Development Of Control Strategies Research Articles

Nonlinear dynamics of a dual-rotor system with active elastic support/dry friction dampers based on complex nonlinear modes

In this study, the nonlinear dynamics of a dual-rotor system with active elastic support/dry friction dampers (ESDFDs) are investigated based on complex nonlinear modes (CNMs). The finite element method (FEM) combined with a full-3D friction model is introduced to construct the governing equation for the system. Additionally, the Craig–Bampton technique is applied to downscale the finite element model of the system. Based on the reduced order model (ROM), the nonlinear modal damping ratio of the target mode is employed to measure the dry friction damping performance of active ESDFD. The effects of the active ESDFD position, normal force, and tangential contact stiffness on the nonlinear modal damping ratio and modal frequency are analysed. Moreover, the softening characteristics of the active ESDFD are revealed, and the critical speed intervals of the active ESDFD/dual-rotor system are determined. Furthermore, by using the harmonic balance–alternating frequency/time domain (HB–AFT) method, the steady-state response of the system under unbalanced excitation is calculated. The accuracy and effectiveness of nonlinear modal analysis are validated based on the relationships between nonlinear modes and steady-state unbalanced responses. Conversely, the vibration mitigation effects of active ESDFD are determined by the unbalanced response amplitude. Additionally, the controllable region and optimal normal force for effective vibration control in the target mode are defined. Depending on the controllable region, a control strategy for turning on/off the optimal normal force is developed. The findings demonstrate that the developed control strategy enables the active ESDFD to significantly reduce the response amplitude of the dual-rotor system across various excitation levels, showing substantial potential for engineering applications.

Unexpected changes in source apportioned results derived from different ambient VOC metrics

Although most source apportionments of VOCs use mixing ratios, about 23 % of published studies use mass concentrations. Thus, systematically exploring the changes in VOC source apportioned results caused by metric differences is important to assess the differences in key precursor apportionment results given the observed increases in O3 pollution situation. Different monitoring instruments measured hourly VOC volumetric concentrations in three typical Chinese cities (i.e., Qingdao, Shijiazhuang, and Zhumadian). Converting volumetric to mass concentrations under standard and/or actual temperature–pressure conditions, VOC values with different metrics were obtained. The impacts of different metrics on the source apportionments were then investigated. Compared to the positive matrix factorization of the volumetric data (VC-PMF), the VOC species concentrations with low relative molecular mass (RMM) in the factor profiles substantially decreased in mass data analyses (MC-PMF). However, those species with high RMM substantially increased. There were no substantial differences in the apportioned source contributions based on standard and actual condition mass concentrations. However, the high-low rankings of percent contributions apportioned using the volumetric and mass data produced substantial differences. Compared with the VC-PMF results, the percent contributions of sources dominated by species with low RMM (e.g., natural gas usage and mixed sources containing natural gas usage) apportioned by MC-PMF decreased, while those of sources that emitted high RMM species (e.g., solvent usage and mixed sources containing solvent usage) increased. Source apportionments based on the volumetric concentration data had more practical significance compared to the mass concentration data results for control strategy development since the mass data analyses created issues.

Biofilm Formation and Antibiotic Resistance Profiles in Carbapenemase-Producing Gram-Negative Rods-A Comparative Analysis between Screening and Pathological Isolates.

(1) Background: Carbapenem-resistant (CR) bacteria pose a significant global public health challenge due to their ability to evade treatment with beta-lactam antibiotics, including carbapenems. This study investigates the biofilm-forming capabilities of CR clinical bacterial isolates and examines the impact of serum on biofilm formation. Additionally, the study evaluates the resistance profiles and genetic markers for carbapenemase production. (2) Methods: Bacterial isolates were collected from the microbiology laboratory of Mures County Clinical Hospital between October 2022 and September 2023. Pharyngeal and rectal swabs were screened for carbapenem-resistant bacteria using selective media. Lower respiratory tract samples were also analyzed for CR Gram-negative bacteria. The isolates were tested for their ability to form biofilms in the presence and absence of fetal bovine serum at 24 and 48 h. Carbapenemase production was detected phenotypically and confirmed via PCR for relevant genes. (3) Results: Out of 846 screened samples, 4.25% from pharyngeal swabs and 6.38% from rectal swabs tested positive for CR bacteria. Acinetobacter baumannii and Klebsiella pneumoniae were the most common species isolated. Biofilm formation varied significantly between clinical isolates and standard strains, with clinical isolates generally showing higher biofilm production. The presence of serum had no significant effect on biofilm formation in Klebsiella spp., but stimulated biofilm formation for Acinetobacter spp. Carbapenemase genes blaKPC, blaOXA-48-like, and blaNDM were detected in various isolates, predominantly in Klebsiella spp., but were not the main determinants of carbapenem resistance, at least in screening isolates. (4) Conclusions: This study highlights the variability in biofilm formation among CR clinical isolates and underscores the differences between the bacteria found as carriage versus infection. Both bacterial species and environmental factors variably influence biofilm formation. These insights are crucial for the development of effective treatment and infection control strategies in clinical settings.

Investigation of conjunctivitis adenovirus spread in human eyes by using bifurcation tool and numerical treatment approach

In order to investigate the dynamics of the system, a mathematical model must be created to comprehend the dynamics of various prevalent diseases worldwide. The purpose of this investigation is to explore the early identification and treatment of conjunctivitis adenovirus by introducing vaccination methods for asymptomatic individuals. A mathematical model is constructed with the aim of strengthening the immune system. The ABC operator is then utilized to convert the model into a fractionally ordered one. The developed system is analyzed with analytical solutions by employing Sumudu transforms, including convergence analysis. The boundedness and uniqueness of the model are investigated using Banach space, which are key properties of such epidemic models. The uniqueness of the system is confirmed to ensure it has a unique solution. The stability of the newly constructed SEVIR system is investigated both qualitatively and statistically, and the system’s flip bifurcation has been verified. The developed system is examined through a Lyapunov function-based local and global stability study. The solution to the system is found using the Atangana-Toufik technique, a sophisticated method for reliable bounded solutions, employing various fractional values. Error analysis has also been conducted for the scheme. Simulations have been carried out to observe the real behavior and effects of the conjunctivitis virus, confirming that individuals with a strong immune response can recover without medication during the acute stage of infection. This helps to understand the real situation regarding the control of conjunctivitis adenovirus after early detection and treatment by introducing vaccination measures due to the strong immune response of the patients. Such investigations are useful for understanding the spread of the disease and for developing control strategies based on the justified outcomes.

Impact of coronavirus disease 2019 on the utilization of hospital services and development of optimal pandemic control strategy in Chinese tertiary hospitals during the Omicron wave

BackgroundThis study aimed to assess the impact of coronavirus disease 2019 (COVID-19) on hospital service utilization and revenue in Chinese tertiary hospitals and develop an optimal pandemic control strategy (OPCS) for the peak period of the Omicron wave.MethodsRetrospective data from three Chinese tertiary hospitals (provincial, city, and county level) were analyzed for three phases: pre-outbreak (Jan-Apr 2019), outbreak (Jan-Apr 2020), and post-outbreak (Jan-Apr 2021). OPCS was developed under the guidance of the China government pandemic control policy during post-break phase of COVID-19. A decision-tree model was constructed to compare OPCS to strict pandemic control strategy during outbreak phase for the hospital service utilization and hospital revenue in a provincial tertiary hospital during the Omicron wave.ResultsOutpatient, emergency room (ER) visits, hospitalizations, and intensive care admissions dropped by 33.8–53.4% during the outbreak, with the provincial hospital being the most affected. Hospital revenue also declined, especially for the provincial hospital (40.1%). Post-outbreak, most services recovered, but ER visits remained lower (11.6% decrease for provincial hospital, 46.5% for county hospital). Total income and expenditure decreased, with the provincial hospital experiencing the most significant revenue reduction (45.7%). OPCS showed greater utilization of medical services (31.6 times more outpatient visits; 1.7 times more inpatient days; 3.4% more surgery volume) and higher revenue (¥220.8 million more) compared to the strict pandemic control strategy.ConclusionsCOVID-19 measures were associated with less hospital service utilization and revenue in Chinese tertiary hospitals. The developed OPCS in Chinese tertiary hospitals, focusing on isolating infected inpatients but not shutting down the hospital facilities exposed to virus, could be effective in optimizing hospital service utilization and hospital revenue during the Omicron wave.

The salivary gland transcriptome of Varroa destructor reveals suitable targets for RNAi-based mite control.

The mite Varroa destructorAnderson and Trueman (Mesostigmata: Varroidae) has a dramatic impact on beekeeping and is one of the main causes of honey bee colony losses. This ectoparasite feeds on honey bees' liquid tissues, through a wound created on the host integument, determining weight loss and a reduction of lifespan, as well as the transmission of viral pathogens. However, despite its importance, the mite feeding strategy and the host regulation role by the salivary secretions have been poorly explored. Here, we contribute to fill this gap by identifying the salivary components of V. destructor, to study their functional importance for mite feeding and survival. The differential expression analysis identified 30 salivary gland genes encoding putatively secreted proteins, among which only 15 were found to be functionally annotated. These latter include proteins with putative anti-bacterial, anti-fungal, cytolytic, digestive and immunosuppressive function. The three most highly transcribed genes, coding for a chitin-binding domain protein, a Kazal domain serine protease inhibitor and a papain-like cysteine protease were selected to study their functional importance by reverse genetics. Knockdown (90%-99%) by RNA interference (RNAi) of the transcript of a chitin-binding domain protein, likely interfering with the immune reaction to facilitate mite feeding, was associated with a 40%-50% decrease of mite survival. This work expands our knowledge of the host regulation and nutritional exploitation strategies adopted by ectoparasites of arthropods and allows the identification of potential targets for RNAi, paving the way towards the development of new strategies for Varroa mite control.

Analytical analysis and bifurcation of pine wilt dynamical transmission with host vector and nonlinear incidence using sustainable fractional approach

To study the dynamical system, it is necessary to formulate a mathematical model to comprehend the dynamics of the diseases that are prevalent around the world by using fractional calculus. A mathematical model is developed with the hypothesis created by adding control and asymptomatic variables to observe the rate of change of pine wilt and the ABC operator is used to turn the model into a fractional ordered model for continuous monitoring. The Boundedness and uniqueness of the developed model are investigated for bounded findings by using Banach space, which are the key properties of such an epidemic model. A newly developed system is examined both qualitatively and quantitatively to determine its stable position, and the verification of flip bifurcation has been made for developed systems. Derived reproductive numbers using the next-generation technique as well as the sensitivity of each involved parameter are verified. The Atangana–Toufik scheme is employed to find the solution for the developed system using different fractional values, which are advanced tools for reliable bounded solutions. Simulations have been made to see the real behavior and effects of pine wilt disease with control and asymptomatic battels in the community. Also, identify the real situation of the spread as well as the control of pine wilt after employing control and asymptomatic battels due to treatment. Such a type of investigation will be useful in investigating the spread of disease as well as helpful in developing control strategies based on our justified outcomes.

Analysis of the relationship between solar greenhouse operation and occupational high incidence diseases

Objective: To understand the health status of solar greenhouse workers, to provide scientific basis for the development of occupational high incidence diseases prevention and control strategies. Methods: In July 2019, a random cluster sampling method was used to select 245 workers engaged in solar greenhouse vegetable cultivation in Daba Village, Jingyuan County, Baiyin City, Gansu Province as the solar greenhouse operation group. Matched by gender, age, marital status, body mass index (BMI), 282 people from adjacent Shaliang Village who did not engage in solar greenhouse operation were selected as the control group. Field investigation and health examination were carried out among the study subjects. The general situation, facial features examination results, ophthalmic examination results, bone and joint examination results and skin examination results were compared between the two groups. And the multiple logistic regression analysis was used to analyze the influencing factors of abnormal bone and joint examination (upper limbs, lower limbs, hands and spine bone joints) in study subjects. Results: There were statistically significant differences in smoking age and alcohol consumption between the two groups (P<0.001). Compared with the control group, the abnormal detection rates of nose examination, throat examination, slit lamp examination, conjunctival examination, lower limb bone joint examination, hand bone joint examination, spine examination, head and neck skin examination, trunk skin examination, upper limb skin examination, and lower limb skin examination in the solar greenhouse operation group were higher, and the differences were statistically significant (χ(2)=11.53, 7.94, 9.92, 27.93, 79.32, 81.42, 9.43, 6.79, 9.76, 4.34, 8.29, P<0.05). Multivariate logistic regression analysis showed that after adjusting for gender, age, marital status, education level, BMI, compared with the control group, solar greenhouse operation was a risk factor for abnormal bone and joint examination (OR=1.178, 95%CI: 1.151-2.143, P=0.001) . Conclusion: Solar greenhouse operation has a certain harmful effect on health of workers, and solar greenhouse workers have an increased risk of abnormal diseases of upper limbs, lower limbs, hands and spine bone joints.

Machine learning-based 3D scan coverage prediction for smart-control applications

Automatic control of a workpiece being manufactured is a requirement to ensure in-line correction and thus move towards a more intelligent manufacturing system. There is therefore a need to develop control strategies which are capable of taking precise account of real working conditions and enabling first-time-right control. As part of such a smart-control strategy, this paper introduces a machine learning-based approach capable of accurately predicting a priori the 3D coverage of a part according to a scan configuration given as input, i.e. predicting before scanning it which areas of the part will be acquired for real. This corresponds to a paradigm shift, where coverage estimation no longer relies on theoretical visibility criteria, but on rules learned from a large amount of data acquired in real-life conditions. The proposed 3D Scan Coverage Prediction Network (3DSCP-Net) is based on a 3D feature encoding and decoding module, which is capable of taking into account the specifics of the scan configuration whose impact on the 3D coverage is to be predicted. To take account of real working conditions, features are extracted at various levels, including geometric ones, but also features characterising the way structured-light projection behaves. The method is thus able to incorporate inter-reflection and overexposure issues into the prediction process. The database used for the training was built using an ad-hoc platform specially designed to enable the automatic acquisition and labelling of numerous point clouds from a wide variety of scan configurations. Experiments on several parts show that the method can efficiently predict the scan coverage, and that it outperforms conventional approaches based on purely theoretical visibility criteria.

Robust and intelligent power control for three-bladed horizontal axis wind turbine system using a real wind profile of the northern Morocco

In this work, a robust and intelligent control approach is developed for three-bladed horizontal axis variable-speed wind turbine (VSWT) operating under real climatic conditions (whether the WT is onshore or offshore). Below the rated power, the main purpose of the controller is to optimize the extraction of energy from wind and ensure the full exploitation of the installation, all while minimizing mechanical stress in the system in the presence of uncertainties and external disturbances. In order to achieve optimized wind energy capture without chattering problems or oscillatory phenomena, this study proposes combining the Integral Sliding Mode Control (ISMC) with an artificial neural network technique such as the Radial Basis Function (RBF) to enhance controller performances. Additionally, the RBF neural network (RBFNN) approach is implemented to estimate the wind turbine (WT) model uncertain part, allowing the use of a lower switching gain to achieve faster convergence and avoid steady-state error. Thus, by means of Lyapunov theory, the stability of the system with this controller can be demonstrated. Simulation results of the proposed Neural Network Integral Sliding Mode Control (NN-ISMC) controller are compared with the usual SMC and the standard ISMC. The comparative analysis indicate a superior efficiency of the developed control strategy in terms of decreasing tracking error and eliminating chattering behavior. Moreover, this approach provides a faster transient system response and higher robustness in presence of uncertainties compared to the other controllers.

Genome Characterization and Phylogenetic Analysis of Scale Drop Disease Virus Isolated from Asian Seabass (Lates calcarifer).

Scale drop disease virus (SDDV), a double-stranded DNA virus in the family Iridoviridae, has been reported widely in southeast Asian countries as a causative agent of scale drop syndrome (SDS) in Asian seabass. SDS has resulted in high mortality and significant economic losses to the aquaculture industry. This study demonstrated the use of metagenomic methods to investigate bacterial and viral communities present in infected fish tissues and recover a complete genome of the causative agent named SDDV TH7_2019. Characterization of the TH7_2019 genome revealed a genome size of 131 kb with 134 putative ORFs encoding viral proteins potentially associated with host apoptosis manipulation. A comparative genome analysis showed a high degree of amino acid identity across SDDV strains, with variations in number of repeat sequences and mutations within core genes. Phylogenetic analyses indicate a close relationship among SDDV genomes. This research enhances our understanding of the genetic diversity and evolutionary relationship of SDDV, contributing valuable insights for further development of effective control strategies of SDDV.

Chromosome-Scale Genome Assembly of the Sheep-Biting Louse Bovicola ovis Using Nanopore Sequencing Data and Pore-C Analysis.

Bovicola ovis, commonly known as the sheep-biting louse, is an ectoparasite that adversely affects the sheep industry. Sheep louse infestation lowers the quality of products, including wool and leather, causing a loss of approximately AUD 123M per annum in Australia alone. The lack of a high-quality genome assembly for the sheep-biting louse, as well as any closely related livestock lice, has hindered the development of louse research and management control tools. In this study, we present the assembly of B. ovis with a genome size of ~123 Mbp based on a nanopore long-read sequencing library and Illumina RNA sequencing, complemented with a chromosome-level scaffolding using the Pore-C multiway chromatin contact dataset. Combining multiple alignment and gene prediction tools, a comprehensive annotation on the assembled B. ovis genome was conducted and recalled 11,810 genes as well as other genomic features including orf, ssr, rRNA and tRNA. A manual curation using alignment with the available closely related louse species, Pediculus humanus, increased the number of annotated genes to 16,024. Overall, this study reported critical genetic resources and biological insights for the advancement of sheep louse research and the development of sustainable control strategies in the sheep industry.

Concurrent event‐triggered adaptive neural control for MASS under cross‐water scenarios

AbstractThis article discusses the control problem of marine autonomous surface ships (MASS) under cross‐water scenarios, that is, from open water to restricted water, where several practical facts, such as uncertain dynamic, unknown disturbance and actuator wear suppression, are taken into account. To resolve such a control design challenge, the predefined performance control (PPC)‐based and barrier Lyapunov function (BLF)‐based ideas are employed, and a prespecified performance function (PPF) is designed to implement the transformation of cross‐water design. Under the adaptive backstepping design framework, with aid of PPC‐based and BLF‐based design ideas, an adaptive neural control solution is developed for MASS under cross‐water scenarios. Furthermore, to reduce the actuator wear and tear caused by high‐frequency corresponding control commands and hull vibration, a new multichannel concurrent event‐triggered protocol (ETP) is constructed in the controller‐actuator (C‐A) channel. Finally, a concurrent event‐triggered adaptive neural control scheme is proposed for MASS under cross‐water scenarios. The theoretical analysis indicates that all signals in the control system are ultimately bounded, and the Zeno behavior is avoided. The simulation and comparison results verify the effectiveness and superiority of the developed control scheme.

The Control of Handling Stability for Four-Wheel Steering Distributed Drive Electric Vehicles Based on a Phase Plane Analysis

For the sake of enhancing the handling and stability of distributed drive electric vehicles (DDEVs) under four-wheel steering (4WS) conditions, this study proposes a novel hierarchical control strategy based on a phase plane analysis. This approach involves a meticulous comparison of the stable region in the phase plane to thoroughly analyze the intricate influence of the front wheel angle, rear wheel angle, road adhesion coefficient, and longitudinal speed on the complex dynamic performances of DDEVs and to accurately determine the critical stable-state parameter. Subsequently, a hierarchical control strategy is presented as an integrated solution to achieve the coordinated control of maneuverability and stability. On the upper control level, a model predictive control (MPC) motion controller is developed, wherein the real-time adjustment of the control weight matrix is ingeniously achieved by incorporating the crucial vehicle stable-state parameter. The lower control level is responsible for the optimal torque allocation among the four wheel motors to minimize the tire load rate, thereby ensuring a sufficient tire grip margin. The optimal torque distribution for the four wheel motors is achieved using a sophisticated two-level allocation algorithm, wherein the friction ellipse is employed as a judgement condition. Finally, this developed control strategy is thoroughly validated through co-simulation utilizing the CarSim 2019 and Simulink 2020b commercial software, demonstrating the validity of the developed control strategy. The comparative results indicate that the presented controller ensures a better tracking capability to the desired vehicle state while exhibiting improved handling stability under both the double lane shifting condition and the serpentine working condition.

Unveiling the Arsenal of Apple Bitter Rot Fungi: Comparative Genomics Identifies Candidate Effectors, CAZymes, and Biosynthetic Gene Clusters in Colletotrichum Species.

The bitter rot of apple is caused by Colletotrichum spp. and is a serious pre-harvest disease that can manifest in postharvest losses on harvested fruit. In this study, we obtained genome sequences from four different species, C. chrysophilum, C. noveboracense, C. nupharicola, and C. fioriniae, that infect apple and cause diseases on other fruits, vegetables, and flowers. Our genomic data were obtained from isolates/species that have not yet been sequenced and represent geographic-specific regions. Genome sequencing allowed for the construction of phylogenetic trees, which corroborated the overall concordance observed in prior MLST studies. Bioinformatic pipelines were used to discover CAZyme, effector, and secondary metabolic (SM) gene clusters in all nine Colletotrichum isolates. We found redundancy and a high level of similarity across species regarding CAZyme classes and predicted cytoplastic and apoplastic effectors. SM gene clusters displayed the most diversity in type and the most common cluster was one that encodes genes involved in the production of alternapyrone. Our study provides a solid platform to identify targets for functional studies that underpin pathogenicity, virulence, and/or quiescence that can be targeted for the development of new control strategies. With these new genomics resources, exploration via omics-based technologies using these isolates will help ascertain the biological underpinnings of their widespread success and observed geographic dominance in specific areas throughout the country.

Observer‐based optimal fault‐tolerant tracking control for input‐constrained interconnected nonlinear systems with mismatched disturbances

SummaryThis paper investigates an observer‐based optimal fault‐tolerant tracking control problem for interconnected nonlinear systems with input constraints and mismatched disturbances via adaptive dynamic programming (ADP). Firstly, an augmented system consisting of tracking error and the reference trajectory is constructed, and then the original optimal tracking control problem is transformed into the optimal regulation control problem of the augmented system. An integral sliding‐mode‐based optimal fault‐tolerant control scheme is developed to eliminate the effect of actuator faults and guarantee the optimal control performance. Furthermore, a neural network‐based observer is designed to identify the completely unknown system dynamics based on the input‐output data, thereby relaxing the restriction on system dynamics. Subsequently, the modified Hamilton‐Jacobi‐Bellman equations are solved by using the ADP algorithm under a critic network framework. According to the Lyapunov approach, all signals in the closed‐loop augmented system are uniformly ultimately bounded. Finally, the effectiveness of the developed control scheme is demonstrated via simulation results.

Strategi Inovatif Pemberantasan Hama Jagung Dengan Sistem Fuzzy Dan Iot Untuk Meningkatkan Produktivitas (Studi Kasus Pemberantasan Hama Jagung Di Ponorogo)

Corn pest control is a significant challenge for corn farmers in Indonesia. Pests can cause substantial losses for farmers, especially if left unattended. Therefore, innovative strategies are needed to enhance productivity and reduce farmer losses. This study explores the potential of Internet of Things (IoT) technology in innovative corn pest control strategies. The study aims to investigate how IoT can assist corn farmers in monitoring and controlling pests, thereby enhancing productivity and reducing losses.The study employs a case study approach, focusing on the application of IoT technology in corn pest control. Data collection involves field observations, interviews with farmers, and analysis of existing literature. The findings indicate that IoT technology can significantly improve the accuracy and speed of pest monitoring, enabling farmers to make more informed decisions about pest control.The study concludes that the integration of IoT technology into corn pest control strategies can lead to increased productivity and reduced losses for farmers. The findings of this study contribute to the development of innovative and effective pest control strategies, ultimately benefiting the corn farming industry in Indonesia.

Intra-event variations of organic micropollutants in highway runoff and a presedimentation-biofilter treatment facility

The study assessed the quality of highway runoff and a stormwater treatment system, focusing on intra-event variations (IEVs: variations within a runoff/effluent event) of the concentration of organic micropollutants (OMPs) including bisphenol-A, alkylphenols, polycyclic aromatic hydrocarbons (PAHs), and petroleum hydrocarbons (PHCs). IEVs of OMPs varied considerably with no particular recurring pattern in highway runoff and presedimentation effluent, displaying sporadic strong first flushes. IEVs are significantly associated with rainfall intensity variations, especially for particle-bound substances such as PAHs and PHCs. However, phenolic substances showed distinct IEV patterns compared to total suspended solids, PAHs, and PHCs, likely due to their higher solubility and mobility. Downstream sand filter (SF) and vegetated biofilter (BFC) mitigated IEVs, leading to more uniform discharge during outflow events. Although BFC’s IEVs were indiscernible due to low effluent concentrations, SF’s IEVs often peaked at the beginning of events (within the first 100 of ⁓600 m3), exceeding the lowest predicted non-effect concentrations for five PAHs, bisphenol-A, and octylphenol. This study highlights the advantage of IEV analysis over conventional event mean concentration analysis for identifying critical effluent stages, crucial for developing control strategies to protect sensitive water recipients or for reuse applications.

Human-Environment Interactions Shape Mosquito Seasonal Population Dynamics.

Mosquito species, including the Asian tiger mosquito, can transmit disease-causing pathogens such as dengue, Zika, and chikungunya, with their population dynamics influenced by a variety of factors including climate shifts, human activity, and local environmental conditions. Understanding these dynamics is vital for effective control measures. Our study, conducted in Jardí Botanic Marimurtra from May to November 2021, monitored Ae. albopictus activity using BG-Traps and investigated larval control effects. We employed Generalized Linear Mixed Models to analyze variables like weather, human presence, and larvicidal control on adult mosquito abundance. Adults of Ae. albopictus exhibited a seasonal pattern influenced by temperature but with bimodal peaks linked to cumulative rainfall. Proximity to stagnant water and visitor influx directly affected mosquito captures. Additionally, the effectiveness of larvicide treatments depended on interactions between preceding rainfall levels and treatment timing. Our research emphasizes the significance of studying vector ecology at local scales to enhance the efficacy of control programs and address the escalating burden of vector-borne diseases. Considering the impacts of extreme weather events and climate shifts is essential for the development of robust vector control strategies. Furthermore, our distinct findings serve as a prime illustration of utilizing statistical modeling to gain mechanistic insights into ecological patterns and processes.

Improved Sliding Mode Control for Tracking Global Maximum Power of Triple Series Parallel Ladder Photovoltaic Array under Uneven Shadowing

Abstract This paper presents an innovative way for enhancing the performance of photovoltaic arrays under uneven shadowing conditions. The study focuses on a triple series parallel ladder configuration to exploit the benefits of increased power generation while addressing the challenges associated with uneven shadowing. The proposed methodology focuses on the implementation of improved sliding mode Control technique for the global maximum power point tracking efficiently. Sliding mode control is known for its robustness in the presence of uncertainties and disturbances, making it suitable for dynamic and complex systems such as photovoltaic arrays. This work employs a comprehensive simulation framework to comment the performance of the suggested improved sliding mode control strategy at uneven shadowing scenarios. Comparative analysis has been done to show the effectiveness of the suggested method than the traditional control strategies. The results demonstrate a remarkable enhancement in the tracking accuracy of the global maximum power point, leading to enhanced energy harvesting capabilities under challenging environmental conditions. Furthermore, the proposed approach exhibits robustness and adaptability in mitigating the effect of shading on the photovoltaic array, thereby increasing overall system efficiency. This research contributes valuable insights into the development of advanced control strategies for photovoltaic arrays, particularly in the context of triple series parallel ladder configurations operating under uneven shadowing conditions. Under short narrow shading condition, the improved sliding mode control method tracking the maximum power more compared to perturb &amp; observe is 20.68%, incremental conductance is 68.78%, fuzzy incremental conductance is 19.8%, and constant velocity sliding mode control is 1.25%. The improved sliding mode control method has 60% less chattering than constant velocity sliding mode control under shading conditions.