Stevens Institute of Technology
  • Hoboken, New Jersey, United States
Recent publications
Ground-based sky cameras, which capture hemispherical images, have been extensively used for localized monitoring of clouds. This paper proposes a short-term forecasting approach based on transfer learning using Total Sky-Imager (TSI) images of the Southern Great Plains (SGP) site obtained from the Atmospheric Radiation Measurement (ARM) dataset. An accurate estimation of solar irradiance using TSI is key for short-term solar energy generation forecasting and optimal energy consumption planning. We make use of deep neural network architectures such as AlexNet and ResNet-101 to extract the underlying deep convolution features from TSI images and then train using an ensemble learning approach to model and forecast solar radiation. We demonstrate the performance of the proposed approach by showcasing the best and worst cases. Thus, the transfer learning approach significantly reduces the time and resources required for modeling solar radiation. We outperform with reference to another state-of-art technique for solar modeling using TSI images at different forecast lead times.
The increasing penetration of renewable energy in power systems has been playing an increasingly important role on power system reliability. However, traditional reliability indices of power systems, such as the loss-of-load probability and the expected unserved load, can only reflect the generation capacity adequacy but neglect its flexibility. Focusing on this issue, a set of comprehensive assessment indices that can accurately quantify both generation capacity adequacy and flexibility is proposed in this paper. Specifically, the proposed up-ward capacity shortage probability and expectation indices assess both generation capacity and ramp-up capacity shortages that may cause load loss, while the down-ward capacity shortage probability and expectation assess both base-load cycling capacity and ramp down capacity shortages that may cause renewable energy curtailment. A probabilistic assessment method with the time sequential probabilistic production simulation technology is developed to effectively calculate the proposed indices. This method can adequately consider uncertainty factors, such as the random outages of generating units and forecasting errors of loads and renewable energy, as well as dynamic operation statuses of generating units. Numerical examples are presented to verify the effectiveness of the proposed indices and the evaluation method.
This paper discusses a tri-layer non-cooperative energy trading approach among multiple grid-tied multi-energy microgrids (MEMGs) in the restructured integrated energy market. The heterogeneous uncertainties from renewable energy, market prices, and electric energy loads are also considered via the risk-averse stochastic programming (SP) approach. First, comprehensive operation models of individual MEMGs are presented with the consideration of practical electric energy and thermal network flows as well as battery degradation. Second, to guarantee fair multi-energy trading among MEMGs and deal with adverse effects from all uncertainty sources, a tri-layer Cournot Nash game-based energy bidding method is developed and solved by the SP approach. In the first layer, i.e., day-ahead multi-energy market, optimal energy bids, dispatches of energy storage assets, and thermal flows against uncertainty scenarios are acquired in a risk-averse manner; In the second layer, i.e., intra-day multi-energy market, optimal intra-day energy bids and dispatches of all resources against uncertainty realizations are sequentially calculated; In the third layer, i.e., the real-time multi-energy market, transactions between each MEMG and the wholesale multi-energy market are finalized. Third, for protecting the privacy of individual MEMGs and alleviating the computation burdens, the distributed alternating search procedure is employed to compute the Nash equilibriums in the day-ahead and intra-day markets. In the end, numerical case studies are conducted to verify the effectiveness of our method. From the simulation results, it can be inferred that compared with the traditional cooperative, deterministic and risk-natural methods in the literature, our proposed method is more practical and economical for real-world applications since it comprehensively considers the market competition, uncertainty handling, and energy trading risk.
Ultra-high-performance concrete (UHPC) has been used to enhance the mechanical behavior of orthotropic steel bridge decks through steel-UHPC composite actions. This research develops a method to evaluate the flexural behavior of steel-UHPC composite sections via experimental and analytical investigations. Four composite deck specimens were fabricated and tested to study the mechanical behaviors under negative moment along the transverse and the longitudinal directions. A polyline model is proposed to link the strain, crack width of UHPC, and the bearing capacity of steel-UHPC composite decks, considering the crack behavior of UHPC with different fiber contents. The polyline model is used to derive engineer-friendly formulae for the flexural behavior of composite decks. Results showed that the proposed method provided adequate predictions for the flexural behavior of composite decks in the evolution of the crack width in UHPC with different fiber contents.
Externally bonding fiber reinforced polymer (FRP) to concrete structures is an effective way to enhance the mechanical performance of concrete structures. Many equations have been proposed to predict the interfacial bond strength for FRP-concrete structures but have limited accuracy due to the complexity of the bond behavior. This study proposes to formulate the FRP-concrete interfacial bond strength based on machine learning (ML) methods, which have emerged as a promising alternative to achieve high prediction accuracy in high-dimension problems. To this end, a database containing 1,375 FRP-concrete direct shear test specimens that failed due to interfacial debonding was established. The database was improved using an unsupervised isolation forest that identified and eliminated anomalous data, and was then used to train six ML models, namely artificial neural networks (ANN), support vector machine, decision tree, gradient boosting decision tree, random forest, and XGboost algorithms, to predict the FRP-concrete interfacial bond strength. The ML predictive models showed higher accuracy than 16 existing equations in the literature. The XGBoost model showed the highest accuracy, and its coefficient of variation was 54% lower than the existing equation with the highest accuracy among those considered. The ANN model was used to perform a parametric study on the influencing parameters, and a new equation was generated to predict the interfacial bond strength, considering the key influencing parameters. The equation enables interpretation of the ML models. The study combines ML models and traditional physical models to achieve a novel, interpretable ML method for predicting FRP-concrete interfacial bond strength.
Electricity cost has become a critical concern of data center operations with the rapid increasing of information processing demand. Data center microgrid (DCMG) is a promising way to reduce electric energy consumption from traditional fossil fuel generators and the billing cost, by effectively utilizing local renewable energy, e.g., wind power. However, uncertainties of wind power generation and real-time workload of data center would have significant impacts on the operational efficiency of DCMG, especially when it is in the island mode. For this reason, a novel affinely adjustable policy based robust multi-objective optimization model under flexible uncertainty set is proposed in this paper, which simultaneously optimizes wind power curtailment, the operation cost, and the over-plus level of computation resource, while considering uncertainties of both the wind power and real-time workload. Through numerical simulation studies, the validity of robust multi-objective optimization model for the island operation of DCMG is verified. Besides, the effectiveness of the proposed methods, i.e., the novel affinely adjustable policy and the flexible uncertainty set, in handling uncertainties are evaluated. Compared to the conventional robust multi-objective optimization model, the proposed approach reduces the operating costs of about 10% in average while maintaining similar reliability in numerical simulations. Moreover, the complex quantitative relationship among these multiple objectives is further investigated. Simulation results indicate the minimization of wind power curtailment and over-plus level of computation resource increases about 25% of the operation cost. These quantitative relationships can well support the decision making of DCMG operation management.
Machine learning has been recently used to predict therapeutic antibody aggregation rates and viscosity at high concentrations (150 mg/ml). These works focused on commercially available antibodies, which may have been optimized for stability. In this study, we measured accelerated aggregation rates at 45°C and viscosity at 150 mg/ml for 20 preclinical and clinical-stage antibodies. Features obtained from molecular dynamics simulations of the full-length antibody and sequences were used for machine learning model construction. We found a k-nearest neighbors regression model with two features, spatial positive charge map on the CDRH2 and solvent-accessible surface area of hydrophobic residues on the variable fragment, gives the best performance for predicting antibody aggregation rates (r = 0.89). For the viscosity classification model, the model with the highest accuracy is a logistic regression model with two features, spatial negative charge map on the heavy chain variable region and spatial negative charge map on the light chain variable region. The accuracy and the area under precision recall curve of the classification model from validation tests are 0.86 and 0.70, respectively. In addition, we combined data from another 27 commercial mAbs to develop a viscosity predictive model. The best model is a logistic regression model with two features, number of hydrophobic residues on the light chain variable region and net charges on the light chain variable region. The accuracy and the area under precision recall curve of the classification model are 0.85 and 0.6, respectively. The aggregation rates and viscosity models can be used to predict antibody stability to facilitate pharmaceutical development.
This paper presents a path-following and collision avoidance system for autonomous surface vehicles based on nonlinear model predictive control. The proposed strategy is capable of following a desired path while maintaining a commanded velocity, whereas it can diverge from the references to safely perform maneuvers to elude unexpected obstacles. A nonlinear dynamic model of the vehicle is applied to predict the vehicle states into a finite horizon. Furthermore, a LiDAR sensor located at the front of the boat is employed for local object detection. Here, the high-performance optimal control framework, acados, is used to solve the optimization problem onboard. Real-time numerical simulations and field experiments demonstrate the effectiveness of the proposed approach against multiple buoys.
Cracks are one of the worsening reasons for concrete failure, which permits the penetration of chemical solutions and could significantly impact the physical, mechanical, and durability characteristics of concrete buildings. To protect, heal and assimilate concrete structures, numerous coating materials, binding materials, and adhesives have been generally exercised. Though these methods are highly appropriate, because of their different essential procedure, critical issues, for instance, lack of effectiveness in cost and delamination, have caused the exploration of substitute procedures for sealing cracks and self-healing concrete. One of the newer self-healing methods is employing bacterial material modified with precipitation of calcite in concrete mixes to fill or heal cracking in concrete. In this method, the mineralization of bacteria is carried out via the decomposition of calcium and urea to form calcium carbonate, which could fill the cracking. To review the methods for this kind of precipitation, the present paper aims to offer an in-depth study of precipitation of calcium carbonate, physical, mechanical, and durability characteristics, and micro-structure performance of concrete. One hundred fifty articles were studied to perform the present study. Their results have been presented about the dose and type of bacteria and its impact on strength and durability characteristics. The present study shows that bio-mineralization largely relies on several factors, for instance, the preservation of bacterial cells and the application procedure. Furthermore, the impact of bacterial material on the environment is observed to be straight related to the proportion of urea in concrete mixes.
Since March 2020, the COVID-19 pandemic has profoundly disrupted higher education in the United States (U.S.). During the first wave of infection and hospitalization, many universities and colleges transitioned classroom instruction to online or a hybrid format. In September 2021, classes largely returned to in-person after the COVID-19 vaccine was widely available and, in some cases, mandated on university and college campuses across the U.S. In the current research, first-year undergraduate students answered a series of questions about their resilience, grit, and perceived academic and career impacts from the ongoing COVID-19 pandemic in Spring (February/March - May) 2021 and 2022. Findings from a series of regression analyses showed that grit and resilience seemed to protect students and help them stay on track, even in the face of the global pandemic. Undergraduate students who reported higher levels of grit and resilience were less likely to worry about job opportunities shrinking as well as less likely to report changing their academic goals, career goals, and proposed major. Future directions and implications are discussed.
Background Nutrition research is relying more on artificial intelligence and machine learning models to understand, diagnose, predict, and explain data. While artificial intelligence and machine learning models provide powerful modeling tools, failure to use careful and well-thought-out modeling processes can lead to misleading conclusions and concerns surrounding ethics and bias. Methods Based on our experience as reviewers and journal editors in nutrition and obesity, we identified the most frequently omitted best practices from statistical modeling and how these same practices extend to machine learning models. We next addressed areas required for implementation of machine learning that are not included in commercial software packages. Results Here, we provide a tutorial on best artificial intelligence and machine learning modeling practices that can reduce potential ethical problems with a checklist and guiding principles to aid nutrition researchers in developing, evaluating, and implementing artificial intelligence and machine learning models in nutrition research. Conclusion The quality of AI/ML modeling in nutrition research requires iterative and tailored processes to mitigate against potential ethical problems or to predict conclusions that are free of bias.
Embedded in the tail base of all snakes is a pair of scent glands that open through ducts at the margin of the cloaca. Scent gland secretions (SGS), which typically are discharged when snakes are disturbed, are thought to deter predators. Previous chemical analyses have indicated carboxylic acids as the chief lipid constituents of SGS. We examined by GC-MS the SGS of the eastern small-scaled burrowing asp (Atractaspis fallax), a venomous, fossorial species in the Lamprophiidae, a diverse family containing more than 60 genera of mostly non-venomous African snakes. Our results confirm that acetic, propionic, 2-methylpropanoic, n-butanoic, and 3-methylbutanoic acids are the major constituents in the SGS samples of A. fallax. These compounds are widespread in the SGS of the Alethinophidia (‘typical snakes’). Some of these short-chain acids also occur in the anal glands of mammals and the uropygial (preen) glands of birds, where they arise via microbial activity. Whether these compounds in SGS have a similar origin is unknown.
Observable signatures of the quantum nature of gravity at low energies have recently emerged as a promising new research field. One prominent avenue is to test for gravitationally induced entanglement between two mesoscopic masses prepared in spatial superposition. Here, we analyze such proposals and what one can infer from them about the quantum nature of gravity as well as the electromagnetic analogues of such tests. We show that it is not possible to draw conclusions about mediators: even within relativistic physics, entanglement generation can equally be described in terms of mediators or in terms of non-local processes—relativity does not dictate a local channel. Such indirect tests, therefore, have limited ability to probe the nature of the process establishing the entanglement as their interpretation is inherently ambiguous. We also show that cosmological observations already demonstrate some aspects of quantization that these proposals aim to test. Nevertheless, the proposed experiments would probe how gravity is sourced by spatial superpositions of matter, an untested new regime of quantum physics.
The roll-to-roll continuous process is a manufacturing technique widely used in several industrial appliances, including electronic devices. Real-time monitoring and fault diagnosis are crucial in this process because it involves several rotary machines, and their failure can cause speed disturbance that can result in tension disturbance and easily degrade the geometrical quality of the coated layer. Therefore, establishing an effective diagnostic system can prevent unexpected damage and reduce maintenance costs by predicting failures in advance. Herein, we propose a diagnostic model based on tension and acceleration data. Further, we establish window size calculation criteria for feature extraction and propose classification and redundancy quantitative evaluation algorithms based on the density between feature classes and the Mahalanobis distance. The results indicate that by using the proposed method, the average accuracy is improved by 6.07% compared to that of the existing method, and the learning time is reduced by 29.44%.
Background Innovation is broadly defined as the act of introducing a new product, idea, or process. The field of surgery is built upon innovation, revolutionizing technology, science, and tools to improve patient care. While most innovative solutions are aimed at problems with a significant patient population, the process can also be used on orphan pathologies without obvious solutions. We present a case of tracheal agenesis, a rare congenital anomaly with an overwhelming mortality and few good treatment options, that benefited from the innovation process and achieved survival with no ventilator dependence at three years of age. Methods Utilizing the framework of the innovation process akin to the Stanford Biodesign Program, 1) the parameters of the clinical problem were identified, 2) previous solutions and existing technologies were analyzed, newly invented solutions were brainstormed, and value analysis of the possible solutions were carried out using crowd wisdom, and 3) the selected solution was prototyped and tested using 3D modeling, iterative testing on 3D prints of actual-sized patient parts, and eventual implementation in the patient after regulatory clearance. Results A 3D-printed external bioresorbable splint was chosen as the solution. Our patient underwent airway reconstruction with “trachealization of the esophagus”: esophageotracheal fistula resection, esophagotracheoplasty, and placement of a 3D-printed polycaprolactone (PCL) stent for external esophageal airway support at five months of age. Conclusions The innovation process provided our team with the guidance and imperative steps necessary to develop an innovative device for the successful management of an infant survivor with Floyd Type I tracheal agenesis. Article summary We present a case of tracheal agenesis, a rare congenital anomaly with an overwhelming mortality and few good treatment options, that benefited from the innovation process and achieved survival with no ventilator dependence at three years of age. The importance of this report is to reveal how the innovation process, which is typically used for problems with significant patient population, can also be used on orphan pathologies without obvious solutions.
Microwave imaging technology is a useful method often applied in medical diagnosis and can be used by the food industry to ensure food safety and quality. For fruit, ripeness is the primary characteristic which determines quality for the consumer. This paper proposes a novel microwave imaging system to determine the ripeness of watermelon as a proof of concept. The design employs a circular array with 10 Coplanar Vivaldi antennas offering wide bandwidth, high gain, and high efficiency. S-parameters between antennas are collected quickly via automated channel switching for fast image generation. Eight different watermelon samples of varying ripeness, type, dimensions, and origin are scanned and imaged. Comparisons with sample cross-sections show distinct differences in image characteristics based on watermelon maturity. Sugar concentration of unripe and ripe watermelon is also measured and plotted for further validation of the imaging technique.
Development of alternative freshwater via desalination can address water scarcity and security. Meanwhile, sustainable renewable energy sources are critical to economically realize seawater desalination. Marine renewable energy has tremendous potential to power blue economy and is co-located with seawater. This paper proposes a compact autonomous ocean-wave-powered reverse osmosis (RO) desalination system by directly pressurizing seawater using a wave energy converter (WEC). The proposed ocean-wave-powered reverse osmosis desalination system consists of an oscillating surge wave energy converter (OSWEC) hinged on the nearshore seabed with a self-rectified piston pump and a RO desalination module on the shore. Seawater is pressurized by the WEC and pumped to the RO desalination module as feed where it then produces permeate that is free of undesired molecules and larger particles. Numerical modeling of the integrated system is created, and simulation is done under realistic irregular wave conditions. Preliminary experiment was implemented in a wave tank. Both simulation result and experiment results reveal the promising integration of ocean wave energy and RO desalination.
Oncology clinical development programs have targeted the RAS/RAF/MEK/ERK signaling pathway with small molecule inhibitors for a variety of cancers during the past decades, and most therapies have shown limited or minimal success. Specific BRAF and MEK inhibitors have shown clinical efficacy in patients for the treatment of BRAF-mutant melanoma. However, most cancers have shown treatment resistance after several months of inhibitor usage, and reports indicate resistance is often associated with the reactivation of the MAPK signaling pathway. It is widely accepted that an effective MAPK therapy will have a significant impact on curtailing cancer growth and improving patient survival. However, despite more than three decades of intense research and pharmaceutical industry efforts, an FDA-approved, effective anti-cancer ERK inhibitor has yet to be developed. Here, we present the design, optimization, and biological characterization of ERK1/2 inhibitors that block catalytic phosphorylation of downstream substrates such as RSK but also modulate the phosphorylation of ERK1/2 by MEK without directly inhibiting MEK. Our series of dual mechanism ERK1/2 inhibitors, in which we incorporated a triazolopyridinone core, may present potential benefits for enhancing efficacy and addressing the emergence of treatment resistance.
Anthropology and science fiction are interlinked traditions of knowledge production and overlap in several different ways, not least by the fact that each of these practices was shaped by the colonial encounter. This afterword to the special section “The Ordinariness of Cross‐Time Relations: Anthropology, Literature, and the Science Fictional” addresses those linkages and suggests that anthropology as a discipline stands to gain from an engagement with speculative and science fiction, for instance, through a questioning of ethnographic realism. [science fiction, speculative fiction, social theory, colonialism, ethnographic realism]
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.
3,683 members
Alexander Ekimov
  • Department of Civil, Environmental & Ocean Engineering
Arthur Ritter
  • Biomedical Engineering, Chemistry and Biolgical Sciences
Santosh Kumar (Gangwar)
  • Department of Physics & Engineering Physics
Marko Zivkovic
  • Department of Physics & Engineering Physics
Qiantao Shi
  • Department of Civil, Environmental & Ocean Engineering
Castle Point on Hudson, 07030, Hoboken, New Jersey, United States