Search Results (110)

Reliable load frequency control (LFC) in interconnected hybrid power systems remains challenging in the presence of nonlinear operating conditions, random demand variations, and parametric uncertainty. This study proposes a PSO-based LFC framework for a two-area hybrid power system and examines its performance through two successive stages. In the first stage, a Particle Swarm Optimization (PSO)-tuned Fuzzy PID controller is developed and benchmarked against reported Fuzzy-PIDF schemes optimized by MPA and COR. In the second stage, three PSO-tuned hybrid fuzzy structures, namely Fuzzy PI-PD + PID, Fuzzy (PI + PD) + PID, and Fuzzy PI + Fuzzy PD + PID, are formulated and comparatively assessed under identical operating conditions. The examined cases include nominal linear operation, Governor Dead Band (GDB) and Generation Rate Constraint (GRC) nonlinearities, random load disturbance, and seven parametric uncertainty scenarios. In the first stage, the PSO-tuned Fuzzy PID controller attains an ITAE of 0.00003433 under linear conditions and 0.00003822 under GDB/GRC nonlinearities, while yielding lower cumulative error than the benchmark controllers. In the second stage, the Fuzzy PI-PD + PID structure records the lowest ITAE and the shortest settling time, with ITAE = 0.000003655 and ST = 0.4234 s under nominal conditions, and ITAE = 0.000004063 and ST = 0.4519 s under nonlinear conditions. Under parametric uncertainty, its ITAE ranges from 2.482 × 10⁻⁶ to 4.833 × 10⁻⁶ with the nominal gains retained. Overall, the results indicate that the proposed PSO-based framework provides improved LFC performance within the examined linear, nonlinear, random-disturbance, and parametric-uncertainty scenarios for the studied two-area hybrid power system. Full article

Photovoltaic (PV)-storage systems operating in weak grids are affected by high grid impedance, transient voltage disturbances, and measurement noise, which can degrade frequency regulation, increase converter current stress, and impose high-frequency current fluctuations on the battery. To address these issues, this paper proposes a multi-timescale transient-state sensing and signal-processing framework for grid-forming PV-hybrid storage systems. The proposed framework combines three coordinated functions. First, a frequency-domain HESS power-decoupling mechanism separates high-frequency transient power components and assigns them to the supercapacitor, while the battery mainly handles low-frequency energy variations. Second, a voltage-deviation-driven adaptive virtual inductance is introduced to increase the equivalent output impedance during voltage-sag events and reduce transient inrush current. Third, a noise-resilient frequency sensing strategy based on a filtered frequency derivative and a dead-band for false-trigger suppression is developed to reduce noise-induced false triggering in adaptive inertia and damping control. Comparative simulations indicate that under the tested weak-grid conditions, the proposed method reduces the transient inrush-current peak by 53.2%, decreases the maximum dynamic frequency deviation by approximately 75%, and improves the active-power regulation speed by more than 50%. These results indicate that the proposed sensing-oriented framework can improve transient response while reducing converter and battery current stress in PV-storage systems connected to high-impedance grids. Full article

Global land use is changing rapidly, particularly in the tropics, where human populations have had relatively high growth rates in recent decades. This has resulted in wildlife increasingly living in or using anthropogenic environments, which often have different thermal properties in comparison to natural habitats. For example, materials used for buildings, such as concrete and brick, typically absorb, retain and radiate more heat than vegetated surfaces. The mosaic of man-made and natural areas formed when anthropogenic environments expand is therefore likely to generate microhabitats with different thermal properties. Here, we investigated the association between microhabitats and the body surface temperature of wild banded mongooses (Mungos mungo), a social mammal living in equatorial Uganda. After controlling for the significant effects of air temperature, humidity, time of day and body contact, we found that mongooses had the highest body surface temperatures when present on anthropogenic substrates, such as discarded roofing straw and refuse, while mongooses present on building materials, dead vegetation and bare soil had intermediate body surface temperatures. In contrast, mongooses had the lowest body surface temperatures when present in more natural, vegetated habitats. Although our study is relatively small scale and limited in scope, our results indicate that anthropogenic modifications to natural environments may result in hotter microhabitats, which may in turn impact space use, movement and thermoregulation in wildlife. We hope that our study encourages further research into this understudied but emerging topic. Full article

Hydraulic manipulators face critical challenges due to valve dead-band nonlinearity and state constraints, which can lead to safety hazards and hardware damage. This study proposes a state-constrained controller with valve dead-band compensation to ensure prescribed positioning accuracy and operational safety. Barrier Lyapunov functions ensure that state constraints are maintained and that boundary violations are avoided. Concurrently, a smooth dead-band inverse model is developed to offset asymmetric valve dead-band effects without inducing chatter. Adaptive laws estimate uncertain parameters and dead-band impact in real time, and a disturbance observer attenuates unmatched uncertainties. Dynamic surface control is employed to diminish the explosion of complexity in backstepping design. Comparative simulations under fixed-angle and arbitrary-angle tracking demonstrate that the proposed controller achieves superior tracking accuracy with steady-state errors below 0.04° compared to 0.06° for non-compensated controllers, while significantly reducing pressure fluctuations and control chattering as adaptive parameters converge. The results indicate that the strategy effectively compensates for valve dead zones while strictly maintaining state constraints, thereby achieving the required control precision for hydraulic servo systems. Full article

Fascioloidosis is a parasitic disease caused by allochthonous parasite Fascioloides magna. In Europe, three types of final hosts are recognised: definitive, aberrant, and dead end. Several countries have launched disease control programmes using medicated feed, with different drugs, to control F. magna infections. In this study, we used corn treated with Albix^® 10 in a total dose of 60 mg/kg of body weight for five consecutive days (12 mg/kg per day). Following successful treatment, a destroyed pseudocyst with different amounts of degrading material and decaying flukes was detected. A total of 136 livers was examined. The average number of pseudocysts per positive liver was seven (min. 1–max. 45), while the average number of adult flukes was 14.17 (2–70). On average, 1.34 juvenile flukes in the migratory phase were detected per infected liver. The average number of pseudocysts was 7.07 per liver in total. Degrading pseudocysts were either absent or present to a maximum of 120 per liver, with an average of 7.99 per liver. Some livers had multifocal to confluent nodules bulging from the liver parenchyma, which were up to 7 cm in diameter. Histologically, these areas showed disruption, containing bands of fibrous connective tissue, dividing parenchyma into pseudolobules of varying size and shape. These septa contained dark brown to black pigment (iron porphyrin), along with remnants of elliptical, operculated, mainly empty trematode eggs. Nodules were surrounded with fibrous tissue and disorganised hyperplastic hepatocytes arranged in irregular trabeculae supported by fibrous bands occasionally containing blood vessels. This study shows the potential of liver regeneration in the case of acute and chronic liver injury, as well as in cases of fatty liver disease. Full article

At temperatures higher than 5 °C in the cooling chambers of refrigeration systems, bacteria multiply rapidly on fresh fishes, thereby leading to an increased risk of foodborne diseases. Maintaining the storage temperature within the recommended bounds of 0 °C and 5 °C is needed to maintain food safety and quality. This study presents model predictive control of a data-driven medium-temperature cold storage system using subspace system identification techniques. The identified linear model presents a holistic view of the whole system, with each subsystem cohesively linked together. The data-driven model was developed from synthetic data derived from a high-fidelity simulation benchmark model of a supermarket refrigeration system from Aalborg University, Denmark. The benchmark model consists of a medium-temperature closed display case, the suction manifold, and the compressor rack. The data of the expansion valve, suction pressure, compressor capacity, heat transfer rate, and ambient temperature were taken as inputs, while the data of the air and goods temperatures were taken as outputs based on expert knowledge. A linear model predictive controller was designed to control the temperature outputs of the identified linear model, and the outputs were compared with the proportional–integral dead band control used in the benchmark. Simulation results for 24 h showed that the model predictive controller was able to achieve an air temperature and a goods temperature within the recommended temperature range of 0 °C and 5 °C that guarantees safe storage of fresh fishes. These results imply that a reduced-order model of a commercial refrigeration system that is robust, reliable, and stable can be developed and controlled to achieve the goal of food safety, thereby guaranteeing food security and reducing costs. Full article

Maintaining frequency regulation in interconnected power systems becomes increasingly difficult in the presence of nonlinear operating conditions. To address this issue, this study develops a hybrid load frequency control scheme in which a fuzzy fractional-order FOPI–FOPD controller is incorporated within a PIDF framework for a two-area LFC system. The controller parameters are optimized using the Dwarf Mongoose Optimization Algorithm (DMOA) and the Catch Fish Optimization Algorithm (CFOA), while the Integral of Time-Weighted Absolute Error (ITAE) is adopted as the performance criterion. The proposed strategy is examined under both linear and nonlinear scenarios, including the effects of Governor Dead Band (GDB) and Generation Rate Constraints (GRC). In the linear case, the DMOA-based design achieves an ITAE of 0.02939 with a tie-line settling time of 13.5478 s, whereas the CFOA-based design produces a bounded and convergent response with an ITAE of 0.03937 and a settling time of 14.4947 s. When GDB nonlinearity is introduced, the DMOA-tuned controller exhibits performance deterioration, yielding an ITAE of 0.1098 and a settling time of 19.0416 s, while the CFOA-tuned design shows more favorable time-domain performance with a lower ITAE of 0.05845 and a bounded settling time of 16.3595 s. These findings indicate that the CFOA-optimized PIDF–Fuzzy FOPI–FOPD controller provides an effective LFC solution under the examined nonlinear operating conditions. Full article

The GCOM-C satellite possesses optimal wavelength bands around 530 nm and 570 nm for monitoring seasonal variations in the photochemical reflectance index (PRI) and chlorophyll–carotenoid index (CCI), which are sensitive to carotenoid contents and its ratio to chlorophyll contents, respectively. As well as NDVI, these indices are excellent indicators for monitoring pigment contents of evergreen trees in winter, which are considered susceptible to climate change impacts. In this study, to investigate the characteristics and usefulness of the GCOM-C-derived indices, the seasonal variations in these indices were analyzed between 2018 and 2024 at two evergreen forest sites in Japan, and compared to CCI and NDVI derived from MODIS, which also has a band near 530 nm. The satellite observation results show that the decreases in all indices for both satellites in winter were observed in the order of PRI, CCI, NDVI. This is thought to indicate that carotenoid contents increased in response to the decrease in land surface temperature to mitigate low-temperature stress, followed by a delayed decrease in chlorophyll contents. GCOM-C showed 0.1 larger NDVI values and 0.2 larger CCI values than MODIS, and the difference was estimated to be largely influenced by the disparity in sensor sensitivity in the red bands. The dispersion of each index was reduced by using data with small sensor zenith angles (below 20 degrees for GCOM-C and 0 to 30 degrees for MODIS); however, MODIS showed a decline in observation accuracy due to satellite drifting in 2024. Spectral measurements of leaves collected at the site also showed similar VI decreases; however, the satellite-derived CCI were 0.12 lower, suggesting that reflection from dead leaves influences the satellite data. This study confirmed that GCOM-C, which can measure both PRI and CCI with high spatial resolution, is suitable for observing seasonal variations in carotenoid and chlorophyll contents in evergreen forests. Full article

Indoor positioning in hospitals is challenging because global navigation satellite systems signals are unavailable and existing solutions struggle with complex indoor propagation and high maintenance requirements. Fingerprinting-based methods using Wi-Fi, Bluetooth Low Energy (BLE), or magnetic field depend on extensive site surveys, while time or angle-based systems such as ultra-wide band, angle of arrival, and Wi-Fi round trip time require additional infrastructure. Recent machine learning approaches improve performance but remain limited by Pedestrian Dead Reckoning (PDR) drift and unstable spatial representations. This study proposes an AI-generated spatial pattern matching framework that integrates an AI-based PDR model with BLE Received Signal Strength Indicator (RSSI) to construct a user RSSI surface. Spatial similarity between user-generated patterns and the pre-built radio map is evaluated using Surface Correlation (SC), and a bi-directional candidate generation strategy with SC-based heading correction is employed to mitigate inertial drift. Experiments in a real hospital setting show that the proposed method achieves robust and accurate localization even in complex indoor environments where conventional fingerprinting and PDR techniques often fail. The results indicate that combining AI-driven inertial modeling with SC-based spatial pattern matching offers a practical and infrastructure-friendly solution for hospital indoor positioning. Full article

Wound dressing coverages (WDC) play a key role in protecting skin lesions and preventing infection. Polymeric membranes have been widely explored as WDC due to their ability to incorporate bioactive agents, including antimicrobial nanoparticles and non-steroidal anti-inflammatory drugs (NSAIDs). In this study, polycaprolactone (PCL)-based membranes functionalized with titanium dioxide nanoparticles (TiO₂ NPs) and ibuprofen (IBP) were fabricated using a film manufacturing approach, and their structural and biocompatibility profiles were evaluated. The membranes were characterized by SEM, FTIR and XPS. Bands at 1725 cm⁻¹, 2950 cm⁻¹, 2955 cm⁻¹, 2865 cm⁻¹ and 510 cm⁻¹ proved molecular stability of reagents during manufacture. In SEM, the control shows the flattest surface, while the PCL-IBP and PCL-IBP-TiO₂ NPs groups had increased rugosity. In vitro biocompatibility was evaluated using human fetal osteoblasts (hFOB). On day 3, the cell adhesion response of hFOB seeded in PCL-IBP and PCL-IBP-TiO₂ NPs groups showed the biggest absorbances (p = 0.0014 and p = 0.0491, respectively). On day 7 PCL-IBP group had lower lectin binding than the control (p = 0.007) and the PCL-IBP-TiO₂ NPs (p = 0.015) membranes, but no evidence of cytotoxicity was observed in any group. Furthermore, the Live/Dead test adds more biocompatibility evidence to conveniently discriminate between live and dead cells. The PCL polymeric membrane elaborated in this study may confer antiseptic, analgesic and anti-inflammatory properties, making these membranes ideal for skin lesions. Full article

In indoor environments, fusion localization methods that combine Wi-Fi fingerprinting and Pedestrian Dead Reckoning (PDR) are constrained by the high sensitivity of traditional filters, such as the Extended Kalman Filter (EKF), to initial states and by their susceptibility to nonlinear drift. This study presents a Wi-Fi/PDR fusion localization approach based on global geometric alignment optimized via a Genetic Algorithm (GA). The proposed method models the PDR trajectory as an integrated geometric entity and performs a global search for the optimal two-dimensional similarity transformation that aligns it with discrete Wi-Fi observations, thereby eliminating dependence on precise initial conditions and mitigating multipath noise. Experiments conducted in a real office environment (14 × 9 m, eight dual-band APs) with a double-L trajectory demonstrate that the proposed GA fusion achieves the lowest mean error of 0.878 m (compared to 2.890 m, 1.277 m, and 1.193 m for Wi-Fi, PDR, and EKF fusion, respectively) and an RMSE of 0.978 m. It also attains the best trajectory fidelity (DTW = 0.390 m, improving by 71.0%, 14.7%, and 27.8%) and the smallest maximum deviation (Hausdorff = 1.904 m, 52.4% lower than Wi-Fi). The cumulative error distribution shows that 90% of GA fusion errors are within 1.5 m, outperforming EKF and PDR. Additional experiments that compare the proposed GA optimizer with Levenberg–Marquardt (LM), particle swarm optimization (PSO), and Procrustes alignment, as well as tests with 30% artificial Wi-Fi outliers, further confirm the robustness of the Huber-based cost and the effectiveness of the global optimization framework. These results indicate that the proposed GA-based fusion method achieves high robustness and accuracy in the tested office-scale scenario and demonstrate its potential as a practical multi-sensor fusion approach for indoor localization. Full article

Sperm cryopreservation produces a series of physicochemical phenomena that negatively impact the function and structure of spermatozoa, including the mobilization of cholesterol from the plasma membrane. The use of attenuated total reflection–Fourier-transform infrared spectroscopy (ATR-FTIR) may be useful to measure the cholesterol efflux in goat spermatozoa. Therefore, the objective of this study was to standardize the use of ATR-FTIR to measure the efflux of cholesterol in goat spermatozoa. Standardization of the technique was carried out in three stages: (i) determination of the appropriate sperm concentration to detect cholesterol in the FTIR spectrum; (ii) determination of the minimum percentage of viable spermatozoa required to observe at least five spectral bands in common with pure cholesterol; (iii) assessment of cholesterol removal in frozen–thawed spermatozoa. Possible differences in the areas of the spectral bands were compared by one-way ANOVA. Nineteen spectra were obtained: pure cholesterol, sperm transport medium, five different sperm concentrations, and ten live/dead sperm proportions (heat and cold-killed). The lowest sperm concentration at which spectral bands were clearly identified was 13 × 10⁶ sperm/mL. Regarding viability, the cut-off value was 50%: higher values produced spectral bands clearly detectable, whereas in smaller values, the band’s areas decreased sharply, making it difficult to quantify them. Five areas of the cholesterol bands decreased in thawed samples compared to fresh spermatozoa; an increase in the proportion of frozen–thawed sperm showing Merocyanine brilliant pattern, indicative of high fluidity, as well as an increase in the proportion of CTC AR pattern, indicative of acrosome reaction, support those results. In conclusion, ATR-FTIR is a useful technique for identifying the movement of cholesterol in goat buck spermatozoa. Full article

The responsibility for risk assessment and user safety in forested and recreational areas lies with the property owner. This study shows that unmanned aerial vehicles (UAVs), combined with remote sensing and GIS analysis, effectively support the identification of high-risk trees, particularly those with reduced structural stability. UAV-based surveys successfully detect 78% of dead or declining trees identified during ground inspections, while significantly reducing labor and enabling large-area assessments within a short timeframe. The study covered an area of 6.69 ha with 51 reference trees assessed on the ground. Although the multispectral camera also recorded the red-edge band, it was not included in the present analysis. Compared to traditional ground-based surveys, the UAV-based approach reduced fieldwork time by approx. 20–30% and labor costs by approx. 15–20%. Orthomosaics generated from images captured by commercial multispectral drones (e.g., DJI Mavic 3 Multispectral) provide essential information on tree condition, especially mortality indicators. UAV data collection is fast and relatively low-cost but requires equipment capable of capturing high-resolution imagery in specific spectral bands, particularly near-infrared (NIR). The findings suggest that UAV-based monitoring can enhance the efficiency of large-scale inspections. However, ground-based verification remains necessary in high-traffic areas where safety is critical. Integrating UAV technologies with GIS supports the development of risk management strategies aligned with the principles of precision forestry, enabling sustainable, more proactive and efficient monitoring of tree-related hazards. Full article

This study investigates the primary frequency regulation dynamic characteristics of industrial steam extraction turbine units under deep peak regulation conditions. A high-fidelity integrated dynamic model was established, incorporating the governor system, steam turbine with extraction modules, and interconnected pipeline dynamics. Through comparative simulations and experimental validation, the model demonstrates high accuracy in replicating real-unit responses to frequency disturbances. For the power grid system in this study, the frequency disturbance mainly comes from three aspects: first, the power imbalance formed by the random mutation of the load side and the intermittence of new energy power generation; second, transformation of the energy structure directly reduces the available frequency modulation resources; third, the system-equivalent inertia collapse effect caused by the integration of high permeability new energy; the rotational inertia provided by the traditional synchronous unit is significantly reduced. In the cogeneration unit and its control system in Guangxi involved in this article, key findings reveal that increased peak regulation depth (30~50% rated power) exacerbates nonlinear fluctuations. This is due to boiler combustion stability thresholds and steam pressure variations. Key parameters—dead band, power limit, and droop coefficient—have coupled effects on performance. Specifically, too much dead band (>0.10 Hz) reduces sensitivity; likewise, too high a power limit (>4.44%) leads to overshoot and slow recovery. The robustness of parameter configurations is further validated under source-load random-intermittent coupling disturbances, highlighting enhanced anti-interference capability. By constructing a coordinated control model of primary frequency modulation, the regulation strategy of boiler and steam turbine linkage is studied, and the optimization interval of frequency modulation dead zone, adjustment coefficient, and frequency modulation limit parameters are quantified. Based on the sensitivity theory, the dynamic influence mechanism of the key control parameters in the main module is analyzed, and the degree of influence of each parameter on the frequency modulation performance is clarified. This research provides theoretical guidance for optimizing frequency regulation strategies in coal-fired units integrated with renewable energy systems. Full article

The combined application of Load Frequency Control (LFC) and Automatic Voltage Regulation (AVR), known as Automatic Generation Control (AGC), manages active and reactive power to ensure system stability. This study presents a novel hybrid controller with a (1+PDD²)-(1+TI) structure, optimized using the Mirage Search Optimization (MSO) algorithm. Designed for dual-area power systems, the controller enhances both LFC and AVR by coordinating voltage and frequency loops. MSO was chosen after outperforming five algorithms (ChOA, DOA, PSO, GTO, and GBO), achieving the lowest fitness value (ITSE = 0.028). The controller was tested under various challenging conditions: sudden load disturbances, stochastic variations, nonlinearities like Generation Rate Constraints (GRC) and Governor Dead Band (GDB), time-varying reference voltages, and ±20% to ±40% parameter deviations. Across all scenarios, the (1+PDD²)-(1+TI) controller consistently outperformed MSO-tuned TID, FOPID, FOPI-PIDD², (1+PD)-PID, and conventional PID controllers. It demonstrated superior performance in regulating frequency, tie-line power, and voltage, achieving approximately a 50% improvement in dynamic response. MATLAB/SIMULINK results confirm its effectiveness in enhancing overall system stability. Full article