Penetration Rate Research Articles

Abstract 4120375: Penetrance and Phenotype Conversion Rate of Genetic Hypertrophic Cardiomyopathy in an Academic Biobank

Introduction: Hypertrophic cardiomyopathy (HCM) is an autosomal dominant condition with incomplete penetrance and variable expressivity. Given the increasing prevalence of sequencing and current guidelines on secondary finding disclosure, HCM P/LP alleles are increasingly being returned to patients who undergo genetic testing for other reasons. Whether individuals carrying P/LP HCM variants discovered in a genome-first context have progressive endophenotypes indicative of HCM remains poorly understood. Methods: To evaluate the penetrance and phenotype conversion rate of adults with P/LP HCM variants, we leveraged the Massachusetts General Brigham Biobank (MGBB), which consists of 36,417 participants with genotyping array and clinical data. Primary images of the earliest and latest TTE studies were reviewed for measurements of the posterior wall (PW), interventricular septum (IVS), and left ventricular morphology. Phenotype-positive (P+) status was defined by a PW or IVS thickness of at least 13 mm. Results: P/LP HCM variants were identified in 42 (0.1%) individuals with mean (SD) age 50.0 (16.6) years and 21 (50%) female sex. The most common HCM variants were in MYBPC3 (44%) and MYH7 (40%), followed by TNNI3(7%), MYL3 (7%), and TNNT2 (2%). Among the 42 genotype-positive (G+) individuals in MGBB, 17 (40%) had pre-existing HCM diagnoses prior to genomic return of results (gRoR). In contrast, 25 (60%) G+ individuals lacked a prior HCM diagnosis, among whom gRoR prompted TTE studies in 13 patients, revealing evidence of HCM in 9 individuals. Among the 42 G+ patients, 25 (58%) patients had completed at least two TTE studies, with a median [IQR] of 6 [3-9] TTE studies per patient, across a span of 10 [7-15] years follow-up. At the time of first TTE, 16 (64%) patients were P+ while 9 (36%) were P-. No G+, P- individuals converted to P+ over 10 years follow-up, nor was there any significant change in IVS or PW thickness. In contrast, individuals who were P+ at the initial TTE exhibited thickening PW (25% increase, CI 97.5% of 6 to 44) and IVS (26% increase, CI 97.5% of 4 to 49) over 8 years follow-up. Conclusion: In a genome-first approach, we identified 42 individuals in the MGBB with a P/LP HCM variant, among whom 60% lacked a prior clinical diagnosis of HCM. Targeted phenotyping revealed that nearly half of this newly identified group were P+. Over a median of 10 years follow-up, we observed a 0% phenotype conversion rate among G+, P- adult carriers of HCM variants.

Modeling the Time-Dependent Variation of Road Salt Concentrations Using Analytical and Machine-Learning Approaches to Advance Service Life Predictions for Concrete Structures

The exposure of concrete structures to environmental and climatic conditions is detrimental to their durability. In northern climates, the key contributor to their degradation is corrosion of the reinforcing steel because of chloride ions originating from de-icing salts applied on roadways during the winter season. In consequence, a key input parameter for predicting the time to the initiation of corrosion for concrete elements is the time history of the concentration of chloride ions at their surfaces. To investigate this issue, a specialized mobile monitoring station was deployed along a roadway over several winter seasons to collect data on salting operations, weather conditions, and the temporal variation of chloride ion levels on the roadway. At first, salting operations were monitored, and then exploratory and machine-learning algorithms were applied to develop relationships between weather conditions, road conditions, and chloride ion concentrations. The first proposed model is based on the simulation modeling approach, while the second is based on the machine-learning XGBoost model. The findings demonstrate that both models can predict the variation of salt concentration on the road surface as a function of time after a salting operation. By accounting for the time dependency of surface chloride in service life models, more accurate predictions of corrosion initiation time are possible, since the rate of penetration of chloride ions is highly dependent on wetting/drying cycles throughout the winter.

Oscillation Mechanism and Suppression of Variable-Speed Pumped Storage Unit with Full-Size Converter Based on the Measured Single-Input and Single-Output Impedances

As the penetration rate of clean energy gradually increases, the demand for flexible regulation resources in the power grid is increasing accordingly. The variable-speed pumped storage unit with a full-size converter (FSC-VSPSU) can provide fast and flexible regulation resources for the power grid, which assists in the stable operation of the clean-energy-dominated power systems. Thus, its application is gradually becoming widespread. FSC-VSPSU relies on the power electronic converter for grid connection. When the impedance mismatch between FSC-VSPSU and the power grid occurs, the grid-connected system will experience oscillations, which seriously threatens its safe and stable operation. Aiming at this, this article firstly relies on the SISO impedance measurement and an equivalent impedance analysis to obtain the stability analysis criterion. Furthermore, applying the impedance matching analysis with the Bode diagram, the influence rules of the parameters of the grid-connected FSC-VSPSU on oscillation are obtained. In addition, with the summarized oscillation analysis results, this study delves into an exploration of pertinent strategies for oscillation suppression. Finally, a grid-connected FSC-VSPSU simulation model is built in MATLAB/SIMULINK, and the simulation results verify the correctness of the oscillation analysis results and the effectiveness of the proposed oscillation suppression strategy.

Predicting Rate of Penetration of Horizontal Wells Based on the Di-GRU Model

Predicting Rate of Penetration of Horizontal Wells Based on the Di-GRU Model

Evaluation of Spatial and Temporal Distribution of Carbon Emissions in Power Grid Based on Cloud Theory

In order to clearly determine the carbon emission distribution of regions, lines or nodes in the power grid, this paper applies cloud theory to the evaluation of the distribution of carbon emissions in the power grid. Based on the theory of carbon emission flow in the whole life cycle, five indicators that can reflect the spatial and temporal distribution of carbon emissions are constructed from the two dimensions of space and time. Cloud theory is used to establish the standard cloud of the carbon emission distribution level to quantify the randomness and fuzziness of the data to be evaluated. The bilateral constraint cloud theory and data-driven cloud transformation are combined to construct five comprehensive standard clouds of excellent, good, medium, poor and inferior, which are used as the evaluation interval of the evaluation index of carbon emission distribution. The reverse cloud is used to convert multiple sets of data into cloud droplets. Through the similarity measurement algorithm based on cloud model overlap, the comprehensive evaluation level of carbon emission distribution state in the time dimension is determined. Taking the IEEE 39 system as the research object, the spatial and temporal distribution of carbon emissions is evaluated, and the rationality and effectiveness of the proposed model are verified. Finally, the influence of the new energy penetration rate and power supply structure on the carbon emission distribution of the power grid is discussed by using cloud computing. Based on this, the targeted carbon reduction strategies for different types of nodes and the method of measuring the optimal new energy penetration rate are proposed and can provide a decision-making reference for optimizing the carbon emissions of the power grid.

Evaluation of Two Mini-implant Lengths in the Infrazygomatic Crest Region: A Randomized Clinical Trial.

Temporary anchorage devices (TADs) have revolutionized fixed orthodontic appliance treatment through anchorage controlling in the clinic and play an essential role in resolving many complex cases. Due to the limited space, there is a risk of injury to the roots while using interradicular microimplants. Therefore, the infrazygomatic crest (IZC) area can be an alternative mini-implant insertion site in the maxillary arch. The aim of the study was to evaluate the primary stability, pain perception, sinus penetration, late stability, and failure rate with two mini-implant lengths in the IZC area. Forty-eight mini-implants (Tusk Dental Co., Ltd., Canada) with two different lengths (length/diameter: 12/2 and 14/2 mm) were grouped by length (24 per group) and inserted bilaterally into the IZC area of 24 patients. The data were statistically analyzed, considering a significance level of p < 0.05. Sinus penetration prevalence did not differ significantly between 12-mm (54.2%) and 14-mm (62.5%) mini-implants (p > 0.05). Primary stability was significantly higher with the 14-mm mini-implants (p < 0.05). The failure rate did not differ significantly between the 12-mm (20.8%) and 14-mm (16.7%) mini-implants (p > 0.05). While the failure rate was similar between 12- and 14-mm mini-implants, the 14-mm mini-implants were more likely to damage adjacent structures. Therefore, shorter mini-implants should be preferred over longer mini-implants for most cases requiring IZC TADs. ClinicalTrials.gov identifier: NCT06293872.

Analysis of failure modes and impact resistance factors of PDC cutters

The service life of PDC cutters directly affects the overall drilling performance. In hard and heterogeneous formations, PDC cutters often fail prematurely due to dynamic impacts, leading to short footage and slow penetration rates. Enhancing the dynamic impact resistance of PDC cutters is crucial for achieving one-run drilling and reducing costs. This paper systematically studies the main factors influencing and optimizing the impact resistance of PDC cutters. First, the failure modes of hundreds of returned PDC drill bits were statistically analyzed, revealing that dynamic impact failure, wear failure, thermal damage, and erosion are the primary failure modes. Among these, dynamic impact failure is the most prevalent, with dynamic impact load being the main cause. Following the successful practices of leading international drill bit companies, a set of dynamic impact test and evaluation devices for PDC cutters was designed, and corresponding testing standards were established. Based on this, 19 types of PDC cutters were tested for impact resistance, exploring the effects of cutter diameter, diamond layer thickness, chamfer, cobalt removal, and cutter design on impact resistance. An optimized design methodology to enhance impact resistance was summarized, leading to the proposal of a new arch-axe cutter design. Indoor impact test data verified the superiority of this cutter. The research results indicate that cutter design (non-standard cutters) can significantly improve the dynamic impact resistance of PDC cutters. Additionally, increasing the cutter diameter and chamfer size while reducing the diamond layer thickness also enhances the dynamic impact resistance of PDC cutters. Through the optimization of the arch-ridge design, the independently developed arch-axe cutter improved the dynamic impact resistance by 106.45% and 71.43% compared to flat circular cutters and axe-shaped cutters, respectively.

An Automated Approach for Discriminating Hole Cleaning Efficiency While Predicting Penetration Rate in Egyptian Western Desert Wells

AbstractHigher rate of penetration (ROP) indicates successful drilling operation but is not the only drilling success measure. However, Conventional ROP prediction methods focus on increasing ROP and neglect the hole cleaning state, which can be altered by ROP changes. Higher ROP in vertical and deviated wells may increase cutting concentration, leading to hole cleaning problems such as overpulling and stuck pipe. With this problem in mind, this paper utilized geological, rheological, and drilling data of 31 vertical wells across four oil fields located in the Egyptian Western Desert, developed intelligent ROP prediction model through back propagation neural network (BPNN), and compared the proposed BPNN results with an empirical model. Finally, the pattern recognition algorithms including discriminant analysis, support vector machines, and neural network pattern recognition (NNPR) were implemented to discriminate hole cleaning efficiency following the ROP prediction process. Recognition models were developed based on predicted ROP, bit wear rate, specific energy, and drilling fluid carrying capacity index to evaluate hole cleaning. The accuracy of the multi-strategy classifier was evaluated using area under curve, confusion matrix, and receiver operating characteristic. The BPNN model outperformed the empirical model in terms of linear correlation coefficient (R = 98.6%) and average absolute error (AAE = 5.5%). Additionally, the best classification performance was achieved using the NNPR algorithm with 91% accuracy and a cross-validation error equal to zero. For validity, the proposed approach predicted ROP and classified hole cleaning efficiency for new vertical well in adjacent oil field, resulting in a 6% improvement in ROP.

WOB fuzzy control and experimental study on horizontal well experimental setup

This paper developed a horizontal drillstring-wellbore-bit experimental setup based on the similarity of physical behaviors. A data acquisition and control system was established to achieve real-time control over the Rotations Per Minute (RPM) and Rate of Penetration (ROP). Combining experts’ experience and practical drilling experiments, a constant Weight on Bit (WOB) fuzzy controller was designed for rock-breaking experiment. To validate the reliability of the control system, control tests were conducted with different WOB settings at varying RPM. The results indicated that the WOB control error was within 60 N, with an error rate below 3%. Rock drilling experiments were conducted on different rocks. In sandstone drilling experiments, as WOB increased from 600N to 900 N, the ROP, Torque on Bit (TOB), and Stick-slip Vibration Intensity (SSVI) increased significantly, reaching 114.12, 3.6, and 2.97 times of the original values, respectively. When the RPM increased from 21 to 30 RPM, TOB, SSVI, and ROP changed to 1.43, 0.89, and 3.84 times of the original values. The increase in ROP during sandstone drilling was evident, suggesting a moderate increase in drilling parameters, with caution against stick-slip vibrations, which can be mitigated by increasing RPM. In limestone drilling experiments, as WOB increased from 900 to 3300 N, TOB, SSVI, and ROP increased by 2.45, 3.17, and 56.34 times, respectively. When RPM was increased from 45 to 63 RPM, TOB, SSVI, and ROP changed by 0.88, 0.74, and 1.68 times, respectively. Under conditions of high WOB and RPM, the limestone drilling speed increased more significantly.

Optimization operation strategy of wind–pumped storage integrated system participation in spot market considering dual uncertainties

Abstract With the gradual increase in the penetration rate of renewable energy, the multifunctional role of pumped storage is becoming increasingly prominent, and the joint operation of “renewable energy + pumped storage” is a current research hotspot. However, during the joint system operation, there are dual risks from internal (renewable energy output) and external (market prices) factors, which significantly impact the system’s overall revenue. Therefore, an analysis is conducted around the operational mechanism of the “wind power–pumped storage” joint operation, and the uncertain factors faced during the system’s operation are identified. Second, an optimization model for wind power–pumped storage under deterministic scenarios is constructed, employing robust optimization theory and information gap decision theory to describe the uncertainty of electricity prices and wind power, thus forming a hybrid of the information gap decision theory and the robust optimization model for wind power–pumped storage. Finally, the results show that: (1) The total revenue of the model proposed in the paper has increased by 2.36% compared to the robust optimization model and by 9.04% compared to the deterministic model, significantly enhancing the model’s robustness and risk resistance capabilities. (2) From the perspective of the economic feasibility of different energy storage system configurations, the wind plant equipped with pumped storage has the highest economic feasibility, with an internal rate of return of 9.8% and net present value of 872 million Chinese Yuan, which is higher than that of compressed air energy storage and electrochemical energy storage systems. (3) Decision makers can set the risk deviation coefficient and the uncertainty budget according to their risk preferences, thereby changing the robustness of the model for differentiated decision making. However, an increase in the uncertainty budget coefficient will cause the total revenue of the joint operation system first to increase and then decrease, with the maximum revenue achievable within the range of 500–625; the total revenue reaches its maximum when the risk deviation coefficient is between 0.1 and 0.125.

A Computational Fluid Dynamics Study on the Effect of Drilling Parameters on Wellbore Cleaning in Oil Wells

Poor wellbore cleaning is a significant challenge in oil drilling, primarily due to the accumulation of cuttings at the bottom of the well, particularly in deviated and horizontal wells. This study addresses this issue by employing Computational Fluid Dynamics (CFD) with the commercial software ANSYS FLUENT (2023-R1) to simulate a solid–liquid multiphase flow in an annulus. The primary objective is to analyze the cuttings concentration, pressure loss, and solid velocity profiles across various drilling parameters, including drill pipe rotation, the flow rate, rate of penetration, inclination angle, and fluid rheology. Our results underscore the critical role of these parameters in enhancing cuttings transport efficiency. Specifically, the drill pipe rotation, flow rate, and rate of penetration emerge as the most influential factors affecting the wellbore cleaning performance. With a validated model exhibiting an average error of 4.24%, this study provides insights into optimizing drilling operations to improve wellbore cleaning and increase hydrocarbon recovery.

Exposure behavior and drilling efficiency of basalt fiber composite impregnated diamond bits in hard granite

Impregnated diamond bits (IDBs) are widely used for drilling in hard formations. To improve the drilling efficiency and exposure behavior of IDBs in granite, three types of Cu-based basalt fiber (BF) composite IDBs were designed and prepared by using the medium-frequency induction hot-pressing and sintering method, in which 0 wt.% BF and 25 vol.% diamond were used in 0BF25D IDB, 1 wt.% BF and 25 vol.% diamond were used in 1BF25D IDB, 1 wt.% BF and 20 vol.% diamond were used in 1BF20D IDB. The drilling efficiency of each IDB was tested under different drilling pressures (WOB), and the exposure behavior of IDBs was investigated by scanning electron microscopy and ultra field microstructural characterization. Results show that drilling granite with grade 9 drillability, low drilling pressure can drill successfully with the addition of BF. The average rate of penetration (ROP) of 1BF20D IBD under 6 kN WOB was 4.78 m/h, which was improved by 60%∼80%, energy consumption decreased by 66% for each meter, torque (TOB) decreased, and rotational speed (RPM) was more stable during the drilling process. The addition of BF and reasonable diamond concentration enhanced the holding power of the diamond which promoted the exposure of the diamond. The average exposed height of the diamond in 1BF20D IDB reached 121.2 μm with 22% to 29% of the whole diamond.

Variable speed limit control strategy considering traffic flow lane assignment in mixed-vehicle driving environment

To accurately predict traffic flow and optimize the operations of freeway bottleneck areas in a mixed-vehicle driving environment, this paper proposes a traffic prediction model and a variable speed limit (VSL) cooperative control strategy. Firstly, a lane-level short-term traffic prediction model, physics informed Transformer and cell transmission model (PIT-CTM), is constructed by combining the Transformer neural network and lane-level cell transmission model (CTM) based on the physics-informed deep learning framework. On this basis, the accuracy and transferability of PIT-CTM are analysed. Secondly, a lane assignment decision model is presented, which enables the dynamic planning of the optimal traffic distribution across lanes. Furthermore, a lane-level VSL control model is constructed based on the model predictive control (MPC) framework. The model induces vehicles to change lanes earlier by setting the speed limit difference between lanes. By regulating the input flow in the bottleneck area of the freeway, it reduces conflicts between mainline vehicles and ramp vehicles. Finally, the feedback regulation between the lane assignment decision model and the lane-level VSL control model promotes the cooperative optimisation of the lateral and longitudinal flows and adapts the control strategy to the dynamic traffic characteristics. A three-lane freeway merging zone is selected, the numerical experiment is conducted and compared with differential lane-level VSL. The results show that the strategy can effectively optimise the mixed-vehicle traffic state and maintain better control performance under any connected and autonomous vehicle (CAV) penetration rates.

Impact of Driving Timing and Road Types on the Operating Regions of Electric Two-wheeler Powertrain Components under Indian Road Conditions

Among the different classes of electric vehicles (EVs), electric two-wheelers (E2Ws) are securing more enticement towards the commutator for regular usage due to their higher suitability in the heterogeneous traffic conditions of India. However, the driving range of the E2Ws is the major concern in increasing its penetration rate in road transportation. To eradicate the range anxiety of commutators by redesigning the current E2W powertrain configuration, this study strives to insights into the operational limits of powertrain elements such as the battery and motor in E2W under multiple operational circumstances such as driving style, pavement, and ambient conditions. Regarding this, the E2W with data acquisition system (DAC) connected with an onboard diagnosis (OBD) port is utilized to acquire the data related to powertrain components and vehicles for four driving trips (trips 1–4) under three major road segments (Rural, expressway, and urban) of India. The operating and computed performance metrics, comprising battery voltage and current, state-of-charge (SOC), energy consumption, motor current, speed, efficiency, and so on, are then compared throughout driving trips and road segments. The mean velocity of E2W for trips 1 to 4 is 31.91, 33.68, 39.03, and 35.58 km/h, respectively. The results disclosed that total energy consumption and energy consumption per km. are higher during trip 3 due to higher average vehicle speed. Also, it has a higher depth-of-discharge (DOD) of 83.91 % than other driving trips. Further, a higher rate of SOC drop, energy consumption and battery temperature rise are found at highway road segments.

Investigation on the interfacial and emulsion stabilized behavior of dextran/ferritin/resveratrol composite nanoparticles

Glycation of ferritin provides a viable approach to enhance its physicochemical and functional properties. However, there is limited research on the interfacial adsorption properties of glycated ferritin-based colloidal particles. Therefore, this study selected recombinant human H-chain ferritin (rHuHF), rHuHF encapsulated with resveratrol (rHuHF–Res), and rHuHF–dextran glycoconjugates loaded with resveratrol (Dex–rHuHF–Res) as emulsifiers to investigate their interfacial adsorption properties. The results revealed that Dex–rHuHF–Res exhibited superior emulsifying properties and rheological behavior. It also increased the hydrophobicity of the microenvironment around Tyr and Trp residues, while hydrogen bonds, hydrophobic force, and salt bridge were identified as the most important intermolecular interactions. Dex–rHuHF–Res exhibited the biggest contact angle and lowest interface tension, which further reduced the diffusion (Kdiff) from the aqueous phase to the interface but promoted the penetration (Kp) and rearrangement (Kr) rates at the interface. Meanwhile, the emulsion stabilized by Dex–rHuHF–Res displayed excellent freeze-thaw stability, and Dex–rHuHF–Res can be more densely accumulated at the O/W interface to form an interface layer. These findings highlight the promising application of glycated ferritin in stabilizing Pickering emulsions, and deepen our understanding of the interplay among particle interaction, interface adsorption properties, and emulsion stability.

Fast and Efficient Mitigation of Coupled Axial-Torsional Drillstring Vibrations through State-Feedback Linear-Quadratic-Integral (LQI) Control

Summary In the exploitation of subsurface hydrocarbon and geothermal energy, more than 50% of total expenses are typically attributed to drilling and well completion. Coupled triaxial vibrations in the drilling system, excited by various mechanical, hydraulic, and geological sources, are major causes of premature downhole tool failures, excessive bit damage, compromised hole quality, reduced rate of penetration (ROP), and increased nonproductive time. Drillstring dynamic models, shock absorption tools, and controllers have been developed since the 1960s to understand and mitigate detrimental vibrations in the drilling system, aiming for smoother and more efficient operations. With advancements in measurement/logging while drilling technologies, rig equipment, mud pulse/wired pipe telemetry, and the digitalization of drilling engineering, a foundation has been established for proactive drilling vibration detection, analysis, and control to achieve efficient and safe drilling. In this study, a reduced-order axial-torsional drillstring dynamic model with soft string lateral contacts is first developed. Nonlinear drilling boundary conditions and inputs, such as bit-rock interactions (BRIs) with torque-on-bit (TOB) velocity-weakening effects, wellbore-drillstring contacts/friction, drillstring gravity, mud buoyancy, and more, are defined within the modeling framework. Equilibrium states and linearization of the model are derived for any given weight-on-bit (WOB) and rotational speed (RPM) setpoints, 3D well trajectories, and drillstring dimensions. A linear-quadratic-integral (LQI) controller is developed and tested for drillstring vibration suppression using a 6,562 ft (2000 m) drillstring model. In comparison to commercially available controllers for stick/slip mitigation, which are designed based on impedance matching and only address the decoupled drillstring torsional dynamics, the proposed state-feedback LQI controller emphasizes the coupling between the axial and torsional drillstring dynamics by simultaneously controlling WOB and RPM. Simulation results demonstrate that the LQI controller can suppress stick/slip and stabilize downhole WOB within 7 seconds, while a commercial drilling controller takes more than 35 seconds to achieve the same. Additionally, valuable insights and potential future directions for nonlinear drillstring vibration mitigation and drilling controller design are provided. With its fast drilling dysfunction stabilization time, small downhole RPM/WOB overshoots, minimal rig control inputs, robustness against drilling nonlinearities, and high efficiency, the proposed multi-input-multi-output state-feedback drilling controller holds great potential for application in proactive drilling vibration control, drilling automation, and real-time drilling optimization.

Performance analysis of hybrid off-grid renewable energy systems for sustainable rural electrification

Delivering sustainable power to rural communities in sub-Saharan Africa, particularly in Somalia, where many areas are far from the electrical grid and suffer from energy shortages, necessitates the deployment of appropriate technologies. This study evaluates the techno-economic and environmental viability of a hybrid renewable energy system (HRES) comprising a 15 kWp photovoltaic (PV) generator, 10 kW wind turbine (WT), 25 kW diesel generator (DG), and a 72-kWh battery energy storage system (BESS). The HRES is proposed to supply sustainable power to a rural community in Somalia. The techno-economic assessment of the proposed HRES and a comparative analysis of other configurations—namely the PV/WT/BESS and PV/DG/BESS systems is conducted using HOMER and MATLAB software. The results indicate that the proposed PV/WT/DG/BESS HRES demonstrates superior techno-economic performance compared to the other configurations. The lowest net present cost of $96,899.16 and the levelized cost of energy (LCOE) of $0.090/kWh were achieved. This system attains 25 % excess electricity, maintains a 0 % unmet electric load, and demonstrates robust environmental performance with a renewable penetration rate of 91.8 %. Approximately 53.34 % of greenhouse gas emissions are reduced compared to the PV/DG/BESS configuration. The study confirmed the significant impacts of all sensitivity parameters on operational costs, LCOE, fuel prices, fuel intake, and renewable fraction. The overall results indicate superior performance in the optimal system case, offering a feasible and suitable choice for the sustainable electrification of rural communities.

Drilling Rate of Penetration Prediction Based on CBT-LSTM Neural Network

Due to the uncertainty of the subsurface environment and the complexity of parameters, particularly in feature extraction from input data and when seeking to understand bidirectional temporal information, the evaluation and prediction of the rate of penetration (ROP) in real-time drilling operations has remained a long-standing challenge. To address these issues, this study proposes an improved LSTM neural network model for ROP prediction (CBT-LSTM). This model integrates the capability of a two-dimensional convolutional neural network (2D-CNN) for multi-feature extraction, the advantages of bidirectional long short-term memory networks (BiLSTM) for processing bidirectional temporal information, and the dynamic weight adjustment of the time pattern attention mechanism (TPA) for extracting crucial information in BiLSTM, effectively capturing key features in temporal data. Initially, data are denoised using the Savitzky–Golay filter, and five correlation coefficient methods are employed to select input features, with principal component analysis (PCA) used to reduce model complexity. Subsequently, a sliding window approach transforms the time series into a two-dimensional structure to capture dynamic changes, constructing the model input. Finally, the ROP prediction model is established, and search methods are utilized to identify the optimal hyperparameter combinations. Compared with other neural networks, CBT-LSTM demonstrates superior performance metrics, with MAE, MAPE, RMSE, and R2 values of 0.0295, 0.0357, 9.3101%, and 0.9769, respectively, indicating the highest predictive capability. To validate the model’s robustness, noise was introduced into the training data, and results show stable performance. Furthermore, the model’s predictive results for other wells achieved R2 values of 0.95, confirming its strong generalization ability. This method provides a new solution for ROP prediction in real-time drilling operations, assisting drilling engineers in better planning their operations and reducing drilling cycles.

Assessment of the Viability of Community-Based Health Insurance Schemes in the Ziguinchor Department from 2016 to 2018 in the Context of Universal Health Coverage (Senegal)

&amp;lt;i&amp;gt;Introduction&amp;lt;/i&amp;gt;: This study examines the viability of community-based health insurance schemes in the Ziguinchor department of Senegal from 2016 to 2018, within the context of universal health coverage. Health insurance schemes have emerged as a promising solution to extend social health protection to informal sector workers and rural populations. However, despite political support, these structures face persistent challenges in terms of enrollment, member retention, and management. The study aims to assess the viability of these schemes, as well as the perceptions of various stakeholders on mutual health insurance. &amp;lt;i&amp;gt;Methodology&amp;lt;/i&amp;gt;: The study adopted a mixed approach, combining quantitative and qualitative methods, based on the theoretical framework developed with the support of the International Labor Office (ILO). This framework evaluates the viability of health insurance schemes according to four dimensions: institutional, technical, functional, and financial/economic. A questionnaire with 87 questions was used to collect quantitative data on these aspects from the seven health insurance schemes in the Ziguinchor department. For the qualitative component, focus groups and individual interviews were conducted with various actors, including beneficiaries, non-beneficiaries, community leaders, scheme managers, and healthcare providers. &amp;lt;i&amp;gt;Results&amp;lt;/i&amp;gt;: The study&amp;apos;s findings reveal a complex situation for the health insurance schemes in the Ziguinchor department. Institutionally, the schemes have a solid foundation with legal status, internal regulations, and affiliation to a departmental union. Technically, membership is voluntary and family-based, with control mechanisms in place, although their effectiveness is limited. Functional viability shows positive signs with a membership growth rate of 20.18%, but the overall penetration rate remains low at 16.99%, and the premium collection rate is very low, averaging 26.78%. The financial situation is particularly of concern: no scheme can meet its short or long-term debts, and the average claims ratio is excessively high at 282%. As for stakeholder perceptions, they are mixed: while the schemes are generally considered beneficial, significant practical difficulties are identified, including lack of information, funding problems, and procedural complexity. &amp;lt;i&amp;gt;Conclusion&amp;lt;/i&amp;gt;: The study highlights the complexity of the role of health insurance schemes in the quest for universal health coverage. Despite undeniable strengths, the schemes face considerable challenges in terms of functional and financial viability. Corrective measures are necessary, including strengthening management capacities, innovating premium collection methods, and improving communication. An evolution of the model is also suggested, potentially towards greater integration into a national social protection system or the development of innovative public-private partnerships.

Research on the Influencing Factors of High-quality Development of Export Enterprises

The Internet and big data have a profound impact on export enterprises, and their high-quality development is also facing unprecedented opportunities and challenges. This paper takes small and medium-sized enterprises of sales oriented export trade as the research sample, and uses the BP neural network model to conduct empirical research on the factors affecting the high-quality development of enterprises. Research conclusions: (1) A influencing factor system for high-quality development of export enterprises has been constructed, which includes 26 specific factors; (2) The factors in order of impacts: convenience of shopping operations, website content, comprehensive quality of employees, product awareness, consumer penetration rate, internationalization level, enterprise scale and reputation; (3) Factors such as overall employee quality, internationalization level, employee training system and effectiveness, network security, internationalization style of website design, convenience of shopping operations, sales growth rate of the enterprise, and profit growth rate do not have a direct, absolute positive relationship with the high-quality development of the enterprise.