Precise Time Window Research Articles

An Economic Optimization Model of an E-Waste Supply Chain Network: Machine Learned Kinetic Modelling for Sustainable Production

Purpose: E-waste management (EWM) refers to the operation management of discarded electronic devices, a challenge exacerbated due to overindulgent urbanization. The main purpose of this paper is to amalgamate production engineering, statistical methods, mathematical modelling, supported with Machine Learning to develop a dynamic e-waste supply chain model. Method Used: This article presents a multidimensional, cost function-based analysis of the EWM framework structured on three modules including environmental, economic, and social uncertainties in material recovery from an e-waste (MREW) plant, including the production–delivery–utilization process. Each module is ranked using Machine Learning (ML) protocols—Analytical Hierarchical Process (AHP) and combined AHP-Principal Component Analysis (PCA). Findings: This model identifies and probabilistically ranks two key sustainability contributors to the EWM supply chain: energy consumption and carbon dioxide emission. Additionally, the precise time window of 400–600 days from the start of the operation is identified for policy resurrection. Novelty: Ours is a data-intensive model that is founded on sustainable product designing in line with SDG requirements. The combined AHP-PCA consistently outperformed traditional statistical tools, and is the second novelty. Model ratification using real e-waste plant data is the third novelty. Implications: The Machine Learning framework embeds a powerful probabilistic prediction algorithm based on data-based decision making in future e-waste sustained roadmaps.

High-frequency visualization of flexible structures using an event-triggered camera: multiple flapping membranes

Optical measurements of fluid–structure interaction (FSI) usually require high-speed imaging techniques. Traditional high-speed cameras are expensive and cannot record data continuously for long periods. An inexpensive and high-frequency measurement method that utilizes an event-triggered camera and a strobe laser is proposed in this paper. In this method, the k-means clustering technique was first employed to determine the precise time window in which to reconstruct frames from event data. Then, a Kalman filtering algorithm was used to extract the dynamic deformation process of the flexible structures from the noisy event data. Wind tunnel flutter tests were conducted using various sets of parallel membranes to examine the proposed method. Under the illumination of a 30 mW strobe laser, membrane flapping was recorded at a resolution of 1280 × 720 pixels with a frame rate of 10 000 fps. With no camera memory storage limitations, the proposed method could continuously record the membrane flapping, thereby making it easy to capture the intermittent flutter phenomenon at the flutter boundary. Two flapping modes, i.e. symmetric mode and in-phase mode, were observed in the flutter of two membranes. Similar symmetric mode and out-of-phase mode were also observed in the flutter of three membranes. Membranes collisions and mode transitions were found in a certain wind speed range. For the first time, the collision processes between membranes were recorded during the flutter of multiple membranes. These collisions were found to be associated with the transitions between different flapping modes.

The Pupa Stage Is the Most Sensitive to Hypoxia in Drosophila melanogaster.

Hypoxia not only plays a critical role in multiple disease conditions; it also influences the growth and development of cells, tissues and organs. To identify novel hypoxia-related mechanisms involved in cell and tissue growth, studying a precise hypoxia-sensitive time window can be an effective approach. Drosophila melanogaster has been a useful model organism for studying a variety of conditions, and we focused in this study on the life cycle stages of Drosophila to investigate their hypoxia sensitivity. When normoxia-grown flies were treated with 4% O2 at the pupa stage for 3, 2 and 1 day/s, the eclosion rates were 6.1%, 66.7% and 96.4%, respectively, and, when 4% O2 was kept for the whole pupa stage, this regimen was lethal. Surprisingly, when our hypoxia-adapted flies who normally live in 4% O2 were treated with 4% O2 at the pupa stage, no fly eclosed. Within the pupa stage, the pupae at 2 and 3 days after pupae formation (APF), when treated for 2 days, demonstrated 12.5 ± 8.5% and 23.6 ± 1.6% eclosion, respectively, but this was completely lethal when treated for 3 days. We conclude that pupae, at 2 days APF and for a duration of a minimum of 2 days, were the most sensitive to hypoxia. Our data from our hypoxia-adapted flies clearly indicate that epigenetic factors play a critical role in pupa-stage hypoxia sensitivity.

Toward chronopharmaceutical drug delivery patches and biomaterial coatings for the facilitation of wound healing

Implantation of a biomaterial entails a form of injury where the integration of the implant into the host tissue greatly depends on the proper healing of the wound. Wound healing, itself, consists of a number of physiological processes, each occurring within a characteristic time window. A composite, multilayered polymeric drug delivery carrier for adhesion to the wound site and its supply with molecules released at precise time windows at which the stages in the healing process that they target occur is conceptualized here. We also present a simplified version of one such multilayered composite fabricated by a combination of solvent casting and dip coating, comprising the base poly(ε-caprolactone) layer reinforced with hydroxyapatite nanoparticles, poly(glutamic acid) mesolayer and poly-l-lysine surface layer, each loaded with specific small molecules and released at moderately distinct timescales, partially matching the chronology of wound healing. To that end, the base layer proved suitable for the delivery of an anti-inflammatory molecule or an angiogenic agent, the mesolayer appeared appropriate for the delivery of an epithelialization promoter or a granulation factor, and the adhesive surface layer interfacing directly with the site of injury showed promise as a carrier of a vasodilator. The drug release mechanisms were diffusion-driven, suggesting that the drug/carrier interaction is a key determinant of the release kinetics, as important as the nature of the polymer and its hydrolytic degradation rate in the aqueous medium. Morphological and phase composition analyses were performed, along with the cell compatibility ones, demonstrating solid adhesion and proliferation of both transformed and primary fibroblasts on both surfaces of the composite films. The design of the multilayered composite drug delivery carriers presented here is prospective, but requires further upgrades to achieve the ideal of a perfect timing of the sequential drug release kinetics and a perfect resonance with the physiological processes defining the chronology of wound healing.

Human-Centric Parcel Delivery at Deutsche Post with Operations Research and Machine Learning

Features such as estimated delivery time windows and live tracking of shipments play a key role in improving the customer experience in last-mile delivery. The building blocks for enabling these features are reliable knowledge about the expected order of deliveries in a tour and precise delivery time window predictions. For Deutsche Post’s parcel delivery service in Germany, we developed a courier-centric routing algorithm and a corresponding state-of-the-art machine learning model for delivery time window predictions. The routing algorithm combines operations research with statistics and machine learning to implicitly gather and use the tacit knowledge of our experienced couriers within the tour generation. This is achieved by deducing and selecting appropriate precedence constraints from historical delivery data. This novel combination of optimization with data-driven constraints enabled us to provide custom routes to the individual couriers. It proved to be a main driver allowing us to provide accurate delivery time window predictions and live tracking of shipments. Our solution is used by Deutsche Post to plan the daily routes of couriers to the approximately 13,000 parcel delivery districts in Germany as well as to provide live tracking and estimated delivery time windows for 1.6 million parcels each day. History: This paper has been accepted for the INFORMS Journal on Applied Analytics Special Issue—2022 Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research.

Ensemble deep clustering analysis for time window determination of event-related potentials

ObjectiveCluster analysis of spatio-temporal event-related potential (ERP) data is a promising tool for exploring the measurement time window of ERPs. However, even after preprocessing, the remaining noise can result in uncertain cluster maps followed by unreliable time windows while clustering via conventional clustering methods. MethodsWe designed an ensemble deep clustering pipeline to determine a reliable time window for the ERP of interest from temporal concatenated grand average ERP data. The proposed pipeline includes semi-supervised deep clustering methods initialized by consensus clustering and unsupervised deep clustering methods with end-to-end architectures. Ensemble clustering from those deep clusterings was used by the designed adaptive time window determination to estimate the time window. ResultsAfter applying simulated and real ERP data, our method successfully obtained the time window for identifying the P3 components (as the interest of both ERP studies) while additional noise (e.g., adding 20 dB to −5 dB white Gaussian noise) was added to the prepared data. ConclusionCompared to the state-of-the-art clustering methods, a superior clustering performance was yielded from both ERP data. Furthermore, more stable and precise time windows were elicited as the noise increased. SignificanceOur study provides a complementary understanding of identifying the cognitive process using deep clustering analysis to the existing studies. Our finding suggests that deep clustering can be used to identify the ERP of interest when the data is imperfect after preprocessing.

Exhibition Review: Tuan Andrew Nguyen: Unburied Sounds

Exhibition Review: <i>Tuan Andrew Nguyen: Unburied Sounds</i>

Long lasting anxiety following early life stress is dependent on glucocorticoid signaling in zebrafish

Chronic adversity in early childhood is associated with increased anxiety and a propensity for substance abuse later in adulthood, yet the effects of early life stress (ELS) on brain development remain poorly understood. The zebrafish, Danio rerio, is a powerful model for studying neurodevelopment and stress. Here, we describe a zebrafish model of ELS and identify a role for glucocorticoid signaling during a critical window in development that leads to long-term changes in brain function. Larval fish subjected to chronic stress in early development exhibited increased anxiety-like behavior and elevated glucocorticoid levels later in life. Increased stress-like behavior was only observed when fish were subjected to ELS within a precise time window in early development, revealing a temporal critical window of sensitivity. Moreover, enhanced anxiety-like behavior only emerges after two months post-ELS, revealing a developmentally specified delay in the effects of ELS. ELS leads to increased levels of baseline cortisol, and resulted in a dysregulation of cortisol receptors’ mRNA expression, suggesting long-term effects on cortisol signaling. Together, these findings reveal a ‘critical window’ for ELS to affect developmental reprogramming of the glucocorticoid receptor pathway, resulting in chronic elevated stress.

Integrated scheduling of a multi-site mining supply chain with blending, alternative routings and co-production

This paper proposes a multi-site global optimisation model of blending operations, alternative routings and order scheduling with a co-production flow. It is performed in a phosphoric supply chain owning three open-pit mines of different geological structures and chemical compositions, each having a dry blending plant. One main challenge of the phosphate industries is to produce merchantable ores (MO) that verify a quality charter (chemical composition) with chemical heterogeneous source ores (SO). Thus, a process of SO blending followed by a treatment is mostly necessary to obtain the required MO. Six alternative routings exist to produce an MO; one of them involving a calcination plant generates a co-product. The model objective is to determine the least costly production programme of a set of MO orders to be delivered within precise time windows, in given quantities and compliance with a specific quality charter, by assigning for each MO its blending plant, routing, blend of SOs and production schedule; it also defines the feeding of the blending plants. A real case study illustrates the proposed model.

Trips-in-Motion Time Matrix to Identify Time Windows as an Input for Time-of-Day Modelling

Time-of-day modelling is an additional step to the conventional four-step Travel Demand Models (TDMs). Here, the target is to obtain more detailed outputs over the temporal dimension. With this additional step, daily (24-hour) travel demand is distributed into a discrete number of time-windows.This paper aims to identify the most precise time windows that maximise the trips that fall within a given time-window and minimise the trip-tailing associated with it. The trips-in-motion method follows a more logical approach to capturing the entire trip duration. The Colombo Metropolitan Region Transport Masterplan database, developed in 2013, is analysed using Bentley Cube Voyager transport demand modelling software. The most precise starting timestamps of two-hour time windows were selected for the morning, mid-day, and evening peaks at 6:30 AM, 01:30 PM and 05:00 PM.This study has developed a systematic approach to identify time-windows as input for time-of-day based modelling. This attempt is an initial step to simulate the third-dimension of a trip, which is called the temporal dimension of TDMs.Finally, it is recommended to study the shift in peak periods with the change in time of demand, which would be the behavioural change most expected to occur post- COVID-19.

The identification of critical time windows of postnatal root elongation in response to Wnt/β-catenin signaling.

In this study, we attempted to define the precise window of time for molar root elongation using a gain-of-function mutation of β-catenin model. Both the control and constitutively activated β-catenin (CA-β-cat) mice received a one-time tamoxifen administration (for activation of β-catenin at newborn, postnatal day 3, or 5, or 7, or 9) and were harvested at the same stage of P21. Multiple approaches were used to define the window of time of postnatal tooth root formation. In the early activation groups (tamoxifen induction at newborn, or P3 or P5), there was a lack of molar root elongation in the CA-β-cat mice. When induced at P7, the root length was slightly reduced at P21. However, the root length was essentially the same as that in the control when β-cat activated at P9. This study indicates that root elongation occurs in a narrow time of window, which is highly sensitive to a change of β-catenin levels. Molecular studies showed a drastic decrease in the levels of nuclear factor I-C (NFIC) and osterix (OSX), plus sharp reductions of odontoblast differentiation markers, including Nestin, dentin sialoprotein (DSP), and dentin matrix protein 1 (DMP1) at both mRNA and protein levels. Murine molar root elongation is precisely regulated by the Wnt/β-catenin signaling within a narrow window of time (newborn to day 5).

Evaluation of promethium-147 production as a by-product of the fission molybdenum-99 process in Tehran research reactor

Abstract Molybdenum-99 and some other fission products such as promethium-147 are formed by the fission of 235U. Applying an efficient separation approach, the 147Pm can be isolated. The production of 147Pm as a by-product of previously optimized conditions for fission molybdenum-99 process is precisely evaluated. Considering the numerous radionuclides with various half-lives accompanying the 147Pm as impurities, a precise time window can be determined to perform an efficient separation process. The fission products activity calculations, using MCNPX and ORIGEN2.1 computer codes, were performed to achieve such a proper time window. Here, it is shown that 300 days after the end of bombardment (EOB), the radioactivity of the other radionuclides is reduced to 1% of 147Pm radioactivity. The chemical process needed to separate the remaining radionuclides is then explained in detail.

Determination of the Time Window of Event-Related Potential Using Multiple-Set Consensus Clustering.

Clustering is a promising tool for grouping the sequence of similar time-points aimed to identify the attention blocks in spatiotemporal event-related potentials (ERPs) analysis. It is most likely to elicit the appropriate time window for ERP of interest if a suitable clustering method is applied to spatiotemporal ERP. However, how to reliably estimate a proper time window from entire individual subjects’ data is still challenging. In this study, we developed a novel multiset consensus clustering method in which several clustering results of multiple subjects were combined to retrieve the best fitted clustering for all the subjects within a group. Then, the obtained clustering was processed by a newly proposed time-window detection method to determine the most suitable time window for identifying the ERP of interest in each condition/group. Applying the proposed method to the simulated ERP data and real data indicated that the brain responses from the individual subjects can be collected to determine a reliable time window for different conditions/groups. Our results revealed more precise time windows to identify N2 and P3 components in the simulated data compared to the state-of-the-art methods. Additionally, our proposed method achieved more robust performance and outperformed statistical analysis results in the real data for N300 and prospective positivity components. To conclude, the proposed method successfully estimates the time window for ERP of interest by processing the individual data, offering new venues for spatiotemporal ERP processing.

Cortico-cortical connectivity: the road from basic neurophysiological interactions to therapeutic applications.

Transcranial magnetic stimulation (TMS) based methods are emerging as a unique approach to evaluate in real-time brain electrical activity in healthy and pathological conditions. By applying TMS pulses in two different bran areas within a short temporal frame of few milliseconds, it is possible to investigate their physiological interactions. These paradigms, collectively termed dual-site TMS, have been inspired by Professor John Rothwell's work, based on the idea that applying a conditioning stimulus over a cortical area may activate putative pathways projecting onto a second target area, thus providing a unique opportunity to test the causal effects between interconnected brain areas. This review highlights the most important features of dual-coil TMS protocols, mainly pioneered in Professor John Rothwell's lab. In the first part, I reviewed development of dual-site TMS protocols leading to the discovery of a distributed system of short-latency interactions within the human parieto-frontal network, likely mediated by direct anatomical pathways. In the second part, the physiological role of these dual-site TMS evoked pathways is considered, describing how these functional interactions are not fixed but vary depending on the brain activation, the condition and on the precise time window in which they are explored. Then, I reviewed recent advances showing that the repeated coupling of interconnected neuronal populations, by means of dual-coil TMS, is able to induce spike-time-dependent plasticity and to determine selective potentiation of physiological connectivity between two human brain regions. Finally, the therapeutic implications of these novel discoveries are discussed, pointing to multi-site TMS as a novel tool to identify early features of synaptic dysfunctions, to monitor disease progression and potentially to provide novel therapeutic approaches by reshaping plasticity in different neurological and psychiatric conditions.

Evolution of Gut Microbiome and Metabolome in Suspected Necrotizing Enterocolitis: A Case-Control Study.

Background: Necrotizing enterocolitis (NEC) is a devastating condition in preterm infants due to multiple factors, including gut microbiota dysbiosis. NEC development is poorly understood, due to the focus on severe NEC (NEC-2/3). Methods: We studied the gut microbiota, microbiome and metabolome of children with suspected NEC (NEC-1). Results: NEC-1 gut microbiota had a higher abundance of the Streptococcus (second 10-days of life) and Staphylococcus (third 10-days of life) species. NEC-1 children showed a microbiome evolution in the third 10-days of life being the most divergent, and were associated with a different metabolomic signature than in healthy children. The NEC-1 microbiome had increased glycosaminoglycan degradation and lysosome activity by the first 10-days of life, and was more sensitive to childbirth, low birth weight and gestational age, than healthy microbiome. NEC-1 fecal metabolome was more divergent by the second month of life. Conclusions: NEC-1 gut microbiota and microbiome modifications appear more distinguishable by the third 10-days of life, compared to healthy children. These data identify a precise window of time (i.e., the third 10-days of life) and provide microbial targets to fight/blunt NEC-1 progression.

Optimal Imaging at the Primary Stroke Center.

Optimal Imaging at the Primary Stroke Center.

Evolution of Gut Microbiome and Metabolome During Suspected Necrotizing Enterocolitis (NEC-1): A Case-Control Study

Necrotizing enterocolitis (NEC) is a devastating condition of the preterm new-born due to multiple factors, including gut microbiota dysbiosis. Since NEC development is poorly understood due to main focus on more severe NEC (NEC-2/3), we studied the gut microbiota and metabolome evolution of of children with suspected NEC (NEC-1). NEC-1 gut microbiota had a higher abundance of Streptococcus (second decade of life) and Staphylococcus (third decade of life) species. NEC-1 children showed a microbiome evolution in the third decade of life being the most divergent and associated to a different metabolomic signature than in healthy children. NEC-1 microbiome had increased glycosaminoglycan degradation and lysosome activity by the first decade of life and was more sensitive to factors such as childbirth, low birth weight and gestational age, than healthy microbiome. NEC-1 fecal metabolome was more divergent by the second month of life. The modifications of gut microbiota and microbiome during NEC-1 development appear more distinguishable by the third decade of life, when compared to healthy children. These data identify a precise window of time (i.e. third decade of life) and provide microbial targets to fight/blunt the progression of NEC-1. Funding Statement: This study was in part supported by the company Longevie SA in the persons of Michel LOSETO and Enzo LOSETO, as a donation to M.S. Longevie SA was not involved in any discussion nor provided any opinion on any part of this manuscript. Declaration of Interests: The authors declare no competing interests. Ethics Approval Statement: This study was approved (number of the approval: DC 2016-2804) by Neonatal and Pediatric Intensive Care Unit and Neonatology Department of Purpan Hospital in Toulouse, France. The parents of the children involved in this study gave their approval by written consensus.

Time Window of Perturbation-Induced Response Triggered by Ankle Motion and Body Sway above the Ankle.

We determined the precise time windows of the electromyographic (EMG) response components triggered by ankle motion and by body sway above the ankle. A support surface under the feet of healthy young adult participants in the quiet stance was moved in translation. The EMG response component triggered by body displacement above the ankle began at 95–100 ms and ended 145–155 ms after the onset of the support surface translation. The EMG response triggered by ankle dorsiflexion began at 35–50 ms and ended 110–115 ms after the onset of the translation in the soleus muscle, indicating that the response component began at a time similar to the short-latency response. In contrast, the response component in the gastrocnemius muscle began noticeably after that. The EMG response triggered by ankle dorsiflexion began at 75–85 ms and ended 125–135 ms after the onset of the translation in the gastrocnemius muscle. Our findings indicate that the threshold of the early response component to the somatic sensation of the ankle motion in the soleus muscle is lower than that in the gastrocnemius muscle. The response component triggered by the ankle motion continued long after the end of ankle dorsiflexion, indicating that the early component is mediated not only by the monosynaptic stretch reflex pathway but also by the polysynaptic pathway.

Phenological responses in a sycamore-aphid-parasitoid system and consequences for aphid population dynamics: A 20year case study.

Species interactions have a spatiotemporal component driven by environmental cues, which if altered by climate change can drive shifts in community dynamics. There is insufficient understanding of the precise time windows during which inter-annual variation in weather drives phenological shifts and the consequences for mismatches between interacting species and resultant population dynamics-particularly for insects. We use a 20year study on a tri-trophic system: sycamore Acer pseudoplatanus, two associated aphid species Drepanosiphum platanoidis and Periphyllus testudinaceus and their hymenopteran parasitoids. Using a sliding window approach, we assess climatic drivers of phenology in all three trophic levels. We quantify the magnitude of resultant trophic mismatches between aphids and their plant hosts and parasitoids, and then model the impacts of these mismatches, direct weather effects and density dependence on local-scale aphid population dynamics. Warmer temperatures in mid-March to late-April were associated with advanced sycamore budburst, parasitoid attack and (marginally) D. platanoidis emergence. The precise time window during which spring weather advances phenology varies considerably across each species. Crucially, warmer temperatures in late winter delayed the emergence of both aphid species. Seasonal variation in warming rates thus generates marked shifts in the relative timing of spring events across trophic levels and mismatches in the phenology of interacting species. Despite this, we found no evidence that aphid population growth rates were adversely impacted by the magnitude of mismatch with their host plants or parasitoids, or direct impacts of temperature and precipitation. Strong density dependence effects occurred in both aphid species and probably buffered populations, through density-dependent compensation, from adverse impacts of the marked inter-annual climatic variation that occurred during the study period. These findings explain the resilience of aphid populations to climate change and uncover a key mechanism, warmer winter temperatures delaying insect phenology, by which climate change drives asynchronous shifts between interacting species.

GRIN2D/GluN2D NMDA receptor: Unique features and its contribution to pediatric developmental and epileptic encephalopathy.

N-methyl-d-aspartate receptors (NMDARs), a subset of ligand-gated ionotropic glutamate receptors, are critical for learning, memory, and neuronal development. However, when NMDAR subunits are mutated, a host of neuropathological conditions can occur, including epilepsy. Recently, genetic variation within the GRIN2D gene, which encodes the GluN2D subunit of the NMDAR, has been associated with a set of early-onset neurological diseases, notably developmental and epileptic encephalopathy (DEE). Importantly, patients with GRIN2D variants are largely refractory to conventional anti-epileptic drug (AED) treatment, highlighting the need to further understand the distinctive characteristics of GluN2D in neurological and pathological functions. In this review, we first summarize GluN2D's unique spatial and temporal expression patterns, electrophysiological profiles, and contributions to both pre- and postsynaptic signaling. Next, we review thirteen unique case studies from DEE patients harboring ten different causal GRIN2D variants. These patients are highly heterogenous, manifesting multiple seizure types, electroencephalographic recordings, and neurological and developmental outcomes. Lastly, this review concludes by highlighting the difficulty in treating patients with DEE-associated GRIN2D variants, and stresses the need for selective therapeutic agents delivered within a precise time window.