Actual Environment Research Articles

Development of α-ray visualization survey meter in high gamma and neutron background environment.

A survey meter was developed to reliably detect and visualize surface contamination of suits and objects by α-nuclides in high γ/n-rays background radiation environment. The survey meter features a semi-opaque ZnS:Ag scintillator mounted directly onto a multi-anode photomultiplier tube (MA-PMT) and amplification circuits, ensuring output gain equalization for all channels. α-ray events induce localized light emission in thin-film scintillators. By directly mounting the scintillator, diffusion of light before reaching the MA-PMT is suppressed, concentrating it in just a few channels, thereby facilitating discrimination from background radiation. This design also enables clear visualization of the shape of surface contamination. The prototyped survey meter is capable of responding up to 2.1×107cpm, with no γ-ray response even in high-radiation environments exceeding 1Sv/h. In actual environments with high background radiation, contamination of ~1/100th of the surface contamination density limit of 4Bq/cm2 could be reliably detected.

Recognition of Sheep Feeding Behavior in Sheepfolds Using Fusion Spectrogram Depth Features and Acoustic Features

In precision feeding, non-contact and pressure-free monitoring of sheep feeding behavior is crucial for health monitoring and optimizing production management. The experimental conditions and real-world environments differ when using acoustic sensors to identify sheep feeding behaviors, leading to discrepancies and consequently posing challenges for achieving high-accuracy classification in complex production environments. This study enhances the classification performance by integrating the deep spectrogram features and acoustic characteristics associated with feeding behavior. We conducted the task of collecting sound data in actual production environments, considering noise and complex surroundings. The method included evaluating and filtering the optimal acoustic features, utilizing a customized convolutional neural network (SheepVGG-Lite) to extract Short-Time Fourier Transform (STFT) spectrograms and Constant Q Transform (CQT) spectrograms’ deep features, employing cross-spectrogram feature fusion and assessing classification performance through a support vector machine (SVM). Results indicate that the fusion of cross-spectral features significantly improved classification performance, achieving a classification accuracy of 96.47%. These findings highlight the value of integrating acoustic features with spectrogram deep features for accurately recognizing sheep feeding behavior.

EDI&S SO07 Future of Low-Cost Laparoscopic Cholecystectomy Simulation (LaChSi) Model: Integration of Segmentation-Based Artificial Intelligence Models for Realism Enhancement in Simulation-Based Laparoscopic Cholecystectomy Training in Lower-Middle-Income Countries

Abstract Background To introduce an approach of employing segmentation-based Artificial Intelligence (AI) models to meticulously isolate the silicon-based Laparoscopic Cholecystectomy Simulation Model (LaChSi) model within the laparoscopic trainer box, thereby enhancing realism. To assess the effectiveness of the AI Model to track camera angles to seamlessly replace the background with a virtual environment reminiscent of authentic intra-operative intra-abdominal visuals. Method The proposed methodology integrates a sophisticated segmentation model that identifies and accurately delineates the LaChSi Model within the laparoscopic Trainer box. Simultaneously, the model exhibits the capability to segregate the background, while closely monitoring camera angles for real-time adjustment. Importantly, the model is trained to perform precise segmentation of surgical tools, ensuring a comprehensive and realistic representation of the laparoscopic environment. Results Implementation of the proposed method is thought to demonstrate a notable improvement in trainees’ adaptation to surgical scenarios. The visual transition experienced by trainees moving from simulation-based surgeries to actual surgical environments will significantly reduce disparities increasing the effectiveness of the Laparoscopic simulation training. This will further avoid the need to use sensors and wires on the LaChSi model to simulate the surgical settings. Conclusion As the visual representation in simulations often deviates substantially from the intricate nuances of actual surgical procedures. This study introduces an innovative AI-based model that will contribute to the augmentation of surgical realism within simulation-based training. By effectively integrating segmentation techniques, our approach will minimise visual discontinuities, providing trainees with a more authentic and immersive experience, and ultimately fostering a smoother transition to acute surgical settings.

A miniature capsule endoscope based on spiral antenna with wireless sensing capabilities

Abstract In this paper, a capsule endoscopy system with a sensing function is proposed for medical devices. A single-arm spiral antenna is designed for data transmission and is combined with the voltage controlled oscillator to achieve sensing capabilities. The designed antenna operates at a 900 MHz industrial scientific medical band. By establishing a three-layer cylindrical model of the stomach, it was concluded that the antenna in the stomach has a high peak gain of −1.1 dBi. Additionally, the antenna achieved a −10 dB impedance bandwidth of 5%. The capsule endoscopy was experimentally measured in both actual stomach and simulated environments. The maximum working distance of the capsule endoscope was measured to be 6.8 m. Additionally, the proposed capsule endoscope was tested for its sensing function using solutions with different dielectric constants. Finally, it was confirmed through link analysis that it has good communication capabilities. The results and analysis confirm that the proposed capsule endoscope can be used for examining gastric diseases.

Trajectory Planning and Adaptive Fuzzy PID Control for Precision in Robotic Vertebral Plate Cutting: Addressing Force Dynamics and Deformation Challenges

This study explores trajectory planning and cutting force control for spinal surgical robots, focusing on managing cutting deformation and vibration during vertebral plate cutting—a critical challenge in robotic spinal surgery. Through theoretical analysis, simulation, and experimental validation, the research addresses the complexities of cutting force dynamics and the associated deformations. A dynamic equation for vertebral plate cutting is established, providing a theoretical foundation for trajectory planning. A new trajectory planning method using B-spline curves and an interpolation point constraint formula is introduced, enabling precise robot trajectory control. Additionally, an adaptive fuzzy PID control strategy with force feedback is proposed to dynamically adjust the feed rate, effectively suppressing cutting deformation and vibration in real time. Simulations and experiments validate the effectiveness of these methods in reducing force fluctuations and enhancing control stability. The findings offer valuable guidance for improving the performance and safety of spinal surgery robots, optimizing their trajectories, incorporating dynamic sensing, and enhancing safety controls. Future research should focus on refining control strategies for more complex surgical scenarios and validating their applicability under conditions closer to actual surgical environments. This work provides theoretical and practical insights into advancing robotic spinal surgery, contributing to better surgical outcomes and patient safety.

The Content Analysis of High-Quality Farmer Cultivation Policy under the Context of Rural Revitalization: A Two-Dimensional Perspective on Policy Tools and Participants

Abstract: As the latest stage in the development of farmer cultivation, the sustainable advancement of high-quality farmer cultivation requires not only the rational application of public policy tools but also the collaborative participation of multiple stakeholders. By collating policy documents on high-quality farmer cultivation at the national, Shandong provincial, and Yantai municipal levels from 2019 to December 2023, a total of 26 policy texts were coded, classified, and quantitatively analyzed using the content analysis method under a two-dimensional framework that includes the dimensions of policy tools and participants. The findings reveal that the composition of high-quality farmer cultivation policy in China is unbalanced, with demand-side policies having a weak driving effect. There is insufficient mobilization of agriculture-related enterprises and social organizations, and policy follow-up requires strengthening. Additionally, the correlation between policy tools and participants is inadequate, resulting in fragmented policies. Based on these findings, it is recommended to optimize the structure of policy tools, emphasize the participation of multiple stakeholders, and align policies with the actual policy environment.

Cross-modal feedback of tactile and auditory stimuli for cyclists in noisy environments

In recent years, with the increasing use of bicycles for environmental and health benefits, the importance of the feedback for cyclists is increasing. Moreover, as bicycles account for 23 % of all traffic accidents in 2022, improving bicycle safety is a crucial challenge. This study aimed to explore methods for enhancing safety and improving feedback to cyclists through auditory and tactile signals. Experiments were conducted using a cycling simulator and cross-modal reaction tests of tactile and auditory signals to simulate bicycle riding under actual external noise environments. The accuracy of the recognition and reaction times for both tactile and auditory signals were evaluated in situations with simulated road traffic sounds. Subsequently, the effectiveness of cross-modal feedback was assessed, optimal signal conditions were examined, and the relationship between variations in environmental noise and optimal auditory signals for cyclists was discussed. The results showed that cross-modal feedback led to faster reaction times, while the recognition accuracy of auditory signals varied depending on environmental noise levels. The present findings suggest the potential of cross-modal feedback to enhance cyclist safety, particularly when optimized for environmental conditions and individual perception.

Investigating the effects of destructive factors on pulse repetition interval modulation type recognition using deep convolutional neural networks based on transfer learning

AbstractAutomation and self‐sufficiency in the complex environment of modern electronic warfare (EW) are critical and necessary issues in electronic intelligence and support systems to detect real‐time and accurate threat radars. The task of these systems is to search, discover, analyse, and identify the parameters of radar signals. However, recognition pulse repetition interval (PRI) modulation is challenging in natural environments due to destructive factors, including missing pulses (MP), spurious pulses (SP), and large outliers (LO) (caused by antenna scanning), which lead to noisy sequences of PRI variation patterns. The current article examines the effects of destructive factors on recognising PRI modulation in radar signals using deep convolutional neural networks (DCNNs). The article uses simulations based on the actual environment to generate data and consider destructive factors with different percentages. The number of images obtained by applying the sum of destructive factors for each range of destructive factors (with different percentages) considered is 30,000. It is common for six types of modulation. Then, the DCNN models, including VGG16, ResNet50V2, InceptionV3, Xception, and MobileNetV2, are trained using the transfer learning method. The simulation results show that the accuracy of training and testing the models decreases significantly with the increase in the percentage of destructive factors. Also, the effects of the model type on the performance of the models have been investigated, and the results have shown that some models are more resistant to destruction and retain more accuracy. Finally, this analysis shows that to improve the performance of deep neural network (DNN) techniques in the face of changes caused by destructive factors, it is necessary to pay attention to these factors and apply appropriate strategies.

Hierarchical core-shell transitional metal chalcogenides Co9S8/ CoSe2@C nanocube embedded into porous carbon for tunable and efficient microwave absorption

In the domain of electromagnetic (EM) wave absorbing materials, the selection of suitable components and microstructures is an effective approach for designing the intense coupling of wave impedance and EM dissipation. In this study, we have successfully fabricated a double-carbon modified Co9S8/CoSe2 nanocube, where the core-shell Co9S8/CoSe2@C cube was anchored onto porous carbon (PC). The existence of three-dimensional (3D) porous carbon and the fabrication of Co9S8/CoSe2@C distributed on carbon nanosheets contribute to the improvement of conduction, magnetic losses, and the optimization of impedance matching. Substantial heterogeneous interfaces are generated between Co9S8, CoSe2, carbon shells, and PC, leading to extensive interfacial polarization and facilitating the transformation of EM energy into thermal energy. The abundant defects, S and Se vacancies in carbon component can act as polarization centers, inducing dipolar polarization and thus increasing the electromagnetic wave (EMW) loss. The Co9S8/CoSe2@C@PC exhibits the best microwave absorption properties, with a minimum reflection loss (RLmin) of −64.1 dB at 2.5 mm and an effective absorption bandwidth (EAB) of 6.3 GHz. The High-Frequency Structure Simulator results indicate the RCS values of the samples within the range of -90 °C < θ < 90 °C are lower than -20 dB m2, which can achieve almost full-angle coverage in the actual environment. This research offers inspiration and strategies for the design and synthesis of multi-component magneto-electric composites based on transitional metal chalcogenides.

Variation of Corrosion Characteristics and Tensile Performances of WE43 Alloy Under Marine Atmospheric Environment.

This study aims to examine the variation in corrosion characteristics and tensile properties of WE43 magnesium alloy in an actual marine atmospheric environment by means of outdoor exposure tests. The macroscopic corrosion morphology, microstructure, and tensile properties were analyzed. The results indicated that WE43 alloy will corrode rapidly during exposure under marine atmospheric environmental conditions, resulting in a loose and porous Mg(OH)2 layer on the surface. The Mg matrix was mainly consumed as an anode, leading to the occurrence of corrosion pits. With the increase in exposure time, both the tensile strength and plasticity of WE43 alloy gradually deteriorated. After exposure for six months, the elongation and area reduction were significantly reduced, with a reduction ratio of more than 50%. After 18 months of exposure, the ultimate strength of the alloy decreased from 359 MPa to 300 MPa. According to an analysis of fractures in the alloy, the corrosion pits on the sample surface were the main reason for the decrease in tensile properties.

Should I Evaluate My Augmented Reality System in an Industrial Environment? Investigating the Effects of Classroom and Shop Floor Settings on Guided Assembly.

Numerous prior studies have investigated real-time assembly instructions using Augmented Reality (AR). However, most such experiments were conducted in laboratory settings with simplistic assembly tasks, failing to represent real-world industrial conditions. To ascertain to what extent results obtained in a laboratory environment may differ from studies in actual industrial environments, we carried out a user study with 32 manufacturing apprentices. We compared assembly task execution results in two settings, a classroom and an industrial workshop environment. To facilitate the experiments, we developed AR-guided manual assembly systems for simple and more complex assets. Our findings reveal a significantly improved task performance in the industrial workshop, reflected in faster task completion times, fewer errors, and subjectively perceived higher flow. This contradicted participants' subjective ratings, as they expected to perform better in the classroom environment. Our results suggest that the actual manufacturing environment is critical in evaluating AR systems for real-world industrial applications.

Analysis of the relationship between neonatal birth weight and meconium metabolites based on birth cohort metabolomics

Neonatal birth weight is a crucial indicator of intrauterine growth and development with important implications for child development and adult health. The birth weight of a newborn is closely linked to the nutrition and health of the mother during pregnancy as well as genetic factors. Therefore, assessing the metabolic status of the fetus in utero is greatly significant for understanding the mechanisms responsible for abnormal birth weight. While previous studies often analyzed the impact of maternal metabolism on fetal development using umbilical cord blood from pregnant women, such blood may not accurately reflect the actual intrauterine environment owing to the barrier function of the placenta; moreover, obtaining biological samples during the fetal period is challenging. Meconium, the first feces excreted by a newborn, provides ideal biological material for studying maternal and infant health. Metabolomics can reveal metabolic changes in living organisms by analyzing small molecules in biological samples; hence studying meconium samples using metabolomics technology is expected to reveal fetal metabolic changes during pregnancy, thereby providing new insights into fetal nutritional intake, growth, and development, as well as metabolic pathways related to birth weight. To gain a deeper understanding of the metabolic changes associated with birth weight, this study collected metabolomic data from the meconium of 484 newborns in the established Xiaogan birth cohort using an untargeted metabolomics technique based on liquid chromatography-high resolution mass spectrometry (LC-HRMS) and analyzed the association between meconium metabolites and birth weight. This cohort exhibited incidence rates of low birth weight (<2500 g) and macrosomia (>4000 g) of 3.3% and 7.2%, respectively, which were roughly equivalent to the national average. Orthogonal partial least squares discriminant analysis revealed significant differences between the meconium metabolomes of the low birth weight and macrosomic groups when compared to the normal weight group. We discovered significant distinctions between the differential metabolites of newborns of low birth weight and those of normal weight, as well as between macrosomic and normal weight newborns that point to disparate biological pathways. Newborns with low birth weight exhibited significantly lower levels of critical amino acids, such as glutamate and proline, compared to the normal weight group, which may be associated with placental dysfunction and maternal nutritional deficiencies. Conversely, the meconium of macrosomic newborns contained significantly elevated levels of hormone metabolites such as estrone that reflected the pathophysiological state associated with maternal metabolic diseases or excessive placental hormone levels. Our study suggests that the metabolomic profile of the meconium reflects the metabolic pathways and regulatory mechanisms at play during fetal growth and development, and offers potential metabolic biomarkers and directions for future in-depth research into diseases related to fetal development. However, this study was based solely on the Xiaogan birth cohort, which was limited to specific regions and populations. A multicenter, multiethnic, and multiregional study is expected to help validate the universality of our research findings.

3D-Micro-Flower like [formula omitted] Nanoplate Solid Solution: A facile and rapid room-temperature synthesis with excellent photocatalytic decomposition of antibiotics in water

This research addresses the environmental threat posed by residual antibiotics in water and explores the potential of the photocatalytic process as an eco-friendly solution for antibiotic wastewater treatment. Here in, a series of BiOBr1−xClx nanoplate solid solutions with varied Br:Cl molar ratios were synthesized through a simple co-precipitation method. These solid solutions exhibited superior visible-light-driven photocatalytic activity compared to pristine BiOCl and BiOBr. Notably, the BiOBr0.25Cl0.75 sample demonstrated exceptional performance, achieving 89% and 99% degradation efficiency for ciprofloxacin (CIP) and tetracycline hydrochloride (TCH), respectively, within 20minutes under optimum conditions. The outstanding performance is attributed to factors such as a large specific surface area, suitable morphology and band gap, effective separation of photo-generated electron-hole pairs, and the presence of meso-size pores in the structure. Thermodynamic studies confirmed the exothermic and spontaneous nature of the photocatalytic reactions. The proposed degradation pathway of CIP and TCH, along with the photocatalytic mechanism, is elucidated. The solid solution, BiOBr1−xClx, exhibits facile recyclability, robust stability, and excellent adaptability to actual aquatic environments, establishing its potential as a promising eco-friendly photocatalyst for antibiotic pollution control.

Development of a method for evaluating fabric surface contamination by particulate matter using fluorescent particles

Functional clothing products are being developed to prevent the attachment of particulate matter (PM). However, currently, PM-blocking performance of such products cannot be quantitatively evaluated. This study developed a method for measuring fabric surface contamination by PM by contaminating textile fabrics with PM and using an image processing technique. Silicate particles exhibiting fluorescence characteristics with a suitable size distribution were selected as PM simulants. The fabric surface contamination was calculated by contaminating textile fabrics under wind speed and PM concentration conditions similar to actual atmospheric environment. The number of pixels was obtained in the black and white binary images converted from the images of the textile fabrics captured under an ultraviolet (UV) light source. Experiments were performed using finished nylon fabric with PM-blocking performance and raw nylon fabric without PM-blocking performance. Comparison of the fabric surface contamination shows that it is possible to evaluate the PM-blocking performance of fabrics.

Integration of an IoT sensor with angle-of-arrival-based angle measurement in AGV navigation: A reliability study

Automated guided vehicle (AGV), which was initially designed for indoor operations in industry, has been increasingly applied in outdoor heavy-duty logistics tasks. In typical navigation tasks, such as the autonomous tracking of a designated object or a person, relative angle and relative distance between AGV and the target is required. To obtain the necessary information, various on-board sensors are extensively integrated. In this paper, the reliability of measuring the relative angle with the Angle-of-Arrival (AoA) method with two different Internet of Things (IoT) sensor sets from Texas Instrument (TI) and u-blox, according to Bluetooth 5.1 was investigated. The performance of IoT sensors was validated with angle accuracy parameters and received signal strength indicator (RSSI). The better IoT sensor was then integrated into the AGV navigation system, and the information gathered from IoT sensor enabled the AGV to turn toward the direction of the target. The process of AGV turning to the targeted direction based on IoT sensor information was respectively tested in the simulation and actual environment and evaluated by the disparity between the real relative angle and the rotation angle of the AGV. The results showed that this disparity was within ±5° in both simulated and actual environments, and methods for higher accuracy were proposed. In this way, the reliability and performance of Angle of Arrival (AoA) sensors in measuring the relative angle, which remains unexplored by other researchers, was systematically assessed contributing to extending the usability of AoA sensors in complex, real-world applications.

A deep learning method for pointer meter reading recognition in inspection robots at refrigeration stations

Abstract To improve the intelligent operation and maintenance of large public building refrigeration stations, various automatic reading methods for pointer meters used in inspection robots have been developed. However, most methods exhibit low detection accuracy for pointer meters in refrigeration stations and poor segmentation of pointers and scale lines, ultimately leading to inaccurate readings. To address the challenges posed by the complex environments of refrigeration stations, this study proposes a deep learning-based image recognition method for precise pointer meter readings. Firstly, the pointer meters in the environment are detected and located by the YOLOV8-CBAM-Wise-IoU model. Secondly, an improved Fast Semantic Segmentation Network (Fast-SCNN) is used for image segmentation of the pointers and scale lines of the pointer meters, and a perspective transformation method is employed to correct images of tilted meters. Then, the image is transformed into a rectangular image using polar coordinate transformation. Finally, the accurate reading is calculated using the distance method, based on the relative distances between the pointers and scale lines. This research conducted experimental tests using an inspection robot in the actual environment of a large public building's refrigeration station. The results demonstrate that the proposed method for pointer meter reading recognition achieves a fiducial error within 0.41%, which can realize the inspection task of the pointer meter in refrigeration stations.

A novel dynamic tracking method for coded targets with complex background noise

To address the issues of low tracking efficiency and poor localization accuracy of artificial coded targets under complex background interference conditions, a new method for dynamic tracking of coded targets is proposed. This method includes a lightweight feature tracker (CBAM-Slim-Net) for adaptive localization of coded circles and a large-capacity coded target solver (CSN-BSSCT). The CBAM-Slim-Net feature tracker achieves a detection accuracy of 0.987 with only 6.030 M parameters. In actual measurement environments with complex background interference, CSN-BSSCT can decode quickly and accurately, with a mean error of 0.036 mm in the three-dimensional Euclidean distance between coded circles in static measurement scenarios. Additionally, this method can analyze the motion trajectory of the target and perform dynamic stitching for 3D measurement from multiple perspectives, making it highly significant for applications in robot motion control and large-field-of-view 3D measurement.

A Study on the Influence of Hydraulic Compactor Reinforcement on the Force Law of an Independent Foundation Under a Column and Its Safety Standard

Due to the complexity of the actual geotechnical environment, the backfill compaction design theory and calculation method are not reflected in the current specification. Therefore, in order to investigate the effect of the hydraulic compactor on the foundation structure during the treatment of the backfill of an independent foundation under a column, the Menard formula was modified. At the same time, relying on an independent foundation project under a column in Jinan, the dynamic model of compactor tamping backfilling soil was established. The applicability of the calculation formula is verified by simulating the single-point multiple tamping on the backfill directly above the foundation tie beam, and the influence law of two factors, the thickness of the backfill and the tamping energy, on the force of the foundation tie beam is elucidated. The results show that after reaching the optimum number of tamping, the cumulative soil settlement and the effective reinforcement depth of tamping show a stable trend, and their simulation results are in good agreement with the analytical solution, which provides a supplement to the relevant safety standards. At this critical point, the force on the tie beams peaked and showed up and down fluctuations under the subsequent ramming action. The tamping action of the compactor has a significant effect on the structural forces within the effective reinforcement range, and there is a negative correlation between the magnitude of the structural forces and the thickness of the backfill. According to the numerical calculation results to choose the best construction programme, the on-site monitoring shows that under 42 KJ tamping energy and 1.5 m single backfilling thickness, the tie beam reinforcement stress reaches 18.5~55.5% of the specification warning value, which meets the safety standard. The research results of this paper can provide important guidance for the hydraulic tamping treatment of an independent foundation backfill project.

Effect of ultraviolet aging and neonicotinoids on the transport of degradable microplastics in saturated porous media

The environmental safety and risk of transporting degradable microplastics (DMPs) and agrochemicals are being actively studied and evaluated. In this study, the aging of polylactic acid (PLA), polyhydroxyalkanoate (PHA), and poly (butylene adipate-co-terephthalate) (PBAT) DMPs were comprehensively explored under ultraviolet (UV) irradiation. The effects of neonicotinoids (acetamiprid, dinotefuran, and nitenpyram) on the transport of DMPs before and after aging were investigated using column experiments and density-functional theory. PHA exhibited stronger resistance to UV degradation than PLA and PBAT owing to its higher hydrophobicity. During aging, ester groups (C–O) were broken in the DMPs and carbonyl groups (C=O) were formed. The calculated molecular electrostatic potential and localized orbital locator as well as the measured zeta potential, verified that the adsorption of neonicotinoids on the DMPs supplemented their surface charge at lower neonicotinoid concentrations, but effectively shielded it at high concentrations. The promoting effect of UV aging on transport was approximately equal for PLA and PBAT but lower for PHA. With the increasing degree of aging, the inhibitory effect of neonicotinoids on DMP recovery became more pronounced. Neonicotinoids, especially dinotefuran, had the most prominent inhibitory effect on the transport of aged PLA and PBAT, with the recoveries of PLA and PBAT UV-aged for 24 h reduced by 38.63% and 34.18%, respectively. These results indicate that the potential ecological hazards arising from the aging and transport of DMPs in the actual environment deserve further investigation.

Role of Sintering Aids in Electrical and Material Properties of Yttrium- and Cerium-Doped Barium Zirconate Electrolytes

Ceramic proton conductors have the potential to lower the operating temperature of solid oxide cells (SOCs) to the intermediate temperature range of 400–600 °C. This is attributed to their superior ionic conductivity compared to oxide ion conductors under these conditions. However, prominent proton-conducting materials, such as yttrium-doped barium cerates and zirconates with specified compositions like BaCe1−xYxO3−δ (BCY), BaZr1−xYxO3−δ (BZY), and Ba(Ce,Zr)1−yYyO3−δ (BCZY), face significant challenges in achieving dense electrolyte membranes. It is suggested that the incorporation of transition and alkali metal oxides as sintering additives can induce liquid phase sintering (LPS), offering an efficient method to facilitate the densification of these proton-conducting ceramics. However, current research underscores that incorporating these sintering additives may lead to adverse secondary effects on the ionic transport properties of these materials since the concentration and mobility of protonic defects in a perovskite are highly sensitive to symmetry change. Such a drop in ionic conductivity, specifically proton transference, can adversely affect the overall performance of cells. The extent of variation in the proton conductivity of the perovskite BCZY depends on the type and concentration of the sintering aid, the nature of the sintering aid precursors used, the incorporation technique, and the sintering profile. This review provides a synopsis of various potential sintering techniques, explores the influence of diverse sintering additives, and evaluates their effects on the densification, ionic transport, and electrochemical properties of BCZY. We also report the performance of most of these combinations in an actual test environment (fuel cell or electrolysis mode) and comparison with BCZY.