement of mixing performance compared to the case without micropillars. In addition, it was also investigated that blade shape micropillars with 0.150 mm gaps deliver best results compared to the other cases with maximum and minimum values of the mixing index 0.95 and 0.72 respectively. The obtained results can be extremely helpful for design and modifications of hybrid microfluidics micro-mixer.

This paper proposes an innovative approach to address the challenge of softening deformation of thermal-curable thermosets during additive manufacturing. This approach involves the development of a composite powder composed of thermosetting materials with distinct curing temperatures, the utilization of Cold Spray Additive Manufacturing (CSAM) technology for 3D printing, and the application of a stepwise isothermal curing process. The corresponding effectiveness is validated by case studies. The results demonstrate that samples printed via CSAM maintain a solid state without deformation. During the post-curing process, each component of the composite material cures independently while the other components remain in a solid state, providing structural support for the whole material, thereby effectively reducing the softening deformation during the post-curing process. This approach offers a novel perspective for the additive manufacturing of thermal-curable thermosets.

The Energy Performance of Buildings Directive 2010 calls for the Nearly Zero Energy Standard for new buildings from 2021 onwards: Buildings using “almost no energy” are powered by renewable sources or energy produced by the building itself. For residential buildings, this ambitious new standard has already been reached. But for other building types this goal is still far away. The potential of these buildings to meet a Nearly Zero Energy Standard was investigated by analyzing ten case studies representing non-residential buildings with different uses. The analysis shows that the primary characteristics common to critical building types are a dense building context with a very high degree of technical installation (such as hospital, research and laboratory buildings). The large primary energy demand of these types of buildings cannot be compensated by building and property-related energy generation including off-site renewables. If the future Nearly Zero Energy Standard were to be defined with lower requirements because of this, the state related properties of Bavaria suggest that the real potential energy savings available in at least 85% of all new buildings would be insufficiently exploited. Therefore, it would be useful to instead individualize the legal energy verification process for new buildings to distinguish critical building types such as laboratories and hospitals.

Enzymes are in high demand for very diverse biotechnological applications. However, natural biocatalysts often need to be engineered for fine-tuning their properties towards the end applications, such as the activity, selectivity, stability to temperature or co-solvents, and solubility. Computational methods are increasingly used in this task, providing predictions that narrow down the space of possible mutations significantly and can enormously reduce the experimental burden. Many computational tools are available as web-based platforms, making them accessible to non-expert users. These platforms are typically user-friendly, contain walk-throughs, and do not require deep expertise and installations. Here we describe some of the most recent outstanding web-tools for enzyme engineering and formulate future perspectives in this field.

Energy harvesting is an emerging topic recently. This harvested energy is used to operate wireless sensors for different industrial monitoring and automation. To maximize the harvested energy in spinning systems, the harvester should vibrate at resonance for different spinning frequencies which can be achieved by active fitting. In this research, a novel methodology was developed by actively customizing the length of vibrating element beam to manipulate its natural frequency according to the spinning frequency. Different models were investigated numerically and experimentally to verify the proposed methodology. COMSOL Multiphysics software V5.1 was used to develop the numerical model, then and tip deformation and voltage difference results were benchmarked. The numerical model was verified experimentally by attaching a piezoelectric MIDE PPA 1021 which its vibrating length was varied by changing the supporting length from 0 mm to 12 mm to a spinning object having variable frequency from 0-200 Hz. It is found that the MIDE PPA 1021 beam vibrated at its resonance throughout the specified spinning frequencies. The proposed harvester can be applied in battery-free sensors being used in automotives, wind-mills blades, and rotating machinery.

Confronting the dual challenges of rapid urbanization and climate change, although extensive research has investigated the influencing factors and practical strategies of urban carbon emissions and urban vibrancy, respectively, the unclear mutual nexus between them and the development strategy of collaborative optimization requires further in-depth analysis. This study explores the delicate balance between urban vibrancy and low-carbon sustainability within the confines of Beijing’s Fifth Ring Road. By integrating OpenStreetMap, land use, population, and building carbon emission data, we have developed a reproducible method to estimate total carbon emission and emission intensity per capita. Furthermore, we have introduced vibrancy index data to distinguish the vibrancy evaluation of residential and non-residential land and applied cross-combinational classification technology to dissect the spatial correlation between urban carbon emissions and urban vibrancy. The results reveal that the four combination typologies show more significant differences and regularity in residential land. Based on the discovery of spatial correlation, this study puts forward corresponding development strategy suggestions for each typology based on geographical location and the requirements of urban development policies of these four combination typologies. In conclusion, our study highlights the importance of integrating carbon emissions and urban vibrancy comprehensively in sustainable urban planning and proposes that various land use combinations need targeted development strategies to achieve this goal, which need to consider population, energy, service facilities, and other diverse aspects.

Intraoperative load sensors have been developed with the aim of balancing the soft tissue in the knee during Total Knee Replacements (TKRs). Literature has shown success in soft tissue bal-ancing however, concerns regarding the accuracy of these sensors remain. For this reason, pre-vious research has attempted to create robust sensors for intraoperative use. The design of the sensor in this research has aimed to address the concerns outlined in the literature to create a new and novel smart-sensor for soft-tissue balancing. This new intraoperative load sensor included design features to increase accuracy while artificial intelligence allowed for comprehensive sensing across the entirety of the sensor, providing unparalleled insight during the operation. Notably, the sensor was designed to withstand loads of at least 450 N, ensuring robust perfor-mance of the sensor. To optimize the design process while minimizing cost, Finite Element Analysis was employed. This approach ensured the design features fulfilled their function while maintaining structural integrity to withstand loads. Moreover, the novelty of using training data from simulated data will reduce the iterative process of the labor-intensive collection of training data for the artificial intelligence while reducing human error associated with it. Results from the evaluation demonstrated the ability of this design to successfully bridge the current gap in the market by fulfilling all essential design criteria established within the literature. This innovative smart-sensing tool will in turn enhance patient outcomes and alleviate financial burdens to pa-tients and the healthcare systems by reducing the need for early revision surgeries associated with improper joint tension.

In recent years there has been an increasing interest in developing novel materials based on natural biopolymers as a renewable alternative of petroleum-based plastics. The availability of proteins derived from agricultural by-products, along with their favourable properties, has fostered a renewed interest in protein-based materials, promoting research in innovative technologies. In this study we propose the use of rapeseed protein-rich meal as main ingredient for the preparation of novel sustainable materials combining excellent environmental properties such as biodegradability and renewable origin. Application as sustainable products in the present high-tech society needs modification of basic native properties of these natural compounds. The original route proposed in this paper consists in the preparation via compression moulding of flexible biomaterials stabilized by crosslinkers/chain extenders. Investigation of the effects of different denaturing and disulfide bond reducing agents, crosslinkers, and preparation conditions on the material mechanical behaviour demonstrated that the novel materials have appreciable strength and stiffness. The results show the potential of utilizing full meal from vegetable by-products to prepare protein-based materials with granted ecofriendly characteristics and mechanical properties adequate to specific structural applications.

The assessment of existing bridges constitutes a serious challenge for the researchers since their safety evaluation is a complex task due to several sources of uncertainty affecting the parameters identified in the knowledge process. In particular, a high level of uncertainties may arise in the case of prestressing systems requiring special investigation strategies and experimental technologies. The paper aims to investigate how the knowledge uncertainties influence the safety assessment of existing post-tensioned bridges. The semi-probabilistic approach is used, and uncertainties related to the knowledge process are described considering the number of tests, the confidence level assumed for the estimation of the problem parameters, and the measurement errors of in-situ tests. Results concerning a typical post-tensioned bridge are reported and the variability of the outcomes is discussed and compared with a reference case of “perfect knowledge”. The main results of the study confirm the general robustness of the semi-probabilistic Eurocodes safety format and provide an overview of dispersion due to different choices about the number of tests and confidence level. The measurement errors may lead to significant under-estimation of the computed structural capacity with respect to the reference one.

Childhood nutrition is a cornerstone of long-term health, yet many children exhibit reluctance to consume healthy foods such as vegetables. This aversion can be influenced by various factors, including food neophobia and the sensory and visual appeal of the foods presented. Understanding how visual cues affect children's willingness behaviors can provide insights into effective strategies to enhance their dietary habits. This study aims to investigate the liking and emotional responses on the relationships between intrinsic and extrinsic cues and children's willingness to eat and to ask parents to buy. To understand how these factors influence children with food neophobia food-related behaviors and to identify effective strategies to promote healthier eating habits. A sample size consisting of 420 children recruited was needed in this cross-sectional study. A validated and reliable self-administered questionnaire was employed. Independent t-test and ANOVA were used to test the differences between gender and food neophobia means. Spearman correlations were used for correlation analysis. Hierarchical regression analyses were conducted to explore the relationships among intrinsic cues, extrinsic cues, and the mediating effect of liking and emotional responses. Attributes such as plate color and shape significantly influence children's food-related behaviors and their emotions. Liking and emotional responses significantly mediates the relationship between intrinsic/extrinsic cues and willingness. Positive visual cues experiences are crucial for increasing children's inclination to purchase and consume. Positive emotional responses enhance acceptance and purchase. There are significant differences between boys and girls. Boys prefer blue and triangular plates, while girls prefer pink plates and generate more positive emotions. Children with food neophobia will initially experience a disorder, but its negative effects can be mitigated by increasing sensory appeal and emotional engagement. By employing strategies designed to enhance visual cues and liking and emotions, children’s willingness to choose healthy eating habits can be effectively increased.

Therapeutic protein engineering has revolutionized medicine by enabling the development of highly specific and potent treatments for a wide range of diseases. This review examines recent advances in computational and experimental approaches for engineering improved protein therapeutics. Key areas of focus include antibody engineering, enzyme replacement therapies, and cytokine-based drugs. Computational methods like structure-based design, machine learning integration, and protein language models have dramatically enhanced our ability to predict protein properties and guide engineering efforts. Experimental techniques such as directed evolution and rational design approaches continue to evolve, with high-throughput methods accelerating the discovery process. Applications of these methods have led to breakthroughs in affinity maturation, bispecific antibodies, enzyme stability enhancement, and the development of conditionally active cytokines. Emerging approaches like intracellular protein delivery, stimulus-responsive proteins, and de novo designed therapeutic proteins offer exciting new possibilities. However, challenges remain in predicting in vivo behavior, scalable manufacturing, immunogenicity mitigation, and targeted delivery. Addressing these challenges will require continued integration of computational and experimental methods, as well as a deeper understanding of protein behavior in complex physiological environments. As the field advances, we can anticipate increasingly sophisticated and effective protein therapeutics for treating human diseases.

Rett syndrome (RTT) is an X-linked neurodevelopment disorder associated with the single monogenic mutation in methyl-CpG binding protein 2 (MeCP2) in up to 95% of cases. The growing number of genome-wide association studies and incomplete concordance for autoimmune diseases in monozygotic twins concur to support the involvement of environmental factors, like infectious agents or chemicals, in the breakdown of tolerance leading to autoimmunity. In fact, the coexistence of a perturbation of the immune system in RTT patients has been previously hypothesized. We herein explored the hypothesis that an autoimmune component derived from environmental bacterial infection of non typeable Haemophilus influenzae may coexist in RTT. At this purpose we screened sera from RTT syndrome patients, non-RTT pervasive developmental disorders patients and healthy controls with the hyperglucosylated adhesin protein HMW1ct(Glc) used as a precise antigen in ELISA in order to identify specific antibodies to N-glucosylation sites. Results showed that HMW1ct(Glc) is able to significantly discriminate antibodies among RTT sera and controls. Competitive ELISA confirmed the specific interaction between antibodies characteristic of RTT syndrome and the N-glucosylation motifs of the bacterial adhesin protein HMW1ct(Glc).

Autonomous navigation in dynamic environments poses a significant challenge in the field of ro-botics with the primary goals of ensuring both smooth and safe movement. This study introduces control strategy based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) to enhance au-tonomous robot navigation in dynamic environments, emphasizing on collision-free path plan-ning. This strategy employs, a path planning technique to develop a trajectory that allows the ro-bot to navigate smoothly while avoiding collisions with both static and dynamic obstacles. The developed control system incorporates four ANFIS controllers: two are tasked with guiding the robot toward its end point, and the other two are activated for obstacle avoidance. The experi-mental setup conducted in CoppeliaSim involves a mobile robot equipped with ultrasonic sensors navigating in an environment with static and dynamic obstacles. Simulation experiments are conducted to demonstrate the model's capability in ensuring collision-free navigation, employing a path planning algorithm to ascertain the shortest route to the target destination. The simulation results highlight the superiority of the ANFIS-based approach over conventional methods, partic-ularly in terms of computational efficiency and navigational smoothness.

Developing and utilising of underground space is an vital direction for urban growth. Underground commercial streets, as a significant component of underground space accommodating extensive human social activities, and consequently necessitate the creation of human-scale spaces. In the evolution of urban design development towards greater refinement, applying architectural theories and excessively subjective designs has resulted in a deficiency of human-centered design and a disordered spatial environment. This study merges environmental psychology and architectural theory to determine the appropriate length of spatial scale. Two experiments focusing on spatial perception evaluation were conducted using a virtual experimental platform that featured varying dimensions of spatial scale combinations. These quantified combinations were correlated with perception evaluation, and regression analysis was employed to identify appropriate scale ranges, which were superimposed on the range of length selection. Finally, the optimal length and scale combination for underground commercial street spaces were established, providing a reference for the human-centered design of these environments.

One of the primary causes of the mechanical seal failure is rotor vibration. Traditional dynamic seal models often would not be able to fully explaining the failure mechanisms. The seal dynamic models proposed in this paper, including those developed by the authors, prove valuable in predicting seal dynamics during operation in specific turbomachinery and in explaining the causes of seal failure. The single-mass dynamic model is suitable for analyzing contact mechanical seal and simple design dry gas seals dynamics. The two-mass dynamic model is used for investigating operational dynamics processes in classical dry gas seals under complex loading conditions. The three-mass dynamic model is used to study various complex mechanical seal types. This model is able to determine the normal operating condition range and to explain leakage mechanisms in the presence of excessive rotor vibrations.

The objective of this study is to determine the relationship between body condition score (BCS) and body carcass fat (BCF) of ewes at two key stages of the production cycle. Data were collected from 88 Coopworth ewes including BCS measurements manually evaluated by the farm manager, BCF measurements determined by computerized tomography (CT) and body parameter measurements automatically determined by an image processing application. Artificial neural network (ANN) statistical analysis method was used to develop a relationship between the body parameters and the BCF with an r2=0.96 and RMSE=1.01 using all top and side variables.

This paper presents an innovative multi-agent, data-driven reinforcement learning (RL) approach to develop and utilize the thermal equivalent network model that represents the motor's thermal dynamics. A multi-agent reinforcement learning is designed and trained to adjust the model parameters using data from several motor driving cycles. To ensure the incoming driving cycle matches the historical data before employing the pre-trained RL agents, offline statistical analysis and clustering techniques are developed and used. Numerical simulations highlighted the RL agent's ability to develop strategies that effectively address the variability of driving cycles. The proposed RL framework showed its capability to accurately reflect the motor's thermal behavior under various driving conditions.

The acoustic environment in learning spaces significantly influences learning effectiveness and occupant well-being. Recent studies have shown that a series of architectural factors within physical learning environments can impact students' educational experiences. This research delves into the relationship between architectural characteristics and the perceived acoustic environment among 467 primary students in 19 learning spaces across 9 schools in Chile. The survey comprises a student questionnaire, a checklist evaluating space characteristics, and acoustic measurements of indoor noise levels during occupied conditions. Examined topics include Indoor Environmental Quality (IEQ) comfort perception, noise annoyance, hearing ability, and acoustic preference. Architectural characteristics considered encompass flexible and acoustic space design parameters. The results indicated that noise discomfort was a common issue, especially in traditional learning spaces without acoustic treatment. The type of learning space (traditional or flexible settings) has a greater impact on acoustic perception particularly concerning noise discomfort and acoustic preference. However, poorly acoustic spaces lacking absorbing materials or exhibiting lower spatial density (m2 per student) contribute to increased indoor noise levels affecting noise annoyance caused by children's talking inside. The study emphasizes the importance of considering both architectural design and acoustic conditioning for optimizing the acoustic experience in educational spaces.

This paper investigates the effects of various process parameters, specifically the substrate temperature, on weld track geometry in Directed Energy Deposition (DED) processes. A specialized experimental setup integrated within a DED machine facilitates controlled thermal conditioning of sample sheets. Through Design of Experiments (DoE) methods, individual weld marks are generated and analysed to assess geometric characteristics. Regression models are constructed to predict machine parameters for desired weld geometry at different substrate temperatures. Validation experiments confirm the accuracy and reliability of the regression models. Results show a consistent trend towards target geometric features across a broad range of substrate temperatures. Despite deviations in measured values, successful fabrication is achieved, demonstrating robust bonding between weld and substrate. Contact angle predictions exhibit precision within a partial temperature range for proper deposition. The developed model offers insights for optimizing DED process parameters to achieve desired weld characteristics, advancing the capabilities and reliability of additive manufacturing technology. Future work aims to refine the regression model and explore additional mathematical relationships for enhanced accuracy. An implication of this work is the potential to vary the local mechanical properties of parts by controlling the temperature profile while maintaining consistent geometric characteristics. By manipulating the substrate temperature, it may be possible to tailor the microstructure and mechanical properties of fabricated components in the future to meet specific requirements in different regions of the part.

L-gulonolactone oxidase enzyme (GULO) catalyzes the last step of L-ascorbic acid (vitamin C) biosynthesis. This enzymatic activity is lost in primates. The full-length rat GULO has been previously produced in plants and demonstrated to be active. In this study, we compare activity of two variants of GULO produced in Escheriachia coli cells, full-length rat GULO (fGULO) and its C-terminal catalytic domain (cGULO). The expression and purification of the recombinant proteins were optimised and their biological activity was confirmed by two methods, the GULO activity assay in the protein extracts and the ‘in-gel’ staining for GULO activity. Both variants of recombinant GULO were biologically active in both assays. However, cGULO is more promising than fGULO for ascorbic acid production because it is more efficiently produced by bacteria.

In order to quickly develop a multi ship collaborative collision avoidance plan in multi ship encounter scenarios, a multi ship collision avoidance decision model is proposed in accordance with the requirements of the International Maritime Collision Avoidance Regulations, taking into account the collision avoidance operations in multi ship encounter scenarios. Firstly, the collision risk between ships is determined by the closest encounter distance (DCPA) and closest encounter time (TCPA). Secondly, a decision objective function is established based on rules, degree of ship deviation, and ship risk. Finally, the prediction mechanism is used to calculate the minimum value of the objective function and complete the collision avoidance decision. The simulation results show that in the scenario of multiple ship encounters, the collision avoidance model can achieve safe collision avoidance with a smaller steering frequency and a shorter col-lision avoidance time. The proposed multi ship collision avoidance model can provide reference for collision avoidance decision-making in multi ship encounter scenarios, which helps to im-prove the collision avoidance efficiency in multi ship collision avoidance

Firefighters Protective Clothing consist of several layers of different properties and functions. The layers create barriers against dangerous outer factor. However, they are also barriers to transport of sweat produced by human organism. It causes that very often sweat in the form of vapor is not transferred effectively from the human organism through the FPC to environment. This leads to sweat condensation on the skin of the FPC user causing his discomfort. In order to ensure as good as possible physiological comfort of firefighter while firefighting and other rescue actions it is necessary to take into consideration the transport of condensed sweat in the multilayer textile set creating the FPC. In the presented work four variants of multilayer textile sets for the FPC have been tested in the aspect of liquid moisture transport. Measurement was performed by means of the Moisture Management Tester (MMT290) – innovative instrument manufactured by the SDL Atlas. Obtained results showed that the transfer of sweat in liquid form occurs only in the inner, next to skin layer of the multilayer sets for the FPC.

This work studies how the Elo rating system can be applied to score-based sports, where it’s gaining popularity, and in particular for predicting the result at any point of a game, extending its statistical basis to stochastic processes. We derive some new theoretical results for this model and use them to implement Elo ratings for basketball and soccer leagues, where the assumptions of our model are tested and found to be mostly accurate. We showcase several metrics for comparing the performance of different ratings systems and determine whether adding a feature has a statistically significant impact. Finally, we propose an Elo model based in a discrete process for the score that allows us to obtain draw probabilities for soccer matches, and has a performance competitive with alternatives like SPI ratings.

Cyclooxygenases (COX), or prostaglandin endoperoxide synthases (PTGS), are key enzimes in the synthesis of prostaglandins, which are chemical species critical in mediating inflammatory processes. There are two highly homologous COX isoforms: COX-1 and COX-2. COX-1 is involved in the production of prostaglandins, chemical compuounds that take part in physiological processes such as: protection of the gastric epithelium, maintenance of renal flow, platelet aggregation, neutrophil migration and, also, are expressed in the vascular endothelium. Meanwhile, COX-2 is induced by proinflammatory stimuli. It is very frequent the use of nonsteroidal antiinflammatory drugs (NSAIDs) to counteract the symptoms of inflammatory processes. These drugs, in addition to its benefits, can cause side effects on people’s health, such as cardiovascular and respiratory problems, among others. In the past years, it has been recognized the potential of plants secondary metabolites as pharmacological agents, prompting the need for investigations that shed light into its mechanism of action. In this work we have applied computational techniques, based on quantum chemistry and mechanical statistics, to study the protein-ligand interaction involving COX’s and secondary metabolites from natural sources. Our aim is to determine the structure activity interplay in processes involving the participation of secondary plant metabolites such as luteolin, galangin, kaempferol, apigenin, morine and quercetin on the inactivation of COX’s. From molecular docking analysis, we have extracted the energetics of the COX-(1,2)/metabolite coupling. By defining energy based factors, we have determined a procedure that predicts the chemical species with highest stability and selectivity towards inactivation of COX-2 over COX-1. The results are discussed with regard to conformational features of the selected ligands and its intermolecular strong/weak interactions inside the active-sites of the COX’s hosts.

Smartwatches are one of the most relevant fitness trends of the past two decades, and they collect increasing amounts of health and movement data. The accuracy of these data may be questionable and requires further investigation. Therefore, the aim of the present study is to validate smartwatches for use in triathlon training. Ten different smartwatches were tested for accuracy in measuring heart rate, distance (via global navigation satellite systems, GNSS), swim stroke rates and the number of swim laps in a 50 m Olympic-size pool. The optical heart rate measurement function of each smartwatch was compared to that of a chest strap. Thirty participants (15 females, 15 males) ran five 3-minute intervals on a motorised treadmill to evaluate the accuracy of the heart rate measurements. Moreover, for each smartwatch, running und cycling distance tracking was tested over six runs of 4,000 m on a 400 m tartan stadium track, six hilly outdoor runs over 3.4 km, and four repetitions of a 36.8 km road bike course, respectively. Three swimming protocols ranging from 200 m to 400 m were performed in triplicate in a 50 m Olympic-size pool, evaluating tracked distance and detected number of strokes. The mean absolute percentage errors (MAPEs) for the average heart rate measurements varied between 3.1% and 8.3%, with the coefficient of determination ranging from 0.22 to 0.79. MAPE results ranged from 0.8% to 12.1% for the 4,000 m run on the 400 m track, from 0.2% to 7.5% for the 3.4 km outdoor run, and from 0.0% to 4.2% for the 36.8 km bike ride. For the swimming tests, in contrast, the deviations from the true distance varied greatly, starting at a 0.0% MAPE for the 400 m freestyle and reaching 91.7% for the 200 m medley with style changes every 25 m. In summary, for some of the smartwatches, the measurement results deviated substantially from the true values. Measurements taken while road cycling over longer distances with only a few curves were in relative terms more accurate than for outdoor runs and even more accurate than for the 400 m track. In the swimming exercises, the accuracy of the measured distances was severely deteriorated by the medley changes among the majority of the smartwatches. Altogether, the results of this study should help in assessing the accuracy and thus suitability of smartwatches for general triathlon training.

Cities are complex systems requiring urban design models that balance order and disorder. Collective creativity initiatives engage citizens in these processes, empowering bottom-up approaches that prioritize people and social well-being within urban development. This paper investigates an 'Urban Laboratory' as a case study, examining the potential of collective creativity to address urban complexity. The successful and ongoing project 'El Campo de Cebada' in Madrid, Spain, demonstrates how a community transformed a vacant lot into a vibrant social hub. The phases of this study include case selection, data collection, data analysis, and presentation of the results. This study identifies key enabling factors, including agents, management, social dynamics, infrastructure, and actions. These insights offer a methodological framework for designing future collaborative, resilient, and inclusive urban spaces, addressing the complex needs of communities within our cities.

This paper proposes an enhanced Quantum Key Distribution (QKD) protocol by incorporating a Three-Dimensional Angular Momentum Representation (3D AMR) to address vulnerabilities inherent in traditional two-dimensional polarization states-based systems. The 3D AMR method expands the state space, potentially increasing security against sophisticated quantum attacks. We present a theoretical and computational framework that transitions from the conventional two-dimensional polarization to a three-dimensional angular momentum space, which inherently increases the complexity and security of the quantum states used in QKD.

We present contrast information, a novel application of some specific cases of relative entropy measures, designed to be useful for cognitive modelling of sequential perception of continuous signals. We explain the relevance of entropy in cognitive modelling of music and language. Then, as a first step to demonstrating the utility of constrast information for that purpose, we show empirically that its discrete case correlates well with existing successful cognitive models in the literature. We explain some interesting properties of constrast information. Finally we propose future work towards a cognitive architecture that uses it.

Driven by mounting environmental apprehensions stemming from the over dependence on fossil fuels in energy and transportation sectors, extensive exploration into alternative energy sources, particularly biomass, has stimulated profound research on harnessing the potential of computational and mathematical techniques. This comprehensive survey delves into optimal models and strategies, unveiling their pivotal role in the design of biomass gasification processes.of hydrogen through biomass gasification. In this study, we aim to provide a comprehensive overview of the computational and mathematical techniques employed in optimizing biomass gasification processes, with a specific focus on enhancing hydrogen yield. Through an extensive literature review, various models and strategies will be examined, including thermodynamic analysis, kinetic modeling, reactor design, and process optimization. By uncovering the power of these techniques, we aim to contribute to the advancement of sustainable and efficient biomass utilization for the hydrogen-based future economy.This paper aims to provide an updated and comprehensive coverage of the investigations conducted on the potential of producing hydrogen from biomass through gasification. To achieve this, we incorporate the latest works that have utilized numerical modeling, simulation, optimization techniques, process heat integration, and co-generation in their studies. By encompassing these aspects, we aim to offer a broader and more in-depth re-appraisal of the subject matter. This will ensure that readers gain a holistic understanding of the advances made in the field and the potential for sustainable hydrogen production of biomass gasification.Through a meticulous re-appraisal and analysis of each subject, we can identify their respective strengths and areas that require further research effort. By thoroughly examining numerical modeling, simulation, optimization techniques, process heat integration, and co-generation, we can assess their effectiveness and applicability in the context of biomass gasification for hydrogen production. This analysis shed light on the areas where these techniques excel, as well as pinpoint limitations or gaps in current understanding. By identifying these areas, we can highlight the need for further research and development, enabling us to make significant advancements in biomass gasification processes and pave the way for a sustainable and efficient hydrogen-based future economy..

Environmental estrogen pollution and estrogen effects on the female reproduction system are well-recognized scientifically. Among the estrogens, 17 β-estradiol is a priority in environmental estrogen pollution, and it is also a major contributor to estrogen which regulates the female reproduction system. 17 β-estradiol is carcinogen and has a tumor promotion effect relating to breast cancer, lung cancer and others. It also affects the psychological well-being such as depression, fatigue and others. Thus, a simple method of detection of 17 β-estradiol will be important for both environmental estrogen pollution and women’s health care. This study demonstrates a simple-use, cost effective 17 β-estradiol biosensor system which can be used for both environment and women’s health care applications. The bio-recognition mechanism is based on the influence of the redox couple, K₃Fe (CN) ₆/K₄Fe (CN) ₆ by the interaction between 17 β-estradiol antigen and its α-receptor (ER- α; α-estrogen antibody). The transduction mechanism is an electrochemical analytical technique, differential pulse voltammetry (DPV). The levels of 17 β-estradiol antigen studied was between 2.25 pg/mL to 2,250 pg/mL, Phosphate buffered saline (PBS), tap water from the Cleveland regional water district, and simulate urine were used as the test media covering the potential application areas for 17 β-estradiol detection. An interference study by testosterone which has a similar chemical structure and molecular weight as those of 17 β-estradiol was carried out, and this 17 β-estradiol biosensor showed excellent specificity without any interference by similar chemicals.

Biohydrogen is a versatile energy carrier for the generation of electric energy from renewable sources. Hydrogenases can be used in enzymatic fuel cells to oxidize dihydrogen. The rate of electron transfer (ET) at the anodic side between the [NiFe]-hydrogenase enzyme distal iron-sulfur cluster and the electrode surface can be described by the Marcus equation. All parameters for the Marcus equation are accessible from DFT calculations. The distal cubane FeS-cluster has a three cysteine and one histidine coordination [Fe₄S₄](His)(Cys)₃ first ligation sphere. The reorganization energy (inner- and outer-sphere) is almost unchanged upon a histidine-to-cysteine substitution. Differences in rates of electron transfer between the wild-type enzyme and the all-cysteine mutant can be rationalized by a diminished electronic coupling between the donor and acceptor molecules in the [Fe₄S₄](Cys)₄ case. The fast and efficient electron transfer from the distal iron-sulfur cluster is realized by a fine-tuned protein environment which facilitates the flow of electrons. This study enables the design and control of electron transfer rates and pathways by protein engineering.

Researchers have explored primary colour-geometric shape associations since Kandinsky's correspondence theory. However, there is a gap in the literature regarding interior colour applications on surface shapes. To address this gap, an online study was employed to gather data with one hundred participants who were asked to choose their preferred colours for shapes on bedroom walls with follow-up questions on the colour-shape semantic associations, their preference of colours and shapes on bedroom walls independently in addition to their favourite colour, their favourite shape, and colour-shape pairs without interior context. The results revealed that colour-shape preferences in interiors differ from those found in psychology studies, other colour applications on bedroom walls, and other preferences, indicating that colour preference is context-dependent. Interestingly, the most popular colour for surface shapes on bedroom walls is purple, followed by brown and blue. The study contributes to the existing literature by providing colour charts for bedroom applications for professionals in the industry, their clients/users, and future studies.

Regulation at the level of translation plays an essential role in modulating gene expression. Given the importance of knowledge about translationally active mRNAs as an indicator of translation efficiency, it is crucial for researchers to have at their disposal organised sources of functional information on translation - plant translatomic maps similar to transcription maps. The development of a general experimental and computational methodology for constructing translatome maps is essential to address this challenge. In this manuscript, we discuss what researchers can do to design plant translatome maps, namely, we review: (i) experimental methods to obtain translationally active mRNA fractions, considering their advantages and limitations; (ii) additional methodological approaches to obtain complete pools of translationally active mRNAs; (iii) sequencing methods to qualitatively and quantitatively assess different mRNA fractions; and (iv) multivariate analyses of sequencing results by bioinformatics methods, including metrics for assessing mRNA translational activity.

This paper discusses the design and implementation of a portable IoT station. Communication and data synchronization issues in several installations are addressed here, making possible a detailed analysis of the entire system during its operation. The system proposed is interpreted as an additional tool which can be utilized in several applications, where the system operator requires a synchronized data stream, combining multiple communication protocols into one single time stamp. The hardware selected for the portable IoT station complies with the International Electrotechnical Commission (IEC) industrial standards. A short discussion regarding interface customization shows how easily the hardware can be modified so that it is integrated with almost any system. A Programmable Logic Controller enables the Node-Red to be utilized. This open-source programming tool employs flows to define operations for each global variable nominated in the Modbus register. Two applications are presented and discussed in this paper, each application has a distinct methodology utilized to publish and visualize the acquired data. The portable IoT Station is highly customizable, consisting of a modular structure and providing the best platform for future research and development of dedicated algorithms.

In the sphere of urban renewal in historic districts, preserving and innovatively reinterpreting traditional architectural styles remains a primary research focus. However, the modernization and adaptive reuse of traditional buildings often necessitate changes in their functionality. To cater to the demands of tourism in historic districts, many traditional residential buildings require conversion to commercial use, resulting in a mismatch between their external form and internal function. This study explores an automated approach to transform traditional residences into commercially viable designs, offering an efficient and scalable solution for the modernization of historic architecture. We developed a methodology based on diffusion models, focusing on a dataset of nighttime shopfront facades. By training a Low-Rank adaptation (LoRA) model and integrating the ControlNet model, we enhanced the accuracy and stability of generated images. The methodology’s performance was validated through qualitative and quantitative assessments, optimizing batch size, repetition, and learning rate configurations. These evaluations confirmed the method’s effectiveness. Our findings significantly advance the modern commercial style transformation of historical architectural facades, providing a novel solution that maintains aesthetic and functional integrity, thereby fostering breakthroughs in traditional de-sign thinking and exploring new possibilities for the preservation and commercial adaptation of historical buildings.

Precise monitoring of individual crop growth and health status is crucial for precision agriculture practices. However, traditional manual inspection methods are time-consuming, labor-intensive, and often lack the spatial resolution required for detailed analysis. This research addresses the need for efficient and high-resolution crop monitoring by leveraging Unmanned Aerial Vehicle (UAV) imagery and advanced computational techniques. The primary objective was to develop a methodology for precise identification, extraction, and monitoring of individual corn crops throughout their growth cycle. This was achieved by integrating UAV-derived data with image processing, computational geometry, and machine learning techniques. UAV imagery was ac-quired bi-weekly at altitudes of 40m and 70m, capturing the entire growth cycle of a corn crop from planting to harvest. A time-series Canopy Height Model (CHM) was generated by analyzing the differences between the Digital Terrain Model (DTM) and the Digital Surface Model (DSM) derived from the UAV data. Local spatial analysis and image processing techniques were em-ployed to determine the local maximum height of each crop. Subsequently, a Voronoi data model was developed to delineate individual crop canopies, successfully identifying 13,000 out of 13,050 corn crops in the study area. For enhanced accuracy in canopy size delineation, vegetation indices were incorporated into the Voronoi model segmentation, refining the initial canopy area esti-mates by eliminating interference from soil and shadows. The proposed methodology enables precise estimation and monitoring of crop canopy size, height, biomass reduction, lodging, and stunted growth over time, providing valuable insights for precision agriculture practices. This work contributes to the scientific community by demonstrating the potential of integrating UAV technology, computational geometry, and machine learning for accurate and efficient crop mon-itoring at the individual plant level.

As a key mediator of chronic pain, neurotrophin nerve growth factor (NGF) binds to two neurotrophin receptors: p75 neurotrophin receptor (p75NTR) and tyrosine kinase receptor A (TrkA). The formation of the p75NTR-NGF-TrkA complex is implicated in the potentiation of chronic pain signals, making it an attractive target for therapeutic explorations. NGF analogues and monoclonal antibodies (mAbs) targeting NGF represent two distinct approaches in modulating the intricate signaling pathways involved in pain transduction mediated by the p75NTR-NGF-TrkA complex. While NGF analogues offer the advantage of tailored design to fine-tune neurotrophic responses, monoclonal antibodies provide a more systemic and comprehensive blockade of NGF, inhibiting its interactions with both p75NTR and TrkA receptors. However, the use of mAbs may pose challenges related to potential side effects and interference with the physiological functions of NGF. As a result, balancing the benefits and drawbacks of the two approaches is critical for advancing therapeutic strategies towards the alleviation of p75NTR-NGF-TrkA-mediated pain. In this study, therefore, a novel structural and biophysical approach was employed for the design of NGF analogues to suppress p75NTR-NGF-TrkA-related chronic pain signaling. Employing high-throughput structural modeling and biophysics-based intermolecular binding affinity calculations, this article for the first time puts forward a set of NGF analogues, including in particular NGF analogues with four site-specific mutations, for whose dimerizations the K d at 37 ^◦C were reduced by three orders of magnitude (from 10^-9 M to 10^-6 M) compared to the K d at 37 ^◦C for the dimerization of native NGFs. Overall, the integration of structural and biophysical perspectives enhances our understanding of the rational design of NGF analogues as promising candidates for the development of NGF-targeted analgesic therapies, which balances the benefits and drawbacks of anti-NGF antibodies and NGF analogues for advancing therapeutic strategies towards the alleviation of p75NTR-NGF-TrkA-mediated pain.

Vertical Indoor Farming (VIF) with hydroponics offers a promising perspective for sustainable food production. Intelligent control of VIF system components plays a key role in reducing operating costs and increasing crop yields. Modern machine vision (MV) systems use Deep Learning (DL) in combination with camera systems for various tasks in agriculture, such as disease and nutrient deficiency detection, and flower and fruit identification and classification for pollination and harvesting. This study presents the applicability of MV technology with DL modelling to detect the growth stages of chilli plants using YOLOv8 networks. The influence of different bird’s eye and side view datasets and different YOLOv8 architectures was analysed. To generate the image data for training and testing the YOLO models, chilli plants were grown in a hydroponic environment and imaged throughout their life cycle using four camera systems. The growth stages were divided into growing, flowering and fruiting classes. All trained YOLOv8 models showed reliable identification of growth stages with high accuracy. The results indicate that models trained with data from both views show better generalisation. YOLO’s middle architecture achieved the best performance.

This paper proposes a product design model for internalising users’ positive experiences through attachment. First, the research commences with a literature review to define the concepts and research status of positive experience design (PED) and product attachment. Subsequently, the design model is constructed, accompanied by relevant formulas for concept generation. Finally, the model’s validity is verified through a workshop, and its effectiveness is evaluated and discussed using the USE questionnaire. This study incorporates the seven factors of product attachment to activate positive states within the user–product relationship and facilitate the cultivation of positive thinking. Grounded in theories of PED and embracing a product attachment perspective, the attachment-driven PED model offers a systematic approach for generating design concepts that are relevant to user experience.

3D bioprinting, like 3D printing, is a process that builds structures by depositing material layer by layer. In the specific case of bioprinting, these layers are composed of a biocompatible material together with living cells, allowing the creation of three-dimensional structures that recreate functional tissues and organs. Bioprinting cell-laden structures is complicated by the high rate of cell damage and stress that occurs during the process, caused by the high pressures and stresses to which they are subjected. To minimise damage during the process, it is important to study and optimise certain bioprinting conditions beforehand and to analyse how they affect the cells by means of computational simulations. In this review, both quantitative and qualitative data are collected to improve the nozzle geometry through computational simulation studies. Optimal ranges for nozzle diameter (0.2-1mm) and length (8-10mm; 300-900μm) have been defined, and recommendations for improving nozzle performance during bioprinting, such as the provision of an inner angle of 20-30°, an inner EDTA coating and a shear stress of less than 10kPa, have been gathered. Finally, based on the data collected, a design of experiments is proposed to obtain an optimal bioprinting configuration for a particular bioink.

Radiation Fireballs are singular phenomena which involve severe thermal radiation and consequently, they need to be duly assessed and prevented. Although the radiative heat transfer produced by a sphere is relatively well known, the shadowing measures implemented to control the fireball’s devastating effects have frequently posed a difficult analytical instance, mainly due to its specific configuration. In this article, since the usual solving equations for the said cases are impractical, the authors propose a novel graphic-algorithm method that sorts the problem efficiently for different kinds of obstructions and relative positions of the fireball and the defenses. Adequate application of this method may improve the safety of a significant number of facilities exposed to such risks.

A series of novel isobutylchalcones (A1-A20) were prepared, evaluated for their cytotoxic activity and characterized by FTIR, ¹H NMR, ¹³C NMR, and elemental analysis data. The logic behind the design is to synthesize and compare chalcones containing electron releasing lipophilic isobutyl substituent on aromatic ring A and the B ring with aromatic ring containing a range of electron releasing and electron withdrawing groups as well as heteroaromatic rings for their cytotoxic activity. The compounds were tested against HT-29 (colon cancer), MCF-7 (breast cancer) and DU-145 (prostate cancer) cell lines using methotrexate (IC₅₀ 12 ± 1 (HT-29), 9 ±1 (MCF-7) 5 ± 1 (DU-145)) as reference standard. Compound A6 having 2,4-difluorphenyl moiety was most potent of the series against all the three cell lines and notably A6 was mainly effective against DU-145 cell lines with an IC₅₀ value of 18 µg/mL. The critical structural features required for the activity against all the cell lines were identified through pharmacophore model using PHASE^TM which has recognised a 5 point AHHRR model and is consistent with the cytotoxic activity of the tested compounds.

Under the oxidative stress condition, the small RNA (sRNA) Oxys that acts as essential post-transcriptional regulators of gene expression is produced and plays a regulatory function with the assistance of the RNA chaperone Hfq protein. Interestingly, experimental studies found that the N48A mutation of Hfq protein could enhance the binding affinity with OxyS while resulting in defection of gene regulation. But, how the Hfq protein interacts with sRNA Oxys and the origin of the stronger affinity of N48A mutation are both unclear. In this paper, molecular dynamics (MD) simulations were performed on the complex structure of Hfq and OxyS to explore their binding mechanism. The molecular mechanics generalized Born surface area (MM/GBSA) and interaction entropy (IE) method were combined to calculate the binding free energy between Hfq and OxyS sRNA, and the computational result is in excellent correlation with the experimental result. Per-residue decomposition of the binding free energy revealed that the enhanced binding ability of the N48A mutation mainly comes from the increased van der Waals interactions (vdW). This research explores the binding mechanism between Oxys and chaperone protein Hfq, and revealed the origin of the strong binding affinity of N48A mutation. The results provided important insights on the mechanism of gene expression regulation affected by protein mutations.

Rural landscapes are acknowledged for their potential to restore human health due to their natural characteristics. However, few studies have systematically analysed the impact of naturalness, landscape types, and combinations of landscape elements on restorativeness using both subjective and objective measurements. This study investigated the restorative effects of various rural landscapes in Guangzhou, employing electroencephalography and eye-tracking technologies to record physiological responses and using the Restorative Components Scale and the Perceived Restorativeness and Naturalness Scale to evaluate psychological responses. The results indicated the following: (1) There was a significant positive correlation between perceived naturalness and restorativeness, surpassing the impact of actual naturalness. (2) Different landscape types had a varying impact on mental health at the equal perceived naturalness. Natural forest landscapes and artificial forest landscapes exhibited the most substantial restorative effects among the natural and semi-natural landscapes examined, respectively, while settlement landscapes showed the highest restorative potential among the artificial landscapes. (3) Restorative properties varied across landscape elements, with identical elements yielding different benefits in different environments. Trees and water significantly enhanced restorativeness, whereas constructed elements such as roads and infrastructure reduced it. This study provides a scientific basis for restorative rural landscape design to enhance mental health and well-being.

Intense climate change and rapid urbanization have increased the risk of urban flooding, seriously affecting urban economic and social stability. Enhancing the urban flood resilience (UFR) has required a new solution to cope with urban flood disasters. In this study, taking Yingtan city as an example, a system of indicators for evaluating UFR was constructed, with 17 representative indicators, comprising three subsystems: socio-economic, ecological and infrastructural. A hybrid (FAHP-EWM)-TOPSIS model was applied, to develop an index-based measurement to compare and evaluate UFR, and Gray Relational Analysis (GRA) was used to discover the main factors affecting UFR. In addition, the natural discontinuous method was innovatively used to divide the UFR grade interval into levels, and the grade change was evaluated based on the TOPSIS method. The results showed that (1) from 2010 to 2022, the UFR in Yingtan City increased by 80.69%, and the factors affecting UFR are highly correlated with urban infrastructure development. However, the ecological resilience in the subsystem showed a fluctuating downward trend, because of the influence of the surface area of lakes and rivers. (2) The grades of UFR for Yingtan City increased from Level III (2010 and 2016) to Level IV (2022), with local financial expenditures and the age structure of the population being the main factors currently limiting the development of UFR. The study provides a theoretical basis for the construction of an indicator system for assessing the UFR of Yingtan and proposes practical improvement directions for UFR.

Although Eulerian approaches are standard in computational acoustics, they are less effective for certain classes of problems like bubble acoustics and combustion noise. A different approach for solving acoustic problems is to compute with individual particles following particle motion. In this paper, a Lagrangian approach to model sound propagation in moving fluid is presented and implemented numerically, using three meshfree methods to solve the Lagrangian acoustic perturbation equations (LAPE) in the time domain. The LAPE split the fluid dynamic equations into a set of hydrodynamic equations for the motion of fluid particles and perturbation equations for the acoustic quantities corresponding to each fluid particle. Then, three meshfree methods, the smoothed particle hydrodynamics (SPH) method, the corrective smoothed particle (CSP) method, and the generalized finite difference (GFD) method, are introduced to solve the LAPE and the linearized LAPE (LLAPE). The SPH and CSP methods are widely used meshfree methods, while the GFD method based on the Taylor series expansion can be easily extended to higher orders. Applications to modeling sound propagation in steady or unsteady fluids in motion are outlined, treating a number of different cases in one and two space dimensions. A comparison of the LAPE and the LLAPE using the three meshfree methods is also presented. The Lagrangian approach shows good agreement with exact solutions. The comparison indicates that the CSP and GFD method exhibit convergence in cases with different background flow. The GFD method is more accurate, while the CSP method can handle higher Courant numbers.

During public health events, Active Mobility (walking and cycling) plays an important role in securing residents' travel. To enhance the resilience of urban transportation, this article integrates the travel environment elements for walking and cycling and constructs a travel environment for Active Mobility evaluation index system. This article uses survey data from the main urban area of Harbin in the context of the COVID-19 pandemic in May 2022, and combines the Gradient Boosting Decision Tree (GBDT) and Impact-Asymmetry Analysis (IAA) methods to analyze the satisfaction of the travel environment for Active Mobility. It proposes strategies for optimizing the travel environment for Active Mobility, ensuring residents' travel during public health events, guiding them to adopt Active Mobility, and improving urban transportation resilience. This research shows that Active Mobility can enhance urban transportation resilience. In addition, there are differences in resident demand for walking and cycling environments in the travel environment for Active Mobility. Residents are more concerned about the comfort of the walking environment, while they prioritize the convenience of the cycling environment.

This research focuses on the computational design and analysis of various winglet Design types on a Boeing 737 aircraft. The idea is to compare the efficiencies of various winglet design types on a Boeing 737 aircraft half wing span and the picking out the best winglet design in respect to the pressure generated at the wing-tip and the lift-to-drag ratio of each winglet designs. The coefficient of lift-drag ratio in this research contribute to the reduction of fuel consumption

Background: Chronic Hepatitis B Virus (HBV) infection is a global health concern, associated with severe liver diseases, necessitating ongoing research on novel drug candidates. This study aims to identify potential drug candidates targeting HBV core protein (HBcAg) and disrupting capsid assembly, a critical step in the virus's life cycle. Methods: HBcAg in complex with HBV inhibitors were obtained from the Protein Data Bank (PDB). CavityPlus server was used for analysis of druggable cavity. Structure-based pharmacophores were extracted from identified cavities, and potential allosteric ligand binding sites were assessed using CavPharmer, CorrSite, and CovCys. LigandScout was employed for ligand-based pharmacophore screening against an FDA-approved library. The ZINC database was screened with features extracted from CavPharmer. Molecular docking studies were conducted using Autodock Vina. Lead compounds were selected based on docking scores, binding modes, and interactions within the druggable cavity. Results: Strong druggable pockets were found for Ciclopirox, while Compound 24, NVR10-001E2, and others showed medium to weak pockets. Ligand-based pharmacophores varied in size and complexity. Screening revealed potential hits matching these pharmacophores, including Ciclopirox olamine, Voriconazole, Enasidenib, and Statins. A large compound database search yielded additional hits like ZINC86859997 and ZINC63280172. Docking analyses confirmed these hits' potential, highlighting their interactions with critical HBc protein residues, offering promising leads for hepatitis B drug development. Conclusions: Voriconazole, Enasidenib, and Lovastatin have shown promises. These hits displayed favorable interactions with crucial HBc protein residues, indicating their potential as lead compounds The mechanism of action of statins with anti-HBV activities also highlighted. This comprehensive approach offers valuable insights into targeting HBc protein for antiviral drug discovery.

Proteomics research has become one of the most important topics in the fields of life science and natural science. At present, research on protein–protein interaction networks (PPINs) mainly focuses on detecting protein complexes or function modules. However, existing approaches are either ineffective or incomplete. In this paper, we investigate function module detection mechanisms in PPIN, including open databases, existing detection algorithms and recent solutions. After that, we describe the proposed solution based on simplified swarm optimization (SSO) algorithm and gene ontology knowledge. The proposed solution implements SSO algorithm for clustering proteins with similar function, and imports biological gene ontology knowledge for further identifying function complexes and improving detection accuracy. Furthermore, we use four different categories of species dataset for experiment: Fruitfly, Mouse, Scere, and Human. The testing and analysis result show that the proposed solution is feasible, efficient and could achieve a higher accuracy of prediction than existing approaches.

Bicyclic peptides have attracted the interest of pharmaceutical companies because of their remarkable properties putting them on a new path in medicine. Their conformational rigidity that improves proteolytic stability, leads to rapid penetration into tissues via any possible route of administration and elimination, occurring by renal metabolism, is of great importance for people with a history of liver diseases. In addition, each ring can function independently, making them extremely versatile molecules for further optimization. In this paper we compared the potentiometric and spectroscopic properties studied by UV-Vis, MCD, and EPR, of four synthetic analogues of the bi-cyclic peptide c(PKKHP-c(CFWKTC)-PKKH). In particular we correlated the structural and spectral properties of complexes with coordinating abilities toward Cu(II) ions of MCL (Ac-PKKHPc(CFWKTC)PKKH-NH2) that contains the unbinding cycle and N- and C-terminal linear parts, with two histidine residues, one per part; two monocyclic ligands containing one histidine residue, both in the N-terminal position i.e., MCL2 (Ac-PKKHPc(CFWKTC)PKKS-NH2) and in the C-terminal position i.e., MCL3 (Ac-PKKSPc(CFWKTC)PKKH-NH2), respectively, and the linear structure LNL (Ac-PKKHPSFWKTSPKKH-NH2).

In recent times, using image-based deep learning techniques has been very useful for classifying fruits. However, mango is a particular challenge because its shelf life is directly related to the degree of maturity at the time of harvest, which is determined mainly through the visual assessment of the operators when sorting them, doing it incorrectly, coupled with the blows and compressions exposed during storage and transport, trigger the deterioration in their quality, hence the importance of this operation. In this sense, the purpose of this study focuses on designing a computational model based on Deep Learning that uses a convolutional neural network (CNN) parameterized to select mangoes according to their color tone based on their maturity stage. A dataset was established from 201 labeled images, then a CNN architecture was built, obtaining a model on which 3 criteria were evaluated: the number of convolution and grouping layers, the resolution of the input images and the number of training epochs required. As a result, an input image resolution of 32 x 32 pixels was achieved, the process included a convolution layer followed by pooling that fed another layer that processed the results and ended in an output layer with 2 neurons under the One Hot Encoding representation. The model required 20 epochs to perfect its training, achieving 96.04 % accuracy in mango image selection. This study supports the potential of CNNs in quality analysis through mango color selection, even in uncertainty scenarios.

Recent discovery of triangular boron Nanotubes makes it a competitor of carbon in many respects. Closed forms of M-polynomial of nanotubes produce closed forms of many degree-based topological indices which are numerical parameters of the structure and, in combination, determine properties of the concerned nanotubes. In this report, we give M-polynomials of triangular boron nanotubes and recover many important topological degree-based indices of these nanotubes. We also plot surfaces associated to these nanotubes.

A central hypothesis on brain functioning is that plasticity regulates the signals transfer function by modifying the efficacy of synaptic transmission. In the cerebellum, granular layer has been shown to control the gain of signals transmitted through the mossy fiber pathway. Until now, the impact of plasticity on incoming activity patterns was analyzed by combining electrophysiological recordings in acute cerebellar slices and computational modeling, unraveling a broad spectrum of different forms of synaptic plasticity in the granular layer, often along with forms of intrinsic excitability changes. Here, we attempt to provide a brief overview of the most prominent forms of plasticity at excitatory synapses formed by mossy fibers onto principal neurons (granule cells, Golgi cells and unipolar brush cells) in the granular layer. Specifically, we will highlight current understanding of the mechanisms and their functional implications of the synaptic and intrinsic plasticity, providing valuable insights into how inputs are processed and reconfigured at the cerebellar input stage.

The combination of immunoPET—where an antibody (Ab) is labeled with an isotope for PET imaging—and radioimmunotherapy (RIT), using the same antibody with a therapeutic isotope, offers significant advantages in cancer management. ImmunoPET allows non-invasive imaging of antigen expression, which aids in patient selection for a subsequent radioimmunotherapy. It also facilitates the assessment of tumor response to therapy, allowing for treatment adjustments if necessary. In addition, immunoPET provides critical pharmacokinetic data, including antibody biodistribution and clearance rates, which are essential for dosimetry calculations and treatment protocol optimization. There are still challenges to overcome. Identifying appropriate target antigens that are selectively expressed on cancer cells while minimally expressed on normal tissues remains a major hurdle to reduce off-target toxicity. In addition, it is critical to optimize the pharmacokinetics of radio-labeled antibodies to maximize tumor uptake and minimize normal tissue uptake, particularly in vital organs such as the liver and kidney. This approach offers the potential for targeted and personalized cancer therapy with reduced systemic toxicity by exploiting the specificity of monoclonal antibodies and the cytotoxic effects of radiation. However, further research is needed to address remaining challenges and to optimize these technologies for clinical use.

Some wrong decisions and faulty practices applied during the repair and restoration of traditional buildings cause more damage to the structures due to the materials used in the repair. In this study, brick, mortar, plaster, and wood samples were taken from one registered building in the Konuralp neighborhood of Düzce Province, which has survived to the present day by preserving its original structural features and reflecting the characteristics of traditional housing. Texture, chemical and petrographic analyses were carried out on the samples. In line with these analyses, a scientific basis was created for selecting material properties in the repair and reuse processes of traditional buildings, and suggestions are made for the analysis of materials specific to traditional buildings in Konuralp.

At the end of the dry season in early March to early April each year, extensive agricultural biomass waste burnings occur throughout insular mainland Southeast Asia. During this biomass burnings period, smoke aerosols blanket the whole regions and were transported and dispersed by predominant westerly and south westerly winds to southern China, Taiwan and as far southern Japan and the Philippines. The extensive and intense burnings coincided with some wildfires in the forests due to high temperature making the region one of the global hot spots of biomass fires. In this study, we focus on the effect of pollutants emitted from biomass burnings in March 2019 at the height of the burnings period to the exposed population and their health impact. The Weather Research Forecast- Chemistry (WRF-Chem) model was used to predict the PM2.5 concentration over the simulating domain and health impacts are then assessed on the exposed population in the four countries of Southeast Asia, namely Thailand, Laos, Cambodia and Vietnam. Using the health impact based on log-linear concentration response function and Integrated Exposure Response (IER), the results show that at the peak period of the burnings from 13 to 20 March 2019, Thailand experienced the highest impact, with an estimated 2170 premature deaths. Laos, Vietnam, and Cambodia followed, with estimated mortalities of 277, 565, and 314 deaths respectively. However, when considering the impact per head of population, Laos exhibited the highest impact, followed by Thailand, Cambodia, and Vietnam. The results highlight the significant health impact and associated economic cost of agricultural waste burnings in Southeast Asia at the end of the dry season. And hence policy makers should take these into account to design measures to reduce the negative impact of widespread burnings on exposed population in the region.

China is experiencing rapid urbanization. The focus on health issues has shifted from the treatment of diseases to passive or active prevention. Diseases caused by the deterioration of the urban environment, such as cardiovascular disease, diabetes, obesity and other chronic diseases and mental illness, have gradually become the main threat to public health. Studies have confirmed that urban green spaces (UGS) can help residents relieve mental stress, improve mood, reduce mental fatigue and restore attention, which has a positive effect on people's physical and mental health. In this study, the relationship between the quantity and quality factors of UGS and the physical and mental health of urban residents in Xihu District of Hangzhou is examined, and on this basis, UGS design strategies are proposed to promote the physical and mental health of residents. This study is of great significance for the future construction of UGS that are beneficial to the health of residents and the promotion of a healthy China and healthy city construction.

Drug-target binding is a crucial parameter in drug discovery and design, ensuring drug efficacy and specificity. Semaglutide, a potent GLP-1 receptor agonist, is widely used to treat type 2 diabetes mellitus by regulating blood glucose levels and promoting weight loss. This study introduces a novel approach utilizing the concept of a general intermolecular binding affinity calculator (GIBAC) for designing semaglutide analogues with enhanced binding affinity to GLP-1R. For the first time, a Val27-Arg28 exchange was manually introduced to strengthen the semaglutide-GLP-1R binding affinity. A comprehensive structural and biophysical analysis was conducted to explore the semaglutide-GLP-1R sequence space, leading to the identification of promising analogues. Among these, one semaglutide analogue demonstrated a binding affinity to GLP-1R that is more than two orders of magnitude (113.3 times) higher than native semaglutide, achieving a Kd of 3.0 x 10^-8 M compared to the Kd of 3.4 x 10^-6 M for native semaglutide. This article proposes a promising structural biophysical approach for developing GLP-1 receptor agonists with improved efficacy. The prototype GIBAC, termed semaGIBAC, represents a paradigm shift in precise drug discovery and design, advocating for the construction of a full-scale GIBAC to be prioritized within the drug discovery and design community.

Drug-target binding is an essential parameter in drug discovery and design to ensure drug efficacy and specificity. In 2022, the concept of a general intermolecular binding affinity calculator (GIBAC) was for the first time coined: $Kd = f(molecules, envPara)$. Technically, GIBAC represents a set of in silico approaches for structural and biophysical data generation towards a paradigm shift in precise drug discovery and design, providing a comprehensive framework for intermolecular binding affinity calculation with adequate accuracy, precision and efficiency. For the first time, this study reports a prototype of GIBAC (semaGIBAC), i.e., a one-dimensional semaglutide-GLP-1R-based mini static GIBAC, based on an experimental complex structure of semaglutide and GLP-1R. Semaglutide is a potent GLP-1 receptor agonist used to treat type 2 diabetes mellitus by regulating blood glucose levels and promoting weight loss. In 2021, a structural modification involving a Val27-Arg28 exchange was manually introduced to enhance semaglutide-GLP-1R binding affinity. This study employs a comprehensive structural and biophysical analysis aimed at thoroughly exploring the sequence space of semaglutide-GLP-1R to design analogues with improved binding affinity, leading to the identification of a promising semaglutide analogue, which binds to GLP-1R with an affinity that is more than two orders of magnitude (113.3 times) higher than native semaglutide. To sum up, this article puts forward a promising structural biophysical approach for developing GLP-1 receptor agonists with enhanced efficacy, and with a GIBAC prototype (semaGIBAC), this article argues again that the time is now ripe for the construction of a real GIBAC to be listed on the agenda of the drug discovery and design community.

Insulin therapy is a cornerstone in the management of diabetes, yet the pursuit of optimizing its pharmacokinetic profile remains a focal point in diabetes research. For instance, insulin icodec of Novo Nordisk is a novel long-acting insulin analogue that exhibits an extended duration of action, providing a promising once-weekly treatment option for diabetic patients. However, designing insulin analogues with lower receptor affinity than that of insulin icodec could still produce a further extended duration of action than that of insulin icodec through the suppression of receptor-mediated internalization of insulin analogues. In this study, therefore, I present the design of a novel series of insulin analogues engineered towards lower binding affinity to the insulin receptor compared to that of insulin icodec. Through computational structural biophysics-based rational design strategies, this article aims to further extend the duration of action while maintaining therapeutic efficacy of insulin analogues. Utilizing homology molecular modeling and structural biophysics-based binding affinity calculations, this article puts forward a set of insulin analogues with lower binding affinity to insulin receptor than that of insulin icodec from a structural and biophysical point of view. Overall, this article calls for subsequent in vitro and in vivo evaluations of the efficacy and prolonged action of the engineered insulin analogues to pharmacokinetically test whether these analogues acutally surpass that of insulin icodec as hopeful candidates for next-generation insulin analogue therapies with improved duration of action and enhanced control of blood glucose levels for diabetic patients in future.

From a structural point of view, understanding interfacial electrostatic features of ligand-receptor complexes is crucial for biophysics-based design of therapeutics with improved efficacy and safety. Here, with a high-throughput structural feature extraction approach, this article reports a set of electrostatic structural features from glucagon-like peptide-1 (GLP-1) and GLP-1 receptor (GLP-1R) complex structures determined using experimental tools. Leveraging a set of computational structural biophysical analyses, this article systematically characterized the electrostatic interactions within the GLP-1-GLP-1R complexes with two sets of criteria to identify interfacial salt bridges and hydrogen bonds. Overall, the results here reveal intricate details of electrostatic interactions critical for GLP-1 binding to its receptor GLP-1R, providing biophysical insights into the stabilization of the GLP-1-GLP-1R complex structure and the molecular basis of GLP-1/GLP-1R signaling. Furthermore, the electrostatic structural features extracted from GLP-1 and GLP-1R complex structures are useful in the development of a machine learning-based ligand-receptor binding affinity calculating model, i.e., a GLP-1-GLP-1R-specific general intermolecular binding affinity calculator (GIBAC), paving the way for accelerated discovery and structure-based design of drug candidates targeting the GLP-1/GLP-1R system with improved efficacy and safety.

This study investigates the spatial and temporal dynamics of Paomo restaurants in Xi'an's central urban area from 2012 to 2023, leveraging Point of Interest (POI) data through a series of sophisticated analytical techniques. Methods including nearest neighbor distance, kernel density estimation, and band set statistical analysis were employed to thoroughly examine the evolutionary patterns and key drivers influencing the spatial distribution of these eateries. Our results highlight a notable trend of expansion from the central city to the periphery, characterized by significant spatial agglomeration. Initially concentrated in the city center, the distribution of Paomo restaurants has progressively shifted towards multiple distinct cores, reflecting the dual influence of urban policy initiatives and the dynamics of market competition. Furthermore, the research identifies several pivotal factors shaping this spatial distribution, particularly transportation access, residential quality of life, and the availability of commercial services. These findings demonstrate the profound effects of urban spatial reconfiguration and socioeconomic development on the specialized catering industry. The insights gleaned from this analysis offer valuable guidance for urban planners and policymakers, providing strategic recommendations for the spatial planning of specialty catering services to optimize urban layouts and foster sustainable economic progress.

The disc brake rotors are designed to withstand both, Maximum possible deceleration (emergency braking) and a series of frequent braking cycles. A effective rotor design and superior heat dissipating material provide better performance during the braking mechanism. In this experiment, modified ventilated disk brake rotors are developed with holes and slots and the stress, deformation, heat flux and temperature distribution has been analysed. Designing of the Finite element models of the rotor are created with SolidWorks and simulated using ANSYS. Structural and thermal characteristics are compared with a reference disk brake rotor of the motorcycle (TVS Apache RTR180). It is found that the modified rotors outperform the conventional one in terms of stress generation, temperature distribution and deformation. Furthermore, this Analysis helps us to find out the best suited material for one of the proposed designs. This experiment provides us an insight of the structural and thermal characteristics of the geometrically modified rotor that can be used to upgrade and outperform the current disc brake rotor used in the motorcycle (TVS Apache RTR180).

Ever since Varela and Maturana proposed the concept of autopoiesis as the minimal requirement for life, there has been a focus on cellular systems that erect topological boundaries to separate themselves from their surrounding environment. Here we reconsider whether the existence of such a spatial boundary is strictly necessary for self-producing entities. This work presents a novel computational model of a minimal autopoietic system inspired by dendrites and molecular dynamics simulations in three-dimensional space. A series of simulation experiments where the metabolic pathways of a particular autocatalytic set are successively inhibited until autocatalytic entities that could be considered autopoietic are produced. These entities maintain their distinctness in an environment containing multiple identical instances of the entities without the existence of a topological boundary. This gives rise to the concept of a metabolic boundary which manifests as emergent self-selection criteria for the processes of self-production without any need for unique identifiers. However, the adoption of such a boundary comes at a cost, as these autopoietic entities are less suited to their simulated environment than their autocatalytic counterparts. Finally, this work showcases a generalized metabolism-centered approach to the study of autopoiesis that can be applied to both physical and abstract systems alike.

This work focuses on the design improvement of a tri-axial piezoresistive accelerometer specifically designed for head injuries monitoring where medium-G impacts are common, for example in sports such as racing cars or American Football. The device requires the highest sensitivity achievable with a single proof mass approach, and a very low error (<1%) as the accuracy for these types of applications is paramount. The optimization method differs from previous work as it is based on the progressive increment of the sensor mass moment of inertia (MMI) in all three axes. The work numerically demonstrates that an increment of MMI determines an increment of device sensitivity with a simultaneous reduction of cross-talk in the particular axis under study. The final device shows a sensitivity increase of about 80% in the Z-axis and a reduction of cross-talk of 18% respect to state-of-art sensors available in the literature. Sensor design, modelling and optimization are presented, concluding the work with results, discussion and conclusion.

Amid escalating urbanization, devising rational commercial space layouts is a critical challenge. Leveraging machine learning, this study uses a Back-propagation (BP) neural network to optimize commercial spaces in Weinan City's central urban area. The results indicate an increased number of commercial facilities with a trend of multi-centered agglomeration and outward expansion. Based on these findings, we propose a strategic framework for rational commercial space development emphasizing aggregation centers, development axes, and spatial guidelines. This strategy provides valuable insights for urban planners in small and medium-sized cities in the Yellow River Basin and metropolitan areas, ultimately showcasing the power of machine learning in enhancing urban planning.

This paper presents a comprehensive comparative analysis of four different computational vision algorithms for implementing an unpalleting system in a factory environment, under typical edge computing constraints. The primary objective is to automate the process of detecting and locating specific variable-shaped objects on a pallet, enabling a robotic system to accurately unstack them. The four algorithms evaluated are Pattern Matching, Scale-Invariant Feature Transform, Oriented FAST and Rotated BRIEF, and Haar cascade classifiers. Each technique is described in detail in this paper, and their implementations are outlined. Experimental results are thoroughly analyzed, assessing the algorithms’ performance based on accuracy, robustness to variability, computational speed, detection sensitivity, and resource consumption. The findings reveal the strengths and limitations of each algorithm, providing valuable insights for selecting the most appropriate technique based on the specific requirements of the unpalleting system. This research contributes to the advancement of automated object detection and localization in industrial environments, paving the way for more efficient and reliable unpalleting systems.