Search Results (10,026)

Wireless communication advancements have significantly improved connectivity and user experience with each generation. The recent release of the framework M.2160 for the upcoming sixth generation (6G or IMT-2030) cellular wireless standard by ITU-R has significantly heightened expectations, particularly for Internet of Things (IoT) driven use cases. However, this progress introduces significant security risks, as technologies like O-RAN, terahertz communication, and native AI pose threats such as eavesdropping, supply chain vulnerabilities, model poisoning, and adversarial attacks. The increased exposure of sensitive data in 6G applications further intensifies these challenges. This necessitates a concerted effort from stakeholders including ITU-R, 3GPP, ETSI, OEMs and researchers to embed security and resilience as core components of 6G. While research is advancing, establishing a comprehensive security framework remains a significant challenge. To address these evolving threats, our research proposes a dynamic security framework that emphasizes the integration of explainable AI (XAI) techniques like SHAP and LIME with advanced machine learning models to enhance decision-making transparency, improve security in complex 6G environments, and ensure effective detection and mitigation of emerging cyber threats. By refining model accuracy and ensuring alignment through recursive feature elimination and consistent cross-validation, our approach strengthens the overall security posture of the IoT–6G ecosystem, making it more resilient to adversarial attacks and other vulnerabilities. Full article

5G, the fifth-generation mobile network, is predicted to significantly increase the traditional trajectory of energy consumption. It now uses four times as much energy as 4G, the fourth-generation mobile network. As a result, compared to previous generations, 5G’s increased cell density makes energy efficiency a top priority. The objective of this paper is to formulate end-to-end power consumption models for three different 5G radio access network (RAN) deployment architectures, namely the 5G distributed RAN, the 5G centralized RAN with dedicated hardware and the 5G Cloud Centralized-RAN. The end-to-end modelling of the power consumption of a complete 5G system is obtained by combining the power models of individual components such as the base station, the core network, front-haul, mid-haul and backhaul links, as applicable for the different architectures. The authors considered the deployment of software-defined networking (SDN) at the 5G Core network and gigabit passive optical network as access technology for the backhaul network. This study examines the end-to-end power consumption of 5G networks across various architectures, focusing on key dependent parameters. The findings indicate that the 5G distributed RAN scenario has the highest power consumption among the three models evaluated. In comparison, the centralized 5G and 5G Cloud C-RAN scenarios consume 12% and 20% less power, respectively, than the Centralized RAN solution. Additionally, calculations reveal that base stations account for 74% to 78% of the total power consumption in 5G networks. These insights helped pioneer the calculation of the end-to-end power requirements of different 5G network architectures, forming a solid foundation for their sustainable implementation. Furthermore, this study lays the groundwork for extending power modeling to future 6G networks. Full article

In response to the current key issues in the field of smart irrigation for farmland, such as the lack of data sources and insufficient integration, a low degree of automation in drive execution and control, and over-reliance on cloud platforms for analyzing and calculating decision making processes, we have developed nodes and gateways for smart irrigation. These developments are based on the EC-IOT edge computing IoT architecture and long range radio (LoRa) communication technology, utilizing STM32 MCU, WH-101-L low-power LoRa modules, 4G modules, high-precision GPS, and other devices. An edge computing analysis and decision model for smart irrigation in farmland has been established by collecting the soil moisture and real-time meteorological information in farmland in a distributed manner, as well as integrating crop growth period and soil properties of field plots. Additionally, a mobile mini-program has been developed using WeChat Developer Tools that interacts with the cloud via the message queuing telemetry transport (MQTT) protocol to realize data visualization on the mobile and web sides and remote precise irrigation control of solenoid valves. The results of the system wireless communication tests indicate that the LoRa-based sensor network has stable data transmission with a maximum communication distance of up to 4 km. At lower communication rates, the signal-to-noise ratio (SNR) and received signal strength indication (RSSI) values measured at long distances are relatively higher, indicating better communication signal quality, but they take longer to transmit. It takes 6 s to transmit 100 bytes at the lowest rate of 0.268 kbps to a distance of 4 km, whereas, at 10.937 kbps, it only takes 0.9 s. The results of field irrigation trials during the wheat grain filling stage have demonstrated that the irrigation amount determined based on the irrigation algorithm can maintain the soil moisture content after irrigation within the suitable range for wheat growth and above 90% of the upper limit of the suitable range, thereby achieving a satisfactory irrigation effect. Notably, the water content in the 40 cm soil layer has the strongest correlation with changes in crop evapotranspiration, and the highest temperature is the most critical factor influencing the water requirements of wheat during the grain-filling period in the test area. Full article

RNA G-quadruplexes (rG4s) in 5′-UTRs represent complex regulatory elements capable of both inhibiting and activating mRNA translation through diverse mechanisms in eukaryotes. This review analyzes the evolution of our understanding of 5′-UTR rG4-mediated translation regulation, from early discoveries of simple translation inhibitors to the current recognition of their multifaceted regulatory roles. We discuss canonical and non-canonical rG4 structures, their interactions with regulatory proteins, including helicases and FMRP, and their function in both cap-dependent and IRES-mediated translation. Special attention is given to the synergistic effects between rG4s and upstream open reading frames (uORFs), stress-responsive translation regulation, and their role in repeat-associated non-AUG (RAN) translation linked to neurodegenerative diseases. We critically evaluate methodological challenges in the field, including limitations of current detection methods, reporter system artifacts, and the necessity to verify rG4 presence in endogenous transcripts. Recent technological advances, including genome editing and high-throughput sequencing approaches, have revealed that rG4 effects are more complex and context-dependent than initially thought. This review highlights the importance of developing more robust methodologies for studying rG4s at endogenous levels and carefully reevaluating previously identified targets, while emphasizing their potential as therapeutic targets in various diseases. Full article

Verbascum nigrum, commonly known as black mullein, is widely used in traditional medicine for its expectorant, mucolytic, sedative, and diuretic properties. This study aimed to develop and optimize a standardized method for extracting phenolic compounds from V. nigrum using enzymatic pretreatment followed by solvent extraction. Enzymatic treatment does not rely on harmful solvents and is a low energy-intensive process, making it a suitable green technology for the food, cosmetic, and pharmaceutical industries. The research explored the use of different lignocellulolytic enzymes, including pectinase, cellulase, α-amylase, and xylanase, to break down plant cell walls, enhancing the release and bioaccessibility of active compounds. The two-step extraction process proposed combined enzymatic pretreatment and hydroalcoholic extraction, resulting in a considerably improved yield of phenolic compounds (24 mg/g DM). Analytical characterization using a high-performance liquid chromatography (HPLC) system coupled with a diode-array-detector (DAD) and ultra-high-performance liquid chromatography (UHPLC) coupled with DAD and tandem mass spectrometry (MS/MS) revealed a higher concentration of target bioactive compounds in enzymatically treated extracts compared to traditional methods, including phenolic derivatives (e.g., caffeic acid, p-coumaric acid, and verbascoside), and flavonoids (e.g., luteolin). Up to 22 phenolic and flavonoid compounds were characterized. This study provides new insight into the potential of enzymatic extraction as a green and efficient alternative to conventional extraction methods, for the production of high-quality herbal products richer in (poly)phenolic compounds, highlighting its potential for industrial applications. Full article

With the development of smart and precision agriculture, new challenges have emerged in terms of response speed and adaptability in agricultural equipment control. Active Disturbance Rejection Control (ADRC), an advanced control strategy known for its strong robustness and disturbance rejection capabilities, has demonstrated exceptional performance in various fields, such as aerospace, healthcare, and military applications. Therefore, investigating the application of ADRC in agricultural control systems is of great significance. This review focuses on the fundamental principles of ADRC and its applications in agriculture, exploring its potential use and achievements in precision agriculture management, intelligent agricultural control, and other agricultural control sectors. These include the control of agricultural machinery, field navigation and trajectory tracking, agricultural production processes, as well as fisheries and greenhouse management in various agricultural scenarios. Additionally, this paper summarizes the integration of ADRC with other control technologies (e.g., LADRC, SMC) in agricultural applications and discusses the advantages and limitations of ADRC in the aforementioned areas. Furthermore, the challenges, development trends, and future research directions of ADRC in agricultural applications are examined to provide a reference for its future development. Full article

The present research work was based on evaluating the effects of Eucommia ulmoides leaf extract (EULE) on the technological quality and protein oxidation of cooked pork sausage during refrigerated storage. Sausages were manufactured with different levels of EULE (0, 0.15, and 0.3 g/kg) and stored at 4 °C for 3, 20, and 40 d, respectively. Quality attributes including cooking loss, texture, and color were evaluated, and the total carbonyl and total sulfhydryl as well as the specific markers α-aminoadipic acid semialdehyde (AAS) and lysinonorleucine (LNL) were analyzed for protein oxidation. The results revealed that the inclusion of EULE exhibited effectiveness in reducing the formation of protein carbonyls, particularly AAS and LNL, while inhibiting the loss of total sulfhydryl. Nevertheless, EULE increased the cooking loss, hardness, and chewiness of the sausages compared to the control group. These findings demonstrated that EULE could be considered a potential natural antioxidant for use in sausage production. Full article

Global climate variability is projected to result in more frequent and severe droughts, which can have adverse effects on New Zealand’s endemic tree species such as the iconic kauri (Agathis australis). Several studies have investigated the physiological response of kauri to medium- and long-term water stress; however, no research has used hyperspectral technology for the early detection and characterization of water stress in this species. In this study, physiological (stomatal conductance (g_s), assimilation rate (A), equivalent water thickness (EWT)) and leaf-level hyperspectral measurements were recorded over a ten-week period on 100 potted kauri seedlings subjected to control (well-watered) and drought treatments. In addition, plant functional traits (PTs) were retrieved from spectral reflectance data via inversion of the PROSPECT-D radiative transfer model. These data were used to (i) identify key PTs and narrow-band hyperspectral indices (NBHIs) associated with the expression of water stress and (ii) develop classification models based on single-date and multitemporal datasets for the early detection of water stress. A significant decline in soil water content and physiological responses (g_s and A) occurred among the trees in the drought treatment in weeks 2 and 4, respectively. Although no significant treatment differences (p > 0.05) were observed in EWT across the whole duration of the experiment, lower mean values in the drought treatment were apparent from week 4 onwards. In contrast, several spectral bands and NBHIs exhibited significant differences the week after water was withheld. The number and category of significant NBHIs varied up to week 4, after which a substantial increase in the number of significant indices was observed until week 10. However, despite this increase, the single-date models did not show good model performance (F1 score > 0.70) until weeks 9 and 10. In contrast, when multitemporal datasets were used, the classification performance ranged from good to outstanding from weeks 2 to 10. This improvement was largely due to the enhanced temporal and feature representation in the multitemporal models. Among the input NBHIs, water indices emerged as the most important predictors, followed by photochemical indices. Furthermore, a comparison of inverted and measured EWT showed good correspondence (mean absolute percentage error (MAPE) = 8.49%, root mean squared error (RMSE) = 0.0026 g/cm²), highlighting the potential use of radiative transfer modelling for high-throughput drought monitoring. Future research is recommended to scale these measurements to the canopy level, which could prove valuable in detecting and characterizing drought stress at a larger scale. Full article

The liquid carbon dioxide energy storage system (LCES), as a highly flexible, long-lasting, and environmentally friendly energy storage technology, shows great potential for application in integrated energy systems. However, research on the combined cooling, heating, and power supply using LCES in integrated energy systems is still limited. In this paper, an optimized scheduling scheme for a low-carbon economic integrated energy system is proposed, coupling LCES with power-to-gas (P2G) technology and the green certificate/carbon trading mechanism. Mathematical models and constraints for each system component are developed, and an optimization scheduling model is constructed, focusing on the economic and low-carbon operation of the integrated energy microgrid system. The objective function aims to minimize total system costs. A case study based on a northern China park is conducted, with seven scenarios set for comparative optimization analysis. The results demonstrate that the use of the combined cooling, heating, and power LCES system reduces total costs by USD 2,706.85 and carbon emissions by 34.57% compared to the single-energy flow operation. These findings validate the effectiveness of the proposed model in optimizing system costs and reducing carbon emissions. Full article

Abstract: The development of 5G application technology has led to a rapid expansion in the scale of internet data center rooms and the number of servers. Due to the high heat generation of data center server equipment and the mixing of hot and cold airflows within the rooms, the thermal environment of these rooms fails to meet operational requirements with increasing energy consumption and thermal density. This study utilized the 6SigmaDC software to simulate and analyze the characteristics and existing problems of airflow distribution in a small-scale data center. Based on identified issues with current airflow patterns, two optimization schemes were proposed, analyzing the effects of raised floor height and the closure of aisles on airflow optimization. The return heat index (RHI) was used as an evaluation metric to assess airflow patterns before and after optimization. When the raised floor height was 600 mm, the maximum temperature at the cabinet inlet and outlet were 19.3 °C and 34 °C respectively, which were the lowest, and the RHI value was 0.9622. Compared with unclosed aisles and closed hot aisles, closed cold aisles effectively reduced the cabinet inlet and outlet temperature and increased the RHI. In addition, closed cold aisles increased the air supply temperature from 18 °C to 20 °C, further reducing the energy consumption of the air conditioning system. This study can provide guidance and act as a reference for optimizing airflow design and energy conservation in small data centers. Full article

The purpose of this study was to extract the lipophilic fraction from one of the largest source of waste in the industrial sector, namely, the tomato residue from processing the fruit. In order to make this process more environmentally sustainable, this study used a green extraction protocol employing natural deep eutectic solvents (NADESs) combined with a less energy-consuming technology, the ultrasound-assisted extraction (UAE) method, to simultaneously recover carotenoids and tocopherol from dried powder tomato waste. Two NADESs, one hydrophilic and one hydrophobic, were prepared and compared to support high extraction efficiency and increase the stability of the extracted compounds. The optimal extraction parameters were identified as choline chloride:1,3-butanediol (1:5)-based NADES, a solid-to-liquid ratio of 1:20 (w/v), time of extraction 12 min, temperature 65 °C, radiation frequency 37 Hz, and an ultrasound power level of 70%. The extraction process was intensified and resulted in extracts rich in lycopene (215.13 ± 4.31 μg/g DW), β-carotene (206.95 ± 3.27 μg/g DW), and tocopherol (130.86 ± 8.97 μg/g DW) content, with the highest antioxidant capacity 93.84 ± 0.18 mM Trolox equivalent. Incorporating NADESs for the extraction of bioactive compounds offers numerous benefits, such as improved sustainability, enhanced extraction efficiency, better protection of sensitive compounds, and reduced environmental impact. These advantages make NADESs a promising alternative to traditional organic solvents, especially in industries that require natural, green, and efficient extraction processes for valuable bioactive molecules. Full article

Malted grains subjected to extrusion technology could have better quality indices than non-malted grains. The effects of malting and extrusion on the functional and physical qualities of foods extruded from malted brown rice and pigeon pea flour blends were investigated. Malted pigeon pea and brown rice flours were processed into blends, extruded under various conditions of feed moisture levels (15–20), feed compositions (8–30%), and barrel temperatures (100–130 °C), and analyzed using Response Surface Methodology with a Box–Behnken design. The impacts of malting and extrusion were assessed on the following functional qualities: bulk density, rheology, swelling capacity, water absorption capacity, and solubility. The physical quality assessment included a 2-D photographic representation of the extrudates, a microscopic assessment of their internal structure, expansion index, color parameters (L*, a*, b*), and alterations in the color index. Increased feed moisture, malted pigeon pea, and decreased barrel temperature resulted in a higher bulk density (0.72 to 0.84 g/cm³) of the extrudates. There was a decrease in water absorption capacity (6.82–4.49%) with an increase in barrel temperature above 100 °C. All the samples showed a decrease in viscosity with increasing shear rate. At low barrel temperatures, feed compositions, and feed moistures, extrusion led to increases in the expansion index (3.5 to 12.94) and the color lightness (66.83–81.71) of the extrudates. Samples with a higher proportion of malted brown rice showed a higher expansion index, lower bulk density, and lighter color of the extrudates. Full article

As 6G development progresses, joint sensing and communication (JSC) is emerging as a transformative technology, promising enhanced spectrum and energy efficiency alongside innovative services. This paper delves into underexplored facets of JSC, particularly its role in vehicular technology and transportation systems. It discusses data fusion techniques that enable cooperative sensing in networked environments and underscores the critical role of resource management in balancing sensing and communication. It suggests modeling extended targets, such as vehicles, within a computationally feasible framework. Moreover, it proposes a novel integration of AI-based target recognition, allowing target-specific tracking parameters and target-based sensing resource allocation. Importantly, a case study is presented to underscore the real-world applicability of these concepts in vehicular scenarios, demonstrating how networked devices can achieve high sensing and communication performance. Full article

Background: Buffaloes are considered an indispensable genetic resource for dairy production. However, improvements in lactation performance have been relatively limited. Advances in sequencing technology, combined with genome-wide association studies, have facilitated the breeding of high-quality buffalo. Methods: We conducted an integrated analysis of genomic sequencing data from 120 water buffalo, the high-quality water buffalo genome assembly designated as UOA_WB_1, and milk production traits, including 305-day milk yield (MY), peak milk yield (PM), total protein yield (PY), protein percentage (PP), fat percentage (FP), and total milk fat yield (FY). Results: The results identified 56 significant SNPs, and based on these markers, 54 candidate genes were selected. These candidate genes were significantly enriched in lactation-related pathways, such as the cAMP signaling pathway (ABCC4), TGF-β signaling pathway (LEFTY2), Wnt signaling pathway (CAMK2D), and metabolic pathways (DGAT1). Conclusions: These candidate genes (e.g., ABCC4, LEFTY2, CAMK2D, DGAT1) provide a substantial theoretical foundation for molecular breeding to enhance milk production in buffaloes. Full article

As an electrically driven artificial muscle, dielectric elastomer actuators (DEAs) are notable for their large deformation, fast response speed, and high energy density, showing significant potential in soft robots. The paper discusses the working principles of DEAs, focusing on their reversible deformation under electric fields and performance optimization through material and structural innovations. Key applications include soft grippers, locomotion robots (e.g., multilegged, crawling, swimming, and jumping/flying), humanoid robots, and wearable devices. The challenges associated with DEAs are also examined, including the actuation properties of DE material, material fatigue, viscoelastic effects, and environmental adaptability. Finally, modeling and control strategies to enhance DEA performance are introduced, with a perspective on future technological advancements in the field. Full article