Modulation Error Research Articles

Stakeholder-led proactive risk assessment of medication safety in the pharmaceutical supply chain

Abstract Introduction In 2022/23, 1.08 billion medications were dispensed to patients using NHS England, at a cost of £19.04 billion1 . However, Elliot et al (2019)2 estimate every year there are 237 million medication errors at some point of the medication process, with a high associated cost. Increasingly, media is reporting medication shortages of critical medicines3 that can contribute to errors of omission. Medication errors suggests system, tasks and processes should be explored to understand if they have been poorly designed. This paper is a pre-Brexit study that examines the pharmaceutical supply chain with stakeholders. Aim To explore risk to medication safety in the pharmaceutical supply chain using qualitative interviews of stakeholders across the supply chain and proactive risk assessment method SHERPA (Systematic Human Error Reduction and Prediction Approach). Method 1. Qualitative interviews with process models System process models were developed (by literature, field observations and interviewees) and employed to elicit stakeholder perceptions about risk to medication safety in 31 qualitative interviews. Participants included pharmacists, wholesalers, pharmaceutical industry representatives, regulators and policymakers. Ethical approval was granted by Cambridge LREC to include healthcare professionals and 6 patients. Interviews were audio-recorded, transcribed and thematically analysed using N-Vivo. 2. Focus group for SHERPA Six stakeholders participated in the SHERPA focus group4 , including pharmacists and representatives from pharmaceutical manufacturing and wholesale industries. Interview participants were invited and those that agreed, attended. The SHERPA template was completed by the group. The session was audio-recorded, transcribed and thematically analysed using N-Vivo. Results The system models led to a greater understanding of the current system ‘as-is’ and created an overview of the supply chain. SHERPA is a validated method and provided the rigour to risk assess the supply chain process and identify sources of risk. However, the error mode terminology required adjustment to healthcare. Across all supply routes, the main source of risk was failure to supply medicines that caused errors of omission and the risk of counterfeit medicines. Failure to supply resulted from shortages that take place from trading activities that result in stockpiling or exporting of medicines. A lack of robust regulation and parallel imports increased the risk of counterfeit medicines. Stakeholders highlighted deficiencies in the supply chain and poor performance of stakeholders, as medication safety concerns. In order of frequency, the deficiencies include fragmented supply chain; complacency by stakeholders; poor communication and integration across supply chain; and end-user often regarded as the pharmacist, rather than the patient. Discussion and conclusion From the findings, strategies emerged as possible solutions. These included communication of shortages in each component of supply chain; robust counterfeit strategies; use of technology (automation/digital tools); and re-design tasks and processes in dispensaries and wards. Staff training, education, and revalidation of stakeholders is vital. Key limitations in this study include biases that can occur from recruitment strategy and small sample size. The SHERPA method posed terminology problems. In conclusion, this multi-method approach reveals risk and possible solutions to improve safety and reduce cost in the NHS. It is clear this study needs to be repeated to understand the impact of Brexit and current reconfiguration of NHS.

An Improved Type of Synchronized Space Vector PWM Based on Switching Angles for High‐Power AC Drive Application

Synchronized space vector pulse width modulation (SSVPWM) is widely employed in high‐power AC drive systems. The existing methods are carrier‐based, in which the carrier ratio, position of samples, and switching sequences used to generate every sample for a given SSVPWM are settled, which causes two problems: (i) the control delay changes, and a voltage phase modulation error appears suddenly during transition between different SSVPWMs with different carrier ratios, which causes motor current spikes; (ii) due to the particular switching sequences, some kinds of SSVPWM cannot get into six‐step operation by overmodulation. This paper proposes an improved type of SSVPWM based on switching angles, in which the carrier ratio of a given SSVPWM can change flexibly, and the problems mentioned above can be solved. Theories including switching angle calculation, overmodulation strategy, carrier ratio setting principle, and transition rules between different SSVPWM modes are given in this paper. Meanwhile, the switching angles change linearly with modulation index which makes it easy to implement, and the modulation error in the overmodulation region is effectively limited so that the proposed scheme is suitable in the whole modulation index range. A case study of a 5.5‐kW experimental platform is presented to validate the proposed method. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Demonstration of a photonic lantern focal-plane wavefront sensor: measurement of atmospheric wavefront error modes and low wind effect in the nonlinear regime

Demonstration of a photonic lantern focal-plane wavefront sensor: measurement of atmospheric wavefront error modes and low wind effect in the nonlinear regime

A seafarers’ cognitive error mining model based on IDA and TRACEr and mitigation measures for ship collision accidents

Human factor is the main cause of maritime accidents. However, the in-depth study on the mechanism of cognitive errors in seafarers’ human error needs to be supplemented. This study empirically analyzes 116 collision cargo ships along the Yangtze River trunk line. To explore the relationship between seafarers’ task errors and the cognitive process of accidents, a seafarers’ cognitive error mining model is proposed, which integrates the Information, Decision, and Action (IDA) cognitive model into the Technique for the Retrospective and Predictive Analysis of Cognitive Errors (TRACEr). According to the approach of mining from the outside to the inside, firstly, the error mode in the cognitive stage of seafarer tasks are identified. Then the fine-grained main manifestation of the task errors is mined by K-means, and Apriori algorithm is employed to mine the hidden features between the external and internal error modes. The results show that there are four main problems of Yangtze River ship accidents, and seafarers have four to seven corresponding causal chains from psychological error mechanism to performance shaping factors for each major issue in the cognitive stage. On the basis of the results, this study summarizes the guiding principles of accident prevention and seafarer management measures to provide decision support for water traffic safety.

High-Precision Bi-Directional Beam-Pointing Measurement Method Based on Space Solar Power Station System.

In the process of wireless energy transmission from a Space Solar Power Station (SSPS) to a satellite, the efficiency of energy transmission is closely related to the accuracy of beam control. The existing methods commonly ignore the impact of array position, structural deviation of the transmitting antenna, and modulation errors, which leads to the deviation error in actual energy transmission beams and the reduction of energy transmission efficiency. This paper innovatively proposes a high-precision bi-directional beam-pointing measurement method, which provides a technical basis for advancing the beam-pointing control accuracy from the perspective of improving the beam-pointing measurement accuracy. The method consists of (1) the interferometer goniometry method to realize high-precision guiding beam pointing measurement; and (2) the power field reconstruction method to realize offset angle measurement of the energy-transmitting beam. Simulation results demonstrate that under dynamic conditions, the guiding beam-pointing measurement accuracy of this method reaches 0.05°, which is better than the traditional 0.1° measurement accuracy based on the guiding beam. The measurement accuracy of the offset distance of the energy center is better than 0.11 m, and the measurement accuracy of the offset angle is better than 0.012°.

On the performance of OFDM-IM systems in the presence of CFO effects

This study presents a comprehensive analysis of the performance degradation effects of carrier frequency offset (CFO) on orthogonal frequency division multiplexing with index modulation (OFDM-IM) systems operating over frequency-selective multipath fading channels. CFO is an impairing factor that degrades the signal-to-noise ratio (SNR) through signal attenuation and inter-carrier interference (ICI). We derive a closed-form expression to quantify the SNR degradation under CFO for OFDM-IM systems. Additionally, we formulate a very tight upper bound for the bit error rate (BER), accounting for index modulation errors, CFO distortion, and multipath fading.The presented analytical formulations capture the unique characteristics of OFDM-IM systems and facilitate precise performance evaluation. The findings yield valuable insights into mitigating CFO-induced BER degradation through appropriate system parameter selection and CFO compensation techniques. Moreover, this investigation makes significant contributions towards designing reliable OFDM-IM communication links resilient to the combined effects of index modulation, frequency offsets, and dispersive channel conditions.

Human error identification and risk assessment in loading and unloading of petroleum products by road trucks using the SHERPA and fuzzy inference system method

Human error constitutes one of the primary causes of accidents, particularly in the context of loading and unloading operations involving road trucks, especially those carrying petroleum products. The process of identifying and evaluating human errors within these operations involves several key steps. Initially, all sub-tasks associated with loading and unloading are meticulously identified and analyzed utilizing Hierarchical Task Analysis (HTA), achieved through direct observation, document examination, and interviews. Subsequently, potential human error modes within each task are delineated using the Systematic Human Error Reduction and Prediction Approach (SHERPA). Finally, essential data for determining the criticality, probability, and severity of each error are gathered through expert elicitation and the application of Fuzzy Inference Systems (FIS).Through the analysis of SHERPA worksheets, a total of 37 errors during loading operations and 14 errors during unloading operations of petroleum products were identified. Among these errors, the predominant category during loading operations was action errors, comprising 31 instances, while communication errors were the least frequent, occurring only twice. Similarly, action errors were most prevalent during unloading operations, constituting 13 instances. These errors were further categorized and ranked based on their risk levels, resulting in 27 levels for loading operations and 12 levels for unloading operations.The consistent occurrence of action errors underscores the need for implementing control measures to mitigate their frequency and severity. Such strategies may include periodic training sessions to reinforce proper work procedures and the development of monitoring checklists, among other interventions.

Machine learning without a processor: Emergent learning in a nonlinear analog network

Standard deep learning algorithms require differentiating large nonlinear networks, a process that is slow and power-hungry. Electronic contrastive local learning networks (CLLNs) offer potentially fast, efficient, and fault-tolerant hardware for analog machine learning, but existing implementations are linear, severely limiting their capabilities. These systems differ significantly from artificial neural networks as well as the brain, so the feasibility and utility of incorporating nonlinear elements have not been explored. Here, we introduce a nonlinear CLLN-an analog electronic network made of self-adjusting nonlinear resistive elements based on transistors. We demonstrate that the system learns tasks unachievable in linear systems, including XOR (exclusive or) and nonlinear regression, without a computer. We find our decentralized system reduces modes of training error in order (mean, slope, curvature), similar to spectral bias in artificial neural networks. The circuitry is robust to damage, retrainable in seconds, and performs learned tasks in microseconds while dissipating only picojoules of energy across each transistor. This suggests enormous potential for fast, low-power computing in edge systems like sensors, robotic controllers, and medical devices, as well as manufacturability at scale for performing and studying emergent learning.

Quantitative assessment of human error of emergency behavior for hazardous chemical spills in chemical parks

In-depth research on human error is of great significance to improve the human reliability in the emergency rescue as well as to avoid the occurrence of secondary accidents. Therefore, the standardized process and key emergency behaviors in the emergency rescue process of hazardous chemical leakage accidents are first analyzed. Based on the Cognitive Reliability and Error Analysis Method, the human error mode was sorted out, and the human error probability estimation model was established. Combining the improved Analytic Hierarchy Process and entropy weight method, a model for estimating the severity of human error was established, realizing the quantitative analysis of the human reliability of the emergency behavior. The study showed that the 21 emergency behaviors were clustered into 3 categories, with clustering 1 including 2 emergency behaviors, which have the highest level of severity, clustering 2 including 11 emergency behaviors, and clustering 3 including 8 emergency behaviors. Finally, countermeasures were proposed in four areas, including increasing the level of Common Performance Conditions and the ability to transmit information on accidents, etc. It indicates that the method can widely guide the improvement of emergency response capability in chemical parks.

Amplified DNA Heterogeneity Assessment with Oxford Nanopore Sequencing Applied to Cell Free Expression Templates.

In this work, Oxford Nanopore sequencing is tested as an accessible method for quantifying heterogeneity of amplified DNA. This method enables rapid quantification of deletions, insertions, and substitutions, the probability of each mutation error, and their locations in the replicated sequences. Amplification techniques tested were conventional polymerase chain reaction (PCR) with varying levels of polymerase fidelity (OneTaq, Phusion, and Q5) as well as rolling circle amplification (RCA) with Phi29 polymerase. Plasmid amplification using bacteria was also assessed. By analyzing the distribution of errors in a large set of sequences for each sample, we examined the heterogeneity and mode of errors in each sample. This analysis revealed that Q5 and Phusion polymerases exhibited the lowest error rates observed in the amplified DNA. As a secondary validation, we analyzed the emission spectra of sfGFP fluorescent proteins synthesized with amplified DNA using cell free expression. Error-prone polymerase chain reactions confirmed the dependency of reporter protein emission spectra peak broadness to DNA error rates. The presented nanopore sequencing methods serve as a roadmap to quantify the accuracy of other gene amplification techniques, as they are discovered, enabling more homogenous cell-free expression of desired proteins.

NetHD: Neurally Inspired Integration of Communication and Learning in Hyperspace

The 6G network, the next‐generation communication system, is envisaged to provide unprecedented experience through hyperconnectivity involving everything. The communication should hold artificial intelligence‐centric network infrastructures as interconnecting a swarm of machines. However, existing network systems use orthogonal modulation and costly error correction code; they are very sensitive to noise and rely on many processing layers. These schemes impose significant overhead on low‐power internet of things devices connected to noisy networks. Herein, a hyperdimensional network‐based system, called , is proposed, which enables robust and efficient data communication/learning. exploits a redundant and holographic representation of hyperdimensional computing (HDC) to design highly robust data modulation, enabling two functionalities on transmitted data: 1) an iterative decoding method that translates the vector back to the original data without error correction mechanisms, or 2) a native hyperdimensional learning technique on transmitted data with no need for costly data decoding. A hardware accelerator that supports both data decoding and hyperdimensional learning using a unified accelerator is also developed. The evaluation shows that provides a bit error rate comparable to that of state‐of‐the‐art modulation schemes while achieving 9.4 faster and 27.8 higher energy efficiency compared to state‐of‐the‐art deep learning systems.

Chaos quantum optimization‐based layered diagnosis framework for faulty sensor node diagnosis and classification in wireless sensor networks

SummaryNode faults are often influenced by factors such as physical component failure, communication module errors, battery exhaustion, and environmental factors. Various researchers have contributed to addressing the problem of detecting, diagnosing, and classifying faults in sensor networks. However, computational complexity and fault occurrence probability increase with the size of the network. In this context, an enhanced optimized layered diagnosis framework (OLDF) is proposed for detecting, diagnosing, and classifying sensor node faults. OLDF utilizes a deep belief network (DBN) with chaos quantum‐behaved particle swarm optimization (CQ‐PSO) for efficient diagnosis and classification of various sensor faults in the network. The proposed OLDF consists of two layers. In the first layer, optimized DBN with CQ‐PSO algorithm is employed to determine the fault type of the sensor nodes. Then, the fault type of sensor nodes that are classified in the first layer is given as input into the second layer of the OLDF algorithm to determine the fault severity in the network. Experimentation has been carried out by using the NS3 simulator for the OLDF framework with performance metrics such as classification accuracy, diagnosis accuracy, false positives, fault alarm rate, and average energy consumption. The validity of the proposed framework has been verified through comparisons with existing works such as MFD, INSA, FDRFC, and HFD. From the comparative analysis carried out, it is apparent that the proposed framework achieves a high classification accuracy of 89.94% and diagnosis accuracy of 93.21%, with less false positive rate, and utilizes minimum energy when compared with its counterparts.

Metasurface Empowered Compact Non‐Paraxial Optical Vortex Mode Sorter

AbstractThe optical coordinate transformation based on the ray model can flexibly and efficiently complete the manipulation of complex light fields through a pair of phase plates, and is successfully used to sort orbital angular momentum (OAM) of light. Generally, the phase modulation required by the optical coordinate transformation is derived analytically under the paraxial approximation, however, this would induce the phase modulation error, and thus worsen the quality of the transformed beam. Here, a non‐paraxial design for the spiral coordinate transformation is proposed, where the phase of the unwrapper is obtained by numerically solving the partial differential equations on the coordinate mapping and the phase corrector is obtained by the angular spectrum diffraction integral method. Due to more accurate phase modulation, the non‐paraxial spiral transformation effectively improves the beam quality, and realizes the demultiplexing of OAM modes with higher efficiency and lower cross‐talk. The proposed approach is verified with a compact metasurface‐based optical vortex mode sorter, which is extensible toward OAM‐based multi‐mode systems involving classical and quantum optical information processing.

Single Photon Detection Technology in Underwater Wireless Optical Communication: Modulation Modes and Error Correction Coding Analysis

Single Photon Detection Technology in Underwater Wireless Optical Communication: Modulation Modes and Error Correction Coding Analysis

Discrete-time zeroing neural network with quintic error mode for time-dependent nonlinear equation and its application to robot arms

Time-dependent nonlinear equation (TDNE) arises in numerous engineering applications. Recently, zeroing neural network (ZNN) has been proven to be an effective alternative for solving the TDNE. In this paper, we present a new solution to the TDNE by using the discrete-time ZNN (DTZNN). Specifically, a special difference formula is first constructed via Taylor series expansion. Then, by utilizing such a formula to discretize the existing continuous-time ZNN model, the new DTZNN model is proposed to determine the TDNE solution. Theoretical analysis and numerical results further indicate the validity and superiority of the proposed DTZNN model in comparison with the previous models. Finally, the DTZNN practicality is presented by the simulation and experiment on the DOBOT arm using the proposed model.

Combining Polarization-Division Multiplexing and Ferromagnetic Nonreciprocity to Achieve In-Band Ultra-High Isolation for Full-Duplex Wireless Systems

The in-band full-duplex (IBFD) wireless system is a promising candidate for 6G and beyond, as it can double data throughput and enormously lower transmission latency by supporting simultaneous in-band transmission and reception of signals. Enabling IBFD systems requires a substantial mitigation of a transmitter (Tx)’s strong self-interference (SI) signal into the receiver (Rx) channel. However, current state-of-the-art approaches to tackle this challenge are inefficient in terms of performance, cost, and complexity, hindering the commercialization of IBFD techniques. In this work, we devise and demonstrate an innovative approach to realize IBFD systems that exhibit superior performance with a low-cost and less-complex architecture in an all-passive module. Our scheme is based on meticulously combining polarization-division multiplexing (PDM) with ferromagnetic nonreciprocity to achieve ultra-high isolation between Tx and Rx channels. Such an unprecedented conception has become feasible thanks to a concurrent dual-mode circulator—a new component introduced for the first time—as a key feature of our module, and a dual-mode waveguide that transforms two orthogonally polarized waves into two orthogonal waveguide modes. In addition, we propose a unique passive tunable secondary SI cancellation (SIC) mechanism, which is embedded within the proposed module and boosts the isolation over a relatively broad bandwidth. We report, solely in the analog domain, experimental isolation levels of 50, 70, and 80 dB over 340, 101, and 33 MHz bandwidth at the center frequency of interest, respectively, with excellent tuning capability. Furthermore, the module is tested in two real IBFD scenarios to assess its performance in connection with Tx-to-Rx leakage and modulation error in the presence of a Tx’s strong interference signal.

Coronagraphic Data Post-processing Using Projections on Instrumental Modes

Directly observing exoplanets with coronagraphs is impeded by the presence of speckles from aberrations in the optical path, which can be mitigated in hardware with wave front control, as well as in post-processing. This work explores using an instrument model in post-processing to separate astrophysical signals from residual aberrations in coronagraphic data. The effect of wave front error (WFE) on the coronagraphic intensity consists of a linear contribution and a quadratic contribution. When either of the terms is much larger than the other, the instrument response can be approximated by a transfer matrix mapping WFE to detector plane intensity. From this transfer matrix, a useful projection onto instrumental modes that removes the dominant error modes can be derived. We apply this approach to synthetically generated Roman Space Telescope hybrid Lyot coronagraph data to extract “robust observables,” which can be used instead of raw data for applications such as detection testing. The projection improves planet flux ratio detection limits by about 28% in the linear regime and by over a factor of 2 in the quadratic regime, illustrating that robust observables can increase sensitivity to astrophysical signals and improve the scientific yield from coronagraphic data. While this approach does not require additional information such as observations of reference stars or modulations of a deformable mirror, it can and should be combined with these other techniques, acting as a model-informed prior in an overall post-processing strategy.

Ridge-Type Pretest and Shrinkage Estimation Strategies in Spatial Error Models with an Application to a Real Data Example

Spatial regression models are widely available across several disciplines, such as functional magnetic resonance imaging analysis, econometrics, and house price analysis. In nature, sparsity occurs when a limited number of factors strongly impact overall variation. Sparse covariance structures are common in spatial regression models. The spatial error model is a significant spatial regression model that focuses on the geographical dependence present in the error terms rather than the response variable. This study proposes an effective approach using the pretest and shrinkage ridge estimators for estimating the vector of regression coefficients in the spatial error mode, considering insignificant coefficients and multicollinearity among regressors. The study compares the performance of the proposed estimators with the maximum likelihood estimator and assesses their efficacy using real-world data and bootstrapping techniques for comparison purposes.

Углеродные кредиты в стратегиях декарбонизации нефтегазовых компаний

Carbon credits are an increasingly important tool for decarbonization. They are of particular importance to oil and gas and other energy companies for which burning fuel comprise the core of the business. The two main sources behind carbon credit emission are renewable energy projects and projects to sequester greenhouse gas emissions from natural systems, mainly forests. Without offsetting mechanisms, including nature-based solutions, companies with high carbon footprint are fundamentally unable to achieve net carbon neutrality. Despite challenges, the voluntary carbon credits markets continue to grow. In 2020–2022 the world’s 34 largest companies by capitalization bought certificates equal to 38 million tons of carbon emissions, which is equivalent to the combined greenhouse gas emissions of Ethiopia and Kenya. 50% of these credits were purchased by oil and gas companies and 28% by automobile concerns. In the near future, voluntary carbon markets will continue develop in an imperfect mode of trial and error, which is inherent to all markets at their early stages of development. The author shares the view that a large-scale non-ideal credit/offset market will be more conducive to solving the strategic task of decarbonizing economic growth than an ideally organized but small-scale market. In principle, this position is supported by authoritative regulators, whose policy is critical for the development of a new market. In May 2024, key economic departments of the US government, including the Department of Agriculture, the Department of Energy and, most importantly, the Department of the Treasury, unequivocally supported the development of a carbon credit market that promotes decarbonization, opens up new opportunities for investment in climate projects and enables realization of wide externalities that positively influence economic growth and preservation of biodiversity.

Sensorless Speed Control of PMSM in Low-Speed by High Frequency Pulsating Injection with Double Modulation Error Compensation

A novel improvement scheme for the High Frequency Pulsating Injection-Sinusoidal (HFPI-S) method that considers the error function is proposed and implemented for PMSM sensorless control. The limitations in extracting the error function and dealing with signal noise at low-speed could appear the estimation errors in rotor position within the HFPI-S system. In this study, a new model for the proposed method, including the addition of dual modulation as error compensation in the process of extracting rotor position and enhancing the observer for conventional rotor position signals was focused. The results of this study showed that the extraction of the error function that is expanded could obtain more accurate results. Therefore, the addition of dual modulation and the observer enhancement could be effectively applied to maintain the accuracy of rotor position estimation even under low-speed and loaded conditions.