Success Ratio Research Articles

A Computationally Efficient Neuronal Model for Collision Detection with Contrast Polarity-Specific Feed-Forward Inhibition

Animals utilize their well-evolved dynamic vision systems to perceive and evade collision threats. Driven by biological research, bio-inspired models based on lobula giant movement detectors (LGMDs) address certain gaps in constructing artificial collision-detecting vision systems with robust selectivity, offering reliable, low-cost, and miniaturized collision sensors across various scenes. Recent progress in neuroscience has revealed the energetic advantages of dendritic arrangements presynaptic to the LGMDs, which receive contrast polarity-specific signals on separate dendritic fields. Specifically, feed-forward inhibitory inputs arise from parallel ON/OFF pathways interacting with excitation. However, none of the previous research has investigated the evolution of a computational LGMD model with feed-forward inhibition (FFI) separated by opposite polarity. This study fills this vacancy by presenting an optimized neuronal model where FFI is divided into ON/OFF channels, each with distinct synaptic connections. To align with the energy efficiency of biological systems, we introduce an activation function associated with neural computation of FFI and interactions between local excitation and lateral inhibition within ON/OFF channels, ignoring non-active signal processing. This approach significantly improves the time efficiency of the LGMD model, focusing only on substantial luminance changes in image streams. The proposed neuronal model not only accelerates visual processing in relatively stationary scenes but also maintains robust selectivity to ON/OFF-contrast looming stimuli. Additionally, it can suppress translational motion to a moderate extent. Comparative testing with state-of-the-art based on ON/OFF channels was conducted systematically using a range of visual stimuli, including indoor structured and complex outdoor scenes. The results demonstrated significant time savings in silico while retaining original collision selectivity. Furthermore, the optimized model was implemented in the embedded vision system of a micro-mobile robot, achieving the highest success ratio of collision avoidance at 97.51% while nearly halving the processing time compared with previous models. This highlights a robust and parsimonious collision-sensing mode that effectively addresses real-world challenges.

Massive MIMO uplink and downlink joint representation based on couple dictionary learning

AbstractThe challenge of jointly representing both the uplink (UL) and downlink (DL) in massive multiple input multiple output (MIMO) systems have been tackled. Considering the angular reciprocity, a couple dictionary learning (CDL) support to enhance performance and address high complexity has been introduced. This approach minimizes the number of pilots and improves accuracy. Currently, accuracy analysis of UL/DL representation primarily relies on simulation. To bridge this gap, a proportion factor (PF) operator is proposed for CDL to assess accuracy. Specifically, a qualitative analysis formula is provided for accuracy and an optimal upper bound is established. Through theoretical proof, it is demonstrated that the accuracy of CDL for representation is mainly influenced by the cross‐correlation between the pilot matrix and the dictionary matrix. Inspired by PF operator, an optimal couple dictionary learning (OCDL) algorithm using singular value decomposition (SVD) is introduced to obtain dictionary updating, aiming at high‐precision representation. By establishing normalized mean squared error (NMSE), successful representation ratio, bit error rate (BER), and constellation performance, an OCDL algorithm that outperforms existing methods is showcased and channel representation accuracy is enhanced significantly.

Experimental simulation of the ancient production of gold granules

Granulation is an ancient and sophisticated decorative technique. The production of granules is a crucial part of this process but has rarely been studied. The present study employed three techniques—pouring method, heating method, and crucible method—to produce gold granules. The success ratio of granule formation, granule surface morphology, microstructure, and formation were analyzed to identify the techniques used in archaeological objects. The cooling medium significantly influenced small granule formation, microstructure, and grain size. Both heating and crucible methods could control the granule formation, but these methods produced distinct microstructures. Based on these experimental granules, the probable production methods of ancient gold balls were identified. The present study provided microscopic information for determining ancient gold granule production techniques.

Performance of large language artificial intelligence models on solving restorative dentistry and endodontics student assessments

ObjectivesThe advent of artificial intelligence (AI) and large language model (LLM)-based AI applications (LLMAs) has tremendous implications for our society. This study analyzed the performance of LLMAs on solving restorative dentistry and endodontics (RDE) student assessment questions.Materials and methods151 questions from a RDE question pool were prepared for prompting using LLMAs from OpenAI (ChatGPT-3.5,-4.0 and -4.0o) and Google (Gemini 1.0). Multiple-choice questions were sorted into four question subcategories, entered into LLMAs and answers recorded for analysis. P-value and chi-square statistical analyses were performed using Python 3.9.16.ResultsThe total answer accuracy of ChatGPT-4.0o was the highest, followed by ChatGPT-4.0, Gemini 1.0 and ChatGPT-3.5 (72%, 62%, 44% and 25%, respectively) with significant differences between all LLMAs except GPT-4.0 models. The performance on subcategories direct restorations and caries was the highest, followed by indirect restorations and endodontics.ConclusionsOverall, there are large performance differences among LLMAs. Only the ChatGPT-4 models achieved a success ratio that could be used with caution to support the dental academic curriculum.Clinical relevanceWhile LLMAs could support clinicians to answer dental field-related questions, this capacity depends strongly on the employed model. The most performant model ChatGPT-4.0o achieved acceptable accuracy rates in some subject sub-categories analyzed.

Assessing the phenology and reproductive output of loggerhead turtles in relation to climatic variables at Patara Beach, Türkiye

AbstractLoggerhead turtles (Caretta caretta), being ectothermic organisms, could be especially susceptible to climate change effects, and may exhibit climate‐related variation in their reproductive behaviours such as phenology, annual nest numbers, clutch size, hatching success, incubation period and sex ratio. This study investigated the reproductive phenology and outputs of loggerhead turtles and their relationships with climatic variables over a 5‐year period (2019–2023) at Patara Beach, Türkiye. We found significant fluctuations in atmospheric temperature, sea surface temperature and relative humidity, and that female turtle emergences on Patara Beach could temporarily adjust their phenology in response to these minor environmental changes. We highlight the importance of understanding the impacts of phenological shifts on the ability to satisfy the conditions over the nesting season that determine reproductive output. Our statistical analyses also showed that increasing sea water temperatures and atmospheric temperatures, as well as decreasing precipitation and relative humidity, had direct and/or indirect effects on the nesting phenology and reproductive output of loggerhead turtles. The findings from this study indicate that atmospheric temperature significantly affected incubation period, hatching success rate, the number of dead embryos and the number of empty eggshells. Additionally, relative humidity had a significant impact on the incubation period and the number of empty eggshells. In this context, rising temperatures led to drier nest conditions, decreased incubation periods and increased nest temperatures, resulting in higher proportions of female offspring. In conclusion, there are still gaps in our understanding of the effects of climate change on the reproductive biology of loggerhead turtles, and more studies are needed at both the Mediterranean and global scales to better understand these effects.

SROR: A Secure and Reliable Opportunistic Routing for VANETs

In Vehicular Ad Hoc Networks (VANETs), high mobility of vehicles issues a huge challenge to the reliability and security of transmitting packets. Therefore, a Secure and Reliable Opportunistic Routing (SROR) is proposed in this paper. During construction of Candidate Forwarding Nodes (CFNs) set, the relative velocity, connectivity probability, and packet forwarding ratio are taken into consideration. The aim of SROR is to maximally improve the packet delivery ratio as well as reduce the end-to-end delay. The selection of a relay node from CFNs is formalized as a Markov Decision Process (MDP) optimization. The SROR algorithm extracts useful knowledge from historical behavior of nodes by interacting with the environment. This useful knowledge are utilized to select the relay node as well as to prevent the malicious nodes from forwarding packets. In addition, the influence of different learning rate and exploratory factor policy on rewards of agents are analyzed. The experimental results show that the performance of SROR outperforms the benchmarks in terms of the packet delivery ratio, end-to-end delay, and attack success ratio. As vehicle density ranges from 10 to 50 and percentage of malicious vehicles is fixed at 10%, the average of packet delivery ratio, end-to-end delay, and attack success ratio are 0.82, 0.26s, and 0.37, respectively, outperforming benchmark protocols.

Forecasting crude oil price: A deep forest ensemble approach

We made an application of a cutting-edge machine learning method, the deep forest ensemble approach (DFEA, Zhou and Feng (2017, 2019)), to empirically predict crude oil prices. We used a large data set with 36 explanatory variables to compare the predictability of the DFEA with seven popular machine learning models and their mean combination method. The out-of-sample forecasting results showed that the DFEA statistically and economically outperforms all the competing models in terms of out-of-sample R square and success ratio. The DFEA also displayed sizable certainty equivalent return (CER) gains for a mean-variance investor in practice from an asset allocation perspective. Furthermore, we found that the predictive power of the DFEA stems from technical indicators, especially momentum predictors. Our results survived in various robustness checks.

Statistical and machine learning models for location-specific crop yield prediction using weather indices.

Crop yield prediction gains growing importance for all stakeholders in agriculture. Since the growth and development of crops are fully connected with many weather factors, it is inevitable to incorporate meteorological information into yield prediction mechanism. The changes in climate-yield relationship are more pronounced at a local level than across relatively large regions. Hence, district or sub-region-level modeling may be an appropriate approach. To obtain a location- and crop-specific model, different models with different functional forms have to be explored. This systematic review aims to discuss research papers related to statistical and machine-learning models commonly used to predict crop yield using weather factors. It was found that Artificial Neural Network (ANN) and Multiple Linear Regression were the most applied models. Support Vector Regression (SVR) model has a high success ratio as it performed well in most of the cases. The optimization options in ANN and SVR models allow us to tune models to specific patterns of association between weather conditions of a location and crop yield. ANN model can be trained using different activation functions with optimized learning rate and number of hidden layer neurons. Similarly, the SVR model can be trained with different kernel functions and various combinations of hyperparameters. Penalized regression models namely, LASSO and Elastic Net are better alternatives to simple linear regression. The nonlinear machine learning models namely, SVR and ANN were found to perform better in most of the cases which indicates there exists a nonlinear complex association between crop yield and weather factors.

Analysis of Instruments for Implementing Intelligent Job Matching Models

Vocational high schools in Indonesia face challenges in producing graduates who meet the needs of the job market. The link and match policy along with the Job Matching system are proposed to enhance curriculum relevance to the workforce. The intelligent job matching model, which integrates machine learning technology, is expected to provide job recommendations aligned with graduates' competencies. Data analysis techniques used include qualitative and quantitative statistical analysis, encompassing Validity Analysis, Practicality Analysis, and Effectiveness Analysis. The intelligent job matching model has proven effective in assisting in matching vocational high school students' competencies with workforce demands in the industry. In the 2023 job fair, the success ratio of students invited for interviews until the employment process reached 53.13%, exceeding the Ministry of education and culture's target of 40%. This indicates that the model can provide job recommendations accurately enough, aiding in improving the direct employment rate of vocational high school students. Research indicates that the intelligent job matching model is effective in facilitating students' transition to the workforce. The model has been tested for validity, practicality, and effectiveness in enhancing students' employability and strengthening the relationship between education and industry.

BRIEF UNDERSTANDING OF CHARACTERISTICS DEPLETED RESERVOIR (INTERVAL-I) USING PORE PRESSURE AND FRACTURE GRADIENT ANALYSIS IN "X" FIELD, SANGA-SANGA AXIS, KUTAI BASIN

Field X, as this study area's focus, boasts over 200 wells proven to be hydrocarbon producers. Two pore pressure regimes exist: normal hydrostatic and overpressure. The primary hydrocarbon reservoir in the Balikpapan Formation within the Sanga- sanga axis at Field X consists of sandstone. Drilling disturbances, particularly in the lower intervals of this reservoir, often occur due to overpressure. The main challenge encountered in this reservoir is mud losses. The objective of this study is to develop the Pore Pressure and Fracture Gradient characteristics and its relation to the geological environment. Furthermore, the distribution of Sand (Interval-I) is determined, which is the primary cause of losses due to production. Besides that, the parameters used to create optimal fracture gradient is also defined. Thirteen wells in this field were examined to identify the controlling factors of pore pressure. This study integrates wireline logging, velocity, mud logs, pressure tests, drilling parameters and event which are subsequently processed for determining shale points, Normal Compaction Trend (NCT), calculating overburden gradient, and estimating pore pressure using the Eaton method. From the analysis result, the distribution of overpressure and the generating mechanism of overpressure are determined. This study also carried out analysis to determine overpressure and depleted Sandstone (Interval-I) distribution. Subsequently, facies and depositional environtment analysis are conducted, followed by modelling Sandstone (Interval-I) and determining Sandstone’s poisson ratio from loss data and shale’s from leak of test data for fracture gradient estimation. Overpressure is found in Interval-I with magnitude of 4000-4700 psi, which corresponds to delta plain to delta front depositional environment. Top of overpressure is observed shallower in the southern than northern due to geological structure conditions. The generating mechanism of overpressure is caused by loading and hydrocarbon generation. The experienced loss in overpressure zone, caused by reservoir production from the initial pressure of 4475 psi and has depleted to 373 psi. From the sandstone distribution model, the loss sandstones are a connected fluvial channel in southern ward. It is also observed several unconnected channel sandstones that did not experience loss. Poisson ratio parameter for sandstone is 0.35, which is calibrated from the loss event with minimum fracture gradient in Interval-I is approximately 4772 psi. It is expected that the understanding of pore pressure and Sandstone (Interval-I) distribution could be used to increase the success ratio for optimal drilling planning, including to choose effective casing design, mud weight, and appropriate total depth.

Fast and accurate distal locking of interlocked intramedullary nails using computer-vision and a 3D printed device

IntroductionDistal locking is a challenging and time-consuming step in interlocked intramedullary nailing of long bone fractures. Current methods have limitations in terms of simplicity, universality, accuracy, speed, and safety. We propose a novel device and software for distal locking using computer vision.Methods and materialsThe device consists of an universal ancillary clamp, a telescopic arm, a viewfinder clamp, and a radio-opaque cross. The software uses a camera photo from the C-arm intensifier and adjusts for geometric projection deformities. The software employs edge detection, Hough transform, perspective interpolation, and vector calculation algorithms to locate the distal hole center. The device and software were designed, manufactured, and tested using 3D CAD, FEM, DRR, and performance testing on phantom bones.ResultsThe device and software showed high accuracy and precision of 98.7% and 99.2% respectively in locating the distal hole center and calculating the correctional vector. The device and software also showed high success ratio in drilling the hole and inserting the screw. The device and software reduced the radiation exposure for the surgeon and the patient. The success ratio of the device and software was validated by the physical testing, which simulated the real clinical scenario of distal locking. The radiation exposure was as low as 5 s with a radiation dose of 0.2mSv, drastically reducing radiation exposure during distal locking.DiscussionOur device and software have several advantages over other distal locking methods, such as simplicity, universality, accuracy, speed, and safety. Our device and software also have some disadvantages, such as reliability and legislation. Our device and software can be compared with other distal locking methods based on these criteria. Our device and software have some limitations and challenges that need to be addressed in the future, such as clinical validation, and regulatory approval.ConclusionThe device showed promising results in terms of low-cost, reusability, low radiation exposure, high accuracy, fast distal locking, high stiffness, and adaptability. The device has several advantages over other distal locking techniques, such as free-hand technique, mechanical aiming devices, electromagnetic navigation systems, and computer-assisted systems. We believe that our device and software have the potential to revolutionize the distal locking technique and to improve the outcomes and quality of life of the patients with long bone fractures.

Quantifying predation on benthos and its overlap with bottom fishing in the NW Atlantic

AbstractContinental shelves experience many human pressures with demersal fisheries central to disturbing the ocean floor. However, ecological processes such as predation rates of benthos and their relationship with bottom fishing are often unknown for large marine ecosystems. We examined the amount of benthos consumed by 14 benthivorous fishes, the overlap between benthos predation and bottom fishing (dredge and trawl gear), and temporal trends in benthivorous fish abundance and the number of fishing trips on the northeast US continental shelf. Mean annual predation (biomass of prey removed) and 95% confidence intervals ranged from .0002 (.0001–.0003) to 3967 (1761–7112) t per 10‐min area squared grid cell and prey taxa for these benthivorous fishes. Predation and bottom fishing had overlapping footprints of activity, which were slightly stronger for dredge gear. Trophic success (ratio of prey biomass eaten to the biomass of the benthivore community per grid cell) revealed more Bivalvia, Gammaridea, and Polychaeta eaten in areas targeted by trawling with more fish biomass. In contrast, dredging did not target fish biomass, but these areas had increased (Echinoidea, Gammaridea, and other benthos) or decreased (Ophiuroidea) trophic success relative to dredging footprint, suggesting habitat preferences for benthic prey and demersal fisheries have converged rather than diverged. Despite declines in bottom fishing, recent increases in benthivorous fish abundance and growing interest in ocean floor use suggest that fisheries managers should ensure benthivores have sufficient prey resources relative to their community size and human pressures to promote long‐term sustainability of demersal fisheries and healthy ecosystems.

Posterior Cervical Foraminotomy Compared with Anterior Cervical Discectomy with Fusion for Cervical Radiculopathy: Two-Year Results of the FACET Randomized Noninferiority Study.

Posterior cervical foraminotomy (posterior surgery) is a valid alternative to anterior discectomy with fusion (anterior surgery) as a surgical treatment of cervical radiculopathy, but the quality of evidence has been limited. The purpose of this study was to compare the clinical outcome of these treatments after 2 years of follow-up. We hypothesized that posterior surgery would be noninferior to anterior surgery. This multicenter, randomized, noninferiority trial assessed patients with single-level cervical radiculopathy in 9 Dutch hospitals with a follow-up duration of 2 years. The primary outcomes measured reduction of cervical radicular pain and were the success ratio based on the Odom criteria, and arm pain and decrease in arm pain, evaluated with the visual analog scale, with a 10% noninferiority margin, which represents the maximum acceptable difference between the new treatment (posterior surgery) and the standard treatment (anterior surgery), beyond which the new treatment would be considered clinically unacceptable. The secondary outcomes were neck pain, Neck Disability Index, Work Ability Index, quality of life, complications (including reoperations), and treatment satisfaction. Generalized linear mixed effects modeling was used for analyses. The study was registered at the Overview of Medical Research in the Netherlands (OMON), formerly the Netherlands Trial Register (NTR5536). From January 2016 to May 2020, 265 patients were randomized (132 to the posterior surgery group and 133 to the anterior surgery group). Among these, 25 did not have the allocated intervention; 11 of these 25 patients had symptom improvement, and the rest of the patients did not have the intervention due to various reasons. At the 2-year follow-up, of 243 patients, primary outcome data were available for 236 patients (97%). Predicted proportions of a successful outcome were 0.81 after posterior surgery and 0.74 after anterior surgery (difference in rate, -0.06 [1-sided 95% confidence interval (CI), -0.02]), indicating the noninferiority of posterior surgery. The between-group difference in arm pain was -2.7 (1-sided 95% CI, 7.4) and the between-group difference in the decrease in arm pain was 1.5 (1-sided 95% CI, 8.2), both confirming the noninferiority of posterior surgery. The secondary outcomes demonstrated small between-group differences. Serious surgery-related adverse events occurred in 9 patients (8%) who underwent posterior surgery, including 9 reoperations, and 11 patients (9%) who underwent anterior surgery, including 7 reoperations (difference in reoperation rate, -0.02 [2-sided 95% CI, -0.09 to 0.05]). This trial demonstrated that, after a 2-year follow-up, posterior surgery was noninferior to anterior surgery with regard to the success rate and arm pain reduction in patients with cervical radiculopathy. Therapeutic Level I. See Instructions for Authors for a complete description of levels of evidence.

Export Potential Index for Textile Industry (EPIT) model proposal with structural equation modelling and application

PurposeThe aim of this study is to develop a new multidimensional index to measure export potential of textile firms by using firm-level data.Design/methodology/approachAfter a conceptual model, a structural equation model is developed with five dimensions and 27 observed variables based on resource-based view theory. The measurement model is solved by Linear Structural Relations (LISREL) with maximum likelihood algorithm by using data collected from 454 textile firms in Türkiye.FindingsIn this study, a new multidimensional index that measures export potential of textile firms is developed. With the proposed model, the export potential of textile firms can be calculated numerically with the five dimensions: Resources, Dynamism, Knowledge, Innovation and Sustainability. The comparison of the output of the proposed model with the control variable, firm’s actual export values, shows a significantly high success ratio of 90.76%.Research limitations/implicationsThe model is applicable for textile firms at different export levels, regions and sub-sectors. The Export Potential Index for Textile Industry model is verified by using Turkish textile industry data. The robustness of the model may be increased by verifying the model by using some other countries data. This model can be implemented to other industrial sectors with some modification of the dimensions and variables.Practical implicationsThe proposed model will contribute to the firms by calculating their export potential in five dimensions with their own variables numerically. The model will help firms to develop strategies to increase their export potential and to the governmental and industrial organizations to develop incentives policies.Originality/valueThis paper fills the gap in the literature by proposing a multidimensional index that determines a firm’s export potential numerically by using firm-level data.

Observation of highly correlated ultrabright biphotons through increased atomic ensemble density in spontaneous four-wave mixing

The pairing ratio, a crucial metric assessing a biphoton source's ability to generate correlated photon pairs, remains underexplored despite theoretical predictions. This study presents experimental findings on the pairing ratio, utilizing a double-Λ spontaneous four-wave mixing biphoton source in cold atoms. At an optical depth (OD) of 20, we achieved an ultrahigh biphoton generation rate of up to 1.3×107 per second, with a successful pairing ratio of 61%. Increasing the OD to 120 significantly improved the pairing ratio to 89%, while maintaining a consistent biphoton generation rate. This achievement, marked by high generation rates and robust biphoton pairing, holds great promise for advancing efficiency in quantum communication and information processing. Additionally, in a scenario with a lower biphoton generation rate of 5.0×104 per second, we attained an impressive signal-to-background ratio of 241 for the biphoton wavepacket, surpassing the Cauchy-Schwarz criterion by approximately 1.5×104 times. Published by the American Physical Society 2024

Research on Privacy Issues in Smart Metering System: An Improved TCN-Based NILM Attack Method and Practical DRL-Based Rechargeable Battery Assisted Privacy Preserving Method

Smart meters, as a key component of Advanced Metering Infrastructure (AMI), collect fine-grained electricity consumption data for demand response in smart grids. While this data improves the grid’s accuracy, it also poses significant threats to users’ privacy. In this paper, we study the privacy issue in smart metering systems from both attacker and defender perspectives. First, we propose an improved Temporal Convolutional Network (TCN) based Non-Intrusive Load Monitoring (NILM) attack method, which infers electrical appliance usage from public load curves, addressing the gradient vanishing, gradient exploding, and other problems while improving attack accuracy. Second, we develop a rechargeable battery-assisted energy management system to hide load characteristics of electrical appliances by adding physical noise, thus resisting NILM attacks. To address the privacy-cost trade-off optimization problem, we propose a Practical Deep Reinforcement Learning-based Rechargeable Battery assist Privacy Preserving Method (PRoP) that learns optimal battery charging/discharging policies. We design a novel privacy measurement method and constraints to ensure the feasibility of system deployment and prove PRoP’s effectiveness in resisting NILM attacks. Comprehensive evaluations demonstrate that our improved TCN-based NILM method achieves an attack success rate of over 80% on various electrical appliances, improving attack performance (MAE, RMSE) by 20% compared to existing methods while reducing model training time. Moreover, our proposed PRoP achieves a better trade-off between privacy protection and electricity cost than existing battery-assisted methods, reducing costs by 5% and the attack success ratio to 36%, while increasing the MAE and RMAE obtained by NILM by 3 times. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Smart grid, which can support bidirectional information transmission, has a series of advantages, such as high efficiency and high stability. However, it also brings a significant threat to users’ electricity privacy. Although encryption-based privacy protection methods have been deployed on terminal devices of smart grids to prevent privacy leaks, this method can often only defend against intrusive attacks and has little effect on non-intrusive attacks. To this end, this paper studies the privacy issues caused by non-intrusive attacks. Specifically, to better study the protection method, we first investigate the attack mechanism and design an improved TCN-based Non-Intrusive Load Monitoring method. Then, we propose a Practical Reinforcement learning-based rechargeable battery-assisted Privacy preserving method (PRoP) to defend against this attack physically. The most practical contribution of this paper is that, compared with existing battery-assisted privacy protection methods, we do not blindly pursue algorithm performance but fully consider the practical factors of deployment, such as limiting battery capacity and constraining battery charging and discharging behavior. This can guide practitioners to better apply this technology in practice.

Holmium laser enucleation of the prostate (HoLEP) in short-circuit outpatient care: Is prostatic volume a limiting factor?

Our goals were to study prostatic volume as a limiting factor after HoLEP surgery with short-circuit outpatient care (4 h) and to define other factors that affect the success of the proposed circuit. An observational analysis and review was performed using a prospective database. Preoperative, intraoperative, and postoperative variables were included for patients who were scheduled for short-circuit outpatient care (SCOC) and who underwent HoLEP between 2020 and 2023. We defined SCOC as a postoperative hospital stay of 4 h. Subjects who required more than 4 h in hospital were categorized as conventional hospital admission (CHA). A descriptive populational study was conducted, expressing the mean using a 95% confidence interval and percentages for the continuous variables. In order to analyze them, we used the Student's t-test for the continuous variables and the chi-squared test for the categorical variables. Sixty-eight patients were included, 54 of which completed SCOC, which represented a success ratio of 79.5%. The mean age and prostatic volume of the whole cohort were 68.9 (±6.8) years and 79.5 (±29.1) mL, respectively. We found no significant differences in age, prostatic volume, antiplatelet drug use, indwelling bladder catheter, or applied energy among the subjects who completed SCOC and those who required CHA. No patient was presented with a complication of Grade 3 (or higher) in the modified Clavien-Dindo classification. At the six-month follow-up, no differences were observed in the uroflowmetry or International Prostate Symptoms Score variables. Prostatic volume does not seem to be a limiting factor after undergoing HoLEP with short-circuit outpatient care.

Amplifying IT Project Success Ratio: the Role of Transformational Leadership, Proactive Behavior, and Psychological Empowerment

Amplifying IT Project Success Ratio: the Role of Transformational Leadership, Proactive Behavior, and Psychological Empowerment

Epidemic dynamics edge caching strategy for 6G networks

By caching popular content on edge servers closer to users to respond to users’ content requests in 6G networks, the transmission load of backhaul links can be reduced. However, the time-varying characteristics of content prevalence leads to the issue that the cache content may not match the user’s needs, resulting in a decrease in cache success ratio. To solve these issues, we proposed a cache distribution strategy based on epidemic dynamics (CDSED) for 6G edge network. First, a 6G edge caching content model (6G ECCM) is constructed to establish the process of cache content propagation among users as an infectious disease propagation process, analyze the distribution of users’ interest in cache content and obtain the cache content state probability prediction equation, and use the cache content state probability prediction equation to predict the cache content prevalence. Second, based on the predicted prevalence results, a prevalence predictive genetic-annealing cache content algorithm (PGAC) is proposed with the optimization objective of maximizing the cache success ratio. The algorithm designs the selection function of the traditional genetic algorithm as a simulated annealing selection function based on the cache content success ratio, which avoids the defect of the genetic algorithm that converges to the locally optimum cache strategy too early and enhances the cache success ratio. Finally, the optimum cache content decision is solved by iterative alternation. Simulation results demonstrate that CDSED strategy can enhance cache success ratio than the LRU strategy, the LFU strategy, and the MPC strategy.

Simultaneously Estimation of Multicomponent Containing Formulation by using UV-Spectroscopy [Aspirin-Atorvastatin

The market is currently offering a wide range of combination dose forms, and the quantity is growing daily. Due to their improved potency, various actions, speedier relief, fewer side effects, and higher patient acceptability, these multicomponent formulations are becoming more and more popular. It is therefore intended that these formulations satisfy all requirements for their efficacy, safety, and quality. This is only feasible if many analytical methods are available to determine them. When doing a simultaneous multicomponent analysis, various UV spectrophotometric techniques are applied.These techniques rely on logging and processing absorption spectra computationally. The various techniques covered in this review include the use of simultaneous equations, derivative spectrophotometry, mean centring of the ratio spectra, absorption factor method, double divisor ratio spectra derivative method, successive ratio derivative spectra, Q-absorbance ratio method, isosbestic point method, absorpitivity factor method, dual wavelength method, mean centring of the ratio spectra, and multivariate methods. A synopsis of the theories and a few uses for these techniques are given.