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In food industries, the usage of eggs in different products plays a very important role. The quality of eggs consists of various aspects of quality, each of which can be related to the quality of interior eggs or the quality of external eggs. Chitin's crisp egg-shell does not bear the collision and extrusion, and is very easy to damage. The external microorganism will reach the shell easily through the crack if the egg-shell has been split, so the decaying of the egg is accelerated or the consistency is decreased. The parameters such as color, thickness, shell strength, eggshell crack, bloodspots, porosity, air cell depth, shell integrity and freshness can also be used to detect the contaminated egg. But the weight loss and change in temperature gives us the variation in quality of the egg gradually and accurately. A simple method is proposed in which the key parameters like weight and temperature is measured using suitable sensor. The analog output from the sensor is fed to the Arduino board as an input signal for further processing. Based on the measured parameters value the quality of the egg and its expiring day will be displayed for consumer.

The state parameter ψ is widely used for soil characterisation and as a controlling parameter in modern constitutive understanding of soil, but there remains a perception that the control of soil strength by ψ is merely that of a correlation. This perception possibly stems from ψ having been introduced from ‘principles’ of critical state theory rather than derived, which is now rectified. It is shown that the control of limiting dilatancy by the state parameter (and thus soil strength through stress–dilatancy) is a formal mathematical consequence of Casagrande's canonical characterisation linking void ratio to soil constitutive behaviour. This formal consequence is independent of soil type, being applicable across the spectrum from clays to sands. Three dimensionless and familiar soil properties are involved in addition to those characterising the critical state locus: Mtc, N and X. The framework is kinematic, with no constitutive model: it is a constraint on models. Example data are shown for sands, silts and clays to illustrate the independence of the theory from geological descriptors.

Complex systems can be defined by “sloppy” dimensions, meaning that their behavior is unmodified by large changes to specific parameter combinations, and “stiff” dimensions whose change results in considerable behavioral modification. In the neocortex, sloppiness in synaptic architectures would be crucial to allow for the maintenance of asynchronous irregular spiking dynamics with low firing rates despite a diversity of inputs, states, and both short- and long-term plasticity. Using simulations on neural networks with first-order spiking statistics matched to firing in murine visual cortex while varying connectivity parameters, we determined the stiff and sloppy parameters of synaptic architectures across three classes of input (brief, continuous, and cyclical). Algorithmically-generated connectivity parameter values drawn from a large portion of the parameter space reveal that specific combinations of excitatory and inhibitory connectivity are stiff and that all other architectural details are sloppy. Stiff dimensions are consistent across input classes with self-sustaining synaptic architectures following brief input occupying a smaller subspace as compared to the other input classes. Experimentally estimated connectivity probabilities from mouse visual cortex are consistent with the connectivity correlations found and fall in the same region of the parameter space as architectures identified algorithmically. This suggests that simple statistical descriptions of spiking dynamics are a sufficient and parsimonious description of neocortical activity when examining structure-function relationships at the mesoscopic scale. Additionally, coarse graining cell types does not prevent the generation of accurate, informative, and interpretable models underlying simple spiking activity. This unbiased investigation provides further evidence of the importance of the interrelationship of excitatory and inhibitory connectivity to establish and maintain stable spiking dynamical regimes in the neocortex.Author SummaryConnections between neurons are continuously changing to allow learning and adaptation to new stimuli. However, the ability of neural networks to vary these connections while avoiding excessively high- or low-activity states is still not well understood. We tackled this question by studying how changes in the parameters of connectivity within and between different neuronal populations impacted network activity in computational models. We identified specific combinations of parameters, deemed “stiff”, that must be maintained to observe activity consistent with recordings from murine visual cortex, while the rest of the parameters can be varied freely with minimal effects on activity. Our results agree with experimentally measured connectivity statistics demonstrating the importance of balancing opposing forces to maintain activity in a natural regime.

У роботі було розглянуто фрагментацію індексів, проаналізовані фактори зменшення продуктивності виконання SQL запитів при збільшенні відсотка фрагментації індексів. В роботі проаналізовано систему індексації, яка використовується в СУБД ORACLE. Розглянуті і проаналізовані сучасні методи зменшення фрагментації та розроблено модель, методи та алгоритм який в автоматичному режимі дозволить робити перебудову та реорганізацію індексів в залежності від відсотка фрагментації. Метою роботи є розробка метода та алгоритмів уникнення фрагментації індексів. Реалізація поставленої мети передбачає вирішення завдання пошуку аналізу та перебудові фрагментованих індексів, що забезпечує запропонований у роботі метод. На базі запропонованого методу було розроблено алгоритм для систем баз даних, та розроблено процедуру яка дозволяє уникати фрагментації індексів у СУБД ORACLE, які відбуваються при роботі серверу ORACLE у режимі OLTP. Методологія вирішення поставленого завдання полягає в проведенні статистичного аналізу існуючих індексів та розробці системи яка дозволяє в запланований час, в який сервер баз даних менш всього навантажений запускати процедуру яка буде перевіряти ступінь фрагментації індексів і при потребі їх перебудовувати. Наукова новизна. В ході виконання роботи набув подальший розвиток метод перебудови індексів, які фрагментовано, у базі даних. В перше запропоновано використання перебудови індексів у автоматичному режимі без втручання адміністратора бази даних. Висновки. Результати даної роботи можуть бути використані для подальших досліджень і розробок, а також для запровадження використання технології уникнення фрагментації для різних типів СУБД. Всі отримані результати представлені в графічному вигляді з детальним описом в даній роботі.

In this paper, a theoretical model has been proposed for the first time to illustrate the living Tulsi (Holy Basil) leaf evolution in describing the branching systems in the intermediate range of frequencies. The proposed fitting function following the applied model portrays the whole stages of the temporal evolution of Tulsi (Holy Basil, "Ocimumtenuiflorum") leaf during the six days of impedance measurements with a high degree of accuracy (with fitting error less than 0.1 percent). The developed fitting parameter enables to identify the three stages of temporal evaluation of the Tulsi leaf as "vital leaf activity" (first stage), "quasi-chaotic behaviour" (second stage), and the "dying" (third stage). The theoretical model proposed in this work incorporated a fractal element having complex conjugated power law of exponents and a fractal element with a time lagged branching process. This novel approach introduces an additional degree of freedom over the previously proposed impedance models in terms of its imaginary part of complex conjugated power-law exponent. Further, it increases flexibility and versatility in accurately modeling the behavioral variations of complex branching systems, whose seemingly nondeterministic temporal nature had been considered earlier as counterintuitive and random. The incorporation of branching processes in comprehensively explaining the complex biological systems allows us to gain a deeper insight into the transfer of charge processes in the intermediate range of available frequency scales. Furthermore, the proposed model validates the presence of new fractal elements with a complex conjugated power law of exponents in naturally occurring biological processes. The experimental confirmation can play a key role in explaining a wide class of branching processes in complex systems and enrich the modern theory of fractional calculus.

There is a direct link between the extent of formation damage and the filtration volume of the drilling fluids in hydrocarbon reservoirs. The filtration volume could be diminished by adding different additives to the drilling fluids. Recently, nanoparticles have been extensively used for enhancing the filtration characteristics of the drilling fluids. However, there is no reliable model for investigating the influence of this class of additives on the performance of drilling fluids. Hence in this study, two powerful tools ELM (Extreme Learning Machine) and PSO-LSSVM (Particle Swarm Optimization-Least Square Support Vector Machine) are applied to determine the effect of various nanoparticles on the filtration volume. The assessment of the models is carried out by computing the statistical parameters and it was found that ELM has a greater ability to predict the filtration volumes, while PSO-LSSVM performs satisfactorily too. The model predictions and experimental results are in excellent agreement as suggested by the values of root mean squared error (RMSE = 0.2459), coefficient of determination (R2 = 0.999), and mean relative error (MRE = 2.028%) for the dataset. The statistical analysis showed that the suggested model can predict the filtration volume with great accuracy. Moreover, through sensitivity analysis of the input parameters, it was found that for a specified nanoparticle, the filtration volume is highly influenced by nanoparticle concentration and it is the essential variable for the optimization process.

Many engineering products have more than one failure mode and the evolution of each mode can be monitored by measuring a performance characteristic (PC). It is found that the underlying multi-dimensional degradation often occurs with inherent process stochasticity and heterogeneity across units, as well as dependency among PCs. To accommodate these features, in this paper, we propose a novel multivariate degradation model based on the inverse Gaussian process. The model incorporates random effects that are subject to a multivariate normal distribution to capture both the unit-wise variability and the PC-wise dependence. Built upon this structure, we obtain some mathematically tractable properties such as the joint and conditional distribution functions, which subsequently facilitate the future degradation prediction and lifetime estimation. An expectation-maximization algorithm is developed to infer the model parameters along with the validation tools for model checking. In addition, two simulation studies are performed to assess the performance of the inference method and to evaluate the effect of model misspecification. Finally, the application of the proposed methodology is demonstrated by two illustrative examples.

Layered assembly is a voxel-based additive manufacturing process that relies on parallel grasping of voxels to produce multi-material parts. Although there exists substantial diversity in mechanism...

Vacuum gas oil (VGO) is the most important feedstock for hydrocracking processes in refineries, but its molecular composition cannot be fully acquired by current analysis techniques owing to its complexity. In order to build an accurate and reliable molecular-level kinetic model for reactor design and process optimization, the molecular composition of VGO has to be reconstructed based on limited measurements. In this study, a modified stochastic reconstruction-entropy maximization (SR-REM) algorithm was applied to reconstruct VGOs, with generation of a general molecule library once and for all via the SR method at the first step and adjustment of the molecular abundance of various VGOs via the REM method at the second step. The universality of the molecule library and the effectiveness of the modified SR-REM method were validated by fifteen VGOs (three from the literature) from different geographic regions of the world and with different properties. The simulated properties (density, elemental composition, paraffin-naphthene-aromatics distribution, boiling point distribution, detailed composition of naphthenes and aromatics in terms of ring number as well as composition of S-heterocycles) are in good agreement with the measured counterparts, showing average absolute relative errors of below 10% for each property.

Since spiking neural networks can effectively simulate the information processing mechanism of the biological cortex, they are expected to bridge the gap between neuroscience and machine learning. The hardware simulation of large-scale spiking neural networks requires a simple and versatile silicon neuron model framework. In this paper, a spiking neuron circuit as the core device of spiking neural networks is presented. The proposed neuron circuit can mimic the dynamics of different types of biological neurons by adjusting the bias voltage. In order to facilitate the implementation of the spiking neuron circuit based on complementary metal-oxide-semiconductor (CMOS) and reduce the overhead of the circuit area, a modified Mihalas-Niebur mathematical model is adopted. The improved Mihalas-Niebur model is biologically plausible and can still successfully display all dynamic behaviors observed in biology. The function of the proposed neuron circuit has been verified by the phase diagram analysis method. The simulation results show the designed neuron circuit can successfully replicate 15 of the 20 firing patterns exhibited by the biological cortex, which proves that the neuron can act as a universal spiking neuron in very large-scale integrated circuit (VLSI) neuromorphic networks.

Symbiotic bacteria often navigate complex environments before colonizing privileged sites in their host organism. Chemical gradients are known to facilitate directional taxis of these bacteria, guiding them towards their eventual destination. However, less is known about the role of physical features in shaping the path the bacteria take and defining how they traverse a given space. The flagellated marine bacteriumVibrio fischeri,which forms a binary symbiosis with the Hawaiian bobtail squid,Euprymna scolopes, must navigate tight physical confinement, squeezing through a bottleneck constricting to ~2 μm in width on the way to its eventual home. Using microfluidicin vitroexperiments, we discovered thatV. fischericells alter their behavior upon entry into confined space, straightening their swimming paths and promoting escape from confinement. Using a computational model, we attributed this escape response to two factors: reduced directional fluctuation and a refractory period between reversals. Additional experiments in asymmetric capillary tubes confirmed thatV. fischeriquickly escape from tapered ends, even when drawn into the ends by chemoattraction. This avoidance was apparent down to a limit of confinement approaching the diameter of the cell itself, resulting in a balance between chemoattraction and evasion of physical confinement. Our findings demonstrate that non-trivial distributions of swimming bacteria can emerge from simple physical gradients in the level of confinement. Tight spaces may serve as an additional, crucial cue for bacteria while they navigate complex environments to enter specific habitats.Significance StatementSymbiotic bacteria that navigate to and through specific host tissues often face tight physical confinement. This work reveals that confinement-associated changes in swimming can dramatically alter taxis, shaping bacterial localization in conjuncture with other motility-directing cues. This work helps explain how bacteria can avoid getting stuck in confined areas while transiting to privileged spaces, adding confinement as an environmental cue that symbiotic bacteria use to shape their motility behavior.

N2-methylguanosine is a post-transcriptional modification of RNA that is found in eukaryotes and archaea. The biological function of m2G modification discovered so far is to control and stabilize the three-dimensional structure of tRNA and the dynamic barrier of reverse transcription. To discover additional biological functions of m2G, it is necessary to develop time-saving and labor-saving calculation tools to identify m2G. In this paper, based on hybrid features and a random forest, a novel predictor, RFhy-m2G, was developed to identify the m2G modification sites for three species. The hybrid feature used by the predictor is used to fuse the three features of ENAC, PseDNC, and NPPS. These three features include primary sequence derivation properties, physicochemical properties, and position-specific properties. Since there are redundant features in hybrid features, MRMD2.0 is used for optimal feature selection. Through feature analysis, it is found that the optimal hybrid features obtained still contain three kinds of properties, and the hybrid features can more accurately identify m2G modification sites and improve prediction performance. Based on five-fold cross-validation and independent testing to evaluate the prediction model, the accuracies obtained were 0.9982 and 0.9417, respectively. The robustness of the predictor is demonstrated by comparisons with other predictors.

The transmission of airborne pathogens via aerosols is considered to be the main route through which a number of known and emerging respiratory diseases infect their hosts. It is therefore essential to quantify airborne transmission in closed spaces and determine the recommendations that should be implemented to minimize exposure to pathogens in built environments. We have developed a method to detect viable virus particles from aerosols by using an aerosolized bacteriophage Phi6 in combination with its host Pseudomonas phaseolicola, which when seeded on agar plates acts as a virus detector that can be placed at a range of distances away from an aerosol-generating source. Based on this method we present two striking results. (1) We consistently detected viable phage particles at distances of up to 18 feet away from the source within 15-minutes of exposure in a classroom equipped with a state of the art HVAC system. (2) Increasing the relative humidity beyond 40% significantly reduces dispersal. Our method can be used to quantify the exposure to pathogens at various distances from the source for different amounts of time, data which can be used to set safety standards for room capacity and to ascertain the efficacy of interventions that aim to reduce pathogen levels in closed spaces of specified sizes and intended uses.SummaryWe present a method to experimentally determine the exposure to airborne pathogens in closed spaces.

Enzyme assisted DNA probes are powerful tools in molecular diagnostics for their simplicity, rapidity, and low detection limit. However, cost of probes, difficulty in optimization and disturbance of secondary structure hindered the wider application of enzyme assisted DNA probes. To solve the problems, we designed a new system named shared-probe system. By introducing two unlabeled single stranded DNA named Sh1 and Sh2 as the bridge between probe and the substrate, the same sequence of dually labeled probe with stable performance was shared for different mutations, thus sparing the expense and time cost on designing, synthesizing and optimizing corresponding probes. Besides, the hybridization between Sh1 and the substrate could overcome secondary structures, which guaranteed the detection of different substrates. The performance and generality of the design were tested by low abundance detection in synthetic single DNA samples and the limit of detection was 0.05% for PTENR130Q, EGFR-L858R and 0.02% for BRCA1-NM007294.3. In genomic DNA samples, the limit of detection of 0.1% can be achieved for EGFR-L858R, demonstrating the potential of clinical application in our design.

The cellular response to the complex extracellular microenvironment is highly dynamic in time and type of extracellular matrix. Accurately reconstructing this process and analyzing the changes in receptor conformation on the cell membrane surface and intracellular or intercellular signaling has been a major challenge in analytical chemistry and biophysical methodology. In this paper, a time-coded multi-concentration microfluidic chemical waveform generator was developed for the dynamic signaling probing with single-cell array of high temporal resolution, high throughput, and multi-concentration combination stimulation. Based on innovative microchannel structure, sophisticated external control methods and multiplexing technology, the system not only allowed for temporally sequential permutations of the four concentrations of stimuli (time code), but also generated pulsed and continuous waveforms at different frequencies in a highly controllable manner. Furthermore, the single-cell trap array was set up to efficiently capture cells in suspension, dramatically increasing throughput and reducing experiment preparation time. The maximum frequency of the platform was 1 Hz, and one cell could be stimulated at multiple frequencies. To show the ability of the system to investigate rapid biochemical events in high throughput, pulse stimulation and continuous stimulation of different frequencies and different time codes, combined with four concentrations of histamine (HA), were generated for probing G protein-coupled receptor (GPCR) signaling in HeLa cells. Then, statistical analysis was performed for the mean peak height and mean peak area of the cellular response. We believe that the time-coded multi-concentration microfluidic chemical waveform generator will provide a novel strategy for analytical chemistry, biophysics, cell signaling, and individualized medicine applications.

There are numerous correlations and thermodynamic models for predicting the natural gas hydrate formation condition but still the lack of a simple and unifying general model that addresses a broad ranges of gas mixture is highly felt. This study was aimed to develop a user-friendly universal correlation based on hybrid group method of data handling (GMDH) for prediction of hydrate formation temperature of a wide range of natural gas mixtures including sweet and sour gas. To establish the hybrid GMDH, the total experimental data of 343 were obtained from open articles. The selection of input variables was based on the hydrate structure formed by each gas species. The modeling resulted in a strong algorithm since the squared correlation coefficient (R2) and root mean square error (RMSE) were 0.9721 and 1.2152, respectively. In comparison to some conventional correlation, this model represents not only the outstanding statistical parameters but also its absolute superiority over others. In particular, the result was encouraging for sour gases concentrated at H2S to the extent that the model outstrips all available thermodynamic models and correlations. Leverage statistical approach was applied on datasets to the discovery of the defected and doubtful experimental data and suitability of the model. According to this algorithm, approximately all the data points were in the proper range of the model and the proposed hybrid GMDH model was statistically reliable.

Drop microfluidics has driven innovations for high throughput, low input analysis techniques such as single-cell RNA-seq. However, the instability of single emulsion (SE) drops occasionally causes significant merging during drop processing, limiting most applications to single-step reactions in drops. Here, we show that double emulsion (DE) drops address this critical limitation and completely prevent content mixing, which is essential for single entity analysis. DEs show excellent stability during thermal cycling. More importantly, DEs undergo rupture into the continuous phase instead of merging, preventing content mixing and eliminating unstable drops from the downstream analysis. Due to the lack of drop merging, the monodispersity of drops is maintained throughout a workflow, enabling the deterministic manipulation of drops downstream. We also developed a simple, one-layer fabrication method for DE drop makers. This design is powerful as it allows robust production of single-core DEs at a wide range of flow rates and better control over the shell thickness, both of which have been significant limitations of conventional two-layer devices. This approach makes the fabrication of DE devices much more accessible, facilitating its broader adoption. Finally, we show that DE droplets effectively maintain the compartmentalization of single virus genomes during PCR-based amplification and barcoding, while SEs mixed contents due to merging. With their resistance to content mixing, DE drops have key advantages for multistep reactions in drops, which is limited in SEs due to merging and content mixing.

(Bio)pharmaceutical products freeze-dried in vials must meet stringent quality specifications: among these, the residual moisture (RM) is crucial. The most common techniques adopted for measuring the RM are destructive, e.g. Karl Fisher titration, thus few samples from each batch are tested. Being a high intra-batch variability an intrinsic feature of batch freeze-drying, a high number of samples needs to be tested to get a representative measurement. Near-Infrared (NIR) spectroscopy was extensively applied in the past as a non-invasive method to quantify the RM. In this paper, an accurate Partial Least Square (PLS) model was developed and calibrated with a single product, focusing on a small but significative wavelength range of NIR spectra (model SR), characteristic of the water and not of the product. The salient feature of this approach is that the model SR appears to provide fairly accurate estimates with the same product but at a higher concentration, with other excipients and in presence of an amino acid at high concentration, without requiring any additional calibration with KF analysis, as in previous techniques; the irrelevance of the vial shape was also shown. This approach was compared to a simpler one, based on a single-variable linear regression, and to more complex one, using a wider wavelength range or calibrating the PLS model with several products. Model SR definitely ended up as the most accurate, and it appeared to have a great potential as a robust model, suitable also for products that were not involved in the calibration step.

In this paper, we investigate a novel multi-sensor odor detection system (electronic nose) for low concentration volatile organic compounds (VOCs). In order to break through the limitation for detecting low concentration volatile organic compounds, we design a conformal symmetric pre-concentration unit structure based on the finite element method. A typical application for exhaled breath is developed to investigate the proposed scheme. The designed pre-concentration system can increase the concentration of the concentrated substance, so that the sensor can detect it. Besides, we choose the alveolar gas at the bottom of the human lung, which can better represent the health status of the human body, to carry out the effectiveness test of the system. In order to combine the gas acquisition system with the pre-concentration system, this paper further designs a cost-effective alveolar gas collection device by a doubled adaptive Pulse Width Modulation (PWM) operation. Finally, the effectiveness of the pre-concentration system is judged by the detection results of gas chromatography-mass spectrometry (GC-MS). We also use the classical algorithm to classify lung cancer and non-lung cancer patients, which proves the effectiveness of our proposed integrated design system and shows the potential of practical application of the system.

Infrared small target detection is a key technique in an infrared system. In the past decade, many methods have concentrated on traditional top-hat transformation, which relies on the hand-crafted shape and value of structural elements. However, these methods are inevitably challenged by the following two aspects: first, the structural elements cannot suppress heavy clutter because the construction of structural elements is always according to the prior information of the target and unable to consider the feature of clutter. Second, adaptively extracting sufficient local feature information for background suppression is hard for the structural element. In this article, we propose an entropy-driven top-hat transformation with guided filter kernel for considering the features of both the clutters and background. First, we propose an entropy-driven top-hat transformation method with our proposed local mean entropy, which can be used to suppress clutter according to the local complex degree of clutter. Then, an adaptive structural element based on a guided filter kernel is further exploited to capture the local feature information of image for background suppression. Finally, an adaptive threshold is combined with our algorithm to achieve target detection in image sequences. The experimental results show that the proposed algorithm is not only robust for suppressing different kinds of backgrounds but can also obtain a higher value of the signal-to-clutter ratio gain and detection accuracy compared with some popular traditional baseline methods and related top-hat methods.

Objective Irreversible electroporation (IRE) is a non thermal tissue ablation therapy which is induced by applying high voltage waveforms across electrode pairs. When multiple electrode pairs are sequentially used, the treatment volume (TV) is typically computed as the geometric union of the TVs of individual pairs. However, this method neglects that some regions are exposed to overlapping treatments. Recently, a model describing cell survival probability was introduced which effectively predicted TV with overlapping fields in vivo. However, treatment overlap has yet to be quantified. This study characterizes TV overlap in a controlled in vitro setup with the two existing methods which are compared to an adapted logistic model proposed here. Methods CHO cells were immobilized in agarose gel. Initially, we characterized the electric field threshold and the cell survival probability for overlapping treatments. Subsequently, we created a 2D setup where we compared and validated the accuracy of the different methods in predicting the TV. Results Overlap can reduce the electric field threshold required to induce cell death, particularly for treatments with low pulse number. However, it does not have a major impact on TV in the models assayed here, and all the studied methods predict TV with similar accuracy. Conclusion Treatment overlap has a minor influence in the TV for typical protocols found in IRE therapies. Significance This study provides evidence that the modeling method used in most pre-clinical and clinical studies seems adequate.

The principle and control for solidification present crucial challenges. Through in situ and real time observation on the solidification of M50 bearing steel, here we discovered that the base of conventional dendritic arms is constituted by a primary semi-solid matrix. Due to the superimposition of temperature fluctuations from the large to the subtle scale, multi-scale dendritic patterns will sequentially emerge and evolve in the primary semi-solid matrix. These findings redefine the essence of multi-scale dendrites as the multi-scale segregation patterns in the primary semi-solid matrix, reconstitute the time and space sequence in the formation of multi-scale dendrites, and reveal the superimposition of temperature fluctuation as the driving principle. These new understandings will fundamentally influence the solidification field. These results also indicate important engineering applications, such as designing multi-scale dendrites, controlling the dendritic segregation and eliminating the detrimental eutectics.

Fourier-transform infrared spectroscopy (FTIR) is a widely adopted technique for characterizing the chemical composition in many physical and chemical analyses. However, FTIR spectra are subject to multiple sources of uncertainty, and thus the analysis of them relies on domain experts and can only lead to qualitative conclusions. This study aims to analyze the effect of a certain treatment on FTIR spectra subject to two commonly observed uncertainties, the offset shift and the multiplicative error. Due to these uncertainties, the pre-exposure FTIR spectra are modeled according to the physical understanding of the uncertainty--observed spectra can be viewed as translating and stretchering an underlying template signal, and the post-exposure FTIR spectra are modeled as the translated and stretchered template signal plus an extra functional treatment effect. To provide engineering interpretation, the treatment effect is modeled as the product of the pattern of modification and its corresponding magnitude. A two-step parameter estimation algorithm is developed to estimate the underlying template signal, the pattern of modification, and the magnitude of modification at various treatment strengths. The effectiveness of the proposed method is validated in a simulation study. Furtherly, in a real case study, the proposed method is used to investigate the effect of plasma exposure on the FTIR spectra. As a result, the proposed method effectively identifies the pattern of modification under uncertainties in the manufacturing environment, which matches the knowledge of the affected chemical components by the plasma treatment. And the recovered magnitude of modification provides guidance in selecting the control parameter of the plasma treatment.

In this study, a system of automatic microparticle patterning that could enable the separation, trapping, and translation of single microbeads in liquid suspension using negative dielectrophoresis (DEP) tweezers was presented to form a single-bead pattern. A microchip with integrated electrodes was flipped and placed above the substrate through a micromanipulator. Microparticles laying on the substrate could be displaced to different positions relative to the electrodes on the microchip, and only the selected particles would be trapped by the electric fields generated from electrodes. Vision-based approaches were used to evaluate the necessary information, such as the gap distance and the positions of electrodes and microparticles in the image. A strategy for separating nearby particles was proposed to achieve single-bead patterning with high accuracy. A controller was used to guide the microparticles toward the position for trapping while avoiding flow disturbance. Different strategies were simulated to decrease the patterning time and find the minimum traveling distance and the best route of movement. The optimization problem is NP-hard. Hence, global optimization algorithms, such as genetic algorithm, particle swarm optimization, and ant colony optimization (ACO), were simulated, and the results were compared with those of the local optimization method. The comparison results showed that ACO obtained the best performance among the methods. The strategy for constructing high-quality microparticle patterns was also examined through experiments. Orange fluorescent polystyrene beads suspended in 6-aminohexanoic acid solution were considered and successfully patterned on a glass substrate by using the proposed system.

The scale of biological discovery is driven by the vessels in which we can perform assays and analyze results, from multi-well plates to microfluidic compartments. We report on the compatibility of sub-nanoliter single-cell containers or “nanovials” with commercial fluorescence activated cell sorters (FACS). This recent lab on a particle approach utilizes 3D structured microparticles to isolate cells and perform single-cell assays at scale with existing lab equipment. Use of flow cytometry led to detection of fluorescently labeled protein with dynamic ranges spanning 2-3 log and detection limits down to ∼10,000 molecules per nanovial, which was the lowest amount tested. Detection limits were improved compared to fluorescence microscopy measurements using a 20X objective and a cooled CMOS camera. Nanovials with diameters between 35-85 µm could also be sorted with purity from 99-93% on different commercial instruments at throughputs up to 800 events/second. Cell-loaded nanovials were found to have unique forward and side (or back) scatter signatures that enabled gating of cell-containing nanovials using scatter metrics alone. The compatibility of nanovials with widely-available commercial FACS instruments promises to democratize single-cell assays used in discovery of antibodies and cell therapies, by enabling analysis of single cells based on secreted products and leveraging the unmatched analytical capabilities of flow cytometers to sort important clones.

In mobile networks, such as fifth generation (5G), the appealing characteristics of the small cells, like easy deployment, affordable, and low power usage, have led to improvement of Heterogeneous Network (HetNet) technique. In HetNet, the restricted resources are usually shared by the small cells, that in turns results in an interference issue challenges. The latter may be regarded as the main barrier for appropriate HetNet performance. In this paper, both Inter Cell Interference and Intra Cell Interference are studied and evaluated for Al-Najaf governorate 5G network. The network performance was investigated before and after interference mitigation. The simulation results are dependent to evaluate the effectiveness of all approaches and schemes that are implemented to cope with both inter and intra cell interference. The simulation of the network is performed by the aid of Integrated Communications System (ICS) telecom software. In Inter-Cell Interference, in term of modulation, it obviously shows that higher modulation occupies only small portion of the covered area due to interference. The modulation scheme 64 Quadrature Amplitude Modulation (QAM) 3/4 occupies 13% from the total covered area with omni profile, while this scheme is being missed with 4-sectors profile due to interference. Intra-cell interference is materialized as Inter Carrier Interference( ICI) and Inter Symbol Interference(ISI), the effect of Cyclic Prefix( CP) on the network performance in terms of ICI is tested based on the simulation of the 3D reflection paths; for the adopted test area, the interfered area equals to 27.06% km2 according to 5.7μsec guard interval, while the interfered area is reduced to 6.42% km2 according to 11.4μsec guard interval. The interfered area is reduced by 21% due to ICI when the CP is extended from 5.7µsec to 11.4µsec. CP adopted in this work equals to (1/8), which is the mostly used.

Amphipathic helix (AH)features the segregation of polar and nonpolar residues and plays important roles in many membrane-associated biological processes through interacting with both the lipid and the soluble phases. Although the AH structure has been discovered for a long time, few ab initio machine learning-based prediction models have been reported, due to the limited amount of training data. In this study, we report a new deep learning-based prediction model, which is composed of a residual neural network and the uneven-thresholds decision algorithm. It is constructed on 121 membrane proteins, in total 51640 residue samples, which are curated from an up-to-date membrane protein structure database. Through a rigid 10-fold nested cross-validation experiment, we demonstrate that our model can achieve promising predictions and exceed current state-of-the-art approaches in this field. This presents a new avenue for accurately predicting AHs. Analysis on the contribution of the input residues and some cases further reveals the high interpretability and the generalization of our model.

Kinetic modeling plays a key role in development of large-scale extraction processes. In this study, we modeled the process of extracting essential oils from pomelo (Citrus maxima) peel materials in different kinetics including pseudo first order and pseudo second order kinetic models in both linear and nonlinear forms. The coefficient of determination, R2, and the percentage of deviation, %q, were used as the basis to determine the most suitable kinetic model kinetics. The results show that non-linear pseudo first order model (equation 7) was best fitted to describe the experimental data. The obtained essential oil was characterized by the abundance of limonene.

The study of chemical reactions in aqueous media is very important for its implications in several fields of science, from biology to industrial processes. However, modeling these reactions is difficult when water directly participates in the reaction, since it requires a fully quantum mechanical description of the system. Ab-initio molecular dynamics is the ideal candidate to shed light on these processes. However, its scope is limited by a high computational cost. A popular alternative is to perform molecular dynamics simulations powered by machine learning potentials, trained on an extensive set of quantum mechanical calculations. Doing so reliably for reactive processes is difficult because it requires including very many intermediate and transition state configurations. In this study we used an active learning procedure accelerated by enhanced sampling to harvest such structures and to build a neural-network potential to study the urea decomposition process in water. This allowed us to obtain the free energy profiles of this important reaction in a wide range of temperatures, to discover several novel metastable states, and improve the accuracy of the kinetic rates calculations. Furthermore, we found that the formation of the zwitterionic intermediate has the same probability of occurring via an acidic or a basic pathway, which could be the cause of the insensitivity of reaction rates to the solution pH.