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During the past few decades, a number of conical tanks have collapsed in various locations around the globe. Previous studies attributed the reason of collapse to inadequate thickness of the conical vessel especially at the bottom part. Most of the previous studies focused on studying the stability of conical tanks under the effect of only hydrostatic pressure. The current study focuses on studying the combined effect of wind loading and hydrostatic pressure on the stability of conical tanks. The study is conducted numerically, using a three-dimensional finite element model that is developed in-house. The critical imperfection shapes leading to minimum buckling capacity of conical shells under wind load alone, and under the combined effect of wind load and hydrostatic pressure, are determined. The study shows that a non-axisymmetric imperfection shape leads to minimum buckling capacity of empty conical tanks subjected to wind loads, while an axisymmetric distribution is noticed in the case of conical tanks under the combined effect of wind loads and hydrostatic pressure. In addition, the current study assesses the adequacy of an existing design procedure, which accounts for hydrostatic pressure, when the combination of hydrostatic pressure and wind load is considered. Key words: shell structures, steel conical tanks, stability, buckling, wind. Au cours des quelques dernieres decennies, plusieurs reservoirs coniques se sont effondres a divers endroits autour du monde. Les etudes anterieures en ont attribue la raison a une epaisseur inadequate du vaisseau conique, particulierement dans sa partie inferieure. La plupart des etudes anterieures portaient sur la stabilite des reservoirs coniques mais uniquement sous l'effet de la pression hydrostatique. La presente etude porte sur l'effet combine de la charge due au vent et de la pression hydrostatique sur les reservoirs coniques. L'etude numerique utilise un modele tridimensionnel par elements finis developpe a l' interne. Les formes d' imperfection critiques menant a une capacite de resistance au flambage minimale des coquilles coniques uniquement sous la charge due au vent et sous l' effet combine de la charge due au vent et de la pression hydrostatique ont ete determinees. L' etude montre qu' une forme d' imperfection non axisymetrique engendre une capacite de resistance au flambage minimale des reservoirs coniques vides soumis a des charges dues au vent, alors qu'une distribution axisymetrique est remarquee dans le cas des reservoirs coniques sous l' effet combine de la charge due au vent et de la pression hydrostatique. De plus, la presente etude evalue la pertinence d'une procedure de conception existante qui tient compte de la pression hydrostatique, lorsque l'on considere la combinaison de pression hydrostatique et de charge due au vent. Mots-cles : coques, reservoirs coniques en acier, stabilite, flambage, vent. [Traduit par la Redaction], 1. Introduction Conical shaped steel vessels are widely used in elevated water storage tanks. A photo of an elevated conical tank, located in Canada, is shown in Fig. 1. During [...]

Demand response-based load scheduling in smart power grids is currently one of the most important topics in energy optimization. There are several benefits to utility companies and their customers from this strategy. The main goal of this work is to employ a load scheduling controller (LSC) to model and solve the scheduling issue for household appliances. The LSC offers a solution to the primary problems faced during implementing demand response. The goal is to minimize peak-to-average demand ratios (PADR) and electricity bills while preserving customer satisfaction. Time-varying pricing, intermittent renewable energy, domestic appliance energy demand, storage battery, and grid constraints are all incorporated into the model. The optimal adaptive wind-driven optimization (OAWDO) method is a stochastic optimization technique designed to manage supply, demand, and power price uncertainties. LSC creates the ideal schedule for home appliance running periods using the OAWDO algorithm. This guarantees that every appliance runs as economically as possible on its own. Most appliances run the risk of functioning during low-price hours if just the real time-varying price system is used, which could result in rebound peaks. We combine an inclined block tariff with a real-time-varying price to alleviate this problem. MATLAB is used to do a load scheduling simulation for home appliances based on the OAWDO algorithm. By contrasting it with other algorithms, including the genetic algorithm (GA), the whale optimization algorithm (WOA), the fire-fly optimization algorithm (FFOA), and the wind-driven optimization (WDO) algorithms, the effectiveness of the OAWDO technique is supported. Results indicate that OAWDO works better than current algorithms in terms of reducing power costs, PADR, and rebound peak formation without sacrificing user comfort., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Alghamdi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The energy optimization in smart power grids (SPGs) is crucial for ensuring efficient, sustainable, and cost-effective energy management. However, the uncertainty and stochastic nature of distributed generations (DGs) and loads pose significant challenges to optimization models. In this study, we propose a novel optimization model that addresses these challenges by employing a probabilistic method to model the uncertain behavior of DGs and loads. Our model utilizes the multi-objective wind-driven optimization (MOWDO) technique with fuzzy mechanism to simultaneously address economic, environmental, and comfort concerns in SPGs. Unlike existing models, our approach incorporates a hybrid demand response (HDR), combining price-based and incentive-based DR to mitigate rebound peaks and ensure stable and efficient energy usage. The model also introduces battery energy storage systems (BESS) as environmentally friendly backup sources, reducing reliance on fossil fuels and promoting sustainability. We assess the developed model across various distinct configurations: optimizing operational costs and pollution emissions independently with/without DR, optimizing both operational costs and pollution emissions concurrently with/without DR, and optimizing operational costs, user comfort, and pollution emissions simultaneously with/without DR. The experimental findings reveal that the developed model performs better than the multi-objective bird swarm optimization (MOBSO) algorithm across metrics, including operational cost, user comfort, and pollution emissions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 International Society of Automation. Published by Elsevier Ltd. All rights reserved.)

Internet of medical things (IoMT) has made it possible to collect applications and medical devices to improve healthcare information technology. Since the advent of the pandemic of coronavirus (COVID-19) in 2019, public health information has become more sensitive than ever. Moreover, different news items incorporated have resulted in differing public perceptions of COVID-19, especially on the social media platform and infrastructure. In addition, the unprecedented virality and changing nature of COVID-19 makes call centres to be likely overstressed, which is due to a lack of authentic and unregulated public media information. Furthermore, the lack of data privacy has restricted the sharing of COVID-19 information among health institutions. To resolve the above-mentioned limitations, this paper is proposing a privacy infrastructure based on federated learning and blockchain. The proposed infrastructure has the potentials to enhance the trust and authenticity of public media to disseminate COVID-19 information. Also, the proposed infrastructure can effectively provide a shared model while preserving the privacy of data owners. Furthermore, information security and privacy analyses show that the proposed infrastructure is robust against information security-related attacks.

This work investigates sensor fault diagnostics and fault-tolerant control for a voltage source converter based microgrid (model) using a sliding-mode observer. It aims to provide a diagnosis of multiple faults (i.e., magnitude, phase, and harmonics) occurring simultaneously or individually in current/potential transformers. A modified algorithm based on convex optimization is used to determine the gains of the sliding-mode observer, which utilizes the feasibility optimization or trace minimization of a Ricatti equation-based modification of H-Infinity (H∞) constrained linear matrix inequalities. The fault and disturbance estimation method is modified and improved with some corrections in previous works. The stability and finite-time reachability of the observers are also presented for the considered faulty and perturbed microgrid system. A proportional-integral (PI) based control is utilized for the conventional regulations required for frequency and voltage sags occurring in a microgrid. However, the same control block features fault-tolerant control (FTC) functionality. It is attained by incorporating a sliding-mode observer to reconstruct the faults of sensors (transformers), which are fed to the control block after correction. Simulation-based analysis is performed by presenting the results of state/output estimation, state/output estimation errors, fault reconstruction, estimated disturbances, and fault-tolerant control performance. Simulations are performed for sinusoidal, constant, linearly increasing, intermittent, sawtooth, and random sort of often occurring sensor faults. However, this paper includes results for the sinusoidal nature voltage/current sensor (transformer) fault and a linearly increasing type of fault, whereas the remaining results are part of the supplementary data file. The comparison analysis is performed in terms of observer gains being estimated by previously used techniques as compared to the proposed modified approach. It also includes the comparison of the voltage-frequency control implemented with and without the incorporation of the used observer based fault estimation and corrections, in the control block. The faults here are considered for voltage/current sensor transformers, but the approach works for a wide range of sensors.

Electricity demand is rising due to industrialisation, population growth and economic development. To meet this rising electricity demand, towns are renovated by smart cities, where the internet of things enabled devices, communication technologies, dynamic pricing servers and renewable energy sources are integrated. Internet of things (IoT) refers to scenarios where network connectivity and computing capability is extended to objects, sensors and other items not normally considered computers. IoT allows these devices to generate, exchange and consume data without or with minimum human intervention. This integrated environment of smart cities maintains a balance between demand and supply. In this work, we proposed a closed-loop super twisting sliding mode controller (STSMC) to handle the uncertain and fluctuating load to maintain the balance between demand and supply persistently. Demand-side load management (DSLM) consists of agents-based demand response (DR) programs that are designed to control, change and shift the load usage pattern according to the price of the energy of a smart grid community. In smart grids, evolved DR programs are implemented which facilitate controlling of consumer demand by effective regulation services. The DSLM under price-based DR programs perform load shifting, peak clipping and valley filling to maintain the balance between demand and supply. We demonstrate a theoretical control approach for persistent demand control by dynamic price-based closed-loop STSMC. A renewable energy integrated microgrid scenario is discussed numerically to show that the demand of consumers can be controlled through STSMC, which regulates the electricity price to the DSLM agents of the smart grid community. The overall demand elasticity of the current study is represented by a first-order dynamic price generation model having a piece-wise linear price-based DR program. The simulation environment for this whole scenario is developed in MATLAB/Simulink. The simulations validate that the closed-loop price-based elastic demand control technique can trace down the generation of a renewable energy integrated microgrid.

There will be a dearth of electrical energy in the prospective world due to exponential increase in electrical energy demand of rapidly growing world population. With the development of internet-of-things (IoT), more smart devices will be integrated into residential buildings in smart cities that actively participate in electricity market via demand response (DR) programs to efficiently manage energy in order to meet this increasing energy demand. Thus, with this incitement, an energy management strategy using price-based DR program is developed for IoT-enabled residential buildings. We propose a wind-driven bacterial foraging algorithm (WBFA), which is a hybrid of wind-driven optimization (WDO) and bacterial foraging optimization (BFO) algorithms. Subsequently, we devised a strategy based on our proposed WBFA to systematically manage the power usage of IoT-enabled residential building smart appliances by scheduling to alleviate peak-to-average ratio (PAR), minimize cost of electricity, and maximize user comfort (UC). This increases effective energy utilization, which in turn increases the sustainability of IoT-enabled residential buildings in smart cities. The WBFA-based strategy automatically responds to price-based DR programs to combat the major problem of the DR programs, which is the limitation of consumer's knowledge to respond upon receiving DR signals. To endorse productiveness and effectiveness of the proposed WBFA-based strategy, substantial simulations are carried out. Furthermore, the proposed WBFA-based strategy is compared with benchmark strategies including binary particle swarm optimization (BPSO) algorithm, genetic algorithm (GA), genetic wind driven optimization (GWDO) algorithm, and genetic binary particle swarm optimization (GBPSO) algorithm in terms of energy consumption, cost of electricity, PAR, and UC. Simulation results show that the proposed WBFA-based strategy outperforms the benchmark strategies in terms of performance metrics.

Objective: Urinary fistulas resulting from obstetric trauma remains a major problem in developing countries. Surgical success rates, however, range from 61% to 95%. This study reviews the main causes of fistulas, and the surgical techniques and success rates of surgical closure., Method: A total of 1086 cases of urinary fistulas resulting from obstetric trauma over 25 years were reviewed. All women received surgical intervention. Various types of fistulas as well as specific surgical techniques are described, and patient age, parity, and probable cause of fistula were compared with findings from studies in Africa., Results: The highest number of cases (47.6%) occurred in women aged between 26 and 35 years; moreover, 54.43% of the women were para 6 and higher. Obstructed labor contributed to 72.78%, cesarean hysterectomy to 7.60%, cesarean section to 7.30%, and other obstetrical traumatic procedures to 12.28% of the cases. Surgical success was achieved in 84.5% of the cases. The major residual problem of stress incontinence was noted in 5% of the cases., Conclusion: Besides prevention, the basic principles of fistula repair are the most important factors in achieving surgical success. Better preoperative preparation and rigorous measures for preventing postoperative infection can help increase the success rate of fistula repair.