Search Results (26,357)

With the launch of OpenAI’s ChatGPT, large language models have garnered significant attention, and applications based on these models have proliferated. A critical challenge has emerged: how to rapidly enhance the capabilities of general LLMs in specialized domains. Compared to fine-tuning and other methods, prompt engineering has proven to be a cost-effective approach for improving the performance of LLMs on specific tasks, yielding remarkable results. However, current prompt example construction methods are numerous and lack a universally applicable approach that spans different models and tasks. Furthermore, existing research is predominantly tested and evaluated on a limited range of specific datasets, failing to explore the broader impact of these methods on a wider array of tasks. This paper proposes a prompt example construction method based on clustering and semantic similarity, which combines clustering algorithms with semantic similarity techniques to significantly improve the quality of prompt examples. In comparative tests conducted on six LLMs and seven datasets, the overall accuracy and stability of the proposed method significantly outperforms five other common methods, demonstrating broad applicability and the potential to enhance the output performance of all LLMs. Through comparative experiments, this paper also identifies that as the parameter scale of LLMs increases, the improvement effect of the prompt example construction method on LLM output performance tends to diminish. Additionally, diversified prompt example sets provide a more pronounced enhancement in LLM output performance. Full article

Comprehending the electrochemical condition of a lithium-ion battery (LiB) is essential for guaranteeing its safe and effective operation. This insight is increasingly obtained through characterization tests such as a differential capacity analysis, a characterization test well suited for the electric transportation sector due to its dependency on the available voltage and current (E–I) data. However, a drawback of this technique is its time dependency, as it requires extensive time due to the need to conduct it at low charge rates, typically around C/20. This work seeks to forecast characterization data utilizing 1C cycle data at increased temperatures, thereby reducing the time required for testing. To achieve this, three neural network architectures were utilized as the following: a recurrent neural network (RNN), feed forward neural network (FNN), and long short-term memory neural network (LSTM). The LSTM demonstrated superior performance with evaluation scores of the mean squared error (MSE) of 0.49 and mean absolute error (MAE) of 4.38, compared to the FNN (MSE: 1.25, MAE: 7.37) and the RNN (MSE: 0.89, MAE: 6.05) in predicting differential capacity analysis, with all models completing their computations within a time range of 49 to 299 ms. The methodology utilized here offers a straightforward way of predicting LiB degradation modes without relying on polynomial fits or physics-based models. This work highlights the feasibility of forecasting differential capacity profiles using 1C data at various elevated temperatures. In conclusion, neural networks, particularly an LSTM, can effectively provide insights into electrochemical conditions based on 1C cycling data. Full article

Anaerobic digestion (AD) is one of the most significant processes for treating fecal sludge. However, a substantial amount of microplastics (MPs) have been identified in septic tanks, and it remains unclear whether they impact the resource treatment of feces. To investigate this, polyethylene terephthalate (PET) was used as an indicator of MPs to study their effect on the anaerobic digestion of fecal sludge (FS). Two digestion systems were developed: FS mono-digestion and FS co-digestion with anaerobic granular sludge. The results indicated that the effects of PET varied between the two systems. PET inhibited volatile fatty acid synthesis in both systems, but the inhibition period differed. During mono-digestion, PET slightly increased gas and methane production, in contrast to the co-digestion system, where PET reduced methane production by 75.18%. Furthermore, in the mono-digestion system, PET increased soluble chemical oxygen demand and ammonia nitrogen concentrations while blocking phosphorus release, whereas the co-digestion system showed the opposite effects. Ultimately, the choice of digestion method is crucial for the resource utilization of septic tank sludge, and the impact of MPs on AD cannot be ignored. Full article

The performance of marine radar constant false alarm rate (CFAR) detection method is significantly influenced by the modeling of sea clutter distribution and detector decision rules. The false alarm rate and detection rate are therefore unstable. In order to address low CFAR detection performance and the modeling problem of non-uniform, non-Gaussian, and non-stationary sea clutter distribution in marine radar images, in this paper, a CFAR detection method in generalized extreme value distribution modeling based on marine radar space-time filtering background clutter is proposed. Initially, three-dimensional (3D) frequency wave-number (space-time) domain adaptive filter is employed to filter the original radar image, so as to obtain uniform and stable background clutter. Subsequently, generalized extreme value (GEV) distribution is introduced to integrally model the filtered background clutter. Finally, Inclusion/Exclusion (IE) with the best performance under the GEV distribution is selected as the clutter range profile CFAR (CRP-CFAR) detector decision rule in the final detection. The proposed method is verified by utilizing real marine radar image data. The results indicate that when the $P_{fa}$ is set at 0.0001, the proposed method exhibits an average improvement in $P_D$ of 2.3% compared to STAF-RCBD-CFAR, and a 6.2% improvement compared to STCS-WL-CFAR. When the $P_{fa}$ is set at 0.001, the proposed method exhibits an average improvement in $P_D$ of 6.9% compared to STAF-RCBD-CFAR, and a 9.6% improvement compared to STCS-WL-CFAR. Full article

The increase in the number of hospital strains of hypervirulent and multidrug resistant (MDR) Pseudomonas aeruginosa is a major health problem that reduces medical treatment options and increases mortality. The molecular profiles of virulence and multidrug resistance of P. aeruginosa-associated hospital and community infections in Mexico have been poorly studied. In this study, we analyzed the different molecular profiles associated with the virulence genotypes related to multidrug resistance and the genotypes of multidrug efflux pumps (mex) in P. aeruginosa causing clinically critical infections isolated from Mexican patients with community- and hospital-acquired infections. Susceptibility to 12 antibiotics was determined using the Kirby–Bauer method. The identification of P. aeruginosa and the detection of virulence and efflux pump system genes were performed using conventional PCR. All strains isolated from patients with hospital-acquired (n = 67) and community-acquired infections (n = 57) were multidrug resistant, mainly to beta-lactams (ampicillin [96.7%], carbenicillin [98.3%], cefalotin [97.5%], and cefotaxime [87%]), quinolones (norfloxacin [78.2%]), phenicols (chloramphenicol [91.9%]), nitrofurans (nitrofurantoin [70.9%]), aminoglycosides (gentamicin [75%]), and sulfonamide/trimethoprim (96.7%). Most strains (95.5%) isolated from patients with hospital- and community-acquired infections carried the adhesion (pilA) and biofilm formation (ndvB) genes. Outer membrane proteins (oprI and oprL) were present in 100% of cases, elastases (lasA and lasB) in 100% and 98.3%, respectively, alkaline protease (apr) and alginate (algD) in 99.1% and 97.5%, respectively, and chaperone (groEL) and epoxide hydrolase (cif) in 100% and 97.5%, respectively. Overall, 99.1% of the strains isolated from patients with hospital- and community-acquired infections carried the efflux pump system genes mexB and mexY, while 98.3% of the strains carried mexF and mexZ. These findings show a wide distribution of the virulome related to the genotypic and phenotypic profiles of antibiotic resistance and the origin of the strains isolated from patients with hospital- and community-acquired infections, demonstrating that these molecular mechanisms may play an important role in high-pathogenicity infections caused by P. aeruginosa. Full article

Background: Heart rate variability (HRV) is the change in time intervals between heart beats, reflecting the autonomic nervous system’s ability to adapt to psychological and physiological demands. Slow breathing enhances parasympathetic activity, increasing HRV. Pranayama, a yoga breathing technique, affords the conscious regulation of respiration frequency. This study aimed to characterize HRV, blood pressure and peripheral oxygen saturation of basic yoga breathing slow techniques with regular yoga practitioners. Methods: In total, 45 yoga practitioners were included in the study (including 7 males, mean age of 54.04 ± 11.97 years) with varying levels of yoga experience (minimum 3 months, maximum 37 years). Participants performed three breathing conditions: baseline (control) and two yoga techniques (abdominal (adham) and complete (mahat)) breathing, each for 10 min in the supine position (i.e., savasana). For each condition, respiratory frequency, heart rate (HR), blood pressure and peripheral oxygen levels were collected. Results: The findings revealed that both abdominal and complete yoga breathing techniques promoted a decrease in respiratory frequency (p < 0.001, r = 0.61; p < 0.001, r = 0.61, respectively), and an increase in peripheral oxygen saturation (p < 0.001, r = 0.50; p < 0.001, r = 0.46, respectively), along with blood pressure decreases in all mean values, and a significant decrease in systolic pressure, considering all conditions (p = 0.034, W = 0.08). There were significant increases in standard deviation of HR during abdominal and complete yoga breathing techniques compared with the baseline (p = 0.003, r = 0.31; p < 0.001, r = 0.47, respectively), indicating enhanced parasympathetic activity. Moreover, the complete breathing technique exhibited the greatest variability in HRV measures, with several significant differences compared with abdominal breathing (standard deviation of HR, p < 0.001, r = 0.42; SD2, standard deviation of points perpendicular to the Poincaré parallel line, p < 0.003, r = 0.31; SD1/SD2, p < 0.003, r = 0.31), suggesting a more profound impact on autonomic modulation. Conclusions: simple, inexpensive and non-intrusive abdominal and complete yoga breathing techniques can effectively and momentarily enhance HRV and oxygen saturation in adults, mature adults and the elderly. Full article

The generation of sustainable energy through wind and hydrokinetic turbines, which convert the kinetic energy from fluid flows into mechanical energy, presents an attractive solution for diversifying the country energy matrix in response to climate change. Consequently, numerous studies have investigated the aerodynamic and hydrodynamic behaviors of various wind and hydrokinetic turbines using numerical simulations to understand their interaction with the surrounding fluid flows and enhance their performance. However, to validate these studies and aiming at improving the turbine design, experimental studies on a laboratory scale employing wind tunnels and hydraulic channels are essential. This work addresses the development and implementation of a reliable control system for experimentally evaluating the power coefficient (C_p) versus the tip speed ratio (TSR) curve of wind and hydrokinetic turbines. The control system, based on a DC motor acting as a generator and aligned with a commercial torque sensor, enables a precise control over the experimental setup. By obtaining and comparing the experimental performance curves of C_p versus TSR for both wind and hydrokinetic turbines with numerical results, the effectiveness and accuracy of the developed control system are demonstrated. A satisfactory fit between numerical and experimental results was achieved, underscoring the utility and reliability of the control system for assessing the turbine performance. Full article

In the heavy-duty vehicle industry, unbalance in the armature is one of the most common problems affecting starters’ performance and durability. This research presents a comprehensive study to improve the balancing process for starting rotors in heavy-duty vehicles. The complete manufacturing process of armatures was analyzed to understand the contribution of assembly processes to unbalancing. The analysis revealed that the primary factor leading to high unbalance in these parts is the misalignment of conductors within the armature winding. During assembly, these conductors experience axial movements, resulting in non-uniform mass distribution and causing unbalanced values ranging from 150 to 350 g·mm. These values surpass the permissible limit, making rectification during the balancing process at the end of the assembly impossible. Consequently, a novel alignment tool was designed to address this issue, significantly reducing the effect and achieving the maximum allowable unbalance of 100 g·mm. This allowed the balancing machine used in the process to correct the initial unbalance of the reinforcements in a single work cycle, improving operation efficiency by about 15%. Full article

This research experimentally demonstrates the practicability of using large-sized direct solar frying as an alternative technology for the predominantly biomass-based injera baking method. The system was designed and developed with fryers 40, 50, and 55 cm in diameter and two operational options: continuous mode and alternating mode. Extensive experimental testing was conducted on each prototype to demonstrate solar frying and determine the relative performance. The findings indicate that the 2 kW heating capacity of the 40 cm-sized solar fryer model conducted baking processes at a relatively lower system temperature in both application modes compared to the larger-sized fryers. As a result, this system maintained a consistent average fryer temperature distribution and shorter initial heating time, without the requirement of a reheating process during the subsequent baking cycles. The experimental testing also demonstrated that alternating-mode applications were more practical for the 40 cm-sized fryers than for the larger ones. Overall, direct solar frying is more efficient and convenient for the 40 cm-sized solar fryers. In contrast, the larger-sized systems required a larger fryer thermal storage capacity coupled with larger-size solar concentrators to maintain equivalent stable operational conditions, conversely leading to a lack of application simplicity and higher system costs. Full article

With people’s concern for health and the development of medical technology, medical products for children are gradually appearing in pharmacies and online stores. However, the appearance of most children’s household medical products tends to meet the needs of adults, which leads to low acceptance of medical products. This study aimed to explore 10- to 16-year-old children’s visual and tactile perception of different materials by researching the relationship between the psychological quantities of visual and tactile perception and the physical quantities of the material surface. Based on the theory of kansei engineering, we measured the physical quantities of nine materials used in insulin syringe products and administered a perceptual questionnaire test for children. By correlating subjective perceptions with the physical attributes of the materials’ surfaces, we determined a strong correlation between the visual and tactile psychological properties and the properties of the materials’ surfaces. Children clearly perceive materials, and materials with lower roughness can elicit calmness, while materials with higher gloss elicit negative emotions. This paper establishes an evaluation model and provides a scientific selection method for surface materials in different children’s household medical products. Full article

Metasurfaces, which are ultrathin planar metamaterials arranged in certain global sequences, interact uniquely with the surrounding light field and exhibit unusual effects of light modulation. Many interesting applications have been discovered based on metasurfaces, particularly in invisibility cloaks. However, most invisibility cloaks are limited to working in specific directions. Achieving effectiveness in multiple directions requires the metasurface to be designed with both perception and modulation capabilities. Current multi-directional metasurface cloak systems are implemented with discrete components rather than an integrated sensing component. Here, we propose an intelligent metasurface cloak system that integrates sensing units, resulting in the cloaking effect with the help of a real-time direction sensor and an adaptive feedback control system. A reconfigurable reflective meta-atom based on phase modulation is presented. Sensing units replace parts of the meta-atoms in the designed tunable metasurface, integrating with an FPGA responsible for measuring the direction and frequency of the incident wave, constituting a closed-loop system together with the feedback parts. Experimental results demonstrate that the metasurface cloak system can recognize the different directions of the incoming wave, and can adaptively manipulate the reflected phase of EM waves to conceal objects without any human participation. Full article

Background: Diabetic retinopathy (DR) is a leading cause of blindness in patients with type 2 diabetes mellitus (T2DM). Cardiovascular risk factors, such as hypertension, obesity, and dyslipidemia, may play a crucial role in the development and progression of DR, though the evidence remains mixed. This study aimed to assess cardiovascular risk factors as independent predictors of DR and to develop a predictive model for DR progression in T2DM patients. Methods: A retrospective cross-sectional study was conducted on 377 patients with T2DM who underwent a comprehensive eye exam. Clinical data, including blood pressure, lipid profile, BMI, and smoking status, were collected. DR staging was determined through fundus photography and classified as No DR, Non-Proliferative DR (NPDR), and Mild, Moderate, Severe, or Proliferative DR (PDR). A Multivariate Logistic Regression was used to evaluate the association between cardiovascular risk factors and DR presence. Several machine learning models, including Random Forest, XGBoost, and Support Vector Machines, were applied to assess the predictive value of cardiovascular risk factors and identify key predictors. Model performance was evaluated using accuracy, precision, recall, and ROC-AUC. Results: The prevalence of DR in the cohort was 41.6%, with 34.5% having NPDR and 7.1% having PDR. A multivariate analysis identified systolic blood pressure (SBP), LDL cholesterol, and body mass index (BMI) as independent predictors of DR progression (p < 0.05). The Random Forest model showed a moderate predictive ability, with an AUC of 0.62 for distinguishing between the presence and absence of DR XGBoost showing a better performance, featuring a ROC-AUC of 0.68, while SBP, HDL cholesterol, and BMI were consistently identified as the most important predictors across models. After tuning, the XGBoost model showed a notable improvement, with an ROC-AUC of 0.72. Conclusions: Cardiovascular risk factors, particularly BP and BMI, play a significant role in the progression of DR in patients with T2DM. The predictive models, especially XGBoost, showed moderate accuracy in identifying DR stages, suggesting that integrating these risk factors into clinical practice may improve early detection and intervention strategies for DR. Full article

The management and exchange of electronic health records (EHRs) remain critical challenges in healthcare, with fragmented systems, varied standards, and security concerns hindering seamless interoperability. These challenges compromise patient care and operational efficiency. This paper proposes a novel solution to address these issues by leveraging distributed ledger technology (DLT), including blockchain, to enhance data security, integrity, and transparency in healthcare systems. The decentralized and immutable nature of DLT enables more efficient and secure information exchange across platforms, improving decision-making and coordination of care. This paper outlines a strategic implementation approach, detailing timelines, resource requirements, and stakeholder involvement while addressing crucial privacy and security concerns like encryption and access control. In addition, it explores standards and protocols necessary for achieving interoperability, offering case studies that demonstrate the framework’s effectiveness. This work contributes by introducing a DLT-based solution to the persistent issue of EHR interoperability, providing a novel pathway to secure and efficient health data exchanges. It also identifies the standards and protocols essential for integrating DLT with existing health information systems, thereby facilitating a smoother transition toward enhanced interoperability. Full article

Currently, water pollution caused by dyes is a serious problem since they are toxic and carcinogenic to living beings. To reduce the presence of these contaminants, natural adsorbents have been considered as they are easy to obtain, inexpensive, and have high removal efficiency. In this work, the adsorption process using natural brushite (nDCPD) was studied for the removal of phenol red (PR), achieving a removal rate of 99.15% and an adsorption capacity of 82.24 mg/g, and gentian violet (GV), achieving a removal rate of 97.03% and an adsorption capacity of 74.22 mg/g. Equilibrium adsorption occurs for both dyes in multiple layers on the surface. The adsorption process is spontaneous for both dyes. The kinetics of the adsorption process involve using a single active site on the surface for PR adsorption, while for GV, two active sites on the surface are required. Analysis via FTIR, EDS, and XRD revealed various mechanisms that intervene in the adsorption process of both dyes on the surface of nDCPD, such as electrostatic forces, functional groups, physisorption, and ion exchange. Full article

The stability of the oil well wellbore is a prerequisite for selecting the optimal completion method. In this paper, based on experimental testing and theoretical models of rock mechanics parameters in deep oil reservoirs, the in situ stress parameters of deep oil wells are accurately predicted. On this basis, a full life cycle assessment model for wellbore and perforation casing stability was established, and the effects of pressure depletion and changes in the production pressure differential on wellbore stability and casing stability were analyzed. The research results indicate that as the formation pressure decreases, the critical collapse pressure difference around the wellbore significantly decreases. The greater the production pressure difference, the more likely the wellbore is to become unstable. Under the original formation pressure coefficient, if there is no casing, the critical failure pressure difference of the wellbore wall is 55 MPa. After cementing and perforation, when the casing is uniformly stressed and the formation pressure drops to a coefficient of 0.83, the casing will not be damaged even when the wellbore is completely emptied. At this time, there is still a certain safety production pressure difference in the perforated formation. This study can effectively guide the optimization of well completion and safe development in deep oil reservoirs. Full article