New Jersey Institute of Technology
  • Newark, New Jersey, United States
Recent publications
Background We sought to determine the association between appendicular adiposity and hypertension, with the purpose of better understanding the role of body fat distribution on blood pressure (BP). Methods We included 7411 adults aged 20 to 59 who were not taking antihypertensives and without cardiovascular disease from the 2011 to 2018 National Health and Nutrition Examination Surveys. Leg & arm adiposity, determined via dual-energy X-ray absorptiometry scans, was defined as percent of total body fat present in legs/arms (leg/total%, arm/total%). Measures were categorized into sex-specific tertiles. We estimated change in BP and odds ratios (ORs) of hypertension (BP ≥ 130/80) and hypertension subtypes using multivariable, survey design-adjusted linear & logistic regression, respectively. Results Of the participants, 49% were female, the average (standard deviation) age was 37.4 (0.3) years, and 24% had hypertension. Those in the highest tertile (T3) of leg/total% had 30% decreased adjusted ORs (aOR) of hypertension compared to the lowest tertile (T1; aOR, 0.70; 95% confidence interval [95% CI], 0.55–0.89). This association was not significant for arm/total% (0.89, 0.68–1.17). T3 of leg/total% was associated with 49% lower, 41% lower, and unchanged relative odds of isolated diastolic hypertension (IDH), systolic-diastolic hypertension (SDH), and isolated systolic hypertension (ISH) compared to T1 (IDH: 0.51, 0.37–0.70; SDH: 0.59, 0.43–0.80; ISH: 1.06, 0.70–1.59). For every 10% increase in leg/total%, diastolic BP decreased by an adjusted mean 3.5 mmHg (95% CI, − 4.8 to − 2.2) in males and 1.8 mmHg (95% CI, − 2.8 to − 0.8) in females ( P < 0.001 for both). Conclusions A greater proportional distribution of fat around the legs is inversely, independently associated with hypertension, and more specifically, diastolic hypertension (IDH and SDH).
Over the years, the photovoltaic market, worldwide, has been witnessing double digit growth rate. The silicon solar cell manufacturing technology has evolved to optimally utilize raw materials to address this growth. One of the ways in which manufacturers are addressing the challenge is by increasing the cell size and making thinner wafers. With this change in parameters, understanding the metal contact formation in solar cells becomes paramount to improve their efficiency. Screen printing is a widely used method to form metal contacts on solar cells and is ideally suited for large volume manufacturing. This paper presents a review of the: (i) role of screen printing in various solar cell architectures, and (ii) existing models for current conduction and contact formation mechanisms. An alternate approach to current conduction and contact formation mechanism in silicon solar cells is proposed.
Humans are impressive social learners. Researchers of cultural evolution have studied the many biases shaping cultural transmission by selecting who we copy from and what we copy. One hypothesis is that with the advent of superhuman algorithms a hybrid type of cultural transmission, namely from algorithms to humans, may have long-lasting effects on human culture. We suggest that algorithms might show (either by learning or by design) different behaviours, biases and problem-solving abilities than their human counterparts. In turn, algorithmic-human hybrid problem solving could foster better decisions in environments where diversity in problem-solving strategies is beneficial. This study asks whether algorithms with complementary biases to humans can boost performance in a carefully controlled planning task, and whether humans further transmit algorithmic behaviours to other humans. We conducted a large behavioural study and an agent-based simulation to test the performance of transmission chains with human and algorithmic players. We show that the algorithm boosts the performance of immediately following participants but this gain is quickly lost for participants further down the chain. Our findings suggest that algorithms can improve performance, but human bias may hinder algorithmic solutions from being preserved. This article is part of the theme issue ‘Emergent phenomena in complex physical and socio-technical systems: from cells to societies’.
To reduce the interference of impulsive noise when the spline adaptive filter (SAF) algorithm is used to identify nonlinear systems, this paper proposes a family of SAF algorithms using the Heaviside step function (HSF). The suitability of those cost functions proposed are investigated; those cost functions are design based on some HSF’s approximate functions. Then based on that, four SAF algorithms have been developed: SAF-HSF-sigmoid, SAF-HSF-erfc, SAF-HSF-atan, and SAF-HSF-tanh. Also, the bound of the learning rate has been derived for those proposed algorithms. The proposed SAF-HSF algorithms have been evaluated for nonlinear system identification and simulation studies to demonstrate their robustness.
In this paper, physics-based analytical models using two-dimensional (2D) Poisson equations for surface potential, channel potential, electric field, and drain current in AlN/β-Ga2O3 high electron mobility transistor (HEMT) is presented. The analytical expression of different quantities is achieved based on full depletion approximation of the AlN barrier layer and polarization charge induced unified two-dimensional electron gas (2DEG) charge density model. For the validation of the developed model, results are compared with 2D numerical simulation results, and a good consistency is found between the two. The drain current model is also validated with experimental results of a similar dimension device. The developed model can be a good reference for different β-Ga2O3-based HEMTs.
Based on the multifractal methodology, we examine how complexity in the dynamics of biological systems can be understood. The possible sources of multifractality have been determined by comparing the multifractal indexes obtained on the original, shuffle, Fourier transform surrogate (FTsurrogate), and iterative amplitude adjusted Fourier transform (iAAFT) time series. This paper shows that the rs-fMRI signal is multifractality. The multifractality is due to the fat-tail probability distribution, long-range dependence (LRD), and the excluded hidden structures. Although the excluded hidden structures also contribute to the sources of the multifractality, it is mainly composed of the fat-tail probability distribution and LRD, especially the fat-tail probability distribution. Specifically, the shuffle, FTsurrogate, and iAAFT series, those three series still display a multifractal behavior. However, based on the shuffle series, we know that the trend of change in the curve of fluctuation functions with q is significantly weakened, indicating that the probability distribution is not the only source of the multifractalionality of the rs-fMRI signal. In addition, the multifractal properties of the shuffle series are not as strong as the original series. The FTsurrogate and iAAFT series also are as powerful multifractals as the original. For the original series, the multifractal spectrum is an asymmetric bell shape, indicating that the multifractal spectrum is on the right. This means that the multifractality of the rs-fMRI signal in this scale range is determined by the multifractal properties of large and small fluctuations. The fat-tailed probability distribution, LRD, and excluded hidden structure are generated by the power-law distributions, scale-free temporal dynamics, and the highly structured dynamical activity in space and time (which means latent variables and connectivity patterns). This study implies that the study on the sources of multifractalionality may also discover new insights into the dynamic mechanism of spontaneous brain activity.
An increasing number of neuroimaging studies indicate functional alterations in cortico‐striatal loops in individuals with substance use disorders (SUD). Dysregulations in these circuits may contribute to drug‐seeking and drug‐consuming behaviour by impeding inhibitory control, habit formation, and reward processing. Despite evidence of network‐level changes in SUD, a shared pattern of functional alterations within and between spatially distributed brain networks has not been systematically investigated. The present meta‐analytic investigation aims at identifying common alterations in resting‐state functional connectivity patterns across different SUD, including stimulant, heroin, alcohol, cannabis, and nicotine use. To this aim, seed‐based whole‐brain connectivity maps for different functional networks were extracted and subjected to multi‐level kernel density analysis to identify dysfunctional networks in individuals with SUD compared with healthy controls. In addition, an exploratory analysis examined substance‐specific effects as well as the influence of drug use status on the main findings. Our findings indicate a hypoconnectivity pattern for the limbic, salience, and frontoparietal networks in individuals with SUD as compared with healthy controls. The default mode network additionally exhibited a complex pattern of hypo‐ and hyperconnectivity across the studies. The observed disrupted connectivity between networks in SUD may associate with deficient inhibitory control mechanisms that are thought to contribute to excessive craving and automatic drug‐related behaviour as well as failure in substance use cessation. The identified network‐based alterations in SUD represent potential treatment targets for neuromodulation, for example, network‐based real‐time functional magnetic resonance imaging (fMRI) neurofeedback. Such interventions can evaluate the behavioural relevance of the identified neural circuits. Our study provides meta‐analytical evidence on network‐level alterations in substance use disorder (SUD). Our findings show altered connectivity within and between distributed brain networks including the salience, limbic, frontoparietal, and default‐mode network in SUD compared with healthy controls.
Solar flares, driven by prompt release of free magnetic energy in the solar corona1,2, are known to accelerate a substantial portion (ten per cent or more)3,4 of available electrons to high energies. Hard X-rays, produced by high-energy electrons accelerated in the flare⁵, require a high ambient density for their detection. This restricts the observed volume to denser regions that do not necessarily sample the entire volume of accelerated electrons⁶. Here we report evolving spatially resolved distributions of thermal and non-thermal electrons in a solar flare derived from microwave observations that show the true extent of the acceleration region. These distributions show a volume filled with only (or almost only) non-thermal electrons while being depleted of the thermal plasma, implying that all electrons have experienced a prominent acceleration there. This volume is isolated from a surrounding, more typical flare plasma of mainly thermal particles with a smaller proportion of non-thermal electrons. This highly efficient acceleration happens in the same volume in which the free magnetic energy is being released².
Swimming microorganisms switch between locomotory gaits to enable complex navigation strategies such as run-and-tumble to explore their environments and search for specific targets. This ability of targeted navigation via adaptive gait-switching is particularly desirable for the development of smart artificial microswimmers that can perform complex biomedical tasks such as targeted drug delivery and microsurgery in an autonomous manner. Here we use a deep reinforcement learning approach to enable a model microswimmer to self-learn effective locomotory gaits for translation, rotation and combined motions. The Artificial Intelligence (AI) powered swimmer can switch between various locomotory gaits adaptively to navigate towards target locations. The multimodal navigation strategy is reminiscent of gait-switching behaviors adopted by swimming microorganisms. We show that the strategy advised by AI is robust to flow perturbations and versatile in enabling the swimmer to perform complex tasks such as path tracing without being explicitly programmed. Taken together, our results demonstrate the vast potential of these AI-powered swimmers for applications in unpredictable, complex fluid environments. Biomedical applications of artificial microswimmers rely on efficient navigation strategies within complex and unpredictable fluid environments. Here, the authors use artificial intelligence to model and design microswimmers that are capable of self-learning efficient navigation strategies by adaptively switching between different locomotory gaits.
Purpose Fine API agglomeration and its mitigation via particle engineering, i.e., dry coating, remains underexplored. The purpose was to investigate agglomeration before and after dry coating of fine cohesive APIs and impact on powder processability, i.e., flowability (FFC), bulk density (BD), and dissolution of BCS Class II drugs. Method Ibuprofen (three sizes), fenofibrate, and griseofulvin (5–20 µm), before and after dry coating with varying amounts of hydrophobic (R972P) or hydrophilic (A200) nano- silica, were assessed for agglomeration, FFC, BD, surface energy, wettability, and dissolution. The granular Bond number (Bog), a dimensionless parameter, evaluated through material-sparing particle-scale measures and particle-contact models, was used to express relative powder cohesion. Results Significant powder processability improvements after dry coating were observed: FFC increased by multiple flow regimes, BD increased by 25–100%, agglomerate ratio (AR) reduction by over an order of magnitude, and greatly enhanced API dissolution rate even with hydrophobic (R972P) silica coating. Scrutiny of particle-contact models revealed non-triviality in estimating API surface roughness, which was managed through the assessment of measured bulk properties. A power-law correlation was identified between AR and Bog and subsequently, between AR and FFC & bulk density; AR below 5 ensured improved processability and dissolution. Conclusion Agglomeration, an overlooked material-sparing measure for powder cohesiveness, was a key indicator of powder processability and dissolution. The significant agglomerate reduction was possible via dry coating with either silica type at adequate surface area coverage. Reduced agglomeration after dry coating also countered the adverse impact of increased surface hydrophobicity on dissolution.
This theoretical work aims to understand the influence of nanopores at CuO–Al nanothermite interfaces on the initial stage of thermite reaction. ReaxFF molecular dynamics simulations were run to investigate the chemical and structural evolution of the reacting interface between the fuel, Al, and oxidizer, CuO, between 400 and 900 K and considering interfaces with and without a pore. Results show that the initial alumina layer becomes enriched with Al and grows primarily into the Al metal at higher temperatures. The modification of alumina is driven by simultaneous Al and O migration between metallic Al and the native amorphous Al2O3 layer. However, the presence of a pore significantly affects the growth kinetics and the composition of this alumina layer at temperatures exceeding 600 K, which impacts the initiation properties of the nanothermite. In the system without a pore, where Al is in direct contact with CuO, a ternary aluminate layer, a mixture of Al, O, and Cu, is formed at 800 K, which slows Al and O diffusion, thus compromising the nanothermite reactivity in fully dense Al/CuO composites. Conversely, the presence of a pore between Al and CuO promotes Al enrichment of the alumina layer above 600 K. At that temperature, any free oxygen molecules in the pore become attached to the reactive alumina surface resulting in a rapid oxygen pressure drop in the pore. This is expected to accelerate the reduction of the adjacent CuO as observed in experiments with Al/CuO composites with porosity at the CuO–Al interfaces.
Developing novel methods for the analysis of intracellular signaling networks is essential for understanding interconnected biological processes that underlie complex human disorders. A fundamental goal of this research is to quantify the vulnerability of a signaling network to the dysfunction of one or multiple molecules, when the dysfunction is defined as an incorrect response to the input signals. In this study, we propose an efficient algorithm to identify the extreme signaling failures that can induce the most detrimental impact on the physiological function of a molecular network. The algorithm finds the molecules, or groups of molecules, with the maximum vulnerability, i.e., the highest probability of causing the network failure, when they are dysfunctional. We propose another algorithm that efficiently accounts for signaling feedbacks. The algorithms are tested on experimentally verified ERBB and T-cell signaling networks. Surprisingly, results reveal that as the number of concurrently dysfunctional molecules increases, the maximum vulnerability values quickly reach to a plateau following an initial increase. This suggests the specificity of vulnerable molecule(s) involved, as a specific number of faulty molecules cause the most detrimental damage to the function of the network. Increasing the number of simultaneously faulty molecules does not further deteriorate the network function. Such a group of specific molecules whose dysfunction causes the extreme signaling failures can better elucidate the molecular mechanisms underlying the pathogenesis of complex trait disorders, and can offer new insights for the development of novel therapeutics.
There has been increasing evidence and growing popularity of orthobiologic treatments, such as platelet-rich plasma, bone marrow aspirate concentrate, and microfragmented adipose tissue. However, real-world data, including patient-reported pain and function outcomes, remains sparse for these procedures. Thus, collecting patient-reported outcome measures is important to evaluate the safety and efficacy of these treatments and hopefully improve patient care. Patient reported outcome measures can systematically be collected through patient registries. This narrative review serves to describe the data collection platforms and registries that obtain patient-reported outcome measures on orthobiologic procedures and provide a discussion on the benefits and limitations of registries. An internet search of the list of orthopedic registries available was conducted, and registries that collect patient-reported outcome measures for orthobiologic procedures were identified. Additional information regarding these various registries was collected by directly contacting these vendors. Publications from these registries, including case series, observational studies, and annual reports, were also reviewed. Providing this review will inform clinicians of a digital tool that can increase the efficiency of collecting outcome measures for orthobiologics and aid physicians in choosing a data collection platform.
Point cloud completion aims at predicting a complete 3D shape from an incomplete input. It has important applications in the fields of intelligent manufacturing, augmented reality, virtual reality, self-driving cars, and intelligent robotics. Although deep learning-based point cloud completion technology has developed rapidly in recent years, there are still unsolved problems. Previous approaches predict each point independently and ignore contextual information. Also, they usually predict a complete 3D shape based on a global feature vector extracted from an incomplete input, which leads to some fine-grained details being lost. In this paper, motivated by the transposed convolution and the ``UNet" structure in neural networks for image processing, we propose a context-aware deep network termed as PCUNet for coarse-to-fine point cloud completion. It adopts an encoder-decoder structure, in which the encoder follows the design of the relation-shape convolutional neural network (RS-CNN), and the decoder consists of fully-connected layers and two stacked decoder modules for predicting complete point clouds. The contributions are twofold. First, we design the decoder module as a coordinate-guided context-aware upsampling module, in which contextual information can be taken into full account by neighbor aggregation. In addition, to preserve fine-grained details lost in the global feature vector, we propose attention-enhanced skip connections for effective information propagation from the encoder to the decoder. Experiments are conducted on the widely used PCN and KITTI datasets. The results show that our proposed approach achieves competitive performance compared to the existing state-of-the-art approaches in terms of the Chamfer distance and the computational complexity metrics.
Neuronal systems are subject to rapid fluctuations both intrinsically and externally. These fluctuations can be disruptive or constructive. We investigate the dynamic mechanisms underlying the interactions between rapidly fluctuating signals and the intrinsic properties of the target cells to produce variable and/or coherent responses. We use linearized and non-linear conductance-based models and piecewise constant (PWC) inputs with short duration pieces. The amplitude distributions of the constant pieces consist of arbitrary permutations of a baseline PWC function. In each trial within a given protocol we use one of these permutations and each protocol consists of a subset of all possible permutations, which is the only source of uncertainty in the protocol. We show that sustained oscillatory behavior can be generated in response to various forms of PWC inputs independently of whether the stable equilibria of the corresponding unperturbed systems are foci or nodes. The oscillatory voltage responses are amplified by the model nonlinearities and attenuated for conductance-based PWC inputs as compared to current-based PWC inputs, consistent with previous theoretical and experimental work. In addition, the voltage responses to PWC inputs exhibited variability across trials, which is reminiscent of the variability generated by stochastic noise (e.g., Gaussian white noise). Our analysis demonstrates that both oscillations and variability are the result of the interaction between the PWC input and the target cell’s autonomous transient dynamics with little to no contribution from the dynamics in vicinities of the steady-state, and do not require input stochasticity.
This research examines the impact of coopetition (i.e., competitor alliances) on the development of internal R&D human capital. The study was conducted using survey data from 111 biotech firms in Spain and United States. Results show a mediation relationship: coopetition increases a firm’s internal R&D human capital via its proactiveness to pursue R&D partnerships. To further examine the link between competitor alliances and R&D partnerships, we also investigate the role of two moderators, alliance satisfaction and alliance coordination. We argue that the two factors exert opposite moderation effects on the relationship between coopetition and proactiveness to pursue R&D partnerships. Results demonstrate that when a firm and its alliance partners are satisfied with each other, the effect of coopetition on proactiveness decreases, but the moderation effect of alliance coordination, though predicted to be in the opposite direction, is not significant.
Background Among systemic abnormalities caused by the novel coronavirus, little is known about the critical attack on the central nervous system (CNS). Few studies have shown cerebrovascular pathologies that indicate CNS involvement in acute patients. However, replication studies are necessary to verify if these effects persist in COVID-19 survivors more conclusively. Furthermore, recent studies indicate fatigue is highly prevalent among ‘long-COVID’ patients. How morphometry in each group relate to work-related fatigue need to be investigated. Method COVID survivors were MRI scanned two weeks after hospital discharge. We hypothesized, these survivors will demonstrate altered gray matter volume (GMV) and experience higher fatigue levels when compared to healthy controls, leading to stronger correlation of GMV with fatigue. Voxel-based morphometry was performed on T1-weighted MRI images between 46 survivors and 30 controls. Unpaired two-sample t-test and multiple linear regression were performed to observe group differences and correlation of fatigue with GMV. Results The COVID group experienced significantly higher fatigue levels and GMV of this group was significantly higher within the Limbic System and Basal Ganglia when compared to healthy controls. Moreover, while a significant positive correlation was observed across the whole group between GMV and self-reported fatigue, COVID subjects showed stronger effects within the Posterior Cingulate, Precuneus and Superior Parietal Lobule. Conclusion Brain regions with GMV alterations in our analysis align with both single case acute patient reports and current group level neuroimaging findings. We also newly report a stronger positive correlation of GMV with fatigue among COVID survivors within brain regions associated with fatigue, indicating a link between structural abnormality and brain function in this cohort.
Institution pages aggregate content on ResearchGate related to an institution. The members listed on this page have self-identified as being affiliated with this institution. Publications listed on this page were identified by our algorithms as relating to this institution. This page was not created or approved by the institution. If you represent an institution and have questions about these pages or wish to report inaccurate content, you can contact us here.
4,157 members
Wenge Guo
  • Department of Mathematical Sciences
Xin Di
  • Department of Biomedical Engineering
Umar Qasim
  • Department of Information Systems
Bernard Friedland
  • Department of Electrical and Computer Engineering
Bruce Bukiet
  • Department of Mathematical Sciences
Information
Address
07102, Newark, New Jersey, United States
Website
www.njit.edu