The irrigation network is a critical infrastructure network consisting of reservoirs, canals, and croplands. However, the operation of irrigation networks is often disrupted by natural disasters like droughts, which can lead to serious losses. Timely and effective maintenance can minimize the yield losses. Many maintenance strategies have been proposed to improve the efficiency of maintenance resource utilization and reduce costs. But in the existing literature, the maintenance of irrigation networks under the influence of droughts is still under-explored. To bridge this gap, this paper proposes a maintenance metric for improving the performance of an irrigation network. A novel optimum maintenance efficiency model is then developed to get the optimal resource allocation. A case study, which investigates an irrigation network consisting of 27 nodes, is used to verify the practicality and effectiveness of the proposed method.
The current study examined the associations among self-criticism, perceptions of autonomy support, and depression prior to and during the onset of the Covid pandemic. 283 students at a large Canadian university participated in a goal related study, and completed questionnaires assessing personality, autonomy support, and depressive symptoms starting in September of 2019 and ending in May of 2020. The results showed that self-criticism was associated with increases in depressive symptomatology, and that autonomy support was inversely associated with depression. The results also showed that autonomy support moderated the effect of self-criticism on depression such that individuals with higher baseline self-criticism who perceived high levels of autonomy support reported lower levels of depression during the beginning of the pandemic. These results confirm the deleterious impact of selfcriticism and the potential benefits of autonomy support. The presence of autonomy support appears to buffer those who are high in self-criticism from increased depressive symptoms. These results have important clinical implications, suggesting the need to address the perniciousness of self-criticism and the need to develop innovative ways to enhance the delivery of autonomy support.
The distribution of zooplankton groups, with an emphasis on fish larvae, in the Oxygen Minimum Zone off southern Mexico (December 2020) was analyzed. A hydrographic section of five sampling stations was made in the confluence of Transitional Water and Tropical Surface Waters. In each station, horizontal zooplankton trawls on three different dissolved oxygen conditions (~100, < 44 and < 4.4 μmol kg⁻¹) were carried out by a MOCNESS net (333 μm). The 100 μmol kg⁻¹ oxypleth (oxic condition) was ~60 m depth along the section, but the 4.4 μmol kg⁻¹ oxypleth (suboxic) rose southward from Transitional Water (~ 150 m) to Tropical Surface Water (~ 90 m), approaching the well oxygenated layer. The distribution of the zooplankton biomass, and the most abundant zooplankton groups (e.g. copepods, chaetognaths, ostracods, euphausiids) and fish larvae showed statistically significant differences (P < 0.01) between the oxic (100 μmol kg⁻¹) and the deeper suboxic conditions. The larvae of typically dominant fish species such as the bathypelagic Vinciguerria lucetia, Diogenichthys laternatus, Diaphus pacificus and Cubiceps pauciradiatus, were present only in the oxygenated depths in the Transitional Water, and were almost absent from all depths in the Tropical Surface Water, where the oxycline shoaled. These differences in larval fish abundance were found despite little change in chlorophyll a concentration (relative units “r.u.”) along the sections, indicating that the oxycline is a limiting factor for the fish larvae. The fish larvae results contrast with previous observations from the mouth of the Gulf of California, where some species have distributions independent of water column dissolved oxygen conditions, probably as a consequence of coastal processes. Overall, our results show that even within the OMZ, variations in oxycline depth have biological implications, particularly on meroplanktonic organisms.
Unmanned aerial vehicles (UAVs) have been widely adopted to assist infrastructure inspection tasks, since they have shown their potential to benefit the inspection in terms of efficiency, cost, and safety. To improve the effectiveness of the inspection, there has been a growing focus of recent research in integrating AI techniques into UAV infrastructure inspection and has achieved promising results. However, no prior work has studied whether such integration will also introduce new security concerns, especially considering the existence of potential vulnerabilities in underlying AI models towards adversarial inputs. In this paper, we perform the first study to fill this critical gap by identifying and validating the security vulnerabilities of AI models in the context of UAV infrastructure inspection with a focus on bridge infrastructure. To understand the security property of AI-assisted UAV bridge inspection, we design a two-stage approach for the construction of effective adversarial inputs, with which we successfully validate the existence of security vulnerability using dynamic analysis. Spatial constraints, physical limits, and dynamic environmental changes are taken into consideration in our analysis to make it practical in the physical world. Our evaluation results on a real-world dataset show that our constructed adversarial inputs can mislead the UAV to miss detecting a significant amount of risk-prone regions by exploiting the identified vulnerability. Based on such observation, this paper also discusses the defenses based on adversarial training to improve the robustness of AI-assisted UAV bridge inspection, which has been demonstrated to be effective according to our experimental evaluation results.
Wearable biosensors represent an opportunity to improve treatment and research into a variety of diseases, including substance use disorder. They provide continuous, real-time data about the wearer’s condition in their natural environment in an unobtrusive, increasingly capable, and cost-effective way. However, generating clinically relevant insights from high-velocity, noisy, multidimensional data streams requires new approaches in real-time anomaly machine learning (ML). We present a survey of the existing algorithms for substance use monitoring in wearable biosensor data streams and how the advent of 5G and 6G wireless communications will drive further changes in this field. Our work highlights trends that have emerged among the different efforts published to-date as well as identifying ongoing challenges not adequately addressed by existing ML algorithms.
In this randomized controlled trial with controls for student characteristics, the authors used a loss-aversion strategy to test whether students achieve greater learning gains from combining research-based instructional strategies with loss aversion. Students in the control group began the class with no grades and built their course average by completing assignments and exams. The treatment group began with a score of 100 percent on each assignment and viewed the deductions on scores from errors and missed work. The results indicated that students in the treatment group experienced gains in learning that were, on average, 5 to 13 percentage points larger than those of the control group. These learning gains were concentrated among students with low SAT math scores.
Over the past several decades, machine learning has been used in many areas—including education. In this article, we generally describe artificial intelligence (AI) and machine learning (ML) and discuss how they relate to one another using everyday examples. Next, we zoom in on ML in terms of its definition, some well-known approaches, its historical roots, and uses in education. We then illustrate how ML can aid instructors and students relative to teaching and learning and present some ideas regarding the future of ML in education. We conclude with some important concerns related to ML applications in education moving forward.
We perform portfolio-level analyses to understand insurance firms’ preferred habitat behavior in the government bond market. Based on portfolio durations and portfolio weights across maturities, we find that interest rate risk exposures of insurers’ portfolios are related to their operating liabilities and financing constraints. We show that this habitat behavior significantly affects bond pricing. During the “quantitative easing” era, bond purchases by the Federal Reserve have a larger impact on the yields of Treasury bonds with a higher habitat demand. (JEL E43, E52, G11, G12, G23) Received February 28, 2019; editorial decision September 15, 2022 by Editor Thierry Foucault. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.
We present a reduced-order surrogate model of gravitational waveforms from nonspinning binary black hole systems with comparable to large mass-ratio configurations. This surrogate model, BHPTNRSur1dq1e4, is trained on waveform data generated by point-particle black hole perturbation theory (ppBHPT) with mass ratios varying from 2.5 to 10,000. BHPTNRSur1dq1e4 extends an earlier waveform model, EMRISur1dq1e4, by using an updated transition-to-plunge model, covering longer durations up to 30,500m1 (where m1 is the mass of the primary black hole), includes several more spherical harmonic modes up to ℓ=10, and calibrates subdominant modes to numerical relativity (NR) data. In the comparable mass-ratio regime, including mass ratios as low as 2.5, the gravitational waveforms generated through ppBHPT agree surprisingly well with those from NR after this simple calibration step. We also compare our model to recent SXS and RIT NR simulations at mass ratios ranging from 15 to 32, and find the dominant quadrupolar modes agree to better than ≈10−3. We expect our model to be useful to study intermediate-mass-ratio binary systems in current and future gravitational-wave detectors.
BACKGROUND Standard of care (SOC) and patient survival in glioblastoma have changed little in the past 17 years. We evaluated in a phase 3 trial whether adding an autologous tumor lysate-loaded dendritic cell vaccine (murcidencel) to SOC extends survival. Patients and METHODS Newly diagnosed glioblastoma patients were randomized 2:1 to either murcidencel or placebo. Under a crossover design, all patients could receive murcidencel following tumor recurrence. All parties remained blinded regarding treatments before recurrence. Patients thus received murcidencel at new diagnosis (nGBM) or at recurrence (rGBM) following crossover from placebo. The primary and secondary endpoints compare overall survival (OS) with contemporaneous, matched external controls. Four sets of analyses were conducted to ensure rigorous matching of the controls, reduce biases, and confirm the robustness of the results. RESULTS 331 patients were enrolled. With the crossover, 89% received murcidencel. Median OS (mOS) for nGBM patients (n = 232) was 19.3 months from randomization (22.4 months from surgery) with murcidencel vs. 16.5 months from randomization in the controls (HR = 0.80, p = 0.002). Survival at 48 months from randomization was 15.7% vs. 9.9%, and at 60 months was 13% vs. 5.7%. For rGBM (n = 64), mOS was 13.2 months from relapse vs. 7.8 months in the controls (HR = 0.58, p < 0.001). Survival at 24 months post-recurrence was 20.7% vs. 9.6%, and at 30 months post-recurrence was 11.1% vs 5.1%. In nGBM patients with methylated MGMT (n = 90), mOS was 30.2 months from randomization (33 months from surgery) with murcidencel vs. 21.3 months from randomization in the controls (HR = 0.74, p = 0.027). The treatment was well tolerated, with only 5 serious adverse events deemed at least possibly related to the vaccine. CONCLUSION Clinically meaningful and statistically significant survival extension was seen in both nGBM and rGBM patients treated with murcidencel and SOC compared with contemporaneous, matched external controls who received SOC alone.
Deep learning-based classification algorithms have been used for automatic modulation recognition (AMR). However, most methods only focus on end-to-end mapping and neglect the classic key features. In this paper, signals are enforced with key classification features to propose a novel deep learning model for AMR by learning the shared latent space of the aligned signals and key features (LLAF); this is done to increase the generalizability of the model and to ensure the physical plausibility of the results. To obtain adequate signal representations, an encoder-decoder architecture is proposed to learn the shared latent space, and the architecture is trained to approximate prior label distributions for precise signal classification. Simulation results verify the high recognition accuracy of the proposed LLAF model under different signal-to-noise ratios (SNRs).
Understanding the effects of climate-mediated environmental variation on the distribution of organisms is critically important in an era of global change. We used wavelet analysis to quantify the spatiotemporal (co)variation in daily water temperature for predicting the distribution of cryptic refugia across 16 intertidal sites that were characterized as ‘no’, ‘weak’ or ‘strong’ upwelling and spanned 2000 km of the European Atlantic Coast. Sites experiencing weak upwelling exhibited high synchrony in temperature but low levels of co-variability at monthly to weekly timescales, whereas the opposite was true for sites experiencing strong upwelling. This suggests upwelling generates temporal thermal refugia that can promote organismal performance by both supplying colder water that mitigates thermal stress during hot Summer months and ensuring high levels of fine-scale variation in temperature that reduce the duration of thermal extremes. Additionally, pairwise correlograms based on the Pearson-product moment correlation coefficient and wavelet coherence revealed scale dependent trends in temperature fluctuations across space, with a rapid decay in strong upwelling sites at monthly and weekly timescales. This suggests upwelling also generates spatial thermal refugia that can ‘rescue’ populations from unfavorable conditions at local and regional scales. Overall, this study highlights the importance of identifying cryptic spatiotemporal refugia that emerge from fine-scale environmental variation to map potential patterns of organismal performance in a rapidly changing world.
The study is aimed to explore the relationship between the social media influence on consumers and E-satisfaction (ESAT). A conceptual model is developed and tested using the data collected from 669 respondents from the southern part of India. After testing the psychometric properties of the survey instrument using the LISREL package of structural equation modeling (SEM), Hayes’s PROCESS macros were used to test the mediation, moderated moderated-mediation hypotheses. The results reveal social media influence is positively associated with ESAT and EWOM. The findings support the positive relationship between EWOM and ESAT. The results also support that EWOM mediates the relationship between social media influence and ESAT. Furthermore, the results support that risk moderates the relationship between EWOM and ESAT. Finally, the results provide strong support for a three-way interaction between EWOM, risk, and trust to influence the ESAT of consumers. The implications for marketing theory and practice are discussed.
Soil biosolarization (SBS) is an alternative technique for soil pest control to standard techniques such as soil fumigation and soil solarization (SS). By using both solar heating and fermentation of organic amendments, faster and more effective control of soilborne pathogens can be achieved. A circular economy may be created by using the residues of a given crop as organic amendments to biosolarize fields that produce that crop, which is termed circular soil biosolarization (CSBS). In this study, CSBS was employed by biosolarizing soil with amended tomato pomace (TP) residues and examining its impact on tomato cropping under conditions of abiotic stresses, specifically high salinity and nitrogen deficiency. The results showed that in the absence of abiotic stress, CSBS can benefit plant physiological performance, growth and yield relative to SS. Moreover, CSBS significantly mitigated the impacts of abiotic stress conditions. The results also showed that CSBS impacted the soil microbiome and plant metabolome. Mycoplana and Kaistobacter genera were found to be positively correlated with benefits to tomato plants health under abiotic stress conditions. Conversely, the relative abundance of the orders RB41, MND1, and the family Ellin6075 and were negatively correlated with tomato plants health. Moreover, several metabolites were significantly affected in plants grown in SS-and CSBS-treated soils under abiotic stress conditions. The metabolite xylonic acid isomer was found to be significantly negatively correlated with tomato Frontiers in Plant Science (2022) The effect of circular soil biosolarization treatment on the physiology, metabolomics, and microbiome of tomato plants under certain abiotic stresses.
Recapitulating inherent heterogeneity and complex microarchitectures within confined print volumes for developing implantable constructs that could maintain their structure in vivo has remained challenging. Here, we present a combinational multimaterial and embedded bioprinting approach to fabricate complex tissue constructs that can be implanted postprinting and retain their three-dimensional (3D) shape in vivo. The microfluidics-based single nozzle printhead with computer-controlled pneumatic pressure valves enables laminar flow-based voxelation of up to seven individual bioinks with rapid switching between various bioinks that can solve alignment issues generated during switching multiple nozzles. To improve the spatial organization of various bioinks, printing fidelity with the z-direction, and printing speed, self-healing and biodegradable colloidal gels as support baths are introduced to build complex geometries. Furthermore, the colloidal gels provide suitable microenvironments like native extracellular matrices (ECMs) for achieving cell growths and fast host cell invasion via interconnected microporous networks in vitro and in vivo. Multicompartment microfibers (i.e., solid, core-shell, or donut shape), composed of two different bioink fractions with various lengths or their intravolume space filled by two, four, and six bioink fractions, are successfully printed in the ECM-like support bath. We also print various acellular complex geometries such as pyramids, spirals, and perfusable branched/linear vessels. Successful fabrication of vascularized liver and skeletal muscle tissue constructs show albumin secretion and bundled muscle mimic fibers, respectively. The interconnected microporous networks of colloidal gels result in maintaining printed complex geometries while enabling rapid cell infiltration, in vivo.
4-Phosphoryloxy-N,N-dimethyltryptamine (psilocybin) is a naturally occurring tertiary amine found in many mushroom species. Psilocybin is a prodrug for 4-hydroxy-N,N-dimethyltryptamine (psilocin), which induces psychedelic effects via agonist activity at the serotonin (5-HT) 2A receptor (5-HT2A). Several other 4-position ring-substituted tryptamines are present in psilocybin-containing mushrooms, including the secondary amine 4-phosphoryloxy-N-methyltryptamine (baeocystin) and the quaternary ammonium 4-phosphoryloxy-N,N,N-trimethyltryptamine (aeruginascin), but these compounds are not well studied. Here, we investigated the structure-activity relationships for psilocybin, baeocystin, and aeruginascin, as compared to their 4-acetoxy and 4-hydroxy analogues, using in vitro and in vivo methods. Broad receptor screening using radioligand binding assays in transfected cells revealed that secondary and tertiary tryptamines with either 4-acetoxy or 4-hydroxy substitutions display nanomolar affinity for most human 5-HT receptor subtypes tested, including the 5-HT2A and the serotonin 1A receptor (5-HT1A). The same compounds displayed affinity for 5-HT2A and 5-HT1A in mouse brain tissue in vitro and exhibited agonist efficacy in assays examining 5-HT2A-mediated calcium mobilization and β-arrestin 2 recruitment. In mouse experiments, only the tertiary amines psilocin, psilocybin, and 4-acetoxy-N,N-dimethyltryptamine (psilacetin) induced head twitch responses (ED50 0.11-0.29 mg/kg) indicative of psychedelic-like activity. Head twitches were blocked by 5-HT2A antagonist pretreatment, supporting 5-HT2A involvement. Both secondary and tertiary amines decreased body temperature and locomotor activity at higher doses, the effects of which were blocked by 5-HT1A antagonist pretreatment. Across all assays, the pharmacological effects of 4-acetoxy and 4-hydroxy compounds were similar, and these compounds were more potent than their 4-phosphoryloxy counterparts. Importantly, psilacetin appears to be a prodrug for psilocin that displays substantial serotonin receptor activities of its own.
Future generation vehicles equipped with modern technologies will impose unprecedented computational demand due to the wide adoption of compute-intensive services with stringent latency requirements. The computational capacity of the next generation vehicular networks can be enhanced by incorporating vehicular edge or fog computing paradigm. However, the growing popularity and massive adoption of novel services make the edge resources insufficient. A possible solution to overcome this challenge is to employ the onboard computation resources of close vicinity vehicles that are not resource-constrained along with the edge computing resources for enabling tasks offloading service. In this paper, we investigate the problem of task offloading in a practical vehicular environment considering the mobility of the electric vehicles (EVs). We propose a novel offloading paradigm that enables EVs to offload their resource hungry computational tasks to either a roadside unit (RSU) or the nearby mobile EVs, which have no resource restrictions. Hence, we formulate a non-linear problem (NLP) to minimize the energy consumption subject to the network resources. Then, in order to solve the problem and tackle the issue of high mobility of the EVs, we propose a deep reinforcement learning (DRL) based solution to enable task offloading in EVs by finding the best power level for communication, an optimal assisting EV for EV pairing, and the optimal amount of the computation resources required to execute the task. The proposed solution minimizes the overall energy for the system which is pinnacle for EVs while meeting the requirements posed by the offloaded task. Finally, through simulation results, we demonstrate the performance of the proposed approach, which outperforms the baselines in terms of energy per task consumption.
Atlantic bluefin tuna (Thunnus thynnus) are large, highly migratory fish that support important fisheries. Oceanic conditions influence Atlantic bluefin tuna distribution and it has been hypothesized that stock distributions have shifted in recent years. Distributional shifts can affect regional availability and fleet catchability, introducing a potential bias in fisheries dependent data used for indexing population trends. We developed a vector auto-regressive spatio-temporal model (VAST) to estimate changes in bluefin tuna spatial distribution in US waters and created standardized indices of abundance for large (>177 cm) and small size classes (≤ 177 cm) of fish. Local-scale environmental factors (sea surface temperature (SST), ocean depth) and regional-scale drivers (e.g., Atlantic Multidecadal Oscillation (AMO) and prey biomass) of spatial distribution were explored. Results indicated that from 1993 to 2020, spatial distribution of the larger size class was highly variable, but on average, the total estimated area occupied increased by 96 km²/year and the center of gravity shifted 2 km/year north and 3 km/year east. Results were similar for the smaller size class fish with an average increase in area occupied of 71 km²/year. The center of gravity shifted an average of 1 km/year north and 2 km/year east. The primary factor driving the spatial shifts for both large and small fish was local-scale SST. Standardized indices of abundance were produced and incorporated SST as a covariate of local density. In comparison to prior standardization results, spatio-temporal indices demonstrated less inter-annual variability and produced similar overall trends. This study advanced our understanding of bluefin tuna spatial distributions and generated indices of relative abundance in US waters of the Northwest Atlantic that are more robust to spatio-temporal changes in tuna distributions for consideration in future stock assessments.
This study investigates the relationship between social currency and customer experience concerning online travel agencies. Riding on the theory of planned behavior (TPB), we explore the moderating effect of attitude and subjective norms on customer experience. First, a conceptual model is developed and tested using the data collected from 392 respondents from India. Then, after checking the instrument's psychometric properties, we used hierarchical regression to test hypotheses. The results indicate that (a) social currency, (b) attitude, and (c) subjective norms are positively associated with customer experience. Furthermore, the findings also support attitude and subjective norms moderating the relationship between social currency and customer experience. Finally, customer experience positively predicts co-creation intention by customers. The implications for theory and practice are discussed.
It has been proposed that humans' exceptional locomotor endurance evolved partly with foraging in hot open habitats and subsequently about 2 million years ago with persistence hunting, for which endurance running was instrumental. However, persistence hunting by walking, if successful, could select for locomotor endurance even before the emergence of any running-related traits in human evolution. Using a heat exchange model validated here in 73 humans and 55 ungulates, we simulated persistence hunts for prey of three sizes (100, 250, and 400 kg) and three sweating capacities (nonsweating, low, high) at 6237 combinations of hunter's velocity (1–5 m s⁻¹, intermittent), air temperature (25–45 °C), relative humidity (30–90%), and start time (8:00–16:00). Our simulations predicted that walking would be successful in persistence hunting of low- and nonsweating prey, especially under hot and humid conditions. However, simulated persistence hunts by walking yielded a 30–74% lower success rate than hunts by running or intermittent running. In addition, despite requiring 10–30% less energy, successful simulated persistence hunts by walking were twice as long and resulted in greater exhaustion of the hunter than hunts by running and intermittent running. These shortcomings of pursuit by walking compared to running identified in our simulations could explain why there is only a single direct description of persistence hunting by walking among modern hunter-gatherers. Nevertheless, walking down prey could be a viable option for hominins who did not possess the endurance-running phenotype of the proposed first persistence hunter, Homo erectus. Our simulation results suggest that persistence hunting could select for both long-distance walking and endurance running and contribute to the evolution of locomotor endurance seen in modern humans.
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