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This paper presents a novel computational process used for determining matching coefficients for optical spectra either in real time or from a database. This simple algorithmic method is capable of outputting a set of coefficients relating to comparative matching datapoints and to a weighted similarity ratio between spectra whilst still maintaining high computational efficiency due to the minimization of floating-point calculations. The process works as effectively on signal data during real time processing or on images of spectra which may be stored in a database. A subset of the algorithm has already been used very effectively in an embedded mobile field device to determine the presence or absence of special security fluorescent emission peaks present in printing inks used in anti-counterfeiting labels. The process involves the division of test spectrum(s) signal/image into a grid of user definable blocks while applying the same grid to the test image or images. Each block is then checked to see if the signal/image passes through the block, if the signal/image passes through a block then the value of this grid block is set to 1 in a grid array. The same check is then applied to the reference signal/image(s).While it is possible to process a signal comparison without the need for data storage, useful for some embedded processes, it may be preferable to store the resultant data in simple data arrays. These arrays can then undergo simple arithmetical processing to quickly calculate a set of values representing the number of matching data points, the variation in non-matching data points and a divergence value. This check can be carried out between the test image or images and a larger number of reference images simultaneously by use of additional counters. In addition, when used with large databases of spectra this novel technique may be used to rapidly reduce a large test set to a more relevant subset which can be further analyzed using more conventional methods or by visual inspection. This grid square analysis method works as efficiently on images as it can for signal data and it is believed by the authors that the method will have applications in other image processing applications where fast and efficient comparative analysis between images is required.

Installing audio-based applications exposes users to the risk of the data processor extracting additional information beyond the task the user permitted. To solve these privacy concerns, we propose to integrate an on-edge data obfuscation between the audio sensor and the recognition algorithm. We introduce a novel privacy loss metric and use adversarial learning to train an obfuscator. Contrary to existing work, our technique does not require users to specify which sensitive attributes they want to protect (opt-out) but instead only provide permission for specific tasks (opt-in). Moreover, we do not require retraining of recognition algorithms, making the obfuscated data compatible with existing methods. We experimentally validate our approach on four voice datasets and show that we can protect several attributes of the speaker, including gender, identity, and emotional state with a minimal recognition accuracy degradation.

The ability to engineer complex multicellular systems has enormous potential to inform our understanding of biological processes and disease and alter the drug development process. Engineering living systems to emulate natural processes or to incorporate new functions relies on a detailed understanding of the biochemical, mechanical, and other cues between cells and between cells and their environment that result in the coordinated action of multicellular systems. On April 3-6, 2022, experts in the field met at the Keystone symposium "Engineering Multicellular Living Systems" to discuss recent advances in understanding how cells cooperate within a multicellular system, as well as recent efforts to engineer systems like organ-on-a-chip models, biological robots, and organoids. Given the similarities and common themes, this meeting was held in conjunction with the symposium "Organoids as Tools for Fundamental Discovery and Translation".

In plants, the robust maintenance of tissue structure is crucial to supporting its functionality. The multi-layered shoot apical meristem (SAM) of Arabidopsis, containing stem cells, is an approximately radially symmetric tissue whose shape and structure is maintained throughout the life of the plant. In this paper, a new biologically calibrated pseudo-three-dimensional (P3D) computational model of a longitudinal section of the SAM is developed. It includes anisotropic expansion and division of cells out of the cross-section plane, as well as representation of tension experienced by the SAM epidermis. Results from the experimentally calibrated P3D model provide new insights into maintenance of the structure of the SAM epidermal cell monolayer under tension and quantify dependence of epidermal and subepidermal cell anisotropy on the amount of tension. Moreover, the model simulations revealed that out-of-plane cell growth is important in offsetting cell crowding and regulating mechanical stresses experienced by tunica cells. Predictive model simulations show that tension-determined cell division plane orientation in the apical corpus may be regulating cell and tissue shape distributions needed for maintaining structure of the wild-type SAM. This suggests that cells' responses to local mechanical cues may serve as a mechanism to regulate cell- and tissue-scale patterning.

Players in serious games may often need multiple trials for correctly completing a game task. Therefore, the number of attempts should be reflected in the score. This article presents three computational score models that take into account the number of attempts that a player makes to be successful. The models, which are extensions of test theory, quantify the random contributions to the scores that need to be removed. They also describe the influence of prior knowledge used for elimination of incorrect options, and take into account that the decision options in a node may not be equally plausible. In a series of simulation studies the score outcomes of the models are compared under various conditions. Results show that the number of trials cannot be ignored as they have a strong impact on the performance scores to be assigned. Neglecting the number of trials leads to inaccurate scores that significantly overrate the observed performances, occasionally up to 100% or even more. The effects depend on the number of decision options, the presence of obvious, correct or incorrect options given the player´s knowledge level and, to a lesser extent, different plausibility levels of the options to decide upon. The practical feasibility is high, because a simple score formula largely solves the problem.

Aquest treball de fi de grau del Grau en Enginyeria Mecànica posa a prova la simulació computacional de dinàmica de fluids mitjançant el programari OpenFOAM on s'intentarà esbrinar si es pot reproduir casos d’experiments reals amb precisió i realisme. S'avaluarà, sota diverses velocitats, el comportament d'un túnel de vent real proporcionat pel Departament de Mecànica de Fluids. Les dades obtingudes durant els experiments s'usaran per calcular una possible simulació i aquesta es durà a terme. D'aquesta manera esbrinarem si realment és possible simular la realitat d'aquests casos. En últim lloc, es deixarà una base sòlida per a la continuació d'aquest treball ampliant els coneixements i afegint camps com l'estudi d'objectes i proves aerodinàmiques. Este trabajo de fin de grado del Grado en Ingeniería Mecánica pone a prueba la simulación computacional de dinámica de fluidos mediante el software OpenFOAM donde se intentará averiguar si se puede reproducir casos de experimentos reales con precisión y realismo. Se evaluará, bajo varias velocidades, el comportamiento de un túnel de viento real proporcionado por el Departamento de Mecánica de Fluidos. Los datos obtenidos durante los experimentos se usarán para calcular una posible simulación y esta se llevará a cabo. De este modo averiguaremos si realmente es posible simular la realidad de estos casos. En último lugar, se dejará una base sólida para la continuación de este trabajo ampliando los conocimientos y añadiendo campos como el estudio de objetos y pruebas aerodinámicas. This final thesis of the Degree in Mechanical Engineering tests the computational simulation of fluid dynamics using OpenFOAM software where we will try to find out if it is possible to reproduce cases of real experiments with accuracy and realism. The behavior of a real wind tunnel provided by the Fluid Mechanics Department will be evaluated under various velocities. The data obtained during the experiments will be used to calculate a possible simulation and this will be carried out. In this way we will find out if it is possible to simulate the reality of these cases. Finally, a solid base will be left for the continuation of this work by extending the knowledge and adding fields such as the study of aerodynamic objects and tests

Epilepsy is a neural disease characterized by rhythmic synchronous firing of neurons, also called seizures. These seizures can be modelled as oscillations of voltage input into neighboring neural regions. In this thesis we put forward a leaky integrate-and-fire network model to investigate how seizure-like inputs interact with heterogeneity. We find that heterogeneity supresses oscillatory behaviour of the network, linearizing the system, with large decreases seen in both resonant and higher-order harmonics. While some frequencies do not follow this trend, heterogeneity is shown to decorrelate excitatory-inhibitory networks, promoting asynchronous firing.

Cell counting and sorting is a vital step in the purification process within the area of biomedical research. It has been widely reported and accepted that the use of hydrodynamic focusing in conjunction with the application of a dielectrophoretic (DEP) force allows efficient separation of biological entities such as platelets from red blood cell (RBC) samples due to their size difference. This paper presents computational results of a multiphysics simulation modelling study on evaluating continuous separation of RBCs and platelets in a microfluidic device design with saw-tooth profile electrodes via DEP. The theoretical cell particle trajectory, particle cell counting, and particle separation distance study results reported in this work were predicted using COMSOL v6.0 Multiphysics simulation software. To validate the numerical model used in this work for the reported device design, we first developed a simple y-channel microfluidic device with square “in fluid” electrodes similar to the design reported previously in other works. We then compared the obtained simulation results for the simple y-channel device with the square in fluid electrodes to the reported experimental work done on this simple design which resulted in 98% agreement. The design reported in this work is an improvement over existing designs in that it can perform rapid separation of RBCs (estimated 99% purification) and platelets in a total time of 6–7 s at a minimum voltage setting of 1 V and at a minimum frequency of 1 Hz. The threshold for efficient separation of cells ends at 1000 kHz for a 1 V setting. The saw-tooth electrode profile appears to be an improvement over existing designs in that the sharp corners reduced the required horizontal distance needed for separation to occur and contributed to a non-uniform DEP electric field. The results of this simulation study further suggest that this DEP separation technique may potentially be applied to improve the efficiency of separation processes of biological sample scenarios and simultaneously increase the accuracy of diagnostic processes via cell counting and sorting.

The reaction between [PtCl2(L-L)] (L-L = dppe, dppp, dppb, dppf, Phen and Bipy) or [PtCl2(PPh3)2] with 1-benzyl-3-phenylthiourea (H2BPT) in a basic medium (CHCl3/EtOH) created new coordinated square planner Pt(II) complexes with [Pt(BPT)(L-L)] (1–4,6,7) and [Pt(BPT)(PPh3)2] (5) types. These complexes were fully characterized by analytical and spectroscopic techniques (i.e., IR, UV. Vis., 1H, and 31P NMR). The results indicated that the thiourea derivative ligand act as a dianion ligand bonded through both S and N atoms in a chelating mode or as a mono-anion ligand coordinated through a sulfur atom with Pt(II) ion. Cytotoxicity activity was performed by the MTT assay to determine anti-cancer activities against MCF-7 breast cancer cells. The study indicated that IC50 values for MCF-7 cells were 10.96–78.90 µM. Additionally, the complexes [Pt(BPT)(dppe)] (1), [Pt(BPT)(PPh3)2] (5), and [Pt(BPT)2(Bipy)] (7) were investigated theoretically, where their quantum parameters were evaluated using the Gaussian 09 program using the theory of B3LYP/Def2TZVP//B3LYP/Lanl2dz. The calculation results confirmed the optimized structures of the complexes square planar geometry. However, the calculated bond lengths and angles showed a slightly distorted square planar geometry due to the trans influence of the sulfur atom. Additionally, complexes of [Pt(BPT)(dppe)] (1) and [Pt(BPT)(PPh3)2] (5) showed higher stability compared to [Pt(BPT)2(Bipy)] (7), which can be attributed to the higher back-donation of (1) and (5) complexes. Furthermore, among the three complexes, the [Pt(BPT)2(Bipy)] (7) complex possessed the lowest HOMO–LUMO gap, which may be a good candidate as the photo-catalyst material.

Three new tripod tetradentate phenolate-amines (H2L1, H2L4 and H2L9), together with seven more already related published ligands, were synthesized, and characterized. With these ligands, two new dinuclear doubly-bridged-phenoxido copper(II) complexes (3, 4), and six more complexes (1, 2, 5–8), a new trinuclear complex (9) with an alternative doubly-bridged-phenoxido and –methoxido, as well as the 1D polymer (10) were synthesized, and their molecular structures were characterized by spectroscopic methods and X-ray single crystal crystallography. The Cu(II) centers in these complexes exhibit distorted square-pyramidal arrangement in 1–4, mixed square pyramidal and square planar in 5, 6, and 9, and distorted octahedral (5+1) arrangements in 7 and 8. The temperature dependence magnetic susceptibility study over the temperature range 2–300 K revealed moderate–relatively strong antiferromagnetic coupling (AF) (|J| = 289–145 cm−1) in complexes 1–6, weak-moderate AF (|J| = 59 cm−1) in the trinuclear complex 9, but weak AF interactions (|J| = 3.6 & 4.6 cm−1) were obtained in 7 and 8. No correlation was found between the exchange coupling J and the geometrical structural parameters of the four-membered Cu2O2 rings.

Solar energy is an essential renewable energy source among all the other renewable energy sources. It is possible to improve the efficiency of the solar energy absorber by increasing the solar energy absorber’s capacity for absorption, which can help in building better solar-based renewable energy devices. The need of covering the whole solar spectrum led us to design this T-shaped metasurface solar absorber which is based on graphene material. The T-shaped absorber gives 90, 88 and 57% absorption in the visible, infrared and UV regions, respectively. This symmetrical structure is also periodic with respect to x-axis and y-axis. This solar absorber demonstrates better efficiency compared to many other existing solar absorbers. The solar absorber is also compared with two other square-1 and square-2 designs to show the improvement in solar energy absorption. The parametric optimization method is applied to optimize the design. The parameters, such as the length and width of the substrate and the thicknesses of the T-shaped metasurface and substrate, are varied to find out the optimized design for maximum solar energy absorption. The optimized parameters obtained from the optimization are 1000, 2500, 3000 and 3000 nm, for resonator thickness, substrate thickness, substrate length and substrate width, respectively. The design results for graphene material and its potential variation are also observed. The design also shows good absorption for a wide-angle of incidence of about 0 to 50°. The increased efficiency of this design can be applied in future solar absorber devices.

Analytical data in the new era of automated synthesis are so extensive that rapid automatic processes are essential to gain information from the data. Manual methods of NMR analysis are incredibly labour intensive and prone to errors, whilst computational methods typically rely on DFT which is too computationally intensive for such high-through put use. In this work new ways of estimating the uncertainty in molecular structures through automated computational analysis of NMR data have been pioneered. This work is divided into five sections. The first section describes the development of DP4-AI, a program which automatically processes and analyses experimental NMR data to calculate the probability of each molecular structure in a list being the correct one given the correct structure is in the list. The second section discusses the characteristics of DFT-NMR prediction errors and shows how these prevent DP4-AI being applied to single molecular structures. The third section explains how new methods from the field of molecular machine learning can be employed to solve these problems. This discussion concludes in the fourth section with the development and evaluation of the DP5 Probability. The DP5 probability utilises principles from the field of molecular machine learning to calculate the probability of a single given molecular structure corresponding to an observed experimental NMR spectrum. The final section describes a complete reformulation of the DP5 probability and redesign of the DP5 program. This affords rapid and DFT-free calculation of the DP5 probability. Overall, the new methods developed and evaluated as part of this work bridge the gap between state-of-the-art robotic synthesis and structure determination and will help usher in the new era of automated chemical synthesis and discovery., EPSRC

This paper is about seagrasses, which are marine plants that form important ecosystems in coastal seas around the world. The paper discusses how little research has been done on seagrass proteins and peptides compared to other marine flora like seaweeds. It also highlights potential benefits of studying these proteins and peptides, such as understanding molecular adaptations for survival underwater or using protein biomarkers for monitoring habitat quality and health of seagrass meadows.

Nature is an inexhaustible source of new therapeutics, and humans have been using it to heal themselves for ages. Shaped by evolution, plants, bacteria, or fungi, among other living organisms, contain hundreds of potential bioactive metabolites, among which many are still to be discovered. Today, Ultra-High Performance Liquid Chromatography (UHPLC) coupled with High-Resolution tandem Mass Spectrometry (HRMS/MS) is the standard method to chemically profile these organisms and get an insight into their composition. Thanks to modern data processing workflows and annotation strategies, large amounts of spectral and structural data can be obtained and linked to biological screening results for each natural extract. In this thesis project, we aimed to develop innovative metabolomics workflows using UHPLC-HRMS/MS and biological screening results of large natural extracts collections to identify bioactive compounds in mixtures. The developed approach should help rationalize efforts to isolate valuable natural products (NP) only. As an application case, we used metabolomics and anti-trypanosomatid activity data - against Trypanosoma cruzi, T. brucei, and Leishmania donovani - obtained on a dataset of 1,600 plant extracts. We developed a novel MS/MS-based sample vectorization method, called MEMO, to highlight chemical similarities among extracts in large chemodiverse libraries without needing any retention time (RT) based feature-alignment step. Because such an alignment makes the iterative addition of new samples difficult, we developed a pythonbased framework to organize automatically, through molecular networking, and annotate, through state-of-the-art annotation tools, the LC-MS features’ fragmentation spectra of each sample independently. While such a sample-centric workflow allows for better iterative addition of samples by avoiding the necessity to recompute post-featurealignment annotations steps, it hinders the direct comparison of the considered samples using LC-MS features’ relative intensity among samples. To maintain a way to compare unaligned samples, the generated and standardized data produced at the sample scale were integrated into a single Experimental Natural Product Knowledge Graph (ENPKG). Such a format allows the integration of both chemical and bioactivity data and the connection of experimental data to other graph databases such as Wikidata and ChEMBL. Using the SPARQL language to query the data, we could demonstrate how such an organization represents an efficient strategy to link and interpret heterogeneous chemical and biological data to annotate bioactive compounds in extracts before their isolation.To confirm the generated bioactivity annotations, we isolated and characterized some metabolites of interest from different extracts using semi-preparative HPLC, NMR, and chiroptical methods. Because they do not require any RT-based feature alignment step and allow the easy addition of new samples over time, the MEMO method and the ENPKG workflow represent sustainable data exploitation and data management strategies in NP research. Integrating crosslinks to other DBs helps the ENPKG workflow avoid data loss into hermetic silos of information. Because of their versatility and integrative character, we believe that the developed tools should contribute to advancing the knowledge of Nature's fascinating chemistry.

The “Ising model” refers to both the statistical and the theoretical use of the same equation. In this article, we introduce both uses and contrast their differences. We accompany the conceptual introduction with a survey of Ising-related software packages in R. Since the model’s different uses are best understood through simulations, we make this process easily accessible with fully reproducible examples. Using simulations, we show how the theoretical Ising model captures local-alignment dynamics. Subsequently, we present it statistically as a likelihood function for estimating empirical network models from binary data. In this process, we give recommendations on when to use traditional frequentist estimators as well as novel Bayesian options.

Methylation of aromatic compounds is a key reaction step in various industrial processes such as the aromatic cycle of methanol-to-hydrocarbons chemistry. The study of isolated methylation reactions and of the influence of catalyst acidity on their kinetics is a challenging task. Herein, we have studied unidirectional metal-substituted H-MeAlPO-5 materials to evaluate the effect of acid strength on the kinetics of benzene methylation with DME. First-principle simulations showed a direct correlation between the methylation barrier and acid site strength, which depends on the metal substituent. Three H-MeAlPO5 catalysts with high (Me = Mg), moderate (Me = Si) and low acidity (Me = Zr) were experimentally tested, confirming a linear relationship between the methylation activation energy and acid strength. The effects of temperature and reactant partial pressure were evaluated, showing significant differences in the byproduct distribution between H-MgAlPO-5 and H-SAPO-5. Comparison with propene methylation suggested that the Mg substituted catalyst is also the most active for the selective methylation of alkenes. (c) 2021 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

The continuous interaction between experimental and theoretical work has proven to be extremely useful for the study of pancreatic cells and, recently, of pancreatic islets. This prolific interaction relies on the capability of implementing computational models and methods to derive quantitative data for the analysis and interpretation of experimental observations. In this addendum I introduce Isletlab, a multiplatform application developed to provide the research community with a user-friendly interface for the implementation of computational algorithms for the characterization and simulation of pancreatic islets.

Protein-based therapeutics such as monoclonal antibodies and cytokines are important therapies in various pathophysiological conditions such as oncology, auto-immune disorders, and viral infections. However, the wide application of such protein therapeutics is often hindered by dose-limiting toxicities and adverse effects, namely cytokine storm syndrome, organ failure and others. Therefore, spatiotemporal control of the activities of these proteins is crucial to further expand their application. Here, we report the design and application of small molecule-controlled switchable protein therapeutics by taking advantage of a previously engineered OFF-switch system. We used Rosetta modeling suite to computationally optimize the affinity between B-cell lymphoma 2 (Bcl-2) protein and a previously developed computationally designed protein partner (LD3) to obtain a fast and efficient heterodimer disruption upon addition of a competing drug (Venetoclax). The incorporation of the engineered OFF-switch system intoαCTLA4, anti-HER2 antibodies or an Fc-fused IL-15 cytokine demonstrated an efficient disruption in vitro, as well as fast clearance in vivo upon addition of the competing drug Venetoclax. These results provide a proof-of-concept for the rational design of controllable biologics by introducing a drug-induced OFF-switch into existing protein-based therapeutics.

There is a growing body of research demonstrating that the cerebellum is involved in language understanding. Early theories assumed that the cerebellum is involved in low-level language processing. However, those theories are at odds with recent work demonstrating cerebellar activation during cognitive tasks. Using natural language stimuli and an encoding model framework, we performed an fMRI experiment where subjects passively listened to five hours of natural language stimuli which allowed us to analyze language processing in the cerebellum with higher precision than previous work. We used this data to fit voxelwise encoding models with five different feature spaces that span the hierarchy of language processing from acoustic input to high-level conceptual processing. Examining the prediction performance of these models on separate BOLD data shows that cerebellar responses to language are almost entirely explained by high-level conceptual language features rather than low-level acoustic or phonemic features. Additionally, we found that the cerebellum has a higher proportion of voxels that represent social semantic categories, which include “social” and “people” words, and lower representations of all other semantic categories, including “mental”, “concrete”, and “place” words, than cortex. This suggests that the cerebellum is representing language at a conceptual level with a preference for social information.Significance StatementRecent work has demonstrated that, beyond its typical role in motor planning, the cerebellum is implicated in a wide variety of tasks including language. However, little is known about the language representations in the cerebellum, or how those representations compare to cortex. Using voxelwise encoding models and natural language fMRI data, we demonstrate here that language representations are significantly different in the cerebellum as compared to cortex. Cerebellum language representations are almost entirely semantic, and the cerebellum contains over-representation of social semantic information as compared to cortex. These results suggest that the cerebellum is not involved in language processing per se, but cognitive processing more generally.

The purpose of this work is to study the impact of a face bypass damper in tackling mould problem caused by an oversized air conditioning system. Comparison was made between the performance of an actual operating AHU and the performance of the AHU after the installation of face bypass damper. Comparison is also made to various simulated model using building energy simulation software to study its effectiveness in solving actual problem. Both actual and simulated result of the face bypass damper shows that it is effective in reducing relative humidity. However, it should not be preferred over a complete replacement with a correctly size AHU due to the energy penalty it incurs.

Frontiers in Physiology, 13, ISSN:1664-042X

According to a World Health Organization study done in 2016, cardiovascular disease is the largest cause of death globally. While strides in the clinical sector over the past few decades have greatly improved understanding of how cardiovascular disease is formed and affects the body, there are still unknowns such as the mechanics involved in disease progression. Computational fluid dynamics for blood flow has potential to greatly improve understanding of cardiovascular disease while offering advantages over traditional clinical methods that use imaging such as CAT scans. Such advantages include greater spatial resolution of the area of interest, non-invasive diagnostics, and the ability to simulate how treatment may affect a patient’s health. Software packages such as “SimVascular”, an open-source project, model hemodynamics by using the Navier-Stokes equations to govern blood flow. While robust, SimVascular cannot model additional blood flow processes such as blood clotting. In this study, an in-house solver is developed using a computing platform, FEniCS, with Python to model blood flow. LEoPart, a particle library, is used to model platelets as they are advected within the flow. Using a collision library, when platelets collide or exceed a specific wall shear stress magnitude, the particle’s color will change, visually indicating an "activation" stage for blood clotting. The velocity and wall shear stress from the simulation model were verified by calculating the absolute error when compared to the analytic solution for Poiseuille flow, 0.000826 m/s and 0.0015567 Pa respectively. From this baseline of modeling blood flow and platelets as particles in an easy-to-use and open-source format with Python and FEniCS, this code can continue to be modified by others to further examine and understand the mechanics involved with blood clotting, shear thinning, and more.&nbsp

Laporan ini berisi penjelasan tentang alur kerja lampu lalu lintas dalam bentuk program python. Program akan dijelaskan terpisah dalam dua kondisi yaitu kondisi satu ruas jalan (waktu mundur dan penyebrangan pejalan kaki) serta pada kondisi perempatan (pergantian nyala lampu antar ruas jalan). Link Video Youtube

Childhood and adolescence are marked by improvements to cognition and by the emergence of neurodevelopmental disorders such as attention deficit hyperactivity disorder (ADHD). What neural mechanisms are associated with cognitive development in ADHD? In this study, I applied machine learning models to functional connectivity profiles to identify patterns of network connectivity that predict various cognitive abilities in a group of participants ages 6 to 16 with ADHD. The models successfully predicted IQ, visual spatial, verbal comprehension, and fluid reasoning in children ages 6 to 11, but not adolescents. Furthermore, the models identified connections with the default mode, memory retrieval, and dorsal attention networks as driving prediction during early and middle childhood, but connections with the somatomotor, cingulo-opercular and frontoparietal networks were more important in middle childhood. These results suggest that computational models can identify neural mechanisms associated with predicting cognitive abilities in children and adolescents with ADHD using naturalistic stimuli.

I cluster di metalli hanno iniziato a svolgere un ruolo importante nella scienza dei materiali negli ultimi decenni. Ciò è dovuto al comportamento peculiare mostrato dalla materia a dimensioni nanometriche. Tali differenti comportamenti vanno da proprietà tipiche (energie di coesione, potenziali di ionizzazione, numeri medi di coordinazione) fino alla reattività (catalisi) e alla risposta ai campi elettromagnetici esterni (comportamenti ottici). Inoltre la materia a nanodimensione pone domande interessanti ai modelli interpretativi: spesso è difficile descrivere i nanosistemi con i paradigmi tipici delle molecole o dei sistemi bulk. Per tali ragioni in questo campo la teoria e in generale gli studi computazionali possono essere molto importanti per razionalizzare i comportamenti osservati. Nel presente studio ci siamo concentrati sulle proprietà ottiche dei cluster metallici, che includono transizioni molecolari standard e fenomeni collettivi come i plasmoni. La necessità di descrivere i plasmoni e quindi il moto collettivo degli elettroni impone di andare oltre l'una particella (approssimazione di particelle indipendenti), considerando un modello di risposta lineare come TDDFT, che offre all'economia computazionale della DFT insieme la capacità di descrivere fenomeni collettivi. In particolare gran parte degli sforzi è stata dedicata al miglioramento dell'algoritmo TDDFT di polarizzabilità complessa (POLTDDFT), un metodo efficiente recentemente sviluppato nel gruppo di chimica teorica dell'Università di Trieste per risolvere le equazioni TDDFT per sistemi di grandi dimensioni e per un ampio intervallo di energia, in per evitare i limiti di schemi più tradizionali come quello dovuto a Casida. A questo proposito un nuovo schema chiamato Hybrid Diagonal Appromation (HDA) ha permesso di applicare il funzionale ibrido nei calcoli TDDFT di grandi sistemi, ottenendo un'ottima corrispondenza con l'esperimento. Anche in questo lavoro è stato realizzato lo sviluppo di un nuovo set di base per il fit della densità, ottimizzato per POLTDDFT, che fornisce all'utente generico uno strumento valido per applicare il metodo ad una gamma molto ampia di target. Oltre allo sviluppo di metodi, anche applicazioni a campi aperti della scienza dei cluster, come il dicroismo circolare elettronico (ECD) di cluster metallici chirali in collaborazione con Thomas Bürgi (Università di Ginevra) e esperimenti di fotoassorbimento a bassa temperatura molto accurati in collaborazione con Flavio Maran (Università di Padova) ) sono stati presi in considerazione. Nel campo specifico della plasmonica è stato studiato anche l'effetto dell'accoppiamento di due sistemi chirali plasmonici sull'ECD ed è stato identificato il meccanismo di accoppiamento sottostante. L'accuratezza del metodo suggerito insieme alla sua efficienza computazionale rendono POLTDDFT un buon candidato per applicazioni ancora più ampie, ma, cosa più importante, l'accuratezza quantitativa ottenuta rende questo metodo predittivo, e quindi un buon candidato anche per la progettazione in-silico di nuovi materiali con le proprietà ottiche richieste. Metal clusters have started to play a major role in material science in the last decades. This is due to the peculiar behaviour showed by the matter at nanosize. Such different behaviours range from typical properties (cohesive energies, ionization potentials, average coordination numbers) up to reactivity (catalysis) and response to external electromagnetic fields (optical behaviours). Moreover matter at nanosize poses interesting questions to interpretative models: it is often hard to describe nanosystem with the paradigms typical of molecules or bulk systems. For such reasons in this field theory and in general computational studies can be very important to rationalize the observed behaviours. In the present study we have focussed on the optical properties of metal clusters, which include standard molecular transitions as well as collective phenomena like plasmons. The necessity to describe plasmons and therefore electron collective motion imposes to go beyond the one-particle (independent particle approximation), considering a linear response model like TDDFT, which offers the computational economy of DFT together the ability to describe collective phenomena. In particular a large part of efforts have been devoted to improve the complex polarizability TDDFT algorithm (POLTDDFT), an efficient method recently developed in the theoretical chemistry group of Trieste University to solve the TDDFT equations for large systems and for a wide energy interval, in order to avoid the limits of more traditional schemes like that due to Casida. In this respect a new scheme called Hybrid Diagonal Approximation (HDA) has allowed to apply hybrid functional in TDDFT calculations of large systems, obtaining excellent match with the experiment. Also in this work the development of a new density fitting basis set, optimized for POLTDDFT, has been carried out, giving to the generic user a valid tool to apply the method to a very wide range of targets. Besides method development, also applications to open fields of cluster science, like Electronic Circular Dichroism (ECD) of chiral metal clusters in collaboration with Thomas Bürgi (Geneve University) as well as very accurate low temperature photoabsorption experiments in collaboration with Flavio Maran (Padua University) have been considered. In the specific field of plasmonics, also the effect of coupling two plasmonic chiral systems on the ECD has been studied, and the underlying coupling mechanism has been identified. The accuracy of the suggested method together with its computational efficiency make POLTDDFT a good candidate for even wider applications, but, more important, the quantitative accuracy obtained makes this method predictive, and therefore a good candidate also for the in-silico design of novel materials with requested optical properties.

In this thesis we focus on computational modelling of flexoelectricity for the design of metamaterials exhibiting apparent piezoelectricity. To do this, we introduce generalized periodicity conditions in an existing finite element code. Given that flexoelectricity is modelled by a set of fourth order PDEs, imposing generalized periodicity conditions requires not only imposing conditions on nodal values, but also on faces, to account for high-order interface conditions.

Creating an interpretative geometric representation of architectural shapes is crucial to learn the essence of any architecture, to preserve it and to hand it down through the ages to our descendants. Nowadays the generation of an accurate reality-based 3D model from a survey is the first step to set up a geometric model that, before its semantic enrichment, must be geometrically correct.In this paper, we compare different workflows adopted in modelling a Renaissance vault in Santa Maria delle Grazie complex in Milan starting from a laser scanner survey acquired within a wider survey campaign of the complex. The vault of the Ancient Sacristy, a Renaissance masterpiece by Donato Bramante, has been chosen according to its integrity, despite the bombings suffered by the Complex in 1943. The workflows have been used to optimize a system of comprehension of the building and to evaluate by analogy a similar vault of the complex added with some reflection at the end: the vault of the Refectory that hosts “the Last Supper” painting. This latter was destroyed by bombing and rebuilt last century on the drawing of a direct survey previously occurred. We can consider the vaulted system as a clear example to practice understanding of the geometric rules of architecture first and the vaulted surface modelling then, to be compared through the principles of descriptive geometry in a dialectical relationship between theory and practice between geometry and construction. In this paper, multi-purpose interpretative models try to read and describe the state of the art and possible declinations of the semantics linked to an architectural object unique in its value.DOI: https://doi.org/10.20365/disegnarecon.26.2021.5

Contents removed due to extenuating circumstances.

An introduction to the role of resolvins in inflammation is outlined in Chapter 1. In addition, a literature survey of the structure-activity relationships of resolvins, protectins and maresins is also included. Chapter 2 briefly outlines the aims and objectives of this project. Chapter 3 focuses on the computational analysis of the outlined virtual library of Resolvin D2 analogues. The key physicochemical properties of each candidate are calculated and unsuitable candidates are eliminated using standard screening protocols. Chapter 4 describes the attempted synthesis of a key intermediate for the preparation of an aromatic resolvin analogue. This attempted synthesis encompasses a monosilylation of a diol, a selective oxidation of an alcohol to an aldehyde, a Wittig olefination and, finally, a comprehensive trial of acetal deprotection methods for a 1,2 dioxane acetal in the presence of a silyl ether. Chapter 5 summarises the overall findings of this project and outlines possible future avenues for exploration. The full computational data set and experimental procedures, including spectroscopic data, are detailed in Chapter 6.

The importance of selenium in biology, in organic catalysis, and green chemistry is well established. Selenoproteins, among which are ubiquitous Glutathione Peroxidases (GPx), play a key role in mitigating oxidative stress by reducing H2O2 and hydroperoxides using glutathione as a cofactor. Organoselenides, particularly diphenyl diselenide, in the presence of H2O2, are efficient and often green oxygen transfer agents in important organic reactions, such as Baeyer-Villiger oxidations of ketones/aldehydes, the conversion of alkenes into epoxides, and the oxidations of alcohols and nitrogen- containing compounds. NMR spectroscopy can facilitate the investigation of the properties of this element in a biological environment and the characterization of the peculiar species of its chemistry. In this short review, a brief overview of the experimental and computational 77Se NMR-based techniques is outlined, with a particular focus on their applications to the study of biologically relevant organoselenium compounds and bio-mimetic systems. Experimental protocols, together with computational methods used in different contexts, are presented and their potential as efficient investigation tools is critically discussed. It emerges that while the 77Se NMR measurement is a consolidated technique, no standard computational protocol is available to compute the shielding constant of the chalcogen nucleus with accuracy and the optimal approach combines molecular dynamics in solution and quantum chemistry calculations to take into account the conformational freedom.

Dengue virus (DENV) is a major mosquito vector based human pathogenic flavivirus which is causing major threat worldwide, yet the availability of therapeutic treatment and several vaccines, still called for advance treatment and vaccine development. The present top down computational approach is a vaccine development step to find novel super antigenic HLA binding epitopes from DENV proteome. The approach used sequence based screening to find complete conserve and high population coverage, common epitopes among all DENV serotype. Propred and Immune Epitope Data Base were used for sequence based screening with recommended parameters. Among top 29 identified epitopes, five structural protein epitopes viz. 33LQGRGPLKL41, 249VVVLGSQEG257, 172LVGIVTLYL180, 146MKILIGVVI154, 72YIIVGVEPG80 and one nonstructural protein epitope 18LKNDIPMTG26 were showed high conserve nature and high population coverage from complete DENV proteome. Further structure based study involving docking and molecular dynamic simulation to confirm stable behavior of HLA allele–peptide complex to give potent cell mediated immune response. Docking of epitope 72YIIVGVEPG80–DRB1 0401 allele and epitope 33LQGRGPLKL41–B*5101 allele complexes showed the best binding energy of − 7.71 and − 7.20 kcal/mol, respectively and stable binding pattern over the time window during molecular dynamic simulation. This computational approach resulted novel epitopes which can be used in the design and development of short epitope based vaccines as well as diagnosis tools for dengue infection.

Gold-centered carbene–metal–amides (CMAs) containing cyclic (alkyl)(amino)carbenes (CAACs) are promising emitters for thermally activated delayed fluorescence (TADF). Aiming at the design and optimization of new TADF emitters, we report a density functional theory study of over 60 CMAs with various CAAC ligands, systematically evaluating computed parameters in relation to photoluminescence properties. The CMA structures were primarily selected based on experimental synthesis prospects. We demonstrate that TADF efficiency of the CMA materials originates from a compromise between oscillator strength coefficients and exchange energy (ΔEST). The latter is governed by the overlap of HOMO and LUMO orbitals, where HOMO is localized on the amide and LUMO over the Au–carbene bond. The S0 ground and excited T1 states of the CMAs adopt approximately coplanar geometry of carbene and amide ligands, but rotate perpendicular in the excited S1 states, resulting in degeneracy or near-degeneracy of S1 and T1, accompanied by a decrease in the S1-S0 oscillator strength from its maximum at coplanar geometries to near zero at rotated geometries. Based on the computations, promising new TADF emitters are proposed and synthesized. Bright CMA complex (Et2CAAC)Au(carbazolide) is obtained and fully characterized in order to demonstrate that excellent stability and high radiative rates up to 106 s−1 can be obtained for the gold–CMA complexes with small CAAC–carbene ligands.

We have presented a solar absorber design with gold–MgF2–tungsten materials. The solar absorber design is optimized with nonlinear optimization mathematical method to find and optimize geometrical parameters. The wideband absorber is made of a three-layer structure composed of tungsten, magnesium fluoride, and gold. This study analyzed the absorber’s performance using numerical methods over the sun wavelength range of 0.25 μm to 3 μm. The solar AM 1.5 absorption spectrum is a benchmark against which the proposed structure’s absorbing characteristics are evaluated and discussed. It is necessary to analyze the behavior of the absorber under a variety of various physical parameter conditions in order to determine the results and structural dimensions that are optimal. The nonlinear parametric optimization algorithm is applied to obtain the optimized solution. This structure can absorb more than 98% of light across the near-infrared and visible light spectrums. In addition, the structure has a high absorption efficiency for the far range of the infrared spectrum and the THz range. The absorber that has been presented is versatile enough to be used in a variety of solar applications, both narrowband and broadband. The design of the solar cell that has been presented will be of assistance in designing a solar cell that has high efficiency. The proposed optimized design with optimized parameters will help design solar thermal absorbers.

Computers have become closely involved with most aspects of modern life, and these developments are tracked in the chemical sciences. Recent years have seen the integration of computing across chemical research, made possible by investment in equipment, software development, improved networking between researchers, and rapid growth in the application of predictive approaches to chemistry, but also a change of attitude rooted in the successes of computational chemistry—it is now entirely possible to complete research projects where computation and synthesis are cooperative and integrated, and work in synergy to achieve better insights and improved results. It remains our ambition to put computational prediction before experiment, and we have been working toward developing the key ingredients and workflows to achieve this.The ability to precisely tune selectivity along with high catalyst activity make organometallic catalysts using transition metal (TM) centers ideal for high-value-added transformations, and this can make them appealing for industrial applications. However, mechanistic variations of TM-catalyzed reactions across the vast chemical space of different catalysts and substrates are not fully explored, and such an exploration is not feasible with current resources. This can lead to complete synthetic failures when new substrates are used, but more commonly we see outcomes that require further optimization, such as incomplete conversion, insufficient selectivity, or the appearance of unwanted side products. These processes consume time and resources, but the insights and data generated are usually not tied to a broader predictive workflow where experiments test hypotheses quantitatively, reducing their impact.These failures suggest at least a partial deviation of the reaction pathway from that hypothesized, hinting at quite complex mechanistic manifolds for organometallic catalysts that are affected by the combination of input variables. Mechanistic deviation is most likely when challenging multifunctional substrates are being used, and the quest for so-called privileged catalysts is quickly replaced by a need to screen catalyst libraries until a new “best” match between the catalyst and substrate can be identified and the reaction conditions can be optimized. As a community we remain confined to broad interpretations of the substrate scope of new catalysts and focus on small changes based on idealized catalytic cycles rather than working toward a “big data” view of organometallic homogeneous catalysis with routine use of predictive models and transparent data sharing.Databases of DFT-calculated steric and electronic descriptors can be built for such catalysts, and we summarize here how these can be used in the mapping, interpretation, and prediction of catalyst properties and reactivities. Our motivation is to make these databases useful as tools for synthetic chemists so that they challenge and validate quantitative computational approaches. In this Account, we demonstrate their application to different aspects of catalyst design and discovery and their integration with computational mechanistic studies and thus describe the progress of our journey toward truly predictive models in homogeneous organometallic catalysis.

In the present study, the effect of the use of false-ceiling on fire-induced smoke flow characteristics in tunnels is investigated using a 3D developed computational fluid dynamics tool. The critical velocity, the minimum required tunnel ventilation velocity to stop the smoke flow from moving toward the tunnel inlet (toward the upstream of the fire source), and the maximum gas temperature beneath the ceiling are selected to evaluate the smoke flow control in presence of the false-ceiling. The hydraulic height of the cross-sectional geometry of the tunnel is used as the characteristic length in order to dimensional analyze and compare the non-dimensional results. The results indicate that the use of the false-ceiling reduces the critical velocity and the smoke backlayering, while increases the maximum ceiling gas temperature. Reducing the critical velocity results in ventilation cost reduction (positive impact), while increasing the maximum gas temperature beneath the ceiling increases the risk of instrumental and life damages (negative impact). The detailed results and corresponding physical discussions are presented to clarify the reason for the significant differences between the results of the tunnels with and without false-ceiling.

International audience; We describe a compressive hyperspectral microscope based on a structured light sheet and a deep convolutional neural network. The setup extends the concept of the computational hyperspectral microscope introduced in Ref. 1. First, we significantly improve the quality of the structured light sheet using a digital micro-mirror device to generate the light patterns directly. Second, to reduce the acquisition time, only fewer light patterns are acquired. The resulting inverse problem is solved using a deep neural network that includes traditional Tikhonov regularization.

This thesis concerns the nature of cognition. It posits that cognitive processes primarily are means to maintain allostasis in organisms whose ecological niches require movement to approach food-resources and avoid predation. Hence triggering, or motivation, of behaviours are a consequence of prediction errors from the body resulting from biological variables moving away from homeostasis. Depending on circumstance and the nature of the particulars of the ecological niche, an organism may require the ability to find the way to a goal-site containing food or water, perceive its surroundings in order to trigger allostatic behaviour, make choices and priorities, and predict outcomes. Hence, cognition is situated in a larger context of staying alive, but efforts are also made to zoom in on exactly how some important cognitive processes may plausibly work, on the level of neural units and networks. These processes include visual perception, spatial cognition, predictive simulation processes (intelligence), and familiarity based trust, as well as reflection, decision-making, and memory.

Variable grouping provides an efficient approach to large-scale optimization, and multipopulation strategies are effective for both large-scale optimization and dynamic optimization. However, variable grouping is not well studied in large-scale dynamic optimization when cooperating with multipopulation strategies. Specifically, when the numbers/sizes of the variable subcomponents are large, the performance of the algorithms will be substantially degraded. To address this issue, we propose a bilevel variable grouping (BLVG)-based framework. First, the primary grouping applies a state-of-the-art variable grouping method based on variable interaction analysis to group the variables into subcomponents. Second, the secondary grouping further groups the subcomponents into variable cells, that is, combination variable cells and decomposition variable cells. We then tailor a multipopulation strategy to process the two types of variable cells efficiently in a cooperative coevolutionary (CC) way. As indicated by the empirical study on large-scale dynamic optimization problems (DOPs) of up to 300 dimensions, the proposed framework outperforms several state-of-the-art frameworks for large-scale dynamic optimization.

St. Lawrence Island Yupik, an endangered language of the Bering Strait region spoken by fewer than one thousand people in western Alaska and far eastern Russia, is currently in a state of generational transition. We survey the existing body of Yupik literature and pedagogical resources developed during the twentieth century, examine the context and use of Yupik in the current educational setting, and describe current challenges for teaching the language in the schools. We then outline our integrated approach to language documentation currently being applied to Yupik, and address how existing resources can be integrated into research and development processes in a way that both supports research efforts and results in tangible modern educational tools for the Yupik community on St. Lawrence Island, and eventually in Russia. This approach is intentionally designed to closely integrate research processes from language documentation and computational linguistics such that the results of each research endeavour positively support the other, and such that both disciplines concretely support community-based efforts to revitalize and teach the language., Le yupik de l’île Saint-Laurent, une langue du détroit de Béring en péril, (moins de 1 000 locuteurs habitent dans l’ouest de l’Alaska et l’extrême est de la Russie) est actuellement en période de transition générationnelle. Cet article examine la littérature disponible sur le yupik et les ressources pédagogiques qui se sont développées au cours du XXe siècle. Il étudie également le contexte et l’utilisation du yupik dans le cadre éducatif actuel et décrit les défis liés à l’enseignement de la langue dans les écoles. On décrit ensuite l’approche intégrée de la documentation linguistique appliquée actuellement au yupik et aborde aussi les façons dont les ressources existantes peuvent être intégrées aux processus de recherche et de développement, de manière à soutenir les efforts de recherche et produire des outils pédagogiques modernes et tangibles pour la communauté yupik de l’île Saint-Laurent (et à plus largement en Russie). Nous avons conçu cette approche pour intégrer étroitement les processus de recherche de la linguistique documentaire et de la linguistique computationnelle, de sorte que les résultats de chaque projet de recherche puissent se soutenir mutuellement, et appuient concrètement les efforts communautaires de revitalisation et d’enseigner de la langue.

The recent renaissance of psychedelic science has reignited interest in the similarity of drug-induced experiences to those more commonly observed in psychiatric contexts such as the schizophrenia-spectrum. This report from a multidisciplinary working group of the International Consortium on Hallucinations Research (ICHR) addresses this issue, putting special emphasis on hallucinatory experiences. We review evidence collected at different scales of understanding, from pharmacology to brain-imaging, phenomenology and anthropology, highlighting similarities and differences between hallucinations under psychedelics and in the schizophrenia-spectrum disorders. Finally, we attempt to integrate these findings using computational approaches and conclude with recommendations for future research.

Simple, fast, and sensitive spectrophotometric method was developed for the determination of metformin in pharmaceutical formulations. The spectrophotometric procedure was based on the oxidation of metformin with potassium permanganate in alkaline medium. The developed method was also validated according to International Conference on Harmonization guidelines parameters such as linearity, accuracy, precision, limit of detection and quantitation. Studies were conducted to investigate different parameters involved in color developments and optimized. The pure drug of metformin was extracted from a pharmaceutical dosage form. The extraction procedure was developed, and a possible reaction mechanism proposed in the manuscript. The IR spectrum of the extracted metformin was taken and compared with the simulated IR spectrum obtained from the density functional theory calculation. The vibrational assignment of the modes was done based on potential energy distribution.

Background The foremost cause of cancer mortality worldwide was lung cancer. Lung cancer is divided into small cell lung cancer and non-small cell lung cancer (NSCLC). The latter is the main type of lung cancer that account for about 90% of the cancer issues and estimate about 25% of the cancer mortality each year in the world. Among the types of lung cancer with about 1.5 million patients and less than 20% survival rate is NSCLC. Overexpression of EGFR tyrosine kinase was recognized to be the cause of NSCLC. Therefore, there is a need to develop more EGFR inhibitors due to drug-resistance development by the mutation. Result Computational virtual screening on some epidermal growth factor receptor inhibitors (EGFRL858R/T790M inhibitors or NSCLC therapeutic agents) against their target protein (EGFR tyrosine kinase receptor pdb entry 3IKA) was performed via molecular docking simulation and pharmacokinetics to identify hit compounds with a promising affinity toward their target. The hit compounds discovered were compound 22 with −9.8 kcal/mol, 24 with −9.7 kcal/mol, 17 with −9.7 kcal/mol, and 19 with −9.5 kcal/mol respectively. These lead compounds were further subjected to drug-likeness and ADME prediction and found to be orally bioavailable. Six (6) new EGFRL858R/T790M inhibitors using compound 22 with the highest binding affinity as a template were designed. Conclusion The six newly EGFRL858R/T790M inhibitors were found to have a better binding affinity than the template used in the designing process and AZD9291 (the positive control). None of the designed compounds was found to violate more than the permissible limit set by RO5 thereby predicting their easy transportation, absorption, and diffusion. More so, the designed compounds were found to have good synthetic accessibility which indicates that these designed compounds can be easily synthesized in the laboratory.

Sumatran lichen has the potential as antiviral, pure isolates that have been isolated and developed as prospective compounds for COVID-19 treatment. Computational methods were used to accelerate the discovery and screening of potential new compounds. The molecular structures of the isolated compounds such as Lobarin, Atranorin, Methyl 2,4-dihydroxy-3,6-dimethylbenzoate, Methyl 3‐formyl‐2,4‐dihydroxy‐6‐methylbenzoate, Ethyl 3-formyl-2,4-dihydroxy-6-methylbenzoate, and Lobaric acid were drawn, then their activities were analyzed, processed by docking with ACE2 protein, and tested for Druglikeness. The activities and druglikeness were determined in the Swiss ADME program, while the ACE2 docking was processed by Blind Docking in Arguslab, AutoDock Vina, Open Babel, and Discovery Studio Visualizer programs. All compounds bound to the ACE2 protein, as apparent from the number of hydrogen bonds between the two. The Gibbs free energy was in the range of -5.6 to -7.0, and the best one was obtained from atranorin. As for lobarin, this compound was found to be non-drug-like.

This study examined the effect of the water channel slope angle on breastshot waterwheel turbine performance using the computational fluid dynamics method with six-degrees of freedom (6-DoF) feature. The method and 6-DoF feature were chosen because turbine rotation is a computational result and the flow pattern can be represented more precisely. Computational results using the 6-DoF feature were used as the basis for recommending a ratio of rotational velocity for a wheel ( U ) with an inlet velocity ( W ), or U ∕ W , for turbine design. Five slope angles were analysed, and the results indicated higher efficiency was produced by a 10 ∘ slope angle while the lowest was 25 ∘ . Based on results, a higher slope angle channel could cause losses or leakage due to water leading to a gap between the wheel and channel. As such, a method of increasing the kinetic energy of water by modifying the channel shape, such as having an angle, requires additional attention to the water velocity direction. Moreover, stable efficiency occurs in a ratio of U ∕ W between 1.1 - 1.6. Thus, it is estimated that the optimum turbine design recommended for U being 1.1 to 1.6 times W .

Preventing and reducing the spread of HIV (HIV) has always been a concern in medical science. One of the most common ways to control the virus is using enzyme-blocking drugs. In this study, we attempted to predict the biological activity (PKi) of organic urea derivatives in protease inhibitor compounds using molecular modeling using QSAR (Quantitative Structure Activity Relation), which is the basis of quantitative study of the structure between And there is activity. Models were presented. In this study, the chemical structure of 41 compounds was optimized by Gaussian 09 software and other properties (descriptors) were obtained using software. The level used in B3LYP calculations and ground state series was 6-31G *. Validation tests were then performed on the obtained models. The results of the statistical data were acceptable. Given the effective variables in the models, it predicts biological activity and invokes appropriate drug design.

The human brain contains billions of neurons that flexibly interconnect to support local and global computational spans. As neuronal activity propagates through the neural medium, it approaches a critical state hedged between ordered and disordered system regimes. Recent work demonstrates that this criticality coincides with the small-world topology, a network arrangement that accommodates both local (sub-critical) and global (super-critical) system properties. On one hand, operating near criticality is thought to offer several neurocomputational advantages, e.g., high dynamic range, efficient information capacity and information transfer fidelity. On the other hand, aberrations from the critical state have been linked to diverse pathologies of the brain, such as post-traumatic epileptiform seizures and disorders of consciousness. Modulation of brain activity, through neuromodulation, presents an attractive mode of treatment to alleviate such neurological disorders, but a tractable neural framework is needed to facilitate clinical progress. Using a variation on Watts and Strogatz’s generative small-world model, and Kuramoto’s model of coupled oscillators, we here show that the topological and dynamical properties of the small-world network are divided into two functional domains based on the range of connectivity, and that these domains play distinct roles in shaping the behavior of the critical state. We demonstrate that short-range network connections shape the dynamics of the system, e.g., its volatility and metastability, whereas long-range connections drive the system state, e.g., a seizure. Together, these findings lend support to combinatorial neuromodulation approaches that synergistically normalize the system dynamic while mobilizing the system state.

Background Detection of locally increased blood concentration and perfusion is critical for assessment of functional cortical activity as well as diagnosis of conditions such as intracerebral hemorrhage (ICH). Current paradigms for assessment of regional blood concentration in the brain rely on computed tomography (CT), magnetic resonance imaging (MRI), and perfusion blood oxygen level dependent functional magnetic resonance imaging (BOLD-fMRI). Results In this study, we developed computational models to test the feasibility of novel magnetic sensors capable of detecting hemodynamic changes within the brain on a microtesla-level. We show that low-field magnetic sensors can accurately detect changes in magnetic flux density and eddy current damping signals resulting from increases in local blood concentration. These models predicted that blood volume changes as small as 1.26 mL may be resolved by the sensors, implying potential use for diagnosis of ICH and assessment of regional blood flow as a proxy for cerebral metabolism and neuronal activity. We then translated findings from our computational model to demonstrate feasibility of accurate detection of modeled ICH in a simulated human cadaver setting. Conclusions Overall, microtesla-level magnetic scanning is feasible, safe, and has distinct advantages compared to current standards of care. Computational modeling may facilitate rapid prototype development and testing of novel medical devices with minimal risk to human participants prior to device construction and clinical trials.