Efficient Performance Research Articles

Enhancing climate monitoring and prediction: The integration of advanced AI techniques

As the global impact of climate change intensifies, the importance of climate monitoring is increasingly highlighted. This study explores the application of artificial intelligence (AI) in climate monitoring, particularly focusing on its potential and challenges in handling large-scale climate data and enhancing prediction accuracy. This study provides an in-depth exploration of the application of AI in the field of climate monitoring, with particular emphasis on the critical role of deep learning models in improving the accuracy of climate predictions. We specifically analyze the efficient performance of these models in processing large-scale climate data, as well as their advanced capabilities in identifying complex climate patterns and extreme weather events.At the same time, the research also points out the challenges faced by deep learning models in practical applications, including the lack of interpretability of the model and the high demand for computing resources. This paper discusses the implications of these challenges for the field of climate science and analyzes potential avenues to overcome them through technological innovation and algorithmic optimization.Furthermore, it presents a vision for optimizing AI models to better serve the field of climate science in the future.

S2−-modified hetero MOF-on-MOF of Ni-MOF/Fe-MOF with efficient seawater oxidation performance

Direct seawater electrolysis offers a viable solution to the excessive consumption of fresh water resources in existing industrial electrolytic water processes. However, electrolysis efficiency and material stability are affected by electrode material corrosion caused by chlorine evolution reactions (CER) at the anode positions. In this study, the prepared S-Ni-MOF/Fe-MOF material exhibits an exceptional performance in the oxygen evolution reaction (OER) both in alkaline solution and seawater. Specifically, the material achieves low overpotentials of 281 and 279 mV in 1.0 M KOH and alkaline seawater at 100 mA cm−2 current density, and maintains stability for at least 100 h in both electrolyte solutions. The electronic structure of MOF-on-MOF was modulated through the introduction of S2−, which induced a large number of vacancies. Furthermore, the reconstruction process on the material surface was accelerated by the formation of NiOOH and FeOOH, that served as protective layers and prevented the corrosion of the electrode material by Cl−, thereby enhancing its stability in seawater. Furthermore, computational studies have indicated that the Fe-Ni active sites at the heterointerface exhibit a synergistic effect, which has been confirmed by density functional theory (DFT) calculations. These results prove that this combination significantly reduces the energy barrier associated with the OER. Consequently, the reaction process is accelerated.

Design and Performance Analysis of Robotic Vertebral-Disc Unit with Cable-Driven Mechanism.

The humanoid torso is crucial for the overall performance of a humanoid robot. Developing an effective humanoid spine is essential for enhancing this mechanism. This paper introduces a one-vertebral-disc unit inspired by human spine anatomy. A prototype was created using 3D-printed parts and commercially available components. Two general human-like motions are achieved using two servo motors and two pulleys, reducing the number of servo motors needed. The results indicate that a one-vertebral-disc unit can bend up to 15 degrees. The proposed mechanism functions effectively and successfully mimics human movements. It holds potential for integration into humanoid torsos, enabling efficient performance in human-like tasks in the future.

GLU-YOLOv8: An Improved Pest and Disease Target Detection Algorithm Based on YOLOv8

In the contemporary context, pest detection is progressively moving toward automation and intelligence. However, current pest detection algorithms still face challenges, such as lower accuracy and slower operation speed in detecting small objects. To address this issue, this study presents a crop pest target detection algorithm, GLU-YOLOv8, designed for complex scenes based on an enhanced version of You Only Look Once version 8 (YOLOv8). The algorithm introduces the SCYLLA-IOU (SIOU) loss function, which enhances the model generalization to various pest sizes and shapes by ensuring smoothness and reducing oscillations during training. Additionally, the algorithm incorporates the Convolutional Block Attention Module (CBAM) and Locality Sensitive Kernel (LSK) attention mechanisms to boost the pest target features. A novel Gated Linear Unit CONV (GLU-CONV) is also introduced to enhance the model’s perceptual and generalization capabilities while maintaining performance. Furthermore, GLU-YOLOv8 includes a small-object detection layer with a feature map size of 160 × 160 to extract more features of small-target pests, thereby improving detection accuracy and enabling more precise localization and identification of small-target pests. The study conducted a comparative analysis between the GLU-YOLOv8 model and other models, such as YOLOv8, Faster RCNN, and RetinaNet, to evaluate detection accuracy and precision. In the Scolytidae forestry pest dataset, GLU-YOLOv8 demonstrated an improvement of 8.2% in mAP@0.50 for small-target detection compared to the YOLOv8 model, with a resulting mAP@0.50 score of 97.4%. Specifically, on the IP102 dataset, GLU-YOLOv8 outperforms the YOLOv8 model with a 7.1% increase in mAP@0.50 and a 5% increase in mAP@0.50:0.95, reaching 58.7% for mAP@0.50. These findings highlight the significant enhancement in the accuracy and recognition rate of small-target detection achieved by GLU-YOLOv8, along with its efficient operational performance. This research provides valuable insights for optimizing small-target detection models for various pests and diseases.

Towards laryngeal cancer diagnosis using Dandelion Optimizer Algorithm with ensemble learning on biomedical throat region images

Laryngeal cancer exhibits a notable global health burden, with later-stage detection contributing to a low mortality rate. Laryngeal cancer diagnosis on throat region images is a pivotal application of computer vision (CV) and medical image diagnoses in the medical sector. It includes detecting and analysing abnormal or cancerous tissue from the larynx, an integral part of the vocal and respiratory systems. The computer-aided system makes use of artificial intelligence (AI) through deep learning (DL) and machine learning (ML) models, including convolution neural networks (CNN), for automated disease diagnoses and detection. Various DL and ML approaches are executed to categorize the extraction feature as healthy and cancerous tissues. This article introduces an automated Laryngeal Cancer Diagnosis using the Dandelion Optimizer Algorithm with Ensemble Learning (LCD-DOAEL) method on Biomedical Throat Region Image. The LCD-DOAEL method aims to investigate the images of the throat region for the presence of laryngeal cancer. In the LCD-DOAEL method, the Gaussian filtering (GF) approach is applied to eliminate the noise in the biomedical images. Besides, the complex and intrinsic feature patterns can be extracted by the MobileNetv2 model. Meanwhile, the DOA model carries out the hyperparameter selection of MobileNetV2 architecture. Finally, the ensemble of three classifiers such as bidirectional long short-term memory (BiLSTM), regularized extreme learning machine (ELM), and backpropagation neural network (BPNN) models, are utilized for the classification process. A comprehensive set of simulations is conducted on the biomedical image dataset to highlight the efficient performance of the LCD-DOAEL technique. The comparison analysis of the LCD-DOAEL method exhibited a superior accuracy outcome of 97.54% over other existing techniques.

The preparation of immunomagnetic beads based on oriented Ab and its application for AFB1 enrichment

The escalating issue of Aflatoxin B1 (AFB1) contamination has posed an alarming concern worldwide. Enrichment efficiency is crucial to accurately detecting trace amounts of AFB1. Immunomagnetic beads (IMBs) enrichment is beneficial for reducing operation time and labor intensity. In this work, oriented MBs@PG@mAbs was prepared as novel IMBs while MBs@SA@mAbs and MBs@mAbs were prepared as traditional IMBs. At equivalent antibody coupling amounts, the normalized Fab orientation of MBs@PG@mAbs, MBs@SA@mAbs, and MBs@mAbs were 100, 70.7, and 68.0%, respectively. The MBs@PG@mAbs exhibit good AFB1 enrichment, with a maximum capture efficiency of 96.2%, and the capture efficiencies of MBs@SA@mAbs, and MBs@mAbs were 95.8 and 94.9%, respectively. The reliability and practicability of the proposed MBs@PG@mAbs for AFB1 enrichment were further evaluated using AFB1-spiked maize samples. The recoveries of MBs@PG@mAbs were 97.6, 95.0 and 94.2% when 2, 5 and 10 ng/g AFB1 were spiked in samples. The quantitative analysis results of the AFB1 in actual maize samples by MBs@PG@mAbs−ELISA and LC-MS/MS were consistent. Thus, the PG-mediated coupling method led to the most efficient enrichment performance and had the potential for further applications.

Efficient magneto-dielectric performance tuned by Gd-substitution in structurally stable La2Zr2O7 pyrochlores

With the fast growth of fifth-generation (5G) technologies, scientists are keenly looking for materials that could offer enhanced magneto-dielectric response, having high permittivity and permeability along low dielectric losses. So, here we report a facile synthesis of Gd-substituted La2Zr2O7 pyrochlores using sol–gel auto-combustion route and comprehensively elaborate the effect of structural phase transformation on the magneto-dielectric response. Structural analysis shows the formation of cubic structure with the decrease of lattice parameters with increasing Gd-substitution. The reliability of measured structural parameters is further ascertained through Rietveld refinement process. A homogenous distribution of grains with an increase of agglomeration is witnessed in morphological study. The detection and calculation of weight percentage of different elements and their consistency with their stoichiometric formula is exhibited by the energy-dispersive X-rays spectroscopy. An increase in magneto-dielectric response was noted with the increase of applied magnetic field strength and the highest value of dielectric constant was recorded for 80 % Gd substitution. However, the maximum dispersion in magneto-dielectric response was recorded for 60 % substitution of Gd which revealed the potential of this composition for 5G communications.

Optimal Knowledge Distillation through Non-Heuristic Control of Dark Knowledge

In this paper, a method is introduced to control the dark knowledge values also known as soft targets, with the purpose of improving the training by knowledge distillation for multi-class classification tasks. Knowledge distillation effectively transfers knowledge from a larger model to a smaller model to achieve efficient, fast, and generalizable performance while retaining much of the original accuracy. The majority of deep neural models used for classification tasks append a SoftMax layer to generate output probabilities and it is usual to take the highest score and consider it the inference of the model, while the rest of the probability values are generally ignored. The focus is on those probabilities as carriers of dark knowledge and our aim is to quantify the relevance of dark knowledge, not heuristically as provided in the literature so far, but with an inductive proof on the SoftMax operational limits. These limits are further pushed by using an incremental decision tree with information gain split. The user can set a desired precision and an accuracy level to obtain a maximal temperature setting for a continual classification process. Moreover, by fitting both the hard targets and the soft targets, one obtains an optimal knowledge distillation effect that mitigates better catastrophic forgetting. The strengths of our method come from the possibility of controlling the amount of distillation transferred non-heuristically and the agnostic application of this model-independent study.

Testing the hydrological performance of live pole drains (LPD) for mitigation of slope instability

Nature-based solutions (NbS) and soil bioengineering techniques have gained considerable attention due to their relevant hydrological functions and ability to mitigate slope instability. Live pole drains (LPD), a lesser-known NbS, have traditionally been deployed on slopes to drain the excess surface water and regulate the soil's water budget, making it a suitable technique for stormwater management and landslide prevention. However, neither the LPD performance as a plant-based drainage system nor its potential to regulate the soil-water budget through hydrological processes have been thoroughly studied. This paper presents a novel pilot, lab-based approach for testing the hydrological performance of LPD under different soil hydrological conditions. We built three different treatments and investigated their hydrological performance under multiple storm events. We explored how LPD regulate the soil-water budget by partitioning the water inputs (i.e., rainfall precipitation) into water outputs (i.e., surface runoff, subsurface flow, and percolation). The study revealed that LPD can effectively manage stormwater by draining excess runoff and buffering water in the soil, outperforming fallow soil. Subsurface flow and percolation were significantly higher under LPD treatments when compared to fallow ground, suggesting that the presence of an enhanced structure in the soil results in high soil hydrological performance. The presence of a secondary species with the LPD showed a more efficient hydrological performance than an LPD alone, which aligns with the current implementation of NbS fostering biodiversity. Antecedent soil moisture impacted the hydrological performance of LPD by altering the relative infiltration capacity of the soil and by potentially modifying the availability of channels for preferential flow. Our findings provide a sound basis for future research to improve our understanding of the hydrological performance of LPD for slope instability mitigation and encourage their reproduction and upscaling.

Highly efficient removal of 1,1,1-trichloroethane from simulated groundwater by polydopamine-modified iron/polylactic acid/biochar composite coupling with Shewanella oneidensis MR-1

Biochar (BC) loaded with nanoscale zero-valent iron (nZVI) has been extensively used to remove chlorinated hydrocarbons from groundwater through biotic/abiotic reductive dechlorination. However, the rapid deactivation due to surface passivation, hydrophobicity, and inadequacy of carbon sources severely limits its practical application. Herein, a novel polydopamine (PDA)-modified iron/polylactic acid (PLA)/biochar composite (PDA@OBC-nZVI) was successfully synthesized to address this issue. Its effect and mechanism for 1,1,1-trichloroethane (1,1,1-TCA) removal from simulated groundwater coupling with Shewanella oneidensis MR-1 (MR-1) were investigated. Results showed that nZVI and PLA particles were uniformly dispersed on the BC surface, being smoother after PDA coating. The composite coupling with MR-1 exhibited efficient and long-term performances for 1,1,1-TCA removal. Under the experimental conditions comprising 10 % PLA mass fraction in the composite, 10 g·L−1 composite dosage and 20 mL·L−1 MR-1 inoculum volume, 83.14 % of 100 mg·L−1 1,1,1-TCA was removed by PDA@OBC-nZVI + MR-1 within 360 h, compared to only 48.12 % by PDA@OBC-nZVI alone. The primary removal pathways included the rapid chemical reductive dechlorination and the long-term microbial dissimilatory iron reduction. The synergistic dechlorination mechanism was manifested in the composite providing sustained carbon sources, accelerating electron transfer, raising sorbed Fe(II) level, and promoting cytochrome c secretion by MR-1. The novelty of this study is enhancing the hydrophilicity and redox activity of carbon-iron composites by PDA modification for the first time in 1,1,1-TCA removal. Overall, the results suggest that PDA@OBC-nZVI + MR-1 can effectively promote the reductive dechlorination process, being great potential for the remediation of chlorinated hydrocarbon-contaminated groundwater.

Study on the mooring systems attaching clump weights and heavy chains for improving the typhoon resistance of floating offshore wind turbines

The southeastern coastal region of China is the most favorable area for offshore wind resources in the country, but typhoon impacts constrain the development of floating wind turbines in this region. Therefore, ensuring reliable and efficient mooring performance is crucial due to significant safety concerns presented by dynamic platform motions and mooring line tensions encountered during typhoon conditions. In this study, two mooring models were proposed to investigate the impact of clump weights or heavy chains on enhancing the typhoon resistance of floating offshore wind turbines. The 10-MW OO-Star semi-submersible platform was used as the reference structure. The aero-hydro-servo-elastic coupled simulations using OpenFAST were conducted for the selected environmental condition. The surge motion and fairlead tension were compared in both time-domain and frequency-domain for different mooring configurations. The results demonstrate that adding clump weights and heavy chains effectively limits the surge motion and tension fluctuations. Additionally, five installation positions were proposed and compared. Furthermore, a comparison between clump weights with heavy chains was conducted. The performance of a mooring system can be improved in the typhoon condition by incorporating optimal clump weights or heavy chains.

A comparison of the usage between financial and non-financial performance metrics in Small and Medium Enterprises (SMEs) in South Africa

The purpose of this study is to explore the performance measurement approach of small and medium enterprises (SMEs) in the South Durban region of South Africa. It has long been known that small and medium enterprises (SMEs) play an important role in economic growth and income distribution in the world's major economies. However, due to globalisation, the fourth industrial revolution and increased competition from multinationals, South African SMEs face new challenges in measuring their performance. To respond to these challenges, SMEs must adopt efficient and effective performance measurement models. However, currently, according to the researchers, there is no such performance measurement framework. This study aims, among others, to close this gap in the literature. This study uses a deductive, positivist research design to comprehensively explore performance measurement practices in small and medium enterprises (SMEs) in a given region. Based on Morgan's sample size table, size 217 was chosen. The use of financial and non-financial performance measures is preferred by SMEs as it shows better performance compared to only one measure. In addition, the study found a positive correlation between business size and age, indicating that larger businesses tend to be older.The results of this study help to understand the complex relationship between job dimensions, age, and performance, highlighting the importance of choosing appropriate performance measures. Future research is recommended to gain a deeper understanding of these relationships and how they vary across contexts and sectors.

Z-Scheme Heterojunction of Hierarchical Cu2S/CdIn2S4 Hollow Cubes to Boost Photoelectrochemical Performance.

The exaltation of light-harvesting efficiency and the inhibition of fast charge recombination are pivotal to the improvement of photoelectrochemical (PEC) performance. Herein, a direct Z-scheme heterojunction is designed of Cu2S/CdIn2S4 by in situ growth of CdIn2S4 nanosheets on the surface of hollow CuS cubes and then annealing at 400°C. The constructed Z-scheme heterojunction is demonstrated with electron paramagnetic resonance and redox couple (p-nitrophenol/p-aminophenol) measurements. Under illumination, it shows the photocurrent 6 times larger than that of hollow Cu2S cubes, and affords outstanding PEC performance over the known Cu2S and CdIn2S4-based photocatalysts. X-ray photoelectron spectroscopy and density functional theory results demonstrate a strong internal electric field formed in Cu2S/CdIn2S4 Z-scheme heterojunction, which accelerates the Z-scheme charge migration, thereby promoting electron-hole separation and enhancing their utilization efficiency. Moreover, the hollow structure of Cu2S is conducive to shortening the charge transport distance and improving light-harvesting capability. In proof-of-concept PEC application, a PEC detection method for miRNA-141 based on the sensitivity of benzo-4-chloro-hexadienone to light absorption on Cu2S/CdIn2S4 modified electrode is developed with good selectivity and a limit of detection of 32 aM. This work provides a simple approach for designing photoactive materials with highly efficient PEC performance.

Enhanced analysis of landslide susceptibility mapping in the proximity of main roads in the province of Skikda, Algeria: using NAS for efficient performance and faster processing

Enhanced analysis of landslide susceptibility mapping in the proximity of main roads in the province of Skikda, Algeria: using NAS for efficient performance and faster processing

Low-cost solar dryer dehydrates silver banana with a prediction of diffusion approximation model

Solar dryers made from recycled materials offer a cost-effective solution for promoting the widespread adoption of this technology and reducing food production costs. In this study, a low-cost solar dryer was constructed using repurposed materials, employing indirect exposure, and its performance was evaluated through a kinetic study on banana slice drying (Musa spp.). Drying was carried out until equilibrium moisture, reaching constant mass. The drying air's temperature and relative humidity (RH, %) were monitored. Fourteen empirical models were used to fit the experimental data. The banana slices took approximately 300 min to dry, with final moisture of 1.8%. The mean operational conditions during natural drying were 59.08±9.16 °C and RH = 39±4%. The diffusion Approximation model fitted the drying curve better, as it presented the lowest reduced Chi-square (χ2 = 2.9×10-5) and a high coefficient of determination (R2 = 0.9998). The effective diffusion coefficient (Def = 5.4×10-9 m2/s, R2 = 0.9935) was determined. Thus, the solar dryer demonstrated efficient performance in the banana drying process, requiring minimal design effort. Furthermore, despite the limitations in controlling the drying conditions, most of the mathematical models successfully predicted the drying process due to the dryer's ability to maintain the continuity of the drying curve, suggesting potential viability for this low-cost dryer.

Active and stable Pd-catalyst modified by sulfonate-tailed zwitterionic phosphine for hydroxycarbonylation of alkynes free of any additive

Pd-catalyzed hydroxycarbonylation of alkynes with CO and H2O is a promising alternative for one-step synthesis of α,β-unsaturated carboxylic acids advantageous with 100 % atom-economy, but suffering from rapid catalyst deactivation and unavoidable side reactions like polymerization and hydration while the auxiliary acid-additive is required as a co-catalyst. Herein, free of any auxiliary acid-additive and under mild conditions (0.5 MPa, 60 °C, 3 h), a flexible zwitterionic phosphine of L1 containing a mono-phosphino-fragment and a hydrophilic sulfonate group (–SO3-) conferred to Pd(OAc)2 catalyst efficient performance for hydroxycarbonylation of terminal alkynes, affording the branched α,β-unsaturated carboxylic acids in the yields of 85 ∼ 91 % with 100 % regioselectivity. In addition, the developed L1-Pd(OAc)2 system was so stable that it could be recycled for 30 runs with well-maintained activity and selectivity, which proved the most active and stable to date under acid-additive-free condition.

Catalytic hydrodeoxygenation of lignin-derived phenolic compounds with Ni-based aluminum phosphate catalyst

Lignin-derived products possess high carbon content and offer significant potential to be used as liquid fuels. However, they also contain high oxygen element and unstable functional groups. Hydrodeoxygenation (HDO) is an effective method for upgrading lignin-derived phenolic compounds to produce high-quality liquid biofuels. In this study, a series of Ni-based phosphate catalysts incorporating various metals (Al, Ce, La) were prepared and employed for the HDO of lignin-derived phenolic compounds. The results showed that Ni/AlPO4 had the most efficient HDO performance, with guaiacol conversion of 99.9 % and cyclohexane selectivity of 91.1 % at the reaction temperature of 250 °C. Various catalyst characterization measurements were conducted to assess the impact of metal phosphate species on the crystallography, morphology, physicochemical characteristics and surface properties. Results indicated that Ni/AlPO4 catalyst exhibited stronger acidity, smaller Ni particle sizes and larger specific surface area than other Ni-based phosphate catalysts. Based on the characterization results and experimental data, the corresponding HDO reaction pathways and mechanisms were also proposed. Ni/AlPO4 catalyst was also applied to the HDO of various lignin-derived phenol compounds and achieved a good HDO effect. Moreover, the catalytic upgrading of lignin-oil was also carried out, and a high proportion of hydrocarbon products that increased from 16.0 % to 88.6 % was obtained. This Ni/AlPO4 catalyst provides potential reference for the HDO application of lignin-derived phenolic compounds for the production of hydrocarbon liquid fuels.

Impact of Electric Vehicles Consumption on Energy Efficient and Self-Sufficient Performance in Building: A Case Study in the Brazilian Amazon Region

Impact of Electric Vehicles Consumption on Energy Efficient and Self-Sufficient Performance in Building: A Case Study in the Brazilian Amazon Region

Performance and emission profiles of petroleum nut (Pittosporum resineferum Hemsl.) biodiesel in combustion ignition engine-generator for power generation

One of the traditional fuels for power generation in the Philippines is the petroleum diesel (PD). However, its extensive usage contributes to environmental degradation, health risks and climate change concerns. Alternative fuels such as petroleum nut biodiesel (PNB) may address the increasing consumption of PD amidst depleting fossil reserves and related issues. This study aimed to produce, characterize, and observe the behavior of PNB as a fuel in a compression ignition (CI) engine-power generation system at various loads of 0 %, 25 %, 50 %, 75 % and 100 %. Petroleum nut fruits were collected, extracted of oil then transesterified to produce PNB. The performance and emission profiles of the latter were determined. Degumming increased the PNB yield by 24.28 %. Additional refining decreased colorants and impurities. Majority of the chemical and physical properties of the PNB showed comparable values with those of PD. Various blends of PNB-PD were prepared and tested in terms of their performance and emissions. The 20 % PNB mixed with 80 % PD (B20) showed the most efficient performance after 100 % PD with at least 3.95 % decrease, whereas PNB for specific fuel consumption (SFC) showed at most 30.78 % higher than all fuels for all loads. The heat release rate (HRR) increases with increasing %PNB in the PNB-PD blend. PNB generally showed the highest CO2 and NOx emissions with at least 16.67 % and 80.52 % lower with PD respectively, but the lowest for CO emission with at least 13.42 % difference compared with PD. Finally, the study confirms that CI engine-generator can be operated with 100 % PNB and its blends without engine modification.

Ultrafast Quasi-Solid-State Microwave Construction of Spongy Cobalt-Molybdenum Phosphide for Hydrogen Production Over Wide pH Range.

The developed strategies for synthesizing metal phosphides are usually cumbersome and pollute the environment. In this work, an ultrafast (30s) quasi-solid-state microwave approach is developed to construct cobalt-molybdenum phosphide decorated with Ru (Ru/CoxP-MoP) featured porous morphology with interconnected channels. The specific nanostructure favors mass transport, such as electrolyte bubbles transfer and exposing rich active sites. Moreover, the coupling effects between metallic elements, especially the decorated Ru, also act as a pivotal role on enhancing the electrocatalytic performance. Benefiting from the effects of composition and specific nanostructure, the prepared Ru/CoxP-MoP exhibits efficient HER performance with a current density of 10mAcm-2 achieved in 1m KOH, 0.5mH2SO4, seawater containing 1mKOH and 1mPBS, with overpotentials of 52, 59, 55, 90mV, and coupling with good stability. This work opens a novel and fast avenue to design metal-phosphide-based nanomaterials in energy-conversion and storage fields.