Intensity Data Research Articles

Glyphosate Use and Mosaic Loss of Chromosome Y among Male Farmers in the Agricultural Health Study.

Glyphosate is the most commonly used herbicide worldwide and has been implicated in the development of certain hematologic cancers. Although mechanistic studies in human cells and animals support the genotoxic effects of glyphosate, evidence in human populations is scarce. We evaluated the association between lifetime occupational glyphosate use and mosaic loss of chromosome Y (mLOY) as a marker of genotoxicity among male farmers. We analyzed blood-derived DNA from 1,606 farmers years of age in the Biomarkers of Exposure and Effect in Agriculture study, a subcohort of the Agricultural Health Study. mLOY was detected using genotyping array intensity data in the pseudoautosomal region of the sex chromosomes. Cumulative lifetime glyphosate use was assessed using self-reported pesticide exposure histories. Using multivariable logistic regression, we estimated odds ratios (ORs) and 95% confidence intervals (CIs) for the associations between glyphosate use and any detectable mLOY (overall mLOY) or mLOY affecting of cells (expanded mLOY). Overall, mLOY was detected in 21.4% of farmers, and 9.8% of all farmers had expanded mLOY. Increasing total lifetime days of glyphosate use was associated with expanded mLOY [highest vs. lowest quartile; (95% CI: 1.00, 3.07), ] but not with overall mLOY; the associations with expanded mLOY were most apparent among older ( years of age) men [ (95% CI: 1.13, 4.67), ], never smokers [ (95% CI: 1.04, 5.21), ], and nonobese men [ (95% CI: 0.99, 4.19), ]. Similar patterns of associations were observed for intensity-weighted lifetime days of glyphosate use. High lifetime glyphosate use could be associated with mLOY affecting a larger fraction of cells, suggesting glyphosate could confer genotoxic or selective effects relevant for clonal expansion. As the first study to investigate this association, our findings contribute novel evidence regarding the carcinogenic potential of glyphosate and require replication in future studies. https://doi.org/10.1289/EHP12834.

High-performance deep transfer learning model with batch normalization based on multiscale feature fusion for tomato plant disease identification and categorization

Deep learning and machine learning are cutting-edge methods for analysing images that have considerable potential. Artificial Neural Networks (A-NNs), one of the most well-known methods of computer intelligence, are now used in machine learning (ML) and deep transfer learning (DL) to raise plant production and quality. Identification and primary prevention of plant diseases at the appropriate time are essential for boosting productivity. Due to the phenomenon of minimally intense data in the background and foreground areas of the image, the extensive colour similarity between regions of unhealthy and normal leaves, the presence of noise in the sampling data, and changes in the location, size, and shape of plant leaf, it is difficult to correctly identify and classify plant diseases. In an effort to address these issues, a reliable technique for classifying plant diseases was developed by using a deep AlexNet CNN architecture as the main network with batch normalisation. In the three-step process, the first annotation is made to obtain the RoI (region of interest). The AlexNet CNN is therefore suggested for deep primary feature extraction in a constructed efficient network. The research demonstrates that the existing strategy is superior to more recent ones in terms of accuracy and dependability in recognising diseases in plants. Based on a deep transfer AlexNet CNN model, this research work developed a model for diseases identification and classification in plant leaves. It is trained using additional datasets that include a variety of plant leaf classifications and background images. From Plant Village and Kaggle, we gathered data on healthy and diseased tomato plant leaves. We are obtaining a near-balanced dataset containing ten different leaf disease kinds, such as bacterial, fungal, viral, and nutrient insufficiency. Ten classes have been considered for this research by gathering a dataset with associated images of the typical and abnormal tomato plant leaves. Considered in this work were the various labels for healthy and diseased tomato leaves, such as early blight, Bacterial spot, late bright mold, healthy, etc. Since deep CNN models have shown notable machine vision results, they are used in this case to diagnose and categorise plant illnesses from their leaves. As a result, the proposed CNN models can thus now be evaluated from confusion matrix using data analysis criteria, primarily focusing on metrics for evaluation like training and validation accuracy, loss, Recall, Precision, F1 score, processing speed, and performance.

Covid-19 detection using modified xception transfer learning approach from computed tomography images

The significance of efficient and accurate diagnosis amidst the unique challenges posed by the COVID-19 pandemic underscores the urgency for innovative approaches. In response to these challenges, we propose a transfer learning-based approach using a recently annotated Computed Tomography (CT) image database. While many approaches propose an intensive data preprocessing and/or complex model architecture, our method focuses on offering an efficient solution with minimal manual engineering. Specifically, we investigate the suitability of a modified Xception model for COVID-19 detection. The method involves adapting a pre-trained Xception model, incorporating both the architecture and pre-trained weights from ImageNet. The output of the model was designed to make the final diagnosis decisions. The training utilized 128 batch sizes and 224x224 input image dimensions, downsized from standard 512x512. No further da processing was performed on the input data. Evaluation is conducted on the 'COV19-CT-DB' CT image dataset, containing labeled COVID-19 and non-COVID-19 cases. Results reveal the method's superiority in accuracy, precision, recall, and macro F1 score on the validation subset, outperforming the VGG-16 transfer model and thus offering enhanced precision with fewer parameters. Furthermore, compared to alternative methods for the COV19-CT-DB dataset, our approach exceeds the baseline approach and other alternatives on the same dataset. Finally, the adaptability of the modified Xception transfer learning-based model to the unique features of the COV19-CT-DB dataset showcases its potential as a robust tool for enhanced COVID-19 diagnosis from CT images.

Macroseismic intensity attenuation models calibrated in Mw for Italy

This study aims at developing new macroseismic intensity attenuation models valid for Italy by exploiting the most updated macroseismic dataset and earthquakes catalogue, as well as the information obtained from a critical analysis of the most recent models in the literature. Several different attenuation models have been calibrated as a function of the moment magnitude (Mw) and epicentral distance from 16,260 intensity data points, that are related to 119 earthquakes occurred after 1900. According to trends and residuals analysis, the preferred calibrated intensity attenuation function is a Log-Linear model for epicentral distance (Repi in km) and a linear model for Mw as:IMCS=1.81-2.61LogR-0.0039R+1.42Mw\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$I\\left( {MCS} \\right) = {1}.{81} - {2}.{61}LogR - 0.00{39}R + {1}.{42}Mw$$\\end{document}with pseudo hypocentral distance R=Repi2+9.872\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R = \\sqrt {R_{epi}^{2} + \\left( {9.87} \\right)^{2} }$$\\end{document}; the estimated standard deviation is σ = 0.75. Also noteworthy is another model for macroseismic intensity attenuation that proved to be as good as the best model and shows higher sensitivity to physical parameters, such as focal depth and magnitude, especially in the epicentral area. Performance of all calibrated models was also checked on an independent set of 15 post-1900 Italian earthquakes. One of the results of the present work is the opportunity to define earthquake scenarios (e.g. probabilistic seismic hazard maps) in terms of Macroseismic Intensity and its related standard deviation, avoiding the uncertainties due to the conversion of various ground shaking parameters into intensity values.

RAINFALL ANALYSIS FOR CREATING INTENSITY-DURATION-FREQUENCY (IDF) CURVE OF PONTIANAK CITY

Pontianak is a lowland area prone to flooding if it rains with high rainfall intensity. Analysis of the planned flood discharge is needed to overcome the flood problem. The design flood discharge analysis can be calculated using the rational method, which requires rain intensity data in terms of duration and frequency, which can be described as an IDF curve.This research was conducted by calculating the average daily maximum rainfall after conducting consistency and homogeneity tests on the rainfall data used and determining the selected distribution based on the results of statistical parameters. The rain design with various return periods is calculated with the desired distribution that matches Pontianak City. The rainfall intensity calculation uses the Mononobe method because the rainfall data owned is daily rainfall data.The analysis results show that the Log Pearson Type III distribution corresponds to the data distribution in Pontianak. From the IDF curve, it can be seen that high rainfall intensity lasts for a short duration; this can prove the general nature of rain that the faster the rain stays, the higher the rainfall intensity and vice versa. The IDF curve can assist in calculating the design flood discharge.

Investigating Moderation Effects at the Within-Person Level Using Intensive Longitudinal Data: A Two-Level Dynamic Structural Equation Modelling Approach in Mplus

Recent technological advances have provided new opportunities for the collection of intensive longitudinal data. Using methods such as dynamic structural equation modeling, these data can provide new insights into moment-to-moment dynamics of psychological and behavioral processes. In intensive longitudinal data (t > 20), researchers often have theories that imply that factors that change from moment to moment within individuals act as moderators. For instance, a person’s level of sleep deprivation may affect how much an external stressor affects mood. Here, we describe how researchers can implement, test, and interpret dynamically changing within-person moderation effects using two-level dynamic structural equation modeling as implemented in the structural equation modeling software Mplus. We illustrate the analysis of within-person moderation effects using an empirical example investigating whether changes in spending time online using social media affect the moment-to-moment effect of loneliness on depressive symptoms, and highlight avenues for future methodological development. We provide annotated Mplus code, enabling researchers to better isolate, estimate, and interpret the complexities of within-person interaction effects.

Pollutant concentration prediction from traffic data analysis for concrete durability studies in Madrid Calle 30 urban tunnels

The possibility of assessing and predicting the concrete damage is essential in order to apply an adequate and accurate maintenance and reparation policy for infrastructure management. In urban tunnels, concrete is exposed to pollutant emissions from the engines of the vehicles which can cause neutralization issues that may lead to the corrosion of the steel reinforcement. As pollutant concentration is an indispensable parameter to evaluate and forecast concrete neutralization damage, the aim of this work was to quantify gas concentrations inside an urban tunnel based on available traffic intensity data. This study was carried out performing in-situ concentration measures, comparing these values with available traffic data during the same period and developing a traffic intensity study to obtain the total concentrations at which the infrastructure had been exposed since its construction. This analysis was performed in a ventilation gallery of the Madrid Calle 30 tunnels as a part of a study in development with the aim of determining the service lifespan of the structure based on a predictive numerical model for concrete carbonation.

Need Fulfillment During Intergroup Contact: Three Experience Sampling Studies.

One challenge of modern intergroup contact research has been the question of when and why an interaction is perceived as positive and improves intergroup relations. We propose to consider the perceived fulfillment of the situationally most relevant need. We conducted three intensive longitudinal studies with recent migrants to capture their interactions with the majority out-group (Nmeasurements = 10,297; Nparticipants = 207). The situational need fulfillment mechanism is consistently a strong predictor of perceived interaction quality and positive out-group attitudes following intergroup interactions. The model is specific to out-group contact, robust to various need types, and works at least as well as Allport's contact conditions. As one of the first studies to test intergroup contact theory using intensive longitudinal data, we offer insight into the mechanisms of positive intergroup contact during real-life interactions and find situational motivations to be a key building block for understanding and addressing positive intergroup interactions.Public significance statement: In this article, we provide evidence that the fulfillment of situational needs during real-life intergroup contacts meaningfully predicts perceived interaction quality and positive outgroup attitudes. Methodologically, this offers a testament to the emerging practice of capturing real-life interactions using intensive longitudinal data. Theoretically, our results give weight to motivational fulfillment as a flexible and effective mechanism for understanding positive intergroup contact.

Does the low-carbon pilot cities policy make a difference to the carbon intensity reduction?

The low-carbon pilot cities policy (LCPCP) aims to stimulate economic growth and ensure the attainment of greenhouse gas emission reduction targets to address climate change. China issued the LCPCP in 2010 and steadily expanded the size of its pilot zones. This study builds a quasi-natural experiment based on China's LCPCP and a difference-in-difference model employing urban carbon intensity data over a 10-year period beginning in 2007 to examine the implementation repercussions of the LCPCP. According to the findings, the LCPCP has significantly reduced the carbon intensity of pilot cities. Additionally, an analysis of heterogeneity suggests that the LCPCP is more prevalent in regions with higher concentrations of secondary industries. Moreover, the mechanism reveals that the decarbonization program reduces carbon intensity through technological innovation, particularly in eastern China. In conclusion, our findings provide strong support for the operation and promotion of China's LCPCP as well as guidance and support for China's goal of reducing carbon emissions.

Predicting high-intensity resuscitation needs in injured patients in the post-hemostasis phase of care following intervention.

Best resuscitation practices in the posthemostasis phase of care are poorly defined; this phase of care is characterized by a range of physiologic derangements and multiple therapeutic modalities used to address them. Using a cohort of injured patients who required an immediate intervention in the operating room or angiography suite following arrival to the emergency department, we sought to define high-intensity resuscitation (HIR) in this posthemostasis phase of care; we hypothesized that those who would require HIR could be identified, using only data available at intensive care unit (ICU) admission. Clinical data were extracted for consecutive injured patients (2016-2019) admitted to the ICU following an immediate procedure in the operating room or angiography suite. High-intensity resuscitation thresholds were defined as the top decile of blood product (≥3 units) and/or crystalloid (≥4 L) use in the initial 12 hours of ICU care and/or vasoactive medication use between ICU hours 2 and 12. The primary outcome, HIR, was a composite of any of these modalities. Predictive modeling of HIR was performed using logistic regression with predictor variables selected using Least Absolute Shrinkage and Selection Operator (LASSO) estimation. Model was trained using 70% of the cohort and tested on the remaining 30%; model predictive ability was evaluated using area under receiver operator curves. Six hundred five patients were included. Patients were 79% male, young (median age, 39 years), severely injured (median Injury Severity Score, 26), and an approximately 3:2 ratio of blunt to penetrating mechanisms of injury. A total of 215 (36%) required HIR. Predictors selected by LASSO included: shock index, lactate, base deficit, hematocrit, and INR. The area under receiver operator curve for the LASSO-derived HIR prediction model was 0.82. Intensive care unit admission data can identify subsequent HIR in the posthemostasis phase of care. Use of this model may facilitate triage, nursing ratio determination, and resource allocation. Therapeutic/Care Management; Level IV.

Evaluation of the Environmental Burden of Cross-Laminated Timber Manufacturing in Japan Using the Input–Output Analysis

Japan is actively promoting the application of cross-laminated timber (CLT) in construction to utilize plantation forests efficiently and fulfil its climate commitments. Although CLT has unique structural properties and environmental advantages, understanding the environmental burden of CLT manufacturing remains scarce. This study uses input–output analysis to evaluate the greenhouse gas (GHG) emissions from CLT manufacturing. An extended input–output table was created to measure the GHG emissions by investigating the revenue and expenditure data of the largest CLT manufacturers in Japan in 2020, combined with the energy and emission intensity data. The results showed that electricity, activities not elsewhere classified, road freight transportation (except self-transportation), timber, and logs were the main sectors contributing to GHG emissions from CLT manufacturing. In addition, the environmental burdens of the cement and steel sectors were evaluated for comparison with the same increase in the final demand. We found that CLT manufacturing emits significantly fewer GHGs than the cement and steel sectors. These findings highlight the potential of CLT in reducing environmental burden, particularly in construction and civil engineering, emphasizing the importance of renewable energy use and efficient raw material transportation.

Variation in women’s attitudes toward intimate partner violence across the rural–urban continuum in Ethiopia

Little is known about the effects of urbanization on women’s attitudes toward intimate partner violence (IPV). The scarcity of empirical studies on this relationship can be partly attributed to the lack of an objective measure of urbanization levels. In this study, we investigate the effects of urbanization on Ethiopian women’s attitudes toward IPV using four continuous measures of urbanization: nightlight intensity, distance to urban areas, total urban area within a 10-km radius, and an urbanization index. These measures are defined from satellite-based nighttime light intensity and multispectral sensor data. We find that despite a generally strong positive association between urbanization and progressive attitudes among women toward IPV, some stages of urbanization show a more significant association than others. The heterogeneities in the effect of all urbanization measures on women’s attitudes toward intimate partner violence further show that the effects of urbanization measures are sharply heterogeneous across wealth indicator terciles. While we find that urbanization measures are associated with an overall decrease in the justification of IPV, the effects are higher and stronger for women in the upper and middle wealth terciles compared to the lowest wealth tercile. Initially less-privileged women gain little from urbanization in the attitudes toward IPV, resulting in increased inequality in women’s empowerment in the short and medium term.

Characteristics and outcomes of neonates hospitalised with SARS-CoV-2 infection in the UK by variant: a prospective national cohort study

ObjectiveNeonatal infection with wildtype SARS-CoV-2 is rare and good outcomes predominate. We investigated neonatal outcomes using national population-level data to describe the impact of different SARS-CoV-2 variants.DesignProspective population-based cohort study.SettingNeonatal,...

Study on diel migration of zooplankton in the Arctic surrounding seas during summer season based on mathematical statistics

Marine zooplankton have diel vertical migration behavior and play a key role in the transport of surface organic compounds to the deep ocean, which is of great significance in studying marine climate change. In this research, the manual visual interpretation method was used to preprocess the biological backscattering intensity data of acoustic Doppler current profiler (ADCP) during the eighth Arctic expedition. Results showed that the extracted vertical migration trajectory was in good agreement with the variation of backscattering intensity data. The amount of zooplankton in the Arctic Ocean is significantly lower than that in the North Pacific and the North Atlantic, and sea ice cover has a significant effect on the diel vertical migration of zooplankton. Characteristics of zooplankton distribution in the North Atlantic Ocean were analyzed based on mathematical statistical methods. The results also prove that the backscattering intensity of -90dB can distinguish scattering layer and water, and that of -78dB can distinguish general scatters and zooplankton. There are two significant concentrations of zooplankton in the Atlantic Ocean which were located at the surface and at depths of 350-450 m, respectively.

The Earthquake of 13 April 1850 near Ston, Croatia: Macroseismic Analyses

Abstract The earthquake that occurred near the city of Ston, Croatia, on 13 April 1850 is, together with the one from 1996, the strongest known event in the northwest part of the Dubrovnik epicentral area. This is the region with the highest seismic hazard in Croatia with a rich history of damaging earthquakes. Although listed in the relevant catalogs, this earthquake has never been addressed by a dedicated study. Herewith, we present analyses of a wealth of newly found material related to the damage and postearthquake actions of the authorities of the Province of Dalmatia, then a part of the Austrian Empire. We were able to estimate intensity at five localities, with a further six where the data were sufficient only to constrain the minimum intensity value. By far, most of the data refer to Ston and Dubrovnik. Intensity data points were inverted for the source parameters by two different methods, each of which yielded similar results. The focus is macroseismically located about 7 km east-southeast from Ston, at a depth of 9 km. Estimated epicentral intensity of 8.2 on the European macroseismic scale is equivalent to macroseismic local magnitude MmL=6.0 or the moment magnitude Mmw=5.9. The location of focus and the epicentral intensity are practically identical to those of the Ston–Slano earthquake of 1996. This is why we propose that these two earthquakes share the same composite seismogenic source consisting of a set of imbricated mostly reverse faults related to the basal thrust of the Dalmatian tectonic unit. The reliable location and quantification of the 1850 earthquake should contribute to a better understanding of the active dynamics of the set of large seismogenic faults in the Dubrovnik epicentral area.

Optimization of membrane thickness for proton exchange membrane electrolyzer considering hydrogen production efficiency and hydrogen permeation phenomenon

Reducing the membrane thickness of Proton exchange membrane (PEM) electrolyzer was found to efficiently promote the hydrogen production rate. However, it can also aggravate the phenomenon of hydrogen permeation, leading to an increased hydrogen content on the anode side and posing a risk of explosions. Thus, optimal design of the membrane thickness of PEM electrolyzer systems is crucial to ensure both safe and efficient hydrogen production. In this paper, we propose two optimization problems for the membrane thicknesses, aiming at achieving a balance between the hydrogen content on the anode side and the hydrogen production rate under constant and varying power input conditions. First, the theoretical model of a PEM electrolyzer is established, and a photovoltaic-PEM electrolyzer system is considered to provide varying power input conditions. Then, the two optimization problems are formulated and resolved using the sequential quadratic programming (SQP) and particle swarm optimization (PSO) algorithms, respectively. Our results reveal that the optimal membrane thickness decreases as the constant input power increases. This suggests that high/low power inputs require thin/thick membranes. For varying power input, we collected one year of solar radiation intensity data from four different regions in China. The findings demonstrate that the selection of the optimal membrane thickness depends not only on the average solar radiation intensity but also on its seasonal variations. By applying these strategies, we effectively optimize the membrane thickness in PEM electrolyzer systems, thereby enhancing the efficiency and safety of hydrogen production. The outcomes provide valuable insights for selecting the appropriate membrane thickness in regions with varying solar radiation intensities and accounting for seasonal variations in solar radiation.

College Students' Day-to-Day Maladaptive Drinking Responses to Stress Severity and Stressor-Related Guilt and Anger.

Stress is a common part of college students' daily lives that may influence their physical activity (PA) and alcohol use. Understanding features of daily stress processes that predict health behaviors could help identify targets for just-in-time interventions. This study used intensive longitudinal data to examine whether prior day stress processes predict current day PA or alcohol use. Participants (N=58, Mage=20.5, 59% women, 70% White) were 18-to-25-year-old students who engaged in binge drinking at least twice monthly and used cannabis or tobacco in the past year. They wore activity (activPAL4) and alcohol (Secure Continuous Remote Alcohol Monitor) monitors for 11 days to assess daily PA (e.g., step counts) and alcohol use (e.g., drinking day), and completed daily surveys about yesterday's stress, including number of stressors (i.e., frequency), stressor intensity (i.e., severity), and frequency of affective states (e.g., guilt). Multilevel models examined prior day stress predicting current day PA or alcohol use. Participants had higher odds of current day drinking (odds ratio=1.21) and greater area under the curve (B=0.08) when they experienced greater than usual stress severity the prior day. Participants had higher current day peak transdermal alcohol concentration (B=0.12) and area under the curve (B=0.11) when they more frequently experienced guilt due to stressors the prior day. College students' unhealthy response of increasing alcohol use due to stress could adversely impact health outcomes. There is a critical need for interventions addressing students' ability to effectively manage and respond to the stress-inducing, daily demands of student life.

Multiple imputation of missing data in multilevel ecological momentary assessments: an example using smoking cessation study data.

Advances in digital technology have greatly increased the ease of collecting intensive longitudinal data (ILD) such as ecological momentary assessments (EMAs) in studies of behavior changes. Such data are typically multilevel (e.g., with repeated measures nested within individuals), and are inevitably characterized by some degrees of missingness. Previous studies have validated the utility of multiple imputation as a way to handle missing observations in ILD when the imputation model is properly specified to reflect time dependencies. In this study, we illustrate the importance of proper accommodation of multilevel ILD structures in performing multiple imputations, and compare the performance of a multilevel multiple imputation (multilevel MI) approach relative to other approaches that do not account for such structures in a Monte Carlo simulation study. Empirical EMA data from a tobacco cessation study are used to demonstrate the utility of the multilevel MI approach, and the implications of separating participant- and study-initiated EMAs in evaluating individuals' affective dynamics and urge.

Is everything really okay?: Using ecological momentary assessment to evaluate daily co-fluctuations in anxiety and reassurance seeking

ObjectiveReassurance seeking, a behavior prominent in anxiety disorders and depression, is associated with poorer quality of interpersonal relationships and acts as a mechanism of stress generation. However, little research has elucidated momentary associations between state anxiety and reassurance seeking behaviors. MethodIn a sample of 104 university-affiliated young adults, we sought to replicate cross-sectional associations of reassurance seeking with trait anxiety (Aim 1) and intolerance of uncertainty (Aim 2). We then used ecological momentary assessment (EMA) to evaluate concurrent fluctuations in daily anxiety and reassurance seeking across 14 consecutive days (Aim 3). Hierarchical multi-level models for intensive longitudinal data were used to evaluate the relationship between state anxiety and daily reassurance seeking. ResultsIn baseline analyses, trait anxiety and intolerance of uncertainty were significantly associated with greater trait reassurance seeking, controlling for depression. Analyses of the EMA data showed that daily reassurance seeking behaviors fluctuated concurrently with daily anxiety during the 14 days, while controlling for trait anxiety and depression. ConclusionGiven evidence of concurrent fluctuations between state anxiety and reassurance seeking, the behavior should be considered as a potential target in treatment for anxiety disorders.

Energy-Aware Framework for Underwater Mine Detection System Using Underwater Acoustic Wireless Sensor Network

Underwater mines are considered a major threat to aquatic life, submarines, and naval activities. Detecting and locating these mines is a challenging task, due to the nature of the underwater environment. The deployment of underwater acoustic sensor networks (UWASN) can provide an efficient solution to this problem. However, the use of these self-powered sensors for intensive data sensing and wireless communication is often energy-scaring and might call into question the viability of their application. One attractive solution to extend the underwater wireless sensor network will be the adoption of cluster-based communication, since data processing and communication loads are distributed in a timely manner over the members of the cluster. In this context, this study proposes an energy-efficient solution for high-accuracy underwater mine detection based on the adequate clustering approach. The proposed scheme uses a processing approach based on wavelet transformation to extract relevant features to efficiently distinguish mines from other objects using the Naïve Bayes algorithm for classification. The main novelty of this approach is the design of a new low-complexity scheme for efficient sensor-based acoustic object detection that outperforms most of the existing solutions. It consumes a low amount of energy, while ensuring 95.12% target detection accuracy.