Efficient Utilization Research Articles

Enhanced photocatalytic hydrogen evolution via yolk–heteroshell TiO2@TiO2/SiO2 nanoreactor with confined Pt nanoparticles

The upper limit of photocatalytic efficiency is largely determined by the light-harvesting capability of a semiconductor. To address this challenge, we developed a yolk-heteroshell TiO2@TiO2/SiO2 (T@T/S) nanoreactor, leveraging a novel design based on an in-depth understanding and innovative utilization of light-matter interactions. The T@T/S nanoreactor significantly enhances solar light capture and utilization efficiency through total internal reflection mechanisms facilitated by the heterogeneous shell. This heteroshell, consisting of an outer SiO2 shell an inner monolayer of TiO2 nanoparticles, maximizes light confinement due to the different refractive indices of SiO2 and TiO2, while also minimizing mass transfer resistance due to the thin inner shell layer. These unique structural features result in high photocatalytic activity under simulated solar light. Additionally, we successfully applied a “ship-in-a-bottle” strategy for the confined growth of platinum nanoparticles within T@T/S nanoreactor. The Pt0.5-T@T/S nanoreactors demonstrated excellent hydrogen production, achieving a hydrogen evolution rate of 24.43 mmol g−1h−1 under simulated sunlight and an apparent quantum efficiency (AQE) of 7.8 % at 350 nm. This study highlights the importance of nano-engineered designs in optimizing semiconductor photocatalysts and shows the significant potential of our approach for sustainable energy conversion and environmental protection.

Shining light on broad-spectrum responded NaYF4:Yb,Er,Tm@Bi@BiOI: Understanding the enhanced photodegradation performance of bisphenol A and mechanisms

This work reported a novel ternary heterogeneous photocatalyst NaYF4:Yb,Er,Tm@Bi@BiOI (NBEG-6h). NBEG-6h exhibits broad-spectrum absorption from ultraviolet to near-infrared. The elimination efficiency of BPA by NBEG-6h under simulated sunlight and near-infrared light irradiation was 88% (24 min) and 93.9% (160 min), respectively. It also has the universality of degrading various pollutants, good reusability and stability. The exceptional photocatalytic activity can be attributed to the absorption of near-infrared light by NaYF4:Yb,Er,Tm, which improves the total efficiency of sunlight utilization. The localized surface plasmon resonance effect of Bi nanoparticles enhances the light absorption performance of the heterostructure. Furthermore, combining Bi and BiOI accelerates carrier migration and improves the separation efficiency of photogenerated electron-hole pairs. h+, ·O2− and 1O2 play the pivotal roles during the photocatalytic process. This research offers new insights into the design, development, and mechanism understanding of full-spectrum-driven heterostructure photocatalysts. It provides an environmentally sustainable approach to the treatment of harmful organic pollutants.

Adaptive tunicate swarm optimization with partial transmit sequence for phase optimization in MIMO-OFDM

<span>Multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM) are widely utilized in wireless systems and maximum data rate communications. The MIMO-OFDM technology increases the efficiency of spectrum utilization. The peak-to-average-power ratio (PAPR) minimization in MIMO-OFDM is a complex task in wireless communications systems. In this research, an adaptive tunicate swarm optimization with partial transmit sequence (ATSO-PTS) algorithm is proposed for a reduction of PAPR in MIMO-OFDM. The nonsquare-matrix-based differential space time coding (N-DSTC) scheme is used for the encoding and decoding process of MIMO-OFDM. The N-DSTC encoding and decoding are linear error-correcting codes that are utilized for message transmission over noisy channels. The pre-specified quadrate phase shift keying (QPSK) symbol is deployed for the modulation and demodulation scheme. On the receiver side, the serial to parallel (S/P) conversion, and fast Fourier transform (FFT) are accomplished, alongside the received data bits being demodulated to obtain the output bits. The proposed ATSO-PTS method achieves better results according to performance metrices PAPR, bit error rate (BER) and signal-to-noise-ratio (SNR), with values of about 2.9, 0.01 and 0.025, respectively. This ensures superior results when compared to the existing methods of twin symbol hybrid optimization applied to partial transmit sequence (TSHO-PTS), selective level mapping and PTS (SLM-PTS), and particle swarm and grey wolf (PS-GW) with PTS, respectively.</span>

Enhancing the bending strength, load-carrying capacity and material efficiency of aspen and black alder plywood through thermo-mechanical densification of face veneers

Many research papers highlight the positive impact of wood densification on mechanical properties of solid wood as well as wood veneers. Previous experimental studies comparing densified to non-densified wood materials of the same thickness consistently demonstrated higher strength properties in bending for densified materials. However, these studies often overlooked an important comparison: the strength and load-carrying capacity of non-densified wood material to its densified state, in which the thickness is reduced and thus the material’s moment of inertia. The current study, aims to address this gap for plywood made from low-quality, underutilized hardwood species, encompassing both non-densified and densified veneers. Additionally, these plywood panels are compared with high-quality birch plywood. Plywood samples made from common aspen (Populus tremula L.) and black alder (Alnus glutinosa L.) species, incorporating densified veneers, are compared to conventional silver birch (Betula pendula Roth.) plywood. The study also considers material utilization efficiency. Results showed that plywood made from non-densified aspen and black alder veneers exhibited comparable strength to standard birch plywood, positioning them as competitive alternatives to high-quality wood species. While densified black alder veneers increased strength in average from 83.6 MPa to 126.3 MPa, a loss in load-carrying capacity was observed from 1930 N to 1637 N due to reduced thickness and moment of inertia.

Anchoring atomic Pd on molybdenum disulfide with state-of-the-art specific activity in the electrochemical dehalogenation of florfenicol

The widespread discharge of halogenated organic pollutants (HOPs) in wastewater brings significant risks to the environment and human health due to potent high toxicity, antibacterial activity, and strong biodegradation resistance. Adsorbed atomic hydrogen (H*ads)-mediated electrocatalytic hydrodehalogenation (EHDH) has been recognized as an efficient strategy for HOPs removal through selectively breaking carbon-halogen bonds. In this work atomic Pd uniformly anchored on molybdenum disulfide (denoted as Pd-MoS2) was synthesized through a facile hydrothermal method and found with state-of-the-art specific activity in florfenicol (FLO) removal via the H*ads-mediated EHDH process. Pd-MoS2 completely dehalogenated (∼100 %) FLO within 30 min at a rate constant of 0.15 min−1, which was 6.0, 6.5 and 33.1 times the value of MoS2, Pd/C and carbon paper samples, respectively. Notably, the toxicity of the wastewater was reduced to 1/6 of its original value after the treatment. Moreover, Pd-MoS2 presented superior catalytic stability in five successive cycles. Furthermore, Pd-MoS2 was also robust in complex water environment with practical applicability for industrial wastewater treatment. A dual-active-center mechanism with enhanced H*ads production capacity and the improved H*ads utilization efficiency was contributed to the increased EHDH activity on Pd-MoS2.

Temperature-dependent optical and dielectric properties of polyvinyl chloride and polystyrene blends in terahertz regime

Terahertz time-domain spectroscopy (THz-TDS) has been used to study the temperature-dependent refractive index, absorption coefficient and dielectric constant of polyvinyl chloride/polystyrene (PVC/PS) blends in frequency range of 0.2–1.8 THz. Moreover, the Sellmeier and thermo optic coefficients of PVC/PS blends with different weight ratios have been explored in the temperature range of 25–80 °C. These parameters are used to evaluate the dispersion properties of these blends in the observed frequency range. A clear indication of temperature dependence on the values of refractive index and the real dielectric constant of these blends have been observed. Their values decrease linearly with increasing temperature up to 80 °C. Whereas, no noticeable change has been observed in the imaginary dielectric constant and the absorption coefficient. These results provide a database of temperature-dependent optical and dielectric parameters of PVC/PS polymer blends for their efficient utilization for device fabrication in THz technology.

Hydrodeoxygenation of guaiacol over modified coconut carbon supported Ni nanoparticles catalysts under alkaline condition

The efficient utilization of lignin from the papermaking black liquor has attracted interest due to its carbon-neutral value and industrial application. This work illustrates a simple support-phosphate-pretreatment strategy to develop superdispersed Ni species in activated carbon (AC) to enhance the hydrodeoxygenation (HDO) reactions of the lignin model compound guaiacol into biohydrocarbons under alkaline conditions. For the first time, sodium polyphosphate was applied as a pretreatment agent for coconut carbon in the synthesis of carbon-supported Ni catalysts. Superdispersed Ni nanoparticles were achieved on Ni/NaPnOAC with a particle size of 2.5 nm, which was much smaller than that on unmodified Ni/C (13.1 nm). The characterization results and reactions revealed that the P−O that formed served as anchoring sites for the Ni species and strengthened the interaction between the Ni species and support (SMSI), which resulted in significantly improved dispersion of the Ni metal sites and, thus, a nearly 6-fold greater yield of hydrocarbons was obtained on Ni/NaPnOAC (58.1%) than on Ni/C (10.1%). In addition, compared with Ni2P/NaPnOAC and NiS/NaPnOAC, Ni/NaPnOAC exhibited higher HDO activity, especially higher direct deoxygenation (DDO) activity and higher benzene yield, reducing hydrogen consumption during the HDO reaction.

The chromosome-level Elaeagnus mollis genome and transcriptomes provide insights into genome evolution, glycerolipid and vitamin E biosynthesis in seeds

Elaeagnus mollis, which has seeds with high lipid and vitamin E contents, is a valuable woody oil plant with potential for utilization. Currently, the biosynthesis and regulation mechanism of glycerolipids and vitamin E are still unknown in E. mollis. Here, we present the chromosome-level reference genome of E. mollis (scaffold N50: ~40.66Mbp, genome size: ~591.48Mbp) by integrating short-read, long-read, and Hi-C sequencing platforms. A total of 36,796 protein-coding sequences, mainly located on 14 proto-chromosomes, were predicted. Additionally, two whole genome duplication (WGD) events were suggested to have occurred ~54.07 and ~35.06 million years ago (MYA), with Elaeagnaceae plants probably experiencing both WGD events. Furthermore, the long terminal retrotransposons in E. mollis were active ~0.23MYA, and one of them was inferred to insert into coding sequence of the negative regulatory lipid synthesis gene, EMF2. Through transcriptomic and metabonomic analysis, key genes contributing to the high lipid and vitamin E levels of E. mollis seeds were identified, while miRNA regulation was also considered. This comprehensive work on the E. mollis genome not only provides a solid theoretical foundation and experimental basis for the efficient utilization of seed lipids and vitamin E, but also contributes to the exploration of new genetic resources.

Surface-enhanced Raman spectroscopy substrates for monitoring antibiotics in dairy products: Mechanisms, advances, and prospects.

Antibiotic residues in dairy products have become an undeniable threat to human health. Surface-enhanced Raman spectroscopy (SERS) has been widely used in efficiently detecting antibiotics because of its characteristics including fast response, high resolution, and strong resistance to moisture interference. However, as a core part of SERS technology, the design principle and detection performance of enhanced substrates used in monitoring antibiotics in dairy products have not yet received enough attention. Thus, it is necessary to give a critical review of the recent developments of SERS substrates for monitoring antibiotics in dairy products, which can be expected to provide inspiration for the efficient utilization of SERS technology. In this work, advances in various SERS substrates applied in sensing antibiotics in dairy products were comprehensively reviewed. First, the enhancement mechanisms were introduced in detail. Significantly, the types of enhanced materials (plasmonic metal particles [PMPs], PMPs/semiconductor composite materials) and biometric design strategies including immunoassay, aptamer, and molecularly imprinted polymers-based SERS biosensors applied in dairy products were systematically summarized for the first time. Meanwhile, the performance of SERS substrates used for the detection of antibiotics in dairy products was addressed from the aspects of dynamic linear range and detection restriction strategy. Finally, the conclusions, challenges, and future prospects of SERS substrates for antibiotic monitoring in dairy products were deeply discussed, which also provide new opinions and key points for constructing SERS substrates applied in complex food matrix in the future.

Innovative Long-Acting Bisoprolol Patch: Synergistic Ion-Pair Skin Adsorption for Drug Delivery Control Coupled with Dynamic Modulation of Penetration Enhancers.

This study aims to develop a sustained release patch for bisoprolol (BSP) to address the issue of blood pressure fluctuations caused by traditional dosing methods, ensuring continuous drug release and efficient utilization. Long-chain saturated fatty acids (C6-C12) were chosen as counterions to precisely control BSP's permeation rate in the patch formulation, and the ion-pairing strategy's mechanism in drug delivery was thoroughly investigated. Molecular docking results revealed significant differences in the adsorption capacities of different ion pairs in the stratum corneum (SC) and epidermis, directly influencing their residence times and thereby regulating BSP's passive diffusion rate. Particularly, the BSP-C10 ion pair successfully reduced BSP's permeation rate to one-third of its baseline. To enhance drug delivery efficiency and reduce costs, chemical permeation enhancers (CPEs) are typically added to sustained release patches. In contrast to traditional static analyses based on cumulative permeation, this study utilized ATR-FTIR dynamic detection of isopropyl myristate (IPM) as a preferred enhancer, studying its disruptive effects on the skin barrier during drug delivery. The study observed that during drug delivery, the interaction between IPM and skin lipids follows a U-shaped trend: initially increasing, then decreasing, with the peak occurring at 10 h. Similarly, the drug delivery rate displays a comparable pattern. The addition of IPM as CPE increased the patch utilization rate from 39.8 ± 4.31 to 79.8 ± 7.27%. This strategy aims to rapidly reduce blood pressure in the initial phase with subsequent weakening of IPM disruption, allowing the ion-pairing strategy to dominate drug delivery control and maintain stable long-term therapeutic effects. Pharmacokinetic studies demonstrated that the newly developed BSP sustained release patch maintains stable blood drug concentrations, reduces burst release effects, increases bioavailability to 84.679%, doubles MRT0-t, halves Cmax, and significantly reduces the occurrence of blood pressure fluctuations.

Optimization of Hybrid Energy Systems Based on MPC-LSTM-KAN: A Case Study of a High-Altitude Wind Energy Work Umbrella Control System

This paper presents an optimization method for hybrid energy systems based on Model Predictive Control (MPC), Long Short-Term Memory (LSTM) networks, and Kolmogorov–Arnold Networks (KANs). The proposed method is applied to a high-altitude wind energy work umbrella control system, where it aims to enhance the stability and efficiency of energy utilization. The work umbrella system integrates wind and solar energy sources, with energy stored in a battery and used to control the umbrella’s operations. The MPC framework is employed to optimize control actions by solving a finite-horizon optimization problem, ensuring the battery State of Charge (SOC) remains within an optimal range. The LSTM network provides accurate predictions of environmental conditions, including wind speed and solar irradiance, which are essential for MPC’s decision-making process. To address complex nonlinearities in the system, the KAN is utilized to model and approximate these dynamics, refining the LSTM predictions. The integration of these advanced control strategies enables the system to handle varying operational conditions and maintain optimal performance. The case study demonstrates the effectiveness of the MPC-LSTM-KAN approach, revealing improvements in the SOC stability, energy efficiency, and operational endurance of the high-altitude wind energy work umbrella system. The results indicate that this hybrid optimization method offers a robust solution for managing hybrid energy systems in dynamic environments.

Value Relevance of Accrual Information in Public Sector: Facts from Practices

ABSTRACT Accrual accounting plays a crucial role in the public sector as it enhances the efficiency of asset utilization. However, much of existing research focuses more on the theoretical ideals rather than the practical realities of its implementation. This research aims to bridge this gap by examining the practical evidence to assess the value relevance of accrual information within the public sector. The findings of this research reveal that managers and public officials in Indonesia consider information relevant when it is simple, easy to understand, and frequently utilized. This research demonstrates that accrual accounting practices in Indonesia predominantly exhibit mimetic behavior.

Global myocardial work index is an independent predictor of functional capacity on 6-minute walk test in stable non-ischaemic cardiomyopathy

Abstract Background Reduction in 6-minute walk test distance (6MWD) in heart failure (HF) is associated with impaired quality of life and adverse cardiovascular outcomes. Myocardial work (MW) indices are novel non-invasive measures of left ventricular (LV) function which characterise myocardial contractile efficiency and energy utility. Purpose We aim to assess the predictive capacity of MW indices for functional capacity in stable non-ischaemic cardiomyopathy (NICM). Methods We prospectively recruited consecutive patients with stable NICM from our HF service from January 2021 to December 2023. All patients received optimal guideline direct therapy (OGDT) for 3-months before undergoing a comprehensive transthoracic echocardiogram and functional assessment with 6-minute walk test (6MWT). We excluded patients with prior cardiac surgery, severe airways disease, valvular heart disease, and those in atrial fibrillation (AF) or had mechanical limitations to their mobility at time of 6MWT. We collected demographic, clinical, and echocardiographic factors to identify the determinates of reduced 6MWD defined as <300 metres. Results Of 249-patients included (69.9% male; age 57.3±16.5yrs), 65-patients had reduced 6MWD. They were significantly older (62.3±17.2 vs 55.5±16.0yrs, p=0.005), had shorter height (166.3±10.3 vs 171.3±9.8cm, p=0.001), higher prevalence of AF history (39.1 vs 21.4%, p=0.001), lower LV-ejection-fraction (38.1±13.3 vs 43.3±10.2%, p=0.001), lower LV-global-longitudinal-strain (LV-GLS; 10.5±4.4 vs 12.7±3.9%, p<0.001), lower global-work-index (GWI; 943±487 vs 1230±455mmHg%, p<0.001), lower global-constructive-work (GCW; 1286±561 vs 1590±494mmHg%, p<0.001), lower global-work-efficiency (GE; 81.2±9.9 vs 85.4±8.1mmHg%, p=0.04), larger indexed left atrial volume (LAV; 45.0±18.4 vs 40.1±24.5ml/m2, p=0.038), higher E/e’ ratio (14.6±8.9 vs 11.8±6.0, p=0.003), and lower right-ventricular-free-wall-strain (RV-FWS; 19.2±6.7 vs 21.8±6.4, p=0.008; Table 1.). On receiver-operating-characteristics-curve analysis, GWI best predicted 6MWD reduction (Figure 1; AUC 0.68) compared to GCW, GE, LVEF and LVGLS (AUC: 0.62, 0.40, 0.62, and 0.62 respectively). Accounting for covariates, our multiple regression was run to predict 6MWD from age, height, AF history, LAV, E/e’, GWI and RV-FWS. These variables statistically significantly predicted 6MWD, F(4,239)=23.5, p<0.001, R2=0.34). With each 100mmHg% rise in GWI being independently associated with a 6.5m increase in 6MWD. Conclusion GWI was independently associated with 6MWD in our cohort of NICM patients on OGDT. MW indices provide some mechanistic insights into exercise capacity in patients with NICM.Table 1.Baseline CharacteristicsFigure 1.ROC of GWI Discriminating 6MWD

Phytoplasma DNA Enrichment from Sugarcane White Leaves for Shotgun Sequencing Improvement

Sugarcane white leaf (SCWL) disease, caused by Candidatus Phytoplasma sacchari, poses a significant threat to sugarcane cultivation. An obligate parasite, phytoplasma is difficult to culture in laboratory conditions, making the isolation of its DNA from the massive amount of plant host DNA extremely challenging. Yet, the appropriate amount and quality of plant microbiome-derived DNA are key for high-quality DNA sequencing data. Here, a simple, cost-effective, alternative method for DNA isolation was applied using a guanidine-HCl-hydroxylated silica (GuHCl-Silica)-based method and microbiome DNA enrichment based on size-selective low-molecular-weight (LMW) DNA by PEG/NaCl precipitation. qPCR analysis revealed a significant enrichment of phytoplasma DNA in the LMW fraction. Additionally, the NEBNext Microbiome DNA enrichment kit was utilized to further enrich microbial DNA, demonstrating a remarkable increase in the relative abundance of phytoplasma DNA to host DNA. Shotgun sequencing of the isolated DNA gave high-quality data on the metagenome assembly genome (MAG) of Ca. Phytoplasma sacchari SCWL with completeness at 95.85 and 215× coverage. The results indicate that this combined approach of PEG/NaCl size selection and microbiome enrichment is effective for obtaining high-quality genomic data from phytoplasma, surpassing previous methods in efficiency and resource utilization. This low-cost method not only enhances the recovery of microbiome DNA from plant hosts but also provides a robust framework for studying plant pathogens in complex plant models.

Research on Intelligent Energy System and Power Metering Optimization Based on Multi-objective Optimization Decision Algorithm

In this paper, the intricate problem of optimizing power metering within an intelligent energy system, utilizing a multi-objective optimization decision-making algorithm, is thoroughly explored. Given the current energy landscape, achieving efficient energy utilization and environmental sustainability has become a focal point of research. As a pivotal aspect of future energy management, the precision and optimization of power metering in intelligent energy systems directly influence the effectiveness and cost of energy consumption. To begin, this paper delves into the fundamental principles and application backdrop of intelligent energy systems, highlighting the significance of power metering in such systems. Subsequently, addressing the multi-objective optimization challenges in power metering, a novel optimization method based on a multi-objective optimization decision algorithm is introduced. This algorithm achieves comprehensive optimization of power metering, encompassing multiple objectives such as power cost reduction, enhanced energy efficiency, and environmental protection. The experimental results underscore the remarkable performance of this algorithm, which not only elevates the precision of power metering but also achieves substantial savings in energy costs and significantly boosts energy efficiency. Furthermore, the algorithm exhibits robust adaptability, making it capable of addressing power metering optimization challenges across diverse scenarios. Finally, this paper discusses the practical application prospects of the optimization algorithm in intelligent energy systems, and points out the direction of future research. The research in this paper provides new ideas and methods for the optimization of power metering in intelligent energy systems, and has important theoretical and practical significance for promoting the intelligence and refinement of energy management.

Recent Advances in Engineering Fe-N-C Catalysts for Oxygen Electrocatalysis in Zn-Air Batteries.

Fe-N-C single-atom catalysts (SACs) have emerged as one of the most promising candidates for oxygen electrocatalysis due to their maximized atom utilization efficiency, high intrinsic activity, and strong metal-support interaction. Significant progress has been made in engineering Fe-N-C SACs for oxygen electrocatalysis in Zn-air batteries (ZABs). This review provides a comprehensive overview of the recent advancements in Fe-N-C SACs, with a special focus on effective engineering strategies, their performance in oxygen electrocatalysis, and their potential applications in ZABs. The review also discusses the key challenges and future directions in the development of Fe-N-C SACs for efficient and durable oxygen electrocatalysis in ZABs. This review aims to offer valuable insights into the current state of research in this field and to guide future efforts in the development of advanced oxygen electrocatalysts for ZABs.

Enhancing Distribution Efficiency Through OTIF Performance Evaluation

Today’s market with more frequent but smaller deliveries complicates the realization of logistics processes and activities. Customers expect products within 24 h or 48 h. This poses a logistics challenge for companies in fulfilling the expected time window. In these situations, companies can either perform self-distribution or they can outsource it. When outsourcing, companies must define key performance indicators (KPIs) to measure the efficiency of the distribution process. One such KPI is On-Time In-Full (OTIF). OTIF fulfillment is increasingly becoming a fundamental requirement not only in distribution but also throughout the entire supply chain. Two essential prerequisites for successful outsourcing collaboration are ensuring that orders are delivered on time and in full. A review of the existing literature revealed that no studies simultaneously address this complex issue. This was exactly the main motive for developing a model that simultaneously takes into account the utilization of logistics resources on one hand, and OTIF on the other when calculating the efficiency. The model developed in this paper was tested on the data of a company operating in the Serbian market. The results showed that seven of the company’s warehouses are inefficient. A two-stage matrix analysis with the three most important indicators (operational efficiency, on-time delivery, and in-full delivery) was performed based on the obtained results. Other than the obvious scientific contributions, the proposed model can help companies increase their resource utilization efficiency as well as OTIF fulfillment.

Sulfonated Covalent Organic Frameworks Anchoring Cobalt as High-Efficient and Stable Catalysts for Peroxymonosulfate Activation.

Heterogeneous cobalt-based catalysts are highly effective in activing peroxymonosulfate (PMS) and produce free radicals to deal with recalcitrant organic pollutants in water. However, unfeasible recyclability and gradual performance degradation remain challenging due to the easy agglomeration and leaching of active cobalt species. Herein, a strategy is proposed to construct stably anchored and highly dispersed Co2+ sites on dual functional sulfonated covalent organic frameworks (COF-Co). The sulfonic acid groups are able to realize the targeted binding with cobalt ions through a two-step cation-exchange method, leading to strong combination with active Co2+ sites and utmost utilization efficiencies. Moreover, the super-hydrophility of sulfonic acid groups favors the rapid accessibility of organic molecules to the catalyst and accelerates the degradation. Remarkably, COF-Co exhibits high activity in PMS activation, effective oxidation for tetracycline degradation (92% within 30min at 30mg L-1) and other coloring contaminants, and excellent recycle stability. This work can guide the rational design of efficient and environmentally friendly PMS-activated catalyst with great potential for application in wastewater treatment.

Prediction of the Relative Resource Abundance of the Argentine Shortfin Squid Illex argentinus in the High Sea in the Southwest Atlantic Based on a Deep Learning Model

To analyze the impact of the marine environment on the relative abundance of Illex argentinus (high and low categories) in the southwest Atlantic, this study collected logbook data from Chinese pelagic trawlers from December 2014 to June 2024, including vessel position data and oceanographic variables such as sea surface temperature, 50 m and 100 m water temperature, sea surface salinity, sea surface height, chlorophyll-a concentration, and mixed layer depth. Vessel positions were used to enhance the logbook data quality, allowing an analysis of the annual trends in the resource center of this squid at a spatial resolution of 0.1° × 0.1° and a temporal resolution of ten days. The findings showed that the resource center is primarily located around 42° S in the north and between 45° S and 47° S in the south, with a trend of northward movement during the study period. Additionally, we constructed two ensemble learning models based on decision trees—AdaBoost and PSO-RF—aiming to identify the most critical environmental factors that affect its resource abundance; we found that the optimal model was the PSO-RF model with max_depth of 5 and n_estimators of 46. The importance analysis revealed that sea surface temperature, mixed layer depth, sea surface height, sea surface salinity, and 50 m water temperature are critical environmental factors affecting this species’ resources. Given that deep learning models generally have shorter running times and higher accuracy than other models, we developed a CNN-Attention model based on the five most important input factors. This model achieved an accuracy of 73.6% in forecasting this squid for 2024, predicting that the population would first appear near the Argentine exclusive economic zone around mid-December 2023 and gradually move east and south thereafter. The predictions of the model, validated through log data, maintained over 70% accuracy during most periods at a time scale of ten days. The successful construction of the resource abundance forecasting model and its accuracy improvements can help enterprises save fuel and time costs associated with blind searches for target species. Moreover, this research contributes to improving resource utilization efficiency and reducing fishing duration, thereby aiding in lowering carbon emissions from pelagic trawling activities, offering valuable insights for the sustainable development of this species’ resources.

Maximizing energy efficiency in HetNets through centralized and distributed sleep strategies under QoS constraint

This research addresses the critical need to optimize the Energy Efficiency (EE) for Ultra-Dense HetNets amid the ever-increasing demands for high-speed data networks. The rapid increase in high-speed devices highlights the urgent necessity for a transformative change in upcoming 5G cellular networks. According to Ericsson's 2022 report, mobile data traffic volume is expected to double by 2027, with mobile video traffic anticipated to rise by nearly 30% each year. In response to these challenges, researchers have identified essential technologies that facilitate 5G networks, including massive Multiple-input-Multiple-output (m-MIMO) systems and HetNets. Although both promise enhanced coverage and throughput, HetNets emerges as a cost-effective solution, surpassing m-MIMO in implementation cost and coverage. However, achieving maximum EE in HetNets necessitates careful consideration of various constraints, including delay, coverage probability, and Signal-to-Interference-plus-Noise Ratio (SINR) thresholds. This research marks a significant milestone in adopting the Distributed Dynamic Opportunistic Sleep Strategy (D-DOSS) approach. The D-DOSS method organizes small clusters throughout the network and evaluates key Quality of Service (QoS) parameters including Energy Utilization Efficiency (EUE), Coverage Probability, Data Throughput, and Success Probability using Monte Carlo simulations. This research analyzes Distributed Sleep (DS) and Centralized Schemes (CS) concerning given QoS parameters. While DS methodologies often exhibit performance trade-offs compared to CS, they provide significant advantages in terms of ease of implementation and management. CS, though representing the most commonly used method in ultra-dense HetNets involves high computational costs that complicate its management. By integrating the D-DOSS and addressing various constraints, this research not only advances HetNet technologies but also makes a significant contribution to optimizing EE while preserving network performance and QoS. The innovative D-DOSS approach offers a promising solution to the challenges of energy efficiency in wireless communication networks and paves the way for future advancements in HetNet deployments. The results and analysis show that D-DOSS effectively addresses the limitations of DS and outperforms existing CS techniques.