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The increasing interest in developing robots capable of navigating autonomously has led to the necessity of developing robust methods that enable these robots to operate in challenging and dynamic environments. Visual odometry (VO) has emerged in this context as a key technique, offering the possibility of estimating the position of a robot using sequences of onboard cameras. In this paper, a VO algorithm is proposed that achieves sub-pixel precision by combining optical flow and direct methods. This approach uses only a downward-facing, monocular camera, eliminating the need for additional sensors. The experimental results demonstrate the robustness of the developed method across various surfaces, achieving minimal drift errors in calculation.

This research aims to develop an intelligent framework for quality control and fault detection in pre-production and post-production systems in Industry 4.0. In the pre-production system, the health of the manufacturing machine is monitored. In this study, we examine the gear system of induction motors used in industries. In post-production, the product is tested for quality using a machine vision system. Gears are fundamental components in countless mechanical systems, ranging from automotive transmissions to industrial machinery, where their reliable operation is vital for overall system efficiency. A faulty gear system in the induction motor directly affects the quality of the manufactured product. Vibration data, collected from the gear system of the induction motor using vibration sensors, are used to predict the motor’s health condition. The gear system is monitored for six different fault conditions. In the second part, the quality of the final product is inspected with the machine vision system. Faults on the surface of manufactured products are detected, and the product is classified as a good or bad product. The quality control system is developed with different deep learning models. Finally, the quality control framework is validated and tested with the evaluation metrics.

Background: The purpose of this study was to compare the accuracy of three different intraoral scanners (IOSs) and to evaluate the patients’ experience. Methods: Thirty subjects were scanned with three different IOSs (TRIOS Color®, iTero Element 5D®, and iTero Lumina®): a sample of 90 maxillary casts was collected. The inclusion criteria were permanent dentition, absence of defects during the dental impression, and complete record of the palatal vault. After the scanning procedure, patients were asked to answer a questionnaire (10 answers, scale from 1 to 10) to compare their experiences with iTero Element and iTero Lumina in the scanning procedure. Results: Differences were recorded in the upper central incisor region (f-ratio of 4.186 for Mesiodistal 1.1 and f-ratio of 4.222 for Mesiodistal 2.1, p < 0.05), while intercanine width Upper 13–23 showed the smallest f-ratio (0.226) when compared to the other results, followed by Mesiodistal 1.4 (0.433). Patients reported more comfort, less pain, decreased duration, and a better technology visualization of the impression with iTero Lumina. No significant differences concerning dryness of the mouth and gag reflex were found. Conclusions: The examined IOSs offer comparable accuracy in capturing dental arch dimensions. The interviewed patients expressed an overall preference for digital impressions performed with iTero Lumina, linked to increased comfort, painless practice, and a better technology visualization of the impression.

This paper proposes, for the first time, the research concept of comparing energy and economy between transcritical cycle high-temperature heat pumps and subcritical cycle high-temperature heat pumps with new refrigerants. Experiments and simulations are conducted to compare the system performance and economy of two heat pumps, and the effects of different factors on the performance of two heat pumps are analyzed. The results show that R744/R1234yf (90/10) and R515-1 are the preferred refrigerants for transcritical cycle heat pumps and subcritical cycle heat pumps, respectively. The COP of the R744/R1234yf (90/10) transcritical heat pump is generally higher than that of the R515B-1 subcritical heat pump, and compared to the R515B-1 subcritical heat pump, the cost recovery period of the R744/R1234yf (90/10) transcritical heat pump is about 9–15 years. Therefore, it is recommended that users who use heat pumps for a long time choose transcritical cycle heat pumps. Meanwhile, with the change of evaporation temperature, the system COP of the R515B-1 subcritical heat pump and R744/R1234yf (90/10) transcritical heat pump increases by 61.11% and 65.91%, respectively. In addition, the optimal charge amount for the R515B-1 subcritical heat pump is 81.8% of that of the R744/R1234yf (90/10) transcritical heat pump.

The multi-modal two-step floating catchment area (MM-2SFCA) method is an extension of the two-step floating catchment area (2SFCA) method that incorporates the impact of different transportation modes, thereby facilitating more accurate calculations of the spatial accessibility of public facilities in urban areas. However, the MM-2SFCA method does not account for the impact of distance within the search radius on supply–demand capacities, and it assumes an idealized supply–demand relationship. This paper introduces the gravity model into the MM-2SFCA method, proposing a multi-modal gravity-based 2SFCA (MM-G2SFCA) method to better account for distance decay and supply–demand relationships. Furthermore, a standardized gravity model is proposed based on the traditional gravity model. This model imposes constraints on upper and lower limits for distance decay weights without compromising the fundamental curve characteristics of the gravity model, thereby avoiding extreme weight scenarios. The accessibility of public hospitals in Shenzhen is evaluated through the integration of basic geographic information data, resident travel data, and official statistical data. The findings demonstrate that the standardized gravity model effectively addresses the issue of excessively high local distance weights in the traditional gravity model, making it more suitable as a distance decay function. The MM-G2SFCA method improves the consideration of distance and supply–demand relationships, thereby facilitating a more rational distribution of accessibility on a global scale. This study discovers differences in the spatial allocation of public hospital resources across the Shenzhen’s districts. Accessibility within the metropolitan core is significantly higher than that outside the core. Additionally, there is a notable difference in the level of accessibility among the districts. Accessibility is found to be better in district centers and along the main traffic arteries.

The development of super high-rise building projects has become crucial for mitigating land shortages in rapidly growing urban areas. Super high-rise steel structures, particularly RC core wall-steel frame systems, have become the preferred choice due to their superior performance, high prefabrication level, and construction efficiency. Despite their benefits, super high-rise buildings face challenges related to higher energy consumption and carbon emissions. Consequently, it is important to analyze the carbon emissions of these buildings throughout their lifecycle and propose low-carbon construction strategies. A carbon emission analysis focused on super high-rise buildings with RC core wall-steel frame structures is conducted in this study. A carbon emission analysis model is constructed based on BIM-enabled LCA through a real-world case study. The emission factor method is combined with the BIM model to calculate carbon emission. Furthermore, carbon emissions across various construction strategies are compared, with a particular focus on the manufacturing processes of the main materials. The results indicate that incorporating admixtures in concrete, along with adopting the electric arc furnace (EAF) method and utilizing recycled scrap steel in steel manufacturing, significantly reduces the carbon emissions of the buildings. Lastly, effective low-carbon approaches for these buildings are proposed.

With the rapid increase in the number of vehicles on the road, minor traffic accidents have become more frequent, contributing significantly to traffic congestion and disruptions. Traditional methods for determining responsibility in such accidents often require human intervention, leading to delays and inefficiencies. This study proposed a fully intelligent method for liability determination in minor accidents, utilizing collision detection and large language models. The approach integrated advanced vehicle recognition using the YOLOv8 algorithm coupled with a minimum mean square error filter for real-time target tracking. Additionally, an improved global optical flow estimation algorithm and support vector machines were employed to accurately detect traffic accidents. Key frames from accident scenes were extracted and analyzed using the GPT4-Vision-Preview model to determine liability. Simulation experiments demonstrated that the proposed method accurately and efficiently detected vehicle collisions, rapidly determined liability, and generated detailed accident reports. The method achieved the fully automated AI processing of minor traffic accidents without manual intervention, ensuring both objectivity and fairness.

The Internet of Things (IoT) is witnessing a surge in sensor-equipped devices. The data generated by these IoT devices serve as a critical foundation for informed decision-making, real-time insights, and innovative solutions across various applications in everyday life. However, data reliability is often compromised due to the vulnerability of sensors to faults arising from harsh operational conditions that can adversely affect the subsequent operations that depend on the collected data. Hence, the identification of anomalies within sensor-derived data holds significant importance in the IoT context. This article proposes a sensor fault detection method using a Long Short-Term Memory autoencoder (LSTM-AE). The AE, trained on normal sensor data, predicts a 20-step window, generating three statistical features via SHapley Additive exPlanations from the estimated steps. These features aid in determining potential faults in the predicted steps using a machine learning classifier. A secondary classifier identifies the type of fault in the sensor signal. Experimentation on two sensor datasets showcases the method’s functionality, achieving fault detection accuracies of approximately 93% and 97%. It is possible to attain a perfect fault classification performance by slightly modifying the feature calculation approach. In a univariate prediction scenario, our proposed approach demonstrates good fault detection and classification performance.

Anomaly detection involves identifying data that deviates from normal patterns. Two primary strategies are used: one-class classification and binary classification. In Industrial Control Systems (ICS), where anomalies can cause significant damage, timely and accurate detection is essential, often requiring analysis of time-series data. One-class classification is commonly used but tends to have a high false alarm rate. To address this, binary classification is explored, which can better differentiate between normal and anomalous data, though it struggles with class imbalance in ICS datasets. This paper proposes a mutation-based technique for generating ICS time-series anomalies. The method maps ICS time-series data into a latent space using a variational recurrent autoencoder, applies mutation operations, and reconstructs the time-series, introducing plausible anomalies that reflect multivariate correlations. Evaluations of ICS datasets show that these synthetic anomalies are visually and statistically credible. Training a binary classifier on data augmented with these anomalies effectively mitigates the class imbalance problem.

The mechanism for improving soil with high-performance ester material is not yet clear, especially changes in soil moisture under dry–wet cycling conditions. Soil moisture is closely related to its ecological performance, which also leads to a lack of scientific bases for the application range, climate conditions, and long-term performance evolution evaluation of high-performance ester materials in ecological restoration. Our research revealed an optimal ratio for the amount of high-performance ester materials that can improve soil moisture and ecological performance under multiple dry–wet cycling conditions. Dry–wet cycling experiments and planting tests were conducted to study the soil moisture improvement mechanism and the changes caused by high-performance ester materials. Changes in the soil’s moisture, EC, and ecological performance were explored. The results indicate that ester materials can effectively improve soil moisture and EC. Even after multiple dry–wet cycles, ester materials can still play an effective role in improving soil moisture and ecological performance. An optimal ratio for the amount of high-performance ester materials was also found. This research reveals the improvement mechanism for ester materials on soil moisture and ecological performance under dry–wet cycling conditions. Our results provide new data and technical guidance for the improvement of soil moisture and ecological restoration by high-performance ester materials.

The purpose of this study was to evaluate the properties and pharmacokinetics of liposomal vitamin C in powder form obtained by a method devoid of organic solvents. The powder and liposome morphology were analyzed using scanning electron microscopy (SEM) and cryogenic transmission electron microscopy (cryo-TEM), respectively. Additionally, the carrier particle size, size distribution (STEP-Technology®; L.U.M. GmbH, Berlin, Germany), and zeta potential value were determined. The pharmacokinetic parameters of liposomal and non-liposomal vitamin C (AUC, Cmax, C10h, and C24h) were compared in a randomized, single-dose, double-blind, cross-over trial (ClinicalTrials.gov ID: NCT05843617) involving healthy adult volunteers (n = 10, 1000 mg dose). The process of spray drying used to transform liquid suspensions of the liposomes into powder form did not adversely affect the quality of the carrier particles obtained. Compared to non-encapsulated vitamin C, oral administration of the liposomal formulation resulted in significantly better absorption of ascorbic acid into the bloodstream, which equated to a higher bioavailability of the liposomal product (30% increase in AUC, p < 0.05). The duration of elevated vitamin C blood levels was also longer (C24h increase of 30%, p < 0.05). Although the results obtained are promising and suggest higher bioavailability for the liposomal form of vitamin C, the limited sample size necessitates further research with a larger cohort to confirm these findings.

Multimodal brain signal analysis has shown great potential in decoding complex cognitive processes, particularly in the challenging task of inner speech recognition. This paper introduces an innovative I nner Speech Recognition via Cross-Perception (ISRCP) approach that significantly enhances accuracy by fusing electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data. Our approach comprises three core components: (1) multigranularity encoders that separately process EEG time series, EEG Markov Transition Fields, and fMRI spatial data; (2) a cross-perception expert structure that learns both modality-specific and shared representations; and (3) an attention-based adaptive fusion strategy that dynamically adjusts the contributions of different modalities based on task relevance. Extensive experiments on the Bimodal Dataset on Inner Speech demonstrate that our model outperforms existing methods across accuracy and F1 score.

A random copolymer (PTBM), utilized as deep ultra-violet (DUV) photoresist, was prepared by reversible addition-fragmentation chain transfer (RAFT) polymerization with tert-butyl methacrylate (tBMA), methyl methacrylate (MMA), triphenylsulfonium p-styrenesulfonate (TPS-SS), and functional poly (sesquicarbonylsiloxanes) (POSS-MA) as the monomer components, and 4-cyano-4-[(dodecylsulfanylthiocarbonyl) sulfanyl]pentanoic acid (CDSPA) as the RAFT reagent. Fourier transform infrared spectroscopy (FT-IR) and proton nuclear magnetic resonance (1H NMR) proved successful synthesis. Ultraviolet absorption spectroscopy (UV) analysis verified the transparency of the polymer in the DUV band. RAFT polymerization kinetics showed that the polymerization rate conformed to the first-order kinetic relationship, and the polymerization process exhibited a typical controlled free radical polymerization behavior. Thermogravimetric analysis (TGA), differential scanning calorimetry (DSC) and static thermo-mechanical analysis (TMA) showed that the incorporation of POSS groups improved the thermal properties of the copolymer. According to scanning electron microscopy (SEM) images, the copolymerization of photoacid monomers (TPS-SS) resulted in photoresist copolymers exhibiting good resistance to acid diffusion and low roughness.

When recording the vegetation distribution with a camera, shadows can form due to factors like camera angle and direct sunlight. These shadows result in the loss of pixel information and texture details, significantly reducing the accuracy of fractional vegetation coverage (FVC) extraction. To address this issue, this study proposes an efficient double-exposure algorithm. The method reconstructs the pixel information in shadow areas by fusing normal-exposure and overexposed images. This approach overcomes the limitations of the camera’s dynamic range in capturing pixel information in shadowed regions. The study evaluates images with five levels of overexposure combined with five vegetation extraction indices. The aim is to determine the best-performing double-exposure combination under shadow conditions and the most suitable vegetation index. Experimental results reveal that the R² value between the best vegetation index and the FVC calculated from the fused double-exposure images and the ground truth FVC increases from 0.750 to 0.969. The root mean square error (RMSE) reduces from 0.146 to 0.046, and the intersection over union (IOU) increases from 0.856 to 0.943. These results demonstrate the excellent vegetation extraction capability of the double-exposure algorithm under shadow conditions, offering a straightforward and effective solution to low accuracy of FVC in shadowed areas.

The emergence of AIGC has significantly improved design efficiency, enriched creativity, and promoted innovation in the design industry. However, whether the content generated from its own database meets the preferences of target users still needs to be determined through further testing. This study investigates the appeal of AI-generated stylized products to users, utilizing 12 images as stimuli in conjunction with eye-tracking technology. The stimulus is composed of top-selling gender-based stylized Bluetooth earphones from the Taobao shopping platform and the gender-based stylized earphones generated by the AIGC software GPT4.0, categorized into three experimental groups. An eye-tracking experiment was conducted in which 44 participants (22 males and 22 females, mean age = 21.75, SD = 2.45, range 18–27 years) observed three stimuli groups. The eye movements of the participants were measured while viewing product images. The results indicated that variations in stimuli category and gender caused differences in fixation durations and counts. When presenting a mix of the two types of earphones, the AIGC-generated earphones and earphones from the Taobao shopping platform, the two gender groups both showed a significant effect in fixation duration with F (2, 284) = 3.942, p = 0.020 < 0.05, and η = 0.164 for the female group and F (2, 302) = 8.824, p < 0.001, and η = 0.235 for the male group. They all had a longer fixation duration for the AI-generated earphones. When presenting exclusively the two types of AI-generated gender-based stylized earphones, there was also a significant effect in fixation duration with F (2, 579) = 4.866, p = 0.008 < 0.05, and η = 0.129. The earphones generated for females had a longer fixation duration. Analyzing this dataset from a gender perspective, there was no significant effect when the male participants observed the earphones, with F (2, 304) = 1.312 and p = 0.271, but there was a significant difference in fixation duration when the female participants observed the earphones (F (2, 272) = 4.666, p = 0.010 < 0.05, and η = 0.182). The female participants had a longer fixation duration towards the earphones that the AI generated for females.

Homography estimation is a crucial task in aligning template images with target images in stamping monitoring systems. To enhance the robustness and accuracy of homography estimation against random vibrations and lighting variations in stamping environments, this paper proposes an improved unsupervised homography estimation model. The model takes as input the channel-stacked template and target images and outputs the estimated homography matrix. First, a specialized deformable convolution module and Group Normalization (GN) layer are introduced to expand the receptive field and enhance the model’s ability to learn rotational invariance when processing large, high-resolution images. Next, a multi-scale, multi-stage unsupervised homography estimation network structure is constructed to improve the accuracy of homography estimation by refining the estimation through multiple stages, thereby enhancing the model’s resistance to scale variations. Finally, stamping monitoring image data is incorporated into the training through data fusion, with data augmentation techniques applied to randomly introduce various levels of perturbation, brightness, contrast, and filtering to improve the model’s robustness to complex changes in the stamping environment, making it more suitable for monitoring applications in this specific industrial context. Compared to traditional methods, this approach provides better homography matrix estimation when handling images with low texture, significant lighting variations, or large viewpoint changes. Compared to other deep-learning-based homography estimation methods, it reduces estimation errors and performs better on stamping monitoring images, while also offering broader applicability.

Conducting the remaining useful life (RUL) prediction for an aircraft engines is of significant importance in enhancing aircraft operation safety and formulating reasonable maintenance plans. Addressing the issue of low prediction model accuracy due to traditional neural networks’ inability to fully extract key features, this paper proposes an engine RUL prediction model based on the adaptive moment estimation (Adam) optimized self-attention mechanism–temporal convolutional network (SAM-TCN) neural network. Firstly, the raw data monitored by sensors are normalized, and RUL labels are set. A sliding window is utilized for overlapping sampling of the data, capturing more temporal features while eliminating data dimensionality. Secondly, the SAM-TCN neural network prediction model is constructed. The temporal convolutional network (TCN) neural network is used to capture the temporal dependency between data, solving the mapping relationship of engine degradation characteristics. A self-attention mechanism (SAM) is employed to adaptively assign different weight contributions to different input features. In the experiments, the root mean square error (RMSE) values on four datasets are 11.50, 16.45, 11.62, and 15.47 respectively. These values indicate further reduction in errors compared to methods reported in other literature. Finally, the SAM-TCN prediction model is optimized using the Adam optimizer to improve the training effectiveness and convergence speed of the model. Experimental results demonstrate that the proposed method can effectively learn feature data, with prediction accuracy superior to other models.

Towing systems are an important component in the automobile industry, having to meet specific quality and resistance conditions. In most cases, the towbar is made of steel. Decreases in vehicle weight and manufacturing price mean that, in this field, research is also being conducted in order to replace the materials that make up the towbar and replace the steel with composite materials or aluminum alloys. In this paper, research was performed on towing systems built from other materials, and the obtained results were compared with those of steel systems. Theoretical calculations and experimental results made it possible to obtain a database and recommendations for the use of new and composite materials. The experimental tests validated the theoretical results obtained. Five towing systems made of different materials were studied. The results of the research are emphasized through recommendations regarding the manufacturing of towing systems.

This article describes a module and method for measuring the surface tension of liquid solders implemented on a measuring device as part of an integrated platform for automatic measurement of brazebility parameters at high temperatures. A concept for constructing a test stand is presented, with a description of the individual functional blocks. The developed stand allows for testing of the solder’s surface tension. The surface tension is one of the parameters that describe the thermodynamics of interfacial reactions and the structure of newly created joints. Determining the physicochemical interactions between liquid and solid substances is crucial for various industrial processes in fields such as metallurgy, electronics, and aviation, mainly where soldering and brazing technologies are employed. A series of bubble experiments in solder for a ceramic capillary is carried out to verify the proposed method using the developed system. One of these experiments is described in this article.

A destructive degradation model was applied on films made from different concentrations of starch, glycerol and nanoclay using various elongation levels as a stress variable at different stress times and stretch cycles. The log tensile quotient (logarithm of the tensile strength to the corresponding break cycle) was recorded as the response variable. The log tensile quotient increased, and the log exact break time decreased, as the elongation level increased. The treatment containing the highest starch and nanoclay and lowest glycerol content proved to be the most resistant to stress conditions and the most versatile in relation to the varying log tensile quotients, while the treatments containing the lowest nanoclay and highest glycerol contents, regardless of the starch concentration, manifested the lowest log tensile quotient at higher levels of log exact break time. According to multiple regression findings, the break cycle governed mostly the stress conditions in the degradation model, followed by the sample ID and the log exact break time. The term log tensile quotient, attempted for the first time on data concerning biodegradable films enhanced with nanoclay, seems very promising for deeper research due to its ability to retrieve predictive information from survival equations and to discriminate the difference between film structures.

In order to investigate the dynamic response and rock mass damage characteristics of cavities with different shapes under blasting loads, this paper, through a combination of model tests and numerical simulations, studies the stress distribution, strain, failure modes, and blasting fragment size distribution of cavities with different shapes subjected to blasting loads. The results show that under the action of blasting loads, the presence of cavities with different shapes significantly affects the blasting effects and rock mass damage. Spherical cavities exhibit excellent blast resistance, whereas rectangular and triangular cavities are prone to stress concentration at their tips, which in turn promotes rock mass damage and failure. Subsequent analysis of the blasting fragment sizes reveals that rectangular and triangular cavities yield more favorable blasting results than spherical cavities. The research findings provide important theoretical foundations and practical guidance for the design and construction of underground engineering blasting, contributing to enhancing engineering safety and promoting the sustainable development of the underground engineering industry.

This study aimed to compare the percentage of bioceramic sealer penetration and maximum penetration sealer depending on the fluorescent staining method when evaluated with confocal laser scanning microscopy (CLSM). In total, 42 single-rooted premolars were instrumented. Two groups of 20 teeth each were filled using the single cone technique. TotalFill BC Sealer® mixed with Rhodamine B was used for the first group and TotalFill BC Sealer® mixed with Fluo-3 for the second group. Two teeth made up the control group. All teeth were sectioned at 3 and 6 mm from the apex and were analyzed with CLSM. The canal perimeter where the tubular penetration happened and the maximum penetration of each sample were calculated. There were no significant differences between the two groups in either the apical or medial sections. In terms of maximum penetration, Rhodamine B had greater tubular penetration in the apical sections, and it had similar results to Fluo-3 in the medial sections. TotalFill BC Sealer® has a higher tubular penetration in the medial slices than in the apical slices regardless of the fluorescent stain used. Rhodamine B has higher results than Fluo-3 in terms of the percentage of sealer penetration and maximum penetration.

Determining the advanced mining influence range of an underground working face is crucial for preventing dynamic disasters, such as coal bursts and gas outbursts. In this study, the occurrence of advanced seismicity before the working face as well as its correlation with the acoustic emission (AE) activity of coal and rocks under axial loading was analyzed. Based on the results, a novel statistical method to determine the advanced mining influence range based on advanced seismicity data was proposed and then validated with a case study. The results show that advanced seismicity is caused by the combined effects of static and dynamic stresses at the working face. This seismicity can be used to assess the mining influence degree of the working face on the advanced coal and rock mass, and determine the advanced mining influence range. Using the novel statistical method, the normalized curves for the total number and total energy of the advanced mining-induced seismicity can be plotted. Then, the advanced mining influence range can be determined using thresholds. The thresholds can be established based on the AE activities observed in coal and rock samples under axial static loading. In the case study in this research, the thresholds for the total seismic number and total seismic energy are 0.076 and 0.052, respectively. The corresponding advanced mining influence ranges are 275 m and 245 m, respectively. Field monitoring confirms an advanced mining influence range of 255 m, which validates the results obtained using the novel statistical method.

This paper emphasizes the crucial needs and reasons to ensure the reliability and repeatability of underwater acoustic measurements. As an exemplification of the aforementioned acoustic measurements challenges, the development of metrological infrastructure in the area of underwater acoustics in GUM (Central Office of Measures, Poland) is presented. An analysis of presented solutions was carried out mainly in the low frequency range, in comparison with other solutions recently developed worldwide. Moreover, factors influencing the sensitivity of hydroacoustic measuring devices are discussed. The summary of this discussion outlines the further works aimed at ensuring the reliability and repeatability of underwater acoustic measurements. The conclusions present the current state of the calibration infrastructure of underwater acoustic devices, with particular emphasis on marine ecology and fisheries sciences.

The moisture content of the human skin, but also the loss of water through the skin, the transepidermal water loss (TEWL), plays a significant role in the skin’s health. Various medical indications require the use of a wound dressing. However, how the skin environment changes under a wound dressing has not yet been sufficiently investigated. Skin moisture and TEWL values were measured in 20 healthy volunteers before and after the application of a total of 23 different wound dressings distributed over the back. Significant changes in the parameters from day 1 to day 2 were tested. Wound dressings change the underlying skin environment. Occlusive dressings significantly increase skin hydration and TEWL. The findings could contribute to quantitative analysis and monitoring of topical-wound therapy in the future.

Molecular communication (MC) represents a paradigm shift in communication technologies, extending beyond traditional electromagnetic methods by incorporating advances in nanotechnology, biotechnology, and communication theory. This innovative approach holds promise for groundbreaking applications in diverse fields such as medicine, military operations, and environmental monitoring. MC employs molecules to carry and transmit data. In concentration-encoded molecular communication (CEMC), information is represented through the concentration levels of the transmitted molecules. In this study, we introduce a novel method named time-hopping concentration-position shift keying (TH-CPSK), designed to facilitate multiple access within MC networks. The TH-CPSK method encodes information based on the position of the transmitted molecular concentration, significantly enhancing the accuracy of data transmission by reducing interference in MC channels. Numerical simulations reveal that an increase in the number of users adversely affects communication performance. Furthermore, our findings indicate that augmenting the number of hops improves communication performance at transmitter-receiver distances of 1 cm and 2 cm. Conversely, at a distance of 3 cm, performance degradation is observed, attributed to the increased complexity. Therefore, it is important to carefully select the number of hops considering the molecular channel’s characteristics. Overall, TH-CPSK can enhance the efficiency and reliability of CEMC systems, offering a significant step forward in the realization of MC’s potential applications.

The increase in mining depth necessitates higher strength requirements for hard rock pillars, making mine pillar stability analysis crucial for pillar design and underground safety operations. To enhance the accuracy of predicting the stability state of mine pillars, a prediction model based on the subtraction-average-based optimizer (SABO) for hyperparameter optimization of the least-squares support vector machine (LSSVM) is proposed. First, by analyzing the redundancy of features in the mine pillar dataset and conducting feature selection, five parameter combinations were constructed to examine their effects on the performance of different models. Second, the SABO-LSSVM prediction model was compared vertically with classic models and horizontally with other optimized models to ensure comprehensive and objective evaluation. Finally, two data sampling methods and a combined sampling method were used to correct the bias of the optimized model for different categories of mine pillars. The results demonstrated that the SABO-LSSVM model exhibited good accuracy and comprehensive performance, thereby providing valuable insights for mine pillar stability prediction.

This study presents the analysis of the UEFA Euro 2024 in Germany, focusing on the impact of the first goal on match outcomes, goal distribution between halves and quarters, and the relationship between offensive, defensive, and goalkeeping metrics and match outcomes. Moreover, a regression model is developed to identify the key factors that significantly contribute to teams’ success. The analysis of the 36 group stage matches revealed that scoring the first goal significantly increased the likelihood of a positive match outcome. There were no significant differences between goals scored in the first and second halves or per 15 min of the game. Kruskal–Wallis tests highlighted that winning teams had more assists, attempts on target and runs into the penalty area. Defensive metrics showed that winning teams recovered more balls, while goalkeeping metrics revealed that winning teams had more clean sheets. The logistic regression model identified “Attempts on Target” and “Passes into Attacking Third” as significant positive predictors of winning, while “Attempts on Target Outside Area” and “Crosses Attempted” were negative predictors. These findings offer valuable insights for coaching staff to develop strategies focusing on key performance indicators that enhance the likelihood of winning.

In the western mining region, weakly cemented rock layers above the coal seams often lead to frequent catastrophic accidents during mining due to their instability. To address this, this paper analyzes the movement characteristics of surrounding rock in the recovery roadway and the effectiveness of from nearby large coal pillar roadways. A mechanical model for the failure of weakly cemented roadways was established, and numerical simulations were used to verify the feasibility of leaving small coal pillars along soft, thick coal seams. Additionally, existing measurements were used to evaluate the impact of leaving small coal pillars on the deformation of the surrounding rock in the recovery roadway. The results show that after changing the coal pillar retention to 5 m in the 130,205 working face of the Yangchangwan mining area, the roadway is in a low-stress zone, with minimal surrounding rock deformation, meeting safety requirements for production.

This study examines the absorption performance of rectangular sonic black holes (SBHs), which are designed to provide broadband anechoic termination for rectangular waveguides. The SBHs explored in this work consist of a series of opposing rib pairs embedded within the waveguide, where the distance between the ribs in each pair decreases towards the end of the structure according to a specific profile. A computationally efficient mathematical model, combined with an evolutionary optimization algorithm, is employed to determine the optimal geometrical parameters, including the SBH profile, which maximize absorption performance over a broad frequency range. As the optimal geometries feature very fine internal structures, which pose challenges for practical implementation, micro-perforated plates are incorporated to introduce additional losses. Numerical simulations and optimizations are again utilized to identify the geometrical and physical parameters that maximize the absorption performance of these modified structures. The results demonstrate superior absorption performance, even with internal structures compatible with contemporary manufacturing processes. The results of the numerical simulations are validated via a comparison with detailed and accurate mathematical model.

Stargardt atrophy and geographic atrophy (GA) represent pivotal endpoints in FDA-approved clinical trials. Predicting atrophy progression is crucial for evaluating drug efficacy. Fundus autofluorescence (FAF), the standard 2D imaging modality in these trials, has limitations in patient comfort. In contrast, spectral-domain optical coherence tomography (SD-OCT), a 3D imaging modality, is more patient friendly but suffers from lower image quality. This study has two primary objectives: (1) develop an efficient predictive modeling for the generation of future FAF images and prediction of future Stargardt atrophic (as well as GA) regions and (2) develop an efficient predictive modeling with advanced 3D OCT features at ellipsoid zone (EZ) for the comparative performance in the generation of future enface EZ maps and prediction of future Stargardt atrophic regions on OCT as on FAF. To achieve these goals, we propose two deep neural networks (termed ReConNet and ReConNet-Ensemble) with recurrent learning units (long short-term memory, LSTM) integrating with a convolutional neural network (CNN) encoder–decoder architecture and concurrent learning units integrated by ensemble/multiple recurrent learning channels. The ReConNet, which incorporates LSTM connections with CNN, is developed for the first goal on longitudinal FAF. The ReConNet-Ensemble, which incorporates multiple recurrent learning channels based on enhanced EZ enface maps to capture higher-order inherent OCT EZ features, is developed for the second goal on longitudinal OCT. Using FAF images at months 0, 6, and 12 to predict atrophy at month 18, the ReConNet achieved mean (±standard deviation, SD) and median Dice coefficients of 0.895 (±0.086) and 0.922 for Stargardt atrophy and 0.864 (±0.113) and 0.893 for GA. Using SD-OCT images at months 0 and 6 to predict atrophy at month 12, the ReConNet-Ensemble achieved mean and median Dice coefficients of 0.882 (±0.101) and 0.906 for Stargardt atrophy. The prediction performance on OCT images is comparably good to that on FAF. These results underscore the potential of SD-OCT for efficient and practical assessment of atrophy progression in clinical trials and retina clinics, complementing or surpassing the widely used FAF imaging technique.

The residues of pharmaceutical compounds are often resistant to degradation, causing an environmental problem. Our research aimed to perform a study of the photocatalytic and photoelectrocatalytic degradation of non-opioid analgesic paracetamol and some of the non-steroidal anti-inflammatory drugs (NSAIDs) (ketoprofen, naproxen, diclofenac, and ibuprofen). Semiconductor WO3, Fe2O3, and WO3/Fe2O3 photocatalysis using solar energy lamps were applied for this purpose. As a result of the photocatalytic processes, high decomposition efficiency was obtained for ketoprofen (97%) and naproxen (70%). Low photodegradation yields were achieved for diclofenac. Under the used measurement conditions, both paracetamol and ibuprofen were not degraded.

In response to the complex working conditions and poor driving stability of high-clearance self-propelled sprayers, a nonlinear model of the chassis power system was established based on the independently controllable torque of each wheel of the developed electric sprayer. A layered-architecture chassis drive control strategy was formulated, and a stability control framework comprising an instability judgment module, an upper controller, and a lower controller was constructed based on the analysis of the impact of the centroid slip angle, the yaw rate, and the wheel slip rate on driving stability. An ideal reference model was established based on the seven-degree-of-freedom model of the sprayer, and the current state of the sprayer body was determined using the instability judgment module. A drive anti-slip controller and a yaw moment controller based on fuzzy PID theory and sliding mode control theory were designed. Additionally, an optimal torque distribution algorithm was developed based on tire characteristics to rationally allocate drive torque to each wheel, ensuring the stability of the sprayer during operation. Simulation tests were conducted using MATLAB/Simulink to evaluate the sprayer under four different driving conditions during transport and field operations. The test results showed that the “SMC + optimal distribution” control method in the chassis stability control strategy reduced the maximum deviations of the yaw rate and centroid slip angle by an average of 89.5% and 13.6%, respectively, compared to no control. The wheel slip rate during straight driving was well maintained at around 15%, enhancing the driving stability of the sprayer.

Tomatoes are among the world’s most significant vegetables, both in terms of production and consumption. Harvesting takes place in tomato production when the important quality attribute of total soluble solids content reaches its maximum possible level. Tomato total soluble solids content (TSS) is among the most crucial attribute parameters for assessing tomato quality and for tomato commercialization. Determination of total soluble solids content by conventional measurement methods is both destructive and time-consuming. Therefore, the tomato processing industry needs a rapid identification method to measure total soluble solids content (TSS). In this study, we aimed to estimate how much soluble solids there are in beef tomato fruit by Artificial Neural Networks (ANN) and Multiple Linear Regression (MLR) methods. The models were assessed using the Coefficient of Determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) metrics. The training data set results of the MLR model established to estimate the amount of brix in tomato fruit, calculated as MAE: 0.2349, RMSE: 0.3048, R2: 0.8441, and MAPE: 5.5368, while, according to the ANN model, MAE: 0.0250, RMSE: 0.031, R2: 0.9982 and MAPE: 0.5814. According to the metric outcomes, the ANN-based model performed better in both the training and testing parts.

This article studies the migration and settlement laws of backflow proppants after fracturing tight sandstone. This paper proposes a fitting method based on a multi-task learning network to address the issue of interference from multiple physical parameters during the transport and settlement processes of proppants. This method can effectively handle multi-dimensional interference factors and fit the mapping logic of multiple engineering parameters to transport patterns through the continuous correction of multi-layer networks. We first introduce the characteristics of tight sandstone reservoirs and their important value in mining, as well as the status of current research on the migration and settlement laws of proppants at home and abroad. Based on this, we then deeply analyze the sedimentation rate model of proppants in tight sandstone backflow and the equilibrium height of proppants under multiple factors of interference while considering the distribution characteristics of proppants. In order to more accurately simulate the transport and settlement laws of proppants, this paper introduces a multi-task learning network. This network can comprehensively consider multi-dimensional parameters, learn the inherent laws of data through training, and achieve accurate fitting of the transport and settlement laws of proppants. This study trained and tested the model using actual production data, and the results showed that the proposed model can fit the input–output relationship well, thus effectively supporting the study of proppant transport and settlement laws.

(1) Background: The use of alumina particles for surface treatment of dental implants is a common practice aimed at enhancing osseointegration. However, the biological effects of residual alumina particles on implant surfaces remain a subject of debate. This systematic review evaluates the impact of residual alumina particles on the osseointegration, biocompatibility, and bacterial adhesion of dental implants based on available in vivo experimental animal studies. (2) Methods: A comprehensive literature search was conducted across PubMed, Web of Science, and Scopus to identify relevant studies. The inclusion criteria focused on experimental animal studies that assessed the biological effects of alumina-blasted dental implants. Data extraction was carried out, and quality assessments were performed using the SYRCLE risk-of-bias tool. (3) Results: Ten studies met the inclusion criteria, involving various animal models, such as rabbits, pigs, dogs, and sheep. The findings demonstrated that residual alumina particles did not negatively impact osseointegration. Some studies reported accelerated bone growth and improved osseointegration with residual alumina. Additionally, residual alumina showed potential bactericidal properties, reducing bacterial adhesion. (4) Conclusions: The available evidence from animal studies suggests that residual alumina particles do not adversely affect the osseointegration and biocompatibility of dental implants. These particles may even enhance bone growth and reduce bacterial adhesion. However, due to the scarcity of human studies and the impracticality of histological assessments in humans, further research, including long-term clinical trials, is necessary to confirm these findings.

In this study, the Bald Eagle Search Algorithm performed hydrogen consumption and battery cycle optimization of a fuel cell electric vehicle. To save time and cost, the digital vehicle model created in Matlab/Simulink and validated with real-world driving data is the main platform of the optimization study. The digital vehicle model was run with the minimum and maximum battery charge states determined by the Bald Eagle Search Algorithm, and hydrogen consumption and battery cycle values were obtained. By using the algorithm and digital vehicle model together, hydrogen consumption was minimized and range was increased. It was aimed to extend the life of the parts by considering the battery cycle. At the same time, the number of battery packs was included in the optimization and its effect on consumption was investigated. According to the study results, the total hydrogen consumption of the fuel cell electric vehicle decreased by 57.8% in the hybrid driving condition, 23.3% with two battery packs, and 36.27% with three battery packs in the constant speed driving condition.

In this work, the lumped model of Rogowski coils is briefly reviewed to illustrate that the reduction in stray capacitance expands the bandwidth and improves the high-frequency performance. Then, a network model as well as explicit formulas for the stray capacitance of Rogowski coils are established, and the influence of geometrical parameters of the skeletons on the stray capacitance is investigated. It is found that the stray capacitance of Rogowski coils is approximately proportional to the perimeter of the skeleton cross-section. Based on the above discussion, optimization of the shape of the skeleton cross-section, aiming at minimizing the perimeter without affecting the magnetic flux and mutual inductance of the coils, is carried out. The widely adopted circular and rectangular cross-sections are discussed first. Then, the cross-section of an arbitrary smooth and convex shape is optimized by solving a constrained variational problem, leading to an explicit equation for the optimal skeleton cross-section of Rogowski coils. Numerical results demonstrate that, compared with the common circular and rectangular skeleton cross-sections, the proposed optimal cross-section exhibits the shortest perimeter and thus the highest upper cutoff frequency under fixed magnetic flux. The optimization method developed by this work can provide a theoretical basis and guidance for the design of Rogowski coils.

In the study of aircraft wake target detection, as the wake evolves and develops, the detection area of the LiDAR often shows the presence of two distinct vortices, one on each side. Sometimes, only a single wake vortex may be present. This can lead to a reduction in the accuracy of wake detection and an increased likelihood of missed detections, which may have a significant impact on the flight safety. Hence, we propose an algorithm based on the YOLOv8n–CBAM–EfficientNetV2 model for wake detection. The algorithm incorporates the lightweight network of EfficientNetV2 and the Convolutional Block Attention Module (CBAM) based on the YOLOv8n model, which achieves the lightweight improvement in the YOLOv8n algorithm and the improvement in detection accuracy. First, this study classifies the wake vortices in the wake greyscale images obtained at Hong Kong International Airport, based on the Range–Height Indicator (RHI) scanning characteristics of the LiDAR and the symmetry of the wake vortex pairs. The classification is used to detect left and right vortices for more accurate wake detection in wind field images, which thereby improves the precision rate of target detection. Subsequently, experiments are conducted using a YOLOv8n–CBAM–EfficientNetV2 model for aircraft wake detection. Finally, the performance of the YOLOv8n–CBAM–EfficientNetV2 model is analysed. The results show that the algorithm proposed in this study can achieve a 96.35% precision rate, 93.58% recall rate, 95.06% F1-score, and 250 frames/s. The results show that the method proposed in this study can be effectively applied in aircraft wake detection.

This study presents an innovative home energy management system (HEMS) that incorporates PV, WTs, and hybrid backup storage systems, including a hydrogen storage system (HSS), a battery energy storage system (BESS), and electric vehicles (EVs) with vehicle-to-home (V2H) technology. The research, conducted in Liaoning Province, China, evaluates the performance of the HEMS under various demand response (DR) scenarios, aiming to enhance resilience, efficiency, and energy independence in green buildings. Four DR scenarios were analyzed: No DR, 20% DR, 30% DR, and 40% DR. The findings indicate that implementing DR programs significantly reduces peak load and operating costs. The 40% DR scenario achieved the lowest cumulative operating cost of $749.09, reflecting a 2.34% reduction compared with the $767.07 cost in the No DR scenario. The integration of backup systems, particularly batteries and fuel cells (FCs), effectively managed energy supply, ensuring continuous power availability. The system maintained a low loss of power supply probability (LPSP), indicating high reliability. Advanced optimization techniques, particularly the reptile search algorithm (RSA), are crucial in enhancing system performance and efficiency. These results underscore the potential of hybrid backup storage systems with V2H technology to enhance energy independence and sustainability in residential energy management.

The development and application of virtual reality (VR) technology significantly enhances consumer immersion. Exploring a multi-sensory evaluation model for virtual packaging is valuable for integrating VR technology with packaging. This study developed a multi-sensory evaluation model for virtual packaging using the analytic hierarchy process (AHP). Eye-tracker experimentation was conducted to identify consumer attention indicators when interacting with virtual packaging. These indicators were quantified using Saaty’s nine-level importance scale and expert input, resulting in a comprehensive multi-sensory evaluation model. Subsequently, a VR shopping system focused on potato chips and cola as packaging design objects. This system was evaluated using the established model, and the results were analyzed. Based on the findings, improvements were made, and the system was re-evaluated using the modified model. The post-improvement evaluation demonstrated significantly enhanced sensory experiences. These results validate that the developed evaluation model effectively guides multi-sensory design approaches for packaging within a VR environment.

We present the generation of attosecond gamma rays via inverse Compton scattering within the framework of laser wakefield acceleration through 2D Particle-In-Cell simulations. Utilizing the near-threshold ionization injection mechanism, an attosecond micro-bunched electron beam characterized by a comb-like current density profile can be achieved with a linearly polarized laser at an intensity of a0 = 1.5. The micro-bunched beam provides a beam energy of approximately 300 MeV and achieves a minimum relative energy spread of about 1.64% after undergoing 2 mm of acceleration. In the inverse Compton scattering scheme, these attosecond electron micro-bunches interact with the reflected driving laser pulse, resulting in the attosecond gamma-ray radiation exhibiting similar structures. Individual spatial-separated gamma-ray pulses exhibit a length of approximately 260–300 as, with a critical energy of 2.0 ± 0.2 MeV. The separated attosecond gamma-ray source owns a peak brilliance of ~1022 photons s−1 mm−2 mrad−2 0.1% BW. This brilliance is competitive in a laboratory for multi-MeV γ-ray sources with a laser intensity of I = 5 × 1018 W/cm2. Such attosecond gamma-ray radiation offers promising applications requiring ultrashort X-ray/gamma ray sources.

On public benchmarks, current macro facial expression recognition technologies have achieved significant success. However, in real-life scenarios, individuals may attempt to conceal their true emotions. Conventional expression recognition often overlooks subtle facial changes, necessitating more fine-grained micro-expression recognition techniques. Different with prevalent facial expressions, weak intensity and short duration are the two main obstacles for perceiving and interpreting a micro-expression correctly. Meanwhile, correlations between pixels of visual data in spatial and channel dimensions are ignored in most existing methods. In this paper, we propose a novel network structure, the Attention-based Spatiotemporal-aware network (ASTNet), for micro-expression recognition. In ASTNet, we combine ResNet and ConvLSTM as a holistic framework (ResNet-ConvLSTM) to extract the spatial and temporal features simultaneously. Moreover, we innovatively integrate two level attention mechanisms, channel-level attention and spatial-level attention, into the ResNet-ConvLSTM. Channel-level attention is used to discriminate the importance of different channels because the contributions for the overall presentation of micro-expression vary between channels. Spatial-level attention is leveraged to dynamically estimate weights for different regions due to the diversity of regions’ reflections to micro-expression. Extensive experiments conducted on two benchmark datasets demonstrate that ASTNet achieves performance improvements of 4.25–16.02% and 0.79–12.93% over several state-of-the-art methods.

The problem of modelling and simulating traditional crafting actions is addressed, motivated by the goals of craft understanding, documentation, and training. First, the physical entities involved in crafting actions are identified, physically, and semantically characterised, including causing entities, conditions, properties, and objects, as well as the space and time in which they occur. Actions are semantically classified into a taxonomy of four classes according to their goals, which are shown to exhibit similarities in their operation principles and utilised tools. This classification is employed to simplify the create archetypal simulators, based on the Finite Element Method, by developing archetypal simulators for each class and specialising them in craft-specific actions. The approach is validated by specialising the proposed archetypes into indicative craft actions and predicting their results in simulation. The simulated actions are rendered in 3D to create visual demonstrations and can be integrated into game engines for training applications.

The friction pair gap affects not only the temperature increase of the multi-disc wet clutch, but also the efficiency of the helicopter transmission system. Consequently, a rotational–axial engagement and disengagement-coupled kinetic model of a wet multi-disc clutch considering asperity contact, hydrodynamic lubrication, spline resistance, and a separating spring model are developed in this paper. The effects of operating conditions on the dynamic characteristics of the wet clutch are investigated. Further, the gap deviation coefficient is proposed to characterize the dynamic behavior of the friction pair gap. As the control oil pressure increases from 1.3 MPa to 1.7 MPa, the gap deviation coefficient increases by 8.6%. Moreover, as the rotation speed increases from 888 rpm to 2488 rpm and the lubricant oil temperature increases from 25 °C to 85 °C, the gap deviation coefficient decreases by 1.1% and 4.44%, respectively. Therefore, an appropriate increase in lubricant oil temperature and rotation speed can facilitate the friction pair gap to be more uniform. These results are useful for the development of optimal control strategies for aviation wet clutch systems.

This paper focuses on predicting road passenger probability and optimizing taxi driving routes based on trajectory big data. By utilizing clustering algorithms to identify key passenger points, a method for calculating and predicting road passenger probability is proposed. This method calculates the passenger probability for each road segment during different time periods and uses a BiLSTM neural network for prediction. A passenger-seeking recommendation model is then constructed with the goal of maximizing passenger probability, and it is solved using the NSGA-II algorithm. Experiments are conducted on the Chengdu taxi trajectory dataset, using MSE as the metric for model prediction accuracy. The results show that the BiLSTM prediction model improves prediction accuracy by 9.67% compared to the BP neural network and by 6.45% compared to the LSTM neural network. The proposed taxi driver passenger-seeking route selection method increases the average passenger probability by 18.95% compared to common methods. The proposed passenger-seeking recommendation framework, which includes passenger probability prediction and route optimization, maximizes road passenger efficiency and holds significant academic and practical value.

In this study, cold plasma treatments are employed to modify peanuts. This study systematically investigates the effects of various plasma treatment conditions, including power, duration, and gas type, on the microstructure of peanut seed coats and embryos. Observations under a scanning electron microscope (SEM) reveal that as plasma treatment power increases from 100 W to 500 W, the etching level of peanut seed coats significantly intensifies, surface roughness deepens, and concavities become more pronounced. Additionally, micro-pores on the seed coat gradually enlarge and form cracks. Specifically, when the plasma treatment is set at 200 W for 60 s, the oxygen (O2) treatment group shows interconnected cracks on the peanut seed coat surface, with lipid particles exuding and protein particles and polymers decomposing. In contrast, the helium (He) treatment group displays clear cell structures and deep grooves, with no noticeable lipid particles exuding around surface cracks. The argon (Ar) treatment group exhibits a distinct rectangular cell structure with clear boundaries, and although surface cracks form, only a few protein particles escape from the cracks. The embryo surface structure becomes looser after plasma treatment, leading to the disintegration of lipid particles, protein particles, and polymers, affecting the fusion and migration of large and small lipid bodies within the peanut’s internal structure. Increasing treatment duration intensifies the etching phenomenon, resulting in more lipid particles exuding, which indicates a positive correlation between lipid particles exuding and treatment duration. This study sheds light on the mechanisms underlying changes in peanut microstructure due to cold plasma treatment, providing scientific evidence for improving peanut quality, enhancing oil extraction efficiency, and optimizing food processing techniques.

The seat characteristics have high relevance in overall comfort on any transportation means. In particular, the foam’s mechanical properties, interface pressure, and contact temperature play an important role in low- or no-vibration situations regarding static comfort. The present work presents the complete protocol for a static evaluation of different foams and seat covers to assess railway seats. Based on the evaluation of the foam’s mechanical properties and interface pressure profiles, it was concluded that higher-density foam (80 kg/m3) is the most favorable. Regarding the foam cover, a thermographic assessment demonstrated that the fabric cover that induces lower temperatures at passenger interface contact promotes higher comfort levels. It should be highlighted that experiments were conducted on real train seat cushions and environments using a thermographic camera and pressure map sensor.

Preserving cosmetic products from microbial contamination is crucial to ensuring their safety, efficacy, and shelf life. A number of synthetic antimicrobial preservatives are available, but, since the global market demand for natural ingredients is increasing, cosmetic manufacturers are considering replacing conventional preservatives with natural alternatives. In this context, the objective of this investigation was to characterize the antimicrobial activity of the natural preservative, intended for cosmetic purposes, Kopraphinol (INCI name: Lactobacillus/Radish Root Ferment Extract Filtrate). It was tested against a panel of selected bacteria and mycetes by using conventional microbiological techniques (determination of MIC, time killing assay), and a challenge test was used to verify its potential preservative in an O/W hydrophilic cream (Cetomacrogol cream base). Kopraphinol has shown an interesting antimicrobial effectiveness, with M.I.C.s ranging from 0.78% to 6.25% for bacteria and from 1.56% to 5% for mycetes. Moreover, it fulfilled challenge test criterion A and has proven effective against microbial contamination, leading to a 3 log reduction of inoculum after 7 days for bacteria and a 2 log reduction at 14 days for fungi. The results obtained show that Kopraphinol can be considered a promising and effective candidate for the antimicrobial preservation of cosmetics and could successfully complement or even replace conventional preservatives.

Two dominant driving forces for evolving communication technologies in the current society have been the proliferation of wireless access networks to the Internet and the broadbandization of access and infrastructure networks. Through these evolutions of communication technologies, high-resolution contents are instantly delivered to wireless devices such as mobile phones, wireless tablets, and headsets. Recently, wireless sensor networks, where up to 1000 low-power sensors are wirelessly connected to each other, have been created and connected to the Internet, which presents a new challenge of efficiently coordinating the transmissions of many wireless sensors with minimal transmission overheads. Developing an efficient Medium Access Control (MAC) protocol governing the transmissions of wireless sensor networks is crucial for the success of wireless sensor networks for the realization of the Internet of Things (IoT). In 2023, the node insertion algorithm was proposed to efficiently derive the minimal number of serially connected multipolling sequences of many wireless sensors, by which Access Points (APs) can poll wireless sensors with minimal polling overheads. In this paper, the combined sweeping and jumping method is presented to dramatically enhance the searching performance of the node insertion algorithm. To validate the performance of the combined sweeping and jumping method, simulation results are presented for wireless sensor networks where wireless sensors with varying transmission ranges exist.