Robust Sliding Mode Control for Flexible Joint Robotic Manipulator via Disturbance Observer

In a flexible joint robotic manipulator, parametric variations and external disturbances result in mismatch uncertainties thus posing a great challenge in terms of manipulator’s control. This article investigates non-linear control algorithms for desired trajectory tracking of a flexible manipulator subjected to mismatch perturbations. The manipulator’s dynamics is derived based on Euler-Lagrange approach followed by the design of nonlinear control laws. The traditional Sliding Mode Control and Integral Sliding Mode Control failed to demonstrate adequate performance due to complex system dynamics. Disturbance Observer-based Sliding Mode Control has been thoroughly examined by defining a novel sliding manifold. The aforementioned control laws are designed and simulated in MATLAB/Simulink environment to characterize the control performance. Results demonstrated that the proposed Disturbance Observer based Sliding Mode Control scheme over-performed on Sliding Mode Control variants and had three prominent features: robustness against mismatch uncertainty, improved chattering behaviour and ability to sustain nominal control performance of the system

Utilization of genes encoding osmoprotectants in transgenic plants for enhanced abiotic stress tolerance

Global agriculture in the context of growing and expanding populations is under huge pressure to provide increased food, feed, and fiber. The recent phenomenon of climate change has further added fuel to the fire. It has been practically established now that the global temperature has been on the increase with associated fluctuations in annual rainfall regimes, and the resultant drought and flood events and increasing soil and water salinization. These challenges would be met with the introduction and utilization of new technologies coupled with conventional approaches. In recent years, transgenic technology has been proved very effective in terms of production of improved varieties of crop plants, resistant to biotic stresses. The abiotic stresses such as salt and drought are more complex traits, controlled by many genes. Transgenic plant development for these stresses has utilized many single genes. However, much emphasis has been placed on genes catalyzing the biosynthetic pathways of osmoprotectants. This review focuses on the current status of research on osmoprotectant genes and their role in abiotic stress tolerance in transgenic plants

Expression of Steroid Receptor RNA Activator 1 (SRA1) in the Adipose Tissue Is Associated with TLRs and IRFs in Diabesity

Steroid receptor RNA activator gene (SRA1) emerges as a player in pathophysiological responses of adipose tissue (AT) in metabolic disorders such as obesity and type 2 diabetes (T2D). We previously showed association of the AT SRA1 expression with inflammatory cytokines/chemokines involved in metabolic derangement. However, the relationship between altered adipose expression of SRA1 and the innate immune Toll-like receptors (TLRs) as players in nutrient sensing and metabolic inflammation as well as their downstream signaling partners, including interferon regulatory factors (IRFs), remains elusive. Herein, we investigated the association of AT SRA1 expression with TLRs, IRFs, and other TLR-downstream signaling mediators in a cohort of 108 individuals, classified based on their body mass index (BMI) as persons with normal-weight (N = 12), overweight (N = 32), and obesity (N = 64), including 55 with and 53 without T2D. The gene expression of SRA1, TLRs-2,3,4,7,8,9,10 and their downstream signaling mediators including IRFs-3,4,5, myeloid differentiation factor 88 (MyD88), interleukin-1 receptor-associated kinase 1 (IRAK1), and nuclear factor-κB (NF-κB) were determined using qRT-PCR and SRA1 protein expression was determined by immunohistochemistry. AT SRA1 transcripts’ expression was significantly correlated with TLRs-3,4,7, MyD88, NF-κB, and IRF5 expression in individuals with T2D, while it associated with TLR9 and TRAF6 expression in all individuals, with/without T2D. SRA1 expression associated with TLR2, IRAK1, and IRF3 expression only in individuals with obesity, regardless of diabetes status. Furthermore, TLR3/TLR7/IRAK1 and TLR3/TLR9 were identified as independent predictors of AT SRA1 expression in individuals with obesity and T2D, respectively. Overall, our data demonstrate a direct association between the AT SRA1 expression and the TLRs together with their downstream signaling partners and IRFs in individuals with obesity and/or T2D

Reinforcement learning for bolus insulin dosing for people with type 1 diabetes

Type 1 Diabetes (T1D) is a chronic metabolic disorder caused by destruction of the insulin producing beta cells in the islets of Langerhans within the pancreas due to an autoimmune reaction. T1D is distinguished by elevated levels of blood glucose (BG) owing to the deficiency of insulin, a hormone responsible for the regulation of BG within the normal range of 70-180 mg/dL. T1D is associated with various micro-vascular and macro-vascular complications such as nephropathy, neuropathy, retinopathy, coronary heart disease, cerebrovascular disease, peripheral artery disease etc. People with T1D rely on the administration of exogenous insulin to restrict the BG in a healthy range. The insulin treatment strategies for T1D can be broadly divided into two categories i.e., multiple daily injections (MDI) and continuous subcutaneous insulin infusion (CSII) to avoid the T1D complications. In the past decade, a significant effort has been made by researchers to reproduce the behavior of beta cells and automate the insulin delivery for the management of T1D paving a way for the rapid development of the artificial pancreas (AP) technology. Integration of a continuous glucose monitor (CGM) with closed-loop control (CLC) algorithms to compute the continuous insulin dosing rate constitute an AP system. The preclinical validation and evaluation of the insulin dosing strategies developed by researchers are performed in the simulation environments that represent virtual patients (VPs) with T1D. The work presented in this thesis provides three contributions. Firstly, a methodology is introduced to generate a cohort of VPs with T1D to replicate the BG metrics of a real cohort of people with T1D from the Hospital Clinic de Barcelona. The clinical data of meals, meal times and insulin (basal and bolus) was utilized to derive realistic scenarios for the generation of VPs. The exercise sessions were introduced as disturbances and were derived from the BG profile of the real patients. The proposed methodology is capable of adopting the daily variations of BG profile from real patients and thus provide a realistic and challenging simulation environment for the validation and evaluation of therapeutic strategies developed for the management of T1D. Secondly, a Q-Learning based Reinforcement Learning (RL) algorithm is proposed for the bolus insulin calculation in patients with T1D and validated on the generated cohort of VPs with T1D. Usually the bolus insulin calculation is based on carbohydrates (CHO) in meal, CHO to insulin ratio (CR) and the insulin sensitivity based correction factor (CF). On the contrary, the proposed algorithm is independent of the CHO content in meals, CR and CF with an aim to avoid the CHO estimation and counting errors and the management burden on patients with T1DLa diabetis tipus 1 (DT1) és un trastorn metabòlic crònic causat per la destrucció de les cèl·lules beta productores d'insulina als illots de Langerhans dins del pàncrees a causa de una reacció autoimmune. La T1D es caracteritza per nivells elevats de glucosa en sang (BG) a causa de la deficiència d'insulina, una de les hormones responsables de la regulació de la glucosa dins del rang normal de 70-180 mg/dL. La T1D s'associa a diverses complicacions microvasculars i macrovasculars, com ara nefropatia, neuropatia, retinopatia, cardiopatia coronària, malaltia cerebrovascular, malaltia arterial perifèrica, etc. Les persones amb T1D depenen de l’administració d’insulina exògena per mantenir la glucèmia en un rang saludable. Les estratègies de tractament amb insulina per evitar les complicacions de la T1D es poden dividir en dues categories, és a dir, múltiples injeccions diàries (MDI) o infusió contínua subcutània d'insulina (CSII). Durant l'última dècada, els investigadors han fet un esforç significatiu per reproduir el comportament de les cèl·lules beta i automatitzar el lliurament d'insulina per a la gestió de la T1D, obrint un camí per al desenvolupament ràpid de la tecnologia del pàncrees artificial (AP). L’integració d'un monitor continu de glucosa (CGM) amb algorismes de control de llaç tancat (CLC) per calcular la taxa de dosificació contínua d'insulina constitueix un sistema AP. La validació i avaluació preclínica de les estratègies de dosificació d'insulina desenvolupades pels investigadors es realitzen en els entorns de simulació que representen pacients virtuals (VP) amb T1D. El treball presentat en aquesta tesi aporta tres contribucions. En primer lloc, una metodologia és introduïda per a la generació d’una cohort de VP amb T1D per replicar les mètriques de BG d'una cohort real de persones amb T1D de l'Hospital Clínic de Barcelona. Les dades clíniques dels àpats, els horaris dels àpats i la insulina (basal i bolus) es van utilitzar per obtenir escenaris realistes per a la generació de VP. Les sessions d'exercici es van introduir com pertorbacions i es van derivar del perfil de glucosa dels pacients reals. La metodologia proposada és capaç d'adoptar les variacions diàries del perfil de glucosa en pacients reals i, per tant, proporcionar un entorn de simulació realista i desafiant per a la validació i avaluació de les estratègies terapèutiques desenvolupades per al maneig de la T1D. En segon lloc, es proposa un algorisme d'aprenentatge per reforç (RL) basat en Q-Learning per al càlcul de bolus d'insulina en pacients amb T1D i es valida en la cohort generada de VP amb T1D. Normalment, el càlcul de la insulina en bolus es basa en els hidrats de carboni (CHO) dels àpats, la proporció CHO a insulina (CR) i el factor de correcció basat en la sensibilitat a la insulina (CF). A diferència d’això, l'algoritme proposat és independent del contingut de CHO en els àpats, del CR i del CF amb l'objectiu d'evitar els errors d'estimació i recompte de CHO i la càrrega de gestió dels pacients amb T1DPrograma de Doctorat en Tecnologi

Super twisting control algorithm for blood glucose regulation in type 1 diabetes patients

This is an increasing belief that consequences due to hyperglycemia can be mitigated using a close loop control system. This paper investigates a robust non-linear control approach based on sliding mode control (SMC) algorithm for type 1 diabetes patients. Bergman's minimal model have been used to analyse the behaviour of glucose and insulin dynamics in blood plasma inside human body. Control law based on super twisting SMC algorithm is formulated and simulated. Results demonstrated the performance and effectiveness of the proposed control scheme. Also the proposed control scheme is compared with traditional SMC on the basis of performance parameters in the presence of external disturbances. Results dictate that the proposed control law exhibits robustness and overperforms by demonstrating accurate trajectory tracking with relatively less control efforts and alleviating chattering

Utilization of genes encoding osmoprotectants in transgenic plants for enhanced abiotic stress tolerance

Global agriculture in the context of growing and expanding populations is under huge pressure to provide increased food, feed, and fiber. The recent phenomenon of climate change has further added fuel to the fire. It has been practically established now that the global temperature has been on the increase with associated fluctuations in annual rainfall regimes, and the resultant drought and flood events and increasing soil and water salinization. These challenges would be met with the introduction and utilization of new technologies coupled with conventional approaches. In recent years, transgenic technology has been proved very effective in terms of production of improved varieties of crop plants, resistant to biotic stresses. The abiotic stresses such as salt and drought are more complex traits, controlled by many genes. Transgenic plant development for these stresses has utilized many single genes. However, much emphasis has been placed on genes catalyzing the biosynthetic pathways of osmoprotectants. This review focuses on the current status of research on osmoprotectant genes and their role in abiotic stress tolerance in transgenic plants

Robust Sliding Mode Control for Flexible Joint Robotic Manipulator via Disturbance Observer

In a flexible joint robotic manipulator, parametric variations and external disturbances result in mismatch uncertainties thus posing a great challenge in terms of manipulator’s control. This article investigates non-linear control algorithms for desired trajectory tracking of a flexible manipulator subjected to mismatch perturbations. The manipulator’s dynamics is derived based on Euler-Lagrange approach followed by the design of nonlinear control laws. The traditional Sliding Mode Control and Integral Sliding Mode Control failed to demonstrate adequate performance due to complex system dynamics. Disturbance Observer-based Sliding Mode Control has been thoroughly examined by defining a novel sliding manifold. The aforementioned control laws are designed and simulated in MATLAB/Simulink environment to characterize the control performance. Results demonstrated that the proposed Disturbance Observer-based Sliding Mode Control scheme over-performed on Sliding Mode Control variants and had three prominent features: robustness against mismatch uncertainty, improved chattering behaviour and ability to sustain nominal control performance of the system

Hypnosis regulation in propofol anaesthesia employing super-twisting sliding mode control to compensate variability dynamics

Regulation of hypnosis level on bi-spectral index monitor (BIS) during a surgical procedure in propofol anaesthesia administration is a challenging task for an anaesthesiologist in multi-tasking environment of the operation theater. Automation in anaesthesia has the potential to solve issues arising from manual administration. Automation in anaesthesia is based on developing the three-compartmental model including pharmacokinetics and pharmacodynamic of the silico patients. This study focuses on regulation of the hypnosis level in the presence of surgical stimulus including skin incision, surgical diathermy and laryngoscopy as well as inter-patient variability by designing super-twisting sliding mode control (STSMC). The depth of the hypnosis level is maintained to 50 on the BIS level in the maintenance phase after improving the induction phase to 60 s using the conventional sliding mode control and 30 s with STSMC. The proposed scheme also compensates the inter-patient variability dynamics including height, age and weight of the different silico patients. Moreover, the surgical stimuli direct the hypnosis level towards the state of consciousness and stimulate the controller to provide continuous drug infusion during the interval 80-90 s. Simulation results witness that the oscillatory behaviour is observed in drug infusion to ensure the moderate level of hypnosis (40-60) for general surgery

FIGURE 5 in Phytodiversity, ecological attributes and phytogeographical distribution of plants in Arang Valley, District Bajaur, a remote area in the Northwest of Pakistan

FIGURE 5. Floristic elements of Arang valleyPublished as part of <i>Haq, Aminul, Badshah, Lal, Ullah, Shariat, Hussain, Wahid, Ullah, Irshad, Ullah, Hafiz & Ahmad, Sayyar, 2023, Phytodiversity, ecological attributes and phytogeographical distribution of plants in Arang Valley, District Bajaur, a remote area in the Northwest of Pakistan, pp. 28-50 in Phytotaxa 625 (1)</i> on page 35, DOI: 10.11646/phytotaxa.625.1.2, <a href="http://zenodo.org/record/10151407">http://zenodo.org/record/10151407</a&gt

FIGURE 6 in Phytodiversity, ecological attributes and phytogeographical distribution of plants in Arang Valley, District Bajaur, a remote area in the Northwest of Pakistan

FIGURE 6. Flowering phenology of the floraPublished as part of <i>Haq, Aminul, Badshah, Lal, Ullah, Shariat, Hussain, Wahid, Ullah, Irshad, Ullah, Hafiz & Ahmad, Sayyar, 2023, Phytodiversity, ecological attributes and phytogeographical distribution of plants in Arang Valley, District Bajaur, a remote area in the Northwest of Pakistan, pp. 28-50 in Phytotaxa 625 (1)</i> on page 36, DOI: 10.11646/phytotaxa.625.1.2, <a href="http://zenodo.org/record/10151407">http://zenodo.org/record/10151407</a&gt