Complex Schemes Research Articles

Thermal Conversion of Coal Bottom Ash and Its Recovery Potential for High-Value Products Generation: Kinetic and Thermodynamic Analysis with Adiabatic TD24 Predictions

Thermal decomposition (pyrolysis) of coal bottom ash (collected after lignite combustion in coal-fired power plant TEKO-B, Republic of Serbia) was investigated, using the simultaneous TG-DTG techniques in an inert atmosphere, at various heating rates. By using the XRD technique, it was found that the sample (CBA-TB) contains a large amount of anorthite, muscovite, and silica, as well as periclase and hematite, but in a smaller amount. Using a model-free kinetic approach, the complex nature of the process was successfully resolved. Thermodynamic analysis showed that the sample is characterized by dissociation reactions, which are endothermic with positive activation entropy changes, where spontaneity is achieved at high reaction temperatures. The model-based method showed the existence of a complex reaction scheme that includes two consecutive reaction steps and one single-step reaction, described by a variety of reaction models as nucleation/growth phase boundary-controlled, the second/n-th order chemical, and autocatalytic mechanisms. It was established that an anorthite I1 phase breakdown reaction into the incongruent melting product (CaO·Al2O3·2SiO2) represents the rate-controlling step. Autocatalytic behavior is reflected through chromium-incorporated SiO2 catalyst reaction, which leads to the formation of chromium(II) oxo-species. These catalytic centers are important in ethylene polymerization for converting light olefin gases into hydrocarbons. Adiabatic TD24 prediction simulations of the process were also carried out. Based on safety analysis through validated kinetic parameters, it was concluded that the tested sample exhibits high thermal stability. Applied thermal treatment was successful in promoting positive changes in the physicochemical characteristics of starting material, enabling beneficial end-use of final products and reduction of potential environmental risks.

A Control Chart for the Joint Monitoring of Mean and Variance With Klein's Supplementary Rule

ABSTRACTSupplementary run rules have been employed since the 1950s to enhance the performance of control charts, such as the control chart, which aims to control the process mean, or control chart, which aims to control the process variance. One simple, effective, and popular suggestion is Klein's 2‐of‐2, which signals an out‐of‐control process only when two sequential sample averages are observed above the upper control limit or below the lower control limit. We propose to implement Klein's 2‐of‐2 for a more complex control chart, the joint control chart, which aims to simultaneously monitor the mean and the variance of a process. Compared with the traditional control chart, using this rule for the control chart reduces the average run length (ARL) and standard deviation of the run length (SDRL) for detecting small process shifts while maintaining high applicability owing to the ease of use in employing the 2‐of‐2 rule in practical environments. Klein's run rule was implemented in the control chart through a 15‐state Markov chain, and its performance was compared with the basic control chart as well as the exponentially weighted moving average (EWMA) chart, known to have excellent capabilities in detecting small process average shifts at the cost of a more complex control scheme. The newly proposed Klein control chart demonstrated superior performance compared to the traditional control chart and competitive performance even against the more complex joint EWMA control chart.

Modeling the successive failure of complex dams systems: A necessity in the light of climatic shifts in extreme storms

This study investigates the effect of extreme storms on the safety of flood protection dams within complex schemes. A modeling methodology is developed using HEC-RAS 2D and presented on an illustrative case, where nine scenarios are simulated. The impact of climatic shifts on extreme storms and dams’ safety is highlighted. The results showed that, despite multi-reservoir systems can be efficient in flood protection against their design storms, they can cause catastrophic damage when subjected to extreme events that have become evident nowadays due to climatic shifts. This is attributed to the successive failure of the water storage structures in a progressive (“domino-like”) manner. The consequences of such breaches can significantly multiply the hazards. These findings can be linked to storm Daniel that occurred at Wadi Derna in Libya and caused two successive dam breaches, leaving a tragedy. The proposed modeling methodology can be useful for designers in evaluating complex scenarios.

An evaluation of single and multi-date Landsat image classifications using random forest algorithm in a semi-arid savanna of Ghana, West Africa

Accurate detection and quantification of land use and land cover (LULC) change is critical for understanding landscape patterns in heterogeneous semi-arid environments. This study investigates the performance of single-date and multi-date Landsat images as well as the relationship between different LULC schemes (simple [2 and 4 classes] and complex [6 and 9 classes]) and the resulting classification accuracy. Specifically, the random forest algorithm was applied to Landsat data comprised of different combinations of image dates (single-date and multi-date) captured in June, October, and December for multiple levels of LULC (scheme) mapping and accuracy evaluations due to its high performance when dealing with large data and heterogeneous landscapes. Results indicated that multi-date images consistently produced higher classification accuracies than single-date images. Significant negative correlations observed between the number of classes in LULC schemes and overall accuracy and kappa coefficient indicate that the more complex the LULC scheme, the lower the accuracy produced. Nevertheless, improvement in overall accuracy was negligible for simple schemes (e.g., ∼1 % for two LULC classes), while it was moderate for complex schemes (∼5 %) when using the best-performing images for multi-date (June-October-December) compared to single-date (October) classifications: however, the improvement was considerable when compared to the least performing single-date image (June, 8–15 %). These varying classification accuracies were due to differences or similarities in spectral responses of target classes in the various LULC schemes applied to the investigated images. Consequently, the resulting differences in the spatial distribution and quantification of LULC classes produced by the different approaches can affect policy and land management decisions, especially if inappropriate image dates are used for LULC mapping. Overall, the findings highlight the reliability of appropriate single-date and multi-date images for mapping LULC change using simple and complex schemes in heterogeneous semi-arid savanna landscapes.

Downstream Migration Success of Atlantic Salmon Smolts in a River Catchment Highly Fragmented by Hydroelectric Impoundments

ABSTRACTRiverine habitat fragmentation by barriers, including impoundments, is common and their effects on obligate aquatic organisms are manifold. Organisms, such as Atlantic salmon (Salmo salar), that make extensive river migrations are particularly vulnerable to the effects of impoundments. In this study, we use acoustic telemetry to examine the migratory behaviour of Atlantic salmon, as they migrate to sea for the first time as juvenile ‘smolts’, in a river with a series of dams that form a complex hydropower scheme. We demonstrate that overall migration success in the River Dee catchment and particularly through standing waters was remarkably high. We speculate that high migration success in standing waters could be due to relative current speeds providing good quality directional cues to migrants. Migration success past the two dams in this study, was relatively high, although the number of unsuccessful passage attempts before a successful passage was also very high. The vast majority of smolts passed the dams when the turbines were operational. At one dam, smolts did not use an available fish pass but migrated through the generating turbines. These findings provide several routes through which generation could be managed to enhance the success of downstream smolt migration in rivers where there are similar patterns of hydrogeneration in place.

Computer Design of the Regulating Working Element of the Channel Cleaning Equipment

Cleaning devices of channel systems used for irrigation of agricultural plants and mechatronic parts regulating their operations were analyzed, the issues of automation of construction design, measurement and management in this field were examined and the purpose of the work was determined. As the goal of the work, the issue of automation of the construction design, technological measurement and management process of the working body for efficient cleaning of small and medium-sized concrete-covered channels, bathtubs from sludge and weeds was set. A complex scheme of information, algorithmic and software support for the automation of the design, technological measurement and control of the mechatronic parts of the working body used for cleaning the channel and bathtub was proposed, and the issues of logical modeling and simulation of the drawing of 2- and 3-dimensional images of the working body were solved. Algorithms were developed using the production modeling method to automate the process of technological measurement, implementation and management, which accurately regulates the operations of the working body of the mechanical device of complex construction. In Solid Edge 2D, 3D software environments, the determination of the geometric shapes of the individual mechanical parts of the channel cleaning unit with a milling cutter, their sizes and types of materials is ensured, and the algorithm was developed using the logical modeling method. For the automation of the structural design process, the information security of the mechanical parts of the application object and the elements of the new design milling cutter device was created for the management of the database of drawings.

Improving the Critical Thinking Capacity of the Subject Protecting the Ideological Foundation of the Communist Party of Vietnam from a Logical Approach

Critical thinking plays an extremely important role in the awareness and practical activities of people in society in general, and particularly for those who protect the ideological foundation of the Communist Party of Vietnam in the current period. Facing increasingly sophisticated and complex schemes and tactics by hostile forces, this team needs to strive to enhance their critical thinking capacity to ensure the most effective protection of the Party’s ideological foundation. Therefore, the study of critical thinking from a logical approach, which suggests requirements and solutions to improve the critical thinking capacity of those protecting the Party’s ideological foundation, holds significant theoretical and practical importance. This is aimed at successfully implementing Resolution No. 35-NQ/TW of the 12th Politburo on Strengthening the protection of the Party’s ideological foundation and combating wrong and hostile views in the new situation.

Dynamic Music at the Macro and Micro Levels

The unpredictability of player behavior in open-world games presents notable challenges, particularly for developers with storytelling ambitions. Most composers have met these challenges by writing music for each distinct location in the game. The issue with a location-focused approach is that most open-world games have stories and characters who develop and change. These changes are communicated through characters’ choices or new abilities they acquire. Aesthetics, especially musical aesthetics, is a neglected storytelling tool. Most discussions of dynamic music have focused on the micro level: how the music may respond to the moment-to-moment actions a player takes within a single play session. There has been less concern with the macro level: how the music may respond to the player’s progress in the game, usually over several play sessions. Through a comparison of The Pathless (Giant Squid, 2020, music by Austin Wintory) and Tchia (Awaceb, 2023, music by John Robert Matz), I argue that different approaches to music composition and implementation may be needed depending on which level is the focus. Although both Wintory and Matz were attentive to musical development on the macro level, I argue that Tchia is more successful at this timescale whereas The Pathless performs better on the micro level. My experiences playing these games suggest that feeling that the score is responding to one’s macro-level progress depends on players noticing that the music has changed and linking that change to something they have done or something that has happened in the story. I argue that Matz’s tuneful themes that respond to a select few in-game parameters serve the macro level better than current tendencies toward ambient music and more and more complex implementation schemes.

Proline substitutions in the ASIC1 β11-12 linker slow desensitization

Desensitization is a prominent feature of nearly all ligand-gated ion channels. Acid-sensing ion channels (ASICs) undergo desensitization within hundreds of milliseconds to seconds upon continual extracellular acidification. The ASIC mechanism of desensitization is primarily due to the isomerization or “flipping” of a short linker joining the 11th and 12th β sheets in the extracellular domain. In the resting and active states this β11-12 linker adopts an “upward” conformation while in the desensitized conformation the linker assumes a “downward” state. It is unclear if a single linker adopting the downward state is sufficient to desensitize the entire channel, or if all three are needed or some more complex scheme. To accommodate this downward state, specific peptide bonds within the linker adopt either trans-like or cis-like conformations. Since proline-containing peptide bonds undergo cis-trans isomerization very slowly, we hypothesized that introducing proline residues in the linker may slow or even abolish ASIC desensitization, potentially providing a valuable research tool. Proline substitutions in the chicken ASIC1 β11-12 linker (L414P and Y416P) slowed desensitization decays approximately 100- to 1000-fold as measured in excised patches. Both L414P and Y416P shifted the steady-state desensitization curves to more acidic pH values while activation curves and ion selectivity were largely unaffected (except for a left-shifted activation pH50 of L414P). To investigate the functional stoichiometry of desensitization in the trimeric ASIC, we created families of L414P and Y416P concatemers with zero, one, two, or three proline substitutions in all possible configurations. Introducing one or two L414P or Y416P substitutions only slightly attenuated desensitization, suggesting that conformational changes in the single remaining faster wild-type subunits were sufficient to desensitize the channel. These data highlight the unusual cis-trans isomerization mechanism of ASIC desensitization and support a model where ASIC desensitization requires only a single subunit.

P.348: Split left and right liver transplantation plus domino liver transplantation: one donor liver and three recipients at the same time, a complex but feasible allocation scheme to alleviate donor liver shortage.

P.348: Split left and right liver transplantation plus domino liver transplantation: one donor liver and three recipients at the same time, a complex but feasible allocation scheme to alleviate donor liver shortage.

Low prevalence of peptic ulcer disease in hospitalized patients with cystic fibrosis: A national database study

BackgroundAlthough cystic fibrosis (CF) is widely considered a lung disease, the prevalence of CF-specific gastrointestinal symptoms and diseases has continued to rise. Peptic ulcer disease (PUD) has not been well-studied among people with CF (PwCF) and may be a common cause of abdominal symptoms. In PwCF, impaired bicarbonate secretion and unbuffered gastric acid production have been attributed to the development of ulcers, although ulcers remain uncommon. The objective of this study was to evaluate the prevalence of PUD in PwCF and assess for possible contributing factors. MethodsThis study utilized the National Inpatient Sample (NIS) database. All patients 18 years or older with CF were identified from 2014 to 2019. Relevant patient characteristics and procedures were identified using ICD-9 and ICD-10 codes. Linear trend, bivariate analyses, and multiple regression analysis were performed. The outcomes of interest were peptic ulcer disease, pancreatic insufficiency, and nonalcoholic steatohepatitis or NASH. All analyses accounted for complex sampling scheme of the NIS. ResultsThe total prevalence of PwCF in the National Inpatient Sample (NIS) database was 0.08 %, and the number was stable year to year from 2014 to 2019. Hispanic patients were more likely to be diagnosed with PUD than other white (aOR 1.802 [1.311,2.476]). Multiple regression analysis indicated that PUD in PwCF was strongly associated with a diagnosis of NASH (aOR 2.421[1.197, 4.898]). PUD patients were less likely to have pancreatic insufficiency compared to the non-PUD group (aOR 0.583 [0.455, 0.745]). ConclusionAlthough cystic fibrosis has been historically known as a disease of childhood, advancements in therapy have led to prolonged life expectancy and higher prevalence for cystic fibrosis-related digestive diseases. This study revealed a low prevalence of PUD in PwCF. Hispanics and those with NASH are more likely to develop peptic ulcers.

GCDkit.Mineral: A customizable, platform-independent R-language environment for recalculation, plotting, and classification of electron probe microanalyses of common rock-forming minerals

Abstract GCDkit.Mineral is a platform-independent (Windows/Mac/Linux) freeware for recalculation, plotting, and statistical treatment of mineral data obtained by microbeam techniques, typically an electron microprobe. It is written in R, a language providing a feature-rich environment for statistics and data visualization. This new program imports compositional data in various commonly used file formats or retrieves them from the clipboard. Routines are available for data management, i.e., grouping, searching, and generation of subsets, using regular expressions and Boolean logic. Raw compositional data (wt%) are recalculated to atoms per formula unit (apfu) based on a required number of O equivalents, atoms, or charges, with or without FeII/FeIII estimation by various methods. Analyses may then be recast to structural formulae, i.e., the atoms are distributed into appropriate crystallographic sites. For minerals forming solid solutions, the molar percentages of end-members are computed. All the data may be treated statistically, either by built-in functions for descriptive and multivariate statistics or using the wealth of tools provided by the wide R community. Raw and recalculated mineral data may be plotted on assorted binary and ternary plots and boxplots. Most are defined as internal templates that provide a means to make later changes to the plot (zooming and scaling, adding comments or legend, identifying data points, altering the size or color of the plotting symbols, etc.). The publication-ready graphics may be saved into several vector-(PostScript, PDF, and WMF) and bitmap-based (e.g., PNG, TIF, and JPG) formats, ready to be imported into a professional graphical, presentation, or desktop publishing software. Importantly, the graphical templates are used as a basis for classification. The general classification routine looks for the name of the polygon within the diagram (= graphical template), into which the analysis falls according to its x–y coordinates. The outcome may be either the name of a mineral or a link to another diagram in the case of more complex classification schemes. Following the rules of the International Mineralogical Association (IMA), in some cases, the classification is not done graphically but using prescribed algorithms. The class mechanism in R provides an elegant solution to the computational problems presented by the differing requirements of each mineral group. By assigning each mineral species to a particular class, all algorithms may be implemented as mutually independent but mineral group-specific methods. The default recalculation options for each mineral class are stored externally in a small and simple text file. The program is designed to cater to three potential user groups. For users with no familiarity with R, the program is fully menu-driven and contains embedded default recalculation options for many common rock-forming minerals. More experienced users may easily tweak these parameters, as they are saved in a logically structured plain text file. Seasoned R users may invoke GCDkit.Mineral in command line mode, use batch scripts or Python-driven notebooks (e.g., of project Jupyter), or modify and develop new recalculations or plugins. The lucid, open, and modular design thus makes GCDkit.Mineral a versatile workbench for everyday use, as well as a promising platform for community-driven development. The GCDkit family of R tools, including GCDkit. Mineral, is distributed through the WWW. The current version may be downloaded from http://mineral.gcdkit.org.

Recognition of Malicious URLs Using Machine Learning

The surge in online activities has heightened the risk of cyber-attacks, with phishing emerging as a notably widespread threat. Conventional phishing detection methods, such as blacklists and heuristic analysis, fall short when it comes to identifying novel and complex phishing schemes. To tackle this issue, we propose a machine learning-based solution for detecting phishing websites. Our model utilizes a combination of features—such as URL structure, domain attributes, and content characteristics—to categorize websites as either phishing or legitimate. The model's outstanding overall accuracy of 96.9% underscores the need to evaluate both precision and recall when determining the effectiveness of machine learning models. The results of this study are significant for developing robust URL classification models and advancing the field of cybersecurity. Future research could aim to improve the model's performance by integrating more features, developing real-time phishing detection systems, and exploring new attributes that can be derived from URLs. Keywords: Phishing, URL, Machine Learning, Classification, Detection

Low complexity and finer granularity quantization scheme for perturbation-based fiber nonlinearity compensation in coherent optical communication systems

In the efficient implementation of perturbation theory-based nonlinearity compensation method for reliable fiber-optic coherent communication systems, different quantization schemes have been proposed to reduce the required computational and implementation complexity. For example, the k-means clustering algorithm focused on reducing the number of perturbation coefficients to alleviate the burden of calculating the perturbation terms. In this paper, from the bit-level architectures of multipliers, we propose a multi-segment mixed-word-length quantization scheme that assigns different word lengths for the quantized perturbative coefficients to save computational resources, where Bayesian optimization is employed to derive the optimal hyperparameters involved in this scheme. The proposed scheme was evaluated by numerical simulations of single-channel and multi-channel optical fiber communication systems with dual-polarization 16-QAM modulation. The comprehensive numerical simulation results show that our proposed scheme demonstrates a complexity reduction of more than 65% without performance degradation compared with the traditional uniform quantization method with fixed-word-length, and was robust to the fiber length and baud-rate. We also carried out an experiment of single-carrier transmission over 18 × 100 km standard single-mode fiber with 20 Gbaud single-polarization 16-QAM signal. Similar to the numerical results, the hardware complexity can be reduced by 70.7% with negligible performance deterioration over the uniform quantization scheme. The proposed scheme shows great potential in real-world applications.

An ML‐Based P3‐Like Multimodal Two‐Moment Ice Microphysics in the ICON Model

AbstractMachine learning (ML) is used to build a bulk microphysical parameterization including ice processes. Simulations of the Lagrangian super‐particle model McSnow are used as training data. The ML performs a coarse‐graining of the particle‐resolved microphysics to multi‐category two‐moment bulk equations. Besides mass and number, prognostic particle properties (P3) like melt water, rime mass, and rime volume are predicted by the ML‐based bulk model. The ML‐based scheme is tested with simulations of increasing complexity. As a box model, the ML‐based bulk scheme can reproduce the simulations of McSnow quite accurately. In 3d idealized squall line simulations, the ML‐based P3‐like scheme provides a more realistic extended stratiform region when compared to the standard two‐moment bulk scheme in ICON. In a realistic case study, the ML‐based scheme runs stably, but can not significantly improve the results. This shows that ML can be used to coarse‐grain super‐particle simulations to a bulk scheme of arbitrary complexity.

False Start on NIL: Public and Private Law Should Treat College Athletes Like Any Other Student

For most of its regulatory existence, the National Collegiate Athletic Association (“NCAA”) has preached the importance of integrating intercollegiate athletics into the campus culture and educational mission, insisting that athletes be an integral part of the student body. A core element of this creed was the amateurism principle—college athletes must not be paid or professionalized. To preserve and enforce the amateurism principle, the NCAA and its divisions promulgated a vast and complex regulatory scheme that paradoxically resulted in segregating, rather than integrating, athletes into campus life. While nonathlete students enjoy increasing autonomy to pursue expressive and economic activity, athletes are subject to paternalistic rules that restrain their lives on and off campus and deny them a share in the wealth generated by their athletic talents. In a watershed moment in the summer of 2021, this began to change. In June 2021, the Supreme Court in National Collegiate Athletic Association v. Alston affirmed that the NCAA had violated antitrust laws by capping the benefits that member institutions could offer to athletes when competing to recruit them to college teams. July 2021 brought the effective date of the first of many state laws preventing the NCAA from penalizing college athletes who monetize their name, image and likeness (“NIL”). In response, the NCAA amended its amateurism bylaws to permit all college athletes, regardless of where they attend school, to engage in NIL commercial activity consistent with state law and NCAA guidelines. What followed was a cycle of NCAA rulemaking to preserve the amateurism principle alternating with state legislative rejoinders to preserve in-state athletic programs’ recruiting advantage. The resulting morass of private and public NIL rules customized to the college athlete led to calls to federalize those regimes. Proposals for federal legislation would establish national standards and a centralized regulatory authority for college athlete NIL exploitation. No such bills have gained traction in Congress. This Article argues that both public and private law regimes singling out college athletes for customized regulation have it wrong. Instead, states, schools, and athletes would benefit from discarding specialized rules and reverting to laws of general application—in other words, let’s treat college athletes like any other student on campus. At least with respect to NIL, do away with the bifurcation of higher education into athletic and nonathletic fiefdoms and rent-seeking special treatment of the university’s relationship with athletes. Adopt a principle of nondiscrimination that deals with all college students the same way when they seek to benefit from and monetize their identities and publicity rights.

Predictive analytics for financial compliance: Machine learning concepts for fraudulent transaction identification

Predictive analytics has emerged as a pivotal tool in financial compliance, offering sophisticated methods for identifying fraudulent transactions through the application of machine learning (ML) concepts. As financial institutions grapple with increasingly complex fraud schemes and stringent regulatory requirements, the integration of predictive analytics with ML provides a proactive approach to fraud detection and prevention. Machine learning algorithms excel in analyzing vast datasets, identifying hidden patterns, and making real-time predictions. In the realm of financial compliance, supervised learning models such as logistic regression, decision trees, and random forests are commonly used to classify transactions as legitimate or fraudulent. These models are trained on historical transaction data, learning to recognize the subtle indicators of fraud by identifying correlations between various features and fraudulent outcomes. This allows for high-accuracy predictions on new, unseen data. Unsupervised learning techniques, such as clustering and anomaly detection, are equally critical in predictive analytics for financial compliance. These methods do not require labeled data and are adept at uncovering novel fraud patterns by detecting outliers and irregularities that deviate from normal transactional behavior. Anomaly detection algorithms, including k-means clustering and isolation forests, can identify transactions that exhibit unusual characteristics, flagging them for further investigation. The integration of predictive analytics with real-time data processing capabilities enhances the agility of fraud detection systems. Streaming analytics and real-time scoring enable the continuous monitoring of transactions, ensuring that suspicious activities are identified and addressed promptly. This real-time aspect is crucial for minimizing the impact of fraudulent transactions and ensuring compliance with regulatory standards. Despite the advancements, implementing predictive analytics for financial compliance involves challenges such as ensuring data quality, addressing privacy concerns, and maintaining model transparency. Financial institutions must navigate these challenges by employing robust data governance practices, leveraging secure data processing techniques, and adopting explainable AI models that provide insights into their decision-making processes. In conclusion, predictive analytics, powered by machine learning concepts, offers a robust framework for identifying fraudulent transactions and enhancing financial compliance. By leveraging advanced ML algorithms and real-time data processing, financial institutions can proactively detect and prevent fraud, thereby safeguarding their operations and ensuring adherence to regulatory mandates. This approach not only mitigates financial losses but also strengthens the overall integrity and trustworthiness of the financial system.

Tapered fiber optic sensor for arterial pulse wave monitoring

Arterial pulse wave monitoring has been recognized as an effective medical diagnostic tool for various cardiovascular diseases. To overcome the barriers in traditional, complex, and expensive detection configurations, new electronic and optical sensors have been developed with their unique advantages. Particularly, various configurations of fiber-optic sensors have been proposed. This paper demonstrates the use of a tapered optical fiber structure to create a sensitive sensor that can detect carotid arterial pulse waves on the skin surface. The demonstrated fiber sensor probe utilizes an in-line Mach-Zehnder interferometer configuration and liquid-gold film coating on the sensor tip to enhance its sensitivity and demonstrate its use by encapsulating the sensor with a flexible elastomer material for detecting arterial pulse waves with a simple testing configuration. Considering the simple fabrication and high sensitivity without a complex demodulation scheme, the proposed sensor has broad implementation in biomedical health monitoring systems.

Investigation of the tradeoffs between tracking performance and energetics in heterogeneous variable recruitment fluidic artificial muscle bundles

In traditional hydraulic robotics, actuators must be sized for the highest possible load, resulting in significant energy losses when operating in lower force regimes. Variable recruitment fluidic artificial muscle (FAM) bundles offer a novel bio-inspired solution to this problem. Divided into individual MUs, each with its own control valve, a variable recruitment FAM bundle uses a switching control scheme to selectively bring MUs online according to load demand. To date, every dynamic variable recruitment study in the literature has considered homogeneous bundles containing MUs of equal size. However, natural mammalian muscle MUs are heterogeneous and primarily operate based on Henneman's size principle, which states that MUs are recruited from smallest to largest for a given task. Is it better for a FAM variable recruitment bundle to operate according to this principle, or are there other recruitment orders that result in better performance? What are the appropriate criteria for switching between recruitment states for these different recruitment orders? This paper seeks to answer these questions by performing two case studies exploring different bundle MU size distributions, analyzing the tradeoffs between tracking performance and energetics, and determining how these tradeoffs are affected by different MU recruitment order and recruitment state transition thresholds. The only difference between the two test cases is the overall force capacity (i.e. total size) of the bundle. For each test case, a Pareto frontier for different MU size distributions, recruitment orders, and recruitment state transition thresholds is constructed. The results show that there is a complex relationship between overall bundle size, MU size distributions, recruitment orders, and recruitment state transition thresholds corresponding to the best tradeoffs change along the Pareto frontier. Overall, these two case studies validate the use of Henneman's Size Principle as a variable recruitment strategy, but also demonstrate that it should not be the only variable recruitment method considered. They also motivate the need for a more complex variable recruitment scheme that dynamically changes the recruitment state transition threshold and recruitment order based on loading conditions and known system states, along with a co-design problem that optimizes total bundle size and MU size distribution.

Advanced Multi-Sampling PWM Technique for Single-Inductor MIMO DC-DC Converter in Electric Vehicles

Amongst the various topologies of multi-input multi-output (MIMO) DC-DC converters, single-inductor MIMO (SI-MIMO) converters have the advantages of a reduced component count, a simpler structure, and low cost. These converters are suitable in electric vehicle (EV) applications involving variable ports, essential for performing different functions. Digital control in SI-MIMO converters is promising for enhancing transient performance due to its numerous benefits. However, delays in digital control, particularly computational and pulse width modulation (PWM) delays, can negatively impact the performance of DC-DC converters. Multi-sampling and double PWM update methods can mitigate these control delays, but they often necessitate complex control schemes, adding computational burden. In this work, an advanced multi-sampling PWM technique, integrating sample shift and multi-sampling, is proposed while employing a simple digital PID control scheme. The proposed method was tested for a shared-switch SI-MIMO converter with battery discharging and charging modes in the MATLAB/Simulink environment and compared with the conventional single- and multi-sampling PWM methods. The results demonstrated that the proposed method significantly improved the converter performance, surpassing the conventional single- and multi-sampling PWM methods. In the battery discharging mode, utilizing the proposed method, the output voltage achieved a settling time of 0.075 s in response to a step change in its reference, significantly outperforming multi-sampling, which yielded a settling time of 0.124 s, and single sampling, which exhibited an even longer settling time of 0.898 s. It also demonstrated a minimal overshoot of 0.06 volts compared to 1.5 volts with multi-sampling during the step change in the input voltage. Similarly, in the battery charging mode, upon a step change in the reference output voltage, the proposed method effectively minimized the overshoot of the output voltage to 0.845 volts compared to 1.175 volts with multi-sampling, and it decreased the inductor current settling time to 0.296 s from 0.330 s recorded under multi-sampling. These findings underscore the potential of the proposed method in enhancing the digital control performance of SI-MIMO DC-DC converters in electric vehicles.