Temporal Delay Research Articles

GEMLIDS-MIOT: A Green Effective Machine Learning Intrusion Detection System based on Federated Learning for Medical IoT network security hardening

The increasing use of Internet of Things (IoT) gadgets in a daily rate has heightened security apprehension, particularly within the healthcare sector. In order to prevent the unauthorized disclosure of sensitive data, it is imperative for Internet of Things (IoT) systems to promptly and effectively respond to harmful activities. Nevertheless, the act of transferring data to distant cloud servers for the purpose of analysis gives rise to both temporal delays and apprehensions regarding privacy. To ensure the security of medical Internet of Things (MIoT) networks, a power-efficient Intrusion Detection System (IDS) is employed for three primary objectives that it will result in three stages of execution: (i) The objective is to categorize different types of attacks, such as Man-in-the-Middle (MitM) and Distributed Denial of Service (DDoS), by utilizing well-established machine learning (ML) techniques. This classification stage will serve to enhance the Intrusion Detection System (IDS) and the reporting system. (ii) Anomaly detection (unknown attack identification), or detection of unknown attacks, will be employed to identify previously unknown attacks. This identification stage involves retraining the ML model to enable future recognition and classification of these unknown attacks when the anomaly attack detector identifies that an unknown attack is recognized. Then, a retraining of the first stage classification model is executed due to the anomaly detection. (iii) To ensure that a remote cloud server remains current with the latest classification model changes, Federated Learning (FL) will be utilized. FL allows for collaborative model training while preserving data privacy and security. The experimental findings indicate that the Enhanced Random Forest (also called ensemble random forest) algorithm achieves a remarkable accuracy rate of 99.98% in classifying attacks. Thus, it will be our first stage classifier. Continuing, the One-Class Support Vector Machine (SVM) algorithm demonstrates a high level of accuracy, reaching 99.7% in detecting anomalies so that it will be our second stage identifier. Finally, the third-stage approach, which has as a target the overall system model updater, will be our introduced Federated Learning approach that works with the Enhanced Random Forests and identifies the ERF differences from the old model in an optimal way. The efficacy of our technique is confirmed through the implementation of experiments involving an Internet of Things (IoT) system and a Raspberry Pi MIoT gateway and with simulations that simulate the FL updating process. These experiments successfully identify known and unknown attacks with a high reliability level while limiting resource utilization and energy consumption. Future studies of this work will focus on enhancing the scalability and efficiency of our Intrusion Detection System in MIoT networks.

Sub-Bin Delayed High-Range Accuracy Photon-Counting 3D Imaging

The range accuracy of single-photon-array three-dimensional (3D) imaging systems is limited by the time resolution of the array detectors. We introduce a method for achieving super-resolution in 3D imaging through sub-bin delayed scanning acquisition and fusion. Its central concept involves the generation of multiple sub-bin difference histograms through sub-bin shifting. Then, these coarse time-resolution histograms are fused with multiplied averages to produce finely time-resolved detailed histograms. Finally, the arrival times of the reflected photons with sub-bin resolution are extracted from the resulting fused high-time-resolution count distribution. Compared with the sub-delayed with the fusion method added, the proposed method performs better in reducing the broadening error caused by coarsened discrete sampling and background noise error. The effectiveness of the proposed method is examined at different target distances, pulse widths, and sub-bin scales. The simulation analytical results indicate that small-scale sub-bin delays contribute to superior reconstruction outcomes for the proposed method. Specifically, implementing a sub-bin temporal resolution delay of a factor of 0.1 for a 100 ps echo pulse width substantially reduces the system ranging error by three orders of magnitude. Furthermore, Monte Carlo simulations allow to describe a low signal-to-background noise ratio (0.05) characterised by sparsely reflected photons. The proposed method demonstrates a commendable capability to simultaneously achieve wide-ranging super-resolution and denoising. This is evidenced by the detailed depth distribution information and substantial reduction of 95.60% in the mean absolute error of the reconstruction results, confirming the effectiveness of the proposed method in noisy scenarios.

Noisy Delay Denoises Biochemical Oscillators.

Genetic oscillations are generated by delayed transcriptional negative feedback loops, wherein repressor proteins inhibit their own synthesis after a temporal production delay. This delay is distributed because it arises from a sequence of noisy processes, including transcription, translocation, translation, and folding. Because the delay determines repression timing and, therefore, oscillation period, it has been commonly believed that delay noise weakens oscillatory dynamics. Here, we demonstrate that noisy delay can surprisingly denoise genetic oscillators. Specifically, moderate delay noise improves the signal-to-noise ratio and sharpens oscillation peaks, all without impacting period and amplitude. We show that this denoising phenomenon occurs in a variety of well-studied genetic oscillators, and we use queueing theory to uncover the universal mechanisms that produce it.

Seed masting as a mechanism for escape from pathogens

Seed masting, a reproductive strategy characterized by variable and synchronous investment in reproduction among years, has attracted much attention. Masting trees incur a cost in delayed reproduction, and thus masting requires an ecological or evolutionary explanation. The two broad causal mechanisms to explain seed masting are resource availability and economies of scale (EOS); the former assumes reproductive investment simply covaries with environment, the latter suggests an adaptive advantage. Two of the most commonly proposed EOS for masting are predator satiation and pollination efficiency. Here we suggest an additional EOS: pathogen escape. We borrow from the disease ecology literature to describe alternative models of pathogen-mediated masting. By comparing and contrasting their ecological dynamics, we show how predator satiation and pathogen escape may favour masting through similar mechanisms of mass-action interactions and temporal delays. However, pathogen- and predator-mediated dynamics may also diverge as a result of host epidemiological structure and the spatial scale of the interaction. We propose that pathogen escape should be considered among the list of putative mechanisms to help explain the many diverse observations of masting across space and phylogeny.

Unlocking The Potential of Lidar: Principles, Applications, And Future Prospects

Light Detecting and Ranging (LiDAR) has been experiencing a surge in popularity within the realm of high-tech innovations. This article elaborates on the operational principles, structural intricacies, and scanning methodologies of contemporary LiDAR devices. Additionally, this article provides a comprehensive overview of the diverse array of applications that LiDAR technology finds utility in, ranging from remote sensing and transportation to geography, atmospheric science, and astronomy. In the context of Time of Flight (ToF) LiDAR, the underlying operational concept revolves around measuring the temporal delay of the laser beam's journey from the device to the target. Conversely, Frequency Modulated Continuous Wave (FMCW) LiDAR operates by quantifying alterations in the wavelength of the reflected laser in comparison to the originally emitted laser, leveraging the Doppler effect. LiDAR products serve as invaluable tools for geologists, enabling them to generate 3D maps for researching various targets such as forestry, coastline morphology, and river bathymetry. Meanwhile, atmospheric scientists harness LiDAR products to delve into studies concerning atmospheric composition and meteorological phenomena. In the foreseeable future, it is anticipated that LiDAR products will witness widespread adoption across an array of applications, including autonomous vehicles and beyond.

Adaptive contrastive learning based network latency prediction in 5G URLLC scenarios

With the increasing demand for 5G Ultra-Reliable Low Latency Communication (URLLC), accurate network time delay analysis is crucial. However, current network latency prediction models designed for 5G URLLC scenarios overlook the multiple downstream tasks case, such as network intrusion detection, recognition of network operational states, and decision-making for network resource scheduling.Furthermore, the robustness of the models needs to be enhanced. In this paper, we propose a Contrastive Convolutional Structure for Network Delay (CCSND), an adaptive contrastive learning framework that utilizes a temporal representation extraction module for capturing temporal periodicity and statistical features. To improve robustness and adaptability in time-series representation, CCSND designs strategic data augmentation methods, including temporal segmentation and delay obscuration. Experiment results on several real-world 5G URLLC scenario datasets demonstrate the superior performance of the proposed CCSND. It outperforms the current state-of-the-art Informer model by 38.7% and the traditional LSTM model by 64.5% in terms of mean squared error (MSE). Our code can be accessed at https://github.com/MerelyBreeze/CCSND.

Creep features and mechanism of active-layer detachment slide on the Qinghai-Tibet Plateau by InSAR

An active-layer detachment slide (ALDS) occurred on September 21, 2018, in the Fenghuoshan mountains of the Qinghai-Tibet Plateau (QTP) (34°39.1′N, 92°53.5′E). With the Sentinel-1A image from Copernicus Open Access Hub, we use small baseline subset to achieve the time series deformation map to analyze the thermo-spatial creep feature, motion pattern, trigger mechanism, and correlation of environmental changes in the ALDS. The SBAS (the Small Baselines Subset) results show that the trailing part of ALDS has the largest downward deformation rate; however, the leading area was small, and the creep feature shows a clear seasonal change corresponding to the freeze-thaw cycle. We also divide the motion pattern into three stages: moderate creep, steady creep, and rapid collapse, based on the deformation rate. Meteorological observation and reanalysis data, as well as borehole data, show that heavy precipitation in the summer of 2017 and 2018 promote the formation of underground ice, while high air temperatures allow the thaw plane to reach the ice-rich zone, and confined water generated by the two-way freezing process result in ALDS. Moreover, there exists a temporal delay of approximately one month in the association between deformation rate and both precipitation and temperature. Furthermore, there is a clear correlation between variations in thawing depth and deformation, which serves as the primary catalyst for ALDS in permafrost regions. Finally, we also identify that ALDS is a mixed-type landslide and that cumulative deformation and creep damage play the main roles in triggering ALDS.

Exploring traffic breakdown with vehicle-level data

Traffic breakdown involves complicated vehicle behavior, and is regarded as a probabilistic event with macroscopic traffic data from fixed detectors. However, with the advent of connected vehicle technologies, traffic data will develop to the vehicle-level, such as trajectory data, and provide unprecedented opportunities to better understand various traffic phenomena. Using novel vehicle-level data from drone videos, this research explores the traffic breakdown by interactions between vehicles. Specifically, this paper categorizes the typical behavior of individual vehicles that causes or resolves traffic congestion. Based on the behavior that occurred in the extensive time–space domain, this research develops a novel measurement method to quantify the behavior as temporal delay or spatial residual. With real-world data, this research verifies the vehicle-level congestion can be estimated from specific vehicle behavior, and their aggregation could describe the change in flow speed or traffic breakdown. The proposed framework can address the traffic phenomenon better when more extensive data is available.

Nailing it: Investigation of elephant toenails for retrospective analysis of adrenal and reproductive hormones.

Hormone monitoring of at-risk species can be valuable for evaluation of individual physiological status. Traditional non-invasive endocrine monitoring from urine and faeces typically captures only a short window in time, poorly reflecting long-term hormone fluctuations. We examined toenail trimmings collected from African (Loxodonta africana) and Asian (Elephas maximus) elephants during routine foot care, to determine if long-term hormone patterns are preserved in these slow-growing keratinized tissues. We first measured the growth rate of elephant toenails biweekly for one year, to establish the temporal delay between deposition of hormones into nail tissue (at the proximal nail bed) and collection of toenail trimmings months later (at the distal tip of the nail). In African elephants, toenails grew ~0.18 ± 0.015mm/day (mean ± SEM) and in Asian elephants, toenails grew ~0.24 ± 0.034mm/day. This slow growth rate, combined with the large toenail size of elephants, may mean that toenails could contain a 'hormone timeline' of over a year between the nail bed and nail tip. Progesterone, testosterone and cortisol were readily detectable using commercial enzyme immunoassays, and all assays passed validations, indicating that these hormones can be accurately quantified in elephant toenail extract. In most cases, variations in hormone concentrations reflected expected physiological patterns for adult females and males (e.g. ovarian cycling and musth) and matched individual health records from participating zoos. Progesterone patterns aligned with our calculations of temporal delay, aligning with female ovarian cycling from over sixmonths prior. Unexpectedly, male testosterone patterns aligned with current musth status at the time of sample collection (i.e. rather than prior musth status). Though this sample type will require further study, these results indicate that preserved hormone patterns in elephant toenails could give conservationists a new tool to aid management of elephant populations.

Hostile-based bipartite containment control of nonlinear fractional multi-agent systems with input delays: a signed graph approach under disturbance and switching networks

<abstract><p>This article addresses the hostile-based bipartite containment control of nonlinear fractional multi-agent systems (FMASs) with input delays. Several fundamental algebraic criteria have been offered by the use of signed graph theory. To make the controller design more realistic, we assumed that the controller was under some disturbance. For the analysis of bipartite containment control, we used a fixed and switching signed network. The commonly used Lyapunov function approach and the Razumikhin technique were used. The use of these techniques can conquer the challenge brought on by switching, temporal delays, and fractional mathematics. To better elucidate the theoretical results, two examples are provided.</p></abstract>

Size onset of metallic behavior in neutral aluminum clusters.

The excited state lifetimes of neutral (Al)n clusters up to ∼1 nm in diameter in size, where n ≤ 43, are systematically measured with femtosecond time-resolved mass spectrometry. The onset of metallic behavior is identified as a distinct change in the relaxation behavior initiated with single ultraviolet (400 nm) photon excitation. The experimentally measured excited state lifetimes gradually decrease with size for small molecular scale clusters (n < 10) before becoming indistinguishable for larger clusters (n > 9), where the measurements are comparable to electron-lattice relaxation time of bulk Al (∼300 fs). Particularly intense, or magic, Aln clusters do not exhibit any significant excited state lifetime behavior. Time-dependent density functional theory quantify the excited state properties and are presented to show that dynamics are strongly tied to the excited state charge carrier distributions and overlap, rather than detailed changes related to changes in the cluster's electronic and geometric structure. The consistency in excited state lifetimes for clusters larger than n = 9 is attributed to the hybridization of the s- and p-orbitals as well as increasing delocalization. Al3 exhibits unique temporal delay in its transient behavior that is attributed to a transition from triangular ground state to linear structure upon excitation.

A synthetic pulse interaction for modelocking at ~100 GHz

Harmonic modelocking allows high repetition rates but has limited controllability and scalability. We propose a synthetic pulse-to-pulse interaction, acting like a saturable absorber, but imparting loss as a function of temporal delay, as a route to ultrahigh frequency fiber lasers.

Cross Relaxation Enables Spatiotemporal Color-Switchable Upconversion in a Single Sandwich Nanoparticle for Information Security.

Smart control of ionic interaction dynamics offers new possibilities for tuning and editing luminescence properties of lanthanide-based materials. However, it remains a daunting challenge to achieve the dynamic control of cross relaxation mediated photon upconversion, and in particular the involved intrinsic photophysics is still unclear. Herein, this work reports a conceptual model to realize the color-switchable upconversion of Tm3+ through spatiotemporal control of cross relaxation in the design of NaYF4:Gd@NaYbF4:Tm@NaYF4 sandwich nanostructure. It shows that cross relaxation plays a key role in modulating upconversion dynamics and tuning emission colors of Tm3+. Interestingly, it is found that there is a short temporal delay for the occurrence of cross relaxation in contrast to the spontaneous emission as a result of the slight energy mismatch between relevant energy levels. This further enables a fine emission color tuning upon non-steady state excitation. Moreover, a characteristic quenching time is proposed to describe the temporal evolution of cross relaxation quantitatively. These findings present a deep insight into the physics of ionic interactions in heavy doping systems, and also show great promise in frontier applications including information security, anti-counterfeiting and nanophotonics.

Body motion of choral singers.

Recent investigations on music performances have shown the relevance of singers' body motion for pedagogical as well as performance purposes. However, little is known about how the perception of voice-matching or task complexity affects choristers' body motion during ensemble singing. This study focussed on the body motion of choral singers who perform in duo along with a pre-recorded tune presented over a loudspeaker. Specifically, we examined the effects of the perception of voice-matching, operationalized in terms of sound spectral envelope, and task complexity on choristers' body motion. Fifteen singers with advanced choral experience first manipulated the spectral components of a pre-recorded short tune composed for the study, by choosing the settings they felt most and least together with. Then, they performed the tune in unison (i.e., singing the same melody simultaneously) and in canon (i.e., singing the same melody but at a temporal delay) with the chosen filter settings. Motion data of the choristers' upper body and audio of the repeated performances were collected and analyzed. Results show that the settings perceived as least together relate to extreme differences between the spectral components of the sound. The singers' wrists and torso motion was more periodic, their upper body posture was more open, and their bodies were more distant from the music stand when singing in unison than in canon. These findings suggest that unison singing promotes an expressive-periodic motion of the upper body.

Impact of the COVID-19 pandemic on the diagnosis and treatment of patients with head and neck cancer in Brandenburg and Berlin

The impact of the COVID-19 pandemic on potential limitations to the diagnosis and treatment of patients with head and neck tumours has not yet been adequately investigated. There are contradictory data on this subject. Data from larger patient collectives do not exist for Germany so far. The aim of the survey was to clarify in alarge cohort whether the COVID-19 pandemic had an influence on the diagnosis and treatment of patients with head and neck tumours. Aretrospective data analysis of the reporting data of the Clinical and Epidemiological Cancer Registry of Brandenburg and Berlin (Klinisch-epidemiologischen Krebsregisters Brandenburg-Berlin, KKRBB) of 4831 cases with head and neck tumours from 2018 to 2020 was performed. The period before April01, 2020, was evaluated as aprepandemic cohort and compared with the cases of the pandemic cohort from April1, 2020, until December31, 2020, in terms of patient-related baseline data, tumour location, tumour stage, tumour board and treatments administered. No differences were observed between the prepandemic and pandemic cohorts with regard to patient-related baseline data, tumour localisation and tumour stage. Likewise, no temporal delay in diagnosis, tumour board and treatment was evident during the pandemic period. On the contrary, the time interval between diagnosis and start of therapy was shortened by an average of 2.7days in the pandemic phase. Tumours with T4 stage were more frequently treated surgically during the pandemic compared to the period before (20.8% vs. 29.6%), whereas primary radio(chemo)therapy decreased during the pandemic (53.3% vs. 40.4%). For all other tumour stages and entities, there were no differences in treatment. Contrary to initial speculation that the COVID-19 pandemic may have led to adecrease in tumour cases, larger tumour stages at initial presentation and adelay in diagnosis and treatment, the cohort studied for Brandenburg and Berlin showed neither adelay in tumour treatment nor an increase in tumour size and stage at initial presentation. The treatments performed, however, were subject to achange in favour of surgery and it remains to be seen whether this trend will be maintained in the long term.

A chromatin-regulated biphasic circuit coordinates IL-1β-mediated inflammation.

Inflammation is characterized by a biphasic cycle consisting initially of a proinflammatory phase that is subsequently resolved by anti-inflammatory processes. Interleukin-1β (IL-1β) is a master regulator of proinflammation and is encoded within the same topologically associating domain (TAD) as IL-37, which is an anti-inflammatory cytokine that opposes the function of IL-1β. Within this TAD, we identified a long noncoding RNA called AMANZI, which negatively regulates IL-1β expression and trained immunity through the induction of IL37 transcription. We found that the activation of IL37 occurs through the formation of a dynamic long-range chromatin contact that leads to the temporal delay of anti-inflammatory responses. The common variant rs16944 present in AMANZI augments this regulatory circuit, predisposing individuals to enhanced proinflammation or immunosuppression. Our work illuminates a chromatin-mediated biphasic circuit coordinating expression of IL-1β and IL-37, thereby regulating two functionally opposed states of inflammation from within a single TAD.

Multidimensional Widefield Infrared-Encoded Spontaneous Emission Microscopy: Distinguishing Chromophores by Ultrashort Infrared Pulses.

Photoluminescence (PL) imaging has broad applications in visualizing biological activities, detecting chemical species, and characterizing materials. However, the chemical information encoded in the PL images is often limited by the overlapping emission spectra of chromophores. Here, we report a PL microscopy based on the nonlinear interactions between mid-infrared and visible excitations on matters, which we termed MultiDimensional Widefield Infrared-encoded Spontaneous Emission (MD-WISE) microscopy. MD-WISE microscopy can distinguish chromophores that possess nearly identical emission spectra via conditions in a multidimensional space formed by three independent variables: the temporal delay between the infrared and the visible pulses (t), the wavelength of visible pulses (λvis), and the frequencies of the infrared pulses (ωIR). This method is enabled by two mechanisms: (1) modulating the optical absorption cross sections of molecular dyes by exciting specific vibrational functional groups and (2) reducing the PL quantum yield of semiconductor nanocrystals, which was achieved through strong field ionization of excitons. Importantly, MD-WISE microscopy operates under widefield imaging conditions with a field of view of tens of microns, other than the confocal configuration adopted by most nonlinear optical microscopies, which require focusing the optical beams tightly. By demonstrating the capacity of registering multidimensional information into PL images, MD-WISE microscopy has the potential of expanding the number of species and processes that can be simultaneously tracked in high-speed widefield imaging applications.

Estimating the effects of roads on migration: a barren-ground caribou case study

A challenge for management of wildlife species is the assessment of the effect of roads on migration. We developed models to estimate the spatial (zone of influence (ZOI)) and temporal (delays in migration) effects of roads, and test whether road closures reduced delays in migration. We analyzed collar (2011–2019) and road survey data from two barren-ground caribou ( Rangifer tarandus groenlandicus (Borowski, 1780)) herds to assess the impact of a 171 km mine road that bisects their migration corridor. We estimated ZOIs of 16–17 km prior to crossing the road during 2018 and 2019, and 3.0 km after crossing. Estimates of mean delay were 4.3 and 2.5 days for 2018 and 2019, which were reduced by 28%–68% (1.2–1.7 days) if roads were closed when caribou were within the ZOI. In 2017–2018, when the road was extended 64 km north, caribou were 12% less likely to deflect north around the road, therefore increasing delays. Road surveys indicated aggregation of caribou prior to crossing the road, with few caribou observed after crossing, a finding supported by collar data. Our methods can estimate the spatial and temporal effects of roads for any wildlife species and assess mitigation strategies in reducing delays in migration.

A compact flexible sub-nanosecond framing photographic system.

A novel high-speed multi-frame photographic system is presented in this paper. The system demonstrates exceptional compactness and flexibility, requiring only the introduction of a cavity comprising multiple beam-splitters in the optical path to enable multi-frame imaging of sub-nanosecond events. The number and temporal delay of frames can be easily adjusted by adjusting the distance and angle between beam-splitters. These capabilities are demonstrated by observing the laser ablation process, highlighting the great potential for application in capturing ultrafast time-evolving events such as optical breakdown, the evolution of laser-produced plasmas, and the propagation of shock waves.

A Comparative Study on Wind Power Forecasting Models Based on the Use of LSTM

In the context of wind power generation's growing significance, this research tackles the critical problem of improving power system stability by reducing peak load and frequency control pressures using sophisticated wind power forecasting methods. Using the rapidly developing area of artificial intelligence and neural networks in particular, the study explores the efficacy of Long Short-Term Memory (LSTM), a recurrent neural network designed for event forecasting in time series data with long intervals and temporal delays. The research presents a novel forecasting model, LSTM that enhances prediction accuracy. This is corroborated by the calculated Root Mean Squared Error (RMSE) of 0.6782 and Mean Absolute Error (MAE) of 0.4614. The sustainability and dependability of wind energy integration into power systems may be enhanced by these findings, which highlight the possibility for more efficient and rapid convergence processes in wind power forecasting.