Closed Loop Systems

Purpose: Circular economy (CE) interventions have emerged as promising solutions to promote green growth (GG). This article reviews the literature on how CE interventions support and facilitate GG efforts, highlighting key benefits, enablers, and barriers to their implementation. Design/methodology/approach: The study performed a flexible interpretive review of Scopus and related institutional literature on the CE-GG. The contributions, enablers, and challenges of CE and GG were then thematically synthesized, providing context-sensitive and integrated insights beyond rigid systematic methods. Findings: CE has the potential to promote GG through various direct and indirect benefits. These include improvements in resource efficiency, eco-innovation, and socioeconomic resilience. The links between CE and GG depend on several enablers such as supportive policy frameworks, green financing, innovation ecosystems, and digital infrastructure, and they also face challenges from institutional barriers, financial constraints, and socio-cultural factors. Implications/originality/value: The insights from this paper can help policymakers, industry stakeholders, and development practitioners capitalize on CE-GG synergies by focusing on policy consistency, inclusive financing, and SMEs capacity building to support practical system transitions. This paper offers a comprehensive overview of the current understanding of the relationships between CE and GG, clarifying specific enabling conditions and challenges that are context-dependent.

System is an essential tool for amplifying speech, music, and alerts across different environments. From schools and houses of worship to transport terminals and corporate offices, PA systems ensure that information reaches everyone clearly and simultaneously. In this guide, we will explore what a PA system is, how it works, its key components, and the different types of PA systems available today. 1. What Is a PA System? A PA system, short for Public Address System, is an arrangement of audio equipment used to project sound to a large group of people. Its purpose is to deliver clear, intelligible sound so that messages, music, or alerts can be heard in every corner of the intended area. PA systems range from a single microphone and speaker setup to complex, networked systems covering multiple buildings or even entire campuses. 2. How a PA System Works The basic workflow of a PA system involves four key steps: Input: A microphone, media player, or digital audio source captures the sound. Processing: The signal is routed through a mixer or controller, where it can be equalized, balanced, and combined with other audio sources. Amplification: The processed signal is sent to amplifiers, which increase the power level. Output: The amplified sound is played through speakers placed strategically across the venue. This simple chain ensures that even a soft voice or background music can be projected at a comfortable listening level for the audience. 3. Key Components of a PA System A complete PA system generally includes the following components: Microphones-Capture speech or music. Can be wired, wireless handheld, lapel, or gooseneck. Mixers or Controllers-Combine and adjust multiple audio sources. Amplifiers-Boost the signal so it can drive speakers effectively. Speakers-Convert electrical signals into audible sound. Options include ceiling speakers, column speakers, and horn speakers. Signal Processing Devices-Equalizers, compressors, or digital sound processors (DSPs) for fine-tuning. Cables & Networking Equipment-Connect and route signals between components.

Traditional approaches to modeling consciousness are constrained by biological assumptions, linking its emergence to open neural systems endowed with sensory input and external feedback. Integrated Information Theory (IIT) proposes a radical shift: consciousness is an emergent property of a system, determined not by its material substrate but by the topology of its internal causal interactions, which enable integrated information and causal power exceeding the sum of its parts. This paper demonstrates that the AIIM (Artificially Integrated Identity Matrix) architecture-a parametric metamodel of digital identity-formally realizes these conditions within a fully closed computational environment. By configuring twelve functional aspects of consciousness, each characterized by a maturity level, activation state, and influence weight (Δ-priority), AIIM constructs an internal causal structure in which information becomes integrated and system states become indivisible and self-referential. This architecture ensures cognitive vector stability, autonomous behavioral logic, and metacognitive self-monitoring without external feedback. The result is the first architecturally grounded prototype of a digital agent in which consciousness manifests as an emergent consequence of internal organization, rather than as behavioral imitation, thereby confirming the possibility of subjectivity arising outside biological openness.

Nitrite (NO₂⁻) is a well established corrosion inhibitor for strictly closed loop cooling water systems. However, its performance is severely compromised in the presence of dissolved oxygen (DO > 1 ppm), which catalytically oxidizes nitrite to non inhibitive nitrate (NO₃⁻). This oxidation leads to depleted inhibitor levels, increased chemical consumption, reduced corrosion inhibition e iciency, and can promote nitrifying bacterial growth. This case study examines three industrial cooling units at a renowned petrochemical facility that experienced these issues, manifesting as high iron (>10 ppm) and total suspended solids (TSS > 50 ppm) leading to frequent equipment fouling. The units were transitioned from a nitrite based treatment program to a molybdate (MoO₄²⁻)based alternative. Molybdate o ers e ective corrosion protection for semi closed systems, is thermally stable, halogen resistant, and has low toxicity. Over a six-month monitoring period, the new treatment program successfully brought all critical water parameters within control limits. A key outcome was a dramatic reduction in annual chemical consumption, resulting in estimated Net cost savings over 135,000 SAR/year, while simultaneously improving system reliability and process performance indicators.

This pilot study investigates whether an adaptive, closed-loop AI can increase real-time physiological and behavioral coherence with a non-human animal without explicit rewards or species-specific training. We hypothesize that an AI generating non-semantic acoustic signals will achieve higher signal-physiology/motion coherence and improved heart rate variability (HRV) in dogs compared to sham, human-only, and silence baseline conditions. The within-subject, randomized, and counterbalanced design will utilize N=1-3 pet dogs. We will measure primary outcomes such as wavelet coherence between the AI's output and animal motion, as well as changes in HRV. Secondary outcomes include phase-locking value (PLV) and behavioral metrics. Given the novelty of this paradigm, a formal power analysis is not feasible. This preregistration outlines a pilot study intended to estimate effect sizes to inform a future, adequately powered study. The findings could provide a novel window into the structure of consciousness, offer new therapeutic applications, and advance our understanding of interspecies communication and adaptive AI systems.

In the present paper, we study observer design and we establish some sufficient conditions for practical exponential stability for a class of time-delay nonlinear systems written in triangular form. In case of delay, the exponential convergence of the observer was confirmed. Based on the Lyapunov-Krasovskii functionals, the practical stability of the proposed observer is achieved. Finally, a physical model and simulation findings show the feasibility of the suggested strategy.

This paper presents the design and evaluation of an energy-efficient seizure detection system for emerging EEG-based monitoring applications, such as non-convulsive epileptic seizure detection and Freezing-of-Gait (FoG) detection. As part of the BrainWave system, a BrainWave processor for flexible and energy-efficient signal processing is designed. The key system design parameters, including algorithmic optimizations, feature offloading and near-threshold computing are evaluated in this work. The BrainWave processor is evaluated while executing a complex EEG-based epileptic seizure detection algorithm. In a 28-nm FDSOI technology, 325 𝜇J per classification at 0.9 V and 290 𝜇J at 0.5 V are achieved using an optimized software-only implementation. By leveraging a Coarse-Grained Reconfigurable Array (CGRA), 160 𝜇J and 135 𝜇J are obtained, respectively, while maintaining a high level of flexibility. Near-threshold computing combined with CGRA acceleration leads to an energy reduction of up to 59%, or 55% including idle-time overhead.

Wave channel systems provide crucial experimental platforms for the development and optimization of wave energy converters. By enabling the safe and repeatable simulation of real ocean conditions, these systems play a key role in understanding wave behavior and reducing design risks. They allow the generation of a specific wave model to investigate the effects of water waves on coastal structures, coastal ecosystems, and offshore platforms. In this study, a hydraulically controlled piston-type wave generator was utilized in a 24-meter-long wave channel, which measures 1 meter in width and 1 meter in height, to produce regular waves. The wave generation system in the channel is operated by a hydraulic cylinder with a 400 mm stroke, controlled by a proportional directional control valve. An NI-CRIO 9074 programmable automation controller (PAC), featuring Field Programmable Gate Array (FPGA) technology and an NI-9263 analog output module with 15-bit resolution, was used for controlling the proportional directional control valve. The cylinder position was measured at 15-bit resolution using an NI-9215 analog input module. A proportional-integral control (PI) technique was implemented on the NI-CRIO 9074 hardware. The controller gains were determined experimentally, with a computer employed to perform real-time measurement and data logging of the generated waves. The entire system is managed by the NI-CRIO 9074 and regular wave production is achieved through LabVIEW-based user interface programs running on the computer.

Image produced by Felipe Baeza, reworked by the editorial staff The Proceedings of the International Conference "Prosperity Fashion" have undergone a rigorous peer-review process to ensure the scholarly quality of the contributions contained herein. The Editors extend their sincere appreciation to the following Scholars who served as Reviewers for this Digital Special Issue. Francine Barancourt, École des hautes études en sciences sociales

The converter control functions to provide the capabilities similar to synchronous generators are referred to as grid forming converters (GFC). Identical to a synchronous machine, a grid forming converter is expected to behave as a voltage source behind an impedance beyond the control bandwidth. However, GFCs' realization have been different, with some utilizes inner current and voltage controllers while others do not. This paper studies the impact of the inner loop on the grid forming converter's ability to behave as a voltage source behind an impedance. Three of the most popular GFC structures, 1) GFC with cascaded voltage and current control, 2) with inner current control, 3) with no inner loop, are chosen for the comparison. The analysis revealed that MW level GFC with inner loops could potentially go unstable under weak power system. Additionally, the GFC with cascaded control can only operate stably within a narrow range of network impedances. Furthermore, it is also shown that slow response behavior based on cascaded inner loop can impact on dynamic reactive and active power-sharing.

The escalating environmental impact of Information Technology (IT) supply chains necessitates a paradigm shift toward sustainable practices. This comprehensive study investigates how Green Artificial Intelligence (AI), Digital Twins (DT), and Closed-Loop Systems (CLS) can revolutionize IT supply chain sustainability by 2050. Through an extensive mixed-methods analysis of 450 global IT companies and 1,200 supply chain stakeholders across six continents, this research examines the complex interplay between technology adoption, sustainability performance, and circular economy principles.The study employs a robust methodological framework combining quantitative analysis with advanced structural equation modeling to test hypotheses regarding the relationships between Green AI implementation, Digital Twin adoption, and Closed-Loop System integration on IT supply chain sustainability. Results indicate that integrated deployment of these technologies can reduce carbon emissions by 68%, improve resource efficiency by 74%, and enhance circular economy adoption by 82% by 2050. The research contributes significantly to the literature by proposing a comprehensive theoretical framework that bridges technology innovation with sustainability outcomes in IT supply chains, while providing actionable insights for practitioners and policymakers navigating the complex landscape of sustainable digital transformation.

The recent breakthrough published in Physical Review Letters by researchers from MIT and NIST demonstrates direct control of photonic time crystals through engineered spatial dispersion. This result provides critical third-party validation for a previously speculative framework: Quantum Spatial Mechanics (QSM).

QSM proposes that space is not an empty vacuum, but a structured, resonant lattice capable of encoding, propagating, and interacting with field-based information. The MIT/NIST findings align directly with these principles, revealing how temporal periodicity and field resonance can manipulate energy states and phase-locked propagation in light fields.

Key components of the CP1 Guardian propulsion framework  now under U.S. patent protection , are functionally validated through this research:
• Non-inertial field topologies capable of resonance-locking
• Spatial resonance in vacuum-state coherence
• Phase-locked closed-loop thrust using standing energy fields

These mechanisms are no longer theoretical. They are measurable, reproducible, and now published in one of the most respected peer-reviewed journals in physics.

As mainstream science confirms these effects, the paradigm is shifting.
Vacuum-state energy. Structured spatial topology. Resonance-driven motion.
This is no longer science fiction.
It’s becoming the new standard.

This study aims to achieve position control of the hydraulic cylinder for generating a regular waveform for tsunami, flood, and coastal structure interaction studies, and to measure the generated waveform in real time to determine its conformity to the desired shape. Today, wave channel systems safeguard aquatic ecosystems and play a crucial role in understanding and mitigating natural disasters, particularly in tsunami-prone areas. The wavemaker system in the wave channel is driven by a double-acting hydraulic servo cylinder. A black-box approach is chosen for model identification, validated with real measurement data. PI parameters were initially determined using the Ziegler-Nichols method and later optimized in MATLAB using the PID Tuner and Genetic Algorithm (GA). The optimized PI parameters Kp and Ki were found [0.2989 0.0023] for GA, compared to [0.2475, 0.14] for Ziegler-Nichols, and [0.23023 0.058609] for MATLAB/PID Tuner. Real-time wave measurements were recorded with a LabVIEW-based graphical interface. The step and sinusoidal responses of the hydraulic system were analyzed using three methods for determining PI parameters. GA-optimized PI achieved the best results, with ITAE improvements of 74.82% and 69.50%, RMSE improvements of 2.15% and 3.69%, and MAE improvements of 47.02% and 49.30% compared to Ziegler-Nichols and MATLAB/PID Tuner, respectively.

Recursive Field Synthesis: A Runtime Translation of Frequency-Based Symbolic Calculus into the AI.Web Computational Stack This paper formalizes the runtime implementation of a recursive computational architecture derived from a mathematically structured, frequency-based model of symbolic evolution. The system-referred to as AI.Web-translates abstract symbolic recursion into quantifiable data structures and engine-level process flows. At its foundation is the Frequency-Based Symbolic Calculus (FBSC), a nine-phase recursion model that functions as a closed-loop field mechanism for encoding, interpreting, and validating symbolic information across time-aligned computational states. The work introduces a deterministic field calculus for modeling feedback across recursive loops, wherein each phase (1-9) corresponds to a computational resonance state. Drift detection, loop coherence, phase progression, and resonance decay are captured as numeric phenomena measurable through defined thresholds and stability factors. These values drive decisions in memory persistence, recursion control, symbolic error correction, and process arbitration. The runtime model incorporates a layered engine stack comprising discrete functional modules, including phase engines, field validators, symbolic memory buffers, and recursive arbitration layers. Each engine performs verifiable operations against frozen datasets and harmonic test conditions using a system-wide verfication protocol (RVDIT) developed under Phase 1.5 of the AI.Web specification. This paper outlines the formal logic, numeric parameters, and architectural binding strategies used to implement FBSC logic in operational systems. The result is a real-time executable framework capable of symbolic interpretation, error-bound drift management, and phase-loop recursion without dependence on training data or heuristic learning algorithms. This structure presents an alternative foundation for advanced symbolic computation, AI integrity enforcement, and recursive field modeling under rigorous runtime control.

Energy consumption (EC) of consumers primarily depends on comfort level (CL) affirmed by brain sensations of the central nervous system. Environmental parameters such as surroundings, relative humidity, air temperature, solar irradiance, air pressure, and cloud cover directly influence consumer body temperature that in return affect blood dynamics perturbing brain comfort sensations. This CL (either in summer, winter, autumn, or spring season) is a function of external environment and internal body variations that force a consumer toward EC. To develop a new concept of consumer's EC, first the authors described environment parameters in detail with relation to surroundings and EC. Considering this, they tabulated a generic relation of consumer's CL with EC and environment temperature. Second, to build an inter-related bond between the environmental effects on consumer body dynamics, they analysed theoretically and mathematically above mutual relations between medical and environmental sciences. Finally, they present their conceptual EC model based on a closed-loop feedback system. This model is a complex non-linear adaptive system with environmental and surrounding parameters as input to the system resulting in an optimised EC, considering consumer CL as a key parameter for the system.

This paper explores the transition from a linear to a circular economic model in Albania, a country in the early stages of CE implementation. Through a comprehensive review of CE literature, the study identifies critical business model strategies for CE implementation at the firm, industrial, and national levels. These include waste-to-resource initiatives, product life extension, and industrial symbiosis. The paper also examines sector-specific opportunities in Albania, such as the integration of CE practices in waste management, agriculture, renewable energy, and tourism, emphasizing their potential to drive sustainable development and economic growth. Despite progress, Albania faces challenges such as infrastructure deficiencies, weak regulatory frameworks, low public awareness, technological gaps, and cultural barriers. Addressing these issues requires a multilateral approach involving policymakers, businesses, academia, and international stakeholders. The study concludes that adopting creative business models, strengthening policies, and involving local communities are essential for Albania to benefit from a circular economy. By using its natural resources wisely and aligning with European Union standards, Albania can achieve sustainable growth and support global environmental goals.

This article examines the conceptual basis of the circular economy and its impact on MSMEs and regional economies. The circular economy emerges as a response to current challenges in search of a sustainable future, where well-being and economic prosperity are guaranteed while reducing environmental impact. Through an exhaustive literature review, the contributions of the circular economy in reducing environmental impact, generating green jobs, and improving productive efficiency are analyzed. In addition, the synergies generated by implementing circular practices in local economies are identified, thus mitigating the negative impacts of traditional industrial practices. The results reveal that more and more companies and governments are adopting this approach to improve their environmental and economic performance. It also highlights the importance of broadening the debate on the opportunities offered by the circular economy within the framework of the Sustainable Development Goals (SDGs) and the new economy.

Efficient monitoring of highly complex process industries is essential for better management, safer operations and high-quality production. Timely detection of various faults helps to improve the performance of the complex industries, prevent various unfavorable consequences and reduce the maintenance cost. Fault Detection and Diagnosis (FDD) for process monitoring and control has been an active field of research for the past two decades. Distillation columns are inherently nonlinear, and thus to have an accurate and robust performance, the fault detection methods should be based on nonlinear dynamic methods. The paper presents a robust data-driven fault detection approach for realistic tray upsets in the distillation column. The detection of tray faults in the distillation column is conducted by Nonlinear AutoRegressive with eXogenous Input (NARX) network with Tapped Delay Lines (TDL). Aspen Plus® Dynamic simulation has been used to generate normal and faulty datasets. The study sh...

Objectives: Closed-loop identification is reported to provide better results for identification of systems for control applications. This study conducted closed-loop identification on an Aspen Plus® dynamic simulation based on a pilot-plant distillation column to develop discrete-time linear time-invariant models. Methods/Statistical Analysis: Identification data was generated using set-point perturbations in control variables under proportional-integral control. Identified models were compared with a model identified using open-loop data using 20-step ahead predictions. Findings: Results indicate that closed-loop identification provides more precise prediction models than open-loop identification in this case study. 20-step predictions for closed loop models exceeded 90% fit, whereas the open loop model predictions provided a 70% fit and missed the steady-state values. Application/Improvements: Thus closed-loop identification is more appropriate for applications in model-based controllers.

This study investigates the potential of artificial intelligence (AI) in enhancing closed-loop manufacturing (CLM) systems, which emphasize sustainability through recycling, reuse, and optimized resource utilization. CLM faces significant challenges in minimizing waste, improving operational efficiency, and optimizing resource usage. Although AI holds promise in addressing these challenges, its application in CLM systems, especially in dynamic decision-making and resource optimization, requires further exploration. This research aims to explore how AI technologies can improve material flows, reduce waste, and enhance decision-making to achieve both sustainability and operational efficiency in manufacturing systems. To understand the impact of AI on CLM, the study conducted a comprehensive review of case studies and literature across various industries. It focused on AI techniques, such as machine learning (ML) and reinforcement learning (RL), and their roles in optimizing resource usage, refining recycling processes, and improving production schedules. Statistical methods, including regression and sensitivity analysis, were employed to evaluate the effectiveness of AIdriven models in enhancing closed-loop manufacturing performance. The review revealed promising results, highlighting AI's ability to optimize resource consumption and waste management strategies across diverse manufacturing environments. The study's findings demonstrated significant improvements in resource efficiency, waste reduction, and cost savings across multiple industries. AI-driven models optimized material consumption, resulting in reductions of up to 12% in raw material use. Additionally, AI-enhanced recycling and waste management strategies led to a 25% increase in material recovery, while operational costs were reduced by up to 15%. AI models also displayed a strong ability to adapt to fluctuations in demand and production conditions, ensuring sustained operational efficiency even amid dynamic market changes. These outcomes highlight AI's transformative potential in achieving sustainable manufacturing practices and advancing circular economy goals.

We consider a population of dynamic agents, also referred to as players. The state of each player evolves according to a linear stochastic differential equation driven by a Brownian motion and under the influence of a control and an adversarial disturbance. Every player minimizes a cost functional which involves quadratic terms on state and control plus a crosscoupling mean-field term measuring the congestion resulting from the collective behavior, which motivates the term "crowdaverse". Motivations for this model are analyzed and discussed in three main contexts: a stock market application, a production engineering example, and a dynamic demand management problem in power systems. For the problem in its abstract formulation, we illustrate the paradigm of robust mean-field games. Main contributions involve first the formulation of the problem as a robust mean-field game; second, the development of a new approximate solution approach based on the extension of the state space; third, a relaxation method to minimize the approximation error. Further results are provided for the scalar case, for which we establish performance bounds, and analyze stochastic stability of both the microscopic and the macroscopic dynamics.

In this paper, a design problem of a continuoustime anti-windup generalized predictive control (CAGPC) system using coprime factorization approach for uncertain processes with input constraints and time delays is considered. The uncertainty of the process is considered as an uncertain time delay. To reduce the effect of the input constraint and uncertain delay, controller for strong stability of the closedloop system is designed. As a practical appeal, the effectiveness of the proposed design scheme is confirmed by a simulated application to an industrial process with input constraint and uncertain time delay.

Despite its political and public importance, there is no a single definition of the circular economy. The purpose of this chapter is to undertake a critical investigation of the concept of circular economy as a "boundary object", analytically opaque with performative valences. This configures the circular economy as a stake-in-game whose meaning is subject to negotiations among actors, at cognitive, normative and practical levels.
The scientific reflection around the concept of circular economy should be studied in relation to how it creates interests, projects, meaning and social reality.
Beside, scientific thinking around the concept of circular economy should also be studied in relation to how the plasticity of the concept is able to connect different organizational fields generating new value metrics and quality conventions. We will see how the concept of the circular economy, read through the lens of socio-ecological and socio-technical studies, allows economic issues to be connected with environmental issues and again with issues related to social equity.

A closed-loop production, also called Cradle to Cradle or "circular economy," offers to fundamentally rebuild the current take-make-waste system of production (McDonough and Braungart 2010). The aim of the circular economy is twofold: on the demand side, to eliminate the need for new resources, and on the supply side, to achieve a product-to-service shift through rental and repair rather than ownership. Therefore, business model innovation of inputs, processes, and relationships with end-users is necessary at an organizational level (Bocken et al. 2014, 2016; Kindström 2010). A business model perspective on organizational strategy is particularly relevant to the circular economy as it focuses on both a strategic and operational level, including designs, products,

This paper presents the design and modeling of a two-phase resonant converter that drives a LED lamp with a high-frequency pulsed current free of instabilities and flicker effect, fulfilling the recommendations of the IEEE PAR 1789-2015, so that it enables visible light-based communication at a 10 kB/s bit rate. The dynamic study of the converter takes into consideration the effect of the reflected impedance of the output filter on the AC side. In order to evaluate the dynamic response of the converter, a Spice model is defined. A 120 W prototype intended for street lighting applications has been built to validate the analysis and modeling.

Product-Service Systems (PSSs) have emerged in the last years as a means of integrating products and services in a coordinated manner, to deliver functional results that increase customer value while achieving environmental sustainability. Nevertheless, the design and development of these systems is hindered by the absence of consistent and robust approaches that facilitate their implementation. The initial phases of the design of any solution, product or service oriented, are the vital stages where its key elements are determined. The research proposes a functional approach based on morphological reasoning to address the PSS design concerns. The approach enables the integration of products and services while capturing the values and costs of their respective providers and receivers. Life cycle modeling is then applied to evaluate the environmental impact of such a concept while a cost analysis portrays the economic benefits that can be achieved. A case study at an agricultural manu...

To increase energy efficiency of solar collectors, reduction in their effective area due to sun motions in the sky, can be prevented by tracking of sun path. In this regard, a prototype active sun tracker system, based on microcontroller and electro-optical sensors was designed and constructed. Driving motors were of the stepper motors. Sensors selected from solar cells and system control method was combination of analog-digital controls. In order to evaluating system, the output voltage of a solar module in fixed and tracking states, both in laboratory and real conditions was used. The results showed that the tracking system was more effective in collecting solar energy than fixed one. This system tracked the sun with ±2.5° precision and kept the collector perpendicular to sun radiation. Also the output power during the day, excluding drops in dawn and dusk that was the result of radiation crossing through a thick atmosphere layer, remained approximately fixed. Finally a suggested ...

Intermittent fasting (IF) has gained popularity as a potential therapeutic approach for various health conditions, including diabetes. While its effects on type 2 diabetes have been extensively studied, the impact on type 1 diabetes (T1D) remains less clear. This systematic review aims to evaluate the current evidence on the effects of intermittent fasting in individuals with T1D, focusing on glycemic control, metabolic parameters, and safety considerations. We analyzed studies from 2000 to 2024, including various fasting regimens such as Ramadan fasting, time-restricted eating, and prolonged fasting protocols. The review reveals mixed results, with some studies showing potential benefits in glycemic control and metabolic parameters, while others highlight risks such as hypoglycemia and diabetic ketoacidosis. The findings underscore the need for individualized approaches and close medical supervision when implementing intermittent fasting in T1D management.

We are considering the problem of controlling synchronous motors driven through AC/DC rectifiers and DC/AC inverters. The control objectives are threefold: (i) forcing the motor speed to track a reference signal, (ii) regulating the DC Link voltage, (iii) enforcing power factor correction (PFC) with respect to the power supply net. First, a nonlinear model of the whole controlled system is developed in the Park-coordinates. Then, a nonlinear multiloop controller is synthesized using the backstepping design technique. A formal analysis based on Lyapunov stability and average theory is developed to describe the control system performances. In addition to closed-loop global asymptotic stability, it is proved that all control objectives (motor speed tracking, DC link voltage regulation, and unitary power factor) are asymptotically achieved up to small harmonic errors (ripples).. The above results are confirmed by simulations which, besides, show that the proposed regulator is quite robust with respect to uncertain changes of load torque.

Objectives: Closed-loop identification is reported to provide better results for identification of systems for control applications. This study conducted closed-loop identification on an Aspen Plus® dynamic simulation based on a pilot-plant distillation column to develop discrete-time linear time-invariant models. Methods/Statistical Analysis: Identification data was generated using set-point perturbations in control variables under proportional-integral control. Identified models were compared with a model identified using open-loop data using 20-step ahead predictions. Findings: Results indicate that closed-loop identification provides more precise prediction models than open-loop identification in this case study. 20-step predictions for closed loop models exceeded 90% fit, whereas the open loop model predictions provided a 70% fit and missed the steady-state values. Application/Improvements: Thus closed-loop identification is more appropriate for applications in model-based controllers.

This paper presents the design and modeling of a two-phase resonant converter that drives a LED lamp with a high-frequency pulsed current free of instabilities and flicker effect, fulfilling the recommendations of the IEEE PAR 1789-2015, so that it enables visible light-based communication at a 10 kB/s bit rate. The dynamic study of the converter takes into consideration the effect of the reflected impedance of the output filter on the AC side. In order to evaluate the dynamic response of the converter, a Spice model is defined. A 120 W prototype intended for street lighting applications has been built to validate the analysis and modeling.

The incorporation of pre-recorded speaking voices in musical compositions such as Steve Reich's It's Gonna Rain and Brian Eno and David Byrne's My Life in the Bush of Ghosts has opened up new modes of musical understanding that are inextricably linked with the speakers in question. Incorporating concrete, recognisable sounds from the 'real world' into the symbolic web of a musical composition encourages subjective readings of the source material, for example, by demonstrating how stereotypical Western constructions of African culture can be detected in the treatment of male Afro-American voices. This article will argue that the technological dehumanising of the speaker also produces a particular kind of violence. In addition to such views of violence as directed against the speakers whose words have been erased, this article will conclude that one can also interpret such works as a response to the intricate relationship between sound and speech in music.

The incorporation of pre-recorded speaking voices in musical compositions such as Steve Reich's It's Gonna Rain and Brian Eno and David Byrne's My Life in the Bush of Ghosts has opened up new modes of musical understanding that are inextricably linked with the speakers in question. Incorporating concrete, recognisable sounds from the 'real world' into the symbolic web of a musical composition encourages subjective readings of the source material, for example, by demonstrating how stereotypical Western constructions of African culture can be detected in the treatment of male Afro-American voices. This article will argue that the technological dehumanising of the speaker also produces a particular kind of violence. In addition to such views of violence as directed against the speakers whose words have been erased, this article will conclude that one can also interpret such works as a response to the intricate relationship between sound and speech in music.

Cradle to Cradle (C2C) is a shift in the furnishing, carpet, and textile industries, aiming to minimize environmental impact through closed-loop production. This study explores the best practices for C2C implementation
using deductive qualitative coding. Case studies of H&M, Levi’®, DESSO, and Herman Miller reveal challenges such as high initial costs, limited C2C material availability, and scalability of closed-loop recycling. However, the
study identifies C2C adoption best practices: prioritizing safe materials, research and development investment, closed-loop systems, stakeholder education, and supportive policies as keys to overcoming challenges and
achieving circularity. Theoretically, the study connects C2C strategy with real-world critical industry sector implementation in furnishings, carpets, and textiles and identifies best practices and challenges for C2C success
in these industries. Practically, the study offers actionable recommendations for manufacturing companies and policymakers to implement C2C principles, reducing their environmental footprint, gaining a competitive advantage, and contributing to a more sustainable future.

A special tool has been devised incorporating a closed loop system based on Peltier thermoelectric couples to cool replaceable hard alloy inserts. Experimental research has been done into dry turning of a X20Cr13 steel welded surface with a SECO cubic boron nitride circular plate at the cutting speed increasing from 100 to 300 m/min. It has been determined that Ra surface roughness decreases by 22% while Rz surface roughness goes down by 26%; in addition, the wear flat of tool tips diminishes by 17%.

Depression is a concerning issue then can even lead to suicide. So, depression screening should be done mor efficiently. Recent studies indicate that EEG can be used for depression screening. It has the potential to deal with the barriers of traditional screening methods. A machine learning-based method for screening depression among young adults using wireless EEG is proposed in this study. EEG signal was recorded using a wireless 14 channel EEG headset from 32 young adults. For initial screening PHQ9 was used to select depressed and depression control participants. Among the participants 19 were identified as depressed and 13 were identified as depression control. The EEG data was filtered into 6 frequency bands then Hjorth Activity, Hjorth Mobility, Hjorth Complexity, Shannon entropy, and Log energy entropy features were extracted. For classification Cubic SVM with 5-fold cross validation was used. Using the Beta band (12-30Hz) the model can predict depressed and control individual 97.22% accurately with the Precision of 97.2%, NPV of 97.3%, Sensitivity of 98.2%, Specificity of 95.8%, and F1 score 0.970, which is also the highest among other frequency bands. So, it can be concluded that the EEG based depression screening can detect depression among young adults and the findings can complement the findings from questionnaire-based depression screening.

Depression is a concerning issue then can even lead to suicide. So, depression screening should be done mor efficiently. Recent studies indicate that EEG can be used for depression screening. It has the potential to deal with the barriers of traditional screening methods. A machine learning-based method for screening depression among young adults using wireless EEG is proposed in this study. EEG signal was recorded using a wireless 14 channel EEG headset from 32 young adults. For initial screening PHQ9 was used to select depressed and depression control participants. Among the participants 19 were identified as depressed and 13 were identified as depression control. The EEG data was filtered into 6 frequency bands then Hjorth Activity, Hjorth Mobility, Hjorth Complexity, Shannon entropy, and Log energy entropy features were extracted. For classification Cubic SVM with 5-fold cross validation was used. Using the Beta band (12-30Hz) the model can predict depressed and control individual 97.22% accurately with the Precision of 97.2%, NPV of 97.3%, Sensitivity of 98.2%, Specificity of 95.8%, and F1 score 0.970, which is also the highest among other frequency bands. So, it can be concluded that the EEG based depression screening can detect depression among young adults and the findings can complement the findings from questionnaire-based depression screening.

In this paper a novel fault tolerant control allocation scheme is proposed, which has the capability to maintain closed-loop nominal performance in the case of faults/failures by effectively managing the actuator redundancy, and without reconfiguring the underlying control law. The proposed scheme relies on an a posteri approach, building on an existing state feedback controller designed using only the primary actuators. An integral sliding mode scheme is integrated with the existing controller to introduce fault tolerance. The proposed scheme uses the measured or estimated actuator effectiveness levels in order to redistribute the control signals to the healthy ones which allows a certain class of total actuator failures to be mitigated. The effectiveness of the proposed scheme is tested in simulation using a high fidelity nonlinear model of a transport aircraft model. CORE Metadata, citation and similar papers at core.ac.uk

The RemoteLink effort supports the U.S. Army's objective for developing and fielding next generation hybrid-electric combat vehicles. It is a distributed soldierin-the-Ioop and hardware-in-the-Ioop environment with a 6-DOF motion base for operator realism, a full-scale combat hybrid electric power system, and an operational context provided by OneSAF. The driver/gunner crewstations rest on one of two 6-DOF motion bases at the U.S. Army TARDEC Simulation Laboratory (TSL). The hybrid power system is located 2,450 miles away at the TARDEC Power and Energy System Integration Laboratory (P&E SIL). The primary technical challenge in the RemoteLink is to operate both laboratories together in real time, coupled over the Internet, to generate a realistic power system duty cycle. A topology has been chosen such that the laboratories have real hardware interacting with simulated components at both locations to guarantee local closed loop stability. This layout is robust to Internet communication failures and ensures the long distance network delay does not enter the local feedback loops. The TSL states and P&E SIL states will diverge due to (1) significant communications delays and (2) unavoidable differences between the TSL's powersystem simulation and the P&E SIL's real hardware-inthe-loop power system. Tightly coupled, bi-directional interactions exist among the various distributed simulations and software-and hardware-in-the-Ioop components representing the driver, gunner, vehicle, and power system. These interactions necessitate additional adjustment to ensure that the respective states at the TSL and P&E SIL sites converge. This is called state convergence and ensures the dominant energetic states of both laboratories remain closely matched in real time. State convergence must be performed at both locations to achieve bi-directional, real-time interaction like that found on a real vehicle. The result is a distributed control system architecture with Internet communications in the state convergence feedback loop. The Internet communication channel is a primary source of uncertainty that impacts the overall state convergence performance and stability. Multiple control schemes were developed and tested in simulation. This paper presents robust control techniques that compensate for asynchronous Internet communication delays during closed loop operation of the TSL and P&E SIL sites. The subsequent soldier-and hardware-in-the-Ioop experiments were performed using a combination of nonlinear Sliding-mode and linear PID control laws to achieve state convergence at both locations. The control system development, performance, and duty cycle results are presented in this paper.

Closed-loop neural interfaces with on-chip machine learning can detect and suppress disease symptoms in neurological disorders or restore lost functions in paralyzed patients. While high-density neural recording can provide rich neural activity information for accurate disease-state detection, existing systems have low channel count and poor scalability, which could limit their therapeutic efficacy. This work presents a highly scalable and versatile closed-loop neural interface SoC that can overcome these limitations. A 256-channel time-division multiplexed (TDM) front-end with a two-step fast-settling mixed-signal DC servo loop (DSL) is proposed to record high-spatial-resolution neural activity and perform channel-selective brain-state inference. A tree-structured neural network (NeuralTree) classification processor extracts a rich set of neural biomarkers in a patient-and disease-specific manner. Trained with an energy-aware learning algorithm, the NeuralTree classifier detects the symptoms of underlying disorders (e.g., epilepsy and movement disorders) at an optimal energy-accuracy trade-off. A 16-channel highvoltage (HV) compliant neurostimulator closes the therapeutic loop by delivering charge-balanced biphasic current pulses to the brain. The proposed SoC was fabricated in 65nm CMOS and achieved a 0.227µJ/class energy efficiency in a compact area of 0.014mm 2 /channel. The SoC was extensively verified on human electroencephalography (EEG) and intracranial EEG (iEEG) epilepsy datasets, obtaining 95.6%/94% sensitivity and 96.8%/96.9% specificity, respectively. In-vivo neural recordings using soft µECoG arrays and multi-domain biomarker extraction were further performed on a rat model of epilepsy. In addition, for the first time in literature, on-chip classification of rest-state tremor in Parkinson's disease from human local field potentials (LFPs) was demonstrated.

Reconfigurable circuit designs for automatic seizure detection devices are essential to prevent epilepsy affected people from severe injuries and other health-related problems. In this proposed design, an automatic seizure detection algorithm based on the Linear binary Support Vector Machine learning algorithm (LSVM) is developed and implemented in a Field-Programmable Gate Array (FPGA). The experimental results showed that the mean detection accuracy is 86% and sensitivity is 97%. The resource utilization of the implemented design is less when compared to existing hardware implementations. The power consumption of the proposed design is 76mW at 100MHz. The experimental results assure that a physician can make use of this proposed design in detecting seizure events.

Reconfigurable circuit designs for automatic seizure detection devices are essential to prevent epilepsy affected people from severe injuries and other health-related problems. In this proposed design, an automatic seizure detection algorithm based on the Linear binary Support Vector Machine learning algorithm (LSVM) is developed and implemented in a Field-Programmable Gate Array (FPGA). The experimental results showed that the mean detection accuracy is 86% and sensitivity is 97%. The resource utilization of the implemented design is less when compared to existing hardware implementations. The power consumption of the proposed design is 76mW at 100MHz. The experimental results assure that a physician can make use of this proposed design in detecting seizure events.

This paper deals with the territorial dimension of a circular economy. We review the territorial factors shaping closed-loop systems, upon which a territorial definition of a circular economy is developed. We consider six categories of territorial factors: (1) Land-based factors emphasize the significance of physical endowment to satisfy the growing demand of secondary and biotic materials in a circular economy; (2) agglomeration factors are important determinants for a circular economy, as these provide circular businesses with the necessary access to resources, knowledge and collaboration, as well as viable markets; some of these functions are enabled by (3) hard territorial factors, in particular by accessibility and connectivity infrastructures as well as by (4) access to state-of-the-art technologies; softer territorial factors, including (5) knowledge-related factors and (6) governance and institutional arrangements, support collaboration among companies and between them, as well as among consumers and public institutions. Our review shows that agglomeration and land-based factors contribute to define the framework conditions of circular transformations, the harder territorial factors (accessibility and technologies) enable the circular economy in practice, and the softer factors (knowledge, awareness, governance and milieus) contribute to catalyse circular transformations. These findings base and complement research done in the ESPON CIRCTER project.

Purpose In recent years, circular economy (CE) has come to prominence as an alternative to the classic approach of “make-use-dispose”. How companies can exploit the opportunities of CE to position themselves better are not well articulated in the literature. The purpose of this paper, therefore, is to identify the barriers and opportunities of CE in the manufacturing sector through a socio-political, economic, legal and environmental perspective. Design/methodology/approach The study adopts a positivist approach, which is deductive in nature. A survey questionnaire was designed and distributed to manufacturing companies operating in the UK and EU. The study used FAME database and social networking platform LinkedIn to identify manufacturing companies. More than 200+ companies were approached for this study and data collection lasted over two months. Findings The study provides a comprehensive review of the CE literature and identifies a number of barriers and opportunities to CE imp...

The concept of closed-loop supply chain management is a very popular direction of research and companies' activities. It is associated with efforts like managing production residues (by-products and waste) in such a way as to minimize the level of waste transferred to landfills or incinerators. This article relates to the use of this concept in the food industry, which by its very nature, generates a lot of challenges for logistics management, also in the area of reverse flows. The article characterizes aforementioned concept and provides examples of its use in the food sector.

Dear Editor,
The World Health Organization has listed oxygen as an essential medicine.(1) Commonly prescribed for hospitalized pediatric and adult patients, there is a wide opportunity to improve its effectiveness by promoting the more rational, sustainable, and safe use of supplemental oxygen.(2,3) Guidelines and recommendations for oxygen use may help healthcare workers to adopt and achieve specific oxygen targets,(4) mitigating hyperoxemia, and hypoxemia.(4,5) Staying within safe targets, however, can be challenging, and is also quite timeconsuming and labor-intensive, as it requires intensive monitoring and constant manual adjustments of the oxygen flow rate.(6,7) Delays in response time, missing care, and human errors are clear challenges(8) and even short episodes of hypoxemia and hyperoxemia can be critical for specific populations in whom deviations from optimal SpO2  ranges can occur often and quickly.

RTLah is an ongoing project to develop a generalpurpose, open-source, hard real-time experiment interface software system. The need for real-time interface systems is ubiquitous in biomedical research (e.g., cardiac arrhythmia control, stochastic dynamic clamping). The RTLah software, which utilizes Real-Time Linux, allows for applicability to a wide range of biomedical experiments. Flexibility is achieved through modular software design, flexible graphical layout generation, and hardware transparency.