System-level Analysis Research Articles

System-level analysis of atmospheric water extraction with MIL-100 (Fe) for design and optimal site selection using meteorological characteristics

This study presents a system-level atmospheric water extraction (AWE) analysis using MIL-100 (Fe) as a representative material. This study considers a range of system features, including (i) modeling the isotherm across a wide temperature range based on the experimental data, (ii) conceptualizing process types and their operating conditions, (iii) analyzing the thermodynamics and economics, and (iv) optimizing the system operation under meteorological variations in temperature and humidity through two-stage stochastic programming. The swing capacity analysis revealed that a vacuum pressure below 0.1 bar and a temperature swing degree of 20–30 °C are necessary for feasible MIL-100 (Fe) AWE operation. Energy analysis shows that higher degrees of vacuum with moderate temperature swings result in lower energy consumption, requiring 4.6 and 1.6 MJ kgH2O−1 for thermal and electrical energy consumption. The estimated unit harvesting cost was 0.058 US$ kgH2O−1, with the potential for cost reduction through material and process development. We make contributions in several key areas: (i) facilitating the selection of optimal sites for pilot or commercial process implementation, (ii) offering insights into process design and operation, (iii) identifying and addressing potential bottlenecks for future process development, and (iv) providing a generalized platform for material screenings for comparative analysis.

Magnetocaloric refrigerator with magnetic pump and liquid metals

One-seventh of the worldwide electricity is used for refrigeration related activities. When compared to the predominantly used vapor compression refrigeration system, magnetocaloric refrigeration provides an energy-efficient and eco-friendly alternative. It utilizes a magnetocaloric material which undergoes temperature changes when exposed to magnetic field changes. Nearly 60% of the total system cost is due to the permanent magnets used. For a given cooling power, the amount of permanent magnet needed is determined by the residence time required by the heat transfer fluid in each cycle. The start-of-the-art systems utilizes water-alcohol mixture as the heat transfer fluid, and a mechanical pump for its circulation. By utilizing non-toxic and non-hazardous gallium-indium-tin based liquid metals as heat transfer fluid, which has three order of magnitude higher thermal diffusivity, the residence time and the permanent magnetic material is reduced nearly by a factor of 10. Even accounting for the cost of the liquid metal, it results in considerable savings in the system cost. We present experimental results on the entropy change of using liquid metal with (Mn,Fe)2(P,Si)-based magnetocaloric material, and numerical results on the system level analysis. Further, to improve the reliability of the system, we propose to use magnetic pumping for heat transfer fluid circulation, which does not have any moving parts. The state-of-the art magnetic pump works at frequencies in the range of 100 to 1000 Hz. However, they cause detrimental heating effects due to eddy current losses. To overcome this, we have designed a pump that operates at 1 Hz and still achieves comparable flow rates. We present numerical results on the pump design by studying the dynamics of ferrofluid rise in a vertical pipe with multiple electromagnetic coils.

Serial adaptive laboratory evolution enhances mixed carbon metabolic capacity of Escherichia coli

Microbes have inherent capacities for utilizing various carbon sources, however they often exhibit sub-par fitness due to low metabolic efficiency. To test whether a bacterial strain can optimally utilize multiple carbon sources, Escherichia coli was serially evolved in L-lactate and glycerol. This yielded two end-point strains that evolved first in L-lactate then in glycerol, and vice versa. The end-point strains displayed a universal growth advantage on single and a mixture of adaptive carbon sources, enabled by a concerted action of carbon source-specialists and generalist mutants. The combination of just four variants of glpK, ppsA, ydcI, and rph-pyrE, accounted for more than 80% of end-point strain fitness. In addition, machine learning analysis revealed a coordinated activity of transcriptional regulators imparting condition-specific regulation of gene expression. The effectiveness of the serial adaptive laboratory evolution (ALE) scheme in bioproduction applications was assessed under single and mixed-carbon culture conditions, in which serial ALE strain exhibited superior productivity of acetoin compared to ancestral strains. Together, systems-level analysis elucidated the molecular basis of serial evolution, which hold potential utility in bioproduction applications.

Tissue-specific nonheritable influences drive endometrial immune system variation.

Although human twin studies have revealed the combined contribution of heritable and environmental factors in shaping immune system variability in blood, the contribution of these factors to immune system variability in tissues remains unexplored. The human uterus undergoes constant regeneration and is exposed to distinct environmental factors. To assess uterine immune system variation, we performed a system-level analysis of endometrial and peripheral blood immune cells in monozygotic twins. Although most immune cell phenotypes in peripheral blood showed high genetic heritability, more variation was found in endometrial immune cells, indicating a stronger influence by environmental factors. Cytomegalovirus infection was identified to influence peripheral blood immune cell variability but had limited effect on endometrial immune cells. Instead, hormonal contraception shaped the local endometrial milieu and immune cell composition with minor influence on the systemic immune system. These results highlight that the magnitude of human immune system variation and factors influencing it can be tissue specific.

C-Slot Circular Polarized Antenna for Hybrid Energy Harvesting and Wireless Sensing

This paper presents a new hybrid energy harvesting on electromagnetic solar for wireless energy harvesting of ambient from sensors of low-power devices. The axial ratio (AR) requirements produce Left-Hand Circular Polarization (LHCP) and Right-Hand Circular Polarization (RHCP) and simultaneously produce a 90-degree phase difference during energy harvesting, adopting a new design in designing a dual-feed broadband circular polarized (CP) antenna. To get the frequency band 2.3–2.4 GHz, we propose a C-Slot antenna with a circular patch dual feed. To estimate the diversity of the phase and magnitude output of the feed configuration under AR value, we used a 50 Ohm feed network output of the characteristic analysis for a dual feed CP antenna. An Axial ratio frequency range of less than 3 dB is achieved using polarization analysis with different branch channel couplers. To produce a DC output voltage, a high-frequency rectifier circuit embedded with a thin-film solar cell on the antenna is then connected to two T-junction power divider rectifiers, resulting in a high-efficiency design. A complete system-level analysis will include multiple signal classification methods of powered ambient RF energy using a wireless energy harvesting array that proposes a compact structure and demonstrates optimal configuration. Reliable operation in typical indoor environments indicates a self-contained sensor Node. Therefore, it has significant implications for powering small electronics and wireless sensor applications independently of the IoT Network or real implementation telecommunications industry.

Thermal management of PV based on latent energy storage of composite phase change material: A system-level analysis with pore-scale model

Perovskite Solar Cell (PSC) have recently emerged as exciting new candidates of Photovatics (PVs) for solar-to-electrical energy conversion. Nevertheless, one huge obstacle to its commercialization is how to improve its tolerance to operation at elevated temperatures. To address this issue, Composite Phase Change Material (CPCM) incorporated with a porous skeleton structure is proposed to integrate with PVs. In this structure, an important theoretical problem is that the temperature dependence of PV and the temperature control behavior of CPCMs interact with each other (bidirectional relationship). In this work, a pore-scale lattice Boltzmann phase change model is established to describe the dynamic thermal behavior of CPCMs and explore the roles of the bidirectional relationship between PVs and CPCMs, the change of power conversion efficiency influenced by temperature variation and glass transmissivity is discussed by comparing the overall liquid fraction of CPCMs, and the average temperature and power conversion efficiency of PVs. Furthermore, the situation of time-varying solar irradiation is discussed to mimic real application conditions. Results indicate that the neglecting temperature dependency of PVs leads to underestimation of PV surface temperatures and overestimation of power conversion efficiencies, while glass transmissivity provides reverse effects. These phenomena not only happen in constant solar irradiation but also in varying solar irradiation, calling for increasing attention to designing PV/CPCMs for varying heat flux conditions. The findings of this work establish a comprehensive manner toward integrated design of CPCMs for PV thermal control.

Systems level analysis of time and stimuli specific signaling through PKA.

It is well known that eukaryotic cells create gradients of cAMP across space and time to regulate the cAMP dependent protein kinase (PKA) and, in turn, growth and metabolism. However, it is unclear how PKA responds to different concentrations of cAMP. Here, to address this question, we examine PKA signaling in Saccharomyces cerevisiae in different conditions, timepoints, and concentrations of the chemical inhibitor 1-NM-PP1, using phosphoproteomics. These experiments show that there are numerous proteins that are only phosphorylated when cAMP and PKA activity are at/near their maximum level, while other proteins are phosphorylated even when cAMP levels and PKA activity are low. The data also show that PKA drives cells into distinct growth states by acting on proteins with different thresholds for phosphorylation in different conditions. Analysis of the sequences surrounding the 118 PKA-dependent phosphosites suggests that the phosphorylation thresholds are set, at least in part, by the affinity of PKA for each site.

Dissecting gene expression networks in the developing hippocampus through the lens of NEIL3 depletion

Gene regulation in the hippocampus is fundamental for its development, synaptic plasticity, memory formation, and adaptability. Comparisons of gene expression among different developmental stages, distinct cell types, and specific experimental conditions have identified differentially expressed genes contributing to the organization and functionality of hippocampal circuits. The NEIL3 DNA glycosylase, one of the DNA repair enzymes, plays an important role in hippocampal maturation and neuron functionality by shaping transcription. While differential gene expression (DGE) analysis has identified key genes involved, broader gene expression patterns crucial for high-order hippocampal functions remain uncharted. By utilizing the weighted gene co-expression network analysis (WGCNA), we mapped gene expression networks in immature (p8-neonatal) and mature (3 m-adult) hippocampal circuits in wild-type and NEIL3-deficient mice. Our study unveiled intricate gene network structures underlying hippocampal maturation, delineated modules of co-expressed genes, and pinpointed highly interconnected hub genes specific to the maturity of hippocampal subregions. We investigated variations within distinct gene network modules following NEIL3 depletion, uncovering NEIL3-targeted hub genes that influence module connectivity and specificity. By integrating WGCNA with DGE, we delve deeper into the NEIL3-dependent molecular intricacies of hippocampal maturation. This study provides a comprehensive systems-level analysis for assessing the potential correlation between gene connectivity and functional connectivity within the hippocampal network, thus shaping hippocampal function throughout development.

Development and application in multiscale and multiphysics methodologies in Spain: Present and future trends

In the field of reactor physics analysis, the coupling of multiple physics phenomena plays a crucial role in enhancing the accuracy of predictions and understanding complex systems. Multiphysics refers to the study and analysis of physical phenomena that involve multiple interconnected physical processes occurring simultaneously in a nuclear system to ensure essential aspects of reactor design, safety analysis, and optimization, among others. Multiphysics encompasses various domains of physics, such as Thermal-Hydraulics, Neutronics, Structural Mechanics, Radiation Transport, Chemistry, and Materials Science. On the other hand, the Multiscale approach involves combining high-fidelity models with lower-fidelity ones to accurately capture the relevant physical phenomena in a Nuclear Power Plant (NPP). Multiscale involves three main scales: system level, core level, and detailed analysis scale. This paper presents an overview of the main research performed by Spanish groups in the field of multiphysics and multiscale calculations, particularly on thermal–hydraulic analysis. This revision is focused mainly on two key aspects: thermal–hydraulic multiscale coupling and thermal–hydraulic / neutronic coupling. The paper also includes the expected future trends in this field.

Computational study of the Nitrogen-16 source term in the ITER vacuum vessel cooling circuit through the coupling of system-level analysis code and CFD

In ITER, the evaluation of the activated water radiation source and its impact on the radiological levels is necessary to demonstrate compliance with the safety requirements. The use of simplified or conservative approaches often results in the application of expensive constraints on the installation that impact its economics, operations, and construction schedule. In this work, we propose a novel methodology to calculate the activated water source term with a higher degree of realism. The methodology is based on the coupling of a system-level code with a Computational Fluid Dynamics (CFD) code in an explicit, one-way approach. We apply this methodology to the evaluation of the 16N radioisotope within the ITER Vacuum Vessel Primary Heat Transfer System (VV-PHTS) cooling circuit in a steady-state and transient scenarios. We chose this system since previous analyses of the VV-PHTS were done with simple, ad-hoc calculations that yielded results that differed by up to a factor of five, underscoring a higher level of uncertainty. As a result, we generate a computational model of the source term that can be used to evaluate the radiological condition surrounding the cooling systems during the operations.

Eurocode-compliant system-level reliability analyses of trussed portal frames under climatic loads

Eurocode 3 provides Geometrically and Materially Nonlinear Analysis with Imperfections-method (GMNIA) in which the entire structure can be designed in system-level. In GMNIA, reliability of structural system is verified by using a system safety factor which is obtained in Eurocode 3 based on numerical or experimental capacity test results. Unfortunately, such test results are scarcely published in the literature thus complicating the determination of the factor for a design engineer. In the so-called Direct Design Method, however, system safety factors are provided in advance for the design engineer by system-level reliability studies. This study determines the Eurocode-compliant GMNIA system safety factor for Warren truss portal frames by using the approach of the Direct Design Method. Advanced numerical models are utilized to perform Monte Carlo-simulations for entire structural systems. These simulations provide statistical distributions of system resistances which are employed in the First-Order Reliability Method to derive the system safety factors. Studied structures are made of S700 cold-formed hollow sections. Various system geometries, system configurations and load combinations consisting of snow and wind loads are investigated and a suitable system safety factor is proposed for the ultimate limit state design of Warren truss portal frames. The proposed system safety factor is applied in a practical comparison in which Warren system is designed both by the conventional Eurocode 3 method and GMNIA. This comparison reveals that GMNIA has a remarkable potential to offer reduced material consumption in design compared to the conventional method.

Food environment research in Canada: a rapid review of methodologies and measures deployed between 2010 and 2021.

Numerous research methodologies have been used to examine food environments. Existing reviews synthesizing food environment measures have examined a limited number of domains or settings and none have specifically targeted Canada. This rapid review aimed to 1) map research methodologies and measures that have been used to assess food environments; 2) examine what food environment dimensions and equity related-factors have been assessed; and 3) identify research gaps and priorities to guide future research. A systematic search of primary articles evaluating the Canadian food environment in a real-world setting was conducted. Publications in English or French published in peer-reviewed journals between January 1 2010 and June 17 2021 and indexed in Web of Science, CAB Abstracts and Ovid MEDLINE were considered. The search strategy adapted an internationally-adopted food environment monitoring framework covering 7 domains (Food Marketing; Labelling; Prices; Provision; Composition; Retail; and Trade and Investment). The final sample included 220 articles. Overall, Trade and Investment (1%, n = 2), Labelling (7%, n = 15) and, to a lesser extent, Prices (14%, n = 30) were the least studied domains in Canada. Among Provision articles, healthcare (2%, n = 1) settings were underrepresented compared to school (67%, n = 28) and recreation and sport (24%, n = 10) settings, as was the food service industry (14%, n = 6) compared to grocery stores (86%, n = 36) in the Composition domain. The study identified a vast selection of measures employed in Canada overall and within single domains. Equity-related factors were only examined in half of articles (n = 108), mostly related to Retail (n = 81). A number of gaps remain that prevent a holistic and systems-level analysis of food environments in Canada. As Canada continues to implement policies to improve the quality of food environments in order to improve dietary patterns, targeted research to address identified gaps and harmonize methods across studies will help evaluate policy impact over time.

Prioritising climate adaptation options to minimise financial and distributional impacts of water supply disruptions

Climate-related disruptions to water supply infrastructure services incur direct financial losses to utilities (e.g. to repair damaged assets) and externalise a societal cost to domestic customers due to additional costs that they may incur (e.g. to acquire water from alternative sources). The latter often represents an uncompensated social burden, which should be properly accounted for in investment planning. Here we present a new framework for quantifying direct financial risks burdened by utilities and alternative water purchase losses incurred by domestic customers, including those in low-income groups, during flood- and drought-induced utility water supply disruptions. This framework enables the comparison of benefit-cost ratios of a portfolio of flood protection and leakage reduction for water supply systems across the island of Jamaica. A system-level optioneering analysis allows the identification of the optimal adaptation option per system. We estimate that 34% of systems would benefit from flood defences and 53% would benefit from leakage reduction to adaptation to droughts. The benefit that could be achieved by implementing all system optimised adaptation options is estimated to be 720 million Jamaican dollars per year on average, representing a substantial saving for the utility and its customers, including low-income customers. We identify options that offer strong synergies between economic and equity objectives for both types of adaptation option. The proposed framework is established to support the business case for climate adaptation in the water supply sector and to prioritise across flood and drought mitigation options. We take a first step towards mainstreaming equity considerations in water supply sector optioneering frameworks by estimating the contribution of adaptation options towards reducing household costs for low-income customers.

Funding Learning Health System Research: Challenges and Strategies.

A growing number of health systems are establishing learning health system (LHS) programs, where research focuses on rapidly improving the health system's internal operations and performance. The authors examine funding challenges facing such initiatives and identify strategies for managing tensions between reliance on external research funding and directly contributing to improvement and learning within the researchers' own system. Qualitative case studies of LHS research programs in 5 health systems were performed via 38 semistructured interviews (October 2019-April 2021) with 35 diverse respondents. Inductive and deductive rapid qualitative analysis supported interview, system-level, and cross-system summaries and analysis. External funding awards to LHS researchers facilitated some internal improvement and learning, scientific advancements, and the reputation of researchers and their systems, but reliance on external funding also challenged researchers' responsiveness to concerns of system leaders, managers, practitioners, and system needs. Gaps between external funding requirements and internally focused projects arose in objectives, practical applicability, audiences, timetables, routines, skill sets, and researchers' careers. To contribute more directly to system improvement, LHS researchers needed to collaborate with clinicians and other nonresearchers and pivot between long research studies and shorter, dynamic improvement, evaluation, and data analysis projects. With support from system executives, LHS program leaders employed several strategies to enhance researchers' internal contributions. They aligned funded-research topics with long-term system needs, obtained internal funding for implementing and sustaining practice change, and diversified funding sources. To foster LHS research contributions to internal system learning and improvement, LHS program leaders need to manage tensions between concentrating on externally funded research and fulfilling their mission of providing research-based services to their own system. Health system executives can support LHS programs by setting clear goals for them; appropriately staffing, budgeting, and incentivizing LHS researchers; and developing supportive, system-wide teamwork, skill development programs, and data infrastructures.

An Analysis of Various Design Pathways Towards Multi-Terabit Photonic On-Interposer Interconnects

In the wake of dwindling Moore’s Law, to address the rapidly increasing complexity and cost of fabricating large-scale, monolithic systems-on-chip (SoCs), the industry has adopted dis-aggregation as a solution, wherein a large monolithic SoC is partitioned into multiple smaller chiplets that are then assembled into a large system-in-package (SiP) using advanced packaging substrates such as silicon interposer. For such interposer-based SiPs, there is a push to realize on-interposer inter-chiplet communication bandwidth of multi-Tb/s and end-to-end communication latency of no more than 10 ns. This push comes as the natural progression from some recent prior works on SiP design, and is driven by the proliferating bandwidth demand of modern data-intensive workloads. To meet this bandwidth and latency goal, prior works have focused on a potential solution of using the silicon photonic interposer (SiPhI) for integrating and interconnecting a large number of chiplets into an SiP. Despite the early promise, the existing designs of on-SiPhI interconnects still have to evolve by leaps and bounds to meet the goal of multi-Tb/s bandwidth. However, the possible design pathways, upon which such an evolution can be achieved, have not been explored in any prior works yet. In this paper, we have identified several design pathways that can help evolve on-SiPhI interconnects to achieve multi-Tb/s aggregate bandwidth. We perform an extensive link-level and system-level analysis in which we explore these design pathways in isolation and in different combinations of each other. From our link-level analysis, we have observed that the design pathways that simultaneously enhance the spectral range and optical power budget available for wavelength multiplexing can render aggregate bandwidth of up to 4 Tb/s per on-SiPhI link. We also show that such high-bandwidth on-SiPhI links can substantially improve the performance and energy-efficiency of the state-of-the-art CPU and GPU chiplets based SiPs.

A reshaped intelligent autonomous driving by using the influence and progress of human factor engineering concept

In modern society, with the improvement of people's taste of life and the change of people's consumption awareness and lifestyle, cars are no longer just a driving tool or means of transportation. People's requirements for cars are constantly improving, such as the comfort of car seats and the convenience of reversing radar. This paper examines the role of human factor engineering in the intelligent driving and gives some examples of the usage of the human factor engineering, and analyse it by Single-vehicle level analysis and Macro system level analysis. Then, the paper lists different human factor problems affecting intelligent driving. The paper discusses one iHCl system of human factor engineering which based on the Cognitive theory at three levels of perception. The paper analyses two cognizant. For the human users, it has the three-layer scene elements, data-driven, and goal-driven information processing mechanisms utilize dynamic mechanisms for information collection and subsequent response, allowing human users to perceive the updated information obtained in the dynamic environment. For intelligent system, For intelligent systems, it has a "top-down" decision-making processing mechanism. The feedback mechanism is composed of subjects such as goals, plans, and tasks.

Towards System-Level Security Analysis of IoT Using Attack Graphs

Most IoT systems involve IoT devices, communication protocols, remote cloud, IoT applications, mobile apps, and the physical environment. However, existing IoT security analyses only focus on a subset of all the essential components, such as device firmware or communication protocols, and ignore IoT systems' interactive nature, resulting in limited attack detection capabilities. In this work, we propose <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Iota</small> , a logic programming-based framework to perform system-level security analysis for IoT systems. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Iota</small> generates attack graphs for IoT systems, showing all of the system resources that can be compromised and enumerating potential attack traces. In building <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Iota</small> , we design novel techniques to scan IoT systems for individual vulnerabilities and further create generic exploit models for IoT vulnerabilities. We also identify and model physical dependencies between different devices as they are unique to IoT systems and are employed by adversaries to launch complicated attacks. In addition, we utilize NLP techniques to extract IoT app semantics based on app descriptions. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Iota</small> automatically translates vulnerabilities, exploits, and device dependencies to Prolog clauses and invokes MulVAL to construct attack graphs. To evaluate vulnerabilities' system-wide impact, we propose three metrics based on the attack graph, which provide guidance on hardening IoT systems. Evaluation on 127 IoT CVEs (Common Vulnerabilities and Exposures) shows that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Iota</small> 's exploit modeling module achieves over 80% accuracy in predicting vulnerabilities' preconditions and effects. We apply <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Iota</small> to 37 synthetic smart home IoT systems based on real-world IoT apps and devices. Experimental results show that our framework is effective and highly efficient. Among 27 shortest attack traces revealed by the attack graphs, 62.8% are not anticipated by the system administrator. It only takes 1.2 seconds to generate and analyze the attack graph for an IoT system consisting of 50 devices.

GINv2.0: a comprehensive topological network integrating molecular interactions from multiple knowledge bases

Knowledge bases have been instrumental in advancing biological research, facilitating pathway analysis and data visualization, which are now widely employed in the scientific community. Despite the establishment of several prominent knowledge bases focusing on signaling, metabolic networks, or both, integrating these networks into a unified topological network has proven to be challenging. The intricacy of molecular interactions and the diverse formats employed to store and display them contribute to the complexity of this task. In a prior study, we addressed this challenge by introducing a “meta-pathway” structure that integrated the advantages of the Simple Interaction Format (SIF) while accommodating reaction information. Nevertheless, the earlier Global Integrative Network (GIN) was limited to reliance on KEGG alone. Here, we present GIN version 2.0, which incorporates human molecular interaction data from ten distinct knowledge bases, including KEGG, Reactome, and HumanCyc, among others. We standardized the data structure, gene IDs, and chemical IDs, and conducted a comprehensive analysis of the consistency among the ten knowledge bases before combining all unified interactions into GINv2.0. Utilizing GINv2.0, we investigated the glycolysis process and its regulatory proteins, revealing coordinated regulations on glycolysis and autophagy, particularly under glucose starvation. The expanded scope and enhanced capabilities of GINv2.0 provide a valuable resource for comprehensive systems-level analyses in the field of biological research. GINv2.0 can be accessed at: https://github.com/BIGchix/GINv2.0.

Systems-level analyses dissociate genetic regulators of reactive oxygen species and energy production.

Respiratory chain dysfunction can decrease ATP and increase reactive oxygen species (ROS) levels. Despite the importance of these metabolic parameters to a wide range of cellular functions and disease, we lack an integrated understanding of how they are differentially regulated. To address this question, we adapted a CRISPRi- and FACS-based platform to compare the effects of respiratory gene knockdown on ROS to their effects on ATP. Focusing on genes whose knockdown is known to decrease mitochondria-derived ATP, we showed that knockdown of genes in specific respiratory chain complexes (I, III, and CoQ10 biosynthesis) increased ROS, whereas knockdown of other low ATP hits either had no impact (mitochondrial ribosomal proteins) or actually decreased ROS (complex IV). Moreover, although shifting metabolic conditions profoundly altered mitochondria-derived ATP levels, it had little impact on mitochondrial or cytosolic ROS. In addition, knockdown of a subset of complex I subunits-including NDUFA8, NDUFB4, and NDUFS8-decreased complex I activity, mitochondria-derived ATP, and supercomplex level, but knockdown of these genes had differential effects on ROS. Conversely, we found an essential role for ether lipids in the dynamic regulation of mitochondrial ROS levels independent of ATP. Thus, our results identify specific metabolic regulators of cellular ATP and ROS balance that may help dissect the roles of these processes in disease and identify therapeutic strategies to independently target energy failure and oxidative stress.

Monolithically Integrated Complementary Ferroelectric FET XNOR Synapse for the Binary Neural Network.

Neuromorphic computing, which mimics the structure and principles of the human brain, has the potential to facilitate the hardware implementation of next-generation artificial intelligence systems and process large amounts of data with very low power consumption. Among them, the XNOR synapse-based Binary Neural Network (BNN) has been attracting attention due to its compact neural network parameter size and low hardware cost. The previous XNOR synapse has drawbacks, such as a trade-off between cell density and accuracy. In this work, we show nonvolatile XNOR synapses with high density and accuracy using a monolithically stacked complementary ferroelectric field-effect transistor (C-FeFET) composed of a p-type Si MFMIS-FeFET at the bottom and a 3D stackable n-type Al:IZTO MFS-FeTFT, achieving 60F2 per cell (2C-FeFET). For adjusting the threshold voltage and improving the switching speed (100 ns) of n-type ferroelectric TFT, we employed a dual-gate configuration and a unique operation scheme, making it comparable to those of Si-based FeFETs. We performed array-level simulation with a 512 × 512 subarray size and a 3-bit flash ADC, demonstrating that the image recognition accuracies using the MNIST and CIFAR-10 data sets were increased by 3.17 and 14.07%, respectively, in comparison to other nonvolatile XNOR synapses. In addition, we performed system-level analysis on a 512 × 512 XNOR C-FeFET, exhibiting an outstanding throughput of 717.37 GOPS and an energy efficiency of 196.7 TOPS/W. We expect that our approach would contribute to the high-density memory systems, logic-in-memory technology, and hardware implementation of neural networks.