Search Results (16)

Background: Dyspeptic symptoms are very common in the general population, with an estimated prevalence of 14% to 41%, and the majority of patients experience symptoms without an organic cause for them (so-called functional dyspepsia). While the pathophysiology of functional dyspepsia remains elusive, the impact of functional dyspepsia on quality of life is detrimental. The treatment involves a change in lifestyle—a healthy diet and physical activity—in combination with pharmacological treatments. However, currently, there is no standard therapy for this condition, although a nutritional approach appears to be feasible and well accepted by patients. In this context, the intake of some mineral water types might be able to play an important role. Objective: The aim of the present study was to evaluate whether the regular intake of bicarbonate natural mineral water (Aqua Carpatica from source “F2 Păltiniș”) is able to positively influence the symptomatic process of dyspepsia, promoting digestion and improving the quality of life of patients. Methods: The patients enrolled in this open-label study had diagnosis of functional dyspepsia formulated in accordance with the Rome III criteria. During the 4-week study, patients had to ingest tap water at 1.5 L/day (wash-out period: 2 weeks), and in the 2-week subsequent period, they had to ingest alkaline natural mineral water at 1.5 L/day. The primary efficacy endpoint of this study includes a statistically significant improvement (p < 0.05) in the “heartburn”, “regurgitation”, and “dyspepsia” subscales and the total Reflux Disease Questionnaire’s (RDQ) score with respect to the effects of alkaline natural mineral water. As secondary endpoints, we considered statistically significant improvements (p < 0.05) in quality-of-life scores (Psychological General Well-Being Index—Short form; PGWB-S), the patient’s self-assessment of the state of efficiency of their digestion, and their sense of post-prandial fullness, as well as the use of antacids. Results: Forty-five patients were enrolled: all were Caucasian and mostly women (25, 55.6% vs. 20 men, 44.4%), and they were aged between 25 and 75 years (50.6 ± 13.5 years; mean ± SD). The results of this preliminary study show a significant improvement in functional dyspepsia symptoms—as assessed via the RDQ—and an improvement in quality of life—as assessed using the PGWB-S score—after the intake of alkaline natural mineral water. Conclusions: This preliminary study provides clinical evidence for a recommendation of alkaline natural mineral water as a symptomatic treatment of dyspepsia. Full article

Background: Immediate post-extraction dental implants are increasingly popular, but ensuring primary stability and managing peri-implant tissues remain challenging. Implant macro-design significantly impacts stability and osseointegration. This study used Cone-beam Computed Tomography (CBCT) to evaluate changes in alveolar bone following immediate placement of two implant designs, System 2P and Dura-Vit 3P, which feature semi-conical microgeometry and apical self-tapping portions for improved stability and bone regeneration. Methods: With a 1:1 allocation ratio, the current investigation was a two-arm parallel group randomized clinical trial. Patients qualified if they required immediate dental replacements with adequate buccal bone support. Two types of implants were placed: System 2P (cylindrical shape) and Dura-Vit 3P (more conical shape, with a particular architecture of threads). Following the intervention, CBCT was performed both immediately (T1) and six months later (T2). Measurements of CBCT horizontal bone level at apical, medial, and bevel height on the palatal/lingual and vestibular sides as well as the buccal vertical gap were the primary results. Complications, implant stability quotient (ISQ), and torque insertion were evaluated. The Mann–Whitney test was used to determine time-based differences within each group, while the Wilcoxon test was used to estimate differences between groups. The impact of baseline marginal gap dimension and gingival biotype was estimated using multiple regressions. Results: Thirty patients were recruited and randomized to treatments, with two lost to follow-up. One System 2P implant failed and two patients of the Dura-Vit 3P group dropped out. At T1, the Dura-Vit 3P group exhibited a lower mean insertion torque and a higher ISQ than the System 2P group. Furthermore, the Dura-Vit 3P group showed lower bone reduction compared to System 2P at horizontal and vertical measurements with significant differences for the vestibular and palatal base and medial level (p-values < 0.05). Regression models indicated a positive effect of thick biotypes on gap filling and dimensional bone reduction. No complications were observed in both groups. Conclusions: The Dura-Vit 3P implant exhibits high primary stability when inserted in post-extraction sites. Furthermore, this kind of implant stimulates higher bone stability on both the palatal and buccal side when compared to the System 2P implant. The present findings support the evidence that the macro-design of the Dura-Vit 3P implant promotes increased primary stability and reduces bone loss. Full article

The integration of photovoltaic (PV) power generation systems has significantly increased the complexity of voltage distribution in power grids, making it challenging for conventional Load Ratio Control Transformers (LRTs) to manage voltage fluctuations caused by weather-dependent PV output variations. Power Conditioning Systems (PCSs) interconnected with PV installations are increasingly considered for voltage control to address these challenges. This study proposes a Machine Learning (ML)-based control method for sub-transmission grids, integrating long-term LRT tap-changing with short-term reactive power control of PCSs. The approach estimates the voltage at each grid node using a Deep Neural Network (DNN) that processes measurable substation data. Based on these estimated voltages, the method determines optimal LRT tap positions and PCS reactive power outputs using Deep Reinforcement Learning (DRL). This enables real-time voltage monitoring and control using only substation measurements, even in grids without extensive sensor installations, ensuring all node voltages remain within specified limits. To improve the model’s transparency, Shapley Additive Explanation (SHAP), an Explainable AI (XAI) technique, is applied to the DRL model. SHAP enhances interpretability and confirms the effectiveness of the proposed method. Numerical simulations further validate its performance, demonstrating its potential for effective voltage management in modern power grids. Full article

This study aimed to estimate the incidence and risk factors for Enterotoxigenic Escherichia coli (ETEC) diarrhea. This was a prospective cohort study of children recruited in a household census. Children were enrolled if they were 36 months or below. A total of 6828 children were followed up passively for 12 months to detect episodes of ETEC diarrhea. Diarrheal stool samples were tested for ETEC using colony polymerase chain reaction (cPCR). Among the 6828 eligible children enrolled, a total of 1110 presented with at least one episode of diarrhea. The overall incidence of ETEC diarrhea was estimated as 2.47 (95% confidence interval (CI): 2.10–2.92) episodes per 100 child years. Children who were HIV-positive (adjusted Hazard ratio (aHR) = 2.14, 95% CI: 1.14 to 3.99; p = 0.017) and those whose source of drinking water was public tap/borehole/well (aHR = 2.45, 95% CI: 1.48 to 4.06; p < 0.002) were at increased risk of ETEC diarrhea. This study found that children whose mothers have at least senior secondary school education (aHR = 0.49, 95% CI: 0.29 to 0.83; p = 0.008) were at decreased risk of ETEC diarrhea. Our study emphasizes the need for integrated public health strategies focusing on water supply improvement, healthcare for persons living with HIV, and maternal education. Full article

The aim of this study was to evaluate the water quality in two streams of the Valles region of Jalisco, Mexico and fully determine if they are being used as tequila vinasse disposal sites. Three sampling campaigns were carried out at eight different points of the two streams that run near tequila factories (TFs). Different physicochemical parameters of water quality were measured: chemical oxygen demand (COD); biochemical oxygen demand (BOD₅); total suspended solids (TSSs); total phosphates; fats, oils, and grease (FOG); Kjeldal nitrogen; nitrite; nitrate; pH; conductivity; temperature; dissolved oxygen (DO); and turbidity. Also, the analysis of samples of tequila vinasses (TVs) diluted with tap water were carried out to have a reference for the level of pollution in the streams. Furthermore, due to the fact that COD could be considered the main indicator of pollution with TVs, a linear regression was performed between COD concentrations and the percentage of dilution of TVs (with tap water). A positive correlation was found between these two variables, and based on this analysis, the vinasse content was estimated at each sampling point of the streams. It was found that on average, a volume of 8.5 ± 6.3% and 11.5 ± 4.9% of TVs were present in each sampling point of the Atizcoa and Jarritos Streams, respectively. Additionally, it was found that, in general, the concentration of pollutants increased as the streams passed through the TFs, particularly the Atizcoa Stream. According to the Water National Commission criteria, most of the points would be classified as highly polluted, since they reach concentrations of COD and BOD₅ up to 6590 mg/L and 3775 mg/L, respectively, temperature values up to 37 °C, and DO values of 0.5 mg/L. Therefore, it was confirmed that the streams are being used as tequila vinasse disposal sites. Due to the above, there is an urgent need for tequila companies to implement treatment systems for the vinasse generated, since under current conditions, the monitored streams are practically devoid of aquatic life. Full article

Simultaneous localization and mapping (SLAM) in rubber plantations is a challenging task for rubber-tapping robots. Due to the long-term stability of tree trunks in rubber plantations, a SLAM system based on semantic segmentation, called Se-LOAM, is proposed in this work. The 3D lidar point cloud datasets of trunks collected in rubber plantations of Hainan University are used to train the semantic model, and the model is used to extract features of trunk point clouds. After clustering the trunk point clouds, each single rubber tree instance is segmented based on the Viterbi algorithm. The point clouds of tree instances are fitted to the cylindrical trunk models for semantic cluster association and positional estimation, which are used for lidar odometry and mapping. The experimental results show that the present SLAM system is accurate in establishing online mapping, and the location of the trunk in the map is clearer. Specifically, the average relative pose error is 0.02 m, which is better than the positioning performance of LOAM and LeGO-LOAM. The average error of estimating the diameter at breast height (DBH) is 0.57 cm, and it only takes 401.4 kB to store a map of the area of approximately 500 ${\mathrm{m}}^2$, which is about 10% less than other classic methods. Therefore, Se-LOAM can meet the requirements of online mapping, providing a robust SLAM method for rubber-tapping robots. Full article

Hand pose estimation (HPE) plays an important role during the functional assessment of the hand and in potential rehabilitation. It is a challenge to predict the pose of the hand conveniently and accurately during functional tasks, and this limits the application of HPE. In this paper, we propose a novel architecture of a shifted attention regression network (SARN) to perform HPE. Given a depth image, SARN first predicts the spatial relationships between points in the depth image and a group of hand keypoints that determine the pose of the hand. Then, SARN uses these spatial relationships to infer the 3D position of each hand keypoint. To verify the effectiveness of the proposed method, we conducted experiments on three open-source datasets of 3D hand poses: NYU, ICVL, and MSRA. The proposed method achieved state-of-the-art performance with 7.32 mm, 5.91 mm, and 7.17 mm of mean error at the hand keypoints, i.e., mean Euclidean distance between the predicted and ground-truth hand keypoint positions. Additionally, to test the feasibility of SARN in hand movement recognition, a hand movement dataset of 26K depth images from 17 healthy subjects was constructed based on the finger tapping test, an important component of neurological exams administered to Parkinson’s patients. Each image was annotated with the tips of the index finger and the thumb. For this dataset, the proposed method achieved a mean error of 2.99 mm at the hand keypoints and comparable performance on three task-specific metrics: the distance, velocity, and acceleration of the relative movement of the two fingertips. Results on the open-source datasets demonstrated the effectiveness of the proposed method, and results on our finger tapping dataset validated its potential for applications in functional task characterization. Full article

In recent years, the emerging fear of an energy crisis in central Europe has caused an increased demand for distributed energy resources (DER), especially small photovoltaic rooftop installations up to 10 kWp. From a technical point of view, distributed PV in low-voltage networks is associated with the risk of power quality violation, overvoltage, voltage unbalance, harmonics, and violation of the thermal limit of phase conductors, neutral conductors, and transformers. Distribution system operators (DSO) are currently in a position to determine the amount of installed PV power for which reliable and safe network operation is ensured, also known as the photovoltaic hosting capacity (PVHC). The presented study describes a stochastic methodology for PVHC estimation and uses it to analyze a typical LV rural network in the Slovak Republic. Detailed and precise calculations are performed on the 4-wire LV model with accurate results. In this study, we, thus, profoundly analyze the problems with voltage violation, unbalanced voltage energy losses, and the thermal loading effect of increasing PV penetration. The results show that overvoltage events are the main factor limiting the PVHC in LV systems. This conclusion is in accordance with the experience of the DSO in the Slovak and Czech Republic. Subsequently, the study focuses on the possibilities of increasing PVHC using those tools typically available for DSO, such as changes in PV inverter power factors and no-load tap changer transformers. The results are compared with those derived from similar analyses, but we ultimately find that the proposed solution is problematic due to the high variability of approaches and boundary conditions. In conclusion, the paper discusses the issue of the acceptable risk of overvoltage violation in the context of PVHC and lowering losses in LV networks. Full article

Wind power can significantly contribute to the transition from fossil fuels to renewable energies. Airborne Wind Energy (AWE) technology is one of the approaches to tapping the power of high-altitude wind. The main purpose of a ground-based kite power system is to estimate the tether force for autonomous operations. The tether force of a particular kite depends on the wind velocity and the kite’s orientation to the wind vector in the figure-eight trajectory. In this paper, we present an experimental measurement of the pulling force of an Airush Lithium 12 ${\mathrm{m}}^2$ kite with a constant tether length of 24 m in a coastal region. We obtain the position and orientation data of the kite from the sensors mounted on the kite. The flight dynamics of the kite are studied using multiple field tests under steady and turbulent wind conditions. We propose a physical model (PM) using Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) deep neural network algorithms to estimate the tether force in the experimental validation. The performance study using the root mean square error (RMSE) method shows that the LSTM model performs better, with overall error values of 126 N and 168 N under steady and turbulent wind conditions. Full article

Two methods for estimating the near-optimal positions and sizes of capacitors in radial distribution networks are presented. The first model assumes fixed-size capacitors, while the second model assumes controllable variable-size capacitors by changing the tap positions. In the second model, we limit the number of changes in capacitor size. In both approaches, the models consider many load scenarios and aim to obtain better voltage profiles by minimizing voltage deviations and active power losses. We use two recently developed heuristic algorithms called Salp Swarm Optimization algorithm (SSA) and Dragonfly algorithm (DA) to solve the proposed optimization models. We performed numerical simulations using data by modifying an actual distribution network in Almaty, Kazakhstan. To mimic various load scenarios, we start with the baseline load values and produce random variations. For the first model, the optimization algorithms identify the near-optimal positioning and sizes of fixed-size capacitors. Since the second model assumes variable-size capacitors, the algorithms also decide the tap positions for this case. Comparative analysis of the heuristic algorithms shows that the DA and SSA algorithms give similar results in solving the two optimization models: the former gives a slightly better voltage profile and lower active power losses. Full article

In this study, an intelligent computing paradigm built on a nonlinear autoregressive exogenous (NARX) feedback neural network model with the strength of deep learning is presented for accurate state estimation of an underwater passive target. In underwater scenarios, real-time motion parameters of passive objects are usually extracted with nonlinear filtering techniques. In filtering algorithms, nonlinear passive measurements are associated with linear kinetics of the target, governing by state space methodology. To improve tracking accuracy, effective feature estimation and minimizing position error of dynamic passive objects, the strength of NARX based supervised learning is exploited. Dynamic artificial neural networks, which contain tapped delay lines, are suitable for predicting the future state of the underwater passive object. Neural networks-based intelligence computing is effectively applied for estimating the real-time actual state of a passive moving object, which follows a semi-curved path. Performance analysis of NARX based neural networks is evaluated for six different scenarios of standard deviation of white Gaussian measurement noise by following bearings only tracking phenomena. Root mean square error between estimated and real position of the passive target in rectangular coordinates is computed for evaluating the worth of the proposed NARX feedback neural network scheme. The Monte Carlo simulations are conducted and the results certify the capability of the intelligence computing over conventional nonlinear filtering algorithms such as spherical radial cubature Kalman filter and unscented Kalman filter for given state estimation model. Full article

This paper describes a methodology for implementing the state estimation and enhancing the accuracy in large-scale power systems that partially depend on variable renewable energy resources. To determine the actual states of electricity grids, including those of wind and solar power systems, the proposed state estimation method adopts a fast-decoupled weighted least square approach based on the architecture of application common database. Renewable energy modeling is considered on the basis of the point of data acquisition, the type of renewable energy, and the voltage level of the bus-connected renewable energy. Moreover, the proposed algorithm performs accurate bad data processing using inner and outer functions. The inner function is applied to the largest normalized residue method to process the bad data detection, identification and adjustment. While the outer function is analyzed whether the identified bad measurements exceed the condition of Kirchhoff’s current law. In addition, to decrease the topology and measurement errors associated with transformers, a connectivity model is proposed for transformers that use switching devices, and a transformer error processing technique is proposed using a simple heuristic method. To verify the performance of the proposed methodology, we performed comprehensive tests based on a modified IEEE 18-bus test system and a large-scale power system that utilizes renewable energy. Full article

The French national public health agency (Santé publique France) has used data from the national health insurance reimbursement system (SNDS) to identify medicalised acute gastroenteritis (mAGE) for more than 10 years. This paper presents the method developed to evaluate this system: performance and characteristics of the discriminatory algorithm, portability in mainland and overseas French departments, and verification of the mAGE database updating process. Pharmacy surveys with certified mAGE from 2012 to 2015 were used to characterise mAGE and to estimate the sensitivity and predictive positive value (PPV) of the algorithm. Prescription characteristics from these pharmacy surveys and from 2014 SNDS prescriptions in six mainland and overseas departments were compared. The sensitivity (0.90) and PPV (0.82) did not vary according to the age of the population or year. Prescription characteristics were similar within all studied departments. This confirms that the algorithm can be used in all French departments, for both paediatric and adult populations, with stability and durability over time. The algorithm can identify mAGE cases at a municipal level. The validated system has been implemented in a national waterborne disease outbreaks surveillance system since 2019 with the aim of improving the prevention of infectious disease risk attributable to localised tap water systems. Full article

The main trough is a part of the blast furnace process for hot metal and molten slag transportation from the tap hole to the torpedo, and mechanical erosion of the trough is an important reason for a short life of a campaign. This article employed OpenFoam code to numerically study and analyze velocity, temperature and wall shear stress of the fluids in the main trough during a full tapping process. In the code, a three-dimensional transient mass, momentum and energy conservation equations, including the standard k-ε turbulence model, were developed for the fluid in the trough. Temperature distribution in refractory is solved by the Fourier equation through conjugate heat transfer with the fluid in the trough. Change velocities of the fluid during the full tapping process are exactly described by a parabolic equation. The investigation results show that there are strong turbulences at the area of hot metal’s falling position and the turbulences have influence on velocity, temperature and wall shear stress of the fluid. With the increase of the angle of the tap hole, the wall shear stress increases. Mechanical erosion of the trough has the smallest value and the campaign of the main trough is estimated to expand over 5 days at the tap hole angle of 7°. Full article

Despite the well-known positive consequences of transformational coaches in sport, there is still little research exploring the mechanisms through which coaches’ transformational leadership exerts its impact on athletes. Multilevel SEM was used to examine the relationship between coaches’ transformational leadership style, a task-involving climate, and leadership effectiveness outcome criteria (i.e., players’ extra effort, coach effectiveness, and satisfaction with their coach), separately estimating between and within effects. A representative sample of 625 Spanish male soccer players ranging from 16 to 18 years old and nested in 50 teams completed a questionnaire package tapping the variables of interest. Results confirmed that at the team level, team perceptions of transformational leadership positively predicted teams’ perceptions of task climate, which in turn positively predicted the three outcome criteria. At the individual level, players’ perceptions of transformational leadership positively predicted teams’ perceptions of task climate, which in turn positively predicted teams’ extra effort and coach effectiveness. Mediation effects appeared at the team level for all the outcome criteria, and at the individual only for extra effort. Transformational leadership is recommended to enhance task climate, in order to increase players’ extra effort, their perceptions of the effectiveness of their coach, and their satisfaction with his/her leadership style. Full article