Head Distribution Research Articles

Novel Energy-efficient Modified LEACH Routing Protocol for Wireless Sensor Networks

Introduction: In wireless sensor networks (WSNs), hierarchical clustered routing protocols play a crucial role in minimizing energy consumption. The Low Energy Adaptive Clustering Hierarchy (LEACH) architecture is commonly employed for application-specific protocols in WSNs. However, the LEACH protocol may lead to increased energy consumption within the network if the rotational distribution of cluster heads (CHs) is not considered. Methods: A novel average energy, residual energy-based modified LEACH (aerem-LEACH) routing protocol for improving the WSN’s energy efficiency is proposed. This approach simultaneously considers the average energy of the networks and the residual node energy for routing, thereby reducing overall power consumption. Results: The suggested approach in aerem-LEACH accounts for optimal CHs numbers, and nodes in close proximity to the sink are forbidden from participating in cluster formation in order to achieve sufficient performance in the form of reduced sensor node energy consumption. Furthermore, a new threshold is employed in the proposed approach for selecting CHs for the network, and the aerem-LEACH uses free space, multiple hopping, and a hybrid communicating model for an energy-efficient network. Conclusion: The simulation result demonstrates that there is a substantial reduction in the consumption of energy in WSNs with the proposed aerem-LEACH routing protocol compared with existing routing protocols, namely Stable Energy Efficient Network (SEEN), Energy Efficient LEACH (EE LEACH), Optical LEACH (O-LEACH), LEACH-Mobile (LEACH-M), LEACHCentralized (LEACH-C), and LEACH for small-scale as well as large-scale sensor field.

Geospatial Mapping of Head and Neck Cancer Research: Assessing Access, Disparities, and Characteristics of Head and Neck Cancer Clinical Trials Across the United States.

To report geographic distribution and characteristics of head and neck cancer (HNC) clinical trials in the United States. We conducted a retrospective analysis of U.S. HNC clinical trials searching ClinicalTrials.gov from January 1, 2017 to December 31, 2021 using the terms "head and neck cancer" or "head and neck neoplasms." A total of 381 clinical trials met inclusion criteria with 2181 trial opportunities, which were correlated with population density. Of the U.S. population, 72% live within a 25-mile radius of trials. California, Pennsylvania, and New York had the greatest number of clinical trial entries. The majority of patients living more than 25 miles from an HNC clinical trial site are located in rural areas. One hundred sixty-five (43.3%) trials were about systemic therapy, of which 138 (83.6%) involved targeted immunotherapy. There were 286 unique principal investigators. One hundred six (37.1%) were females and 180 (62.9%) were males. We demonstrate disparity in the geographic distribution of HNC trials favoring densely populated urban areas, which may limit patient access due to travel burden. Studies are skewed towards immunotherapy drug trials, with fewer radiation and surgery investigations. Level III.

Combining WetSpass and MODFLOW to assess the groundwater recharge in the agricultural highlands of Ethiopia

ABSTRACT Estimating the temporal and spatial patterns of recharge is crucial in Jedeb Watershed, which has shallow aquifers, a proliferation of wells, and limited observations. This paper uses the WetSpass-MODFLOW coupling to evaluate the groundwater recharge in the Jedeb watershed. The ArcGIS tool is used to build grid maps that contain the input data for the WetSpass-M model. These findings are then used to simulate the hydraulic head distribution in the groundwater flow simulation program MODFLOW. WetSpass-M calculated that the long-term spatial and temporal average annual precipitation of 1394 mm is distributed as 99 mm (7.1%) groundwater recharge and 722 mm (51.79%) surface runoff, while 574 mm (41.11%) is lost through evapotranspiration. With such seasonal variations, the groundwater level due to the annual stress period varied from 2164.2 to 2213.1 m. The findings could serve as a benchmark for future research by academics, policymakers, and specialists in regional water resources.

Convolutional long short-term memory neural network for groundwater change prediction

Forecasting groundwater changes is a crucial step towards effective water resource planning and sustainable management. Conventional models still demonstrated insufficient performance when aquifers have high spatio-temporal heterogeneity or inadequate availability of data in simulating groundwater behavior. In this regard, a spatio-temporal groundwater deep learning model is proposed to be applied for monthly groundwater prediction over the entire Choushui River Alluvial Fan in Central Taiwan. The combination of the Convolution Neural Network (CNN) and Long Short-Term Memory (LSTM) known as Convolutional Long Short-Term Memory (CLSTM) Neural Network is proposed and investigated. Result showed that the monthly groundwater simulations from the proposed neural model were better reflective of the original observation data while producing significant improvements in comparison to only the CNN, LSTM as well as classical neural models. The study also explored the performance of the Masked CLSTM model which is designed to handle missing data by reconstructing incomplete spatio-temporal input images, enhancing groundwater forecasting through image inpainting. The findings indicated that the neural architecture can efficiently extract the relevant spatial features from the past incomplete information of hydraulic head observations under various masking scenarios while simultaneously handling the varying temporal dependencies over the entire study region. The proposed model showed strong reliability in reconstructing and simulating the spatial distribution of hydraulic heads for the following month, as evidenced by low RMSE values and high correlation coefficients when compared to observed data.

Urban-rural geographic distribution of Otorhinolaryngologist, Head and Neck surgeons in Aotearoa New Zealand.

We identified geographic distribution of Otorhinolaryngologist, Head and Neck surgeons in Aotearoa New Zealand. To identify the future workforce pipeline, we explored trainee intentions for specialist practice. A survey was distributed to all New Zealand Society of Otolaryngology, Head and Neck Surgery (NZSOHNS) members and all current New Zealand Otolaryngology, Head and Neck surgery trainees. Data were gathered on work location and patterns of work, including on-call commitments and full-time equivalent hours worked. Trainees were asked about future career plans. An 88% response rate was achieved encompassing senior medical officers (SMOs) and trainees. A total of 64.8% (68) of respondents reported primarily working in a metropolitan hospital and 26.7% (28) reported working in a regional centre. Rates of internationally trained surgeons were significantly higher in regional centres compared to metropolitan hospitals (64.3% vs 32.4%, p<0.05). Regional respondents had higher after hours on-call burden, a higher full-time equivalent (FTE) worked and higher average hours worked per week. Retirement intentions within the next 10 years were high in both groups (64% regional and 52.9% metropolitan, p<0.05). The regional workforce in ORLHNS work longer hours, are older and are reliant on internationally trained surgeons. Current training of ORLHNS surgeons is unlikely to keep pace with expected retirements.

Acceleration of Numerical Modeling of Uranium In Situ Leaching: Application of IDW Interpolation and Neural Networks for Solving the Hydraulic Head Equation

The application of In Situ Leaching (ISL) has significantly boosted uranium production in countries like Kazakhstan. Given that hydrodynamic and chemical processes occur underground, mining enterprises worldwide have developed models of reactive transport. However, modeling these complex processes demands considerable computational resources. This issue is particularly significant in the context of numerical analyses of mining processes or when modeling production scenarios in uranium mining by the ISL technique, given that a substantial portion of computational resources is allocated to solving the hydraulic head equation. This work aims to explore the applicability of PINNs to accelerate hydrodynamic simulations of the ISL process. The solution of the Poisson equation is accelerated by generating an initial approximation for the iterative method through the application of the Inverse Distance Weighting (IDW) interpolation and PINNs. The impact of various factors, including the computational grid and the spacing between wells, on both the accuracy and efficiency of initial approximation and the overall solution of the elliptic equation are explored. Employing the hydraulic head distribution obtained through PINNs as the initial approximation led to a significant reduction in computation time and a decrease in the number of iterations by a factor of 2.8 to 7.10.

BiPC: Bidirectional Probability Calibration for Unsupervised Domain Adaption

Unsupervised Domain Adaptation (UDA) leverages a labeled source domain to solve tasks in an unlabeled target domain. While Transformer-based methods have shown promise in UDA, their application is limited to plain Transformers, excluding Convolutional Neural Networks (CNNs) and hierarchical Transformers. To address this issues, we propose Bidirectional Probability Calibration (BiPC) from a probability space perspective. We demonstrate that the probability outputs from a pre-trained head, after extensive pre-training, are robust against domain gaps and can adjust the probability distribution of the task head. Moreover, the task head can enhance the pre-trained head during adaptation training, improving model performance through bidirectional complementation. Technically, we introduce Calibrated Probability Alignment (CPA) to adjust the pre-trained head’s probabilities, such as those from an ImageNet-1k pre-trained classifier. Additionally, we design a Calibrated Gini Impurity (CGI) loss to refine the task head, with calibrated coefficients learned from the pre-trained classifier. BiPC is a simple yet effective method applicable to various networks, including CNNs and Transformers. Experimental results demonstrate its remarkable performance across multiple UDA tasks. Our code will be available at: https://github.com/Wenlve-Zhou/BiPC.

An improved steady-state semianalytical solution for assessing the two-dimensional hydraulic head distribution induced by an underground dam in a sloping unconfined aquifer

An improved steady-state semianalytical solution for assessing the two-dimensional hydraulic head distribution induced by an underground dam in a sloping unconfined aquifer

Application of coupled WetSpass-M and MODFLOW models to estimate spatial–temporal water balance components in the Chemoga watershed, Ethiopia

ABSTRACT The groundwater level in the Chemoga watershed has been declining due to an increase in water demand, anthropogenic activities, and climate change effects. This paper uses the WetSpass-MODFLOW coupling to evaluate the groundwater recharge in the Chemoga watershed. The MODFLOW groundwater flow simulation model is then used to simulate the hydraulic head distribution based on these findings. The input data of WetSpass models are soil, land cover, topography, slope, and groundwater depth, as well as monthly meteorological characteristics (such as temperature, wind speed, and rainfall). The long-term spatial and temporal average annual precipitation of 1,453 mm is distributed as 169 mm (11.63%) groundwater recharge and 879 mm (60.5%) surface runoff, while 405 mm (27.87%) is lost through evapotranspiration. In such seasonal variations, the groundwater head due to the wet/summer stress period varied from 4 to 41 m. While in the dry/winter stress period groundwater head varied from 3.5 to 39.8 m, and also the groundwater head due to the annual stress period varied from 3.7 to 40 m. The findings are extensive and can be applied to water resource management and groundwater resource development in a sustainable manner by safeguarding high groundwater recharge locations, and reevaluating allowable groundwater abstraction rates.

MFNet: Multi-scale feature enhancement networks for wheat head detection and counting in complex scene

Developing a high-precision wheat head detection algorithm is challenging due to the dense distribution and diverse sizes of wheat head in the field, as well as serious coverage from weeds. In this work, we propose multi-scale feature enhancement networks for wheat head detection and counting in complex scene (MFNet). Firstly, we introduce a deformable spatial attention mechanism (DSAM) and embed it in the backbone network to enhance the extraction of wheat head features while suppressing irrelevant features, effectively improving detection of wheat head in occluded environments. Secondly, we design a multi-scale receptive field feature fusion (MRFF) module in combination with an improved light-weight feature pyramid module to achieve more accurate detection of wheat head of different sizes and improve localization accuracy simultaneously. Additionally, the modified detection head with deformable convolution is able to adapt to different shapes of wheat head features and accurately predict the bounding boxes. Our method achieves 94.2% AP@50 at a speed of 30 FPS on the GWHD dataset, and to verify the generalization of the proposed method, we constructed a dense wheat head detection (DWHD) dataset with annotations, conducted experiments on the DWHD and SPIKE datasets and compared them with state-of-the-art algorithms. The experimental results show that the proposed method outperformed most of the existing methods, which further proved the superiority and robustness of the proposed method, and demonstrated that our model possesses excellent adaptability, enabling it to flexibly cope with scenarios of significant scale differences and severe occlusion in complex field environments for wheat head, and provide a technological reference for monitoring the wheat phenotype.

Steady-state semi-analytical solutions for assessing the two-dimensional groundwater hydraulic head depletion induced by river dam removal

Dams are pervasive features of the world’s river systems. The reservoir associated with the dam modifies the distribution of the hydraulic head in the aquifer and the natural groundwater flow. However, such modifications must be defined to forecast their environmental, economic and/or social impacts. Based on the method of fundamental solutions (MFS), steady-state semi-analytical solutions are proposed for evaluating the long-term spatial distribution of the rise or decline of the hydraulic head in an aquifer caused by a dam reservoir on a watercourse or its removal. MFS was chosen because of its ability to account for different boundary conditions along the banks of the watercourse. The dam reservoir can have a triangular or rectilinear geometry. The aquifer is assumed unconfined, homogeneous, inclined and finite. The solutions take into account both the reservoir-aquifer interaction and those between the stream and the aquifer up- and downstream of the reservoir. The reservoir-stream system fully penetrates the aquifer, and is separated from the aquifer by semi-pervious banks (Robin condition). Analysis of the semi-analytical solution included sensitivity tests, and assessment of the influence of several parameters: reservoir leakance parameter, width of the reservoir and width of the aquifer. The results show that the reservoir and river leakage parameters up- and downstream of the reservoir, as well as the geometry of the reservoir dam, are the main determining parameters of the extent of the induced groundwater mound. They show that low aquifer-river exchanges in the river upstream and downstream of the reservoir increases the spatial extension of the mound. The degree of error introduced by the method of linearization used to solve the governing partial differential equation is also discussed. A solution has been also developed with a pumping well, particularly to assess what the depletion of the water level at the well location might be if the dam is removed. The proposed solutions were applied at two field sites, giving satisfactory results.These semi-analytical solutions will be useful applications for assessing the long-term spatial impact on the aquifer of the emplacement, removal or leveling of a reservoir dam.

Ultimate Support Pressure Determination for Shield Tunnel Faces in Saturated Strata Based on Seepage Flow Control

Existing methods for calculating the ultimate support pressure of tunnel faces do not consider the control of seepage flow. Therefore, a model for calculating the ultimate support pressure under seepage conditions was established based on a two-dimensional water head distribution model and the upper bound theorem of limit analysis. The reliability of this method was verified through comparisons with other studies. Subsequently, the influence of water level and tunnel face water pressure coefficient on stability was analyzed. The results indicate that the ultimate support pressure is linearly positively correlated with the water level and tunnel face water pressure coefficient; as the water level increases and the water pressure coefficient decreases, the failure area extends and enlarges. Finally, an existing seepage flow calculation formula was introduced, and a method for calculating the ultimate support pressure based on seepage control was proposed. The appropriate tunnel face water pressure coefficient is determined through the seepage flow calculation formula, and the corresponding ultimate support pressure is then calculated. The results demonstrate that this method can provide better theoretical guidance for seepage control in tunnel faces in practical engineering.

Design Emission Uniformity of Horizontal Tapered Drip Laterals from the Normalized Pressure Head Distribution Approach

Design Emission Uniformity of Horizontal Tapered Drip Laterals from the Normalized Pressure Head Distribution Approach

Pattern of Fine Needle Aspiration Cytology of Head and Neck Swelling in Patients Attending a Tertiary Health Care Center: A Descriptive Cross-sectional Study.

Fine needle aspiration cytology is a simple, rapid, cost-effective method in diagnosis of head and neck swelling with minimal risk of complications. Head and neck swellings include a broad spectrum of diseases with different management for each. Fine needle aspiration cytology is a suitable and useful method for assessment of these swelling. This study was done with the objective to study the frequency and distribution of various head and neck lesions detected by fine needle aspiration cytology. A descriptive cross-sectional study was conducted at the Department of Pathology in a tertiary care center from February 1 to July 31, 2023 after obtaining ethical approval from Institutional Review Committee (Reference number: IRC-PA-191/2078-79). All the patients presenting with head and neck swelling during the study period were included in this study. Total sampling was done. Fine needle aspiration was done and cytological diagnosis was made. Descriptive analysis was done where frequency and percentage were calculated. Out of 112 cases included in the study, 43 (38.40%) were of lymph nodes, 36 (32.14%) of thyroid, 22 (19.64%) of skin and soft tissue and 11 (9.82%) of salivary glands. Among the lymph nodes cases, there were 11 (25.57%) metastases. In thyroid lesions, beingn lesions were seen in 24 (66.68%). This study found that lymph nodes were the most common site for head and neck swellings, frequently involving metastatic lesions.

Suitability evaluation of an equivalent porous medium method for characterizing 2-D flow based on the fracture fractal dimension

ABSTRACT An equivalent porous medium (EPM) method is widely utilized in various practical applications of hydrogeological engineering with areas of a fractured geological medium (FGM). However, the suitability of the EPM model still lacks sufficient scientific evaluation when considering the distribution of water head and flow velocity. In this study, the suitability of the EPM model to the fractal dimension of a discrete fracture network (DFN) is analyzed based on numerical simulation experiments. The simulation results of the EPM model are compared with those of the DFN model. Then, a formula is proposed to evaluate the suitability of the EPM model for characterizing two-dimensional flow distribution. The results show that with the increase of the fractal dimension, the suitability of the EPM model decreases first and then increases, and the inflection point gradually appears in advance with the increase of the length–width ratio of the simulation domain. The length–width ratio of the simulation domain has a linear relationship with the suitability of the EPM model, and the slope increases with the increase of the fractal dimension. Finally, a quantitative evaluation method is proposed to calculate the suitability of the EPM model based on the fractal dimension and the length–width ratio of the simulation domain.

Groundwater inverse modeling: Physics-informed neural network with disentangled constraints and errors

This study combines a physics-informed neural network (PINN) and Karhunen-Loeve expansion (KLE) (i.e., KLE-PINN) to solve the groundwater inverse problem. The hydraulic head (u) distribution is approximated by a deep neural network (DNN), while the hydraulic conductivity (K) field is constructed by KLE with given prior geostatistical information. KLE-PINN is applied to investigate the inversion using data from a single pumping test, natural gradient flow (NG), and hydraulic tomography (HT). The results from these cases demonstrate that our inverse method is robust. Our error analysis endeavors to quantify the sources of error by using two custom reference indicators, eforward and ecoupling. Moreover, the study finds that the inversion using data from multiple pumping tests (HT) yields more accurate estimates, leads to faster training convergence, and maintains higher stability. In addition, by investigating cases with different outer boundary conditions (BCs), we find that KLE-PINN is more flexible. Precisely, in scenarios with missing BCs, our network still fits well with the observed data, and the estimates capture the approximate spatial pattern in the region where the observation wells are distributed. Even with incorrect BCs, our network still performs well because the observational data constraints are strongly enforced during training.

The morphological diagnosis of 2 economically important subterranean termites in Western Indonesia, Coptotermes curvignathus and Coptotermes gestroi (Blattodea, Rhinotermitidae).

In Indonesia, the control of Coptotermes curvignathus populations as living tree termite pests has been carried out since the early 20th century. Recently, C. curvignathus has been considered the main termite pest and designated as the only species model for wood and wood products resistance tests against subterranean termite attack testing protocol in the Indonesian National Standard (SNI). However, the species distribution range of C. curvignathus has been long questioned as Coptotermes gestroi is commonly reported as a species found in urban areas of Southeast Asian region, particularly in Indonesia. One of the reasons for the species distribution discrepancies is the lack of morphological markers to distinguish both species in the field. Thus, limiting the field inspection effectiveness in termite pest management in Indonesia. This study reexamined and clarified the morphological differences between C. curvignathus and C. gestroi, based on soldier caste. The head shape, mandible shape, and distribution of genal setae on the ventro-anterior head part and pronotum marked the difference between the 2 species. These current results support previous findings of C. curvignathus as the forest dweller while clarifying C. gestroi as the common urban dweller in Indonesia. The putative benefit of morphological features related to the head, mandible, and setae distribution to the defensive adaptation in their common habitat was discussed. Ultimately, the inclusion of C. gestroi into the Indonesian National Standard (SNI) as an alternative species for efficacy tests against subterranean termites is highly recommended.

Head and neck tumors detected in dogs and cats between 2011 and 2021: A retrospective study

The aim of this study is to determine and compare the distribution, localization, breed, age and gender incidences of head and neck region tumors by years, by evaluating the results of dog-cat biopsies and/or operation material brought to Ankara University, Faculty of Veterinary Medicine, Department of Pathology between the years 2011 and 2021. In the study, 244 (15.9%) of 1533 tumors diagnosed in dogs and cats between 2011 and 2021 belonged to the head and neck region, of which 159 were in dogs and 85 in cats. Dogs with tumors were generally more than 6 years old (n=108) and cats were mostly 1 year old and older (n=75). In the head and neck region tumors observed in dogs, malignant and benign tumors of epithelial origin were most common (n=81; 50.9%), with sebaceous tumors (n=22) being the most common, and mostly observed around the eyes (n=26), while malignant and benign tumors of mesenchymal origin were most commonly found in the mouth region. Among the tumors of this region, epithelial tumors were mostly encountered in cats (n=39, 45.8%), and 51.2% of the epithelial tumors were squamous cell cancer (n=20), with epithelial tumors being the most common in the mouth (n=12) and nose (n=9). As a result, epithelial malignant and benign tumors were mostly seen in the eyes, and mesenchymal malignant and benign tumors were detected in the mouth. Among tumor types, papilloma in benign tumors and squamous cell carcinoma in malignant tumors were noted.

Physics‐Informed Convolutional Decoder (PICD): A Novel Approach for Direct Inversion of Heterogeneous Subsurface Flow

AbstractWe propose a physics‐informed convolutional decoder (PICD) framework for inverse modeling of heterogenous groundwater flow. PICD stands out as a direct inversion method, eliminating the need for repeated forward model simulations. The framework combines data‐driven and physics‐driven approaches by integrating monitoring data and domain knowledge into the inversion process. PICD utilizes a convolutional decoder to effectively approximate the spatial distribution of hydraulic heads, while Karhunen–Loève expansion (KLE) is employed to parameterize hydraulic conductivities. During the training process, the stochastic vector in KLE and the parameters of the convolutional decoder are adjusted simultaneously to minimize the data‐mismatch and the physical violation. The final optimized stochastic vectors correspond to the estimation of hydraulic conductivities, and the trained convolutional decoder can predict the evolution and distribution of hydraulic heads. Various scenarios of groundwater flow are examined and results demonstrate the framework's capability to accurately estimate heterogeneous hydraulic conductivities and to deliver satisfactory predictions of hydraulic heads, even with sparse measurements.

Analytical Solution of the Two-Dimensional Steady-State Seepage Field of a Seepage Anisotropy Pit Considering the Free Surface

An anisotropic foundation pit steady-state seepage field under a suspended waterproof curtain support considering the position of the free surface is studied analytically, and an analytical solution for the free surface position is given. The head distribution in the three zones is expressed as a series solution using the separation of variables method, and the explicit solution for the extent of the seepage field in each zone is obtained by combining the continuity condition between zones and the series orthogonality condition. The free surface position is determined according to the condition that the total head of the free surface is equal to the position head. A comparison of the calculation results of the analytical method and the indoor test and finite element analysis results verifies the correctness of the analytical solution, and the analytical method has more calculation efficiency than the finite element numerical method. Employing the aforementioned methods to analyze the influence parameters of the free surface position, the results show that drawdown increases as the ratio of the vertical permeability coefficient to the horizontal permeability coefficient increases; the greater the ratio of pit width to depth, the more significant the drawdown, but when the ratio continues to exceed 1.5, the drawdown is negligible.