Efficient Application Research Articles

Differentially Private Client Selection and Resource Allocation in Federated Learning for Medical Applications Using Graph Neural Networks.

Federated learning (FL) has emerged as a pivotal paradigm for training machine learning models across decentralized devices while maintaining data privacy. In the healthcare domain, FL enables collaborative training among diverse medical devices and institutions, enhancing model robustness and generalizability without compromising patient privacy. In this paper, we propose DPS-GAT, a novel approach integrating graph attention networks (GATs) with differentially private client selection and resource allocation strategies in FL. Our methodology addresses the challenges of data heterogeneity and limited communication resources inherent in medical applications. By employing graph neural networks (GNNs), we effectively capture the relational structures among clients, optimizing the selection process and ensuring efficient resource distribution. Differential privacy mechanisms are incorporated, to safeguard sensitive information throughout the training process. Our extensive experiments, based on the Regensburg pediatric appendicitis open dataset, demonstrated the superiority of our approach, in terms of model accuracy, privacy preservation, and resource efficiency, compared to traditional FL methods. The ability of DPS-GAT to maintain a high and stable number of client selections across various rounds and differential privacy budgets has significant practical implications, indicating that FL systems can achieve strong privacy guarantees without compromising client engagement and model performance. This balance is essential for real-world applications where both privacy and performance are paramount. This study suggests a promising direction for more secure and efficient FL medical applications, which could improve patient care through enhanced predictive models and collaborative data utilization.

Distributed Neural Representation for Reactive In Situ Visualization.

Implicit neural representations (INRs) have emerged as a powerful tool for compressing large-scale volume data. This opens up new possibilities for in situ visualization. However, the efficient application of INRs to distributed data remains an underexplored area. In this work, we develop a distributed volumetric neural representation and optimize it for in situ visualization. Our technique eliminates data exchanges between processes, achieving state-of-the-art compression speed, quality and ratios. Our technique also enables the implementation of an efficient strategy for caching large-scale simulation data in high temporal frequencies, further facilitating the use of reactive in situ visualization in a wider range of scientific problems. We integrate this system with the Ascent infrastructure and evaluate its performance and usability using real-world simulations.

Dynamic simulation of a combined solar system including phase change material storage and its experimental validation

Thermal energy storage is a key issue in efficient energy system applications, especially in renewable energies. In this respect, phase change materials (PCM) have attracted interest as an active solution for efficient energy management, particularly in the building sector. This paper presents a modelling of a PCM thermal battery in solar system assisted by heat pump. The storage stores the heat produced by unglazed solar panels (Batisol®) and releases it into a heat pump. Then this heat is sent to two other PCM storages. In this way, the low-temperature heating and domestic hot water needs of a commercial building in winter are met. The storage consists of a block of PCM contained between two plates of heat transfer fluid. It acts as a plate heat exchanger that can be charged and discharged simultaneously. The objective of the study is to dynamically simulate the thermal behaviour of this storage for different hot inlet temperature profiles. A rectangular profile is used to validate the model using experimental results. Then, a solar system assisted by heat pump and three PCM storages is dynamically simulated. This 2D model would be useful to validate a simpler model for optimising the operational parameters of the system.

Prediction and analysis of dominant factors influencing moisture content during vacuum screening based on machine learning

The study of the dominant factors influencing moisture content is essential for investigating vacuum filtration mechanisms. In view of the present situation where there is insufficient experimental data and the dominant factors influencing the moisture content of a filter cake have not been identified, in this study a vacuum filtration apparatus was designed and constructed. Quartz sand particles were used as the filtration material. 300 datasets of moisture contents of a filter cake were obtained under different experimental conditions. Multiple Linear Regression, artificial neural network, decision tree, random forest, and extreme gradient boosting were used to establish a prediction model for moisture content during vacuum screening. By comprehensively analyzing the feature importance rankings and the effects of positive and negative correlations, the dominant factors influencing the moisture content of the filter cake during vacuum screening were the particle ratio, screen mesh, and airflow rate. This finding not only provides a scientific basis for the optimization of vacuum screening technology but also points the way for improving screening efficiency in practical applications. It is of significant importance for deepening the understanding of the vacuum screening mechanism and promoting its extensive application.

New Public Management and Human Resource Planning Process In Delta State Hospital Management Board, Asaba, Nigeria

The new strategy for public management has given rise to a shift in human resource planning. The purpose of the study is to assess how the new public management approach's theoretical modifications have affected its practical applications, particularly in the area of human resource planning. The main goal of this study was to ascertain how the New Public Management approach has affected human resource planning in Delta State Hospital Management Board, Asaba. Convenience sampling and a cross-sectional research design were used. For this study, a questionnaire was used as the research tool for data collection, and 109 respondents were deemed useful. Statistical Package for Social Sciences (SPSS) software version 23.0 was used to perform inferential statistics of correlation and multiple regression analysis on the collected data. The study's findings demonstrated a favorable and significant correlation between new public management and human resource planning and a high-performance culture. The study also showed that the New Public Management approach had a direct impact on the culture of high commitment and performance and human resource planning. The study makes several recommendations, including that the Delta State Hospital Management Board and other related public sector organizations work on clearly outlined HR policies that can form the cornerstone of an institutional framework to ensure the efficient application of HRP policies. Additionally, they should implement New Public Management processes that include human resource planning scope and allocate more funds for the capacity building and professional growth of the resources in order to provide need-based training to their staff. The study is unique in two ways. First, while there are numerous studies that describe the traditional approach to public management in the literature, this study offers a critical viewpoint on it. Second, no other studies in Nigeria have looked into the connection between the new public management approach and human resource planning; the study is a leader in the field in this regard.

Data-Driven Optimization of Plasma Electrolytic Oxidation (PEO) Coatings with Explainable Artificial Intelligence Insights

PEO constitutes a promising surface technology for the development of protective and functional ceramic coatings on lightweight alloys. Despite its interesting advantages, including enhanced wear and corrosion resistances and eco-friendliness, the industrial implementation of PEO technology is limited by its relatively high energy consumption. This study explores the development and optimization of novel PEO processes by means of machine learning (ML) to improve the coating thickness. For this purpose, ML models random forest and XGBoost were employed to predict the thickness of the developed PEO coatings based on the key process variables (frequency, current density, and electrolyte composition). The predictive performance was significantly improved by including the composition of the used electrolyte in the models. Furthermore, Shapley values identified the pulse frequency and the TiO2 concentration in the electrolyte as the most influential variables, with higher values leading to increased coating thickness. The residual analysis revealed a certain heteroscedasticity, which suggests the need for additional samples with high thickness to improve the accuracy of the model. This study reveals the potential of artificial intelligence (AI)-driven optimization in PEO processes, which could pave the way for more efficient and cost-effective industrial applications. The findings achieved further emphasize the significance of integrating interactions between variables, such as frequency and TiO2 concentration, into the design of processing operations.

Design and testing of an intelligent variable-rate spraying system based on LiDAR-guided application

Traditional plant protection machinery cannot apply pesticides according to the actual growth demand of crops, which causes a lot of pesticide waste and serious environmental pollution. Based on this, an intelligent variable-rate application system based on 3D LiDAR (light detection and ranging) scanning and PWM (pulse-width modulation) control for maize canopy was developed in this paper. After completing the performance testing, the system was mounted on a lightweight high-clearance self-propelled sprayer by remote controlling with an operating width of 6.0 m, and a ground gap of 2.0 m. And the actual operational performance of the sprayer showed that the prediction error of LiDAR for plant height was within 10%, which illustrated the accuracy of the detection system and the reliability of the variable application based on the plant height change. And the variable-rate system could adjust the spray volume in real-time according to the plant height change. The penetration rate of the upper, middle, and bottom layers maintained a relatively stable state (upper > middle > bottom) at variable-rate application and could reach the normal application level. This study can provide new ideas for efficient pests and diseases control of high stalk crops, especially suitable for small fields, and gives a reference for the structural design of new precise variable-rate application machinery and performance optimization of efficient application technology.

Efficient energy conversion mechanism and energy storage strategy for triboelectric nanogenerators

Energy management strategy is the essential approach for achieving high energy utilization efficiency of triboelectric nanogenerators (TENGs) due to their ultra-high intrinsic impedance. However, the proven management efficiency in practical applications remains low, and the output regulation functionality is still lacking. Herein, we propose a detailed energy transfer and extraction mechanism addressing voltage and charge losses caused by the crucial switches in energy management circuits. The energy conversion efficiency is increased by 8.5 times through synergistical optimization of TENG and switch configurations. Furthermore, a TENG-based power supply with energy storage and regularization functions is realized through system circuit design, demonstrating the stable powering electronic devices under irregular mechanical stimuli. A rotating TENG that only works for 21 s can make a hygrothermograph work stably for 417 s. Even under hand driving, various types of TENGs can consistently provide stable power to electronic devices such as calculators and mini-game consoles. This work provides an in-depth energy transfer and conversion mechanism between TENGs and energy management circuits, and also addresses the technical challenge in converting unstable mechanical energy into stable and usable electricity in the TENG field.

Genetically engineered whole-cell biocatalyst for efficient CO2 capture by cell surface display of carbonic anhydrase from Bacillus cereus GLRT202 on Escherichia coli

CO2 sequestration is important for reducing greenhouse effects. Carbonic anhydrase (CA) from bacteria has a promising role because it can be modified by genetic techniques and bioengineering. In this study, the CA from B. cereus GLRT202 (Bc-CA) was genetically engineered and anchored on the surface of E. coli by using the N-domain of the ice nucleation protein from P. syringae (INPN). Both surface-displayed and cytosolic Bc-CA yielded high expression levels of CA when induced with 0.5 mM IPTG. It exhibited no adverse influence on the host cell growth. Additionally, surface-displayed Bc-CA enhanced its stability and specificity compared to cytosolic expressed Bc-CA. The CA activity of whole-cell surface-displayed cells was 1.66-fold higher (5.19 U/mL) than that of the cytosolic form. Besides the advantages of higher activity, the whole-cell displaying CA was comparatively stable, with better storage (at 4 ℃) and resting culture stability (at 37 ℃). The whole-cell biocatalyst induced the calcite precipitation, which indicated that the cell facilitated the CO2 capture. XRD, FTIR, and FESEM characterized calcite precipitates thus obtained. This study demonstrates that Bc-CA can be correctly expressed on the E. coli surface through fusion with the INPN. This leads to an effective whole-cell biocatalyst with enhanced stability and specificity of the enzyme for efficient CO2 capture applications.

Sequencing-Dependent Impact of Carbon Coating on Microstructure Evolution and Electrochemical Performance of Pre-lithiated SiO Anodes: Enhanced Efficiency and Stability via Pre-Coating Strategy.

Silicon monoxide (SiO) has attracted considerable interest as anode material for lithium-ion batteries (LIBs). However, their poor initial Coulombic efficiency (ICE) and conductivity limit large-scale applications. Prelithiation and carbon-coating are common and effective strategies in industry for enhancing the electrochemical performance of SiO. However, the involved heat-treatment processes inevitably lead to coarsening of active silicon phases, posing a significant challenge in industrial applications. Herein, the differences in microstructures and electrochemical performances between prelithiated SiO with a pre-coated carbon layer (SiO@C@PLi) and SiO subjected to carbon-coating after prelithiation (SiO@PLi@C) are investigated. A preliminary carbon layer on the surface of SiO before prelithiation is found that can suppress active Si phase coarsening effectively and regulate the post-prelithiation phase content. The strategic optimization of the sequence whereprelithiation and carbon-coating processes of SiO exert a critical influence on its regulation of microstructure and electrochemical performances. As a result, SiO@C@PLi exhibits a higher ICE of 88.0%, better cycling performance and lower electrode expansion than SiO@PLi@C. The pouch-type full-cell tests demonstrate that SiO@C@PLi/Graphite||NCM811 delivers a superior capacity retention of 91% after 500 cycles. This work provides invaluable insights into industrial productions of SiO anodes through optimizing the microstructure of SiO in prelithiation and carbon-coating processes.

Layered oxyiodide CdBiO2I: An efficient visible light responsive and scalable photocatalyst

Developing high-performance visible-light driven photocatalyst (λ≥420 nm) makes significance for the efficient utilization of solar energy. Mass production and easy recycling are equally important for the practical application of powdery photocatalyst. However, it is challenging to meet the above requirements at the same time. In this work, we develop an efficient visible-light responsive layered oxyiodide CdBiO2I nanosheets prepared by a facile direct precipitation method at ambient atmosphere, and demonstrate its upgradable features well fitting potential application. CdBiO2I has a layered crystal structure consisting of [CdBiO2]+ layer and I– single layer, differing from that of BiOI composed of [BiO2]2+ layer and I- double layers. It displays an absorption edge of 520 nm in visible region, with a band gap of 2.52 eV. CdBiO2I has a large intrinsic effective mass difference of hole/electron, exceeding BiOI by 8-fold, which results in much higher charge separation efficiency and production of more reactive species (superoxide radicals and holes). The degradation efficiency of CdBiO2I nanosheets for tetracycline hydrochloride reaches 84% under the visible light irradiation within 1 h, in which the degradation rate is 10 times that of BiOI. In addition, mass production (12 g catalyst at one time) and immobilization onto porous polyurethane foam of CdBiO2I powder are also demonstrated, which indicates the scalable properties and easy recovery of the catalyst, highlighting the advantages of the current preparation method and prefiguring its potential in practical applications. This work may enlighten future research on the exploitation of solar-driven catalyst with high efficiency and strong practical applicability.

Efficient fruit disease diagnosis on resource-constrained agriculture devices

Climate change in recent years has caught the attention of researchers worldwide, with drastic effects on many economic sectors, especially agriculture and livestock. Due to global warming and declining water levels, food security is becoming an increasingly urgent concern. The situation is more challenging in underdeveloped nations such as Pakistan, where little attention has been paid to improving the processes to produce effective seeds and herbicides. The problem has been exacerbated due to financial constraints and a lack of technology to monitor crops. It has been observed that even a noncritical disease can ruin the crop if timely inspections aren’t performed. Although many solutions have been proposed recently, most are based on heavy mobile applications and cloud-based solutions, which results in considerable computational cost, power consumption, and latency. This work proposes a Deep Neural Networks (DNNs)-based light-weight solution for diagnosing and classifying apple crop diseases for farmers working in rural places (where the internet is not accessible). With the increasing need for efficient and robust Machine Learning (ML) applications on edge devices, such as mobile phones, Raspberry Pi, and Jetson Nano-based devices, there has been a demand for more efficient Deep Neural Networks (DNN) models. To meet this demand, various DNN-based models have been experimented with, including Basic CNN Architecture, AlexNet, and EfficientNet Lite to measure performance After a thorough examination and evaluation of each model’s performance, efficiency, and resource usage, the most optimal model was picked and developed applications for classifying apple crop disease. Using a transfer learning strategy on a specially developed EfficientNet DNN architecture, achieving 85% test accuracy.

Efficient application of Pyrosin B for nano-gram level assay of antiparkinsonian medication, rasagiline: Evaluation of tablets and content uniformity.

Rasagiline (RAS) is a medication for Parkinson's disease that increases dopamine levels in the brain by inhibiting monoamine oxidase, helping to alleviate symptoms. The proposed study aims to develop an efficient, feasible, and sensitive method for RAS assay, utilizing Pyrosin B dye, a convenient fluorescent ligand. Combining the RAS analyte with Pyrosin B ligand in a mildly acidic buffered solution rapidly quenches the native fluorescence of the ligand. This quenching results from the formation of a specific ion-dipole association complex between the lone pair-bearing atoms of the ligand and the protonated amine moiety of RAS, highlighting their interactive chemistry under these conditions. The degree of this interaction demonstrated superior sensitivity compared with reported alternatives, exhibiting a linear range of 50.0 to 1000.0ng/mL. The method is characterized by a limit of detection (LOD) of 16.0ng/mL and a limit of quantification (LOQ) of 48.0ng/mL. By optimizing the RAS-Pyrosin B system, the variable parameters were finely tuned, ensuring the assay method's reliability. The method's accuracy, precision, selectivity, and robustness were validated according to International Council for Harmonization (ICH) guidelines, enabling precise and efficient analysis of RAS in the nanogram range. This method streamlines the analysis procedure and reduces environmental impact, making it a promising approach for the quality control of ParkintreatR tablets (1mg) and other analytical applications.

Efficiency improvement and application prospects of solar photovoltaic power generation

The background of efficiency improvement and application prospects of solar PV power generation reflects a dynamic and evolving landscape. As technology continues to advance and as society places greater emphasis on sustainability and clean energy, solar PV is expected to play an increasingly significant role in meeting the worlds energy needs while mitigating the environmental impact of energy production. This paper is mainly about how to improve the efficiency of solar photovoltaic power generation and the application of solar power generation. We put together an article mainly by looking up technology and summarizing other peoples opinions. Among them, we have studied the principle of solar power supply systems in the first part. The author analyzed how to choose monocrystalline silicon and polysilicon and how they can improve the efficiency and the use of HJT batteries and PERC solar cells. Both HJT and PERC applications can greatly improve efficiency

Designing a Web-Based Accounting Information System Using the Object Oriented Analysis and Design Method

The development of information technology in the digital era has created many new efficient applications. With the development of computer technology as a means of processing data into information which is then processed again in such a way in its presentation. Perum BULOG is one of the state-owned companies engaged in rice logistics and food security. As a company that continues to carry out public duties from the government, BULOG in carrying out activities that can stabilize the basic purchase price for grain, stabilize prices, especially basic prices, distribute rice for the poor (Raskin) and manage food stocks. This research aims to optimize the process of purchasing food and distributing computerized social assistance. The development method used in making this system is the Object Oriented Analysis and Design (OOAD) method which can model objects in the system, in the context of AIS, objects such as "accounts, transactions" can be represented as objects in the OOAD model. The results showed that the sales and distribution activities of social assistance are increasingly managed according to the needs equipped with a transaction process that can be stored.

EFFECT OF STEM CELL THERAPY ON BONE REPAIR OF THE MAXILLA AND MANDIBLE

Maxillary and mandibular defects can result from a number of issues, such as periodontal disease, tumors, trauma and congenital anomalies, and can affect not only speech and mastication, but also aesthetics and self-esteem. In this sense, stem cell therapy is a promising, effective and safe option in the growing search for new forms of treatment for these dysfunctions. In this context, the aim of this study is to evaluate, through a literature review, the use of stem cells in bone regeneration of the maxillary and mandibular bones. The information was obtained through a bibliographic search carried out in February and March 2024 on the Scielo, Pubmed, Medline and Google Scholar platforms using the descriptors "Stem Cells", "Bone Repair", "Mandible", "Maxilla" and their Portuguese versions. Only papers published in English and Portuguese that dealt with the use of stem cells to regenerate defects in the mandible and maxilla were selected. During the search, x papers were initially selected, which 18 were included after consulting the abstracts and then reading them in full. Mesenchymal stem cells have been shown to improve and accelerate healing processes and increase bone density in the long term, thus promoting a faster and more complete recovery for patients, reducing the need for autologous grafts or synthetic materials. However, they have been shown to have a limited effect in cases of extensive bone defects. It can be concluded that stem cell therapy is an effective and safe alternative for promoting bone regeneration in cases of maxillary and mandibular defects, but further controlled studies are still needed to improve its performance by developing more efficient application strategies.

An Efficient Application of Modified YOLOv5 in Basketball Player Detection and Analysis

Effective analysis helps players evaluate their performance, make necessary adjustments, and develop diverse game strategies. Moreover, the analysis provides viewers with different perspectives, enhancing their understanding of the game. This study aims to develop a basketball player detection and analysis system to assist in analyzing on-court situations. The system uses perspective transformation to obtain player tracking information on the top view image in basketball games. The system uses a modified you only look once (YOLO) v5 model that replaces the backbone of YOLOv5s with the MobileNetv3-small architecture for player detection. Compared to the original YOLOv5, the modified YOLOv5 reduces parameters from 7.02 × 106 to 3.5 × 106, a decrease of 49.8%. The number of frames obtainable per second increases from 12.4 to 17.5, an improvement of about 41.1%. Finally, the system performs perspective transformation and tracks the detected player positions onto the top-view court image using the YOLOv5 model.

Metagenomics Analysis of the Impact of Protein-Degrading Functional Microbial Agents on Composting of Chicken Manure from Cereal Hulls

In this study, four highly efficient protein-degrading bacteria (Siccibactercolletis, Bacillus thuringiensis, Bacillus cereus, and Bacillus sp. (in: Firmicutes)) were screened from soil and fermentation beds and prepared into a mixed microbial agent in a ratio of 1:1:1:1. The effects of inoculation with protein-degrading functional bacteria on nitrogen transformation rate, microbial community, and functional genes during chicken manure–rice husk composting were studied. With the addition of functional agents, the nitrogen loss in chicken manure composting was reduced to 17.05%, and ammonia emissions were also reduced. Firmicutes, Proteobacteria, Bacteroidetes, Cocci, and Actinobacteria became the dominant bacterial communities, accounting for 85.41%~98.52% of the overall bacterial community in the compost; it promoted the growth of microorganisms such as Pseudogracilibacillus and Lachnospiraceae in the compost. Metagenomic analysis revealed that the addition of functional bacterial agents enhanced the expression of nitrogen fixation genes (nifK, nifH, and glnA) during the high-temperature phase, increased the diversity of bacteria associated with the nitrogen cycle in the compost, and improved the absorption and fixation of nitrogen source elements by microorganisms. Additionally, it strengthened the correlation between microbial communities, the composting environment, and functional genes. This study provides a theoretical basis for the efficient application of microbial agents and the reduction of pollution in chicken manure hull composting.

Plasmon-Enhanced Light Absorption Below the Bandgap of Semiconducting SnS2 Microcubes for Highly Efficient Solar Water Evaporation.

Semiconducting materials show high potential for solar energy harvesting due to their suitable bandgaps, which allow the efficient utilization of light energy larger than their bandgaps. However, the photon energy smaller than their bandgap is almost unused, which significantly limits their efficient applications. Herein, plasmonic Pd/SnS2 microcubes with abundant Pd nanoparticles attached to the SnS2 nanosheets are fabricated by an in situ photoreduction method. The as-prepared Pd/SnS2 microcubes extend the light-harvesting ability of SnS2 beyond its cutoff wavelength, which is attributed to the localized surface plasmon resonance (LSPR) effect of the Pd nanoparticles and the 3D structure of the SnS2 microcubes. Pd nanoparticles can also enhance the light absorption of TiO2 nanoparticles and NiPS3 nanosheets beyond their cutoff wavelengths, revealing the universality for promoting absorption above the cutoff wavelength of the semiconductors. When the plasmonic Pd/SnS2 microcubes are integrated into a hydrophilic sponge acting as the solar evaporator, a solar-to-vapor efficiency of up to 89.2% can be achieved under one sun. The high solar-to-vapor conversion efficiency and the broad applicability of extending the light absorption far beyond the cutoff wavelength of the semiconductor comprise the potential of innovative plasmonic nanoparticle/semiconductor composites for solar desalination.

Correcting implicit solvation at metal/water interfaces through the incorporation of competitive water adsorption.

Conventional continuum solvation models are ubiquitous in computational catalysis, including for describing metal/water interfaces, which are relevant to both solution-phase heterogeneous catalysis and electrocatalysis. Nonetheless, we find that such continuum models qualitatively fail to describe both the adsorption free energy and conformational preference for many organic molecules at such interfaces, largely due to the failure of continuum models to incorporate the role of competitive water adsorption. We develop a simple phenomenological model that accounts for competitive water adsorption and show that the model, when used in conjunction with continuum solvation, provides a dramatic improvement in the description of both adsorption and conformational preference. The model is also extended to additionally incorporate the influence of applied potential at the electrode surface, thus facilitating computationally efficient applications to scenarios including electrocatalysis.