Processing Demands Research Articles

A Consortium Blockchain-Based Edge Task Offloading Method for Connected Autonomous Vehicles

In recent years, the proliferation of Connected Autonomous Vehicles(CAV) has revolutionizing the transportation industry. However, these vehicles often face limitations in terms of local computing resources, leading to the need for offloading interactive-intensive application tasks to servers for processing. Traditional paradigm has its limitations in meeting the demands of massive task processing. The combination of Web3.0 and edge computing offers users high-reliable, low-latency, and highly flexible services. Nevertheless, the new paradigm also presents its own challenges such as ensuring privacy data protection, and reducing the time and energy costs associated with task offloading. To tackle these challenges, a edge task offloading framework based on consortium blockchain for CAVs has been developed. Within this framework, a consortium blockchain-based interaction-intensive task offloading method, called CBIToMe, has been designed. CBIToMe specifically addresses the multi-stage nature of interactive-intensive CAV tasks and aims to minimize task completion time and offloading costs, particularly when the waiting time for interaction is uncertain. Additionally, CBIToMe effectively utilizes consortium blockchain technology to safeguard the CAV privacy data. Results from experiments conducted in various scenarios demonstrate that CBIToMe outperforms three representative methods, showcasing its superior performance.

Construction of heterogeneous frustrated lewis pairs based on covalent organic frameworks stabilized boron cations and investigation of cycloaddition reaction performance

Frustrated Lewis Pairs (FLPs) catalysts have been widely designed and synthesized in current chemistry, with the key being the selection of boron-based Lewis acids. Among novel boron-based Lewis acids, boron cations with higher Lewis acidity caused by cationic charge have been proven to have special reactivity. However, the current boron cations have only been used to construct homogeneous FLPs, which would be difficult to meet the industrial process demand for FLPs catalysts, possibly due to the lack of suitable stabilizers. In this paper, we propose a strategy to construct heterogeneous FLPs using covalent organic frameworks (COFs) stabilized boron cations as Lewis acid. Firstly, heterogeneous Lewis acid COFs@[B(C6F5)2]+[Al2Cl7]- was prepared by stabilizing boron cations with CN bonds in COFs, followed by introducing tBu3P to construct heterogeneous FLPs COFs@[B(C6F5)2]+[Al2Cl7]-/tBu3P. Meanwhile, these FLPs catalysts exhibited excellent catalytic and cyclic performance in the preparation of cyclic carbonates by CO2 addition from epoxy compounds.

Assessment of Hazards in the High‐Pressure Continuous Flow Reactor for Dimethyl Ether (DME) Synthesis

AbstractThe objective of this study is to conduct a thorough hazard analysis specifically tailored to identify hazards and suggest precautions associated with the intricate process of dimethyl ether (DME) production. This exothermic nature of DME production introduces the critical concern of thermal runaway. Furthermore, the DME synthesis process demands precise control over both pressure and flow rate. These parameters are important for increasing DME selectivity and CO conversion. Inadequate monitoring of these values may not only prevent the synthesis from proceeding but may also lead to major risks to health and safety. For this reason, proactive measures must be implemented to eliminate or minimize the risks. The significance of such measures cannot be overstated, as they serve to safeguard not only the integrity of the synthesis process but also the surrounding environment and human health. To delve deeper into the hazard evaluation, a comprehensive Hazard and Operability (HAZOP) analysis has been carried out, focusing specifically on the reactor within the DME process. This methodical examination involves a systematic review of potential deviations, identifying the associated hazards, and proposing effective preventive measures. The HAZOP analysis serves as a pivotal tool in ensuring the overall safety and reliability of the DME process.

Pure water and resource recovery from municipal wastewater using high-rate activated sludge, reverse osmosis, and mainstream anammox: A pilot scale study

In response to the escalating global water scarcity and the high energy consumption associated with traditional wastewater treatment plants, there is a growing demand for transformative wastewater treatment processes that promise greater efficiency and sustainability. This study presents an innovative approach for municipal wastewater treatment that integrates high-rate activated sludge with membrane bio-reactor (HRAS-MBR), reverse osmosis (RO) and partial nitrification-anammox (PN/A). With an influent of 8.4 m³/d, the HRAS-MBR demonstrated a removal efficiency of approximately 85 % for chemical oxygen demand (COD), with over 70 % of it being recovered for energy production. The RO system achieved a recovery rate of 75 % for the influent, producing pure water with an electrical conductivity of 50 μS/cm. Concurrently, it concentrated ammonia, thereby enhancing the effectiveness of the PN/A process for nitrogen removal in the mainstream, resulting in a removal efficiency exceeding 85 %. Notably, the HRAS-MBR achieved significant phosphorus removal without chemical additives, attributed to the presence of influent calcium and magnesium ions. Overall, this integrated system reduced the net energy consumption for reclaimed water production by about 26 % compared to conventional methods. Additionally, the new process produced a revenue of 0.75 CNY/m³, demonstrating considerable economic and environmental benefits. This pilot-scale study offers a viable alternative for wastewater treatment and water reuse in water-scarce regions, contributing to sustainable water resource management.

Linear symmetric self-selecting 14-bit kinetic molecular memristors.

Artificial Intelligence (AI) is the domain of large resource-intensive data centres that limit access to a small community of developers1,2. Neuromorphic hardware promises greatly improved space and energy efficiency for AI but is presently only capable of low-accuracy operations, such as inferencing in neural networks3-5. Core computing tasks of signal processing, neural network training and natural language processing demand far higher computing resolution, beyond that of individual neuromorphic circuit elements6-8. Here we introduce an analog molecular memristor based on a Ru-complex of an azo-aromatic ligand with 14-bit resolution. Precise kinetic control over a transition between two thermodynamically stable molecular electronic states facilitates 16,520 distinct analog conductance levels, which can be linearly and symmetrically updated or written individually in one time step, substantially simplifying the weight update procedure over existing neuromorphic platforms3. The circuit elements are unidirectional, facilitating a selector-less 64 × 64 crossbar-based dot-product engine that enables vector-matrix multiplication, including Fourier transform, in a single time step. We achieved more than 73 dB signal-to-noise-ratio, four orders of magnitude improvement over the state-of-the-art methods9-11, while consuming 460× less energy than digital computers12,13. Accelerators leveraging these molecular crossbars could transform neuromorphic computing, extending it beyond niche applications and augmenting the core of digital electronics from the cloud to the edge12,13.

Advancements in TinyML: Applications, Limitations, and Impact on IoT Devices

Artificial Intelligence (AI) and Machine Learning (ML) have experienced rapid growth in both industry and academia. However, the current ML and AI models demand significant computing and processing power to achieve desired accuracy and results, often restricting their use to high-capability devices. With advancements in embedded system technology and the substantial development in the Internet of Things (IoT) industry, there is a growing desire to integrate ML techniques into resource-constrained embedded systems for ubiquitous intelligence. This aspiration has led to the emergence of TinyML, a specialized approach that enables the deployment of ML models on resource-constrained, power-efficient, and low-cost devices. Despite its potential, the implementation of ML on such devices presents challenges, including optimization, processing capacity, reliability, and maintenance. This article delves into the TinyML model, exploring its background, the tools that support it, and its applications in advanced technologies. By understanding these aspects, we can better appreciate how TinyML is transforming the landscape of AI and ML in embedded and IoT systems.

H2-driven biocatalysis for flavin-dependent ene-reduction in a continuous closed-loop flow system utilizing H2 from water electrolysis

Despite the increasing demand for efficient and sustainable chemical processes, the development of scalable systems using biocatalysis for fine chemical production remains a significant challenge. We have developed a scalable flow system using immobilized enzymes to facilitate flavin-dependent biocatalysis, targeting as a proof-of-concept asymmetric alkene reduction. The system integrates a flavin-dependent Old Yellow Enzyme (OYE) and a soluble hydrogenase to enable H2-driven regeneration of the OYE cofactor FMNH2. Molecular hydrogen was produced by water electrolysis using a proton exchange membrane (PEM) electrolyzer and introduced into the flow system via a designed gas membrane addition module at a high diffusion rate. The flow system shows remarkable stability and reusability, consistently achieving >99% conversion of ketoisophorone to levodione. It also demonstrates versatility and selectivity in reducing various cyclic enones and can be extended to further flavin-based biocatalytic approaches and gas-dependent reactions. This electro-driven continuous flow system, therefore, has significant potential for advancing sustainable processes in fine chemical synthesis.

Defending Discomfort: A Critical Social Work Case Against Trigger Warnings

ABSTRACT Trigger warnings have become a hotly contested practice in higher education, including within the field of social work. Learning to become a social worker can be a demanding process that requires in-depth study about often socially taboo and traumatic topics. The learning process can, understandably, cause discomfort that may result in a disconcerting or anxiety-provoking experience for social work students. However, the inclusion of challenging topics in social work education, including human rights violations, domestic violence, sexual abuse, racism, sexism, and many other social injustices is essential in the development of competency in social work practice. What remains unclear is the role and responsibility of universities and subsequently educators, in how we include or exclude trigger warnings, their relevance to social work education, and how we manage the expectations of students and their responses to the exposure of sensitive material. This article argues that trigger warnings may contribute to the pathologization of the experience of discomfort while simultaneously silencing oppressed students who may not identify with the language or experience of being triggered. This article investigates trigger warnings from a critical social work perspective and considers if the use of trigger warnings may potentially be more harmful than helpful.

Enhancing Energy Efficiency and Mitigating Environmental Degradation through Anaerobic Co-digestion of Palm Oil Mill Effluent and Solid Residues

The utilization of palm oil mill residues for sustainable biogas production presents a promising avenue to mitigate environmental challenges associated with waste disposal and energy demand in palm oil processing. The study explores the use of palm oil mill residues for sustainable biogas production, focusing on the impact of mixing ratios of POME and three raw milled residues on biogas yield through anaerobic co-digestion. Results indicate that co-digestion significantly enhanced biogas production compared to mono-digestion of POME alone. The highest biogas yield of 370 ml was recorded in the digester with 40% residues followed by 50% with a cumulative biogas of 190 ml. The two digesters were higher than 80 ml recorded for POME alone. The findings underscore the potential of integrating palm oil mill residues into biogas systems to achieve dual objectives of waste management and renewable energy generation

Testing of battery separators: transducers and methods for air-coupled ultrasonic sensing.

Abstract Battery separator me3mbranes are thin polymeric microporous films placed between battery electrodes. They are a key component that strongly affects both battery performance and security and controlling its main properties is critical for the industry. In particular, the manufacturing process demands tests that allow checking both the mechanical integrity (porosity, thickness, pore size, etc.) of the materials and the consistency of these properties during the construction of the batteries. The use of an air-coupled ultrasonic system is proposed in this work. It is expected that this will make possible the non-destructive and quick testing of these materials, in order to evaluate thickness, stiffness, pore distribution and pore size of the separator. By means of three pairs of transducers, the modulus and phase of the transmission coefficient was measured from 0.35 to 1.4 MHz in different materials. The measurements show a clear modification of the film response produced by the presence of porosity. This suggests that the transmission of ultrasonic waves in battery separators using air-coupled ultrasound may have two contributions: one corresponds to the propagation though the solid part, the other corresponds to propagation in the pores. The former allows to determine properties like film thickness and elastic constants, while the latter allows to determine porosity and pore size and pore tortuosity. This work set the basis for the development of a quality test for the in-line inspection of these materials during their fabrication. The observed ultrasonic response in microporous separators suggest the possibility to use low frequency air-coupled transducers with limited sensitivity, where the use of MEM transducers at industrial scale can be advantageous.

Vigilant Attention During Cognitive and Language Processing in Aphasia.

Persons with aphasia (PWA) experience differences in attention after stroke, potentially impacting cognitive/language performance. This secondary analysis investigated physiologically measured vigilant attention during linguistic and nonlinguistic processing in PWA and control participants. To evaluate performance and attention in a language task, seven PWA read sentences aloud (linguistic task) and were compared to a previous data set of 10 controls and 10 PWA. To evaluate performance and attention in a language-independent task, 11 controls and nine PWA completed the Bivalent Shape Task (nonlinguistic task). Continuous electroencephalogram (EEG) data were collected during each session. A previously validated EEG algorithm classified vigilant-attention state for each experiment trial into high, moderate, distracted, or no attention. Dependent measures were task accuracy and amount of time spent in each attention state (measured by the number of trials). PWA produced significantly more errors than controls on the linguistic task, but groups performed similarly on the nonlinguistic task. During the linguistic task, controls spent significantly more time than PWA in a moderate-attention state, but no statistically significant differences were found between groups for other attention states. For the nonlinguistic task, amount of time controls and PWA spent in each attention state was more evenly distributed. When directly comparing attention patterns between linguistic and nonlinguistic tasks, PWA showed significantly more time in a high-attention state during the linguistic task as compared to the nonlinguistic task; however, controls showed no significant differences between linguistic and nonlinguistic tasks. This study provides new evidence that PWA experience a heightened state of vigilant attention when language processing demands are higher (during a linguistic task) than when language demands are lower (during a nonlinguistic task). Collectively, results of this study suggest that when processing language, PWA may allocate more attentional resources than when completing other kinds of cognitive tasks.

Double‐sided queues and their applications to vaccine inventory management

AbstractWe consider a double‐sided queueing model with batch Markovian arrival processes (BMAPs) and finite discrete abandonment times, which arises in various stochastic systems such as perishable inventory systems and financial markets. Customers arrive at the system with a batch of orders to be matched by counterparts. While waiting to be matched, customers become impatient and may abandon the system without service. The abandonment time of a customer depends on its batch size and its position in the queue. First, we propose an approach to obtain the stationary joint distribution of age processes via the stationary analysis of a multi‐layer Markov modulated fluid flow process. Second, using the stationary joint distribution of the age processes, we derive a number of queueing quantities related to matching rates, fill rates, sojourn times and queue length for both sides of the system. Last, we apply our model to analyze a vaccine inventory system and gain insight into the effect of uncertainty in supply and demand processes on the performance of the inventory system. It is observed that BMAPs are better choices for modeling the supply/demand process in systems with high uncertainty for more accurate performance quantities.

Additive manufacturing of quartz glass using coaxial wire feeding technology

This study explores the process of coaxial wire feeding and melting for quartz additive manufacturing using Direct Energy Deposition (DED). Quartz glass, valued for its excellent mechanical, optical, and thermal properties, is widely used in semiconductor, aerospace, and optical communication industries. Additive manufacturing offers advantages such as cost-effective materials, rapid formation, and customization of structures. However, current methods for additive manufacturing of quartz glass often result in low quality or require complex post-processing, particularly for optical components. In optics, there is a demand for high-quality, straightforward processes, and custom-shaped quartz glass. This article presents a coaxial wire feeding mechanism wherein quartz wire is fed vertically downward along the optical axis into the melting zone created by a CO2 laser, facilitating smooth material deposition onto the molten zone’s surface. Laser power, wire feeding speed, substrate movement speed, and defocusing distance are key factors influencing the quality of single-layer quartz glass formation. Positive defocusing was found to have a beneficial impact on single-layer quartz glass additive manufacturing. Subsequently, optimized process parameters enabled the production of uniformly cross-sectional single-layer quartz glass. Microscopic morphology analysis and temperature field simulation were conducted on the experimental results before and after optimization. Finally, complex quartz glass structures were additively manufactured using the optimized experimental parameters. Results demonstrate that coaxial wire feeding technology has significant potential for achieving uniform dimensions and complex structures in quartz glass additive manufacturing.

Parallel PSO for Efficient Neural Network Training Using GPGPU and Apache Spark in Edge Computing Sets

The training phase of a deep learning neural network (DLNN) is a computationally demanding process, particularly for models comprising multiple layers of intermediate neurons.This paper presents a novel approach to accelerating DLNN training using the particle swarm optimisation (PSO) algorithm, which exploits the GPGPU architecture and the Apache Spark analytics engine for large-scale data processing tasks. PSO is a bio-inspired stochastic optimisation method whose objective is to iteratively enhance the solution to a (usually complex) problem by approximating a given objective. The expensive fitness evaluation and updating of particle positions can be supported more effectively by parallel processing. Nevertheless, the parallelisation of an efficient PSO is not a simple process due to the complexity of the computations performed on the swarm of particles and the iterative execution of the algorithm until a solution close to the objective with minimal error is achieved. In this study, two forms of parallelisation have been developed for the PSO algorithm, both of which are designed for execution in a distributed execution environment. The synchronous parallel PSO implementation guarantees consistency but may result in idle time due to global synchronisation. In contrast, the asynchronous parallel PSO approach reduces the necessity for global synchronization, thereby enhancing execution time and making it more appropriate for large datasets and distributed environments such as Apache Spark. The two variants of PSO have been implemented with the objective of distributing the computational load supported by the algorithm across the different executor nodes of the Spark cluster to effectively achieve coarse-grained parallelism. The result is a significant performance improvement over current sequential variants of PSO.

Res-GCN: Identification of protein phosphorylation sites using graph convolutional network and residual network

An essential post-translational modification, phosphorylation is intimately related with a wide range of biological activities. The advancement of effective computational methods for correctly recognizing phosphorylation sites is important for in-depth understanding of various physiological phenomena. However, the traditional method of identifying phosphorylation sites experimentally is time-consuming and laborious, which makes it difficult to meet the processing demands of today's big data. This research proposes the use of a novel model, Res-GCN, to recognize the phosphorylation sites of SARS-CoV-2. Firstly, eight feature extraction strategies are utilized to digitize the protein sequence from multiple viewpoints, including amino acid property encodings (AAindex), pseudo-amino acid composition (PseAAC), adapted normal distribution bi-profile Bayes (ANBPB), dipeptide composition (DC), binary encoding (BE), enhanced amino acid composition (EAAC), Word2Vec, and BLOSUM62 matrices. Secondly, elastic net is utilized to eliminate redundant data in the fused matrix. Finally, a combination of graph convolutional network (GCN) and residual network (ResNet) is used to classify the phosphorylated sites and output predictions using a fully connected layer (FC). The performance of Res-GCN is tested by 5-fold cross-validation and independent testing, and excellent results are obtained on S/T and Y datasets. This demonstrates that the Res-GCN model exhibits exceptional predictive performance and generalizability.

Preliminary treatment of landfill leachate by hydrodynamic cavitation supported by Fenton process

Hydrodynamic cavitation as an effective and environmentally friendly method of treating wastewater. Massive amounts of energy may be released into the surrounding liquid during hydrodynamic cavitation, resulting in mechanical , chemical and thermal impacts. Bacteria and organic materials in sewage can be broken down by these circumstances. Furthermore, a coupling effect may be created by combining hydrodynamic cavitation with other water treatment techniques. In this study it is aim to investigate and improve the hydrodynamic cavitation (HDC) process supported by Fenton process for the pretreatment of landfill leachate. In the second phase of the study, the effectiveness of the hydrodynamic cavitation process was investigated in conjunction with the Fenton process. The parameters such as the number of cavitation events, pH, and temperature were evaluated. The effluent was characterized and monitored for COD measurements. The consequences of operational variables such H2O2, Fe+2 , and pH values were investigated to determine the optimal Fenton oxidation process parameters. The findings of the experiment showed that pH values were ideal for Fenton oxidation of 3.5-4.5, 30 mM H2O2, and 5 mM Fe+2. A combined treatment process of Fe+2+H2O2, HDC + Fenton, and Cavitation alone were conducted for the treatment of landfill leachate. The results showed that the removal rates of chemical oxygen demand (COD) for the combined processes were 32.85%, 44.28%, and 7%, respectively. Temperature, pH, and the number of cavitation events were among the parameters that were assessed. The effluent was measured for COD and was characterized.

Generation of a tripartite photonic state via a double-Λ configuration in a four-level system

Triphotons have a more abundant energy structure compared to biphotons. Furthermore, as the number of photons increases, excellent properties such as entangled multi-qubit states, high security, flexibility, and information capacity are observed. This leads to a growing demand for multi-body quantum information processing. Here, a method is proposed to generate a three-photon entangled state using a single six-wave mixing process in an atomic ensemble. The research examines the temporal correlation characteristics of the triphoton produced in photon coincidence counting measurements, with a focus on the linear and nonlinear susceptibilities of the six-wave mixing process. These properties primarily depend on the fifth-order nonlinear coupling coefficients responsible for the damping Rabi oscillations and the group delay determined by the longitudinal detuning function. To enhance the nonlinear interaction between the optical field and the atomic ensemble, placing the atomic ensemble in a high-quality cavity and utilizing laser cooling techniques to eliminate the internal Doppler broadening effect in the atomic gas hold promise.

Design and Assessment of Fighter Pilot Assistance Systems for Air-to-Air Refuelling with Probe-and-Drogue-Equipment

Air-to-air refuelling (AAR) with a probe-and-drogue system is a highly demanding process for fighter pilots. In the frame of the project “Future Air-to-Air Refuelling” (F(AI)2R) the German Aerospace Center (DLR) is designing concepts for assistance systems to support fighter pilots during this manoeuvre. The AAR process was examined using a hierarchical task analysis (HTA) based on a literature review and semi-structured interviews with Eurofighter-, Tornado- and test pilots. “Situation Awareness Requirements” (SAR), which represent relevant parameters for the situation awareness of the fighter pilot were derived from the HTA and rated by pilots according to their relevance. The collected data was then used to design different concepts of pilot assistance systems using a morphological box and a decision matrix. The concepts were visualised as storyboards, assessed by test pilots of the “Wehrtechnische Dienststelle 61” (WTD 61) and optimized based on their feedback. In this paper, the used methodology as well as the results of the HTA and SAR analysis are presented. Besides that, storyboards for pilot assistance systems displaying the overtake speed “German Patent No. 10.2022.110.505” and the “Contact Assistance, Status and Warning System” (CASWS) “German Patent No. 10.2022.123.653” are described. Additionally, the planned implementation using a HoloLens 2 is explained and the strategy for the subsequent simulator study on the DLR fighter aircraft simulator “Military AiR vehicle Simulator – Fast Integration Testbed” (MARS-FIT) is described.

Proteomic insights into adventitious root formation in Larix kaempferi

The adventitious root formaton (ARF) in excised plant parts is essential for the survival of isolated plant fragments. In this study, we explored the complex mechanisms of ARF in Larix kaempferi by conducting a comprehensive proteomic analysis across three distinct stages: the induction of adventitious root primordia (C1, 0–25 d), the formation of adventitious root primordia (C2, 25–35 d), and the elongation of adventitious roots (C3, 35–45 d). We identified 1976 proteins, with 263 and 156 proteins exhibiting increased abundance in the C2/C1 and C3/C2 transitions, respectively. In contrast, a decrease in the abundance of 106 and 132 proteins suggests a significant demand for metabolic processes during the C2/C1 phase. The abundance of IAA-amino acid hydrolase and S-adenosylmethionine synthase were increased in the C2/C1 phase, underscoring the role of auxin in adventitious root induction. The decrease in abundance of photosynthesis-related proteins during the C2/C1 phase highlights the significance of initial light conditions in adventitious root induction. Moreover, variation in cell wall synthesis and metabolic proteins in the C2/C1 and C3/C2 stages suggests that cell wall metabolism is integral to adventitious root regeneration. Gene Ontology enrichment analysis revealed pathways related to protein modification enzymes, including deubiquitinases and kinases, which are crucial for modulating protein modifications to promote ARF. Furthermore, the increased abundance of antioxidant enzymes, such as peroxidases and glutathione peroxidases, indicates a potential approach for enhancing ARF by supplementing the culture medium with antioxidants. Our study provides insights into metabolic changes during ARF in L. kaempferi, offering strategies to enhance adventitious root regeneration. These findings have the potential to refine plant propagation techniques and expedite breeding processes. SignficanceThe main challenge in the asexual reproduction technology of Larix kaempferi lies in adventitious root formation (ARF). While numerous studies have concentrated on the efficiency of ARF, proteomic data are currently scarce. In this study, we collected samples from three stages of ARF in L. kaempferi and subsequently performed proteomic analysis. The data generated not only reveal changes in protein abundance but also elucidate key metabolic processes involved in ARF. These insights offer a novel perspective on addressing the challenge of adventurous root regeneration.

Even laypeople use legalese

Whereas principles of communicative efficiency and legal doctrine dictate that laws be comprehensible to the common world, empirical evidence suggests legal documents are largely incomprehensible to lawyers and laypeople alike. Here, a corpus analysis (n = 59) million words) first replicated and extended prior work revealing laws to contain strikingly higher rates of complex syntactic structures relative to six baseline genres of English. Next, two preregistered text generation experiments (n = 286) tested two leading hypotheses regarding how these complex structures enter into legal documents in the first place. In line with the magic spell hypothesis, we found people tasked with writing official laws wrote in a more convoluted manner than when tasked with writing unofficial legal texts of equivalent conceptual complexity. Contrary to the copy-and-edit hypothesis, we did not find evidence that people editing a legal document wrote in a more convoluted manner than when writing the same document from scratch. From a cognitive perspective, these results suggest law to be a rare exception to the general tendency in human language toward communicative efficiency. In particular, these findings indicate law's complexity to be derived from its performativity, whereby low-frequency structures may be inserted to signal law's authoritative, world-state-altering nature, at the cost of increased processing demands on readers. From a law and policy perspective, these results suggest that the tension between the ubiquity and impenetrability of the law is not an inherent one, and that laws can be simplified without a loss or distortion of communicative content.