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Molecular dynamics triggered by interaction with light often involve the excitation of several electronic, vibrational, and rotational states. Characterizing the resulting coupled electronic and nuclear wave packet motion represents a severe challenge, even for small polyatomic systems. In this Letter, we demonstrate how the interplay between vibrational, rotational, and electronic degrees of freedom governs the evolution of molecular wave packets in the low-lying states of strong-field-ionized sulfur dioxide. Using time-resolved Coulomb explosion imaging (CEI) in combination with quantum mechanical wave packet simulations, we directly map bending vibrations of the molecule, show how the vibrational wave packet is influenced by molecular alignment, and elucidate the role of the coupling between the two lowest electronic states of the cation. A conical intersection between these states couples the bending and asymmetric stretching coordinates, which is clearly reflected in the correlated fragment momenta. Our results suggest that multi-coincident CEI represents an efficient experimental tool for characterizing coupled electronic and nuclear motion in polyatomic molecules.

Coulomb explosion imaging (CEI) with x-ray free electron lasers has recently been shown to be a powerful method for obtaining detailed structural information of gas-phase planar ring molecules [R. Boll et al. Nat. Phys. 18, 423-428 (2022)]. In this Letter, we investigate the potential of CEI driven by a tabletop laser and extend this approach to differentiating three-dimensional (3D) structures. We study the static CEI patterns of planar and nonplanar organic molecules that resemble the structures of typical products formed in ring-opening reactions. Our results reveal that each molecule exhibits a well-localized and distinctive pattern in 3D fragment-ion momentum space. We find that these patterns yield direct information about the molecular structures and can be qualitatively reproduced using a classical Coulomb explosion simulation. Our findings suggest that laser-induced CEI can serve as a robust method for differentiating molecular structures of organic ring and chain molecules. As such, it holds great promise as a method for following ultrafast structural changes, e.g., during ring-opening reactions, by tracking the motion of individual atoms in pump-probe experiments.

In this study, we present a novel platform based on scanning microwave microscopy for manipulating and detecting tiny vibrations of nanoelectromechanical resonators using a single metallic tip. The tip is placed on the top of a grounded silicon nitride membrane, acting as a movable top gate of the coupled resonator. We demonstrate its ability to map mechanical modes and investigate mechanical damping effects in a capacitive coupling scheme, based on its spatial resolution. We also manipulate the energy transfer coherently between the mode of the scanning tip and the underlying silicon nitride membrane, via parametric coupling. Typical features of optomechanics, such as anti-damping and electromechanically induced transparency, have been observed. Since the microwave optomechanical technology is fully compatible with quantum electronics and very low temperature conditions, it should provide a powerful tool for studying phonon tunnelling between two spatially separated vibrating elements, which could potentially be applied to quantum sensing.

Molecular photoabsorption results in an electronic excitation/ionization which couples to the rearrangement of the nuclei. The resulting intertwined change of nuclear and electronic degrees of freedom determines the conversion of photoenergy into other molecular energy forms. Nucleobases are excellent candidates for studying such dynamics, and great effort has been taken in the past to observe the electronic changes induced by the initial excitation in a time-resolved manner using ultrafast electron spectroscopy. The linked geometrical changes during nucleobase photorelaxation have so far not been observed directly in time-resolved experiments. Here, we present a study on a thionucleobase, where we extract comprehensive information on the molecular rearrangement using Coulomb explosion imaging. Our measurement links the extracted deplanarization of the molecular geometry to the previously studied temporal evolution of the electronic properties of the system. In particular, the protons of the exploded molecule are well-suited messengers carrying rich information on the molecule's geometry at distinct times after the initial electronic excitation. The combination of ultrashort laser pulses to trigger molecular dynamics, intense X-ray free-electron laser pulses for the explosion of the molecule, and multi-particle coincidence detection opens new avenues for time-resolved studies of complex molecules in the gas phase., Comment: 19 pages, 8 figures

Each person has a unique gait, i.e., walking style, that can be used as a biometric for personal identification. Recent works have demonstrated effective gait recognition using deep neural networks, however most of these works predominantly focus on classification accuracy rather than model efficiency. In order to perform gait recognition using wearable devices on the edge, it is imperative to develop highly efficient low-power models that can be deployed on to small form-factor devices such as microcontrollers. In this paper, we propose a small CNN model with 4 layers that is very amenable for edge AI deployment and realtime gait recognition. This model was trained on a public gait dataset with 20 classes augmented with data collected by the authors, aggregating to 24 classes in total. Our model achieves 96.7% accuracy and consumes only 5KB RAM with an inferencing time of 70 ms and 125mW power, while running continuous inference on Arduino Nano 33 BLE Sense. We successfully demonstrated realtime identification of the authors with the model running on Arduino, thus underscoring the efficacy and providing a proof of feasiblity for deployment in practical systems in near future.

Identifying multiple rival reaction products and transient species formed during ultrafast photochemical reactions and determining their time-evolving relative populations are key steps towards understanding and predicting photochemical outcomes. Yet, most contemporary ultrafast studies struggle with clearly identifying and quantifying competing molecular structures/species amongst the emerging reaction products. Here, we show that mega-electronvolt ultrafast electron diffraction in combination with ab initio molecular dynamics calculations offer a powerful route to determining time-resolved populations of the various isomeric products formed after UV (266 nm) excitation of the five-membered heterocyclic molecule 2(5H)-thiophenone. This strategy provides experimental validation of the predicted high (~50%) yield of an episulfide isomer containing a strained 3-membered ring within ~1 ps of photoexcitation and highlights the rapidity of interconversion between the rival highly vibrationally excited photoproducts in their ground electronic state.

Following the changes in molecular structure throughout the entirety of a chemical reaction with atomic resolution is a long-term goal in femtochemistry. Although the development of a plethora of ultrafast technique has enabled detailed investigations of the electronic and nuclear dynamics on femtosecond time scales, direct and unambiguous imaging of the nuclear motion during a reaction is still a major challenge. Here, we apply time-resolved Coulomb explosion imaging with femtosecond near-infrared pulses to visualize the ultraviolet-induced ultrafast molecular dynamics of gas-phase furan. Widely contradicting predictions and observations for this molecule have been reported in the literature. By combining the experimental Coulomb explosion imaging data with ab initio molecular dynamics and Coulomb explosion simulations, we reveal the presence of a strong ultrafast ring-opening pathway upon excitation at 198 nm that occurs within 100 fs., Comment: 18 pages, 4 figures

Just like electrical engineers understand how microprocessors execute programs in terms of how transistor currents are affected by their inputs, neuroscientists want to understand behavior production in terms of how neuronal outputs are affected by their inputs and internal states. This dependency of neuronal outputs on inputs can be described by a state-dependent input-output (IO)-function. However, to reliably identify these IO-functions, we need to perturb each input and combinations of inputs while observing all the outputs. Here, we argue that such completeness is possible in C. elegans; a complete description that goes all the way from the activity of every neuron to predict behavior. The established and growing toolkit of optophysiology can non-invasively capture and control every neuron's activity and scale to countless experiments. The information from many such experiments can be pooled while capturing the inter-individual variability because neuronal identity and function are largely conserved across individuals. Just like electrical engineers use transistor IO-functions to simulate program execution, we argue that neuronal IO-functions could be used to simulate the impressive breadth of brain states and behaviors of C. elegans., Comment: 28 pages, 2 figures, opinion paper

Synthesis of high-performance single crystal LiNi0.8Mn0.1Co0.1O2 (NMC811) in the absence of molten salt is challenging with no success yet. An innovative drop-in approach is discovered to synthesize single crystal NMC811 by controlling the morphology of transition metal hydroxide TM(OH)2 precursors followed by a simple decomposition step to form transition metal oxide (TMO) intermediates. Ni redistribution in TMO, as a result of the concurrent formation of mixed spinel and rock salt phases, helps deagglomerate the later formed NMC811 clusters of single crystals. As-prepared single crystal NMC811 is validated in a 2Ah pouch cell demonstrating 1000 stable cycling. The fundamentally new reaction mechanism of single crystal growth and segregation without molten salt provides a new direction towards cost-efficient manufacturing of single crystal NMC811 cathode for advanced lithium-based batteries., Comment: 13 pages, 5 figures

There's a gap between funders' perspectives on how much they support indirect costs ("overhead") and organisational development, and NGOs' perspectives on just how much funders are willing to support it. Improved communication between funders and NGOs would help to build trust and shift entrenched attitudes and practices. This research along with our assessment guide and tips can support NGOs and funders in this important work. [For the assessment guide, "Organisational Development Assessment Guide," see ED622362. For the tips, "Tips for Grant Pitches: Know Your Funder's Mindset," see ED622361.]

Graph transformers have emerged as a promising architecture for a variety of graph learning and representation tasks. Despite their successes, though, it remains challenging to scale graph transformers to large graphs while maintaining accuracy competitive with message-passing networks. In this paper, we introduce Exphormer, a framework for building powerful and scalable graph transformers. Exphormer consists of a sparse attention mechanism based on two mechanisms: virtual global nodes and expander graphs, whose mathematical characteristics, such as spectral expansion, pseduorandomness, and sparsity, yield graph transformers with complexity only linear in the size of the graph, while allowing us to prove desirable theoretical properties of the resulting transformer models. We show that incorporating Exphormer into the recently-proposed GraphGPS framework produces models with competitive empirical results on a wide variety of graph datasets, including state-of-the-art results on three datasets. We also show that Exphormer can scale to datasets on larger graphs than shown in previous graph transformer architectures. Code can be found at \url{https://github.com/hamed1375/Exphormer}.

Let $G$ and $H$ be two graphs, each one of them being a path, a cycle or a star. In this paper, we determine the $b$-chromatic number of every subdivision-vertex neighbourhood corona $G\boxdot H$ or $G\boxdot K_n$, where $K_n$ is the complete graph of order $n$. It is also established for those graphs $K_n\boxdot G$ having $m$-degree not greater than $n+2$. All the proofs are accompanied by illustrative examples., Comment: 25 pages, 24 figures

In this work, we present the effects of stochastic force generated by white noise on the nonlinear dynamics of a circular silicon nitride membrane. By tuning the membrane to the Duffing nonlinear region, detected signals switching between low- and high- amplitudes have been observed. They are generated by noise-assisted random jumps between bistable states at room temperature and exhibit high sensitivity to the driving frequency. Through artificially heating different mechanical vibration modes by external input of white noise, the switching rate exhibits exponential dependence on the effective temperature and follows with Kramer's law. Furthermore, both the measured switching rate and activation energy exhibit sensitivity to the width of the hysteresis window in nonlinear response and the driving force, which is in qualitative agreement with the theoretical descriptions. Besides, white noise-induced hysteresis window squeezing and bifurcation point shifting have also been observed, which are attributed to the stochastic force modulation of the spring constant of the membrane. These studies are essential for the generation and manipulation of stochastic switching effects, paving the way to explore new functions based on probability distributions in nanomechanical resonators.

Key Distillation is an essential component of every Quantum Key Distribution system because it compensates the inherent transmission errors of quantum channel. However, throughput and interoperability aspects of post-processing engine design often neglected, and exiting solutions are not providing any guarantee. In this paper, we propose multiple protocol support high throughput key distillation framework implemented in a Field Programmable Gate Array (FPGA) using High-Level Synthesis (HLS). The proposed design uses a Hadoop framework with a map-reduce programming model to efficiently process large chunks of raw data across the limited computing resources of an FPGA. We present a novel hardware-efficient integrated post-processing architecture that offer dynamic error correction, a side-channel resistant authentication scheme, and an inbuilt high-speed encryption application, which uses the key for secure communication. We develop a semi automated High level synthesis framework capable of handling different QKD protocols with promising speedup. Overall, the experimental results shows that there is a significant improvement in performance and compatible with any discrete variable QKD systems.

This paper proposes a data and Machine Learning-based forecasting solution for the Telecommunications network-rollout planning problem. Milestone completion-time estimation is crucial to network-rollout planning; accurate estimates enable better crew utilisation and optimised cost of materials and logistics. Using historical data of milestone completion times, a model needs to incorporate domain knowledge, handle noise and yet be interpretable to project managers. This paper proposes partition-based regression models that incorporate data-driven statistical models within each partition, as a solution to the problem. Benchmarking experiments demonstrate that the proposed approach obtains competitive to better performance, at a small fraction of the model complexity of the best alternative approach based on Gradient Boosting. Experiments also demonstrate that the proposed approach is effective for both short and long-range forecasts. The proposed idea is applicable in any context requiring time-series regression with noisy and attributed data.

We demonstrate the linear and nonlinear characterization of plasma-enhanced chemical vapor deposited silicon-rich silicon nitride (SRSN) racetrack ring resonator for on-chip application within the telecommunication wavelength range. The SRN waveguide parameters are optimized by employing the refractive index profile measured by ellipsometry to achieve flat dispersion in the telecom band. Furthermore, we measure the thermo-optic coefficient (TOC) of the micro-resonator by analyzing the temperature-dependent transmission spectra and assessing it to be \(3.2825\) $\times$ \(10^{-5}\) \(^o{} C^{-1}\). Additionally, we perform power-dependent transmission spectra to investigate the effect of local heating and nonlinear absorption. The power-dependent transmission spectra exhibit a blue-shifting of the resonance peak in the visible and near-IR regions, which indicates the presence of nonlinear losses in that range. The power-dependent transmission spectra almost remain unchanged in the telecom band, revealing the absence of nonlinear losses and excellent thermal stability in that wavelength range. Our experimental results reveal that the SRSN-based structure can be employed potentially to realize linear and nonlinear applications in the telecom band.

Unmanned ground vehicles (UGVs) are being used extensively in civilian and military applications for applications such as underground mining, nuclear plant operations, planetary exploration, intelligence, surveillance and reconnaissance (ISR) missions and manned-unmanned teaming. We consider a multi-objective, multiple-vehicle routing problem in which teams of manned ground vehicles (MGVs) and UGVs are deployed respectively in a leader-follower framework to execute missions with differing requirements for MGVs and UGVs while considering human-robot interactions (HRI). HRI studies highlight the costs of managing a team of follower UGVs by a leader MGV. This paper aims to compute feasible paths, replenishments, team compositions and number of MGV-UGV teams deployed such that the requirements for MGVs and UGVs for the missions are met and the path, replenishment, HRI and team deployment costs are at minimum. The problem is first modeled as a a mixed-integer linear program (MILP) that can be solved to optimality by off-the-shelf commercial solvers for small-sized instances. For larger instances, a variable neighborhood search algorithm is offered to compute near optimal solutions and address the challenges that arise when solving the combinatorial multi-objective routing optimization problem. Finally, computational experiments that corroborate the effectiveness of the proposed algorithms are presented., Comment: 10 pages

Coupled electromechanical resonators that can be independently driven/detected and easily integrated with external circuits are essential for exploring mechanical modes based signal processing. Here, we present a room temperature phonon-cavity electromechanical system, consisting of two distinct resonators: a silicon nitride electromechanical drum capacitively coupled to an aluminum one. We demonstrate electromechanically induced transparency and amplification in a two-tone driving scheme and observe the phonon-cavity force affecting the mechanical damping rates of both movable objects. We also develop an analytical model based on linearly coupled motion equations, which captures the optomechanical features in the classical limit and enables to fit quantitatively our measurements. Our results open up new possibilities in the study of phonon-cavity based signal processing in the classical and potentially in the future in the quantum regimes.

The ability to know and verifiably demonstrate the origins of messages can often be as important as encrypting the message itself. Here we present an experimental demonstration of an unconditionally secure digital signature (USS) protocol implemented for the first time, to the best of our knowledge, on a fully connected quantum network without trusted nodes. Our USS protocol is secure against forging, repudiation and messages are transferrable. We show the feasibility of unconditionally secure signatures using only bi-partite entangled states distributed throughout the network and experimentally evaluate the performance of the protocol in real world scenarios with varying message lengths., Comment: Preprint, 9 pages, 7 figures, 1 table

Traditional industrial systems, e.g., power plants, water treatment plants, etc., were built to operate highly isolated and controlled capacity. Recently, Industrial Control Systems (ICSs) have been exposed to the Internet for ease of access and adaptation to advanced technologies. However, it creates security vulnerabilities. Attackers often exploit these vulnerabilities to launch an attack on ICSs. Towards this, threat hunting is performed to proactively monitor the security of ICS networks and protect them against threats that could make the systems malfunction. A threat hunter manually identifies threats and provides a hypothesis based on the available threat intelligence. In this paper, we motivate the gap in lacking research in the automation of threat hunting in ICS networks. We propose an automated extraction of threat intelligence and the generation and validation of a hypothesis. We present an automated threat hunting framework based on threat intelligence provided by the ICS MITRE ATT&CK framework to automate the tasks. Unlike the existing hunting solutions which are cloud-based, costly and prone to human errors, our solution is a central and open-source implemented using different open-source technologies, e.g., Elasticsearch, Conpot, Metasploit, Web Single Page Application (SPA), and a machine learning analyser. Our results demonstrate that the proposed threat hunting solution can identify the network's attacks and alert a threat hunter with a hypothesis generated based on the techniques, tactics, and procedures (TTPs) from ICS MITRE ATT&CK. Then, a machine learning classifier automatically predicts the future actions of the attack.

Temporal and causal relations play an important role in determining the dependencies between events. Classifying the temporal and causal relations between events has many applications, such as generating event timelines, event summarization, textual entailment and question answering. Temporal and causal relations are closely related and influence each other. So we propose a joint model that incorporates both temporal and causal features to perform causal relation classification. We use the syntactic structure of the text for identifying temporal and causal relations between two events from the text. We extract parts-of-speech tag sequence, dependency tag sequence and word sequence from the text. We propose an LSTM based model for temporal and causal relation classification that captures the interrelations between the three encoded features. Evaluation of our model on four popular datasets yields promising results for temporal and causal relation classification., Comment: Accepted at the 33rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2021)

The mechanical properties of amorphous materials (glasses) at low temperatures are dominated by effects of low energy excitations that are thought to be atomic-scale tunneling two level systems (TTLS). In nanometer-scale glass samples, the temperature dependence of the sound speed and dissipation is modified relative to that of bulk glass samples. In addition to this size effect, the usual presence of a polycrystalline metal in nanomechanical resonators leads to a further departure from the well-studied behavior of insulating bulk glass. We report a dual chip optomechanical measurement technique used to characterize non-metallized amorphous SiN strings at low temperatures. A harp consisting of SiN strings of width 350 nm and lengths 40 to 80 $\mu$m is coupled to an Al superconducting microwave cavity on a separate chip. The strings are driven dielectrically and their motion is detected via its modulation of the microwave resonance frequency.

Silicon nitride (SiN) mechanical resonators with high quality mechanical properties are attractive for fundamental research and applications. However, it is challenging to maintain these mechanical properties while achieving strong coupling to an electrical circuit for efficient on-chip integration. Here, we present a SiN drum resonator covered with an aluminum thin film, enabling large capacitive coupling to a suspended top-gate. Implementing the full electrical measurement scheme, we demonstrate a high quality factor ~ 1E4 (comparable to that of bare drums at room temperature) and present our ability to detect ? 10 mechanical modes at low temperature. The drum resonator is also coupled to a microwave cavity, so that we can perform optomechanical sideband pumping with a fairly good coupling strength G and demonstrate mechanical parametric amplification. This SiN drum resonator design provides efficient electrical integration and exhibits promising features for exploring mode coupling and signal processing.

In this paper, we introduce an application of a high-performance MultiPeriod AC Optimal Power Flow (MPOPF) solver, called "BATTPOWER", to simulate active distribution grids for a near-future scenario. A large-scale Norwegian distribution grid along with a large population of Electric Vehicles (EV) are here taken as the case-study. We suggest and analyse three operational strategies (in terms of control of charge scheduling fleet of EV) for the Distribution System Operator (DSO): (a) uncoordinated/dumb charge scheduling, (b) coordinated charge scheduling with the objective of energy cost-minimisation without operational constraints of the grid, and (c) coordinated charge scheduling with the objective of energy cost-minimisation along with the operational constraints of the grid. The results demonstrate that the uncoordinated charging would lead to: 1) overloading of lines and transformers when the share of EVs is above 20%, and 2) higher operational costs than the proposed control strategies of (b) and (c). In strategy (b) operational line/transformer limits are violated when the populations of EVs are growing above 36%. This implies that current market design must be altered to allow active control of a large proportion of DERs within grid operational limits to achieve cost minimization at system level. To our knowledge, the work presented in this paper is the first ever attempt to do a comprehensive analysis of the impact of EV charging demand on a real Norwegian distribution grid. Moreover, the inference of the analysis says that the Norwegian distribution networks are more prone to congestion problems than the voltage problems for the EV demand which includes a smart charging scheme accounting for grid conditions., Comment: Preprint submitted to Electric Power Systems Research (EPSR)

The global interest in quantum networks stems from the security guaranteed by the laws of physics. Deploying quantum networks means facing the challenges of scaling up the physical hardware and, more importantly, of scaling up all other network layers and optimally utilising network resources. Here we consider two related protocols, their experimental demonstrations on an 8-user quantum network test-bed, and discuss their usefulness with the aid of example use cases. First, an authentication transfer protocol to manage a fundamental limitation of quantum communication -- the need for a pre-shared key between every pair of users linked together on the quantum network. By temporarily trusting some intermediary nodes for a short period of time (<35 min in our network), we can generate and distribute these initial authentication keys with a very high level of security. Second, when end users quantify their trust in intermediary nodes, our flooding protocol can be used to improve both end-to-end communication speeds and increase security against malicious nodes., Comment: New version includes changes suggested by referees, and a modification to an incorrect calculation. Fig. 6 has been updated correspondingly. With thanks to Rui Wang for spotting the mistake, and the referees for detailed feedback

A novel coronavirus disease has emerged (later named COVID-19) and caused the world to enter a new reality, with many direct and indirect factors influencing it. Some are human-controllable (e.g. interventional policies, mobility and the vaccine); some are not (e.g. the weather). We have sought to test how a change in these human-controllable factors might influence two measures: the number of daily cases against economic impact. If applied at the right level and with up-to-date data to measure, policymakers would be able to make targeted interventions and measure their cost. This study aims to provide a predictive analytics framework to model, predict and simulate COVID-19 propagation and the socio-economic impact of interventions intended to reduce the spread of the disease such as policy and/or vaccine. It allows policymakers, government representatives and business leaders to make better-informed decisions about the potential effect of various interventions with forward-looking views via scenario planning. We have leveraged a recently launched open-source COVID-19 big data platform and used published research to find potentially relevant variables (features) and leveraged in-depth data quality checks and analytics for feature selection and predictions. An advanced machine learning pipeline has been developed armed with a self-evolving model, deployed on a modern machine learning architecture. It has high accuracy for trend prediction (back-tested with r-squared) and is augmented with interpretability for deeper insights.

Anonymity in networked communication is vital for many privacy-preserving tasks. Secure key distribution alone is insufficient for high-security communications, often knowing who transmits a message to whom and when must also be kept hidden from an adversary. Here we experimentally demonstrate 5 information-theoretically secure anonymity protocols on an 8 user city-wide quantum network using polarisation-entangled photon pairs. At the heart of these protocols is anonymous broadcasting, which is a cryptographic primitive that allows one user to reveal one bit of information while keeping her identity anonymous. For a network of $n$ users, the protocols retain anonymity for the sender, given less than $n-2$ users are dishonest. This is one of the earliest implementations of genuine multi-user cryptographic protocols beyond standard QKD. Our anonymous protocols enhance the functionality of any fully-connected Quantum Key Distribution network without trusted nodes., Comment: 11 pages, 4 figures, 1 table, experimental work. ZH and SKJ contributed equally to this work and are joint first authors

Heterogeneous unmanned vehicles (UVs) are used in various defense and civil applications. Some of the civil applications of UVs for gathering data and monitoring include civil infrastructure management, agriculture, public safety, law enforcement, disaster relief, and transportation. This paper presents a two-stage stochastic model for a fuel-constrained UV mission planning problem with multiple refueling stations under uncertainty in availability of UVs. Given a set of points of interests (POI), a set of refueling stations for UVs, and a base station where the UVs are stationed and their availability is random, the objective is to determine route for each UV starting and terminating at the base station such that overall incentives collected by visiting POIs is maximized. We present an outer approximation based decomposition algorithm to solve large instances, and perform extensive computational experiments using random instances. Additionally, a data driven simulation study is performed using robot operating system (ROS) framework to corroborate the use of the stochastic programming approach.

An increase in greenhouse gases emission from the transportation sector has led companies and the government to elevate and support the production of electric vehicles (EV). With recent developments in urbanization and e-commerce, transportation companies are replacing their conventional fleet with EVs to strengthen the efforts for sustainable and environment-friendly operations. However, deploying a fleet of EVs asks for efficient routing and recharging strategies to alleviate their limited range and mitigate the battery degradation rate. In this work, a fleet of electric vehicles is considered for transportation and logistic capabilities with limited battery capacity and scarce charging station availability. We introduce a min-max electric vehicle routing problem (MEVRP) where the maximum distance traveled by any EV is minimized while considering charging stations for recharging. We propose an efficient branch and cut framework and a three-phase hybrid heuristic algorithm that can efficiently solve a variety of instances. Extensive computational results and sensitivity analyses are performed to corroborate the efficiency of the proposed approach, both quantitatively and qualitatively.

This research is about the development a fuzzy decision support system for the diagnosis of coronary artery disease based on evidence. The coronary artery disease data sets taken from University California Irvine (UCI) are used. The knowledge base of fuzzy decision support system is taken by using rules extraction method based on Rough Set Theory. The rules then are selected and fuzzified based on information from discretization of numerical attributes. Fuzzy rules weight is proposed using the information from support of extracted rules. UCI heart disease data sets collected from U.S., Switzerland and Hungary, data from Ipoh Specialist Hospital Malaysia are used to verify the proposed system. The results show that the system is able to give the percentage of coronary artery blocking better than cardiologists and angiography. The results of the proposed system were verified and validated by three expert cardiologists and are considered to be more efficient and useful.

Electric vehicles (EVs) provide a cleaner alternative that not only reduces greenhouse gas emissions but also improves air quality and reduces noise pollution. The consumer market for electrical vehicles is growing very rapidly. Designing a network with adequate capacity and types of public charging stations is a challenge that needs to be addressed to support the current trend in the EV market. In this research, we propose a choice modeling approach embedded in a two-stage stochastic programming model to determine the optimal layout and types of EV supply equipment for a community while considering randomness in demand and drivers' behaviors. Some of the key random data parameters considered in this study are: EV's dwell time at parking {\sv location}, battery's state of charge, distance from home, willingness to walk, drivers' arrival patterns, and traffic on weekdays and weekends. The two-stage model uses the sample average approximation method, which asymptotically converges to an optimal solution. To address the computational challenges for large-scale instances, we propose an outer approximation decomposition algorithm. We conduct extensive computational experiments to quantify the efficacy of the proposed approach. In addition, we present the results and a sensitivity analysis for a case study based on publicly available data sources.

Limiting flight delays during operations has become a critical research topic in recent years due to their prohibitive impact on airlines, airports, and passengers. A popular strategy for addressing this problem considers the uncertainty of day-of-operations delays and adjusts flight schedules to accommodate them in the planning stage. In this work, we present a stochastic programming model to account for uncertain future delays by adding buffers to flight turnaround times in a controlled manner. Specifically, our model adds slack to flight connection times with the objective of minimizing the expected value of the total propagated flight delay in a schedule. We also present a concurrent solution framework that integrates an outer approximation decomposition method and column generation. Further, we demonstrate the scalability of our approach and its effectiveness in reducing delays with an extensive simulation study of five different flight networks using real-world data.

An important challenge confronting healthcare is the effective management of access to primary care. Robust appointment scheduling policies/templates can help strike an effective balance between the lead-time to an appointment (a.k.a. indirect waiting time, measuring the difference between a patient's desired and actual appointment dates) and waiting times at the clinic on the day of the appointment (a.k.a. direct waiting time). We propose methods for identifying effective appointment scheduling templates using a two-stage stochastic mixed-integer linear program model. The model embeds simulation for accurate evaluation of direct waiting times and uses sample average approximation method for computational efficiency. The model accounts for patients' no-show behaviors, provider availability, overbooking, demand uncertainty, and overtime constraints. The model allows the scheduling templates to be potentially updated at regular intervals while minimizing the patient expected waiting times and balancing provider utilization. Proposed methods are validated using data from the U.S. Department of Veterans Affairs (VA) primary care clinics.

Significant advances in sensing, robotics, and wireless networks have enabled the collaborative utilization of autonomous aerial, ground and underwater vehicles for various applications. However, to successfully harness the benefits of these unmanned ground vehicles (UGVs) in homeland security operations, it is critical to efficiently solve UGV path planning problem which lies at the heart of these operations. Furthermore, in the real-world applications of UGVs, these operations encounter uncertainties such as incomplete information about the target sites, travel times, and the availability of vehicles, sensors, and fuel. This research paper focuses on developing algebraic-based-modeling framework to enable the successful deployment of a team of vehicles while addressing uncertainties in the distance traveled and the availability of UGVs for the mission., Comment: 10 Pages, 5 Figures

Ferroelectricity-the switchable intrinsic electric polarization-has not yet been attained in a metal experimentally and is in fact generally deemed irreconcilable with free carriers, although polar metal has been achieved recently. Multiferroic metal has never even been proposed though multiferroics have been widely investigated. Here we report a room-temperature coexistence of multiferroicity and metallic behavior in PbNb0.12Ti0.88O3-delta films. The oxygen-vacancy-induced electrons become delocalized and ameliorate the ferromagnetic properties of these films, whereas they fail to vanish the polar displacements nor the individual dipole in each unit cell. This concurrent appearance of multiferroicity and metallicity is also confirmed by our first-principles calculation performed on 12.5% Nb-doped PbTiO3 with oxygen vacancies. These findings break a path to multiferroic metallic materials and offer a potential application for multiferroic spintronic devices.

Polar metals with ferroelectric-like displacements in metals have been achieved recently, half century later than Anderson and Blount's prediction. However, the genuine ferroelectricity with electrical dipolar switching has not yet been attained experimentally in the conducting materials, especially the ones possessing magnetic responses. Here we report the coexistence of ferroelectricity and magnetoresistance (MR) in the metallic PbNb0.12Ti0.88O3 (PNTO) thin films. We found that the conducting and magnetic responses of PNTO films are highly asymmetric. Negative MR up to 50% is observed under an in-plane magnetic field; the MR switches to positive with the magnetic field applied parallel to the surface normal. Such unique behavior is attributed to the moving electron caused effective magnetic field which couples with the spins of electrons, which form a dynamic multiferroic state in the metallic PNTO. These findings break a path to multiferroic metal and offer a great potential to the multi-functional devices., Comment: 21 pages, 3 figures

The past decade has seen a substantial increase in the use of small unmanned aerial vehicles (UAVs) in both civil and military applications. This article addresses an important aspect of refueling in the context of routing multiple small UAVs to complete a surveillance or data collection mission. Specifically, this article formulates a multiple-UAV routing problem with the refueling constraint of minimizing the overall fuel consumption for all of the vehicles as a two-stage stochastic optimization problem with uncertainty associated with the fuel consumption of each vehicle. The two-stage model allows for the application of sample average approximation (SAA). Although the SAA solution asymptotically converges to the optimal solution for the two-stage model, the SAA run time can be prohibitive for medium- and large-scale test instances. Hence, we develop a tabu-search-based heuristic that exploits the model structure while considering the uncertainty in fuel consumption. Extensive computational experiments corroborate the benefits of the two-stage model compared to a deterministic model and the effectiveness of the heuristic for obtaining high-quality solutions., Comment: 18 pages

This article presents a framework and develops a formulation to solve a path planning problem for multiple heterogeneous Unmanned Vehicles (UVs) with uncertain service times for each vehicle--target pair. The vehicles incur a penalty proportional to the duration of their total service time in excess of a preset constant. The vehicles differ in their motion constraints and are located at distinct depots at the start of the mission. The vehicles may also be equipped with disparate sensors. The objective is to find a tour for each vehicle that starts and ends at its respective depot such that every target is visited and serviced by some vehicle while minimizing the sum of the total travel distance and the expected penalty incurred by all the vehicles. We formulate the problem as a two-stage stochastic program with recourse, present the theoretical properties of the formulation and advantages of using such a formulation, as opposed to a deterministic expected value formulation, to solve the problem. Extensive numerical simulations also corroborate the effectiveness of the proposed approach., Comment: 8 pages, 2 figures, submitted to International Conference on Unmanned Aircraft Systems (ICUAS)

Advantages of electric vehicles (EV) include reduction of greenhouse gas and other emissions, energy security, and fuel economy. The societal benefits of large-scale adoption of EVs cannot be realized without adequate deployment of publicly accessible charging stations. We propose a two-stage stochastic programming model to determine the optimal network of charging stations for a community considering uncertainties in arrival and dwell time of vehicles, battery state of charge of arriving vehicles, walkable range and charging preferences of drivers, demand during weekdays and weekends, and rate of adoption of EVs within a community. We conducted studies using sample average approximation (SAA) method which asymptotically converges to an optimal solution for a two-stage stochastic problem, however it is computationally expensive for large-scale instances. Therefore, we developed a heuristic to produce near to optimal solutions quickly for our data instances. We conducted computational experiments using various publicly available data sources, and benefits of the solutions are evaluated both quantitatively and qualitatively for a given community.

In this paper,we propose a four channel Dense Wavelength Division Multiplexing demultiplexer with two dimensional photonic crystal square resonant cavity that fulfill the ITU-T recommendation of G.694.1 DWDM systems.DWDM demultiplexer consists of a waveguide and Microscopic Square Resonant (MSR) cavity to enable filtering of the desired wavelength. The MSR cavity design has inner rods, outer rods and coupling rods. As the radius of the inner rod in MSR cavity changes, the cavity has the ability to filter different ITU.T G.6941 standard wavelengths like 1555.3 nm, 1555.5 nm, 1555.7 nm, and 1555.9 nm with 0.2 nm / 25 GHz channel spacing. From the simulation of various wavelengths, helps achievement of the quality factor of 8000, uniform spectral linewidth of 0.2 nm, transmission efficiency of 100 %, crosstalk of -42 dB and footprint is about 395 micro meter square.

Original equipment manufacturers (OEMs) manufacture, inventory and transport new vehicles to franchised dealers. These franchised dealers inventory and sell new vehicles to end users. OEMs rely on logistics companies with a special type of truck called an auto-carrier to transport the vehicles to the dealers. The process of vehicle distribution has a common challenge. This challenge involves determining routes, and the way to load the vehicles onto each auto-carrier.In this paper, we present a heuristic to determine the route for each auto-carrier based on the dealers' locations, and subsequently, a branch-and-price algorithm to obtain optimal solutions to the loading problem based on the generated route. The loading problem considers the actual dimensions of the vehicles, and the restrictions imposed by vehicle manufacturers and governmental agencies on the loading process. We perform extensive computational experiments for the loading problem using real-world instances, and our results are benchmarked with a holistic model to corroborate the effectiveness of the proposed method. For the largest instance comprising of 600 vehicles, the proposed method computes an optimal solution for the loading problem within a stipulated runtime.

We present a decomposition method for stochastic programs with 0-1 variables in the second-stage with absolute semi-deviation (ASD) risk measure. Traditional stochastic programming models are risk-neutral where expected costs are considered for the second-stage. A common approach to address risk is to include a dispersion statistic in addition with expected costs and weighted appropriately. Due to the lack of block angular structure, stochastic programs with ASD risk-measure possess computational challenges. The proposed decomposition algorithm uses another risk-measure `expected excess', and provides tighter bounds for ASD stochastic models. We perform computational study on a supply chain replenishment problem and standard knapsack instances. The computational results using supply chain instances demonstrate the usefulness of ASD risk-measure in decision making under uncertainty, and knapsack instances indicate that the proposed methodology outperforms a direct solver.

This paper addresses a fuel-constrained, autonomous vehicle path planning problem in the presence of multiple refueling stations. We are given a set of targets, a set of refueling stations, and a depot where $m$ vehicles are stationed. The vehicles are allowed to refuel at any refueling station, and the objective of the problem is to determine a route for each vehicle starting and terminating at the depot, such that each target is visited by at least one vehicle, the vehicles never run out of fuel while traversing their routes, and the total travel cost of all the routes is a minimum. We present four new mixed-integer linear programming formulations for the problem. These formulations are compared both analytically and empirically, and a branch-and-cut algorithm is developed to compute an optimal solution. Extensive computational results on a large class of test instances that corroborate the effectiveness of the algorithm are also presented., Comment: A version of this paper accepted to Unmanned Systems under the title "Analysis of Mixed-Integer Linear Programming Formulations for a Fuel-Constrained Multiple Vehicle Routing Problem"

Two-stage stochastic mixed-integer programming (SMIP) problems with general integer variables in the second-stage are generally difficult to solve. This paper develops the theory of integer set reduction for characterizing the subset of the convex hull of feasible integer points of the second-stage subproblem which can be used for solving the SMIP. The basic idea is to consider a small enough subset of feasible integer points that is necessary for generating a valid inequality for the integer subproblem. An algorithm for obtaining such a subset based on the solution of the subproblem LP-relaxation is then devised and incorporated into the Fenchel decomposition method for SMIP. To demonstrate the performance of the new integer set reduction methodology, a computational study based on randomly generated test instances was performed. The results of the study show that integer set reduction provides significant gains in terms of generating cuts faster leading to better bounds in solving SMIPs than using a direct solver., Comment: 28 pages, 5 figures

This paper describes the application of stochastic grey-box modeling to identify electrical power consumption-to-temperature models of a domestic freezer using experimental measurements. The models are formulated using stochastic differential equations (SDEs), estimated by maximum likelihood estimation (MLE), validated through the model residuals analysis and cross-validated to detect model over-fitting. A nonlinear model based on the reversed Carnot cycle is also presented and included in the modeling performance analysis. As an application of the models, we apply model predictive control (MPC) to shift the electricity consumption of a freezer in demand response experiments, thereby addressing the model selection problem also from the application point of view and showing in an experimental context the ability of MPC to exploit the freezer as a demand side resource (DSR)., Comment: Submitted to Sustainable Energy Grids and Networks (SEGAN). Accepted for publication

We consider a multiple depot, multiple vehicle routing problem with fuel constraints. We are given a set of targets, a set of depots and a set of homogeneous vehicles, one for each depot. The depots are also allowed to act as refueling stations. The vehicles are allowed to refuel at any depot, and our objective is to determine a route for each vehicle with a minimum total cost such that each target is visited at least once by some vehicle, and the vehicles never run out fuel as it traverses its route. We refer this problem as Multiple Depot, Fuel-Constrained, Multiple Vehicle Routing Problem (FCMVRP). This paper presents four new mixed integer linear programming formulations to compute an optimal solution for the problem. Extensive computational results for a large set of instances are also presented., Comment: 6 pages, 2 figures, submitted to American Control Conference 2016

We derive a variational formula for the optimal growth rate of reward in the infinite horizon risk-sensitive control problem for discrete time Markov decision processes with compact metric state and action spaces, extending a formula of Donsker and Varadhan for the Perron-Frobenius eigenvalue of a positive operator. This leads to a concave maximization formulation of the problem of determining this optimal growth rate., Comment: 35 pages

The secondary structure that maximizes the number of non-crossing matchings between complimentary bases of an RNA sequence of length n can be computed in O(n^3) time using Nussinov's dynamic programming algorithm. The Four-Russians method is a technique that will reduce the running time for certain dynamic programming algorithms by a multiplicative factor after a preprocessing step where solutions to all smaller subproblems of a fixed size are exhaustively enumerated and solved. Frid and Gusfield designed an O(\frac{n^3}{\log n}) algorithm for RNA folding using the Four-Russians technique. In their algorithm the preprocessing is interleaved with the algorithm computation. (Algo. Mol. Biol., 2010). We simplify the algorithm and the analysis by doing the preprocessing once prior to the algorithm computation. We call this the two-vector method. We also show variants where instead of exhaustive preprocessing, we only solve the subproblems encountered in the main algorithm once and memoize the results. We give a simple proof of correctness and explore the practical advantages over the earlier method. The Nussinov algorithm admits an O(n^2) time parallel algorithm. We show a parallel algorithm using the two-vector idea that improves the time bound to O(\frac{n^2}{log n}). We discuss the organization of the data structures to exploit coalesced memory access for fast running times. The ideas to organize the data structures also help in improving the running time of the serial algorithms. For sequences of length up to 6000 bases the parallel algorithm takes only about 2.5 seconds and the two-vector serial method takes about 57 seconds on a desktop and 15 seconds on a server. Among the serial algorithms, the two-vector and memoized versions are faster than the Frid-Gusfield algorithm by a factor of 3, and are faster than Nussinov by up to a factor of 20., Comment: Peer-reviewed and presented as part of the 13th Workshop on Algorithms in Bioinformatics (WABI2013). Editor's note: abstract was shortened to comply with arxiv requirements. Full abstract in PDF

A system of electrons in two dimensions and strong magnetic fields can be tuned to create a gapped 2D system with one dimensional channels along the edge. Interactions among these edge modes can lead to independent transport of charge and heat, even in opposite directions. Measuring the chirality and transport properties of these charge and heat modes can reveal otherwise hidden structure in the edge. Here, we heat the outer edge of such a quantum Hall system using a quantum point contact. By placing quantum dots upstream and downstream along the edge of the heater, we can measure both the chemical potential and temperature of that edge to study charge and heat transport, respectively. We find that charge is transported exclusively downstream, but heat can be transported upstream when the edge has additional structure related to fractional quantum Hall physics., Comment: 24 pages, 18 figures

Electrons in two dimensions and strong magnetic fields effectively lose their kinetic energy and display exotic behavior dominated by Coulomb forces. When the ratio of electrons to magnetic flux quanta in the system is near 5/2, the unique correlated phase that emerges is predicted to be gapped with fractionally charged quasiparticles and a ground state degeneracy that grows exponentially as these quasiparticles are introduced. Interestingly, the only way to transform between the many ground states would be to braid the fractional excitations around each other, a property with applications in quantum information processing. Here we present the first observation of localized quasiparticles at nu=5/2, confined to puddles by disorder. Using a local electrometer to compare how quasiparticles at nu=5/2 and nu=7/3 charge these puddles, we are able to extract the ratio of local charges for these states. Averaged over several disorder configurations and samples, we find the ratio to be 4/3, suggesting that the local charges are e/3 at seven thirds and e/4 at five halves, in agreement with theoretical predictions. This confirmation of localized e/4 quasiparticles is necessary for proposed interferometry experiments to test statistics and computational ability of the state at nu=5/2., Comment: 6 pages, 4 figures corrected title