Objective Monitoring Research Articles

Integrating digital gait data with metabolomics and clinical data to predict outcomes in Parkinson’s disease

Parkinson’s disease (PD) presents diverse symptoms and comorbidities, complicating its diagnosis and management. The primary objective of this cross-sectional, monocentric study was to assess digital gait sensor data’s utility for monitoring and diagnosis of motor and gait impairment in PD. As a secondary objective, for the more challenging tasks of detecting comorbidities, non-motor outcomes, and disease progression subgroups, we evaluated for the first time the integration of digital markers with metabolomics and clinical data. Using shoe-attached digital sensors, we collected gait measurements from 162 patients and 129 controls in a single visit. Machine learning models showed significant diagnostic power, with AUC scores of 83–92% for PD vs. control and up to 75% for motor severity classification. Integrating gait data with metabolomics and clinical data improved predictions for challenging-to-detect comorbidities such as hallucinations. Overall, this approach using digital biomarkers and multimodal data integration can assist in objective disease monitoring, diagnosis, and comorbidity detection.

Epidemiological features and psychoneurological consequences of infection of people with the Nipah virus

The lecture outlines modern ideas about the etiology, features of the epidemic process, mechanisms of damage to the human body, clinical manifestations, diagnosis, treatment and prevention of infection caused by the Nipah virus. Particular attention is paid to the characteristics of individual outbreaks of the infectious process in various geographical regions of the world, early and long-term psychopathological, neurological and cognitive consequences due to human infection with the Nipah virus. The risk factors for the emergence and rapid spread of infection with a very high mortality rate, reaching 100%, are emphasized, which determines the high pandemic potential and hidden threats to human society. Recommendations are outlined for the prevention of transmission of infection caused by the Nipah virus at all levels: from animals to humans, from humans to humans, in medical institutions, which, in the absence of effective vaccine prevention and specific antiviral treatment, are the basis for containing the spread of the infectious process. In this regard, measures taken and planned to provide patients and contact persons with adequate medical and psychological care, features of the development and implementation of effective anti-epidemic, clinical diagnostic algorithms, timely and high-quality social and hygienic monitoring of environmental objects against the backdrop of increasing biological threats from outside, and within the country will minimize the risks of biological threats. The lecture is intended for doctors of various specialties, including infectious disease specialists, epidemiologists, general practitioners, neurologists, psychiatrists, laboratory diagnosticians, radiologists etc.

Comparing cost‐effectiveness of radio and drone telemetry with playback surveys for assessing translocation outcomes

Abstract Post‐release monitoring is critical for assessing translocation outcomes. Yet the quality of information gained from monitoring can vary greatly, and perceived monitoring costs often results in reduced monitoring effort. Selecting cost‐effective monitoring strategies that provide high quality data are therefore important for assessing translocation outcomes and making informed management decisions. To compare how monitoring strategy affects information gained, we monitored a toutouwai/North Island robin (Petroica longipes) reintroduction in Aotearoa New Zealand, based on monitoring objectives of determining survival, site fidelity and whether the extent of management was large enough to protect dispersing individuals. We compared how these objectives were met through four monitoring strategies: (1) comprehensive surveys with ground radio telemetry and playback; (2) aerial drone telemetry; (3) dedicated playback by trained surveyors and (4) opportunistic playback by predator control contractors. We undertook a viewshed analysis to determine search coverage of each strategy and compared detection rates, efficiency and cost. Comprehensive ground telemetry and playback, while costly, covered the largest area and provided the most accurate data on dispersal, survival and the translocation outcome. In comparison, opportunistic playback monitoring detected substantially fewer individuals, giving a false impression of low site fidelity and survival and a failed translocation. Although drone telemetry had considerable site‐specific limitations, which limited its effectiveness during our study, it was the most cost‐effective with a high detection rate and low search effort. Synthesis and applications: Our study shows the value of intensive monitoring in facilitating management decisions for wildlife translocations. Comprehensive telemetry and playback, while costly, were invaluable for gaining high quality information on the translocation outcome. Without suitable monitoring, reintroduction outcomes can be difficult to assess and potentially result in unnecessary, ineffective or overly expensive management actions. We recommend that monitoring intensity and methodology should reflect the site, species and level of uncertainty regarding the translocation outcome. Prioritising monitoring can help reduce long‐term costs, increase quality of information gained and allow for more informed management decisions that can improve subsequent translocation outcomes.

Improved unmanned aerial vehicle control for efficient obstacle detection and data protection

<p>The article centers on the research objectives and tasks associated with developing a swarm control system for unmanned aerial vehicles (UAVs) utilizing artificial intelligence (AI). A comprehensive literature review was undertaken to assess the effectiveness of the "swarm" method in UAV management and identify key challenges in this domain. Swarm algorithms were implemented in the MATLAB/Simulink environment for modeling and simulation purposes. The study successfully instantiated and simulated a UAV swarm control system adhering to fundamental principles and laws. Each UAV operates autonomously, following target-swarm principles inspired by the collective behavior of bees and ants. The collective movement and behavior of the swarm are controlled by an AI-based program. The system demonstrated effective obstacle detection and avoidance through computer simulations. Results obtained highlight key features contributing to success, including decentralized autonomy, collective intelligence, UAV coordination, scalability, and flexibility. The deployment of a local radio communication system in UAV swarm control and remote object monitoring is also discussed. The research findings hold practical significance as they enable the effective execution of complex tasks and have potential applications in various fields.</p>

Relationship Between Patient-reported Outcome Measures and Accelerometer-measured Physical Activity and Sleep in Patients With Postherpetic Neuralgia.

Available treatment for chronic neuropathic pain is still limited, and the positive effects are modest. Thus, clinicians aim to improve activity and quality of life despite pain. The objective monitoring of activity is attracting attention in chronic pain assessments. Therefore, we objectively evaluated daytime activity and sleep in patients with postherpetic neuralgia (PHN), using actigraphy to determine risk factors for decreased activity. Participants with PHN wore an actigraph (a wristwatch-like accelerometer) on the nondominant hand. The actigraph measured day-time activity and sleep, which were compared with participant-reported subjective pain and sleep assessments. Fifty-four individuals with PHN who visited our outpatient clinic completed questionnaires and a week of actigraph monitoring. Subjective scores of pain intensity, neuropathic pain, disability in daily life, pain-catastrophizing thoughts, and insomnia were all well correlated. However, the actigraph-monitored activity levels, using 2 equations, and sleep quality were not associated with any pain or sleep-related subjective scores. The discrepancy between the subjective and objective scores in this study may be due to (1) features of PHN, an archetype of peripheral neuropathic pain affecting no motor nerves, (2) actigraph measurement limitations regarding the sedentary life of the elderly, or (3) activity misperception, a new proposition explaining the discrepancy between subjective and objective measures of activity, similar to the sleep state misperception. In patients with PHN, high pain intensity may be reported in those with highly maintained activity, in which treatment must be selected cautiously to prevent interruption of their physical abilities.

Cost-effectiveness of lures in attracting mammals: a large scale camera-trapping field test on European species

The cost-effectiveness of different attractants during camera trapping surveys has been seldom evaluated. To contribute in filling this knowledge gap we (1) compare the effectiveness of a suite of attractants in detecting widely distributed mammals in Europe and (2) evaluate the cost-effectiveness of these attractants, by calculating the costs associated to reach a specific monitoring objective. We conducted a large-scale field experiment across four study areas in central and northern Italy, encompassing a variety of environments, from lowland forest to alpine beech forest. We focused on comparing the following low cost and readily available attractants: sardines, peanut butter, a commercial lure and we used a camera with no attractant as control, collecting data on a suite of small to large mammals. We found that for seven of our 13 target species detectability varied with the type of attractant used. Specifically, sardines proved to be the most effective attractant for canids and the porcupine, peanut butter was most effective for mustelids but was avoided by the roe deer, whereas the commercial lure was the most effective with red deer. Through a power analysis combined with a cost function analysis we were able to show striking differences in the cost-effectiveness of the different methods, sometimes in the order of magnitude of tens of thousands of euros, which strongly emphasizes the critical importance played by the choice of whether to use an attractant or not and the type of attractant to be used.

Monitoring of the radioactivity in the marine environment: a White Paper - Part I

Radioactivity in the marine environment, although present since the Earth’s formation, is comparatively understudied in contrast to aerial and terrestrial environments. A thorough examination of the radioactivity levels in aquatic environments can establish a robust foundation for comprehending various geochemical processes and phenomena within the water column and near the seabed, and as a result estimate the impact of radioactivity on local ecosystems. To achieve this objective, in-situ, long-term, and continuous monitoring is required. The present part I of the white paper highlights the fundamentals of marine radioactivity, describes the main objectives of an ambitious EU-funded project (RAMONES: Radioactivity Monitoring in Ocean Ecosystems) and introduces the innovative aspects of the technology developed as novel solutions to open problems.

A Lightweight Machine-Learning Method for Cloud Removal in Remote Sensing Images Constrained by Conditional Information

Reconstructing cloud-covered regions in remote sensing (RS) images holds great promise for continuous ground object monitoring. A novel lightweight machine-learning method for cloud removal constrained by conditional information (SMLP-CR) is proposed. SMLP-CR constructs a multilayer perceptron with a presingle-connection layer (SMLP) based on multisource conditional information. The method employs multi-scale mean filtering and local neighborhood sampling to gain spatial information while also taking into account multi-spectral and multi-temporal information as well as pixel similarity. Meanwhile, the feature importance from the SMLP provides a selection order for conditional information—homologous images are prioritized over images from the same season as the restoration image, and images with close temporal distances rank last. The results of comparative experiments indicate that SMLP-CR shows apparent advantages in terms of visual naturalness, texture continuity, and quantitative metrics. Moreover, compared with popular deep-learning methods, SMLP-CR samples locally around cloud pixels instead of requiring a large cloud-free training area, so the samples show stronger correlations with the missing data, which demonstrates universality and superiority.

Analgesia Nociception Index Monitor is an Objective Monitor for Predicting Successful Treatment after Epidural Blood Patch in a Spontaneous Intracranial Hypotension Patient - A Case Report

Analgesia Nociception Index Monitor is an Objective Monitor for Predicting Successful Treatment after Epidural Blood Patch in a Spontaneous Intracranial Hypotension Patient - A Case Report

Comparison of Home Sleep Devices and Sleep Study Testing in Hypoglossal Nerve Stimulation Patients.

Hypoglossal nerve stimulation (HGNS) is an implantable therapy for obstructive sleep apnea (OSA). Therapy efficacy is currently confirmed by a formal sleep study after empiric adjustment by the patient at home based on their subjective experience with the device. Home-based longitudinal apnea hypopnea index (AHI) measurements have the potential to refine HGNS therapeutic amplitude selection with objective data. Our objective was to compare AHI derived from routine sleep studies and two different home sleep devices in new HGNS recipients. Prospectively enrolled patients receiving HGNS therapy were provided a Sleep Tracking Mat (Withings, Issy-les-Moulineaux, France) and NightOwl peripheral arterial tonometry (PAT) sensor (Ectosense, Leuven, Belgium) for longitudinal, home AHI monitoring from 1 to 6 months post-implant. Therapy efficacy was assessed at 3 and 6 months post-implant using in-lab polysomnography (PSG) or home sleep apnea test (HSAT). The sleep mat and PAT sensor AHI were compared against PSG and HSAT for accuracy of OSA severity identification. Sixty patients were enrolled across 5 centers and followed for 6 months. The sleep mat had sensitivity and specificity for identifying AHI <15 of 61% and 82% and AHI <30 of 77% and 100%. The PAT device had sensitivity and specificity for identifying AHI <15 of 57% and 77% and AHI <30 of 81% and 80%. The sleep mat and PAT sensor demonstrated high sensitivity and specificity for detection of mild and moderate OSA in patients with HGNS therapy and may enable longitudinal objective monitoring of HGNS efficacy in the home setting. 3 Laryngoscope, 2024.

Artificial intelligence-driven volumetric CT outcome score in cystic fibrosis: longitudinal and multicenter validation with/without modulators treatment.

Holistic segmentation of CT structural alterations with 3D deep learning has recently been described in cystic fibrosis (CF), allowing the measurement of normalized volumes of airway abnormalities (NOVAA-CT) as an automated quantitative outcome. Clinical validations are needed, including longitudinal and multicenter evaluations. The validation study was retrospective between 2010 and 2023. CF patients undergoing Elexacaftor/Tezacaftor/Ivacaftor (ETI) or corticosteroids for allergic broncho-pulmonary aspergillosis (ABPA) composed the monocenter ETI and ABPA groups, respectively. Patients from six geographically distinct institutions composed a multicenter external group. All patients had completed CT and pulmonary function test (PFT), with a second assessment at 1 year in case of ETI or ABPA treatment. NOVAA-CT quantified bronchiectasis, peribronchial thickening, bronchial mucus, bronchiolar mucus, collapse/consolidation, and their overall total abnormal volume (TAV). Two observers evaluated the visual Bhalla score. A total of 139 CF patients (median age, 15 years [interquartile range: 13-25]) were evaluated. All correlations between NOVAA-CT to both PFT and Bhalla score were significant in the ETI (n = 60), ABPA (n = 20), and External groups (n = 59), such as the normalized TAV (ρ ≥ 0.76; p < 0.001). In both ETI and ABPA groups, there were significant longitudinal improvements in peribronchial thickening, bronchial mucus, bronchiolar mucus and collapse/consolidation (p ≤ 0.001). An additional reversibility in bronchiectasis volume was quantified with ETI (p < 0.001). Intraclass correlation coefficient of reproducibility was > 0.99. NOVAA-CT automated scoring demonstrates validity, reliability and responsiveness for monitoring CF severity over an entire lung and quantifies therapeutic effects on lung structure at CT, such as the volumetric reversibility of airway abnormalities with ETI. Normalized volume of airway abnormalities at CT automated 3D outcome enables objective, reproducible, and holistic monitoring of cystic fibrosis severity over an entire lung for management and endpoints during therapeutic trials. Visual scoring methods lack sensitivity and reproducibility to assess longitudinal bronchial changes in cystic fibrosis (CF). AI-driven volumetric CT scoring correlates longitudinally to disease severity and reliably improves with Elexacaftor/Tezacaftor/Ivacaftor or corticosteroid treatments. AI-driven volumetric CT scoring enables reproducible monitoring of lung disease severity in CF and quantifies longitudinal structural therapeutic effects.

Clinical Impact of Self-Recognition of Recurrent Acute Myocardial Infarction: From KRMI-RCC.

Background/Objectives: Self-recognition of recurrent myocardial infarction (re-MI) may be essential for reducing prehospital time contrast to awareness of re-MI symptoms. However, data on the current status and clinical impact of self-recognition of re-MI are limited in the contemporary period. Thus, this study aimed to increase this body of knowledge. Methods: We enrolled 1018 patients with re-MI using data from the Korean Registry of Acute Myocardial Infarction for Regional Cardiocerebrovascular Centres. The patients were classified into self-recognised MI and unrecognised MI groups, and the differences between them were compared. Results: The rate of self-recognition among the patients with previous experience of MI was only 52.4%. Among the patients with re-MI, factors associated with self-recognition included recent first MI within 3 years, prior dyslipidaemia, two or more MI symptoms, and the male gender (p < 0.05). Factors associated with a lack of recognition were older age (≥70 years), prior stroke, and cancer history (p < 0.05). The proportion of symptoms-to-emergency room arrival time within 90 min among the patients with ST-elevation MI was significantly higher in the self-recognised group than in the unrecognised group (52.6% vs. 31.6%, p < 0.001). The self-recognised group showed a lower in-hospital mortality rate (1.5% vs. 6.2%, p < 0.001), and this benefit was maintained even after 1 year (hazard ratio: 0.53; p < 0.001). Conclusions: Only half of the patients who previously experienced a MI recognised a re-MI when it occurred. This recognition reduced prehospital delay and led to higher survival rates, which highlights the importance of patient education as well as objective monitoring devices, irrespective of individual recognition ability for immediate response.

Wearable-Based Kinematic Analysis of Upper-Limb Movements During Daily Activities Could Provide Insights into Stroke Survivors’ Motor Ability

Background Frequent and objective monitoring of motor recovery progression holds significant importance in stroke rehabilitation. Despite extensive studies on wearable solutions in this context, the focus has been predominantly on evaluating limb activity. This study aims to address this limitation by delving into a novel measure of wrist kinematics more intricately related to patients’ motor capacity. Objective To explore a new wearable-based approach for objectively and reliably assessing upper-limb motor ability in stroke survivors using a single inertial sensor placed on the stroke-affected wrist. Methods Seventeen stroke survivors performed a series of daily activities within a simulated home setting while wearing a six-axis inertial measurement unit on the wrist affected by stroke. Inertial data during point-to-point upper-limb movements were decomposed into movement segments, from which various kinematic variables were derived. A data-driven approach was then employed to identify a kinematic variable demonstrating robust internal reliability, construct validity, and convergent validity. Results We have identified a key kinematic variable, namely the 90th percentile of movement segment distance during point-to-point movements. This variable exhibited robust reliability (intra-class correlation coefficient of .93) and strong correlations with established clinical measures of motor capacity (Pearson’s correlation coefficients of .81 with the Fugl-Meyer Assessment for Upper-Extremity; .77 with the Functional Ability component of the Wolf Motor Function Test; and −.68 with the Performance Time component of the Wolf Motor Function Test). Conclusions The findings underscore the potential for continuous, objective, and convenient monitoring of stroke survivors’ motor progression throughout rehabilitation.

B-Spline signature responses in structural change detection: method development

Damage detection is perhaps the most critical objective of structural health monitoring. Proper documentation of the progression of structural damage from onset to failure enables performance-based maintenance that mitigates risk and contains socioeconomic impact. Recently, damage detection methods that focus on change detection through analyses of the structural global response in the frequency or time domain have garnered attention due to their efficiency and accuracy. This work proposes a novel time-domain, nonparametric, data-driven change detection method. The time-domain B-Spline transfer function is defined as a signature time history response of the structure, referred to in this work as the B-Spline signature response (BSR). BSR may be computed directly through a physics-based simulation, or it be extracted in a discrete form from measurements of displacement, velocity, or acceleration time histories of structural response to any external excitation. The BSR is independent of loading, is a characteristic of the system, and captures the condition of the structure at the time of acquisition without the need for identifying structural properties. In this work, the fundamentals of the BSR concept are introduced first along with the process of extracting a BSR from structural responses to arbitrary excitations. Verification and validation studies are conducted based on computer simulations and laboratory testing and are discussed in detail. Subsequently, an approach is proposed to detect change through correlation of BSR signals acquired at different times during the monitoring period. Any changes in the intrinsic characteristics affecting the response cause BSR changes and correlation loss. The change detection methodology is demonstrated through computer simulations and experimental investigations. Initial parametric studies are conducted to investigate the suitability of the proposed approach to detect small structural changes. This article demonstrates the validity of the proposed method and establishes the framework for further development and case-specific applications.

Full-waveform automatic location of small seismic events in an underground mine using synthetic strain Greens tensors

A reliable method to locate microseismic evets is an important objective for routine mine seismic monitoring. We developed an algorithm that can automatically and reliably locate an event from a single uniaxial trace. Signals created by microseismic events are recorded as seismograms that consist of direct P- and S-waves as well as the waves reflected off the underground excavations. We take advantage of the extra complexity these reflections introduce as they make the signal more unique. In our investigation we cross-correlated unaltered synthetic seismograms against trial waveforms created from a linear combination of appropriate waveforms from a library of numerically calculated strain Green's tensors. We use a single uniaxial synthetic seismogram to invert for the source location, six moment tensor components, and frequency. The resulting cost functions are constructed as maximal correlations at each node in the mine grid through the true source x-planes. This function is maximized by differential evolution to simultaneously yield the source parameters. Sensitivity tests such as shifting the mine plans and altering the background velocities of the media are investigated to test the robustness of the method. We contaminate the data with 40% white noise to test the resolution against simulated real recorded seismograms. The method is demonstrated using synthetic data calculated with a realistic underground 3D velocity model.

Multi-frame temporal dense nested attention method for detecting GEO objects

The geostationary orbit is a special and important spatial orbital resource. However, space resident objects in this orbit pose potential threats to space security, characterized as dim and small targets in imaging features. To solve the problem of detecting dim and small targets in geostationary orbit, we propose a multi-frame temporal dense nested attention method, which consists of a uniform linear motion constraint module and a dense nested attention module. The dense nested attention module adopts a multi-layer U-shaped structure and channel spatial attention mechanism, which can preserve the deep features of small targets, extract and fuse features from different scales of feature maps, and finally obtain the preliminary detection results of single-frame images. The uniform linear motion constraint module uses the prior information of the target motion in the image sequence, removes false positives and supplements false negatives, and improves the detection performance. Experiments were conducted on the SpotGEO dataset publicly released by the European Space Agency. The results of our experiments indicate that our method outperforms the state-of-the-art approach, showing a 12.83% reduction in Mean Squared Error compared to the current leading method. Our method, characterized by its minimal parameter requirement and independence from extensive dataset pre-training, is ideally suited for integration into small satellite devices, enabling efficient monitoring and mitigation of resident space objects.

Probing Thermal Electrons in Gamma-Ray Burst Afterglows

Particle-in-cell simulations have unveiled that shock-accelerated electrons do not follow a pure power-law distribution, but have an additional low-energy “thermal” part, which owns a considerable portion of the total energy of the electrons. Investigating the effects of these thermal electrons on gamma-ray burst (GRB) afterglows may provide valuable insights into the particle acceleration mechanisms. We solve the continuity equation of electrons in energy space, from which multiwavelength afterglows are derived by incorporating processes including synchrotron radiation, synchrotron self-absorption, synchrotron self-Compton scattering, and γ–γ annihilation. First, there is an underlying positive correlation between the temporal and spectral indices due to the cooling of electrons. Moreover, thermal electrons result in simultaneous nonmonotonic variations of both the spectral and temporal indices at multiple wavelengths, which could be individually recorded by the 2.5 m Wide Field Survey Telescope and Vera Rubin Observatory Legacy Survey of Space and Time (LSST). The thermal electrons could also be diagnosed using afterglow spectra from synergistic observations in the optical (with LSST) and X-ray (with the Microchannel X-ray Telescope on board the Space Variable Objects Monitor) bands. Finally, we use Monte Carlo simulations to obtain the distribution of the peak flux ratio (R X) between the soft and hard X-rays, and of the time delay (Δt) between the peak times of the soft X-ray and optical light curves. The thermal electrons significantly raise the upper limits of both R X and Δt. Thus, the distribution of GRB afterglows with thermal electrons is more scattered in the R X−Δt plane.

Use of Transdermal Alcohol Sensors in Conjunction With Contingency Management to Reduce Alcohol Consumption in People With Alcohol Dependence Attending Alcohol Treatment Services: Protocol for a Pilot Feasibility Randomized Controlled Trial.

Wearable technology for objective, continuous, and reliable alcohol monitoring has been developed. These are known as transdermal alcohol sensors (TASs). They can be worn on the wrist or ankle with the sensor pressed against the skin and can measure sweat vapors being emitted from the skin, to record transdermal alcohol concentration (TAC). Previous studies have investigated the accuracy and acceptability of the available TAS brands, but there has been little research into their use in people with alcohol use disorders (AUD). This feasibility randomized controlled trial aims to explore the feasibility, strengths, and limitations of using a TAS to monitor alcohol consumption in individuals in treatment for AUD with or without contingency management (CM) to promote abstinence or low-level alcohol consumption. The target sample size is 30 (15 randomized to each group). Participants will be recruited through poster adverts at alcohol services. Both groups (control and CM) will wear the TAS (BACtrack Skyn) for 2 weeks in the context of their usual treatment, meeting with the researcher every other weekday. In the last meeting, the participants will complete a postwear survey on their experience of wearing the TAS. The CM group will also receive small financial incentives for low or no alcohol consumption, as measured by the TAS. On days where the TAC peak is below a set threshold (<115.660 g/L), CM group participants will be rewarded with a £5 (US $6.38) voucher. There are financial bonuses if this target is achieved on consecutive days. The researcher will monitor TAC for each day of the study at each research visit and allocate financial incentives to participants according to a set reinforcement schedule. The first participant was enrolled in June 2023, and the last in December 2023. Data analysis is underway and is estimated to be completed by June 2024. A total of 32 participants were enrolled. Most TAS brands have had limited application in clinical settings, and most studies have included healthy adults rather than people with AUD. TAS has the potential to enhance treatment outcomes in clinical alcohol treatment. The accuracy, acceptability, and feasibility of TAS for people with AUD in clinical settings need to be investigated. This is the first study to use TAS in specialized alcohol services with diagnosed AUD individuals currently receiving treatment from a south London alcohol service. ISRCTN Registry ISRCTN46845361; https://www.isrctn.com/ISRCTN46845361. DERR1-10.2196/57653.

An Efficient Approach for Localizing Sensor Nodes in 2D Wireless Sensor Networks Using Whale Optimization-Based Naked Mole Rat Algorithm

Localization has emerged as an important and critical component of research in Wireless Sensor Networks (WSNs). WSN is a network of numerous sensors distributed across broad areas of the world to conduct numerous activities, including sensing the data and transferring it to various devices. Most applications, like animal tracking, object monitoring, and innumerable resources put in the interior as well as outdoor locations, need to identify the position of the occurring incident. The primary objective of localization is to identify the locality of sensor nodes installed in a network so that the location of a particular event can be traced. Different optimization approaches are observed in the work for solving the localization challenge in WSN and assigning the apt positions to undiscovered sensor nodes. This research employs the approach of localizing sensor nodes in a 2D platform utilizing an exclusive static anchor node and virtual anchors to detect dynamic target nodes by projecting these six virtual anchors hexagonally at different orientations and then optimizing the estimated target node co-ordinates employing Whale Optimization-based Naked Mole Rat Algorithm (WONMRA). Moreover, the effectiveness of a variety of optimization strategies employed for localization is compared to the WONMRA strategy concerning localization error and the number of nodes being localized, and it has been investigated that the average error in localization is 0.1999 according to WONMRA and is less than all other optimization techniques.

The Influence of Metro Tunnel Construction Parameters on the Settlement of Surrounding Buildings

The construction of tunnels in conditions of dense urban development affects buildings, structures, and engineering communications located at the surface. In this work, through dispersion analysis, factors influencing the settlement of the earth’s surface and buildings during tunneling were selected. Subsequently, a model based on statistically significant parameters that can predict deformations at the pre-design stage was created. This research was conducted using data from geotechnical monitoring obtained during the construction of underground structures, with information about the technological parameters of shield tunneling in the construction of the single-track lines of the Moscow Metro using TBM with face-support pressure and engineering–geological conditions. In the obtained model, there is a clear dependency between the additional displacement of the monitoring object located above the projected tunnel and the average face-support pressure causing the mentioned deformations. The response is also affected by the soil deformation model at the tunnel face, the depth of the tunnel, and the soil excavation for the installation of one ring.