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Purpose To compare the refractive power profile, subjective depth-of-field and objective optical quality of two advanced monofocal intraocular lenses (IOLs) designed to improve intermediate vision. Methods This prospective study evaluated forty-six eyes of twenty-three patients, aged 54–68 years, binocularly implanted with two monofocal enhanced intraocular lenses (IOLs), the Tecnis Eyhance and the Physiol Isopure. Subjective through-focus visual acuity curves were obtained by placing trial lenses in front of the eye while wearing its best spherical-cylindrical correction for distance. Objective optical quality was defined as the area under the modulation transfer function, calculated from the wavefront maps measured with a high-resolution aberrometer. The optical design of both lenses was compared based on their refractive power profiles measured with the lenses immersed in saline solution. Results Both lenses have progressive aspherical geometries, in which the sagittal power decreases rapidly from the center to the edge of the optical zone. Mean monocular through-focus curves show a best corrected distance visual acuity of − 0.02 logMAR with both lenses. Through-focus visual acuity was marginally higher for the Eyhance, with a difference of 1 letter at the defocus position of − 0.5D and 3 letters between − 1.0D and − 2.0D. Objective assessment of optical quality revealed only a difference of about 2 points in MTF area at distance. Conclusion Both IOLs use a similar approach to improve intermediate vision. The Eyhance showed marginally better subjective performance than the Isopure at the target vergences between − 1.00D and − 2.00D, although these results did not reach statistical significance and were not replicated by the objective findings.

To investigate the influence of road gradient on cycling power output in male professional cyclists, and to determine whether cyclist typology (i.e., flat or climbing specialist) moderates this influence. Observational study. Ninety-eight professional cyclists (27 ± 6 years; 53 flat and 45 climbing specialists). We collected power output data during both training sessions and competitions over 10 years (2013–2022). We determined the maximal mean power values attained for efforts lasting 1, 5, 10 and 20 min, during both level cycling and uphill cycling (average slope< or ≥5 %, respectively), as well as the average road gradients on which cyclists attained their maximal mean power. Maximal mean power values were higher during uphill cycling than during level cycling for all effort durations (difference ranging between 0.4 and 3.6 %, all p < 0.003). This finding was confirmed for flat and uphill specialists separately (p < 0.003 for both), with a similar increase in maximal mean power values between level cycling and uphill cycling in the two typologies except for longer efforts (≥10 min), in which maximal mean power values tended to increase more in climbers. Participants attained maximal mean power at an average slope of 6.0–7.3 %, with no differences between effort durations or cyclist typologies. Professional cyclists attain higher maximal mean power values on steep than on level road gradients regardless of their typology, with an average gradient of 6–7 % appearing optimal (or at least the most common) for achieving the highest maximal mean power values.

Durability is emerging as a key performance determinant in cycling, but scarce evidence exists on the durability of female cyclists, and particularly on whether there are sex differences. We therefore aimed to determine potential sex differences in durability. Observational field-based study. Power output data from training and competitions were registered in female and male professional cyclists (n = 42 each) during 1–5 seasons. Participants' highest power output values achieved for different effort durations (10 s, 1 min, 5 min, and 20 min) (or ‘record power profile’) were determined under non-fatigued conditions (0 kJ/kg) and after varying levels of accumulated work (10, 20 and 30 kJ/kg). A significant reduction in the record power profile compared with non-fatigued conditions was observed after > 10 kJ/kg in both female and male cyclists (p < 0.001), with no significant impairment observed below this level of accumulated work (p > 0.05 for all). A similar relative decay (% decline compared with the fresh condition) was observed between sexes for 10-s efforts (p > 0.05). However, a significantly higher relative decay was observed in female cyclists after 20 kJ/kg for 1-min, 5-min, and 20-min efforts (4 %, 4 % and 2 %, respectively; p < 0.05), with these differences enlarging after 30 kJ/kg (8 %, 6 % and 7 %; p < 0.001). Professional female cyclists show a greater relative decay in the record power profile after a given accumulated work compared to male cyclists, which might reflect a lower durability.

Purpose This study is to evaluate the optical characteristics of a non-diffractive wavefront-shaping intraocular lens which incorporates surface refractive modifications for shaping the wavefront in order to achieve extended depth of focus (EDoF) and to assess whether the nominal power of this IOL influences the attainable add power. Methods A commercially available optical bench NIMO TR1504 device (LAMBDA-X, Nivelles, Belgium) was employed to obtain full optical characterization of three non-diffractive EDoF intraocular lenses with + 10 D, + 20 D, and + 30 D powers. After NIMO measurements, data were computed using a custom-made MATLAB program (Mathworks, Inc., Natick, MA, USA) to evaluate the optical quality functions, such as the point spread function (PSF), wavefront profiles, and modulation transfer function (MTF) for two pupil sizes: 3 mm and 4.0 mm. Results The non-diffractive EDoF intraocular lens showed a central serrated power profile behavior with additions of + 2.00 to + 2.50 D over the nominal power. Higher order aberrations were found to be driven mainly by the spherical aberration, with almost null comatic influence. Optical quality metrics showed good values, better for a 3 mm pupil compared to a 4.5 mm one, as expected. The three IOL powers tested showed a very similar behavior in terms of power and aberrometric profiles, with minimal to null differences related to the nominal power. Conclusion The non-diffractive wavefront-shaping EDoF intraocular lens achieves a near addition up to + 2.50 D aiming for an extended range of vision, almost independently of the base power.

Climate change mitigation, adaptation to intensifying hurricanes, and decarbonization challenges in developing countries emphasize the urgent need for resilient high-voltage grids to facilitate the expansion of renewables. This research explores the technical feasibility of extending the U.S.–Caribbean Super Grid to include the Virgin Islands, Guadeloupe, Martinique, Trinidad and Tobago, Guyana, Suriname, French Guyana, and the northeastern part of Brazil in South America. This proposed extension aims to capitalize on the recent introduction of a new generation of wind turbines certified for operation under strong hurricane forces. The research utilizes modeling and simulation techniques to evaluate the performance of the proposed extension. A method for modeling and estimating spatiotemporal wind power profiles is applied, and the results demonstrate a reduction in maximum wind power variability within the U.S.–Caribbean Super Grid. Depending on the hurricane trajectory, the variability is reduced from 56.6% to less than 43.2%. This reduction takes effect by distributing peak surplus wind power alongside the proposed U.S.–Caribbean–South America Super Grid (UCASG). The research concludes by acknowledging the merits and limitations of the study and discussing potential directions for future research in this field.

Narrowband-Internet of Things (NB-IoT) displays high-quality connectivity underpinned by fifth-generation (5G) networks to cover a wide array of IoT applications. The devices’ development and integration into different smart systems require permanent control, supervision, and the study of power consumption models to determine the performance of the network topology and allow for the measurement of the efficiency of the network topology’s application. This paper reports on an architecture and open-sourced simulation that was developed to study NB-IoT in Device-to-Device (D2D) mode, which includes the Physical (PHY), network, and application layers, as well as a queuing model, the model for uplink and downlink delays, the throughput, the overall NB-IoT D2D network performance, and the energy consumption based on the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. Our results prove that the suggested framework contributes to a reduction in power consumption, a minimization of queuing delays, a decrease in communication cost, a reduction in inter-cluster collisions, and the prevention of attacks from malicious nodes. Consequently, the framework manages the battery’s State of Charge (SOC), improves the battery’s State of Health (SOH), and maximizes the whole network lifetime. The proposed framework, the code of which has been open-sourced, can be effectively used for scientific research and development purposes to evaluate different parameters and improve the planning of NB-IoT networks.

Solar photovoltaic (PV) generation technology stands out as a scalable and cost-effective solution to enable the transition toward decarbonization. However, PV solar output, beyond the daily solar irradiance variability and unavailability during nights, is very sensitive to weather events like hurricanes. Hurricanes nucleate massive amounts of clouds around their centers, shading hundreds of kilometers in their path, reducing PV power output. This research proposes a spatiotemporal method, implemented in MATLAB R2023b coding, to estimate the shading effect of hurricanes over a wide distribution of PV solar plants connected to a high-voltage power infrastructure called the U.S.–Caribbean–South America super grid. The complete interconnection of the U.S., the Caribbean, and South America results in the lowest power valley levels, i.e., an overall percentual reduction in PV power output caused by hurricane shading. The simulations assess the impact of hurricanes in 10 synthetic trajectories spanning from Texas to Florida. The Caribbean would also experience lower power valleys with expanded interconnectivity schemes. The U.S.–Caribbean–South America super grid reduces Caribbean variability from 37.8% to 8.9% in the case study. The proposed spatiotemporal method for PV power profile estimation is a valuable tool for future solar power generation expansion, transmission planning, and system design considering the impact of hurricanes.

Various power meters are used to assess road-cycling performance in training and competition, but no previous study has analyzed their interchangeability in these conditions. Therefore, the purpose was to compare the data obtained from two different power meters (PowerTap vs. Power2Max) during cycling road races. A national-level under-23 male competitive cyclist completed six road-cycling official competitions (five road races and one individual time trial), in which power output was simultaneously registered with the two power meters. After this, the main power output variables were analyzed with the same software. The average and critical power obtained from the PowerTap power meter were slightly lower than from the Power2Max power meter (3.56 ± 0.68 and 3.62 ± 0.74 W·kg−1, 5.06 and 5.11 W·kg−1, respectively), and the correlations between both devices were very high (r ≥ 0.996 and p < 0.001). In contrast, the PowerTap power meter registered a significantly higher (p < 0.05) percentage of time at <0.75 and >7.50 W·kg−1 and power profile at 1, 5 and 10 s. In conclusion, the data obtained in competitions by the two power meters were interchangeable. Nevertheless, the Power2Max power meter underestimated the pedaling power during short and high-intensity intervals (≤10.0 s and >7.50 W·kg−1) compared to the PowerTap power meter. Therefore, the analysis of these efforts should be treated with caution.

Nowadays, the development of the Internet of Things (IoT) concept has increased the interest in some technologies, one of which is the detection of anomalies in home appliances before they occur. In this work, in order to contribute to the works that use appliance power profiles for anomaly detection, a novel Appliance Monitoring and Anomaly Detection System (AM-ADS) is presented. AM-ADS consists of a main controller, a database, IoT-based communication units, home appliances, and power measurement units (smart plugs or special measurement equipments) mounted on appliances. In AM-ADS, a new Control Chart (CC) based method, for the cases that a limited number of historical power profiles are available; and a new Artificial Neural Network (ANN) based method, for the cases that a sufficient number of historical power profiles of each anomaly free and anomalous situations are available, are used according to the developed rule-based AM-ADS procedure to maximize the advantages and to eliminate the disadvantages of these methods as much as possible. According to the AM-ADS procedure, power consumptions of appliances, which provide meaningful information about the health of appliances, are measured during their operations and the corresponding power profiles are created. Active power, power factor, and operation duration features of power profiles are considered as decisive control parameters and different characteristics of these parameters are used as inputs for CC and ANN-based methods. The efficiency and performance of AM-ADS are validated by application case studies, where the ability to detect anomalies varies between 94.56% and 99.03% when a limited number of historical data is available; and the ability to detect and classify anomalies varies between 96.34% and 99.45% when a sufficient number of historical data is available.

In this paper, a clustering method with proposed distance measurement to extract base load profiles from arbitrary data sets is studied. Recently, smart energy load metering devices are broadly deployed, and an immense volume of data is now collected. However, as this large amount of data has been explosively generated over such a short period of time, the collected data is hardly organized to be employed for study, applications, services, and systems. This paper provides a foundation method to extract base load profiles that can be utilized by power engineers, energy system operators, and researchers for deeper analysis and more advanced technologies. The base load profiles allow them to understand the patterns residing in the load data to discover the greater value. Up to this day, experts with domain knowledge often have done the base load profile realization manually. However, the volume of the data is growing too fast to handle it with the conventional approach. Accordingly, an automated yet precise method to recognize and extract the base power load profiles is studied in this paper. For base load profile extraction, this paper proposes Sample Pearson Correlation Coefficient (SPCC) distance measurement and applies it to Mean-Shift algorithm based nonparametric mode-seeking clustering. The superiority of SPCC distance over traditional Euclidean distance is validated by mathematical and numerical analysis.