Output Function Research Articles

Charge Capacitive Signatures at the Interface of E. coli/MOF Biohybrids to Create a Live Cell Biocapacitor.

The chemistry of the extracellular electron transfer (EET) process in microorganisms can be understood by interfacing them with abiotic materials that act as external redox mediators. These mediators capture and transfer extracellular electrons through redox reactions, bridging the microorganism and the electrode surface. Understanding this charge transfer process is essential for designing biocapacitors capable of modulating and storing charge signatures as capacitance at the electrode interface. Herein, a novel biointerfacial strategy is presented to investigate directional charge injection from a non-exoelectrogenic living microbe to an electrode surface using the porous metal-organic framework (MOF), MIL-88B. The biohybrid, formed by interfacing Escherichia coli (E. coli) with MIL-88B, demonstrates symbiotic interactions between the biotic and abiotic components, facilitating EET from E. coli to the electrode via the MOF. Acting as a redox mediator, the MOF catalyzes E. coli's exoelectrogenic activity, generating distinct charge capacitive signatures at the E. coli-MOF interface. This system integrates the capacitive signatures resulting from the EET process with the MOF's intrinsic pseudocapacitive properties and surface-controlled capacitive effects, functioning as a highly efficient biocapacitor. Furthermore, this approach of converting the biochemical energy of a non-exoelectrogenic microorganism into capacitive signatures opens a new pathway for translating biological signals into functional outputs, paving the way for autonomous biosensing platforms.

Calibration of RAFM Micro Mechanical Model for Creep using Bayesian Optimization for Functional Output

Calibration of RAFM Micro Mechanical Model for Creep using Bayesian Optimization for Functional Output

Global output feedback stabilization of stochastic uncertain nonlinear systems with large constant delays

This paper studies the problem of output feedback stabilization for a class of stochastic uncertain nonlinear systems with large constant delays in state and input. For stochastic nonlinear systems, the maximal open sector Δ of output function is given. As long as output function belongs to any closed sector included in Δ, by constructing a reduced-order observer and C 2 Lyapunov–Krasovskii functional and using the homogeneous domination method, an output feedback controller can be developed to guarantee the closed-loop system globally asymptotically stable in probability.

Acceptability of improved cook stoves-a scoping review of the literature.

Improved cooking stoves (ICS) are intended to reduce indoor air pollution and the inefficient use of fuel yet there is often reticence to shift permanently to ICS. Drawing on a scoping review, this article aims to provide a comprehensive overview of factors affecting the acceptability of ICS. A scoping review was carried out using a systematic search strategy of literature. All articles identified in three major databases that included Pubmed/Medline, Scopus and Web of Science underwent screening followed by content analysis to generate major and minor themes using a structured social level analysis. The analysis identified factors at micro, meso, and macro-social levels that potentially contribute to an adoption of an improved cooking stove (ICS). The findings from the review were discussed and refined among a group of experts identified based on their prior academic or commercial contributions related to ICS. Adoption of ICS was dependent on functional outputs (e.g. cleanliness, and cooking efficiency) while meeting local social and cultural demands (e.g. cooking large meals, traditional meals, and taste). Health and cost benefits played an important role in the adoption and sustained use of ICS. The adoption of ICS was enabled by use among neighbors and other community members. Sustained use of ICS depended on fuel supply, fuel security and policies promoting its use. Policies offering subsidies in support of supply-chain garnered institutional trust among community members and resulted in the sustained use of ICS. In addition to design attributes of ICS that could meet both scientific and social demands, policies supporting promotion of clean energy, subsidies and supplies can substantially enhance the adoption of ICS.

Functional microbiome assembly in food environments: addressing sustainable development challenges.

The global food system faces numerous challenges, creating an urgent need for sustainable reform. Functional microbiome assemblies offer transformative potential by endowing microbial foods with diverse, beneficial characteristics. These assemblies can dynamically influence specific food systems, positioning them as a promising approach for reshaping food production. However, the current applications and types of microbiome assemblies in foods remain limited, with a lack of effective screening and regulatory methods. This review introduces the functions and practical approaches for implementing microbiome assemblies in food systems alongside future directions for enhancing their applications. Several ecological studies evaluated how to regulate functional output and revealed that environmental conditions, which shape the niche for species survival, significantly influenced the functional output of microbiomes. Building on this theoretical foundation, this review presents a model for functional output comprising niche conditions, functional gene codes, and corresponding functional outputs. This model is illustrated with examples to explore sustainable applications and regulatory mechanisms for functional microbiome assemblies. By highlighting the roles of functional outputs in food systems and examining the potential for food environments to induce and modulate microbiome functions, this review provides a roadmap to address emerging challenges in food sustainability.

Control of Synaptotagmin-1 Trafficking by SV2A-Mechanism and Consequences for Presynaptic Function and Dysfunction.

Synaptic vesicle protein 2A (SV2A) is an abundant synaptic vesicle cargo with an as yet unconfirmed role in presynaptic function. It is also heavily implicated in epilepsy, firstly being the target of the leading anti-seizure medication levetiracetam and secondly with loss of function mutations culminating in human disease. A range of potential presynaptic functions have been proposed for SV2A; however its interaction with the calcium sensor for synchronous neurotransmitter release, synaptotagmin-1 (Syt1), has received particular attention over the past decade. In this review we will assess the evidence that the primary role of SV2A is to control the expression and localisation of Syt1 at the presynapse. This will integrate biochemical, cell biological and physiological studies where the interaction, trafficking and functional output of Syt1 is altered by SV2A. The potential for SV2A-dependent epilepsy to be a result of dysfunctional Syt1 expression and localisation is also discussed. Finally, a series of key open questions will be posed that require resolution before a definitive role for SV2A in Syt1 function in health and disease can be confirmed.

Utilising artificial neural networks for optimising supercritical CO2 extraction of Pongamia pinnata (L.) seed oil

ABSTRACT The application of various mathematical models in supercritical CO2 extraction processes is constrained by the high sensitivity to slight variations in process variables. Consequently, supercritical fluid extraction (SFE) processes have become an appealing field for employing artificial neural networks (ANNs). Feed forward back propagation (FFBP) and Cascade forward back propagation (CFBP) networks have been used for optimising SC-CO2 extraction of Pongamia pinnata (L.) (PP) seed oil. Both networks included TRAINLM and LEARNGDM as training function and learning function respectively, PURELIN and TANSIG as transfer functions for output and hidden layer respectively. A single layer with six hidden neurons was used for predicting cumulative extraction yield (CEY) of PP seed oil. The influence of five extraction variables – namely pressure, temperature, particle size, flow rate – CO2 and co-solvent percentage was examined with regard to CEY of PP seed oil. The values of performance parameters e.g. mean square error = 0.00013, average-absolute-relative deviation percentage = 3.18649, and correlation coefficient = 0.99195 for CFBPN model and 0.00017, 4.39994, and 0.98741 respectively for FFBPN model indicating that CFBPN model is superior over FFBPN model. Gas chromatography confirmed essential fatty acids in the oil extracted from PP seeds.

ALGORITHMIC DIFFERENCES OF COMPLETE AND PARTIAL ALGEBRAIC SYNTHESIS OF A FINITE STATE MACHINE WITH DATAPATH OF TRANSITIONS

Context. The problem of algorithmization the search for formal solutions of the problem of algebraic synthesis of a finite state ma-chine with datapath of transitions is considered. The concept of complete and partial solutions of this problem is proposed. The object of research is the automated synthesis of the finite state machine in the part of the function of transitions without taking into account the function of outputs. The basis of the algebraic implementation of the transition function is the author's approach to the transformation of state codes using a set of arithmetic and logical operations. The search for formal solutions to the problem of algebraic synthesis is a complex process that requires the use of appropriate methods and algorithms aimed at special coding of states and mapping of operations of transitions to individual state machine transitions. The use of partial solutions of the problem of algebraic synthesis can contribute to reducing the executing time of such algorithms and reducing the overall design time of digital control devices based on a finite state machine with an datapath of transitions. Objective. Development and research of algorithms for finding complete and partial solutions to the problem of algebraic synthesis of a finite state machine with datapath of transitions. Method. The research is based on the structure of a finite state machine with datapath of transitions. The synthesis of the circuit of the state machine involves the preliminary solution of the problem of algebraic synthesis. The result is the so-called formal solution of this problem, which contains two components. The first component is defined state codes, the second component is arithmetic and logic operations mapped to separate state machine transitions. Finite state machine can be synthesized in that case if transformation of given state codes in the process of making transitions is possible using a given set of operations. Verification of this possibility is carried out using the known matrix approach. It involves the formation and element-by-element mapping of two matrices – the matrix of transitions and the combined matrix of operations. As a result, a coverage matrix is formed, which reflects the possibility of implementing (covering) of state machine transitions by specified arithmetic and logic operations. Changing the way of encoding states or the set of operations can give different solutions to the problem of algebraic synthesis with a greater or lesser number of covered state machine transitions. Results. Using the example of an abstract graph-scheme of the control algorithm, it is demonstrated that the solution of the problem of algebraic synthesis of a finite state machine with datapath of transitions can be considered a situation when one or more state machine transitions cannot be implemented using a given set of operations. It is proposed to call such situation as a partial solution of the problem of algebraic synthesis. The implementation of all transitions by specified operations gives a complete solution of this problem, but the number of complete solutions is always much smaller than the number of partial solutions. Therefore, in the general case, the search for complete solutions takes much more time and, moreover, is not always possible. Conclusions. The design of a logical circuit of a finite state machine with datapath of transitions is possible in the case of a complete or partial solution of the problem of algebraic synthesis. In the case of a partial solution, those transitions that cannot be implemented by any of the available operations are implemented in a canonical way using a system of Boolean equations. The search for complete solutions generally takes more time than the search for partial solutions. This makes actual the development of algorithms and methods of synthesis of this class of finite state machine, based on the search for partial solutions of the problem of algebraic synthesis.

Discussion on the Splitting Treatment Technique in Gamma Knife Treatment Plans

Objective: In clinical Gamma Knife treatment, when patients have multiple or large lesions, a single Gamma Knife plan may require extended treatment time, making it difficult for patients to complete the session. This study explores the proper application of the splitting treatment technique in Gamma Knife treatment plans to create segmented plans for patients. Methods: Utilizing the design and output functions of the radiotherapy planning system, this study examines the typical errors in clinical treatment plans designed for the Moonlight Gamma Knife. Different splitting approaches were analyzed by comparing beam-on time for each target and calculating beam-on time error rates. Based on this, the appropriate splitting treatment technique for Gamma Knife treatment plans was discussed. Results: Scenarios where dose curves of multiple lesions intersect were categorized into three types: complete intersection, partial intersection, and no intersection. Complete intersection cases were further divided into Type I and Type II complete intersections. For cases with completely intersecting dose curves, the Gamma Knife plans should be split using the upper-lower segmentation method. For cases with no intersection, plans can be split based on individual lesions. For partial intersection cases, either the upper-lower segmentation or lesion-based segmentation method may be used. However, careful handling of target weighting at the dose curve intersection is necessary to ensure dose accuracy. For large lesions, the upper-lower segmentation method is recommended. Conclusion: To meet clinical treatment requirements, the proper application of the splitting treatment technique in Gamma Knife treatment plans is essential. This ensures dose accuracy in radiotherapy, thereby guaranteeing treatment efficacy and patient safety.

Analyzing the efficacy of transforaminal lumbar interbody fusion (TLIF) surgery for degenerative spondylolisthesis based on clinical outcomes and spinopelvic metrics

Aims: Transforaminal lumbar interbody fusion (TLIF) is an increasingly used approach for treating degenerative spondylolisthesis, but limited data exist regarding its impact on spinopelvic alignment (SA) and related functional outcomes. Methods: 150 patients who underwent TLIF were enrolled and evaluated pre-operatively and post-operatively in this study. Radiographic analysis was used to measure spinopelvic parameters, including lumbar lordosis (LL), pelvic tilt (PT) and sacral slope (SS). Oswestry disability index (ODI) and visual analog scale (VAS) functional outcomes were assessed. Methods statistical analyses included paired t-tests, Pearson correlation coefficients, and multivariate regression for differences in parameters before and after surgery, associations between changes in spinopelvic parameters and functional outcomes, and predictors of greater improvement in the ODI, respectively. Results: Changes in LL, PT and SS from pre- to post-operative day 10, 30-points posterior pelvic plane re-orientation score. Functional output assessments showed improvement in mean ODI (34 to 20) and VAS (6.5 to 3.2) scores (p

An Extended Analysis of the Correlation Extraction Algorithm in the Context of Linear Cryptanalysis

In cryptography, techniques and tools developed in the subfield of linear cryptanalysis have previously successfully been used to allow attackers to break many sophisticated cryptographic ciphers. Since these linear cryptanalytic techniques require exploitable linear approximations to relate the input and output of vectorial Boolean functions, e.g., the plaintext, ciphertext, and key of the cryptographic function, finding these approximations is essential. For this purpose, the Correlation Extraction Algorithm (CEA), which leverages the emerging field of quantum computing, appears promising. However, there has been no comprehensive analysis of the CEA regarding finding an exploitable linear approximation for linear cryptanalysis. In this paper, we conduct a thorough theoretical analysis of the CEA. We aim to investigate its potential in finding a linear approximation with prescribed statistical characteristics. To support our theoretical work, we also present the results of a small empirical study based on a computer simulation. The analysis in this paper shows that an approach that uses the CEA to find exploitable linear approximations has an asymptotic advantage, reducing a linear factor to a logarithmic one in terms of time complexity, and an exponential advantage in terms of space complexity compared to a classical approach that uses the fast Walsh transform. Furthermore, we show that in specific scenarios, CEA can exponentially reduce the search space for exploitable linear approximations in terms of the number of input bits of the cipher. Neglecting the unresolved issue of efficiently checking the property of linear approximations measured by the CEA, our results indicate that the CEA can support the linear cryptanalysis of vectorial Boolean functions with relatively few (e.g., n≤32) output bits.

Solving the traveling salesman problem by statistical testing using complex Markov chains

A method was proposed for solving the traveling salesman problem (TSP) using N -complex Markov chains (N -MC), the state sequence of which simulates a path through N points, each visited only once. Transition probabilities from each point to one of the next l points were set, where l < N. The complexity of implementing all stages of the method, depending on the values of N and l, was analyzed. Estimates of the complexity of the N -MC generator were obtained based on the composition of a finite deterministic automaton and a probabilistic memoryless automaton. The complexity of the N -MC generator is characterized by the volume of input sets, internal states, and outputs, as well as the amount of memory required to implement the transition and output functions of the automata. The delay time of the N -MC generator operation was also estimated. The probability of generating valid paths, i. e., those resolving TSP, and the memory requirements for storing valid paths were calculated.

Mapping the FGF2 Interactome Identifies a Functional Proteoglycan Coreceptor.

Fibroblast growth factor 2 (FGF2) is a multipotent growth factor and signaling protein that exhibits broad functions across multiple cell types. These functions are often initiated by binding to growth factor receptors and fine-tuned by glycosaminoglycan (GAG)-modified proteins called proteoglycans. The various outputs of FGF2 signaling and functions arise from a dynamic and cell type-specific set of binding partners. However, the interactome of FGF2 has yet to be comprehensively determined. Moreover, the identity of the proteoglycan proteins carrying GAG chains is often overlooked and remains unknown in most cell contexts. Here, we perform peroxidase-catalyzed live cell proximity labeling using an engineered APEX2-FGF2 fusion protein to map the interactome of FGF2. Across two cell lines with established and distinct FGF2-driven functions, we greatly expand upon the known FGF2 interactome, identifying >600 new putative FGF2 interactors. Notably, our results demonstrate a key role for the GAG binding capacity of FGF2 in modulating its interactome.

COMPREHENSIVE EVALUATION OF DIMENSIONAL DEVIATION, FLANK WEAR, SURFACE ROUGHNESS AND MATERIAL REMOVAL RATE IN DRY TURNING OF C45 STEEL

The study investigated the turning of C45 steel in a dry environment. The input parameters that were varied were cutting speed, feed, depth of cut, corner radius and insert type. The experimental investigations were carried out according to a custom experimental design using the D-optimality criterion. The measured output parameters were dimensional deviation, flank wear and surface roughness, while the material removal rate was calculated. A detailed analysis and evaluation of the effects of the input parameters on the output parameters was carried out. The model was diagnosed and appropriate regression equations was established. Based on the obtained regression equations, multi-objective optimisation was performed using particle swarm optimisation. The objective function was to simultaneously minimise dimensional deviation, flank wear and surface roughness and maximise material removal rate. The optimisation was carried out for different weighting coefficients of each output function for different production requirements. The obtained models and optimal values were verified by additional confirmation experiments.

Optimizing engine performance and emission properties with Garcinia indica biodiesel: a holistic RSM-TLBO-DFA strategy

Garcinia indica (GI) feedstock poses high oil content (45.2%) and after transesterification resulted with 94.8% yield. The GI crude oil, biodiesel, and their blends were tested for fuel characteristics and run in a diesel engine. Response surface methodology based on central composite design experimental matrices was used to model and examine the input variables (engine load, injection timing, injection pressure, and blend type) on engine performance (brake thermal efficiency (BTE), brake specific fuel consumption (BSFC)) and emission characteristics (nitrogen oxide (NOx), unburnt hydrocarbon (UHC), carbon monoxide (CO)). All factors showed significant effects (except injection pressure and injection time for NOx) on all responses. The empirical equations predicted 27 experimental cases with 4.75% accuracy. Desirability function approach was applied to transform all output functions (maximize BTE and minimize BSFC, CO, NOx, and UHC) with different weight fractions (WF) to single composite desirability function for maximization. Teaching learning-based optimization (TLBO) determined optimal condition corresponding to case 4 (maximum WF to CO, minimum WF to BTE, BSFC, UHC, NOx) resulting in highest desirability function value (0.9432) with a percent deviation of 7.09%. The developed models assist novice users in predicting unknown parametric conditions and improving engine performance and emission characteristics without practical experiments.

RNA encodes physical information.

Most amino acids are encoded by multiple codons, making the genetic code degenerate. Synonymous mutations affect protein translation and folding, but their impact on RNA itself is often neglected. We developed a genetic algorithm that introduces synonymous mutations to control the diversity of structures sampled by an mRNA. The behavior of the designed mRNAs reveals a physical code layered in the genetic code. We find that mRNA conformational heterogeneity directs physical properties and functional outputs of RNA-protein complexes and biomolecular condensates. The role of structure and disorder of proteins in biomolecular condensates is well appreciated, but we find that RNA conformational heterogeneity is equally important. This feature of RNA enables both evolution and engineers to build cellular structures with specific material and responsive properties.

Energy Trading in Local Energy Markets: A Comprehensive Review of Models, Solution Strategies, and Machine Learning Approaches

The increasing adoption of renewable energy sources and the emergence of distributed generation have significantly transformed the traditional energy landscape, leading to the rise of local energy markets. These markets facilitate decentralized energy trading among different market participants at the community level, fostering greater energy autonomy and sustainability. As local energy markets gain momentum, the application of artificial intelligence techniques, particularly reinforcement learning, has gained substantial interest in optimizing energy trading strategies by interacting with the environment and maximizing the rewards by addressing the decision complexities by learning. This paper comprehensively reviews the different energy trading projects initiated at the global level and machine learning approaches and solution strategies for local energy markets. State-of-the-art reinforcement learning algorithms are classified into model-free and model-based methods. This classification examines various algorithms for energy transactions considering the agent type, learning methods, policy, state space, action space, and action selection for state, action, and reward function outputs. The findings of this work will serve as a valuable resource for researchers, stakeholders, and policymakers to accelerate the adoption of the local energy market for a more efficient, sustainable, and resilient energy future.

A One-Dimensional Depthwise Separable Convolutional Neural Network for Bearing Fault Diagnosis Implemented on FPGA.

This paper presents a hardware implementation of a one-dimensional convolutional neural network using depthwise separable convolution (DSC) on the VC707 FPGA development board. The design processes the one-dimensional rolling bearing current signal dataset provided by Paderborn University (PU), employing minimal preprocessing to maximize the comprehensiveness of feature extraction. To address the high parameter demands commonly associated with convolutional neural networks (CNNs), the model incorporates DSC, significantly reducing computational complexity and parameter load. Additionally, the DoReFa-Net quantization method is applied to compress network parameters and activation function outputs, thereby minimizing memory usage. The quantized DSC model requires approximately 22 KB of storage and performs 1,203,128 floating-point operations in total. The implementation achieves a power consumption of 527 mW at a clock frequency of 50 MHz, while delivering a fault diagnosis accuracy of 96.12%.

Does current forest management affect the ecologic and esthetic assets of the landscape in Poland?

Abstract Forestry in Poland finds itself currently at a crossroads. On the one hand, there is powerful social pressure and concern for sustainable development and landscape assets of forests, while on the other hand, the output function is still important. Forest complexes are a crucial element contributing to the esthetic value of landscapes. The esthetic and landscape assets of forests at a regional or local level are determined by numerous elements. Examples of measures at the regional level are afforestation programs, while at the local level, one of the most important factors influencing the landscape is the extent of clear-cut areas or the increase in the proportion/use of natural regeneration. Forest management must take into account the multifaceted role of forests, in which the landscape function is extremely important. Allowing for a variety of ecosystem services that forests supply can contribute to minimizing the risk of potential conflicts whilst maximizing the benefits derived from forest ecosystems by different stakeholder groups. The aim of this paper is to present the results of a literature review relating to the impact of forest management (with its tools) on ecologic and esthetic landscape values at both regional and local levels.

Global Asymptotic Stabilization Control for Uncertain Nonlinear Systems With Input Quantization and Unknown Output Function

Global Asymptotic Stabilization Control for Uncertain Nonlinear Systems With Input Quantization and Unknown Output Function