Reliable Solution Research Articles

Determination of dimethyl sulfate genotoxic impurity in pantoprazole sodium sesquihydrate by derivatization method and UPLC/MS

In this study, a new method has been developed and, the method has been validated to analyze dimethyl sulfate (DMS) which known as a genotoxic impurity. DMS has been detected in trace levels within the drug substance pantoprazole sodium sesquihydrate using the derivatization method. Triethylamine has been used as derivatization reagent. Due to the highly polar nature of the derivatization product, which is a quaternary ammonium ion, a polar retention Hilic column has been used for chromatographic separation. The Waters QDA detector has been used in the single ion recording (SIR) mode at m/z 116 for the detection of the quaternary ammonium ion. In this study, the recovery rates have been achieved within the range of 96.46–105.98%. The LOD has been determined as 1.94E-07 mg/mL, and the LOQ has been determined as 6.46E-07 mg/mL. From the results, it can be said that the improved method in this study, would enables fast and reliable solutions for routine quality control analyses for DMS in API companies that especially is producing PSS.

Research on an Identification and Grasping Device for Dead Yellow-Feather Broilers in Flat Houses Based on Deep Learning

The existence of dead broilers in flat broiler houses poses significant challenges to large-scale and welfare-oriented broiler breeding. To ensure the timely identification and removal of dead broilers, a mobile device based on visual technology for grasping them was meticulously designed in this study. Among the multiple recognition models explored, the YOLOv6 model was selected due to its exceptional performance, attaining an impressive 86.1% accuracy in identification. This model, when integrated with a specially designed robotic arm, forms a potent combination for effectively handling the task of grasping dead broilers. Extensive experiments were conducted to validate the efficacy of the device. The results reveal that the device achieved an average grasping rate of dead broilers of 81.3%. These findings indicate that the proposed device holds great potential for practical field deployment, offering a reliable solution for the prompt identification and grasping of dead broilers, thereby enhancing the overall management and welfare of broiler populations.

Adaptation of an Eddy Current Model for Characterizing Subsurface Defects in CFRP Plates Using FEM Analysis Based on Energy Functional

In this work, a known Eddy Current (EC) model is adapted to characterize subsurface defects in carbon fiber-reinforced polymer (CFRP) plates intended for the civil aerospace industry. The considered defects include delaminations, microcracks, porosity, fiber breakage, and the simultaneous presence of these defects. Each defect is modeled as an additive variation in the material’s electrical conductivity tensor, allowing for a detailed mathematical representation of the defect’s influence on the CFRP’s electromagnetic behavior. The additivity of the variations in the conductivity tensor is justified by the assumption that the defects are not visible to the naked eye, implying that the material does not require non-destructive testing. The adapted EC model admits a unique and stable solution by verifying that all analytical steps are satisfied. To reconstruct 2D maps of the magnetic flux density amplitude, a FEM formulation is adopted, based on the energy functional because it ensures a stable and consistent numerical formulation given its coercivity. Moreover, the numerical approach allows precise and reliable numerical solutions, enhancing the capability to detect and quantify defects. The numerical results show that the obtained 2D maps are entirely superimposable on those highlighting the distribution of mechanical stress states known in the literature, offering a clear advantage in terms of detection costs. This approach provides an effective and economical solution for the non-destructive inspection of CFRP, ensuring accurate and timely defect diagnosis for maintaining structural integrity.

Biomass microcrystalline cellulose based solar evaporator with aligned channels for clean water and electricity co-generation

Solar-driven interfacial evaporation co-generation (SDIE-CG) technology can effectively solve problems such as low latent heat utilization in the evaporation process, which provides a reliable solution to the problem of hydropower shortage in remote areas. Herein, we report a tea residue microcrystalline cellulose (TMCC) aerogel (abbreviated as TCA), in which TMCC is regenerated by dissolution through the co-solvent method, and microcrystalline cellulose is rearranged by the directional slow freezing technique to get a TCA with a directional aligned pore structure, resulting in a good mechanical and salt-resistant property. Due to the internal doping of carbon black particles, the TCA has an average ultra-high absorbance of 97.46 %. The evaporation rate of TCA is up to 3.066 kg m−2h−1 under 3 kW m−2 illuminations. The maximum output voltage of the TCA is 44.801 mV under 2 kW m−2 illuminations through the introduction of a suitable wind-assisted hydropower generation device. In addition, the TCA can effectively purify the wastewater containing traditional Chinese medicine, and the chemical oxygen demand (COD) value in wastewater shows a significant decrease after treatment. It is worth noting that TCA can maintain structural integrity and stable evaporation performance in saltwater under extreme pH conditions (2<pH<11), and has potential application value in water treatment and cogeneration.

A solar-powered multi-functional portable charging device (SPMFPCD) with internet-of-things (IoT)-based real-time monitoring—An innovative scheme towards energy access and management

In the absence of portable charging devices, sectors such as transportation, communication, and emergency services deal with various challenges towards electric power needs while compromising on (1) operational efficiency, (2) insufficient portable charging solutions, and (3) limited versatility. This highlights the critical need for reliable and multi-functional power solutions. To provide a portable charging solution across diverse sectors, this paper proposes an innovative development of a solar-powered multi-functional portable charging device (SPMFPCD) with internet- of-thing (IoT)-based monitoring capabilities. The proposed scheme introduces a comprehensive model integrating advanced technologies which include a highly efficient solar panel, charge controller, sensors, and IoT module. The proposed system facilitates versatile charging solutions for a wide range of power requirements with real-time monitoring and data analysis through the IoT platform. Moreover, the proposed work explores the applications of the SPMFPCD in (1) emergency medical scenarios, (2) outdoor adventures, (3) disaster management, and 4) public spaces. Performance evaluation was made by proposing case studies to validate the (1) economic viability, (2) power management, and (3) environmental impact of widespread deployment of SPMPFCD in public spaces. Furthermore, detailed analysis of battery energy storage system (BESS) and photovoltaic (PV) integration for load management, seasonal dynamics, and renewable energy integration (REI) contribute to a comprehensive understanding of the proposed solution.

The Zero-Velocity Correction Method for Pipe Jacking Automatic Guidance System Based on Fiber Optic Gyroscope.

The pipe jacking guidance system based on a fiber optic gyroscope (FOG) has gained extensive attention due to its high degree of safety and autonomy. However, all inertial guidance systems have accumulative errors over time. The zero-velocity update (ZUPT) algorithm is an effective error compensation method, but accurately distinguishing between moving and stationary states in slow pipe jacking operations is a major challenge. To address this challenge, a "MV + ARE + SHOE" three-conditional zero-velocity detection (TCZVD) algorithm for the fiber optic gyroscope inertial navigation system (FOG-INS) is designed. Firstly, a Kalman filter model based on ZUPT is established. Secondly, the TCZVD algorithm, which combines the moving variance of acceleration (MV), angular rate energy (ARE), and stance hypothesis optimal estimation (SHOE), is proposed. Finally, experiments are conducted, and the results indicate that the proposed algorithm achieves a zero-velocity detection accuracy of 99.18% and can reduce positioning error to less than 2% of the total distance. Furthermore, the applicability of the proposed algorithm in the practical working environment is confirmed through on-site experiments. The results demonstrate that this method can effectively suppress the accumulated error of the inertial guidance system and improve the positioning accuracy of pipe jacking. It provides a robust and reliable solution for practical engineering challenges. Therefore, this study will contribute to the development of pipe jacking automatic guidance technology.

Bearing faults classification using novel log energy-based empirical mode decomposition and machine Mel-frequency cepstral coefficients

The accurate diagnosis of faults in bearing components is crucial for the safe and efficient operation of electrical and power drives. These machines generate sound and vibration signals that indicate their operational state. While vibration signals are often utilized for fault diagnosis, they require costly transducers. On the other hand, sound signal transducers are more affordable, but their lower signal-to-noise ratio complicates the differentiation between healthy and faulty bearings. This paper addresses these challenges by introducing a machine sound-based bearing fault diagnosis system. The proposed method employs a novel Log Energy-based Empirical Mode Decomposition and Reconstruction for advanced sound preprocessing. Feature extraction is performed using Machine Mel-frequency Cepstral Coefficients, with feature selection facilitated by a Genetic Algorithm. Classification is achieved through Support Vector Machines. The system demonstrated a high classification accuracy of 99.26% on the SUBF v2.0 dataset, outperforming other diagnostic methods, even in noisy conditions. This approach is particularly suited for industrial applications, offering a reliable solution for preventing downtime and ensuring the reliability of equipment.

Face recognition using deep learning on Raspberry Pi

Abstract Facial recognition on resource-limited devices such as the Raspberry Pi poses a challenge due to inherent processing limitations. For real-time applications, finding efficient and reliable solutions is critical. This study investigated the feasibility of using transfer learning for facial recognition tasks on the Raspberry Pi and evaluated transfer learning that leverages knowledge from previously trained models. We compared two well-known deep learning (DL) architectures, InceptionV3 and MobileNetV2, adapted to face recognition datasets. MobileNetV2 outperformed InceptionV3, achieving a training accuracy of 98.20% and an F1 score of 98%, compared to InceptionV3’s training accuracy of 86.80% and an F1 score of 91%. As a result, MobileNetV2 emerges as a more powerful architecture for facial recognition tasks on the Raspberry Pi when integrated with transfer learning. These results point to a promising direction for deploying efficient DL applications on edge devices, reducing latency, and enabling real-time processing.

Accelerating the Speed of Convergence for High-Order Methods to Solve Equations

This article introduces a multistep method for developing sequences that solve Banach space-valued equations. It provides error estimates, a radius of convergence, and uniqueness results. Our approach improves the applicability of the recommended method and addresses challenges in applied science. The theoretical advancements are supported by comprehensive computational results, demonstrating the practical applicability and robustness of the earlier method. We ensure more reliable and precise solutions to Banach space-valued equations by providing computable error estimates and a clear radius of convergence for the considered method. We conclude that our work significantly improves the practical utility of multistep methods, offering a rigorous and computable approach to solving complex equations in Banach spaces, with strong theoretical and computational results.

Exploring Porous Media for Compressed Air Energy Storage: Benefits, Challenges, and Technological Insights

The global transition to renewable energy sources such as wind and solar has created a critical need for effective energy storage solutions to manage their intermittency. This review focuses on compressed air energy storage (CAES) in porous media, particularly aquifers, evaluating its benefits, challenges, and technological advancements. Porous media-based CAES (PM-CAES) offers advantages, including lower costs and broader geographical availability compared to traditional methods. This review synthesizes recent advancements in numerical modeling, simulation, and experimental studies, which have enhanced the understanding of air–water–heat flow interactions and improved efficiency in these systems. Field studies demonstrate that using existing idle and abandoned wells can minimize infrastructure costs and environmental impact. This review underscores the potential of CAES in porous media to support the growing demand for sustainable and reliable energy storage solutions.

Incorporating Radioactive Decay Batteries into the USA's Energy Grid: Solutions for Winter Power Challenges

This paper explores the integration of radioactive decay batteries into the USA's energy grid as a strategic solution for addressing winter power challenges. Radioactive decay batteries, leveraging isotopes such as tritium or americium-241, offer significant advantages over traditional fission and fusion-based nuclear batteries. Their design inherently ensures enhanced durability, providing long-lasting energy outputs that can span several decades with minimal maintenance. Unlike conventional nuclear reactors, these batteries operate safely outside a reactor environment, significantly reducing the risks associated with nuclear power generation. This operational safety is further augmented by the relatively low radiation levels they emit, making them viable for diverse applications, including residential, commercial, and remote off-grid systems. The implementation of these batteries promises a stable and reliable energy supply during winter months when energy demands peak, mitigating the risk of power outages and enhancing the resilience of the energy grid. This study examines the potential deployment strategies, technical feasibility, and regulatory considerations necessary to incorporate radioactive decay batteries effectively into the USA's energy infrastructure, positioning them as a transformative technology for sustainable and reliable winter energy solutions.

Sustainable hybrid energy system’s reliability optimization by solving RRAP-CM with integration of metaheuristic approaches

PurposeThis research introduces an innovation strategy aimed at bolstering the reliability of a renewable energy resource, which is hybrid energy systems, through the application of a metaheuristic algorithm. The growing need for sustainable energy solutions underscores the importance of integrating various energy sources effectively. Concentrating on the intermittent characteristics of renewable sources, this study seeks to create a highly reliable hybrid energy system by combining photovoltaic (PV) and wind power.Design/methodology/approachTo obtain efficient renewable energy resources, system designers aim to enhance the system’s reliability. Generally, for this purpose, the reliability redundancy allocation problem (RRAP) method is utilized. The authors have also introduced a new methodology, named Reliability Redundancy Allocation Problem with Component Mixing (RRAP-CM), for optimizing systems’ reliability. This method incorporates heterogeneous components to create a nonlinear mixed-integer mathematical model, classified as NP-hard problems. We employ specially crafted metaheuristic algorithms as optimization strategies to address these challenges and boost the overall system performance.FindingsThe study introduces six newly designed metaheuristic algorithms. Solve the optimization problem. When comparing results between the traditional RRAP method and the innovative RRAP-CM method, enhanced reliability is achieved through the blending of diverse components. The use of metaheuristic algorithms proves advantageous in identifying optimal configurations, ensuring resource efficiency and maximizing energy output in a hybrid energy system.Research limitations/implicationsThe study’s findings have significant social implications because they contribute to the renewable energy field. The proposed methodologies offer a flexible and reliable mechanism for enhancing the efficiency of hybrid energy systems. By addressing the intermittent nature of renewable sources, this research promotes the design of highly reliable sustainable energy solutions, potentially influencing global efforts towards a more environmentally friendly and reliable energy landscape.Practical implicationsThe research provides practical insights by delivering a comprehensive analysis of a hybrid energy system incorporating both PV and wind components. Also, the use of metaheuristic algorithms aids in identifying optimal configurations, promoting resource efficiency and maximizing reliability. These practical insights contribute to advancing sustainable energy solutions and designing efficient, reliable hybrid energy systems.Originality/valueThis work is original as it combines the RRAP-CM methodology with six new robust metaheuristics, involving the integration of diverse components to enhance system reliability. The formulation of a nonlinear mixed-integer mathematical model adds complexity, categorizing it as an NP-hard problem. We have developed six new metaheuristic algorithms. Designed specifically for optimization in hybrid energy systems, this further highlights the uniqueness of this approach to research.

SpemNet: A Cotton Disease and Pest Identification Method Based on Efficient Multi-Scale Attention and Stacking Patch Embedding.

We propose a cotton pest and disease recognition method, SpemNet, based on efficient multi-scale attention and stacking patch embedding. By introducing the SPE module and the EMA module, we successfully solve the problems of local feature learning difficulty and insufficient multi-scale feature integration in the traditional Vision Transformer model, which significantly improve the performance and efficiency of the model. In our experiments, we comprehensively validate the SpemNet model on the CottonInsect dataset, and the results show that SpemNet performs well in the cotton pest recognition task, with significant effectiveness and superiority. The SpemNet model excels in key metrics such as precision and F1 score, demonstrating significant potential and superiority in the cotton pest and disease recognition task. This study provides an efficient and reliable solution in the field of cotton pest and disease identification, which is of great theoretical and applied significance.

Analysis of Early-Stage Behavior and Multi-Parameter Early Warning Algorithm Research for Overcharge Thermal Runaway of Energy Storage LiFePO4 Battery Packs

Overcharging of lithium-ion batteries may lead to severe thermal runaway (TR) incidents, resulting in significant economic losses and safety hazards. Therefore, it is crucial to research early warning methods for TR behavior in overcharged lithium batteries. This study initially conducted overcharging experiments on LiFePO4 battery packs under different initial charging states and charging rates, analyzing variations in temperature, voltage, and inter-group pressure during overcharging. The TR process was divided into three stages: non-overcharged, early, and middle. Based on this, temperature change rate, pressure change rate, and voltage were extracted as input feature parameters, and the Mean Shift algorithm was employed for stage identification and classification of overcharging experiments on LiFePO4 battery packs. According to experimental results, the algorithm achieved an accuracy of over 96% in stage identification and classification of TR in overcharged lithium batteries, accurately determining the current stage of TR and providing a reliable and effective solution for preventing TR in overcharged lithium batteries.

Reliability of pre-resection ligament tension assessment in imageless robotic assisted total knee replacement

BackgroundLigament tension balance is a major determinant for the success of total knee replacement (TKR). The present study aimed at determining the inter-rater and intra-rater reliability in performing ligament tension assessment using an imageless robotic-assisted TKR.MethodsTwenty-four knees in 21 patients who received robotic-assisted TKR for end-stage varus osteoarthritis were examined. Three orthopedic specialists and six orthopedic trainees participated in the operations. Data from the ligament tension assessment were collected during the operations.ResultsFor the inter-rater reliability, “extension medial” and “flexion medial” had excellent reliability whilst “extension lateral” and “flexion lateral” had good-to-excellent reliability. For the intra-rater reliability, “extension medial” showed excellent reliability, “extension lateral” and “flexion medial” showed good-to-excellent reliability, and “flexion lateral” showed moderate-to-excellent reliability.ConclusionsRobotic-assisted technology provides a reliable solution to improve ligament tension assessment. All ligament tension assessments with the use of the technology could demonstrate at least good-to-excellent reliability except for the intra-rater reliability of “flexion lateral”.

Latent Semantic Consensus for Deterministic Geometric Model Fitting.

Estimating reliable geometric model parameters from the data with severe outliers is a fundamental and important task in computer vision. This paper attempts to sample high-quality subsets and select model instances to estimate parameters in the multi-structural data. To address this, we propose an effective method called Latent Semantic Consensus (LSC). The principle of LSC is to preserve the latent semantic consensus in both data points and model hypotheses. Specifically, LSC formulates the model fitting problem into two latent semantic spaces based on data points and model hypotheses, respectively. Then, LSC explores the distributions of points in the two latent semantic spaces, to remove outliers, generate high-quality model hypotheses, and effectively estimate model instances. Finally, LSC is able to provide consistent and reliable solutions within only a few milliseconds for general multi-structural model fitting, due to its deterministic fitting nature and efficiency. Compared with several state-of-the-art model fitting methods, our LSC achieves significant superiority for the performance of both accuracy and speed on synthetic data and real images.

Self-stabilizing multivalued consensus in asynchronous crash-prone systems

The multivalued consensus problem is a fundamental issue in fault-tolerant distributed computing. It encompasses a wide range of agreement problems where processes must unanimously decide on a specific value v∈V, with |V|≥2. Existing solutions that handle process crash failures simplify the multivalued consensus problem by reducing it to the binary consensus problem. Examples of such solutions include Mostéfaoui-Raynal-Tronel [IPL 2000] and Zhang-Chen [IPL 2009].In this work, we aim to design an even more reliable solution by leveraging the concept of self-stabilization, which provides a strong form of fault tolerance. Self-stabilizing algorithms can recover from transient faults, which represent any deviation from the system's intended behavior (as long as the algorithm code remains intact) in addition to process and communication failures.To the best of our knowledge, this work presents the first self-stabilizing algorithm for multivalued consensus in asynchronous message-passing systems susceptible to process failures and transient faults. Our solution uses, at most, n concurrent invocations of binary consensus. This is another way we advance state-of-the-art solutions compared to previous non-self-stabilizing ones. For example, Mostéfaoui-Raynal-Tronel's solution requires an unbounded number of sequential invocations of binary consensus.

AI-based R&D for frozen and thawed meat: Research progress and future prospects.

Frozen and thawed meat plays an important role in stabilizing the meat supply chain and extending the shelf life of meat. However, traditional methods of research and development (R&D) struggle to meet rising demands for quality, nutritional value, innovation, safety, production efficiency, and sustainability. Frozen and thawed meat faces specific challenges, including quality degradation during thawing. Artificial intelligence (AI) has emerged as a promising solution to tackle these challenges in R&D of frozen and thawed meat. AI's capabilities in perception, judgment, and execution demonstrate significant potential in problem-solving and task execution. This review outlines the architecture of applying AI technology to the R&D of frozen and thawed meat, aiming to make AI better implement and deliver solutions. In comparison to traditional R&D methods, the current research progress and promising application prospects of AI in this field are comprehensively summarized, focusing on its role in addressing key challenges such as rapid optimization of thawing process. AI has already demonstrated success in areas such as product development, production optimization, risk management, and quality control for frozen and thawed meat. In the future, AI-based R&D for frozen and thawed meat will also play an important role in promoting personalization, intelligent production, and sustainable development. However, challenges remain, including the need for high-quality data, complex implementation, volatile processes, and environmental considerations. To realize the full potential of AI that can be integrated into R&D of frozen and thawed meat, further research is needed to develop more robust and reliable AI solutions, such as general AI, explainable AI, and green AI.

Enhanced ResNet50 Model for Aqueous Solubility Prediction of Drug Compounds Using Deep Learning Techniques

Background: Aqueous Solubility (AS) is a critical factor in drug discovery (DD), directly influencing a drug’s bioavailability and overall efficacy. Accurate prediction of AS remains a challenge despite the advancement in machine learning techniques, which are essential for improving the pharmacokinetics and formulation of new compounds. Methods: This study determines an enhanced ResNet50 deep learning architecture for predicting AS in drug compounds. Deep-net models with 8, 16, and 20-layer ResNet50 Convolutional Neural Network (CNN) architectures were developed. A dataset of 9,532 drug compounds, represented by molecular footprints, was used to train the models. The training process utilized a ten-fold cross-validation technique to optimize the model's predictive performance. Results: The 20-layer ResNet50 model outperformed human experts and shallower models, achieving an R² value of 0.423 and an RMSE of 0.678. The model also demonstrated an impressive ASP accuracy rate of 90.6%, significantly surpassing the predictions made by human experts and simpler neural network models. Conclusion: This study demonstrates that deeper-net architectures, particularly the 20-layer ResNet50 model, offer superior performance in predicting AS. These deep learning models provide a reliable and efficient solution for improving solubility predictions, crucial for advancing drug discovery efforts.

Techno-Economic Analysis of Solar, Wind, and Biomass Hybrid Renewable Energy Systems to Meet Electricity Demand of a Small Village in Bihar State of India

This study examines the potential use of Hybrid Renewable Energy Systems (consisting of photovoltaic, wind, bio, and diesel sources) both with and without the inclusion of battery storage in the eastern region of India. An evaluation is conducted to determine the economic viability of several system configurations, and the most efficient system is selected using HOMER software. The investigation focused on six distinct scenarios to meet the energy needs of a village community. The goal was to satisfy a daily load need of 1093.7 kWh, with a peak demand of 153.63 kW. The study examined many factors, such as system efficiency, financial viability, and ecological consequences. The primary aim of the research was to compare the power costs associated with different designs of HRES. Detailed techno-commercial assessments were carried out to examine the energy production, consumption, and financial impacts of each scenario. This research provides valuable insights for individuals and organizations seeking reliable and long-lasting energy solutions by analyzing the potential benefits and drawbacks of implementing HRES in rural areas. An evaluation is conducted to determine the energy contribution of each element within an RES, as well as the influence of HRES on energy expenses and net present value. The findings of this study reveal that the optimized hybrid system comprises 133 kW photovoltaic arrays, a 130-kW wind turbine, a 0.2 kW biogas generator, a 100-kW diesel generator, a 540-kWh battery bank with nominal capacity, and a 58-kW converter. This system has a minimum COE of 0.347$/kWh and NPC of $1.71M. The research offers useful insights for designers, scholars, and policymakers on the existing design constraints and policies of biomass-based hybrid systems.