In this study, conical shell vibration with distributed piezoelectric layers on the shell surface is controlled by a distributed optimal controller. Two piezoelectric layers are distributed on the conical shell surface with the same geometry and they are segmented into the same numbers of patches. One piezoelectric layer is considered to be a sensor layer and the other one is considered to be an actuator layer. An optimal controller with various constants for each piezoelectric patch is determined with an optimal input voltage. The conical shell electromechanical equations of motions with piezoelectric layers are extracted. The Galerkin method is used for obtaining the time domain equations and after that optimal constants of the controller are determined. Various kinds of distribution for the piezoelectric layer are considered and their effects on the conical shell vibration control are evaluated. For a better assessment, free vibration response, forced vibration response with concentrated and distributed force, and the frequency response of the considered system are computed and compared with the uncontrolled response. The results show the high impact of the optimal controller on the vibration mitigation of the conical shell and also the actuator applied voltage amplitude is considerably low. The applicability of the piezoelectric layer in the conical shell vibration mitigation is vividly determined by using an optimal controller which decreases the actuator applied voltage amplitude dramatically.Quality-relevant process monitoring has attracted much attention for its ability to assist in maintaining efficient plant operation. However, when the process suffers from non-stationary and over-complex (with noise, multiplicative faults, etc.) characteristics, the traditional methods usually cannot be effectively applied. To this end, a novel method, termed as Robust adaptive boosted canonical correlation analysis (Rab-CCA), is proposed to monitor the wastewater treatment processes. First, a robust decomposition method is proposed to mitigate the defects of standard CCA by decomposing the corrupted matrix into a low-matrix and a sparse matrix. Second, to further improve the performance of the standard process monitoring method, a novel criterion function and control charts are reconstructed accordingly. Moreover, an adaptive statistical control limit is proposed that can adjust the thresholds according to the state of a system and can effectively reduce the missed alarms and false alarms simultaneously. The superiority of Rab-CCA is verified by Benchmark Simulation Model 1 (BSM1) and a real full-scale wastewater treatment plant (WWTP).In this paper, a practical and systematic tuning procedure combining both frequency-domain (FD) and time-domain (TD) specifications is proposed to obtain an optimal robust fractional order (FO) PIλD (FOPIλD) controller for the first order plus time delay (FOPTD) processes. The FD specifications (i.e. Bleximenib phase margin (PM), gain crossover frequency (ωgc) and flat phase constrain (FPC)) guarantee the systemic stability and robustness to plant gain variations. Meanwhile, the TD specification (i.e. the smallest JITAE) achieves optimal dynamic performance. Furthermore, the entire feasible regions of two frequency-domain specifications ωgc and PM have been obtained with a synthesis scheme and visualized in three-dimensional plots which can be used as prior knowledge before the controller design. The comparisons of feasible region with FOPI and integer order PID (IOPID) controllers clearly present the superiority of proposed FOPIλD controller. Simulation illustration for delay dominant systems, lag dominant systems and high order system with one zero, using the proposed optimal robust FOPIλD controller is presented to demonstrate the significant performance improvement over FOPI controller, three-parameter FOPID controller, Ziegler-Nichols FOPID controller, fractional filter-FOPID controller and SIMC-PI controller.The mammalian skin is essential to protect the organism from external damage while at the same time enabling communication with the environment. Aging compromises skin function and regeneration, which is further exacerbated by external influences, such as UVR from the sun. Aging and UVR are also major risk factors contributing to the development of skin cancer. Whereas aging research traditionally has focused on the role of DNA damage and metabolic and stress pathways, less is known about how aging affects tissue architecture and cell dynamics in skin homeostasis and regeneration and whether changes in these processes promote skin cancer. This review highlights how key regulators of cell polarity and adhesion affect epidermal mechanics, tissue architecture, and stem cell dynamics in skin aging and cancer.Available tools to evaluate patients with central nervous system (CNS) tumors such as magnetic resonance imaging (MRI), cerebrospinal fluid (CSF) cytology, and brain biopsies, have significant limitations. MRI and CSF cytology have poor specificity and sensitivity, respectively, and brain biopsies are invasive. Circulating tumor DNA in CSF (CSF-ctDNA) could be used as a biomarker in patients with CNS tumors, but studies in this area are limited. We evaluated four CSF-ctDNA extraction methods and analyzed mutations in CSF-ctDNA with the Oncomine Pan-Cancer cell-free assay. CSF-ctDNA was extracted from 38 patients with primary or metastatic CNS tumors and 10 patients without CNS malignancy. Commercial ctDNA controls were used for assay evaluation. CSF-ctDNA yields ranged from 3.65 to 3120 ng. Mutations were detected in 39.5% of samples. TP53 was the most commonly mutated gene and copy number alterations were detected in CCND1, MYC, and ERBB2/HER2. Twenty-five percent of CSF-cytology-negative samples showed mutations in CSF-ctDNA. There was good concordance between mutations in CSF-ctDNA and matching tumors. The QIAamp Circulating Nucleic Acid Kit was the optimal method for extraction of CSF-ctDNA and the Oncomine cell-free DNA assay is suitable for detection of mutations in CSF-ctDNA. Analysis of CSF-ctDNA is more sensitive than CSF-cytology and has the potential to improve the diagnosis and monitoring of patients with CNS tumors.