Accurate Monitoring Research Articles

Cascading hazards in volcanic environments: monitoring, modelling and impact analysis of tsunamigenic flows for risk reduction

Volcanic environments present complex multi-hazard scenarios where primary volcanic activity can trigger cascading hazards or where multiple hazards can occur simultaneously, leading to cascading and compounding impacts on communities and ecosystems. Stromboli, one of the most active volcanoes globally, exemplifies these challenges with its frequent eruptions, pyroclastic density currents, landslides, and tsunamis. In the present study, the mechanisms behind tsunamigenic flows at Stromboli are investigated by using extensive monitoring data from previous studies and numerical modelling. Key findings from simulations and parametric analysis are presented, showing the relationships between mass flow dynamics, morphological factors, and tsunami generation. A more comprehensive context is then considered by drawing the impact chain related to tsunamigenic flows during a volcanic eruption. This includes a broad evaluation of the exposure, vulnerability and possible mitigation measures. Starting from the study of the hazard and then following up with the evaluation of the impacts, this study emphasizes the necessity of a holistic approach in multi-hazards environment, where accurate hazards modelling and monitoring, interdisciplinary collaboration, and community engagement need to be considered to reduce risk.

Factors impacting target-enriched long-read sequencing of resistomes and mobilomes.

We investigated the efficiency of target-enriched long-read sequencing (TELSeq) for detecting antimicrobial resistance genes (ARGs) and mobile genetic elements (MGEs) within complex matrices. We aimed to overcome limitations associated with traditional antimicrobial resistance (AMR) detection methods, including short-read shotgun metagenomics, which can lack sensitivity, specificity, and the ability to provide detailed genomic context. By combining biotinylated probe-based enrichment with long-read sequencing, we facilitated the amplification and sequencing of ARGs, eliminating the need for bioinformatic reconstruction. Our experimental design included replicates of human fecal microbiota transplant material, bovine feces, pristine prairie soil, and a mock human gut microbial community, allowing us to examine variables including genomic DNA input and probe set composition. Our findings demonstrated that TELSeq markedly improves the detection rates of ARGs and MGEs compared to traditional sequencing methods, underlining its potential for accurate AMR monitoring. A key insight from our research is the importance of incorporating mobilome profiles to better predict the transferability of ARGs within microbial communities, prompting a recommendation for the use of combined ARG-MGE probe sets for future studies. We also reveal limitations for ARG detection from low-input workflows, and describe the next steps for ongoing protocol refinement to minimize technical variability and expand utility in clinical and public health settings. This effort is part of our broader commitment to advancing methodologies that address the global challenge of AMR.

An ultrasensitive multimodal intracranial pressure biotelemetric system enabled by exceptional point and iontronics.

The accurate monitoring of vital physiological parameters, exemplified by heart rate, respiratory rate, and intracranial pressure (ICP), is of paramount importance, particularly for managing severe cranial injuries. Despite the rapid development of implantable ICP sensing systems over the past decades, they still suffer from, for example, wire connection, low sensitivity, poor resolution, and the inability to monitor multiple variables simultaneously. Here, we propose an ultrasensitive multimodal biotelemetric system that amalgamates an iontronic pressure transducer with exceptional point (EP) operation for the monitoring of ICP signals. The proposed system can exhibit extraordinary performance regarding the detection of minuscule ICP fluctuation, demonstrated by the sensitivity of 115.95 kHz/mmHg and the sensing resolution down to 0.003 mmHg. Our system excels not only in the accurate quantification of ICP levels but also in distinguishing respiration and cardiac activities from ICP signals, thereby achieving the multimodal monitoring of ICP, respiratory, and heart rates within a single system. Our work may provide a pragmatic avenue for the real-time wireless monitoring of ICP and thus hold great potential to be extended to the monitoring of other vital physiological indicators.

A Deep Learning Dependent Controller for Advanced Ultracapacitor SoC Concept to Increase Battery Life Span of Electric Vehicles

ABSTRACTOn a global scale, significant progress is being made in the field of battery technology for Electric Vehicle (EV) applications, driven by the need to combat carbon emissions and mitigate the effects of global warming. Accurately determining critical parameters, making sure battery storage system diagnosis, and functioning are correct are critical to the feasibility of EVs. However, insufficient supervision and safety measures for these storage systems may lead to serious problems like a thermal runaway, overcharging, overheating, cell imbalances, and fire hazards. To tackle these challenges, the presence of an efficient battery management system becomes paramount. By facilitating accurate monitoring, managing heat dissipation, regulating charging‐discharging procedures, guaranteeing battery safety, and offering protection measures, this system is essential to maximizing battery performance. The key intention of this innovative approach is to improve the longevity of EV batteries during extended periods of operation. By assessing vehicle velocity, remaining battery energy, and State of Charge (SoC), the proposed method effectively manages SoC in both the battery and ultracapacitor. This control is accomplished through a two‐stage convolutional neural network‐based system known as the Charge Sustain‐CNN Controller and the Charge Deplete‐CNN Controller. These controllers are fine‐tuned using the Fractional Latrans‐Hunt optimization (FLHO) algorithm to optimize the performance. The evaluation criteria encompass the battery and ultracapacitor's energy efficiency, as well as vehicle velocity. This novel approach significantly improves the energy storage system in EVs, leading to enhanced energy efficiency and prolonged battery life. Ultimately, experimental results validate the practicality and effectiveness of this developed method. Specifically, the proposed approach attained the Battery's SoC of 72.47%, 91.99%, and 82.88% for the different drive cycles including the FTP75, J1015, and UDDS, respectively.

Recognizing human activities using light-weight and effective machine learning methodologies

Background Human activity recognition (HAR) is increasingly important in enhancing healthcare systems by enabling accurate monitoring of individuals' movements through sensor data. This paper is motivated by the need to improve the accuracy of HAR, particularly for applications in e-health systems, where reliable activity detection can lead to better health outcomes. The study explores six prominent machine learning techniques—decision tree, random forest, linear regression, Naïve Bayes, k-nearest neighbor, and neural networks—to determine which methods can most effectively predict activities like walking, sitting, standing, laying, walking upstairs, and walking downstairs. Methods We employed these six machine learning algorithms to analyze a comprehensive dataset derived from various sensors. Each model was rigorously trained and evaluated to compare its effectiveness in recognizing human activities. The experiments aimed to identify strengths and weaknesses in each approach, with particular emphasis on advanced techniques such as random forest, convolutional neural networks (CNNs), and gated recurrent networks (GRNs). Results The experimental evaluation revealed that the random forest classifier, CNN, GRN, and neural networks delivered promising results, achieving high accuracy levels. Notably, the neural network model excelled, attaining an impressive accuracy of 98%. In contrast, the Naïve Bayes model did not meet the performance expectations set by the other algorithms. Conclusions This research effectively classifies activities such as sitting, standing, laying, walking, walking downstairs, and walking upstairs, underscoring the potential of machine learning in HAR. The findings highlight the superior performance of neural networks in enhancing activity recognition, which could lead to advanced applications in e-health systems and improve overall healthcare monitoring strategies.

RSSDI Expert Consensus for Optimal Glucose Monitoring in Diabetes Mellitus in India and Recommendations for Clinical Practice

Effective management of diabetes warrants accurate and regular monitoring of glucose levels to prevent serious diabetes-associated complications and long-term health consequences. Hence, achieving target glucose levels and maintaining adequate glycemic control is essential in diabetes to prevent episodes of hypoglycemia. Multiple glucose monitoring tools have been developed over time, each with differing degrees of precision, convenience, and real-time input. The management of diabetes has been considerably improved by using these various glucose monitoring devices, both in terms of treatment outcomes and patient quality of life. However, in India, the lack of awareness, patient education, availability, and affordability of glucose monitoring devices poses a significant concern resulting in poor adherence to glucose monitoring and low medication adherence thereby increasing disease burden. Consequently, there is an urgent need for healthcare professionals and policymakers in India to collaborate and address the challenges associated with glucose monitoring in patients with diabetes, while also providing recommendations to overcome these concerns. This recommendation article offers a comprehensive evaluation of various glucose monitoring tools available for use by patients suffering from different types of diabetes, explores the challenges associated with glucose monitoring in India, and also presents expert panel recommendations to enhance diabetes management effectively.

Utilizing Multiple Triboelectric Nanogenerator Sensors and Signal Processing Technology for Monitoring Periodic Leg Movements of Sleep

High-quality sleep is essential for both physiological and cognitive functions. However, periodic leg movements of sleep (PLMS), an involuntary phenomenon during sleep, affects millions of people worldwide, contributing to sleep fragmentation and functional impairments. The accurate monitoring of PLMS is important for identifying and addressing these issues. Traditional methods, such as polysomnography (PSG), which monitor the bare tibialis muscle movements in clinical environments, may not adequately reflect the natural sleep patterns at home. They are costly and unsuitable for long-term studies. In recent years, there has been growing interest in using flexible sensors for sleep monitoring. Previous studies have applied triboelectric nanogenerators (TENGs) as flexible sensors to detect muscle movements during sleep. However, distinguishing true PLMS from false signals caused by external factors, such as blankets, remains a challenge. This study proposes a method using three TENG sensors placed on the dorsum, ankle, and tibialis, respectively, along with signal processing techniques to enhance the accuracy of PLMS detection. This study provides a cost-effective, comfortable method for PLMS monitoring, with the potential for widespread use in home-based sleep studies and long-term care in the future.

Development of Ultrathin, Breathable, Waterproof, and Durable Nanonet‐Supported Ionogel Sensors for Electrophysiological Monitoring

AbstractIonogel has gained attention as a promising material for flexible electronic skin (e‐skin), due to its exceptional conductivity, compositional stability, and biocompatibility. Nevertheless, establishing a seamless interface between ionogel and human skin remains a significant challenge. This study presents the design and fabrication of a nanonet‐supported ionogel with a thickness of ≈16 µm, marking it the thinnest ionogel reported to date. This ultrathin ionogel exhibits remarkable toughness (5.51 MJ m−3), a broad strain range (0–375%), and impressive fatigue resistance, enduring over 3000 cycles. Additionally, it offers excellent water resistance and a high water vapor transmission rate (WVTR), supporting perspiration and enhancing skin breathability. Moreover, the ultrathin ionogel has potential as a carrier in transdermal drug delivery systems (TDDS), highlighting its suitability for biomedical applications. Wearable devices incorporating this ultrathin ionogel facilitate continuous, sensitive, and highly accurate monitoring of physiological signals, positioning it as a promising material for future flexible e‐skin material.

Novel Endoplasmic Reticulum-Targeted Luminescent Probe for Visualization of Carbon Monoxide in Drug-Induced Liver Injury.

Drug-induced liver injury (DILI) is a major hepatic dysfunction commonly caused by hepatotoxic drug overdose, resulting in a considerable number of fatalities worldwide. Recent studies have highlighted the regulatory and hepatoprotective effects of carbon monoxide (CO) during the liver injury process. However, precisely tracking the dynamic changes in the composition of CO in DILI is still a great challenge. In this work, leveraging the innovative "quencher-insertion" strategy, a unique endoplasmic reticulum (ER)-targetable lanthanide complex-based luminescence probe, ER-ANBTTA-Eu3+/Tb3+, has been developed for the selective and accurate monitoring of CO fluxes in live cells and laboratory animals. The new probe is composed of three covalently linked functional moieties: the terpyridine polyacid-Eu3+/Tb3+-mixed chelates as the long-lived luminophore, a p-toluenesulfonamide moiety as the ER-anchoring motif, and an allyloxy-nitrobenzyl ether moiety as the CO-specific recognition unit. Upon reaction with CO in the presence of Pd2+ ions, the Tsuji-Trost reaction leads to the cleavage of the allyloxy-nitrobenzyl group from the Eu3+/Tb3+-mixed chelates, which results in the restoration of Tb3+ emission at 538 nm and the attenuation of Eu3+ emission at 688 nm, leading to a dramatic increase of the I538/I688 ratio. In addition to the exceptional response sensitivity and selectivity toward CO, ER-ANBTTA-Eu3+/Tb3+ also exhibits the outstanding ER-locating capability, which allows the probe to be used for imaging of CO in the ER of live cells. Using this probe, combined with the time-gated luminescence imaging mode, the exogenous and endogenous CO in ER of live cells were monitored without the interference of background autofluorescence. Moreover, the upregulation of hepatic CO in DILI mice was successfully visualized. The results suggested the potential of ER-ANBTTA-Eu3+/Tb3+ for deeply exploring the functions of CO in DILI pathogenesis.

Digital twin water conservancy early warning system based on physical level and numerical simulation in intelligent water conservancy construction

Abstract. In 2023, the Central Committee of the Communist Party of China and The State Council issued the Overall Layout Plan for the Construction of Digital China, emphasizing the construction of a smart water conservancy system with digital twin basins as the core. The Guangxi Zhuang Autonomous Region government has clearly proposed to actively build a "digital twin Pinglu Canal", and attaches great importance to the construction of digital twin platform application services during the construction of the Western land-Sea New Passage (Pinglu) canal. Aiming at the problems such as unsatisfactory early warning effect, inaccurate data and imprecise model of existing smart water conservancy technology, this paper proposes a digital twin system for disaster early warning based on physical level and numerical simulation technology, establishes a visual digital water conservancy model and digital twin smart water conservancy cloud platform, and combines multi-physics field and multi-dimensional coupling algorithm to respond to technical pain points. To achieve scientific and accurate monitoring and intelligent management, and contribute to the construction of digitally empowered Pinglu Canal.

The effect of breed on ivermectin residues in the edible tissues of cattle and the estimated withdrawal period

This study analyzed the residue depletion kinetics of ivermectin (IVM) in Nelore and crossbred (Nelore x Angus) cattle aiming to compare the profiles between the breeds and evaluate the residue levels at the injection site. IVM 1%, at a dose of 0.2 mg/kg, was administered via the subcutaneous route, and tissue samples were collected on different days post administration for analysis by LC–MS/MS. The results revealed that the detection of the marker residue in conventional matrices such as the liver, perirenal fat, and trapezius muscle (injection site) had relatively high residue concentrations. The maximum residue limit (MRL) was exceeded at the injection site at 21- and 35-days post administration in crossbred and Nelore animals, respectively, with significant variations between animals. This study highlighted significant challenges in accurately determining the pharmacokinetic profile and withdrawal periods of IVM in cattle due to high variability in tissue residue data, particularly at injection sites. The comparison of IVM concentrations between cattle breeds was hindered by high standard errors, emphasizing the need for more rigorous sampling protocols. The results suggest that current guidelines may not adequately account for the erratic depletion kinetics of injectable formulations like IVM, especially at injection sites. Therefore, improving sampling techniques and revising guidelines are essential for accurate residue monitoring and withdrawal period determination.

Integrating Multisource Data and Machine Learning for Supraglacial Lake Detection: Implications for Environmental Management and Sustainable Development Goals in High Mountainous Regions

The accurate detection and monitoring of supraglacial lakes in high mountainous regions are crucial for understanding their dynamic nature and implications for environmental management and sustainable development goals. In this study, we propose a novel approach that integrates multisource data and machine learning techniques for supra-glacial lake detection in the Passu Batura glacier of the Hunza Basin, Pakistan. We extract pertinent features or parameters by leveraging multisource datasets such as radar backscatter intensity VH and VV parameters from Sentinel-1 Ground Range Detected (GRD) data, near-infrared (NIR), NDWI_green, NDWI_blue parameters from Sentinel-2 Multi-spectral Instrument(MSI) data, and surface slope, aspect, and elevation parameters from topographic data. The entire dataset is partitioned into training and testing sets, with machine learning models including the artificial neural network (ANN), the support vector machine (SVM), logistic regression (LR), random forest (RF), and K-nearest neighbour (KNN) trained on the training data (70%). Accuracy assessment employs testing data and involves the evaluation of metrics such as ROC curves and confusion matrices. The best-performing model, ANN, is validated against manually digitized lake polygons derived from Sentinel-2 and Google Earth Pro imagery. Furthermore, the digitized lake polygons are used to analyze glacial lake dynamics from 2016 to 2022. Key findings of this research presented that the NDWI_green, Sigma0_VH, and elevation are the most significant predictors in detecting supra-glacial lakes. Among the various trained and evaluated models, the Artificial Neural Network (ANN) achieved the highest performance (accuracy: 95%, AUC: 0.99) and accurately mapped supra-glacial lakes regardless of their small size. The findings have significant implications for understanding glacial lake behavior in the context of climate change and informing future research and monitoring efforts.

Feasibility of Using Toroidal Transceivers for Acquiring Intraoperative MR Images Around Deep Brain Stimulation Electrodes

IntroductionMagnetic resonance imaging (MRI) provides excellent soft tissue contrast for visualizing of deep brain stimulation (DBS) targets, allowing validation of the electrode placement, and assessing complications such as microhemorrhage and edema. However, the presence of the electrodes can introduce challenges such as radiofrequency (RF) induced current artifacts and excessive heating of the electrode contacts. Additionally, extended procedure times are also considered a disadvantage when using MRI as an intraoperative imaging modality following DBS electrode placement. MethodWe propose a novel approach of using toroidal resonators to inductively couple the shaft of the electrode to the scanner's transmit-receive chain thereby utilizing it as a localized imaging antenna. The small extent of the field generated by the electrode antenna allows fast imaging with smaller field-of-views (FOVs) spanning only a few centimeters. Furthermore, we present a fast and accurate safety monitoring strategy that can be used to predict the temperature increase at the electrical contacts of the electrode. Results and DiscussionImaging with the toroidal transceiver yields a higher signal-to-noise ratio (SNR) efficiency in proximity to the electrodes. This approach reduced the RF induced current artifacts around the electrode which enhanced the visibility of the shaft and improved electrode localization. Moreover, the limited sensitivity around the electrode can be exploited to perform fast scans with small FOVs. The predicted heating around DBS contacts was in quantitative agreement with the experimental heating in swine studies with a normalized root-mean-square error (NRMSE) ≤ 0.09.

Strand displacement amplification combining photothermal-colorimetric dual mode test strip for ultra-sensitive detection of patulin

Patulin (PAT) is a fungal toxin often found in fruit and juice products. The sensitive and fast detection of PAT can help the control of PAT-induced health risk. However, the lack of efficient and stable antibody for PAT hindered the advancement of its test strips. Here, we constructed a nucleic acid-based lateral flow test strip (NALFTS) and used photothermal-colorimetric dual mode for the detection of PAT. The binding of PAT in samples to the aptamer on magnetic separation probe MNPs-Apt@cDNA induced the release of cDNA competitively. Subsequently, cDNA initiates strand displacement amplification (SDA), generating numerous single-strand DNA (ssDNA). These ssDNA molecules were analyzed by a test strip with photothermal-colorimetric dual signal. The developed method demonstrated a limit of detection (LOD) of 5 pg mL−1 and a quantitative range of 0.01 ∼ 320 ng mL−1 in photothermal mode, and a LOD of 0.1 ng mL−1 and a quantitative range of 0.1 ∼ 40 ng mL−1 in colorimetric mode. This SDA-NALFTS offered dual signals corroborating each other to provide more reliable results for rapid screening or accurate monitoring of PAT in juice.

An Arrhenius-Based Simulation Tool for Predicting Aging of Lithium Manganese Dioxide Primary Batteries in Implantable Medical Devices

This article presents a novel aging-coupled predictive thermo-electrical dynamic modeling tool tailored for primary lithium manganese dioxide (Li-MnO2) batteries in active implantable medical devices (AIMDs). The aging mechanisms of rechargeable lithium batteries are well documented using computationally intensive physics-based models, unsuitable for real-time onboard monitoring in AIMDs due to their high demands. There is a critical need for efficient, less demanding modeling tools for accurate battery health monitoring and end-of-life prediction as well as battery safety assessment in these devices. The presented model in this article simulates the battery terminal voltage, remaining capacity, temperature, and aging during active discharge, making it suitable for real-time health monitoring and end-of-life prediction. We incorporate a first-order dynamic for internal resistance growth, influenced by time, temperature, discharge depth, and load current. By adopting Arrhenius-type kinetics and polynomial relationships, this model effectively simulates the combined impact of these variables on battery aging under diverse operational conditions. The simulation handles both the continuous micro-ampere-level demands necessary for device housekeeping and periodic high-rate pulses needed for therapeutic functions, at a constant ambient temperature of 37 °C, mimicking human body conditions. Our findings reveal a gradual, nonlinear increase in internal resistance as the battery ages, rising by an order of magnitude over a period of 5 years. Sensitivity analysis shows that as the battery ages and load current increases, the terminal voltage becomes increasingly sensitive to internal resistance. Specifically, at defibrillation events, the ∂V∂R trajectory dramatically increases from 10−12 to 10−8, indicating a fourth-order-of-magnitude enhancement in sensitivity. A model verification against experimental data shows an R2 value of 0.9506, indicating a high level of accuracy in predicting the Li-MnO2 cell terminal voltage. This modeling tool offers a comprehensive framework for effectively monitoring and optimizing battery life in AIMDs, therefore enhancing patient safety.

The Effect of Safety Training and Supervision on The Use of Personal Protective Equipment Which Has Implications on Work Safety in Loading and Unloading at Maccini Baji Port, Maros, South Sulawesi

This study aims to analyze the influence of training and safety supervision on the use of personal protective equipment (PPE) and its implications for loading and unloading safety at Maccini Baji Port, Maros, South Sulawesi. The issues addressed include the low awareness and compliance of workers in using PPE and the lack of adequate safety supervision. The research population consists of 155 employees and dockworkers at the port, with a saturated sampling technique used. The research method employed is quantitative, with data collected through questionnaires and observations. Data analysis was conducted using the SMART PLS method. The results show that training and safety supervision have a positive and significant effect on the use of PPE, which also has a positive implication for loading and unloading safety. Training proved to be effective through the instructor dimension, while safety supervision was greatly influenced by the accuracy of measurement. In conclusion, improving the quality of training and supervision can enhance compliance with PPE usage and work safety at the port. Managerial recommendations include increasing the frequency of training, maintaining equipment, and regularly supervising PPE usage by introducing more accurate and modern monitoring technologies.

Mitochondria-Targetable Cyclometalated Iridium(III) Complex-Based Luminescence Probe for Monitoring and Assessing Treatment Response of Ferroptosis-Mediated Hepatic Ischemia-Reperfusion Injury.

Ferroptosis plays an essential role in the pathological progression of hepatic ischemia-reperfusion injury (HIRI), which is closely related to iron-dependent lipid peroxidation. Since mitochondria are thought to be the major site of reactive oxygen species (ROS) production and iron storage, monitoring the variations of mitochondrial hypochlorous acid (HClO) (an important member of ROS) has important implications for the assessment of ferroptosis status, as well as the formulation of treatment strategies for HIRI. However, reliable imaging tools for the visualization of mitochondrial HClO and monitoring its dynamic changes in ferroptosis-mediated HIRI are still lacking. Herein, in this work, an HClO-activated near-infrared (NIR) cyclometalated iridium(III) complex-based probe, named NIR-Ir-HClO, was developed for the visual monitoring of the mitochondrial HClO fluxes in ferroptosis-mediated HIRI. The newly prepared probe showed fast response (<30 s), good sensitivity, excellent selectivity, good cell biocompatibility, and satisfactory mitochondrial-targeting performance, making it suitable for accurate monitoring of mitochondrial HClO in living cells. Moreover, visualization of the variations of mitochondrial HClO in ferroptosis-mediated HIRI and monitoring of the treatment response of ferroptosis-mediated HIRI to the ferroptosis inhibitors were achieved for the first time. All these show that probe NIR-Ir-HClO can be utilized as a reliable imaging tool for revealing the pathological mechanism of mitochondrial HClO in ferroptosis-mediated HIRI, as well as for the formulation of new treatment strategies for HIRI.

Research on the Registration of Aerial Images of Cyclobalanopsis Natural Forest Based on Optimized Fast Sample Consensus Point Matching with SIFT Features

The effective management and conservation of forest resources hinge on accurate monitoring. Nonetheless, individual remote-sensing images captured by low-altitude unmanned aerial vehicles (UAVs) fail to encapsulate the entirety of a forest’s characteristics. The application of image-stitching technology to high-resolution drone imagery facilitates a prompt evaluation of forest resources, encompassing quantity, quality, and spatial distribution. This study introduces an improved SIFT algorithm designed to tackle the challenges of low matching rates and prolonged registration times encountered with forest images characterized by dense textures. By implementing the SIFT-OCT (SIFT omitting the initial scale space) approach, the algorithm bypasses the initial scale space, thereby reducing the number of ineffective feature points and augmenting processing efficiency. To bolster the SIFT algorithm’s resilience against rotation and illumination variations, and to furnish supplementary information for registration even when fewer valid feature points are available, a gradient location and orientation histogram (GLOH) descriptor is integrated. For feature matching, the more computationally efficient Manhattan distance is utilized to filter feature points, which further optimizes efficiency. The fast sample consensus (FSC) algorithm is then applied to remove mismatched point pairs, thus refining registration accuracy. This research also investigates the influence of vegetation coverage and image overlap rates on the algorithm’s efficacy, using five sets of Cyclobalanopsis natural forest images. Experimental outcomes reveal that the proposed method significantly reduces registration time by an average of 3.66 times compared to that of SIFT, 1.71 times compared to that of SIFT-OCT, 5.67 times compared to that of PSO-SIFT, and 3.42 times compared to that of KAZE, demonstrating its superior performance.

Predictive Modelling of Land Cover Changes in the Greater Amanzule Peatlands Using Multi-Source Remote Sensing and Machine Learning Techniques

The Greater Amanzule Peatlands (GAP) in Ghana is an important biodiversity hotspot facing increasing pressure from anthropogenic land-use activities driven by rapid agricultural plantation expansion, urbanisation, and the burgeoning oil and gas industry. Accurate measurement of how these pressures alter land cover over time, along with the projection of future changes, is crucial for sustainable management. This study aims to analyse these changes from 2010 to 2020 and predict future scenarios up to 2040 using multi-source remote sensing and machine learning techniques. Optical, radar, and topographical remote sensing data from Landsat-7, Landsat-8, ALOS/PALSAR, and Shuttle Radar Topography Mission derived digital elevation models (DEMs) were integrated to perform land cover change analysis using Random Forest (RF), while Cellular Automata Artificial Neural Networks (CA-ANNs) were employed for predictive modelling. The classification model achieved overall accuracies of 93% in 2010 and 94% in both 2015 and 2020, with weighted F1 scores of 80.0%, 75.8%, and 75.7%, respectively. Validation of the predictive model yielded a Kappa value of 0.70, with an overall accuracy rate of 80%, ensuring reliable spatial predictions of future land cover dynamics. Findings reveal a 12% expansion in peatland cover, equivalent to approximately 6570 ± 308.59 hectares, despite declines in specific peatland types. Concurrently, anthropogenic land uses have increased, evidenced by an 85% rise in rubber plantations (from 30,530 ± 110.96 hectares to 56,617 ± 220.90 hectares) and a 6% reduction in natural forest cover (5965 ± 353.72 hectares). Sparse vegetation, including smallholder farms, decreased by 35% from 45,064 ± 163.79 hectares to 29,424 ± 114.81 hectares. Projections for 2030 and 2040 indicate minimal changes based on current trends; however, they do not consider potential impacts from climate change, large-scale development projects, and demographic shifts, necessitating cautious interpretation. The results highlight areas of stability and vulnerability within the understudied GAP region, offering critical insights for developing targeted conservation strategies. Additionally, the methodological framework, which combines optical, radar, and topographical data with machine learning, provides a robust approach for accurate and detailed landscape-scale monitoring of tropical peatlands that is applicable to other regions facing similar environmental challenges.

LIG-Based High-Sensitivity Multiplexed Sensing System for Simultaneous Monitoring of Metabolites and Electrolytes

With improvements in medical environments and the widespread use of smartphones, interest in wearable biosensors for continuous body monitoring is growing. We developed a wearable multiplexed bio-sensing system that non-invasively monitors body fluids and integrates with a smartphone application. The system includes sensors, readout circuits, and a microcontroller unit (MCU) for signal processing and wireless communication. Potentiometric and amperometric measurement methods were used, with calibration capabilities added to ensure accurate readings of analyte concentrations and temperature. Laser-induced graphene (LIG)-based sensors for glucose, lactate, Na+, K+, and temperature were developed for fast, cost-effective production. The LIG electrode’s 3D porous structure provided an active surface area 16 times larger than its apparent area, resulting in enhanced sensor performance. The glucose and lactate sensors exhibited high sensitivity (168.15 and 872.08 μAmM−1cm−2, respectively) and low detection limits (0.191 and 0.167 μM, respectively). The Na+ and K+ sensors demonstrated sensitivities of 65.26 and 62.19 mVdec−1, respectively, in a concentration range of 0.01–100 mM. Temperature sensors showed an average rate of resistance change per °C of 0.25%/°C, within a temperature range of 20–40 °C, providing accurate body temperature monitoring.