Typical Performance Research Articles

Modelling construction performance variability for probabilistic time estimation of tunnelling projects

ABSTRACT Budget overrun and schedule delay of infrastructure projects result in the mismanagement of huge amounts of resources. Studies show that accounting for uncertainties in time and cost estimation of such projects can help the decision-makers in better understanding the involved risks. Several probabilistic models have been developed during the last two decades to facilitate such estimations. A common aspect of all these models is the subjective assessment of unit activities' duration, which affects the accuracy of the estimated time and cost significantly. Despite this, the mechanism governing the variability of unit activities' duration has seldom been studied. Thus this paper focuses on addressing this gap by using a unique set of data from a tunnel project. The variability in unit activities' duration (construction performance variability) is governed by three main components: typical performance variability, minor machinery delays and minor performance delays. Using these components, a novel method for modelling construction performance variability in probabilistic time estimation of tunnelling projects is proposed. The application of the proposed method is demonstrated through a case example with a discussion on its important aspects, advantages and limitations. The findings of this paper offer a resource to improve the accuracy of time estimation for tunnelling projects.

An Enhanced State-Space Modeling for Detecting Abnormal Aging in VRLA Batteries

The knowledge of battery aging is an indicator that allows controlling the performance of large battery banks. State of Health (SOH) is typically the metric used, encompassing all possible mechanisms in a percentage indicator, with the Coulomb Counting as the most common method. Hence, an in-depth study of aging based on known models provides proper information for correctly managing batteries. This article proposes an aging-sensitive 3-RC-array-equivalent electrical circuit model to characterize the behavior of batteries throughout their useful life, identifying parametric changes as complementary information to the state of health. This model was validated based on experimental tests with 2 V and 6 Ah VRLA batteries aged according to the manufacturer’s recommended use. The results reveal a proportionality through capacity degradation. Then, a control group of batteries was subjected to overcharge and over-discharge conditions. The information given by Coulomb Counting SOH and the proposed method were evaluated. The proposed method provides additional information to the SOH, enhancing the distinguishing capability between typical aging performance and misused aging performance, resulting in a useful tool capable of identifying the aging associated with parametric changes in a time-invariant system where aging is treated as an imminent multiplicative fault.

보편적 학습설계와 증거기반교수를 적용한 수학 수업이 초등학생의 곱셈·나눗셈 성취도와 수학적 학습 태도에 미치는 효과

Objectives The purposes of this study was to examine the effects of mathematics instruction, incorporate Universal Design for Learning(UDL) and evidence-based instruction, on the multiplication and division achievement and mathematical learning attitudes of 4th grade elementary school students with typical or low achievement in mathematics. Methods The study involved 46 subjects, comprising students from two 4th-grade classes in a public elementary school in Seoul. The subjects were divided into experimental and control groups, with each group further subdivided based on their level of mathematics achievement. The experimental design employed a pretest-posttest control group design along with a repeated measures design. Improvements in students’ multiplication and division achievements were analyzed using a hierarchical linear growth model, while differences in attitudes towards mathematical learning were assessed using paired samples t-tests and analysis of covariance(ANCOVA). Results First, the rate of change in multiplication and division achievement among students with typical math performance in the experimental group was statistically significantly higher than that in the control group. Second, the rate of change in multiplication and division achievement among students with low math performance in the experimental group was significantly higher than that in the control group. Third, within the experimental group, students with low math performance demonstrated a significantly greater rate of improvement in achievement compared to students with typical math performance. Fourth, students in the experimental group showed statistically significant improvement in most areas of mathematical learning attitudes. Finally, there was no statistically significant difference in mathematical learning attitudes between the two subgroups of the experimental group, except for their level of confidence in multiplication and division. Conclusions First, mathematics instruction incorporating Universal Design for Learning (UDL) and evidencebased instruction was effective in improving multiplication and division achievement as well as mathematical learning attitudes among 4th grade elementary students. Finally, it can be inferred that this approach could be an effective method for ensuring foundational academic skills and bridging achievement gaps in general education settings with diverse student characteristics.

Analysis of the potential of the electroactive biofilm growth in a microbial fuel cell type H

Microbial fuel cells (MFC) constitute an attractive alternative as an environmental remediation technology since they can generate electrical current using organic waste as a substrate. Since the performance of MFCs depends on the characteristics of the biofilm on the anode surface, it is important to assess the genetic information of the microorganisms that grow on the electrode. For this purpose, a sewage sludge sample was obtained from a wastewater treatment plant and used to inoculate a type H MFC. Electrochemical characterization, on one hand, indicates that while the biofilm has a typical electrochemical performance reflected by the generated voltage (near 0.4 V) and by the electroactivity observed in cyclic voltammetry experiments, and on the other hand, the metagenomic analysis shows that the most abundant genera are Pseudomonacea, Nitrosomonas, Hyphomonas, and Opitutus. The study also indicates that the biofilm’s electroactive microorganisms can metabolize amino acids, lipids, and carbohydrates and possess genetic tools for ionic transport and energy production. Regarding the electron acceptor/donator capabilities, several oxidases, reductases, and complexes were identified, mainly terminal cytochrome C oxidase and respiratory complex I, which could be associated with the exoelectrogenic capacity of the microorganisms. Finally, the metagenomic information indicates that the biofilm can synthesize rhamnose, sialic acid, and alginate molecules, which could possibly be associated with the formation and consolidation of the microbial biofilm.

A Three-Stage Dynamic Comparator for SAR ADC Optimized for Reduced Kickback Noise and Ultra-Low Delay

This paper presents a novel footless single clock-phase three-stage comparator with internally generated regenerative voltage signals for low kickback noise and high speed. The preamplifier and clocked latch topology of dynamic comparators are considered in this brief. The proposed design has been compared by analyzing and optimizing three state-of-the-art comparator designs: Modified StrongARM, Miyahara’s and Three-Stage. These designs are simulated using the 40[Formula: see text]nm CMOS technology process. The area of the proposed comparator is 28.025[Formula: see text][Formula: see text]m2. When simulated in the SPICE-based simulator HSPICE, under typical performance conditions of 0.9[Formula: see text]V supply voltage, 25∘C, 1 GHz operational frequency, typical process corner (tt), and 1[Formula: see text]mV differential voltage, the proposed comparator produces the best post-layout results, with a minimum delay of 59.9[Formula: see text]ps from meta-stability analysis, energy per comparison of 0.175[Formula: see text]pJ/comparison and a kickback noise of 44.55 [Formula: see text]A. The proposed design also shows a significant improvement in the measured performance parameters over the other state-of-the-art dynamic comparators that have been discussed in this brief. The obtained results show that the proposed comparator is appropriate for 12-bit Successive Approximation Resister Analog-to-Digital Converters (SAR-ADCs) in IoT applications.

Occupational performance patterns in children with paediatric acute-onset neuropsychiatric syndrome.

Paediatric acute-onset neuropsychiatric syndrome (PANS) is a neuroimmune condition characterised by episodic exacerbations and remissions. This study investigates the impact of PANS on children's occupational performance during these phases, using the Vineland Adaptive Behaviour Scale (VABS). Understanding these variations is crucial for developing tailored interventions and managing the condition effectively. A two-period bidirectional case-crossover design was employed to assess occupational performance in children with PANS at exacerbation (T-E) and remission (T-R) phases. Parents of children aged 4.6-13.1years with PANS were recruited globally, with data collected via online surveys and monthly updates. The VABS evaluated occupational performance, with analysis using linear mixed models and Least Squares Means (LSMeans) for accurate representation. Data from 27 participants showed significantly lower scores in the Adaptive Behaviour Composite and socialisation domain of the VABS at T-E compared with T-R, indicating a decline in occupational performance and social skills during exacerbations. During remission, participants demonstrated typical performance across all domains. The study highlights a marked decline in occupational performance and socialisation during PANS exacerbations, with improvements to typical levels during remission. These findings emphasise the need for targeted interventions to address occupational and social challenges in children with PANS during exacerbations, underscoring the episodic nature of the condition and the risk of misinterpreting behaviours if assessed during exacerbations. Multidisciplinary allied health support may benefit children with PANS. Although there was no direct consumer and community involvement, two researchers on our team have family members with PANS. Their personal experiences provided vital insights into the challenges faced by children with PANS, deeply influencing our study's design, focus, and interpretation, ensuring it reflects the realities of those affected. Our study looked at how PANS affects the things children do in their daily lives. We asked parents of children ages 4.6-13.1years old to complete surveys measuring this at two times: once during a symptom flare-up and once during a period of wellness. Our results showed that during symptom flare-ups, children's social skills and general ability to do daily tasks were much lower compared with periods of wellness. When children were well, their abilities were similar to those of typically developing children of the same age. This highlights the need for occupational therapy during flare-ups to help improve the lives of children with PANS.

Effect of ethanol content, water injection, and compression ratio on combustion and detailed emissions in an SI-GDI engine

The transportation sector accounts for 29 % of the total greenhouse gas emissions produced in the US. Steadily tightening criteria emissions and fuel efficiency requirements have led to current research efforts evaluating multiple mixed strategies or extending beyond traditional engine architectures and fuels. Ethanol is used extensively as both a fuel additive and primary fuel replacement for gasoline. Modern engines utilizing boosting or high compression ratios require fuels such as ethanol that are more resistant to pre-ignition to operate efficiently at high load conditions. Water dilution has historically been used for knock reduction and some potential has been found for emissions and efficiency improvements. The studies on these topics, however, often focus on wide-open-throttle (WOT) conditions and neglect part load where engines normally operate. This investigation evaluates the combined effects of fuel ethanol content, compression ratio, and water dilution on the combustion and emissions characteristics A 2.4L 4-cylinder naturally aspirated (NA) gasoline direct injection (GDI) engine at part load conditions.The results indicated that the varied combinations of compression ratio and fuel ethanol content did not change the typical performance or emissions results expected for either parameter alone. Water dilution only significantly degraded stability at conditions with coefficient of variation (CoV) of indicated mean effective pressure (IMEP) values near or above 3 %. Water dilution at 10 % and 20 % W/F rations reduced average oxides of nitrogen (NOx) emissions by 20 % and 40 % respectively. The water dilution effect on unburned hydrocarbons (UHCs) was more mixed with flame ionization detector (FID) results showing mixed positive and negative effects for lower ethanol content fuels and up to a 20 % increase in UHC emissions with the high ethanol fuel (E85). Fourier transform infrared (FTIR) results agreed with the fuel blend UHC sensitivity reported with the FID. Ethanol emissions were found to increase by two to three times more for each level of water dilution than unburned gasoline emissions.

Experimental study on the compressive behavior of UHPC filled stainless steel tubes subjected to monotonic and cyclic loading

Ultra-High Performance Concrete Filled Stainless Steel Tubular (UHPCFSST) column is a novel type of composite columns appealing to be adopted in corrosive and marine environment. To explore the axial performance of UHPCFSST columns, this paper initiates an experimental program containing 14 UHPCFSST columns subjected to either monotonic or cyclical axial compressive loading. The test variables in this program mainly include the column dimension, tube thickness and the loading schemes. Based on the test results, the damage evolution process, ultimate failure mode, load-deformation response and the typical mechanical performance indexes inclusive of loading-carrying capacity, stiffness degradation, as well as ductility coefficient are thoroughly examined. After that, theoretical analysis is carried out to explore the interaction behavior between UHPC and stainless-steel tube and hence judge the degree of confinement effect. To assess the applicability of the code equations stipulated in current international design guidelines and the empirical formulas proposed by other researchers, a small-scale dataset containing 41 UHPCFSST columns was compiled to make comparison of axial capacity between test results and analytical predictions. In view of the limited accuracy or the burdensome calculation process, a new formula considering actual loading carrying mechanism was proposed to calculate the axial capacity of UHPCFSST columns, which exhibits enhanced accuracy and efficiency than the current formulas.

Enhancing global sensitivity and uncertainty quantification in medical image reconstruction with Monte Carlo arbitrary-masked mamba

Deep learning has been extensively applied in medical image reconstruction, where Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) represent the predominant paradigms, each possessing distinct advantages and inherent limitations: CNNs exhibit linear complexity with local sensitivity, whereas ViTs demonstrate quadratic complexity with global sensitivity. The emerging Mamba has shown superiority in learning visual representation, which combines the advantages of linear scalability and global sensitivity. In this study, we introduce MambaMIR, an Arbitrary-Masked Mamba-based model with wavelet decomposition for joint medical image reconstruction and uncertainty estimation. A novel Arbitrary Scan Masking (ASM) mechanism “masks out” redundant information to introduce randomness for further uncertainty estimation. Compared to the commonly used Monte Carlo (MC) dropout, our proposed MC-ASM provides an uncertainty map without the need for hyperparameter tuning and mitigates the performance drop typically observed when applying dropout to low-level tasks. For further texture preservation and better perceptual quality, we employ the wavelet transformation into MambaMIR and explore its variant based on the Generative Adversarial Network, namely MambaMIR-GAN. Comprehensive experiments have been conducted for multiple representative medical image reconstruction tasks, demonstrating that the proposed MambaMIR and MambaMIR-GAN outperform other baseline and state-of-the-art methods in different reconstruction tasks, where MambaMIR achieves the best reconstruction fidelity and MambaMIR-GAN has the best perceptual quality. In addition, our MC-ASM provides uncertainty maps as an additional tool for clinicians, while mitigating the typical performance drop caused by the commonly used dropout.

A quantitative model of temperature-dependent diapause progression

Winter diapause in insects is commonly terminated through cold exposure, which, like vernalization in plants, prevents development before spring arrives. Currently, quantitative understanding of the temperature dependence of diapause termination is limited, likely because diapause phenotypes are generally cryptic to human eyes. We introduce a methodology to tackle this challenge. By consecutively moving butterfly pupae of the species Pieris napi from several different cold conditions to 20 °C, we show that diapause termination proceeds as a temperature-dependent rate process, with maximal rates at relatively cold temperatures and low rates at warm and extremely cold temperatures. Further, we show that the resulting thermal reaction norm can predict P. napi diapause termination timing under variable temperatures. Last, we show that once diapause is terminated in P. napi, subsequent development follows a typical thermal performance curve, with a maximal development rate at around 31 °C and a minimum at around 2 °C. The sequence of these thermally distinct processes (diapause termination and postdiapause development) facilitates synchronous spring eclosion in nature; cold microclimates where diapause progresses quickly do not promote fast postdiapause development, allowing individuals in warmer winter microclimates to catch up, and vice versa. The unveiling of diapause termination as one temperature-dependent rate process among others promotes a parsimonious, quantitative, and predictive model, wherein winter diapause functions both as an adaptation against premature development during fall and winter and for synchrony in spring.

Proprioceptive intervention improves reading performance in developmental dyslexia: An eye-tracking study

Developmental dyslexia is characterized by difficulties in learning to read, affecting cognition and causing failure at school. Interventions for children with developmental dyslexia have focused on improving linguistic capabilities (phonics, orthographic and morphological instructions), but developmental dyslexia is accompanied by a wide variety of sensorimotor impairments. The goal of this study was to examine the effects of a proprioceptive intervention on reading performance and eye movement in children with developmental dyslexia. Nineteen children diagnosed with developmental dyslexia were randomly assigned to a regular Speech Therapy (ST) or to a Proprioceptive and Speech Intervention (PSI), in which they received both the usual speech therapy and a proprioceptive intervention aimed to correct their sensorimotor impairments (prism glasses, oral neurostimulation, insoles and breathing instructions). Silent reading performance and eye movements were measured pre- and post-intervention (after nine months). In the PSI group, reading performance improved and eye movements were smoother and faster, reaching values similar to those of children with typical reading performance. The recognition of written words also improved, indicating better lexical access. These results show that PSI might constitute a valuable tool for reading improvement children with developmental dyslexia.

Performance evaluation of a low power Hall thruster with carbon dioxide propellant

Carbon dioxide (CO2) is an attractive candidate for condensable propellants because its availability and storability. Although several studies have been reported on adopting CO2 as a propellant for Hall thrusters, there have been few studies on CO2 standalone operation, and none of them have focused on physical phenomena in plasmas. This study experimentally evaluated, thrust performance and plume characteristics of a 100 W-class Hall thruster under CO2 operation. CO2 operation was compared to xenon operation under the same discharge power conditions to determine its unique properties. As typical thrust performance under this experimental condition, a specific impulse of 727 s, thrust-power-ratio of 19.8 mN/kW, and anode efficiency of 0.0711 were achieved at a discharge voltage of 200 V and a discharge power of 352 W under a background pressure of 3.4 × 10−2 Pa. Plume measurements showed that mass utilization efficiency was 0.228 under CO2 operation, which was particularly poor compared to under xenon operation. The first reason for this is that the ionization mean free path of CO2 is 5.18 mm, about eight times longer than that of xenon. The second reason is that the energy is used for other processes, such as dissociative ionization and dissociation etc., considering the reaction process of the particles. Therefore, the results suggest narrowing the channel width to increase the channel density, and increasing the magnetic field strength to suppress the discharge current as ways to improve thrust efficiency.

Amplitude and duration hazard-consistent ground-motion selection for seismic risk assessment in Mexico City

The emphasis of seismic design regulations on applying nonlinear dynamic analyses (NDAs) promotes using accelerograms that characterize site-specific ground motions. Commonly, amplitude levels of such accelerograms are defined by a target spectrum that could be based on a uniform hazard spectrum (UHS), which is determined by a probabilistic seismic hazard analysis (PSHA) and represents a response spectrum with ordinates having an equal probability of being exceeded within a given return period, Tr\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${T}_{r}$$\\end{document}. Conversely, the definition of ground-motion duration levels is not yet properly defined in current regulations to select accelerograms. Thus, adhering to data handling as that for amplitude ground-motion parameters, this study motivates executing PSHAs to define hazard-consistent levels for the ground-motion duration. That is, accelerograms can be selected to match both amplitude and duration ground-motion levels associated with Tr\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${T}_{r}$$\\end{document}. Further, fragility functions conditional on Tr\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${T}_{r}$$\\end{document} that cover typical performance objectives can be developed using sets of hazard-consistent accelerograms to implement, e.g., multiple stripe analyses (MSAs). To demonstrate the importance of choosing fully hazard-consistent accelerograms to perform NDAs, this study includes the displacement- and energy-based seismic-response evaluation of a steel frame building located at different soil-profile sites in Mexico City. Sets of fully hazard-consistent accelerograms and solely amplitude-based hazard-consistent accelerograms were artificially generated per site for values of Tr\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${T}_{r}$$\\end{document} up to 5000 years. Results indicate that the probability of failure can be underestimated if the ground-motion duration is unvaried in MSAs, e.g., structural damage caused by 50-year return-period or higher events can be more noticeable when fully hazard-consistent accelerograms take place.

Black phosphorus quantum dots functionalized with photochromic poly(vinylspiropyran)-grafted polydopamine for transient digital-type memristors

Abstract The covalent functionalization of black phosphorus quantum dots (BPQDs) with organic species or polymers will inevitably change or damage their electronic structure and intrinsic structure. To address this problem and explore the application of BPQDs in transient digital-type memristors, a polydopamine (PDA) thin film is first synthesized in situ onto the surface of BPQDs to produce a donor–acceptor-type BPQDs@PDA composite that is directly used to react with 2-bromoisobutyryl bromide to give BPQDs@PDA-Br. By using BPQDs@PDA-Br as an atom transfer radical polymerization agent, a large number of polyvinylspiropyran (PSP) chains are in situ grown from the PDA surface to yield BPQDs@PDA-PSP. Upon ultraviolet (UV)–visible light illumination, the 2 isomers of the spiropyran (ring-closed spiropyran form and ring-opened merocyanine) in the PSP moieties will interconvert into each other rapidly. As expected, the as-fabricated indium tin oxide (ITO)/BPQDs@PDA-PSP/ITO device exhibits typical nonvolatile digital-type memristive performance under visible irradiation, with a small turn-on voltage of −1.52 V, a turn-off voltage of +1.16 V, and an ON/OFF ratio current ratio of 1.02 × 104. Upon UV illumination, the information stored in the device is quickly and completely erased within 6 s. By utilizing a simple memristor-based convolutional neural network, one can easily realize handwritten digit recognition. After 10 epochs of training, numeral recognition accuracy can reach up to 96.21%.

Cyclopentadithiophene-based conjugated polymer artificial synapses for dual information encryption

High-performance polymer memristors with analog-type switching behaviors and low power consumption, which is a perfect candidate for brainoid intelligence, have triggered the research campaign of the novel in-memory computing paradigm. A novel poly[cyclopentadithiophene- alt-thiophene] with triazole and ferrocene moieties in the sidechains (PCTF) is synthesized by copper-catalyzed azide-alkyne Click chemistry reaction of poly[4,4-bis(6-azidohexyl)-4H- cyclopenta(1,2-b: 5,4-b′) dithiophene-alt-thiophene], which is prepared by the reaction of poly[thiophene-alt-4,4- bis(6-bromohexyl)- 4H-cyclopenta(1,2-b:5,4-b′)dithiophene](PTT-Br) with NaN3, with ethynylferrocene. The PTT-Br film sandwiched between the Al and ITO electrodes shows a typical history-dependent memristive switching performance at a small sweep voltage range of ± 1 V, with 6 distinguishable conductance states. The device current can be modulated continuously when being subjected to consecutive positive and negative voltage sweeps. In contrast to PTT-Br, PCTF exhibits excellent nonvolatile memristive performance, with 16 distinguishable conductance states due to the combined action of charge transfer between the triazole and ferrocene moieties, and redox activity from both the ferrocene and polymer backbone. As an important component related to human memory, the short-term synaptic plasticity to long-term potentiation/plasticity transition is, for the first time, successfully used to realize dual information encryption at a high information security level. Only by inputting a given number of voltage pulses and at a specific time can classified information be correctly read out.

From brain to education through machine learning: Predicting literacy and numeracy skills from neuroimaging data

Abstract The potential of using neural data to predict academic outcomes has always been at the heart of educational neuroscience, an emerging field at the crossroad of psychology, neuroscience, and education sciences. Although this prospect has long been elusive, the exponential use of advanced techniques in machine learning in neuroimaging may change this state of affairs. Here we provide a review of neuroimaging studies that have used machine learning to predict literacy and numeracy outcomes in adults and children, in both the context of learning disability and typical performance. We notably review the cross-sectional and longitudinal designs used in such studies, and describe how they can be coupled with regression and classification approaches. Our review highlights the promise of these methods for predicting literacy and numeracy outcomes, as well as their difficulties. However, we also found a large variability in terms of algorithms and underlying brain circuits across studies, and a relative lack of studies investigating longitudinal prediction of outcomes in young children before the onset of formal education. We argue that the field needs a standardization of methods, as well as a greater use of accessible and portable neuroimaging methods that have more applicability potential than lab-based neuroimaging techniques.

Progressive Monitoring of Micro-Dumps Using Remote Sensing: An Applicative Framework for Illegal Waste Management

Illegal dumping is one of the major issues to be addressed by public managers in both developed and developing countries. The adequate tackling and enforcement of such unlawful activities require continuous territory monitoring, the lack of which is the principal cause of the failure of traditional systems based on patrolling, eventually triggered by sparse crowdsourced data. This work proposes the digitalization of the entire illegal waste management monitoring process through an innovative decision support system based on multiscale remote sensing data. These data enable the assessment of the risk level of specific areas, thus determining inspection priorities in line with the available patrolling resources. The proposed methodology provides a tool to assess the impact of diverse monitoring system parameters on overall the performance in light of the expected operating costs and to understand whether remote sensing can help a better management of illegal waste in a specific operational scenario, thus helping in the determination of the best cost–benefit trade-off. Simulation results over a demonstration scenario, based on realistic dumping/restoration dynamics and the typical performance of satellite detection systems, show that the adoption of remote sensing technologies in the monitoring process can increase the restoration capacity by about 10% compared to traditional patrolling.

Deliberate Starvation: Hunger Artists in Kafka, Różewicz, and Sinking Ship

How can a negative action, the decision to abstain from food, be enacted on stage? Examining hunger as a conscious choice to avoid food for spectacle rather than an unwanted need arising from lack of basic nourishment, this article illustrates several ways to make hunger visible in performance. Through the critical lens of actor-network theory (ANT), hunger’s apparently scarce dramatic action is shown to be framed by and networked with the performance of several other human and non-human actors. Case studies chart a process of “translation” of the typical hunger artist performance from its historical examples to its contemporary theatre-within-the-theatre adaptations. In the West, self-inflicted starvation became a form of entertainment more distinctly in the late nineteenth century, when living skeletons and hunger artists were shown at circuses, fairs, and amusement parks, and produced income for themselves and their impresarios. Franz Kafka’s short story “Ein Hungerkünstler” (“A Hunger Artist,” 1922) looked back at the history of the profession, identifying the main components of the spectacle of hunger. Polish playwright Tadeusz Różewicz turned the short story into a play, Odejście głodomora (The Hunger Artist Departs, 1977), exploring the potential for dialogic interactions at the textual level and developing side-characters only implied by Kafka. By contrast, the contemporary NYC-based company Sinking Ship created A Hunger Artist (2017), an adaptation that expanded the short story’s theatricality around a single performer who plays multiple characters with the aid of all the resources of theatre, from puppets to audience members “enrolled” in the show.

A deep autoencoder network connected to geographical random forest for spatially aware geochemical anomaly detection

Machine learning (ML) and deep learning (DL) techniques have recently shown encouraging performance in recognizing metal-vectoring geochemical anomalies within complex Earth systems. However, the generalization of these techniques to detect subtle anomalies may be precluded due to overlooking non-stationary spatial structures and intra-pattern local dependencies contained in geochemical exploration data. Motivated by this, we conceptualize in this paper an innovative algorithm connecting a DL architecture to a spatial ML processor to account for local neighborhood information and spatial non-stationarities in support of spatially aware anomaly detection. A deep autoencoder network (DAN) is trained to abstract deep feature codings (DFCs) of multi-element input data. The encoded DFCs represent the typical performance of a nonlinear Earth system, i.e., multi-element signatures of geochemical background populations developed by different geo-processes. A local version of the random forest algorithm, geographical random forest (GRF), is then connected to the input and code layers of the DAN processor to establish nonlinear and spatially aware regressions between original geochemical signals (dependent variables) and DFCs (independent variables). After contributions of the latter on the former are determined, residuals of GRF regressions are quantified and interpreted as spatially aware anomaly scores related to mineralization. The proposed algorithm (i.e., DAN‒GRF) is implemented in the R language environment and examined in a case study with stream sediment geochemical data pertaining to the Takht-e-Soleyman district, Iran. The high-scored anomalies mapped by DAN‒GRF, compared to those by the stand-alone DAN technique, indicated a stronger spatial correlation with locations of known metal occurrences, which was statistically confirmed by success-rate curves, Student's t‒statistic method, and prediction-area plots. The findings suggested that the proposed algorithm has an enhanced capability to recognize subtle multi-element geochemical anomalies and extract reliable insights into metal exploration targeting.

Grating design methodology for laser cooling

We present a design strategy for grating magneto-optical traps (GMOTs). It takes the three most relevant optical properties for laser cooling (radiation pressure balance, specular reflection cancellation, and diffracted polarization) to build a scalar figure of merit. We use a rigorous coupled wave analysis (RCWA) simulation to find a geometry that maximizes this figure of merit. We also introduce a criterion that takes into account the robustness of the manufacturing processes to select a geometry that is reliable to manufacture. Finally, we demonstrate that the fabricated grating exhibits the expected optical properties and achieves typical GMOT performance.