Dynamic multi-objective big data optimization problems (DMBDOPs) are challenging because of the difficulty of dealing with large-scale decision variables and continuous problem changes. In contrast to classical multi-objective optimization problems, DMBDOPs are still not intensively explored by researchers in the optimization field. At the same time, there is lacking a software framework to provide algorithmic examples to solve DMBDOPs and categorize benchmarks for relevant studies. This paper presents a metaheuristic software framework for DMBDOPs to remedy these issues. The proposed framework has a lightweight architecture and a decoupled design between modules, ensuring that the framework is easy to use and has enough flexibility to be extended and modified. Specifically, the framework now integrates four basic dynamic metaheuristic algorithms, eight test suites of different types of optimization problems, as well as some performance indicators and data visualization tools. In addition, we have proposed an experience reuse method, speeding up the algorithm’s convergence. Moreover, we have implemented parallel computing with Apache Spark to enhance computing efficiency. In the experiments, algorithms integrated into the framework are tested on the test suites for DMBDOPs on an Apache Hadoop cluster with three nodes. The experience reuse method is compared to two restart strategies for dynamic metaheuristics. Full article

This article proposes a distributed intelligent Coordinated Multi-Point Non-Orthogonal Multiple-Access (CoMP-NOMA) collaborative transmission model with the assistance of reconfigurable intelligent surfaces (RISs) to address the issues of poor communication quality, low fairness, and high system power consumption for edge users in multi-cellular networks. By analyzing the interaction mechanisms and influencing factors among RIS signal enhancement, NOMA user scheduling, and multi-point collaborative transmission, the model establishes RIS-enhanced edge user grouping and coordinates NOMA user clusters based on this. In the multi-cell RIS-assisted JT-CoMP NOMA downlink transmission, joint optimization of the power allocation (PA), user clustering (UC), and RIS phase-shift matrix design (PS) poses a challenging Mixed-Integer Non-Linear Programming (MINLP) problem. The original problem is decomposed by optimizing the formulas into joint sub-problems of PA, UC, and PA and PS, and solved using an alternating optimization approach. Simulation results demonstrate that the proposed scheme effectively reduces the system’s power consumption while significantly improving the system’s throughput and rates. Full article

The tunnel portal section is often in extremely weak and fragmented strata, and the deformation of the portal side and slope will affect the stability of the surrounding rock and the tunnel-supporting structure. However, the deformation characteristics and displacement development patterns of slopes in the tunnel portal section are not clear. In this paper, the multifractal characteristics and displacement prediction of the deformation sequence of the tunnel portal slope at of a weak and water-rich shallow buried tunnel adjacent to an important structure are studied in depth. Combined with the deformation characteristics of the tunnel portal slope, a suitable slope monitoring and measurement scheme is designed to analyze the deformation pattern of the tunnel portal slope. Based on the multifractal detrended fluctuation analysis (MF-DFA) method, the multifractal characteristics of the deformation monitoring sequences at each monitoring point of the tunnel portal slope are analyzed. The multifractal characteristics of displacement sequences at different monitoring points of the tunnel portal slope are consistent with the actual monitoring results. Furthermore, the Long Short-Term Memory (LSTM) model is optimized using the Particle Swarm Optimization (PSO) algorithm to predict the deformation of the tunnel portal slope. The results show that the maximum mean square error (MSE) of the horizontal displacement test set prediction results is 0.142, and the coefficient of determination (R²) is higher than 91%. The maximum value of MSE for vertical displacement test set prediction is 0.069, and the R² are higher than 91%. The study shows that the performance of the PSO-LSTM prediction model can meet the requirements for predicting the displacement of the tunnel portal slope. Based on the MF-DFA method and PSO-LSTM prediction model, the fluctuation characteristics of the displacement value of the tunnel portal section can be accurately identified and the displacement development pattern can be effectively predicted. The conclusions of the study are of great practical significance for the safe construction of the tunnel portal section. Full article

The electrical impedance of dilute aqueous solutions containing extracts from five brands of canned tuna is analyzed using impedance spectroscopy in order to analyze their salt content and detect the potential presence of other salts beyond the well-stated NaCl. A complex electrical impedance is modeled with an equivalent electrical circuit, demonstrating good agreement with experimental data. This circuit accounts for the contribution of ions in the bulk solution, as well as those contributing to electrode polarization. The parameters describing the equivalent circuits, obtained through fitting data to the electrical impedance, are discussed in terms of the various ion contributions to both the electrical double layer at the electrode interface and the electrical conductivity of each solution. The ionic contribution to the electrical impedance is compared with that of a pure NaCl solution at the same concentration range. This comparison, when extended to real samples, allows for the development of a model to estimate the electrical conductivity of canned tuna samples, thereby determining the salt concentration in tuna. The model enables differentiation among the various samples of tuna studied. Subsequently, the potential presence of other ions besides Na⁺ and Cl⁻ and their contribution to the electrical properties of each canned tuna extract is considered, especially for samples with a higher ratio of the sum of K⁺ and phosphates to Na⁺ concentration. This analysis shows the potential of impedance spectroscopy for on-site and rapid analysis of salt content and/or detection of additives in canned tuna fish. Full article

The plant hormone jasmonic acid plays an important role in plant growth and development, participating in many physiological processes, such as plant disease resistance, stress resistance, organ development, root growth, and flowering. With the improvement in living standards, people have higher requirements regarding the quality of vegetables. However, during the growth process of vegetables, they are often attacked by pests and diseases and undergo abiotic stresses, resulting in their growth restriction and decreases in their yield and quality. Therefore, people have found many ways to regulate the growth and quality of vegetable crops. In recent years, in addition to the role that JA plays in stress response and resistance, it has been found to have a regulatory effect on crop quality. Therefore, this study aims to review the jasmonic acid accumulation patterns during various physiological processes and its potential role in vegetable development and quality formation, as well as the underlying molecular mechanisms. The information provided in this manuscript sheds new light on the improvements in vegetable yield and quality. Full article

This study examines the impact of artificial intelligence (AI) on workforce skill requirements as AI becomes increasingly integrated into business operations. Using foresight analysis and scenario-based methods, we anticipate the necessary skills for future AI-integrated workplaces. A SWOT analysis evaluates three potential paths for AI adoption—gradual, aggressive, and selective—to project the evolving skills needed for employee success in changing business environments. The findings emphasize the critical need for both enhanced technical proficiency and soft skills, such as creative problem-solving and interpersonal abilities, across all AI adoption scenarios. The study highlights the importance of strategic reskilling and continuous learning to align employee skills with the new business paradigms shaped by AI. It provides a roadmap for businesses, educators, and policymakers to collaboratively develop a resilient and adaptable workforce for an AI-enhanced future. Full article

This article introduces a novel middleware that utilizes cost-effective, low-power computing devices like Raspberry Pi to analyze data from wireless sensor networks (WSNs). It is designed for indoor settings like historical buildings and museums, tracking visitors and identifying points of interest. It serves as an evacuation aid by monitoring occupancy and gauging the popularity of specific areas, subjects, or art exhibitions. The middleware employs a basic form of the MapReduce algorithm to gather WSN data and distribute it across available computer nodes. Data collected by RFID sensors on visitor badges is stored on mini-computers placed in exhibition rooms and then transmitted to a remote database after a preset time frame. Utilizing MapReduce for data analysis and a leader election algorithm for fault tolerance, this middleware showcases its viability through metrics, demonstrating applications like swift prototyping and accurate validation of findings. Despite using simpler hardware, its performance matches resource-intensive methods involving audiovisual and AI techniques. This design’s innovation lies in its fault-tolerant, distributed setup using budget-friendly, low-power devices rather than resource-heavy hardware or methods. Successfully tested at a historical building in Greece (M. Hatzidakis’ residence), it is tailored for indoor spaces. This paper compares its algorithmic application layer with other implementations, highlighting its technical strengths and advantages. Particularly relevant in the wake of the COVID-19 pandemic and general monitoring middleware for indoor locations, this middleware holds promise in tracking visitor counts and overall building occupancy. Full article

Lung squamous cell carcinoma (LSCC) is refractory to various therapies for non-small cell cancer; therefore, new therapeutic approaches are required to improve the prognosis of LSCC. Although immunotherapies targeting B7 family molecules were explored as treatments for several cancer types, the expression and significance of B7-H3 in the tumor microenvironment (TME) and its relationship with other immune checkpoint molecules have not yet been investigated in detail. We used high-throughput quantitative multiplex immunohistochemistry to examine B7-H3 expression in the TME. We investigated the relationship between B7-H3 expression and prognosis as well as changes in the TME with B7-H3 expression using 110 surgically resected pathological specimens retrospectively. We examined the correlation between B7-H3 and programmed cell death-ligand 1 (PD-L1) expression in single cells. High B7-H3 expression in tumor cells was associated with a better prognosis and a significant increase in the number of CD163⁺PD-L1⁺ macrophages. Quantitative analysis revealed that there is a positive correlation between B7-H3 and PD-L1 expression in tumor and stromal cells, as well as in intratumoral tumor-infiltrating lymphocytes and tumor-associated macrophages in the same cells. CD68⁺, CD163⁺, and CK⁺ cells with PD-L1⁺ phenotypes had higher B7-H3 expression compared to PD-L1⁻ cells. Our findings demonstrate a correlation between B7-H3 and PD-L1 expression in the same cells, indicating that therapies targeting B7-H3 could provide additional efficacy in patients refractory to PD-L1-targeting therapies. Full article

Ultra-widefield (UWF) retinal imaging stands as a pivotal modality for detecting major eye diseases such as diabetic retinopathy and retinal detachment. However, UWF exhibits a well-documented limitation in terms of low resolution and artifacts in the macular area, thereby constraining its clinical diagnostic accuracy, particularly for macular diseases like age-related macular degeneration. Conventional supervised super-resolution techniques aim to address this limitation by enhancing the resolution of the macular region through the utilization of meticulously paired and aligned fundus image ground truths. However, obtaining such refined paired ground truths is a formidable challenge. To tackle this issue, we propose an unpaired, degradation-aware, super-resolution technique for enhancing UWF retinal images. Our approach leverages recent advancements in deep learning: specifically, by employing generative adversarial networks and attention mechanisms. Notably, our method excels at enhancing and super-resolving UWF images without relying on paired, clean ground truths. Through extensive experimentation and evaluation, we demonstrate that our approach not only produces visually pleasing results but also establishes state-of-the-art performance in enhancing and super-resolving UWF retinal images. We anticipate that our method will contribute to improving the accuracy of clinical assessments and treatments, ultimately leading to better patient outcomes. Full article

Aging leads to tissue and cellular changes, often driven by oxidative stress and inflammation, which contribute to age-related diseases. Our research focuses on harnessing the potent anti-inflammatory and antioxidant properties of Korean Ulmus macrocarpa Hance, a traditional herbal remedy, to address muscle loss and atrophy. We evaluated the effects of Ulmus extract on various parameters in a muscle atrophy model, including weight, exercise performance, grip strength, body composition, muscle mass, and fiber characteristics. Additionally, we conducted Western blot and RT-PCR analyses to examine muscle protein regulation, apoptosis factors, inflammation, and antioxidants. In a dexamethasone-induced muscle atrophy model, Ulmus extract administration promoted genes related to muscle formation while reducing those associated with muscle atrophy. It also mitigated inflammation and boosted muscle antioxidants, indicating a potential improvement in muscle atrophy. These findings highlight the promise of Ulmus extract for developing pharmaceuticals and supplements to combat muscle loss and atrophy, paving the way for clinical applications. Full article

A virtual rail train (VRT) is a multi-articulated vehicle as well as a novel public transportation system due to its low economic cost, environmental friendliness and high transit capacity. Equipped with all-wheel steering (AWS) and a tracking control method, the super long VRT can travel on urban roads easily. This paper proposed a tracking control approach using only interoceptive sensors with high scene adaptivity. The kinematic model was established first under reasonable assumptions when the sensor configuration was completed simultaneously. A hierarchical controller consists of a front axle controller and a rear axle controller. The former applies virtual axles theory to avoid motion interference. The latter generates a first-axle reference path with path segmentation and a data updating method to improve storage and computational efficiency. Then, a fast curvature matching rear axles control method is developed with an actuator time delay considered. Finally, the proposed approach is verified in a hardware in loop (HIL) simulation under various situations with predefined evaluation standards, which shows better tracking performance and applicability. Full article

Neurodegenerative disorders, including Alzheimer’s disease (AD) and Parkinson’s disease (PD), represent debilitating conditions with complex, poorly understood pathologies. Epichaperomes, pathologic protein assemblies nucleated on key chaperones, have emerged as critical players in the molecular dysfunction underlying these disorders. In this study, we introduce the synthesis and characterization of clickable epichaperome probes, PU-TCO, positive control, and PU-NTCO, negative control. Through comprehensive in vitro assays and cell-based investigations, we establish the specificity of the PU-TCO probe for epichaperomes. Furthermore, we demonstrate the efficacy of PU-TCO in detecting epichaperomes in brain tissue with a cellular resolution, underscoring its potential as a valuable tool for dissecting single-cell responses in neurodegenerative diseases. This clickable probe is therefore poised to address a critical need in the field, offering unprecedented precision and versatility in studying epichaperomes and opening avenues for novel insights into their role in disease pathology. Full article

Autoimmune thyroid diseases (AITD) are among the most frequent autoimmune disorders, with a multifactorial etiology in which both genetic and environmental determinants are probably involved. Celiac disease (CeD) also represents a public concern, given its increasing prevalence due to the recent improvement of screening programs, leading to the detection of silent subtypes. The two conditions may be closely associated due to common risk factors, including genetic setting, changes in the composition and diversity of the gut microbiota, and deficiency of nutrients like vitamin D. This comprehensive review discussed the current evidence on the pivotal role of vitamin D in modulating both gut microbiota dysbiosis and immune system dysfunction, shedding light on the possible relevance of an adequate intake of this nutrient in the primary prevention of AITD and CeD. While future technology-based strategies for proper vitamin D supplementation could be attractive in the context of personalized medicine, several issues remain to be defined, including standardized assays for vitamin D determination, timely recommendations on vitamin D intake for immune system functioning, and longitudinal studies and randomized controlled trials to definitely establish a causal relationship between serum vitamin D levels and the onset of AITD and CeD. Full article

Electrochemical sensors play a pivotal role in various fields, such as biomedicine and environmental detection, due to their exceptional sensitivity, selectivity, stability, rapid response time, user-friendly operation, and ease of miniaturization and integration. In addition to the research conducted in the application field, significant focus is placed on the selection and optimization of electrode interface materials for electrochemical sensors. The detection performance of these sensors can be significantly enhanced by modifying the interface of either inorganic metal electrodes or printed electrodes. Among numerous available modification materials, conductive polymers (CPs) possess not only excellent conductivity exhibited by inorganic conductors but also unique three-dimensional structural characteristics inherent to polymers. This distinctive combination allows CPs to increase active sites during the detection process while providing channels for rapid ion transmission and facilitating efficient electron transfer during reaction processes. This review article primarily highlights recent research progress concerning CPs as an ideal choice for modifying electrochemical sensors owing to their remarkable features that make them well-suited for biomedical and environmental applications. Full article

The deposition of combustion residues in the nozzle ring (NR) of a turbocharger turbine stage changes the NR geometry significantly in a random manner. The resultant complex and highly asymmetric geometry induces low engine order (LEO) excitation, which may lead to resonance excitation of rotor blades and high cycle fatigue (HCF) failure. Therefore, a suitable prediction workflow is of great importance for the design and validation phases. The prediction of LEO excitation is, however, computationally expensive as high-fidelity, full annulus CFD models are required. Previous investigations showed that a steady-state computational model consisting of the volute, the NR, and a radial extension is suitable to reduce the computational costs massively and to qualitatively predict the level of LEO forced response. In the current paper, the aerodynamic excitation of 69 real contaminated NRs is analyzed using this simplified approach. The results obtained by the simplified simulation model are used to select 13 contaminated NR geometries, which are then simulated with a model of the entire turbine stage, including the rotor, in a transient time-marching manner to provide high-fidelity simulation results for the verification of the simplified approach. Furthermore, two contamination patterns are analyzed in a more detailed manner regarding their aerodynamic excitation. It is found that the simplified model can be used to identify and classify contamination patterns that lead to high blade vibration amplitudes. In cases where transient effects occurring in the rotor alter the harmonic pressure field significantly, the ability of the simplified approach to predict the LEO excitation is not sufficient. Full article

Background: Therapeutic hypothermia is the standard treatment for neonates with hypoxic–ischemic encephalopathy. Preclinical evidence indicates that the time to initiate therapeutic hypothermia correlates with its therapeutic success. This study aims to explore whether there is a correlation between the early initiation of therapeutic hypothermia and improved short-term neurological outcomes in cooled asphyxiated newborns. Methods: A retrospective analysis was conducted, involving 68 neonates from two different neonatal intensive care units. The impact of time to initiate treatment, time to reach the target temperature, and time between initiation and target temperature was correlated with short-term outcomes on MRI. Results: We did not find a significant difference between outcomes regarding the time to start treatment and the time to achieve the target temperature. Interestingly, neonates with a poor outcome were treated on average earlier than neonates with a favorable outcome but required more time to reach the target temperature. Additionally, the study results did not support the hypothesis that a shorter time to initiate treatment would lead to shorter times to achieve the target temperature. Conclusion: Based on our findings, it is recommended to prioritize a thorough evaluation of neonatal encephalopathy before initiating therapeutic hypothermia. Early initiation of treatment should be balanced with the time required for precise assessment to ensure better outcomes. Full article

In recent years, distributed generation systems based on renewable energy sources have gained increasing prominence. Thus, the DC/AC converters based on power electronics devices have become increasingly important. In this context, this article presents an integrated Zeta inverter for low-power conditions, which operates in continuous conduction mode (CCM), achieving efficiency greater than 95%. The proposed topology is composed of four power switches, two operating at high frequency and two operating at low frequency, i.e., at the output frequency. Compared with the topologies in the literature, these configurations make it a competitive solution from the point of view of efficiency, number of elements, and, consequently, implementation cost. The proposed converter operates as a sinusoidal voltage source for local loads and is supplied by a DC source, such as batteries or a photovoltaic array. A multi-resonant voltage controller was used to guarantee the sinusoidal voltage provided to the non-linear load while dealing with the complex dynamics of the Zeta converter in the CCM. Experimental results from a 324 W prototype show the converter’s implementation feasibility and the high efficiency of the DC/AC conversion. Full article

This article examines the structure and functions of the plant synthetic promoters frequently used to precisely regulate complex regulatory routes. It details the composition of native promoters and their interacting proteins to provide a better understanding of the tasks associated with synthetic promoter development. The production of synthetic promoters is performed by relatively small libraries produced generally by basic molecular or genetic engineering methods such as cis-element shuffling or domain swapping. The article also describes the preparation of large-scale libraries supported by synthetic DNA fragments, directed evolution, and machine or deep-learning methodologies. The broader application of novel, synthetic promoters reduces the prevalence of homology-based gene silencing or improves the stability of transgenes. A particularly interesting group of synthetic promoters are bidirectional forms, which can enable the expression of up to eight genes by one regulatory element. The introduction and controlled expression of several genes after one transgenic event strongly decreases the frequency of such problems as complex segregation patterns and the random integration of multiple transgenes. These complications are commonly observed during the transgenic crop development enabled by traditional, multistep transformation using genetic constructs containing a single gene. As previously tested DNA promoter fragments demonstrate low complexity and homology, their abundance can be increased by using orthogonal expression systems composed of synthetic promoters and trans-factors that do not occur in nature or arise from different species. Their structure, functions, and applications are rendered in the article. Among them are presented orthogonal systems based on transcription activator-like effectors (dTALEs), synthetic dTALE activated promoters (STAPs) and dCas9-dependent artificial trans-factors (ATFs). Synthetic plant promoters are valuable tools for providing precise spatiotemporal regulation and introducing logic gates into the complex genetic traits that are important for basic research studies and their application in crop plant development. Precisely regulated metabolic routes are less prone to undesirable feedback regulation and energy waste, thus improving the efficiency of transgenic crops. Full article

In recent years, China has experienced significant economic growth and some degree of environmental pollution control. However, achieving a perfect balance between the environment and economic development remains a challenge. In order to seek solutions to this issue and promote the sustainable development of cities, this paper starts from the urban level, which is relatively lacking in existing research. Based on the panel data of urban indicators from 2013 to 2021, it quantifies the environmental performance of key cities using the slack-based measure (SBM) model of super-efficiency based on a non-expected output. Furthermore, it utilizes the Tobit panel regression model suitable for limited dependent variables to analyze the impact of driving factors on the environmental performance of key cities, and it further explores the reasons for the loss of urban environmental performance from the dual perspectives of inputs and outputs. The research findings indicate the following. (1) The average environmental performance of 30 key cities has shown an increasing trend but has not yet reached a valid state. The cities’ environmental performance rises in the range of [0.444, 0.821], indicating that there is room for improvement in urban environmental management. (2) Cities in the northeastern region of China have lagged behind the eastern, central, and western regions in terms of environmental performance over this nine-year period, and the redundancy of undesirable outputs is partly responsible for this decline. (3) The large proportion of the secondary industry, the number of vehicles on the road, and the population density have a significantly negative impact on urban environmental performance, while the per capita regional GDP and urban maintenance and construction funds make a positive difference. These research findings provide a scientific basis and valuable insights into urban environment performance enhancement and can serve as a reference for areas in need of balanced development between the urban environment and economic growth. Full article

With the advancement of network technology, there has been an increase in the volume of data being transmitted across networks. Due to the bandwidth limitation of communication channels, data often need to be quantized or event-triggered mechanisms are introduced to conserve communication resources. On the other hand, network uncertainty can lead to data loss and destroy data integrity. This paper investigates the identification of finite impulse response (FIR) systems under the framework of stochastic noise and the combined effects of the event-triggered mechanism and uncertain communications. The study provides a reference for the application of remote system identification under transmission-constrained and packet loss scenarios. First, a congruential summation-triggered communication scheme (CSTCS) is introduced to lower the communication rate. Then, parameter estimation algorithms are designed for scenarios with known and unknown packet loss probabilities, respectively, and their strong convergence is proved. Furthermore, an approximate expression for the convergence rate is obtained by data fitting under the condition of uncertain packet loss probability, treating the trade-off between convergence performance and communication resource usage as a constrained optimization problem. Finally, the rationality and correctness of the algorithm are verified by numerical simulations. Full article

The purpose of this study was to explore the effect of Semaglutide on intrauterine adhesions and discover new drugs for such adhesions. In this study, the cell model was simulated by TGF-β1-induced human endometrial epithelial cells, and the animal model was established through mechanical curettage and inflammatory stimulation. After co-culturing with TGF-β1 with or without different concentrations of Semaglutide for 48 h, cells were collected for RT-qPCR and Western blotting analyses. Three doses were subcutaneously injected into experimental mice once a day for two weeks, while the control group received sterile ddH2O. The serum and uterine tissues of the mice were collected. HE and Masson staining were used for the uterine histomorphological and pathological analyses. RT-qPCR and Western blotting were used for mRNA and protein expression analyses. Serum indicators were detected using ELISA kits. The results showed that Semaglutide significantly reduced the mRNA levels of fibrosis indicators ACTA2, COL1A1, and FN and inflammatory indicators TNF-α, IL-6, and NF-κB in the two models. Semaglutide improved endometrium morphology, increased the number of endometrial glands, and reduced collagen deposition in IUA mice. The results also showed that Semaglutide could inhibit vimentin, E-Cadherin, and N-Cadherin in the two models. In summary, Semaglutide can ameliorate fibrosis and inflammation of intrauterine adhesions as well as inhibit epithelial–mesenchymal transition in IUA models. Full article

The genus Tarzetta is distributed mainly in temperate forests and establishes ectomycorrhizal associations with angiosperms and gymnosperms. Studies on this genus are scarce in México. A visual, morphological, and molecular (ITS-LSU) description of T. americupularis, T. cupressicola, T. davidii, T. durangensis, T. mesophila, T. mexicana, T. miquihuanensis, T. poblana, T. pseudobronca, T. texcocana, and T. victoriana was carried out in this work, associated with Abies, Quercus, and Pinus. The results of SEM showed an ornamented ascospores formation by Mexican Taxa; furthermore, the results showed that T. catinus and T. cupularis are only distributed in Europe and are not associated with any American host. Full article