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Abstract Material science has historically evolved in tandem with advancements in technologies for characterization, synthesis, and computation. Another type of technology to add to this mix is machine learning (ML) and artificial intelligence (AI). Now increasingly sophisticated AI‐models are seen that can solve progressively harder problems across a variety of fields. From a material science perspective, it is indisputable that machine learning and artificial intelligence offer a potent toolkit with the potential to substantially accelerate research efforts in areas such as the development and discovery of new functional materials. Less clear is how to best harness this development, what new skill sets will be required, and how it may affect established research practices. In this paper, those question are explored with respect to increasingly more sophisticated ML/AI‐approaches. To structure the discussion, a conceptual framework of an AI‐ladder is introduced. This AI‐ladder ranges from basic data‐fitting techniques to more advanced functionalities such as semi‐autonomous experimentation, experimental design, knowledge generation, hypothesis formulation, and the orchestration of specialized AI modules as stepping‐stones toward general artificial intelligence. This ladder metaphor provides a hierarchical framework for contemplating the opportunities, challenges, and evolving skill sets required to stay competitive in the age of artificial intelligence.

Abstract Owing to the highly parallel network structure of the biological neural network and its triggered processing mode, tactile sensory neurons can realize the perception of external signals and the functions of perception, memory, and data processing by adjusting the synaptic weight. In this paper, a piezoresistive pressure sensor is combined with a memristor to design an artificial tactile sensory neuron. The polyurethane sponge sensor has excellent sensitivity and can convert physical stimuli into electrical signals, and the chitosan‐based memristor has stable bipolar resistive switching characteristics, allowing further information to be memorized and processed. The neuron can respond to tactile stimuli of different degrees, durations, and frequencies; realize potentiation/depression modulation, paired‐pulse facilitation, and spike‐timing‐dependent plasticity; exhibit spike‐rate‐dependent plasticity; and store and erase tactile information through memistor state switching, which has great application potential in biological sensing systems.

Abstract Endoscopes navigate within the human body to observe anatomical structures with minimal invasiveness. A major shortcoming of their use is their narrow field‐of‐view during navigation in large, hollow anatomical regions. Mosaics of endoscopic images can provide surgeons with a map of the tool's environment. This would facilitate procedures, improve their efficiency, and potentially generate better patient outcomes. The emergence of magnetically steered endoscopes opens the way to safer procedures and creates an opportunity to provide robotic assistance both in the generation of the mosaic map and in navigation within this map. This paper proposes methods to autonomously navigate magnetic endoscopes to 1) generate endoscopic image mosaics and 2) use these mosaics as user interfaces to navigate throughout the explored area. These are the first strategies, which allow autonomous magnetic navigation in large, hollow organs during minimally invasive surgeries. The feasibility of these methods is demonstrated experimentally both in vitro and ex vivo in the context of the treatment of twin‐to‐twin transfusion syndrome. This minimally invasive procedure is performed in utero and necessitates coagulating shared vessels of twin fetuses on the placenta. A mosaic of the vasculature in combination with autonomous navigation has the potential to significantly facilitate this challenging surgery.

Abstract This paper presents the first quantitative relationship between the cold point tropopause (CPT) and tropical easterly jet (TEJ) using radiosonde observations over Gadanki (13.45° N, 79.2° E) during the Indian summer monsoon season 2006–2014. CPT and TEJ peak altitudes (HCPTandHTEJ) show amalgams of two categories of variability on the day‐to‐day scale. In category1 HTEJ occurs close to HCPT and they show in‐phase variation. While in Category2 HTEJ occurs far apart from HCPT and they do not show any relationship. For Category1 HCPT and HTEJ are strongly correlated (0.70), as well as HCPT and TCPT (CPT temperature) are moderately anticorrelated (−0.55) significant at a 95% confidence level, indicating the dominance of adiabatic processes. Whereas in Category2 HCPT and TCPT are not significantly anti‐correlated. Thus, when TEJ and CPT are close to each other, it may serve as an indicator for the prevalence of the synoptic‐scale effect.

Abstract This study assesses the midsummer precipitation prediction over eastern China by the dynamic downscaling method. Based on the Climate Forecast System version 2 of the National Centers for Environmental Prediction and the Weather Research and Forecasting Model, the prediction performance of global and regional models on the July precipitation over eastern China is further analyzed by hindcast experiments from 1982 to 2010 and prediction experiments from 2011 to 2021. The results suggest that the regional model forced by the global model can noticeably improve the prediction skill for precipitation in eastern China, especially in the region from the South of North China to the Yangtze River Basin, referred as the northern China in this paper. In addition, we perform a diagnostic analysis of the reason for the improvement of the model prediction skill. The results indicate that the high resolution of the regional model and the refinement of physical process parameterizations contribute to improving the simulation ability for the East Asian atmospheric circulation pattern, heat flux, especially for the meridional teleconnection pattern in East Asia and the sensible heat flux in the northern China, thus further improving precipitation prediction.

Abstract Objectives Endoscopic ultrasound‐guided tissue acquisition (EUS‐TA) is used for pathological diagnosis and obtaining samples for molecular testing, facilitating the initiation of targeted therapies in patients with pancreatic cancer. However, samples obtained via EUS‐TA are often insufficient, requiring more efforts to improve sampling adequacy for molecular testing. Therefore, this study investigated the use of oil blotting paper for formalin fixation of samples obtained via EUS‐TA. Methods This prospective study enrolled 42 patients who underwent EUS‐TA for pancreatic cancer between September 2020 and February 2022 at the Osaka International Cancer Institute. After a portion of each sample obtained via EUS‐TA was separated for routine histological evaluation, the residual samples were divided into filter paper and oil blotting paper groups for analysis. Accordingly, filter paper and oil blotting paper were used for the formalin fixation process. The total tissue, nuclear, and cytoplasm areas of each sample were quantitatively evaluated using virtual slides, and the specimen volume and histological diagnosis of each sample were evaluated by an expert pathologist. Results All cases were cytologically diagnosed as adenocarcinoma. The area ratios of the total tissue, nuclear, and cytoplasmic portions were significantly larger in the oil blotting paper group than in the filter paper group. The frequency of cases with large amount of tumor cells was significantly higher in the oil blotting paper group (33.3%) than in the filter paper group (11.9%) (p = 0.035). Conclusions Oil blotting paper can increase the sample volume obtained via EUS‐TA on glass slides and improve sampling adequacy for molecular testing.

Abstract It is no longer a myth that climate change‐related disasters are on the rise globally, with severe and devastating consequences in developing countries, particularly in Sub‐Saharan Africa, where many of the world's poor people reside. Thus, the emphasis on adaptation to climate change is no longer a choice or a deferred problem into the future, but rather an urgent matter of concern which must be considered a policy priority. As a response to retrogressive climate change challenges on social‐economic development and environmental degradation, the government of South Africa has in the last two and half decades, embarked on the progressive formulation of different policy instruments and strategic frameworks to curb and minimize the impacts of climate change on all sub‐sectors of the economy. Although much remains to be achieved, some level of success has been realized, particularly in the space of policy formulation, albeit the poor record of implementation. Using research methods inspired by the tradition of qualitative research and an appraisal of existing literature, this paper discusses the complexities of climate change adaptation governance at a local government level in South Africa, focusing on the Eastern Cape Province. It is argued in the paper that one of the major challenges for climate change governance at local municipality levels in South Africa emanates from the gap between aspirations and actions across the entire national governance system. The absence of an integrated climate change governance system has posed significant systemic challenges, and these have tended to constrain decision‐making and implementation processes. These issues have been discussed within the broader discourse of the sustainable development goals, particularly goals number 13, 16 and 17.

Key Clinical Message Main objective of root canal therapy is to locate all the canals, cleaning and shaping, and obturation to obtain fluid tight seal. Failure to locate all canals can lead to the failure of root canal therapy. Abstract Variation of pulp aperture, among teeth with multiple roots, constitutes recurring issue during diagnosing and completing efficient endodontic procedures. Understanding normal anatomy features and associated likely modifications is critical in the effective execution of the dental procedure, since the inability to effectively treat simply one canal may end up into endodontic unsuccessful therapy. The paper covers a procedure whereby the root pattern and canals of the maxillary second molar were modified employing an operative microscope and verified with cone‐beam computed tomography (CBCT). Cone‐beam computed tomography revealed that the maxillary second molar containing two different palatal roots and canals and two distinct buccal roots and canals. This Research paper presents and investigates the morphological difference observed on the maxillary second molar in order to guarantee the effectiveness of root canal treatment examined utilizing imaging techniques like CBCT.

Key Clinical Message The phenomenon of vessel pedicle ossification is a noteworthy aspect of the repair and reconstruction of maxillofacial defects. Imaging findings typically reveal high‐density shadows within the vascular pedicle pathway, which may be managed through conservative observation or surgical intervention as deemed appropriate. Abstract Vessel pedicle ossification is a relatively uncommon complication associated with the reconstruction of oral and maxillofacial tissue defects using free tissue flap repair. In this paper, we report a case of pedicle ossification and conduct a comprehensive review of previous literature. A 39‐year‐old man presented with a limited ability to open his mouth 6 months after fibular flap reconstruction of the mandible. Plain film X‐ray and computed tomography (CT) indicated pedicle ossification. Two years after the initial operation, the restriction in the patient's ability to open his mouth had not worsened, although there were more pronounced radiographic abnormalities.

Key Clinical Message Transverse colonic volvulus (TCV) is a serious condition with a mortality rate of up to 33%. It is very rare, especially in children. Despite its rarity, surgeons should have a high index of suspicion and include it in the list of differential diagnoses, especially in patients with developmental delays and associated uncommon syndromes. Resection and anastomosis, whether as a one‐stage or two‐stage procedure, proved to be the best treatment options for children. Since prompt identification and management are vital, this paper presents useful information on the presentation, treatment, and outcome of this case report.

Key Clinical Message Chyle leakage is a rare postoperative complication of breast cancer, and conservative treatments should be prioritized, with careful monitoring of drainage volume and timely surgical intervention when conservative treatments are ineffective. Abstract Chyle leaks following surgery for breast cancer are seldom encountered. Management varies with no consensus in the literature. This paper reports a case of a chylous leak after axillary dissection in a patient with breast cancer eventually cured with conservative treatment and discusses management options varied with both conservative and surgical options available to clinicians.

Abstract Conservation planners often rely on heuristic indices when challenged with prioritizing potential projects under a constrained budget. This paper presents a comparative analysis of several prioritization indices (PIs) of culvert fish passage barriers, which can contribute to declines in anadromous fish populations. A federal injunction requires Washington state to restore 90% of habitat blocked by state‐owned culverts by 2030, prompting the development of numerous PIs, by various entities (i.e., counties, cities) within the injunction area. Our comparative analysis of PIs within the injunction Case Area investigates their ability to distinguish between barriers, their transferability in terms of scoring metrics, how scoring weights differ, and the preferences implied thereby. We document the use of six distinct PI methods by 10 entities and find that some PIs used many shared metrics, whereas others used a high percentage of unique metrics that would be difficult to replicate outside the entity's jurisdiction. Although habitat potential, habitat quantity, and connectivity were considered across all PIs, we found a high level of variation in terms of the metric weights. Our methods can be employed in other geographies or for other restoration PI planning efforts, and our results may facilitate the development and refinement of future PIs.

Abstract Sedentary, inadequate sleep and exercise can affect human health. Artificial intelligence (AI) and Internet of Things (IoT) create the Artificial Intelligence of Things (AIoT), providing the possibility to solve these problems. This paper presents a novel approach to monitor various human behaviors for AIoT‐based health management using triboelectric nanogenerator (TENG) sensors. The insole with solely one TENG sensor, creating a most simplified system that utilizes machine learning (ML) for personalized motion monitoring, encompassing identity recognition and gait classification. A cushion with 12 TENG sensors achieves real‐time identity and sitting posture recognition with accuracy rates of 98.86% and 98.40%, respectively, effectively correcting sedentary behavior. Similarly, a smart pillow, equipped with 15 sensory channels, detects head movements during sleep, identifying 8 sleep patterns with 96.25% accuracy. Ultimately, constructing an AIoT‐based health management system to analyze these data, displaying health status through human‐machine interfaces, offers the potential to help individuals maintain good health.

Abstract As the power source of the engine, the Fuel Pump(FP) plays a vital role in the safe operation of the aircraft. Due to the complexity of the working mechanism of Aviation Fuel Pumps (AFP) and the strong correlation between the components, the assessment model cannot reflect the health state of the FPs better, while the initial parameters in the assessment model will affect the assessment effect of the model. Therefore, this paper proposes a health status assessment model that can fully integrate monitoring data. To improve the accuracy of the model parameters, the Random Forest algorithm is used to give the feature weights to make up for the limitation of relying on expert knowledge, and the model parameters are optimized by the Covariance Matrix Adaptive Evolutionary Strategy algorithm, which achieves an accurate assessment of the state. Finally, the AFP test bed was built and the AFP was tested. Compared with other methods, the accuracy of the proposed method in this question reaches 96%, which is greatly superior to other methods and verifies the effectiveness of the proposed method. It also provides an outlook on future research directions for health state assessment.

Abstract Wind power systems (WPs) are complex non‐linear systems with varying parameters affected by environmental changes, including wind speed fluctuations. Extracting maximum power from WPs poses a significant challenge due to these factors. Direct power control (DPC) is a highly effective technique known for its simplicity and ease of implementation. However, it suffers from power ripples caused by the use of hysteresis comparators and switching tables that operate at variable frequencies. To address this issue, this paper presents the robust neural controller (NC) based on DPC, which replaces the switching tables. The Double‐Fed Induction Generator (DFIG) is the chosen generator for the studied WP system due to its advantageous features. The NC‐DPC effectively regulates the exchange of active and reactive powers between the DFIG and the system, maximizing power extraction from the WP system while reducing Total Harmonic Distortion and enhancing overall system quality. The effectiveness of the NC‐DPC is evaluated through MATLAB simulations and further supported by experimental data obtained using the Real‐Time Interface of the dSPACE‐DS1104 Controller card.

Abstract This paper presents an energy‐efficient digital‐to‐analog converter (DAC) switching method with low common‐mode variations for high‐resolution successive approximation register (SAR) analog‐to‐digital converters (ADCs), while enabling to implement resolutions such as 14‐bit as compared to the typical 10‐bit. The proposed switching method enables high resolution by having a nearly constant common‐mode voltage and employing input‐swapping to use the reference voltage (Vref) only in the sampling phase. This method eliminates the need for the third reference voltage during the entire DAC switching steps, which reduces the required number of switches at the bottom plates of the capacitors. The use of lower number of switches not only lowers the DAC control logic complexity, but also results in a faster operation, lower power, and smaller area. When compared to conventional 10‐bit SAR ADCs, the proposed switching method in a 10‐bit implementation reduces the average energy and area by 96.09% and 75%, respectively, while offering high‐resolution implementation options such as 14 bits.

Abstract Under the background of green and low‐carbon transformation of industrial high‐energy‐consuming parks, in order to realize the coordinated development planning of energy and transportation under different peaking capacity requirements, this paper proposes a collaborative optimization model of industrial high‐energy‐consuming parks under green and low‐carbon. Firstly, by studying the energy balance mechanism between industrial load and new energy, an energy topology prior model of energy interaction in the next generation of industrial new energy power grid is constructed. Then, based on the probability characteristics of the equilibrium state of different regions in the high‐energy‐consuming industrial zone, combined with the prior knowledge of industrial load and low‐carbon power system, a collaborative development planning model of high‐energy‐consuming industrial parks is constructed. Finally, the proposed model is simulated. From the simulation results the authors can see the dynamic division model and solution algorithm of energy balance region established in this paper can effectively improve the energy balance ability of power grid, reduce peak shaving demand and use more renewable energy.

Abstract Occluded person re‐identification (Re‐ID) is to identify a particular person when the person's body parts are occluded. However, challenges remain in enhancing effective information representation and suppressing background clutter when considering occlusion scenes. This paper proposes a novel attention map‐driven network (AMD‐Net) for occluded person Re‐ID. In AMD‐Net, human parsing labels are introduced to supervise the generation of partial attention maps, while a spatial‐frequency interaction module is suggested to complement the higher‐order semantic information from the frequency domain. Furthermore, a Taylor‐inspired feature filter for mitigating background disturbance and extracting fine‐grained features is proposed. Moreover, a part‐soft triplet loss, which is robust to non‐discriminative body partial features is also designed. Experimental results on Occluded‐Duke, Occluded‐Reid, Market‐1501, and Duke‐MTMC datasets show that this method outperforms existing state‐of‐the‐art methods. The code is available at: https://github.com/ISCLab‐Bistu/SA‐ReID.

Abstract The wind power generation system of a 5 MW horizontal axis wind turbine has a high wind energy conversion efficiency. The proportion of installed capacity in practical production applications is increasing year on year, so that the stability of its operation becomes a central factor in determining the productivity of the wind farm in question. This paper takes a 5 MW wind turbine as the research object and proposes a parameter‐adaptive robust model predictive control method to achieve self‐optimization of controller parameters through a Bayesian optimization approach. A robust model predictive control strategy, aiming to reduce the power fluctuation while maximizing the power output, is developed in this paper to enhance the dynamic economic performance under uncertain wind speed variation. A Bayesian algorithm is used in this paper to optimize the parameters of the controller. Moreover, wind speeds are simulated using TurbSim for different turbulence intensities of 5%, 10%, and 15% turbulence. Finally, the robust model predictive control toolbox in MATLAB is designed and simulated. The results show that the operational instability of the wind energy system is overcome. Meanwhile, the robustness of the wind energy system operation is improved compared to the traditional model predictive control approach.

Abstract Adsorption cooling and desalination systems have a distinct advantage over other systems that use low‐grade waste heat near ambient temperature. Since improving their performance, including reliability and failure prediction, is challenging, developing an efficient diagnostic system is of great practical significance. The paper introduces artificial intelligence (AI) and an automated machine learning approach (AutoML) in a real‐life application for a computational diagnostic system of existing adsorption cooling and desalination facilities. A total of 1769 simulated data points containing data indicating a failure status are applied to develop a comprehensive AI‐based Diagnostic (AID) system covering a wide range of 42 input parameters. The paper introduces a conditional monitoring system for adsorption cooling and desalination systems. The novelty of the presented study mainly consists of two aspects. First, the intelligent system predicts the health or failure states of various components in a complex three‐bed adsorption chiller installation using the extensive input data sets of 42 different operating parameters. The developed AID expert tool, based on selecting the best from 42 models generated by the DataRobot platform, was validated on the complex, existing three‐bed adsorption chiller. The AID system correctly identified healthy and failure states in various installation components. The developed expert system is very efficient (AUC = 0.988, RMSE = 0.20, LogLoss = 0.14) in predicting emergency states. The proposed method constitutes a quick and easy technique for failure prediction and represents a complementary tool compared to the other condition monitoring methods.

Abstract Microgrids with large‐scale photovoltaic systems constitute a large part of distributed renewable generation in many grids around the world. Managing the performance of such microgrids and especially their interaction with the main power grid is a challenging task, because it requires the control of renewable resources. This paper presents a comprehensive overview of DC‐DC converter structures used in microgrids and presents a new classification for converters. This paper also provides an overview of the control techniques of DC‐DC converters in DC microgrids and the advantages and disadvantages of the control methods are discussed. In connection with the increasing penetration of distributed generation sources (DGS) and renewable sources in power systems and their power management has been raised as a major concern and methods for power management have been investigated in this paper. Finally, a DC microgrid system, which includes a solar system, wind turbine, and battery, is simulated in MATLAB/Simulink software and its performance is analyzed.

Abstract Saturation characteristics of water and capillary pressure are important for hydrofracturing or water flooding, which is widely adopted in dust suppression, methane displacement, and gas and coal outburst elimination. In this paper, we have studied the water saturation characteristics of coal samples with different coal ranks and their effect on capillary pressure. It was found that the isothermal adsorption of water vapor from coal samples can be divided into three processes: monolayer adsorption, multilayer adsorption, and capillary condensation. The dent isothermal adsorption model can well describe water vapor adsorption of coal samples, indicating water molecules were first adsorbed on the first‐order adsorption site of pore surfaces at low relative humidity and then on the second‐order adsorption site with increasing relative humidity. An obvious hysteresis occurred in the process of water imbibition and drainage. A capillary pressure of ~325 MPa or even larger was required to displace the water from the coal. Besides, micro‐ and mesopores play a more important role in capillary pressure. The capillary pressure of coal samples was positively correlated with wettability at lower water saturation, but it is not significant at higher capillary pressures. Therefore, the article's results provide a new direction for solving the water‐locking effect in coal seams.

Abstract Environmental issues are the most discussed in today's industrialized world, and this has always been associated with the emission of greenhouse gases. CO2 emissions, especially from power plants, are considered the major greenhouse gas source. Capturing CO2 from large emission sources is necessary to address the worst impacts of climate change, including more frequent and severe droughts, heat waves, and intermittent rainfall. Conventional post‐combustion CO2 capture has always been questioned because of its high impact on energy costs. Rotating packed bed technology has shown the potential to reduce the cost of CO2 capture. This paper presents an experimental study on an in‐house designed rotating packed bed absorber for the capture of CO2 from a flue gas stream. Effects of process parameters such as rotating speed, liquid‐to‐gas ratio, and monoethanolamine (MEA) concentration on CO2 capture efficiency and overall volumetric mass transfer coefficient were studied. The study showed that as rotor speed increased, the CO2 capture efficiency increased, this can be attributed to higher rotor speed, resulting in more droplet flow, indicating an increase in the area of contact between the flue gas and the MEA solvent. Also, as the concentration of MEA increased CO2 capture efficiency increased, since an increase in concentration results in an increase in the rate of reaction. Finally, the study showed that a rotating packed bed absorber is an efficient unit for CO2 capture since it enhances mass transfer performance.

Abstract In the chemical flooding process, the premature breakthrough of chemical agents in production wells results in a large waste of chemical agents and increases the volume and processing difficulty of the produced fluids. The early warning method of chemical agent channeling can predict the strength of agent channeling in advance. The practice of chemical flooding shows that the production performance can be used for early warning of chemical agent channeling. In this paper, we analyze the relationship between cumulative oil production and cumulative polymer production of production wells in polymer–surfactant flooding. Three types of curves according to the enhanced oil production characteristics of chemical flooding, including convex type, S‐type, and concave type. We use the drop speed of the water–oil ratio and the rapid‐decline speed of water cut as early warning indicators to predict the channeling coefficient. A Latin hypercube experimental design method is used to design a polymer–surfactant flooding scheme with the main control factors of the channeling coefficient and early warning indicators. Numerical simulation is used to calculate samples of the channeling coefficient and early warning indicators under various conditions. The drop speed of the water–oil ratio reference value model and the rapid‐decline speed of the water cut reference value model are determined with a multiple regression method. A prediction model for the chemical agent channeling coefficient is established using the deviation coefficient of an early warning index. The method is applied in the Ng54‐61 polymer–surfactant flooding pilot area in the west of the Gudong Seventh District, Shengli Oilfield, China, and the error between the predicted result and the actual value is less than 10%. This research is helpful in taking effective antichanneling measures and improving the oil recovery degree of chemical flooding reservoirs.

Abstract Before starting stability analysis of the multivirtual synchronous machine (n‐VISMA) power system, it is necessary to obtain the steady‐state operating points (SSOPs) of all dynamic nodes in the network. Modified traditional iterative schemes using the concept of droop bus technique in an islanded microgrid are not feasible for load flow analysis of VISMA microgrid incorporating non‐control dynamics. This paper proposes closed‐form steady‐state, fundamental‐frequency models for islanded VISMA microgrids using the concept of virtual swing buses. In this technique, the virtual internal buses of all VISMAs in the network are governed by the swing equation. The voltage at all buses is variable except the virtual buses in which the pole wheel voltages are prespecified. The algorithm was extended by a droop control localized to each VISMA. The suitability of the proposed algorithm to obtaining SSOPs of VISMA was tested on IEEE‐9 bus system with VISMA replacing electromechanical synchronous machines and also on a low‐voltage distribution system. To validate the applicability of the proposed algorithm and prove its accuracy, the case study systems were also modeled in the SIMULINK environment for detailed time domain analysis. The algorithm was found to be computationally effective for a load flow analysis of the VISMA microgrid. The results also reveal that the addition of external droop control improves the frequency stability of the system.

Abstract Automakers spend billions of dollars annually towards warranty costs, and warranty reduction is typically high on their priorities. An accurate understanding of warranty performance plays a critical role in controlling and steering the business, and it is of crucial importance to fully understand the actual situation as well as be able to predict future performance, for example, to set up adequate financial reserves or to prioritize improvement actions based on expected forthcoming claims. Data maturation, a major nuisance causing changes in performance metrics with observation time, is one of the factors complicating warranty data analysis and typically leads to over‐optimistic conclusions. In this paper, we propose a sequence of steps, decomposing and addressing the main reasons causing data maturation. We first compensate for reporting delay effects using a Cox regression model. For the compensation of heterogeneous build quality, sales delay, and warranty expiration rushes, a constrained quadratic optimization approach is presented, and finally, a sales pattern forecast is provided to properly weigh adjusted individual warranty key performance indicators. The results are shown to dramatically improve prior modeling approaches.

Abstract The goal of deploying LTE‐Advanced heterogeneous networks is to meet the growing need for Quality of Service (QoS), high data rates, and more coverage. Load balancing is one of the biggest problems, especially when User Equipment (UE) uses received signal strength to connect to different transmission power network tiers. Spectrum for small cells has not been used to its full potential. While all the cells are using the same frequency spectrum and the small cells overlap the Macro Cells, preventing interference is another big problem. The proposed Cell Range Extension (CRE) and Almost Blank Sub‐frame (ABS) provides a solution to overcome these challenges. The proposed ABS in this paper is novel because it is a partial muting one rather than the full muting ABS in recent research studies. In addition, the cell association method is by distance rather than conventional Reference Signal Received Power (RSRP). CRE‐ABS has been applied simultaneously to solve both load balancing and interference. Simulation results show that the proposed scheme achieves substantial throughput and Jain's Fairness Index (JFI).

Abstract Named Data Networking is a novel network architecture for the future internet in which data is named rather than addressed. This enables routers to forward data more efficiently based on its name, outperforming IP‐based routing. Another networking architecture that divides the control plane and the data plane is software‐defined networking (SDN). In an SDN network, the control plane decides on routing while the data plane forwards data packets. This separation increases routing flexibility and scalability. Numerous techniques to improve routing can be achieved with NDN and SDN integration. This paper provides an in‐depth examination of routing approaches in NDN based on SDN, emphasizing design principles, algorithms, and performance measures. We begin by summarizing the NDN architecture and delving into its essential components. We next go into the core routing ideas in NDN and categorize and study several routing solutions based on Software Defined Networks. Finally, we highlight the need for scalable, effective, and secure routing systems that may satisfy the expanding requirements of the contemporary internet. We also suggest open research topics in NDN routing based on SDN. This review provides an extensive overview of current centralized routing approaches in NDN, including their limitations and future possibilities.

Abstract MnS which has good plasticity is a non‐metallic inclusion commonly found in steel. For most steel types, the size, shape, and distribution of MnS have a significant influence on the properties of steel. The large‐sized MnS inclusions disrupt the continuity of the steel and cause the anisotropy in steel. The result is a decline of steel's overall performance. In contrast, the small‐sized MnS inclusions which in the shape of spherical or spindle in steel can diminish the incidence of thermal embrittlement and improve the machinability of steel. The morphology of MnS in steel is mainly affected by the ingredients of steel and heat treatment manner. MnS inclusions in steel are present in spherical, polyhedral, dendritic, and irregular shapes. The precipitation behavior is mainly affected by the steel ingredients, heat treatment system, and other factors. This paper summarizes the latest research results about the factors affecting MnS inclusions and controlling measures in high‐quality steel in recent years.

Abstract In this paper, we explored the influence of lipase on cuticle wax and anthocyanin metabolism in blueberries. The results demonstrated a progressive alteration in wax crystal structure, transitions from rod‐like to short rod‐like and flaky, and even disappearance of the cuticle wax. Gas chromatography–mass spectrometry analysis revealed a reduction in esters, aldehydes, long‐chain alkanes, and total wax content following lipase treatment of blueberry fruits. Additionally, ultra high performance liquid chromatography–mass spectrometry analysis showed significant decreases in peonidin, petunidin, cyanidin, and delphinidin, accompanied by an increase in malvidin content due to lipase treatment. Subsequent investigations uncovered that lipase treatment inhibited the activities of key anthocyanin synthetic enzymes, namely phenylalanine ammonia‐lyase (PAL), chalcone isomerase (CHI), and dihydroflavonol reductase (DFR). Moreover, lipase treatment downregulated the expression of synthesis‐related genes, including PAL, 4‐coumarate: CoA ligase, chalcone synthase (CHS), CHI, and DFR. In summary, our findings indicate that lipase disrupts the integrity of cuticle wax structure, inhibits anthocyanin synthesis, and reduces anthocyanin content. Accordingly, our studies suggest that inhibiting lipase could offer a novel approach to preserving the freshness and anthocyanin concentration of blueberries.

Abstract This review examines various methods of reducing acrylamide levels in processed foods, focusing on thermal and nonthermal methods. Acrylamide, which is mainly formed by the Maillard reaction, poses a health risk and therefore requires the implementation of successful mitigation strategies. The processes by which acrylamide is formed, particularly at temperatures above 120°C, such as frying, roasting, and cooking (however, the practical temperature in the inner of foods does not exceed 120°C), serve as a basis for understanding intervention methods. The effectiveness of thermal technologies, including optimization of time and temperature as well as pretreatment and posttreatment techniques, will be studied in detail. In addition, vacuum‐based technologies such as baking, predrying, frying, deep‐frying, and impregnation are examined to shed light on their underlying mechanisms. Advanced thermal techniques such as microwaves and irradiation will be investigated to evaluate their effectiveness in reducing acrylamide. Furthermore, nonthermal methods, including pulsed electric fields, ultrasound treatments, and novel combinations such as pulsed electric fields and blanching, are being investigated. Various enzymatic interventions with asparaginase and glucose oxidase as well as yeast treatments and fermentations offer a wide range of intervention possibilities. The use of additives/coatings and plant extracts, such as edible coatings, polyphenols, and specific ingredient formulations, has shown promise for acrylamide reduction. This paper highlights the commercial implications, future prospects, and barriers to implementation of these methods. By examining different approaches, this comprehensive analysis emphasizes the importance of using different strategies to successfully reduce acrylamide levels in processed foods and provides guidance to the food industry to improve product safety and quality.

Abstract Marine resources are rich. In recent years, more and more people have paid attention to the development of blue food. Fish protein is one of the essential biomolecules in blue food. It is a natural emulsifier because of its natural, nontoxic, easily obtained, degradable, and good surface activity. The research and application of fish protein as an emulsifier were reviewed in this paper. The emulsifying mechanism of fish protein was discussed. The emulsifying property of fish protein was affected by natural factors (source and protein concentration) and processing environment (ion strength, temperature, and pH). The emulsifying property of fish protein was improved by ultrasound, high pressure, microgel, complexation, glycosylation, and enzyme cross‐linking. It was pointed out that future research of fish protein applications should focus on the problems of shortening the gap between laboratory and commercial; stable emulsion of fish protein is difficult to preserve and single loading. It provides a reference for the development of fish protein emulsifiers.

Abstract The conventional techniques for protein extraction from biomass are not fully aligned with sustainability goals, so it is important to look for some alternate solutions. By simultaneously extracting both soluble and insoluble proteins, deep eutectic solvents (DESs) offer a viable method for valorizing protein‐rich biomass from a variety of sources. Notably, the molecular crowding effects of DESs may have helped unfolded proteins acquire compact and stable conformations, facilitating solubilization and effective extraction. However, there is still a lack of information regarding how DESs interact with proteins and affect the structure and properties of the recovered proteins. To enable their widespread usage as a sustainable method for extracting dietary proteins, the safety of DES‐extracted proteins must also be addressed. In this paper, we review the state of the science in DES‐mediated protein extraction, focusing on the extraction mechanism and the interactions between DESs and proteins. Additionally, important aspects of DES‐mediated protein extraction that could affect the structure, technofunctional, nutritional characteristics, and safety of extracted protein are explored. DES‐based protein extraction could be helpful to valorize different biomasses for the production of food proteins due to the specific features.

Abstract In Ethiopia, a diverse array of cereal‐based alcoholic beverages is being prepared and consumed. These traditional fermented drinks are distinct to specific regions and are prepared by locals using locally available raw materials according to cultural traditions. Notable among these are Tella, Areki, Keribo, Borde, and Shamita, renowned for their nutritional benefits and their role in ensuring food security. This paper explores existing literature regarding the production, quality, and safety of traditional cereal‐based alcoholic beverages in Ethiopia. Despite the widespread consumption of these beverages, they have yet to be commercialized, mainly due to their perceived low quality. The uncommercialized processes and products demand more attention, particularly in light of the country's inflationary pressures. Additionally, these traditional fermented beverages significantly enhance health due to the presence of bioactive compounds and their nutritional value. Standardizing and modernizing production methods by integrating scientific knowledge, such as optimizing fermentation practices, is essential to fully capitalize on these traditional beverages. Equipping local producers with this knowledge can facilitate the transition to larger scale production. Furthermore, continued research is essential to maintaining overall quality and safety standards. Therefore, it is crucial to concentrate on enhancing the nutritional value and quality of traditional cereal‐based beverages in the future. By illuminating these aspects, this review aims to enhance understanding of the traditional Ethiopian alcoholic beverage industry and its potential for elevating quality and safety standards. Moreover, the review explores these beverages' cultural significance, consumption patterns, and associated health risks.

Abstract Communication security has become particularly crucial with the rapid development of the Internet of Things (IoT). Frequency hopping spread spectrum (FHSS) technology, a prevalent method in wireless communication, has a wide range of applications in the Internet of Things. Enhancing the security of frequency hopping sequences is an essential means to improve the security of frequency hopping communication in the Internet of Things, as the performance of frequency hopping sequences plays a crucial role in frequency hopping systems. This paper proposes constructing secure adaptive frequency hopping sequence sets based on the advanced encryption standard (AES) algorithm. As a block cipher algorithm with superior security, the AES algorithm can provide a fundamental guarantee for the security of the proposed frequency hopping sequences. The mapping methods from ciphertext sequences to frequency hopping sequences proposed in this paper can achieve the construction of frequency hopping sequences of any frequency set size to meet the requirements of adaptive frequency hopping. In addition, we also model and analyse the problem of overlapping spectrum band of the IoT groups in the industrial, scientific, and medical (ISM) band, aiming to achieve better packet transmission performance by adjusting the frequency set size.

Abstract In unmanned aerial vehicle ad‐hoc network (UANET), the node speed of unmanned aerial vehicles (UAVs) may reach up to 400 km/h. The fast or slow movement of UAV nodes leads to different speeds of topology change of the nodes. Traditional optimized link state routing (OLSR) protocol cannot adaptively adjust the routing update period when the network topology changes, which may lead to the nodes calculating incorrect routing tables. This increases the average end‐to‐end delay and packet loss rate for packet transmission. To enhance the adaptability of OLSR routing protocol to network topology changes, this paper proposes a multi‐agent independent deep deterministic policy gradient‐OLSR (MA‐IDDPG‐OLSR) routing protocol based on distributed multi‐agent reinforcement learning. The protocol deploys DDPG algorithm on each UAV node, and each UAV node adaptively adjusts the Hello and TC message sending intervals, according to the one‐hop neighbouring nodes as well as its own state. Simulation results show that the proposed protocol is able to improve the throughput and reduce the packet loss rate as compared to traditional AODV, GRP, OLSR, and distributed multiple‐agent independent proximal policy optimization‐OLSR (MA‐IPPO‐OLSR), distributed multiple‐agent independent twin delayed deep deterministic policy gradient‐OLSR (MA‐ITD3‐OLSR) routing protocols. Since MA‐IDDPG‐OLSR relies only on local information, there is a minor performance degradation in MA‐IDDPG‐OLSR compared to centralized single‐agent DQN‐OLSR routing protocol. But it is more suitable to a completely distributed UAV network without a centralized node.

Abstract This paper is concerned with the synchronization of stochastic uncertain complex dynamic networks with time‐varying delays. In contrast to existing synchronization network models, the current study considers both internal time‐varying delays and coupling time‐varying delays. By analyzing the two factors (i.e. time‐varying delays and uncertainties) on the synchronization performance of stochastic uncertain complex dynamic networks, a new inverse optimal adaptive control is proposed. The analysis tool developed in this paper is based on Lyapunov technique, algebraic graph theory and inverse optimality, which does not need to solve Hamilton‐Jacobi‐Bellman equation. Some sufficient conditions are established for the synchronization of stochastic uncertain complex dynamic networks with time‐varying delays. To demonstrate the effectiveness of the present approach, simulation results are illustrated.

Abstract The stabilization problem for uncertain discrete‐time singular Markovian jump systems (DSMJSs) with time‐varying delays is comprehensively covered in this paper. An updated Lyapunov–Krasovskii functional is presented via a discrete state decomposition method. With the help of this constructed Lyapunov–Krasovskii functional, some delay‐ and mode‐dependent sufficient conditions for the open‐loop DSMJSs are derived. Based on these circumstances, a memory mode‐dependent state feedback control is used to create a closed‐loop DSMJS with parameter uncertainties that is regular and causal. And then, the stochastically admissible conditions are attained. Through the exact calculation of each decomposition component for the designed memory state feedback controller, the intended memory state feedback controller settings are determined. It should be mentioned that the algorithm suggested in this article expands the controller design's feasibility and flexibility. The numerical results show how the approach is superior to previous ones, and the given results are less conservative.

Abstract This paper considers the problem of communication protocols between leaders and its followers for motion planning in an initially partially known environment. In this setting, the leader observes the environment information to satisfy its own local objective and and the follower completes its own local objective by estimating the states of the leader and communicating with the leader to update its knowledge about the environment when it is necessary, where the local objectives can be expressed in temporal logic. A verifier construction is built first to contain all possible communication protocols between the leaders and the followers. Then, a two‐step synthesis procedure is proposed to capture all feasible communication protocol that satisfy the local objectives for the leader and follower, respectively. In the first step, a sub‐verifier is synthesized to satisfy the objective of the follower. In the second step, based on the obtained sub‐verifier, an iterative algorithm is proposed to extract communication protocols such that the objectives of the leader and follower are satisfied, respectively. A running example is provided to illustrate the proposed procedures.

Abstract In the realm of evolutionary game theory, the majority of scenarios involve players with incomplete knowledge, specially regarding their opponents' actions and payoffs compounded by the ever‐shifting landscape of players' interactions. These dynamics present formidable challenges in both the analysis and optimization of game evolution. To address this, a novel model named the networked evolutionary game (NEG) is proposed based on incomplete information with switched topologies. This model captures situations where players possess limited insight into their opponents' benefits, yet make decisions based on their own payoffs while adapting to different networks and new players. To bridge the gap between incomplete and complete information games, R. Selten's transformation method is leveraged, a renowned approach that converts an incomplete information game into an interim agent game, thereby establishing the equivalence of pure Nash equilibria (NE) in both scenarios. Employing the semi‐tensor product (STP) of matrices, a powerful tool in logistic system, the evolution of the model is articulated through algebraic relationships. This enables to unravel the patterns of game evolution and identify the corresponding pure Nash equilibria. By introducing control players, strategically positioned within the game, optimized control is facilitated over the evolutionary trajectory, ultimately leading to convergence towards an optimal outcome. Finally, these concepts are illustrated with a practical example within the paper.

Abstract In the field of optoelectronic tracking, precisely modeling the motion equations of the tracked target is often challenging, and in some cases, they may even be entirely unknown. This necessitates the use of a robust state estimator for accurate state estimation. Additionally, atmospheric turbulence, variations in illumination, and intricate observation backgrounds may introduce a significant increase in observation noise for the tracked target. To address these challenges, one approach is to introduce adaptive factors, such as the Mahalanobis method, into the robust state estimator to enhance estimation accuracy. However, further exploration has revealed that adaptive factors designed using different methods offer unique advantages in scenarios with varying levels of noise amplification. In this paper, different adaptive factors are further combined using an interacting multiple model approach, allowing the designed state estimator to exhibit stronger adaptability to noise amplification. The stability and effectiveness of this algorithm are validated through program simulations, double reflection mirror experiment, and drone trace prediction, demonstrating its applicability and reliability in diverse scenarios.

Abstract This paper extends the characteristic ratio approach using novel inequalities to ensure zero/low overshoot for linear‐time‐invariant systems with zeros. The extension provided by this paper is based on the maximally‐flatness property of a transfer function, where the square‐magnitude of the transfer function is ensured to be a low‐pass filter. In order to be able to design low‐order/fixed structure controllers, a partial pole‐assignment approach is used instead of the full pole‐assignment used in the Characteristic Ratio Assignment (CRA) method. The developed inequalities and additional stability conditions are combined into an optimization problem using time domain restrictions when necessary. Although the method given in the paper is general, particular inequalities are developed for PI and PI‐PD controller cases, due to their frequent use in industrial applications. Similarly, First‐Order‐Plus‐Delay‐Time (FOPDT) and Second‐Order‐Plus‐Delay‐Time (SOPDT) systems are considered specifically, since most of the practical systems can be approximated by one of these types. The study is extended to plants with uncertainties where a theorem is developed to decrease computation time dramatically. The benefits of the proposed methods are demonstrated by several examples.

Abstract Five‐phase permanent magnet synchronous machine (PMSM) can operate under open‐circuit faults with the appropriate current regulations. However, the traditional current regulations based on the five‐phase half‐bridge inverter have to satisfy the constraint of zero neutral point current, which limits the fault‐tolerant performance of the machine. Thus, this paper investigates the current regulation based on the five‐phase six‐leg (FPSL) inverter, which relieves the zero‐neutral point current limitation, and both current regulations considering the maximum torque (MT) and minimum copper loss (MC) are derived under different open‐circuit conditions. The cost of the system has increased, but with the new regulations, better fault‐tolerant operation capability of the machine can be obtained under all kinds of open‐circuit faults. Moreover, with the introduction of the neutral point current, the machine can work under triple‐phase open‐circuit faults. In conclusion, the proposed control scheme based on the FPSL inverter expands the fault‐tolerant operation capability of the five‐phase PMSM. The experiment results prove the effectiveness of the proposed current regulation on increasing average torque or reducing copper loss under fault‐tolerant operation.

Abstract Power outages can cause harm to both society and individual health. Among the crucial elements of power systems, power transformers play a vital role, and their failures can lead to incidents and disruptions. According to statistics, almost 50% of the explosions and fires in the power transformer are caused by oil‐impregnated paper (OIP) bushing failures. Moreover, moisture is a crucial factor that affects the health of OIP bushings, and assessing the moisture level in OIP bushings is vital to guarantee the safety of power systems. While the frequency‐domain dielectric spectroscopy (FDS) method has been widely used to assess the moisture content of OIP bushings on‐site, it has the disadvantage of requiring a long measurement time. Therefore, a novel technique was proposed in this study that involves measuring the OIP bushing dissipation factor (tanδ) at 1 Hz to estimate the moisture content in a significantly shorter time. Five samples of OIP with various moisture levels have been selected, and an equation based on the OIPs' value of tanδ at 1 Hz was proposed to assess the moisture level of OIP bushings. Furthermore, the validity of the proposed method was successfully verified, proving its precision and time efficiency.

Abstract Contemporary distribution networks can be seen with diverse dispatchable and non‐dispatchable energy resources. The coordinated scheduling of these dispatchable resources with non‐dispatchable resources can provide several techno‐economic and social benefits. Since battery energy storage systems (BESSs) and microturbine units are capital intensive. A thorough investigation of their coordinated scheduling on a purely economic basis will be an interesting and challenging task while considering dynamic electricity price and uncertainty of renewable power generation and load demand. This paper proposes a new methodology for optimal coordinated scheduling of BESSs and microturbine units considering existing renewable energy resources and dynamic electricity price to maximize daily profit function of the utility. In this study, a recently explored modified African buffalo optimization algorithm is employed. The key attributes of the proposed methodology are comprised of mean price‐based adaptive scheduling embedded within a decision mechanism system to maximize arbitrage benefits. Decision mechanism system keeps a track of system states as a‐priori thus guides the artificial intelligence‐based solution technique for sequential optimization. This may also reduce the computational burden of complex real‐life engineering optimization problems. Further, a novel concept of fictitious charges in coordination with BESS management algorithm is proposed to restrict the counterproductive operational management of BESSs. The application results investigated and compared on a benchmark 33‐bus test distribution system highlights the importance of the proposed methodology.

Abstract The explosion accident due to the internal arc fault in the metal‐enclosed switchgear cannot be ignored. Therefore, improving the ability of switchgear to resist the arc fault has important significance. In this paper, a passive protection measure by installing the buffer volume (BV) was proposed. The short‐circuit arcing tests were carried out in the small‐scale switchgear compartments, and the pressure rise was measured in the different models. The changes of pressure rise before and after installing BV were analyzed. Then, the calculation methods of pressure rise in the compartments and the mechanical response of the enclosure were put forward. By calculating the distributions of the pressure rise and temperature rise in the compartments, as well as the time‐varying displacement and stress of the enclosure, the pressure relief effect of the BV has been discussed. Results show that after installing the BV, the pressure rise, displacement, and stress are all decreased obviously in the initial of arcing. Hence, the arc fault protection effect for the switchgear can be improved by properly installing the BV.

Abstract Precisely locating the voltage sag sources is significant for improving power quality and clarifying the responsibilities of both power supply and consumption. However, the existing location models are complex, computationally intensive, and influenced by the measurement error and fault type, which have low positioning efficiency and large errors. Therefore, this paper proposes a method for locating voltage sag sources based on power polarity and twin characteristics matching. The method firstly uses the polarity of the sequence sag component disturbance reactive power at the monitoring point to determine the sag line. Secondly, the basic realization method of the twin power system is discussed, and the twin characteristics are defined by combining the positive and negative sequence sag component disturbance reactive power and the twin database is established. Thirdly, the spectral norm and Pearson product‐moment correlation coefficient are used to define the data matching degree, and the precise location model of the sag source is established with the objective function of maximizing the data matching between the twin characteristics of the preset and actual sag. Finally, the Improved Whale Optimization Algorithm is used to solve the location model. The correctness and superiority of the proposed method is verified based on IEEE33 node modelling.

Abstract This paper presents a state‐space average model of a three‐level photovoltaic (PV) inverter to understand short‐circuit currents transient characteristics. Analytical solution of the model is also derived. The proposed model is validated with PV system controller hardware in‐loop (HIL) test method. The errors between simulation and HIL tests results under various conditions are evaluated, testifying the validity of the proposed model. With the model and analytical solution, the decaying time constants of sub‐transient and transient processes are derived, which shows that the time constants of a three‐level PV inverter are not fixed and they vary with voltage dips. The analytical expression of short‐circuit currents is also formulated. The findings from this study are essential to short‐circuit current calculations of a power grid with large‐scale PV plants, coordination of power system protections, and grid planning.

Abstract Existing interturn fault detecting methods rely on winding impedance, winding current, and dissolved gases. They are effective only when the insulation is severely damaged. This paper proposes a novel detection method based on fusion analysis of electrothermal characteristics including winding currents, temperatures of four areas on the tank wall, top oil and ambient temperatures, which can identify the interturn fault at an early stage. When an incipient interturn fault occurs, the heat generated by the faulty turns is transferred to the oil and tank wall, leading to an increase in top oil and tank wall temperatures. Thus, the incipient fault can be detected by analysing these electrothermal characteristic parameters. Borrowing the idea of digital twin (DT), this method establishes a high‐fidelity simulation model to simulate the transformer electrothermal characteristics under different operating conditions. Afterward, an intelligent neural network is adopted to extract the quantitative relationship between the eight feature attributions and fault conditions. Finally, this neural network is utilized to detect the incipient interturn fault for the transformer entity. Case studies are conducted on a 100 kVA transformer with oil natural air natural (ONAN) cooling mode. The detection accuracy is improved by 68.5% compared to the winding current‐based method.

Abstract In distribution networks, among the planning problems, optimal placement of medium voltage to low voltage (MV/LV) transformers is a vital and challenging issue. Electrical load uncertainty is an important factor that affects the result of this planning problem. This paper investigates optimal allocation of service transformers with respect to the load uncertainty modelled by information gap decision theory (IGDT). For this aim, the planning problem is solved in risk‐neutral (RN) and risk‐averse (RA) frameworks. In RN strategy, objective function is defined to minimize the cost of service transformers and low voltage feeders as well as the cost of power losses. On the other hand, in RA strategy, objective function is defined to maximize the radius of the uncertainty in such a way that any deviation of the uncertain parameter results in an objective function value that is not worse than the critical limit. The optimization problem is solved by crow search algorithm (CSA) and particle swarm optimization (PSO) and the results are compared. In mid‐term planning, with respect to the deviation factors of 0.05, 0.1, 0.15, 0.2, 0.25 and 0.3, optimal values of the uncertainty radius are 5.89%, 13.64%, 21.37%, 28.97%, 34.39% and 43.46%, respectively. In long‐term planning, with respect to the deviation factors of 0.05, 0.1, 0.15, 0.2, 0.25 and 0.3, optimal values of the uncertainty radius are 6.92%, 13.33%, 20.39%, 27.03%, 34% and 40.46%, respectively. Moreover, on average, CSA finds more promising results than PSO.