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Background and Objectives: Polydeoxyribonucleotides (PDRN), composed of DNA fragments derived from salmon DNA, is widely recognized for its regenerative properties. It has been extensively used in medical applications, such as dermatology and wound healing, due to its ability to enhance cellular metabolic activity, stimulate angiogenesis, and promote tissue regeneration. In the field of dentistry, PDRN has shown potential in promoting periodontal healing and bone regeneration. This study aims to investigate the effects of PDRN on the morphology, survival, and osteogenic differentiation of gingiva-derived stem cell spheroids, with a focus on its potential applications in tissue engineering and regenerative dentistry. Materials and Methods: Gingiva-derived mesenchymal stem cells were cultured and formed into spheroids using microwells. The cells were treated with varying concentrations of PDRN (0, 25, 50, 75, and 100 μg/mL) and cultivated in osteogenic media. Cell morphology was observed over seven days using an inverted microscope, and viability was assessed with Live/Dead Kit assays and Cell Counting Kit-8. Osteogenic differentiation was evaluated by measuring alkaline phosphatase activity and calcium deposition. The expression levels of osteogenic markers RUNX2 and COL1A1 were quantified using real-time polymerase chain reaction. RNA sequencing was performed to assess the gene expression profiles related to osteogenesis. Results: The results demonstrated that PDRN treatment had no significant effect on spheroid diameter or cellular viability during the observation period. However, a PDRN concentration of 75 μg/mL significantly enhanced calcium deposition by Day 14, suggesting increased mineralization. RUNX2 and COL1A1 mRNA expression levels varied with PDRN concentration, with the highest RUNX2 expression observed at 25 μg/mL and the highest COL1A1 expression at 75 μg/mL. RNA sequencing further confirmed the upregulation of genes involved in osteogenic differentiation, with enhanced expression of RUNX2 and COL1A1 in PDRN-treated gingiva-derived stem cell spheroids. Conclusions: In summary, PDRN did not significantly affect the viability or morphology of gingiva-derived stem cell spheroids but influenced their osteogenic differentiation and mineralization in a concentration-dependent manner. These findings suggest that PDRN may play a role in promoting osteogenic processes in tissue engineering and regenerative dentistry applications, with specific effects observed at different concentrations. Full article

Automated packing is urgently needed in apple production. This paper proposes an improved genetic algorithm fused with an optimal parameter selection algorithm to optimize the two-posture manipulator working path of packing robots. First, the structure and working principle of the packing robot were designed. Second, the kinematics and packing paths of the two-posture manipulator were analyzed. Finally, the path optimization method for the two-posture manipulator was introduced. The method was based on the improved genetic algorithm by using a two-level coding and region crossover operator. The parameter values can be automatically determined by the optimal parameter selection algorithm. Ten repeated comparative tests show that the total packing time is 23.86 s under the working conditions of four grasping points and fourteen placing points. The optimal performance of the proposed algorithm is better than that of the traditional genetic algorithm, and the average optimization amplitudes are 14.63%, 15.42%, 16.24%, and 13.82% for 9-groove, 12-groove, 14-groove, and 16-groove trays, respectively. The proposed algorithm can effectively prevent the premature convergence problem of the traditional genetic algorithm and the optimization process instability problem, improve the range of optimization, and reduce the manipulator working time during packing. Full article

Additive manufacturing (AM) and topology optimization (TO) emerge as vital processes in modern industries, with broad adoption driven by reduced expenses and the desire for lightweight and complex designs. However, iterative topology optimization can be inefficient and time-consuming for individual products with a large set of parameters. To address this shortcoming, machine learning (ML), primarily neural networks, is considered a viable tool to enhance topology optimization and streamline AM processes. In this work, a machine learning (ML) model that generates a parameterized optimized topology is presented, capable of eliminating the conventional iterative steps of TO, which shortens the development cycle and decreases overall development costs. The ML algorithm used, a conditional generative adversarial network (cGAN) known as Pix2Pix-GAN, is adopted to train using a variety of training data pairs consisting of color-coded images and is applied to an example of cantilever optimization, significantly enhancing model accuracy and operational efficiency. The analysis of training data numbers in relation to the model’s accuracy shows that as data volume increases, the accuracy of the model improves. Various ML models are developed and validated in this study; however, some artefacts are still present in the generated designs. Structures that are free from these artefacts achieve 91% reliability successfully. On the other hand, the images generated with artefacts may still serve as suitable design templates with minimal adjustments. Furthermore, this research also assesses compliance with two manufacturing constraints: the limitations on build space and passive elements (voids). Incorporating manufacturing constraints into model design ensures that the generated designs are not only optimized for performance but also feasible for production. By adhering to these constraints, the models can deliver superior performance in future use while maintaining practicality in real-world applications. Full article

DC–DC buck converters have been designed by incorporating different control stages to drive the switches. Among the most commonly used controllers, the sliding mode control (SMC) and proportional-integral-derivative (PID) controller have shown advantages in accomplishing fast slew rate, reducing settling time and mitigating overshoot. The proposed work introduces the implementation of both SMC and PID controllers by using the field-programmable gate array (FPGA) device. The FPGA is chosen to exploit its main advantage for fast verification and prototyping of the controllers. In this manner, a DC–DC buck converter is emulated on an FPGA by applying an explicit multi-step numerical method. The SMC controller is synthesized into the FPGA by using a signum function, and the PID is synthesized by applying the difference quotient method to approximate the derivative action, and the second-order Adams–Bashforth method to approximate the integral action. The FPGA synthesis of the converter and controllers is performed by designing digital blocks using computer arithmetic of 32 and 64 bits, in fixed-point format. The experimental results are shown on an oscilloscope by using a digital-to-analog converter to observe the voltage regulation generated by the SMC and PID controllers on the DC–DC buck converter. Full article

The “dual carbon” goal has made it a mainstream trend for new energy stations (NESs) and energy storage stations (ESSs) to jointly participate in market regulation. This paper proposes a multiple time scale distributed optimization method for NESs and ESSs based on the alternate direction multiplier method (ADMM). By first considering the uncertainty of new energy output and the volatility of electricity market prices, a multi time scale revenue model is constructed for day-ahead, intraday, and real-time markets. Then, the objective function is built by maximizing the comprehensive market revenues and is simplified using the synergistic effect of NESs and ESSs. Next, the simplified objective function is solved by the ADMM, and the revenues are maximized while each energy meets the relevant constraints. Lastly, the 33-node network topology is used to illustrate the feasibility of the proposed method. The simulation results show that after optimization, the output of NESs and ESSs can coordinate work in day-ahead, intraday, and real-time markets, while the abandonment power of wind and light is significantly improved. Full article

Pesticide use in crops is a severe problem in some countries. Each country has its legislation for use, but they differ in the degree of tolerance for these broadly toxic products. Several synthetic pesticides can cause air, soil, and water pollution, contaminating the human food chain and other living beings. In addition, some of them can accumulate in the environment for an indeterminate amount of time. The agriculture sector must guarantee healthy food with sustainable production using environmentally friendly methods. In this context, biological biopesticides from microbes and plants are a growing green solution for this segment. Several pests attack crops worldwide, including weeds, insects, nematodes, and microorganisms such as fungi, bacteria, and viruses, causing diseases and economic losses. The use of bioproducts from microorganisms, such as microbial biopesticides (MBPs) or microorganisms alone, is a practice and is growing due to the intense research in the world. Mainly, bacteria, fungi, and baculoviruses have been used as sources of biomolecules and secondary metabolites for biopesticide use. Different methods, such as direct soil application, spraying techniques with microorganisms, endotherapy, and seed treatment, are used. Adjuvants like surfactants, protective agents, and carriers improve the system in different formulations. In addition, microorganisms are a tool for the bioremediation of pesticides in the environment. This review summarizes these topics, focusing on the biopesticides of microbial origin. Full article

In agriculture, abiotic and biotic stress reduce yield by 51–82% and 10–16%, respectively. Applications of biological agents such as plant growth-promoting rhizobacteria (PGPR) and arbuscular mycorrhizal fungi (AMF) can improve plant growth. Applications of lone PGPR and AMF also help plants resist abiotic and biotic stressors. The reports for dual inoculation of AMF and PGPR to benefit plants and tackle stressors are largely unknown. It is speculated that PGPR colonization in plants enhances AMF infection during dual AMF and PGPR application, although increased AMF colonization does not always correlate with the increased benefits for the plant hosts. Further research is needed regarding molecular mechanisms of communication during dual inoculations, and dual-inoculation enhancement of induced systemic resistance under pathogen stress, to understand how dual inoculations can result in enhanced plant benefits. The influence of application timing of AMF and PGPR dual inoculations on mitigating abiotic and biotic stress is also not well understood. This review documents the factors that govern and modulate the dual application of AMF and PGPR for plant benefits against stress responses, specifically abiotic (drought) stress and stress from pathogen infection. Full article

Modern advances in technology have increased the demand for traditional accounting systems to be upgraded for real-time data processing, security, and standardized reports. Thus, this paper proposes a new accounting information system that integrates IoT, blockchain, and XBRL. The proposed system aims to automate the accounting process by using IoT to collect data and send it automatically to a blockchain, which acts as a database that will generate journal entries automatically through smart contracts. XBRL will then be used as an output method for standardized financial reports based on the data transferred from the blockchain. This paper uses a qualitative research design based on semi-structured interviews with 13 industry experts from IT engineering, academia, and financial systems analysis. NVivo software was used to conduct a thematic analysis of interview transcripts. The findings demonstrated that integrating IoT, blockchain, and XBRL is technically feasible, with significant potential to enhance accounting systems. Additionally, the findings identified key challenges of the proposed system, including the complexity of integration, data validation across technologies, costs, user adoption, and scalability concerns. However, the results showed that this system offers substantial benefits, such as real-time data capture from IoT devices, secure data storage and immutability through blockchain, standardized financial reporting via XBRL, accounting process automation, improved data accuracy, and enhanced security and transparency in financial reporting. The study also identified an optimal mechanism for ensuring seamless data transmission between these technologies. The study makes a valuable contribution to the accounting field by providing a new framework for automating data collection, enhancing data security, and streamlining financial reporting, with significant potential to advance accounting systems and improve transparency, accuracy, and efficiency in financial reporting. The study’s potential to impact accounting systems and financial reporting research and practice emphasizes its importance. Full article

As network technology continues to advance, network congestion has become an inevitable aspect of network communication. Considering the external interference, unmodeled uncertainty and the interaction between nodes, a multi-bottleneck TCP/AWM network model is established in this paper. A new fixed-time congestion controller was designed by combining a neural network and the backstepping technique. The neural network approximation property is used to eliminate the interference of unmodeled uncertainty and UDP flow in the system. The controller designed in this paper can ensure the stability of the TCP/AWM closed-loop system in a fixed time. Finally, the simulation results demonstrate the effectiveness of the proposed TCP/AWM controller. Full article

Intelligent traffic control decision-making has long been a crucial issue for improving the efficiency and safety of the intelligent transportation system. The deficiencies of the Type-1 fuzzy traffic control system in dealing with uncertainty have led to a reduced ability to address traffic congestion. Therefore, this paper proposes a Type-2 fuzzy controller for a single intersection. Based on real-time traffic flow information, the green timing of each phase is dynamically determined to achieve the minimum average vehicle delay. Additionally, in traffic light control, various factors (such as vehicle delay and queue length) need to be balanced to define the appropriate reward. Improper reward design may fail to guide the Deep Q-Network algorithm to learn the optimal strategy. To address these issues, this paper proposes a deep reinforcement learning traffic control strategy combined with Type-2 fuzzy control. The output action of the Type-2 fuzzy control system replaces the action of selecting the maximum output Q-value of the target network in the DQN algorithm, reducing the error caused by the use of the max operation of the target network. This approach improves the online learning rate of the agent and increases the reward value of the signal control action. The simulation results using the Simulation of Urban MObility platform show that the traffic signal optimization control proposed in this paper has achieved significant improvement in traffic flow optimization and congestion alleviation, which can effectively improve the traffic efficiency in front of the signal light and improve the overall operation level of traffic flow. Full article

Rural adolescents are at higher risk of reduced academic persistence due to socioeconomic barriers. Educational expectations are theoretically viewed as important for adolescents’ learning behaviors, and cross-sectional research has supported this assumption. However, few longitudinal studies have investigated the influence of educational expectations on adolescents’ academic persistence. In addition, research has not clearly identified whether self-esteem moderates this link among adolescents who experience greater economic risk. Using data from two time points (i.e., six months apart), this study aims to provide a more complete understanding of whether, and under what conditions, rural adolescents’ educational expectations influence academic persistence. The participants consist of 631 adolescents (M_age = 13.34 years at T1), and all the adolescents are from families with rural household registrations. The results show that the interaction term of educational expectations and self-esteem significantly predicts academic persistence. Specifically, after controlling for baseline academic persistence, educational expectations positively predict later academic persistence for rural adolescents with lower self-esteem, and educational expectations do not significantly predict later academic persistence for those with higher self-esteem. This study reveals the protective role of self-esteem in rural adolescents. High self-esteem benefits rural adolescents by protecting them from the effects of lower educational expectations on academic persistence. This finding also emphasizes the importance of developing self-esteem interventions for rural adolescents with low educational expectations to prevent them from experiencing weaker academic persistence. Full article

In the realm of traffic signal operations, the Signal Timing Manual second edition (STM2) serves as a foundational guide for professionals engaged in multimodal signal retiming projects. However, it is acknowledged that the STM2 has its limitations, and real-world conditions often necessitate adaptations in the established procedures. Considering this context, this research endeavors to bridge this gap by conducting a comprehensive survey aimed at traffic signal professionals. This study presents the findings of a comprehensive survey conducted among traffic signal professionals to explore the methodologies, challenges, and practices involved in multimodal signal retiming projects. The survey aimed to obtain detailed insights into the current state of signal retiming, the types of data and tools utilized, and the adaptations necessary to address the complexities of multimodal urban transportation networks. The survey highlights and summarizes responses from 36 professionals across North America, providing insight into both the common strategies and unique challenges faced by those responsible for optimizing signal timings in diverse and dynamic urban environments. The survey results reveal a reliance on diverse tools and data types for signal optimization, highlighting the complexities of accommodating different transportation needs. The findings underscore the importance of tailored approaches and advanced technologies in enhancing signal retiming processes. The insights gained from this study will inform future strategies and enhance the effectiveness of signal retiming procedures in urban areas, thereby contributing to improved traffic management and multimodal transportation efficiency. Full article

In this work, a new methodology is proposed for the online and non-invasive extraction of partial discharge (PD) pulses from raw measurement data obtained using a simplified setup. This method enables the creation of sub-windows with optimized size, each containing a single candidate PD pulse. The proposed approach integrates mathematical morphological filtering (MMF) with kurtosis, a first-order Savitzky-Golay smoothing filter, the Otsu method for thresholding, and a specific technique to associate each sub-window with the phase angle of the applied voltage waveform, enabling the construction of phase-resolved PD (PRPD) patterns. The methodology was validated against a commercial PD detection device adhering to the IEC (International Electrotechnical Commission) standard. Experimental results demonstrated that the proposed method, utilizing an off-the-shelf 8-bit resolution data acquisition system and a low-cost high-frequency current transformer (HFCT) sensor, effectively diagnoses and characterizes PD activity in high-voltage equipment, such as surge arresters and instrument transformers, even in noisy environments. It was able to characterize PD activity using only a few cycles of the applied voltage waveform and identify low amplitude PD pulses with low signal-to-noise ratio signals. Other contribution of this work is the diagnosis and fault signature obtained from a real surge arrester (SA) with a nominal voltage of 192 kV, corroborated by destructive disassembly and internal inspection of the tested equipment. This work provides a cost-effective and accurate tool for real-time PD monitoring, which can be embedded in hardware for continuous evaluation of electrical equipment integrity. Full article

In this study, we propose a method for assessing the temporal and spatial distribution of Carcharhinus melanopterus in shallow waters using unmanned aerial vehicles (UAVs). Aerial surveys were conducted in Tien Og Bay (Koh Tao, Thailand) thrice daily (morning, afternoon, evening) along a 360 m transect at a 30 m altitude. Environmental factors, including cloudiness, sea conditions, wind, tide, and anthropogenic disturbance, were recorded for each time slot. We developed a Python/AppleScript application to facilitate individual counting, correlating sightings with GPS data and measuring pixel-based length. Abundance varied significantly across time slots (p < 0.001), with a strong morning preference, and was influenced by tide (p = 0.040), favoring low tide. Additionally, abundance related to anthropogenic disturbance (p = 0.048), being higher when anthropogenic activity was absent. Spatial distribution analysis indicated time-related, sector-based abundance differences (p < 0.001). Pixel-based length was converted to Total Length, identifying juveniles. They exhibited a strong sector preference (p < 0.001) irrespective of the time of day. Juvenile abundance remained relatively stable throughout the day, constituting 94.1% of afternoon observations. Between 2020 and 2022, an underwater video survey was conducted to determine the sex ratio of the individuals. Only females and juveniles were sighted in the bay. Full article

Combining aluminum and steel offers a promising solution for reducing structural weight and fuel consumption across various industries. Pulse in tungsten inert gas (TIG) weld brazing effectively suppresses interfacial brittle intermetallics and enhances joint strength by influencing pool stirring and heat input during aluminum-to-steel joining. However, optimizing the pulsed TIG weld brazing process is challenging due to its numerous welding parameters. This study established statistical models for Al/steel joint strength without reinforcement using response surface methodology (RSM) based on central composite design (CCD). The models’ adequacy and significance were verified through analysis of variance (ANOVA). The four welding parameters influence weld strength in the following descending order: pulse on time > base current > pulse current > pulse frequency. Additionally, interactions between pulse current and pulse frequency, and between pulse on time and base current, were observed. Numerical optimization using RSM determined the optimal pulsed GTA weld brazing parameters for aluminum and stainless steel. With these optimized parameters, the joint strength reached 155.73 MPa, and the intermetallic compound (IMC) thickness was reduced to 3.4 μm. Full article