Search Results (7,206)

Even while living circumstances and construction techniques have generally improved, occupants of these spaces frequently feel unsatisfied with the sense of security they provide, which leads to looking for and eventually enacting ever-more-effective safety precautions. The continuous uncertainty that contemporary individuals experience, particularly with regard to their protection in places like cities, prompted the field of computing to design smart devices that attempt to reduce threats and ultimately strengthen people’s sense of protection. Intelligent apps were developed to provide protection and make a residence a smart and safe home. The proliferation of technology for smart homes necessitates the implementation of rigorous safety precautions to protect users’ personal information and avoid illegal access. The importance of establishing cyber security has been recognized by academic and business institutions all around the globe. Providing reliable computation for the Internet of Things (IoT) is also crucial. A new method for enhancing safety in smart home environments’ sustainability using IoT devices is presented in this paper, combining the Whale Optimization Algorithm (WOA) with Deep Convolutional Neural Networks (DCNNs). WOA-DCNN hybridization seeks to enhance safety measures by efficiently identifying and averting possible attacks in real time. We show how effective the proposed approach is in defending smart home systems from a range of safety risks via in-depth testing and analysis. By providing a potential path for protecting smart home surroundings in a world that is growing more linked, this research advances the state of the art in IoT security. Full article

Ocimum sanctum Linn (O. sanctum L.), commonly known as Holy Basil or Tulsi, is a fragrant herbaceous plant belonging to the Lamiaceae family. This plant is widely cultivated and found in north-central parts of India, several Arab countries, West Africa and tropical regions of the Eastern World. Tulsi is known to be an adaptogen, aiding the body in adapting to stress by harmonizing various bodily systems. Revered in Ayurveda as the “Elixir of Life”, Tulsi is believed to enhance lifespan and foster longevity. Eugenol, the active ingredient present in Tulsi, is a l-hydroxy-2-methoxy-4-allylbenzene compound with diverse therapeutic applications. As concerns over the adverse effects of conventional antibacterial agents continue to grow, alternative therapies have gained prominence. Essential oils (EOs) containing antioxidants have a long history of utilization in traditional medicine and have gained increasing popularity over time. Numerous in vitro, in vivo and clinical studies have provided compelling evidence supporting the safety and efficacy of antioxidant EOs derived from medicinal plants for promoting health. This comprehensive review aims to highlight the scientific knowledge regarding the therapeutic properties of O. sanctum, focusing on its antibacterial, anti-diabetic, anti-carcinogenic, radioprotective, immunomodulatory, anti-inflammatory, cardioprotective, neurogenesis, anti-depressant and other beneficial characteristics. Also, the extracts of O. sanctum L. have the ability to reduce chronic inflammation linked to neurological disorders such as Parkinson’s and Alzheimer’s disease. The information presented in this review shed light on the multifaceted potential of Tulsi and its derivatives in maintaining and promoting health. This knowledge may pave the way for the development of novel therapeutic interventions and natural remedies that harness the immense therapeutic potential of Tulsi in combating various health conditions, while also providing valuable insights for further research and exploration in this field. Full article

Hydrophobic eutectic solvents (HES) show significant promise as extractants for metal ions. At their current stage of development, however, they have many disadvantages, such as high costs, limited data on reusability and, often, lower extraction efficiency when compared with traditional extraction systems. This study investigates the physico-chemical properties of five HES formulations based on 1-octanol in combination with camphor, 2′-hydroxypropiophenone, menthol, 1-octanoic acid, and thymol. The 1-octanol/camphor HES exhibited substantially higher extraction efficiency for Fe(III) ions than a solution of 1-octanol in toluene at the same concentration. Furthermore, it showed stability when used in a mixer-settler type extractor. The 1-octanol/camphor HES achieved a rapid extraction and re-extraction rate, with phase contact time reduced to just 2 min, without loss of extraction efficiency. Using the supported liquid membrane method, the proposed Oct/Cam HES enabled a threefold concentration of iron ions in the raffinate phase under continuous operation, confirming its potential for reusability. Full article

With the continued recognition of the devastating effects of natural hazards, the construction of shelters has become essential in urban disaster preparedness planning systems. After analyzing the deficiency of the conventional spatial allocation model of shelters and the hierarchy of evacuation assignments, this study proposes a bi-level and two-phase spatial configuration methodology of shelters. The first hierarchy aims to evacuate refugees from demand blocks to both emergency shelters and resident emergency congregate shelters. The second hierarchy aims to transfer refugees from selected shelters in the first hierarchy to resident emergency congregate shelters. Each hierarchy contains two phases of optimizing calculations. The optimization objects for the first phase and second phase are minimizing the number of new shelters and the evacuation time, respectively. A genetic algorithm and exhaustive approach are programmed to determine the solution of the model in the first and second phases, respectively. The evacuation assignment rule is proposed based on the gravity model, which distributes evacuees proportionally to nearby shelters. This study uses the deterministic user equilibrium problem to present the evacuation traffic flow allocation, which improves the scientificity of the location model of shelters. The refuge demands differentiate the population between daytime and nighttime through mobile signaling data and improve the accuracy from the plot scale to the building scale. Using mobile signaling data to differentiate refuge demands between day and night populations enhances the model’s precision. Finally, to validate the proposed methodology, this study selected the main area of Changshu City, Jiangsu Province, China, which has a population of 1.6 million, as a case study area. Full article

Due to the intensification of human activities, the ecosystems are being polluted by heavy metals. The pollution of heavy metals in agricultural systems has become a serious issue of global concern. This study detected the bioaccumulation of cadmium (Cd) in broad beans and aphids through continuous exposure to varying concentrations of Cd pollution (0, 3.125, 6.25, 12.5, 25, 50 mg/L) and subsequently examined its effects on aphid energy metabolism and reproductive ability. The results showed that Cd can be transmitted and accumulated between Vicia faba L. and aphids along the food chain, and the amount of accumulation was related to the Cd treatment concentration. Quantitative real-time PCR results showed that the expression levels of trehalase (TRE) and trehalose-6-phosphate synthase (TPS) in F1 were significantly upregulated, and those of vitellogenin (Vg) were varied across the five generations of aphids after Cd treatment, which were up-regulated, and others down-regulated. Compared with the control group, the glycogen content and two types of trehalase activities of the first-generation Cd-treatment aphids were decreased, while trehalose content increased; there was no significant change in the carbohydrate content and trehalase activity of the fourth and fifth generations of aphids. In addition, the reproduction of female aphids was inhibited. This research is helpful for studying the toxic effects of heavy metals on insects and the adaptation mechanisms of insects to extreme environments. It also provides a theoretical basis for further exploring the molecular mechanisms of Cd homeostasis in plants and insects under Cd stress. Full article

Skid-steer mobile robots (SSMRs) are ubiquitous in indoor and outdoor applications. Their accurate trajectory tracking control is quite challenging due to the uncertainties arising from the complex behavior of frictional force, external disturbances, and fluctuations in the instantaneous center of rotation (ICR) during turning maneuvers. These uncertainties directly disturb velocities, hindering the robot from tracking the velocity command. This paper proposes a nonsingular terminal sliding mode control (NTSMC) based on backstepping for a four-wheel SSMR to cope with the aforementioned challenges. The strategy seeks to mitigate the impacts of external disturbances and model uncertainties by developing an adaptive law to estimate the integrated lumped outcome. The finite time stability of the closed-loop system is proven using Lyapunov’s theory. The designed NTSMC input is continuous and avoids noticeable chattering. It was noted in the simulation analysis that the proposed control strategy is strongly robust against disturbance and modeling uncertainties, demonstrating effective trajectory tracking performance in the presence of disturbance and modeling uncertainties. Full article

This article presents research work on an intelligent system that was developed to monitor and continuously evaluate the quality of metal powder distribution in the laser powder bed fusion (LPBF) 3D printing process. The 3D printer that was used to carry out the work was equipped with an industrial vision system to capture images immediately after spreading powder on the work field. The powder distribution tests showed that the most common defects were identified as an insufficiently thick layer of powder applied to the working field (super elevation), unevenly distributed powder as a result of recoater vibration (so called recoater hopping), and its wear (so called recoater streaking). In the first stage of research, a set of training data (images) was collected. Then, the implementation of the machine learning process was prepared in the Roboflow environment. After that, the learning, validation, and prediction process was carried out several times using the selected machine learning model (YOLO model implemented in a Python environment) in order to select the most effective parameters. The study showed that deep machine learning can be effectively used to identify defects in powder distribution during the laser powder bed fusion (LPBF) process. Full article

In response to the difficulties and poor timeliness in detecting feeding metallic foreign objects during high-yield continuous crushing operations in coal mines, this paper proposes a new method for detecting metallic foreign objects, combining pulsed eddy current testing with the Truncated Region Eigenfunction Expansion (TREE) method. This method is suitable for the harsh working conditions in coal mine crushing stations, which include high dust, strong vibration, strong electromagnetic interference, and low temperatures in winter. A model of the eddy current field of feeding metallic foreign objects in the truncated region is established using a coaxial excitation and receiving coil with a Hall sensor. The full-cycle time-domain analytical solution for the induced voltage and magnetic induction intensity of the reflective field under practical square wave signals is obtained. Simulation and experimental results show that the effective time range, peak value, and time to peak of the received voltage and magnetic induction signals can be used to classify and identify the size, thickness, conductivity, and magnetic permeability of feeding metallic foreign objects. Experimental results meet the actual needs for removing feeding metallic foreign objects in coal mine sites. This provides core technical support for the establishment of a predictive fault diagnosis system for crushing equipment. Full article

Transmission networks face continuous electrical and mechanical stresses due to increasing system challenges and power losses. Transmission networks require special focus and detailed studies each time a load or a generator emerges to the grid. The interline power flow controller (IPFC) is a relatively new scheme that is implemented in the transmission network to improve transmission efficiency, decrease transmission losses, and enhance voltage profile. In this paper, the interline power flow controller’s impact on transmission network performance is investigated as it is implemented within the IEEE 5-bus, 14-bus, and IEEE 57-bus systems. In addition, the whale optimization algorithm (WOA) is used to optimize the interline power flow controller locations within the system to achieve optimal transmission system performance. WOA performance is also compared to genetic algorithm (GA) and particle swarm optimization (PSO) algorithms, and the superiority of the proposed WOA-based control is proved. The robustness of the optimized system against load variations is investigated and the results introduced affirm the capability of the interline power flow controller to enhance transmission network efficiency. Full article

Task allocation is an important problem in multi-robot systems, particularly in dynamic and unpredictable environments such as offshore oil platforms, large-scale factories, or disaster response scenarios, where high change rates, uncertain state transitions, and varying task demands challenge the predictability and stability of robot operations. Traditional static task allocation strategies often struggle to meet the efficiency and responsiveness demands of these complex settings, while optimization heuristics, though improving planning time, exhibit limited scalability. To address these limitations, this paper proposes a task allocation method based on the Monte Carlo Tree Search (MCTS) algorithm, which leverages the anytime property of MCTS to achieve a balance between fast response and continuous optimization. Firstly, the centralized adaptive MCTS algorithm generates preliminary solutions and monitors the state of the robots in minimal time. It utilizes dynamic Upper Confidence Bounds for Trees (UCT) values to accommodate varying task dimensions, outperforming the heuristic Multi-Robot Goal Assignment (MRGA) method in both planning time and overall task completion time. Furthermore, the parallelized distributed MCTS algorithm reduces algorithmic complexity and enhances computational efficiency through importance sampling and parallel processing. Experimental results demonstrate that the proposed method significantly reduces computation time while maintaining task allocation performance, decreasing the variance of planning results and improving algorithmic stability. Our approach enables more flexible and efficient task allocation in dynamically evolving and complex environments, providing robust support for the deployment of multi-robot systems. Full article

Background: Insulin pumps coupled with continuous glucose monitoring sensors use algorithms to analyze real-time blood glucose levels. This allows for the suspension of insulin administration before hypoglycemic thresholds are reached or for adaptive tuning in hybrid closed-loop systems. This longitudinal retrospective study aims to analyze real-world glycemic outcomes in a pediatric population transitioning to such devices. Methods: We evaluated children with type 1 diabetes mellitus (T1D) admitted to the Pediatric Diabetes Department from a major University Hospital in Bucharest, Romania, who transitioned to hybrid closed-loop or predictive low-glucose suspend system from either non-automated insulin pumps or multiple daily injections. The primary outcome was assessing the change in glycated hemoglobin (HbA1c) after initiating these devices. Secondary outcomes analyzed changes in glucose metrics from the 90 days prior to the baseline and follow-up visit. Results: 51 children were included (58.8% girls), the mean age was 10.3 ± 3.7 years, and the mean follow-up duration was 13.2 ± 4.5 months. The analyzed parameters, such as HbA1c (6.9 ± 0.7% vs. 6.7 ± 0.6%, p = 0.023), time in range (69.3 ± 11.2% vs. 76 ± 9.9%, p < 0.001), time in tight range (47.4 ± 10.9% vs. 53.7 ± 10.7%, p < 0.001), time below range (5.6 ± 2.9% vs. 3.5 ± 1.9%, p < 0.001), time above range (25 ± 11.2% vs. 20.4 ± 9.4%, p = 0.001), and coefficient of variation (37.9 ± 4.8% vs. 35.6 ± 4.6%, p = 0.001), showed significant improvements. Conclusions: The application of these sensor-integrated insulin pumps can significantly enhance metabolic control in pediatric populations, minimizing glycemic variations to mitigate complications and enrich the quality of life. Full article

The aircraft smart skin (ASS) with structural health monitoring capabilities is a promising technology. It enables the real-time acquisition of the aircraft’s structural health status and service environment, thereby improving the performance of the aircraft and ensuring the safety of its operation, which in turn reduces maintenance costs. In this paper, a miniaturized and ultra-low-power wireless multi-parameter monitoring system (WMPMS) for ASS is developed, which is capable of monitoring multiple parameters of an aircraft, including random impact events, vibration, temperature, humidity, and air pressure. The system adopts an all-digital monitoring method and a low-power operating mechanism, and it is integrated into a low-power hardware design. In addition, considering the airborne resources limitations, an energy self-supply module based on a thermoelectric generator (TEG) is developed to continuously power the system during flight. Based on the above design, the system has a size of only 45 mm × 50 mm × 30 mm and an average power consumption of just 7.59 mW. Through experimental validation, the system has excellent performance in multi-parameter monitoring and operating power consumption, and it can realize the self-supply of energy. Full article

This study introduces a multi-agent reinforcement learning approach to address the challenges of real-time scheduling in dynamic environments, with a specific focus on healthcare operations. The proposed system integrates the Human-in-the-Loop (HITL) paradigm, providing continuous feedback from human evaluators, and it employs a sophisticated reward function to attenuate the effects of human-driven events. Novel mapping between reinforcement learning (RL) concepts and the Belief–Desire–Intention (BDI) framework is developed to enhance the explainability of the agent’s decision-making. A system is designed to adapt to changes in patient conditions and preferences while minimizing disruptions to existing schedules. Experimental results show a notable decrease in patient waiting times compared to conventional methods while adhering to operator-induced constraints. This approach offers a robust, explainable, and adaptable solution for the challenging tasks of scheduling in the environments that require human-centered decision-making. Full article

Flywheels have been largely used in rotating machine engines to save inertial energy and to limit speed fluctuations. A stress distribution problem is created due to the centrifugal forces that are formed when the flywheel is spinning around, which leads to different levels of pressure and decompression inside its structure. Lack of balance leads to high energy losses through various mechanisms, which deteriorate both the flywheel’s expectancy and their ability to rotate at high speeds. Deviation in the design of flywheels from their optimum performance can cause instability issues and even a catastrophic failure during operation. This paper aims to analytically examine the stress distribution of radial and tangential directions along the flywheel structure within a linear elastic range. The eigenvalues and eigenvectors, which are representative of free vibrational features, were extracted by applying finite element analysis (FEA). Natural frequencies and their corresponding vibrating mode shapes and mass participation factors were identified. Furthermore, Kirchhoff–Love plate theory was employed to model the transverse vibration of the system. A general solution for the radial component of the equation of flywheel motion was derived with the help of the Bessel function. The results show certain modes of vibration identified as particularly influential in specific directions. Advanced time-frequency analysis techniques, including but not limited to continuous wavelet transform (CWT) and Hilbert–Huang transform (HHT), were applied to extract transverse vibration features of the flywheel system. It was also found that using CWT, low-frequency vibrations contribute to the majority of the energy in the extracted signal spectrum, while HHT exposes the high-frequency components of vibration that may cause significant structural damage if not addressed in time. Full article

Buildings are responsible for approximately 40% of global energy consumption, putting pressure on the construction industry to mitigate its environmental impact. Therefore, there is an urgent need for innovative solutions to reduce power consumption, particularly in lighting systems. This study’s primary objective was to investigate novel integrated lighting solutions that significantly reduce energy use, as well as to explore their enhancement through Building Information Modelling (BIM) and the Internet of Things (IoT) to improve energy efficiency further and reduce the carbon footprint of buildings. Hence, this literature review examined energy-saving actions, retrofitting practices and interventions across a range of multi-use buildings worldwide, focusing on research from 2019 to 2024. The review was conducted using Scopus and Web of Science databases, with inclusion criteria limited to original research. The objective was to diagnose the goals being undertaken and ultimately validate new actions and contributions to minimise energy consumption. After applying eligibility criteria, 48 studies were included in the review. First, daylight harvesting and retrofitting solutions were examined using the latest technologies and external shading. The review indicates a lack of proper coordination between daylight and electrical lighting, resulting in energy inefficiency. Secondly, it reviews how the integration of BIM facilitates the design process, providing a complete overview of all the building variables, thus improving indoor daylight performance and proper lighting with energy analysis. Lastly, the review addresses the role of the Internet of Things (IoT) in providing real-time data from sensor networks, allowing for continuous monitoring of building conditions. This systematic literature review explores the integration of these fields to address the urgent need for innovative strategies and sustainability in the built environment. Furthermore, it thoroughly analyses the current state of the art, identifying best practices, emerging trends and concrete insight for architects, engineers and researchers. The goal is to promote the widespread adoption of low-carbon systems and encourage collaboration among industry professionals and researchers to advance sustainable building design. Ultimately, a new parametric design framework is proposed, consisting of five iterative phases that cover all design stages. This framework is further enhanced by integrating BIM and IoT, which can be used together to plan, reconfigure, and optimise the building’s performance. Full article