Switching Framework Research Articles

DIGITAL TRANSFORMATION IN HEALTHCARE FOR EFFECTIVE INFORMATION GOVERNANCE

Serving as the first Chief Information Governance Officer (CIGO) at a venerable healthcare facility, this paper provides a thorough and strategic framework for switching from traditional paper-based record-keeping to an advanced Electronic Health Records (EHR) system. The organisation's long history spanning more than 50 years and its enormous, diversified data gathering present serious problems relating to data integrity, privacy, and security to this significant transformation that aims to improve patient care and operational efficiency. An in-depth discussion of information governance in healthcare is provided in this paper, with particular attention paid to data quality management, stringent privacy and security regulations, and the tactical use of data to spur innovation in healthcare. Along with providing a comprehensive overview of the digital transformation in healthcare, it critically examines the effects on patient care of emerging digital technologies such as wearable health devices, big data analytics, and the Internet of Things (IoT). An extensive literature review and an annotated bibliography bolster these analyses. Moreover, the paper offers a thorough plan for digital transformation that will guarantee compliance with legal and moral requirements, improve patient care, and establish the company as a pioneer in patient data management and healthcare innovation. In addition to addressing concerns such as data integrity problems brought on by a lack of solid administrative control and data handling standards, the study examines social media's potential as a marketing tool while considering pertinent legal and policy considerations. Finally, the paper offers particular metrics to evaluate the impact and efficacy of the information governance program, ensuring that it aligns with the organisation's objectives and the changing landscape of digital healthcare to guarantee these initiatives' successful implementation and oversight.

Base Station Energy Saving based on Imitation Learning in 5G Network

Abstract With the rapid development of communication technology, the large-scale deployment of base stations (BSs) has led to an increase in power consumption. To reduce power consumption, energy saving technologies for BSs have emerged, which are in line with the concept of green communications and can save operators’ costs. In this paper, our goal is to minimize the total power consumption of the base station by dynamically controlling the switching status of the base station. This article first proposes a dynamic base station switching framework based on deep reinforcement learning (DRL), which optimizes the power consumption of switching BSs. Then, considering the dynamic nature of the environment, this article proposes a dynamic BS switching algorithm that introduces the idea of imitation learning (IL) to update the policy by learning the expert’s action. The simulation results show that this scheme can effectively reduce power consumption.

A Markov‐switching spatio‐temporal ARCH model

Stock market indices are volatile by nature, and sudden shocks are known to affect volatility patterns. The autoregressive conditional heteroskedasticity (ARCH) and generalized ARCH (GARCH) models neglect structural breaks triggered by sudden shocks that may lead to an overestimation of persistence, causing an upward bias in the estimates. Different regime‐switching models that have abrupt regime‐switching governed by a Markov chain were developed to model volatility in financial time series data. Volatility modelling was also extended to spatially interconnected time series, resulting in spatial variants of ARCH models. This inspired us to propose a Markov switching framework of the spatio‐temporal log‐ARCH model. In this article, we discuss the Markov‐switching extension of the model, the estimation procedure and the smooth inferences of the regimes. The Monte Carlo simulation studies show that the maximum likelihood estimation method for our proposed model has good finite sample properties. The proposed model was applied to 28 stock indices' data that were presumably affected by the 2015–2016 Chinese stock market crash. The results showed that our model is a better fit compared to that of the one‐regime counterpart. Furthermore, the smoothed inference of the data indicated the approximate periods where structural breaks occurred. This model can capture structural breaks that simultaneously occur in nearby locations.

Arduino Based Framework for Automatic Street Light Switching and Fault Detection System

ABSTRACT- The necessity for automated street lighting systems in India is underscored by the significant potential for energy savings, crucial for a country where efficient energy use is imperative. Research indicates that implementing LED street lighting could lead to an estimated 30% reduction in energy consumption, translating to a substantial electricity saving of approximately 2,326 GWh. LED technology, known for reducing energy usage by up to 80% compared to conventional lighting, offers a greener alternative that significantly lowers electricity demand. The adoption of energy- efficient lighting systems not only aids in diminishing energy consumption but also plays a vital role in environmental preservation by reducing CO2 emissions. Furthermore, integrating automatic controls in street lights, equipped with daylight sensors and adaptive solar panels, maximizes energy efficiency. These systems tailor lighting levels to real-time environmental conditions and activity, such as the presence of vehicles, thus optimizing power usage and minimizing waste. The extended lifespan of LED lamps, reaching up to 50,000 hours, further contributes to economic savings and a decrease in maintenance frequency, supporting ongoing conservation efforts. Therefore, the implementation of automated and energy- efficient street lighting in India is a pivotal step towards attaining energy conservation, economic benefits, environmental sustainability, and enhanced urban safety. This move towards innovative and sustainable lighting solutions positions India on a path to a brighter, more sustainable future.

Forward starting options pricing under a regime-switching jump-diffusion model with Wishart stochastic volatility and stochastic interest rate

Vanilla options become effective immediately after they are entered, while some exotic options will only come to effective some time after they are bought or sold. Forward starting options are one kind of such exotic options started actually at some pre-specified future date. This paper presents an extension of regime-switching jump diffusion model, in which the parameters are driven by a continuous time and stationary Markov chain on a finite state space, by introducing the Wishart process into the instantaneous variance-covariance matrix of the risky asset price and stochastic interest rate. We derive the discounted conditional joint characteristic function and the forward characteristic function of the log-asset price and its the instantaneous variance-covariance process, and thereby the price of forward starting options are well evaluated by the probabilistic approach combined with the Fourier-cosine (COS) method. We also provide efficient Monte Carlo simulation of this proposed model, and simulated solutions to forward starting options pricing within a two-state regime switching framework. Numerical results show that the COS method is accurate and efficient for pricing forward starting options. Finally, we analyse impacts of some main parameters (especially, parameters in the Wishart stochastic volatility) in this proposed model on option prices and Δ values. Also, we consider the forward implied volatility. Furthermore, the forward starting options under the regime-switching jump diffusion model with Wishart stochastic volatility and stochastic interest rate which we derived are more generalized than those recently appeared in the derivatives pricing literature, and thus have wider application.

Fuzzy Interactions Compensation in Adaptive Control of Switched Interconnected Systems

In this work, tracking decentralized control scheme can be addressed for switched interconnected systems. To remove the strict assumption for interconnected systems, a new fuzzy interactions compensation function is presented. By directly employing the dynamic loop gain function, it is difficult to solve algebraic loop issue under switching framework. To relax such restriction, one novel mode-dependent dynamic loop gain function can be developed. Furthermore, adaptive controller can be designed to ensure stability of closed-loop system. Finally, availability of developed control scheme can be proven by the examples. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Under the switching framework, the internal performances of different modes are usually different, and repeating switching of different subsystems may result in the discontinuity of Nussbaum function’s argument. Therefore, this paper proposes one novel mode-dependent dynamic loop gain function. It can be shown that the proposed mode-dependent dynamic loop gain function technique in which every mode in underlying system has its own dynamic loop gain function, which can remove such difficulty. The examples are applied to confirm availability of developed design.

Exploration of a wide bandgap semiconducting supramolecular Mg(II)-metallohydrogel derived from an aliphatic amine: a robust resistive switching framework for brain-inspired computing

A rapid metallohydrogelation strategy has been developed of magnesium(II)-ion using trimethylamine as a low molecular weight gelator in water medium at room temperature. The mechanical property of the synthesized metallohydrogel material is established through the rheological analysis. The nano-rose like morphological patterns of Mg(II)-metallohydrogel are characterized through field emission scanning electron microscopic study. The energy dispersive X-ray elemental mapping analysis confirms the primary gel forming elements of Mg(II)-metallohydrogel. The possible metallohydrogel formation strategy has been analyzed through FT-IR spectroscopic study. In this work, magnesium(II) metallohydrogel (Mg@TMA) based metal–semiconductor-metal structures have been developed and charge transport behaviour is studied. Here, it is confirmed that the magnesium(II) metallohydrogel (Mg@TMA) based resistive random access memory (RRAM) device is showing bipolar resistive switching behaviour at room temperature. We have also explored the mechanism of resistive switching behaviour using the formation (rupture) of conductive filaments between the metal electrodes. This RRAM devices exhibit excellent switching endurance over 10,000 switching cycles with a large ON/OFF ratio (~ 100). The easy fabrication techniques, robust resistive switching behaviour and stability of the present system makes these structures preferred candidate for applications in non-volatile memory design, neuromorphic computing, flexible electronics and optoelectronics etc.

Resolving the discrepancy between coercive voltages extracted from C-V and P-V measurements in a ferroelectric capacitor

In this work, we study hafnium zirconate (HZO) based ferroelectric (FE) capacitors (FeCAP) intended for random access memory and compute-in-memory (CiM) applications. We show that there exists a discrepancy between the coercive voltages (VC) extracted from a small-signal capacitance–voltage (C-V) measurement ({VC}CV) and dynamic polarization-voltage (P-V) measurement ({VC}PV):|{VC}CV|<|{VC}PV|. Utilizing the Nucleation Limited Switching (NLS) framework, we attribute the origin of this discrepancy to the difference in voltage–time dynamics experienced by the FE during the C-V and P-V measurements, resulting in a higher switching probability at the same applied voltage for a longer time during the C-V measurement: |{VC}CV| <|{VC}PV|. Therefore, experimentally obtained VC values do not solely reflect an intrinsic material property of the FE as they depend on the voltage–time dynamics of the measurement.

A trust-aware openflow switching framework for software defined networks (SDN)

Software Defined Networks (SDN) and Network Function Virtualisation (NFV) are prime driving technologies behind 5G and Beyond 5G (B5G) communications. The network control intelligence segregation in the SDN infrastructure enables dynamic network features (such as dynamic end-to-end management of security and quality of service (QoS)) offering significantly improved network performance. Even if one assumes that the centralised SDN controller can be security hardened and hence can be trusted, a fundamental challenge in such networks is that the data plane and switching devices are susceptible to cyberattacks. A malicious adversary can compromise them during run-time making them unreliable for secure and trusted communications. Furthermore, the controller communicating with OpenFlow switching devices is unable to accurately assess the state of the switching devices, which serves as the communication base for NFVs in 5G networks. Vulnerable switching devices can put the whole 5G network infrastructure at risk. Hence, there is a clear need for the controller and the management layer to determine the trustworthiness of the switching devices at run-time. The current trend is for many such devices to deploy trusted computing functionality such as Trusted Platform Module (TPM) or Software Guard Extension (SGx) to achieve local as well as remote attestation. In this paper, we present a dynamic trust management framework for evaluating the trustworthiness of the OpenFlow switching devices deployed in the SDN based networks. We formulate device properties that need to be assessed to determine the trust status of the device. We develop a trust enhanced security architecture which can be used to evaluate the trustworthiness of devices and determine their deployment in the provision of network services. The proposed framework uses subjective logic based techniques to derive trust levels of the switching devices at run-time, which are then used by the architecture to make trust enhanced decisions on the provision of network services. A prototype implementation of the proposed architecture is described, which demonstrates how the trustworthiness of the OpenFlow devices are assessed at run-time. The paper concludes with the performance and security analysis of the implemented trust enhanced architecture services.

WALS: A Lightweight Stateless Label Switching Framework Based on Prime Theorem for MANETs

This article proposes an innovative unicast/multicast unified, cross-layer, and control/user-plane splitting forwarding method, called Wireless Arithmetic Label Switching (WALS), for mobile ad-hoc networks (MANETs). The WALS is based on a stateless approach: an arithmetic source-routed label is processed by the source and intermediate nodes without table lookup, causing lower protocol overheads and forwarding latency than the conventional stateless unicast/multicast routing protocols such as the Dynamic Source Routing (DSR) and the Differential Destination Multicast (DDM) respectively. Different from conventional unicasting/multicasting designs demanding distinct routing protocol stacks, the WALS requires a unified protocol stack, avoiding the need to distinguish unicast and multicast frames, thus simplifying the design of MANET devices. Mathematical analysis indicates that the WALS is highly scalable for both the network sizes and the number of multicast groups. Simulation results reveal that WALS effectively performs network scalability and the required overhead for the unicast and multicast capabilities. Additionally, WALS achieves superior to the other three state-of-the-art competitive multicast routing methods including DVMRP, ODMRP, and MAODV on the performance of the packet processing latency and packet delivery ratio. As a result, the WALS can significantly enable the employment and management of integrated unicast and multicast services for MANETs.

Encoding multistate charge order and chirality in endotaxial heterostructures

High-density phase change memory (PCM) storage is proposed for materials with multiple intermediate resistance states, which have been observed in 1T-TaS2 due to charge density wave (CDW) phase transitions. However, the metastability responsible for this behavior makes the presence of multistate switching unpredictable in TaS2 devices. Here, we demonstrate the fabrication of nanothick verti-lateral H-TaS2/1T-TaS2 heterostructures in which the number of endotaxial metallic H-TaS2 monolayers dictates the number of resistance transitions in 1T-TaS2 lamellae near room temperature. Further, we also observe optically active heterochirality in the CDW superlattice structure, which is modulated in concert with the resistivity steps, and we show how strain engineering can be used to nucleate these polytype conversions. This work positions the principle of endotaxial heterostructures as a promising conceptual framework for reliable, non-volatile, and multi-level switching of structure, chirality, and resistance.

Operations Management Curriculum Development Under Uncertain Situations: Lessons Learned From the Pandemic

The COVID-19 pandemic caused a major disruption to higher education as face-to-face teaching had to shift to distance teaching overnight. In this paper, we describe the adaptation of an operations management undergraduate core module, taught to first-year students, from face-to-face delivery to emergency online delivery. In our online version, we used a mix of traditional and flipped classroom techniques. We adopted a “live” online lecture, where students were encouraged to interact with their instructors and ask questions, and flipped live online tutorials, where students and instructors discussed case studies. In addition, we used a web-based simulation game as a learning and assessment tool. After the simulation game was complete, we assessed our students using a reflective group presentation regarding the decisions they made throughout the game. Based on student feedback, the curriculum design and the module adaptations that we adopted for its emergency online delivery can be considered successful. In particular, students were satisfied with the module quality and structure. Following our experience from adapting our face-to-face module for emergency online delivery, we provide a framework for switching to emergency online teaching along with some useful insights and recommendations to operations management educators.

Optimisation of generation unit commitment and network topology with the dynamic thermal rating system considering N-1 reliability

The electrical grid is a vital component of modern society that requires significant and cost-effective investments to ensure its reliability. The dynamic thermal rating system (DTR) and network topology optimization (NTO) aim to increase the capacity of existing networks and provide flexible options for enhancing power system reliability. The co-optimization framework for generation unit commitment and network switching is formulated with for N-1 reliability, a gap that has not been previously addressed. This is important as it ensures: (1) economic efficiency in power system despatch, (2) optimal operation of both generation and transmission systems, and (3) power grid operation that can withstand the loss of any single component without causing a cascading failure. The reliability impacts of these new proposed operating strategies are also tested on complex, large-scale power systems under long-term, multi-area weather conditions. This proposed work is useful for power system planners, as it helps them identify ways to improve the reliability and efficiency of power systems under various weather conditions. This paper shows that using the DTR-NTO combination improves EENS by 50% in both test cases compared to the original condition. Additionally, it also makes the reliability assessment more realistic when taking into account multi-area weather conditions.

An artificial intelligence approach to forecasting when there are structural breaks: a reinforcement learning-based framework for fast switching

An artificial intelligence approach to forecasting when there are structural breaks: a reinforcement learning-based framework for fast switching

Multi-phase synthetic contrast enhancement in interventional computed tomography for guiding renal cryotherapy

PurposeMinimally invasive treatments for renal carcinoma offer a low rate of complications and quick recovery. One drawback of the use of computed tomography (CT) for needle guidance is the use of iodinated contrast agents, which require an increased X-ray dose and can potentially cause adverse reactions. The purpose of this work is to generalise the problem of synthetic contrast enhancement to allow the generation of multiple phases on non-contrast CT data from a real-world, clinical dataset without training multiple convolutional neural networks.MethodsA framework for switching between contrast phases by conditioning the network on the phase information is proposed and compared with separately trained networks. We then examine how the degree of supervision affects the generated contrast by evaluating three established architectures: U-Net (fully supervised), Pix2Pix (adversarial with supervision), and CycleGAN (fully adversarial).ResultsWe demonstrate that there is no performance loss when testing the proposed method against separately trained networks. Of the training paradigms investigated, the fully adversarial CycleGAN performs the worst, while the fully supervised U-Net generates more realistic voxel intensities and performed better than Pix2Pix in generating contrast images for use in a downstream segmentation task. Lastly, two models are shown to generalise to intra-procedural data not seen during the training process, also enhancing features such as needles and ice balls relevant to interventional radiological procedures.ConclusionThe proposed contrast switching framework is a feasible option for generating multiple contrast phases without the overhead of training multiple neural networks, while also being robust towards unseen data and enhancing contrast in features relevant to clinical practice.

Odor Source Localization in Obstacle Regions Using Switching Planning Algorithms with a Switching Framework.

Odor source localization (OSL) robots are essential for safety and rescue teams to overcome the problem of human exposure to hazardous chemical plumes. However, owing to the complicated geometry of environments, it is almost impossible to construct the dispersion model of the odor plume in practical situations to be used for probabilistic odor source search algorithms. Additionally, as time is crucial in OSL tasks, dynamically modifying the robot's balance of emphasis between exploration and exploitation is desired. In this study, we addressed both the aforementioned problems by simplifying the environment with an obstacle region into multiple sub-environments with different resolutions. Subsequently, a framework was introduced to switch between the Infotaxis and Dijkstra algorithms to navigate the agent and enable it to reach the source swiftly. One algorithm was used to guide the agent in searching for clues about the source location, whereas the other facilitated the active movement of the agent between sub-environments. The proposed algorithm exhibited improvements in terms of success rate and search time. Furthermore, the implementation of the proposed framework on an autonomous mobile robot verified its effectiveness. Improvements were observed in our experiments with a robot when the success rate increased 3.5 times and the average moving steps of the robot were reduced by nearly 35%.

Zeros and zero dynamics of switching systems

A notion of zero dynamics for switching linear systems is introduced by using a structural geometric approach. The zero dynamics for a switching system is shown to be represented by a family of dynamics that characterize the way in which the stability of each mode is affected when the overall system is made maximally unobservable by means of a state feedback. The comparison between the elements of the zero dynamics of a switching system and the classical zero dynamics of each mode shows differences that are due to the dynamics generated by the switching and by the interaction between the modes. The role of the zero dynamics in characterizing the stability of the compensated system is similar to that found in the classical linear case, although the results in the switching framework are weaker.

Distributionally Robust Chance-Constrained Optimal Transmission Switching for Renewable Integration

Increasing integration of renewable generation poses significant challenges to ensure robustness guarantees in real-time energy system decision-making. This work aims to develop a robust optimal transmission switching (OTS) framework that can effectively relieve grid congestion and mitigate renewable curtailment. We formulate a two-stage distributionally robust chance-constrained (DRCC) problem that assures limited constraint violations for any uncertainty distribution within an ambiguity set. Here, the second-stage recourse variables are represented as linear functions of uncertainty, yielding an equivalent reformulation involving linear constraints only. We utilize moment-based (mean-mean absolute deviation) and distance-based ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\infty$</tex-math></inline-formula> -Wasserstein distance) ambiguity sets that lead to scalable mixed-integer linear program (MILP) formulations. Numerical experiments on the IEEE 14-bus and 118-bus systems have demonstrated the performance improvements of the proposed DRCC-OTS approaches in terms of guaranteed constraint violations and reduced renewable curtailment. In particular, the computational efficiency of the moment-based MILP approach, which is scenario-free with fixed problem dimensions, has been confirmed, making it suitable for real-time grid operations.

Neural-Adaptive Switching Control of Task-Space Objectives on Strict-Feedback Robot

Switching between tasks in robot applications is necessary for autonomous behaviour, requiring a control framework for switching between objectives. Thus, this paper proposes a switching control framework that allows transitions in position-dependent objectives for robot systems in strict-feedback form with adaptive capabilities. The proposed controller integrates artificial neural networks in two ways; one approximates control transforms while another approximates additive terms. Furthermore, the proposed method includes a dynamic damped inverse to ensure the invertible control transform matrices. The proposed controller is derived using the backstepping method and is stable in a Lyapunov sense. The proposed task-switching controller is first demonstrated on a generic robot manipulator. Then, a simulated two-degree-of-freedom manipulator with a camera at the end-effector tasked with position tracking and visual servoing verifies the capabilities of the proposed controller. A baseline switching controller shows that the proposed controller has superior performance, specifically, root mean square (RMS) error and control effort. Also, executing the simulation for multiple cycles shows that the proposed controller's artificial neural network and adaptive laws are stable.

The Dollar Exchange Rate, Adjustment to the Purchasing Power Parity, and the Interest Rate Differential

This study applies a Markov switching error correction model to describe the single most important real exchange rate (Deutsche mark versus US dollar) over the flexible exchange rates period from 1973 to 2004. We show an alternative way of modelling non-linear adjustment to the purchasing power parity (PPP) besides standard threshold models. The model merges the two possible sources of non-linearity by additionally allowing the probability of a mean-reverting regime to increase with the distance from PPP. The interest rate differential as an additional determinant of real exchange rate behaviour in a Markov switching framework is introduced in the model. The study finds that the real dollar exchange rate during the post-Bretton Woods era is well described by a Markov switching error correction model with (PPP) as long-run equilibrium. There is one mean reversion regime where PPP and the interest parity condition are valid. Contrary, the second regime is characterised by persistent mean aversion, where a regime switch does not become more likely with increasing distance from PPP. The unconditional half-life of shocks is about 1.5 years.