Efficient Modulation Research Articles

Lightweight pyramid attention residual network for intelligent fault diagnosis of machine under sharp speed variation

Deep learning based diagnostic methods have obtained great success in intelligent fault diagnosis of machines. However, most of the existing methods are developed for fault diagnosis task under stable working conditions, and cannot handle the data generated under sharp speed variation favorably. Besides, the large model parameters and high computing costs of these methods fail to meet the requirement of small memory and fast-precise prediction in practical applications. To address these challenges, a lightweight pyramid attention residual network (PARNet) is proposed in this paper. First, an efficient pyramid squeeze convolution module is proposed to capture multi-scale information from raw signals. Then, a soft squeeze-and-excitation attention is designed to establish long-range channel dependency, and fuse features of different scales. Based on the above improvements, a pyramid attention residual block is proposed as basic feature learning unit, and a concise CNN-based model PARNet is built using PAR block. Extensive experiments in two case studies are conducted to validate the effectiveness and adaptability of PARNet under different speed variation conditions. The test results demonstrate the superiority of the proposed method for machine fault diagnosis and reliability assessment in terms of recognition accuracy and model parameter complexity, compared with other advanced diagnostic methods.

Sodium alginate grafted phase-changing electrolyte for energy-efficient thermochromic and electrochromic synergy with uniquely tunable optical states and simultaneous Vis-NIR modulation

Conventional smart windows, limited by their single-stimulus response, often fail to meet diverse practical needs, and cannot achieve zero transmission across the Visible-NIR spectrum. Integrating thermochromic, electrochromic, and synergetic-responsiveness into a single device is both challenging and essential for effective light modulation and energy efficiency. Herein, we present a novel green phase-changing electrolyte integrated with a versatile chromogen, to create an “all-in-one” smart window. This device autonomously switches functions and can also simultaneously respond to dual-stimuli, achieving an absolute private state. The device’s tunable performance in optical contrast, switching kinetics, coloration efficiency, and cyclic stability, influenced by lattice water concentration is also meticulously assessed. Our comprehensive study highlights the device’s robust multifunctional properties, including thermal insulation, lifestyle interventions, and environmental remediation. These features make the smart window a highly energy-efficient solution with exceptional climate adaptability, such as shutting down light transmission with environmentally-benign operations and on-demand color changes. This innovative approach transforms smart windows from mere energy savers to versatile, energy-efficient systems for modern buildings. By bridging the gap between advanced materials and practical applications, our “all-in-one” smart window represents a significant advancement, paving the way for broader adoption in various environmental and energy-saving contexts.

DNIM: Deep-sea netting intelligent enhancement and exposure monitoring using bio-vision

Intelligent monitoring of deep-sea nets is affected by light attenuation, light scattering, and limited dynamic range factors of the camera, which can cause color shift, low visibility, and over/underexposure in monitoring, resulting in reduced farming efficiency, and misdetection of biological behavior. Therefore, we propose a method for underwater net tank scene enhancement and exposure using multicolor space and bio-vision, called DNIM. Specifically, we first propose an intelligent enhancement and exposure monitoring method for deep-sea nets using bio-vision, which eliminates the effect of exposure on underwater imaging by introducing a bio-vision system that uses a brightness transformation function to generate a series of exposure images. Inspired by the image fusion method, we designed a color correction and visibility recovery method using bio-vision. In the Lab color model, color shifts in bio-vision are corrected by guiding feature transformations with contrast-constrained adaptive histogram equalization; in the RGB color model, depth information is recovered by creating a linear model for the depth of field of underwater blurred images based on the characteristics of the bio-vision system. To obtain more qualified results, we also propose an efficient perceptual fusion module to mix the output of visibility recovery and color correction. Finally, we propose two deep-sea net tank culture datasets: salmon bait-eating behavior dataset (SBBD) and salmon non-bait-eating behavior dataset (SNBD). By comparing the experimental analysis in four real scenario datasets, our DNIM achieved superior results compared to 16 advanced underwater enhancement methods. The code is publicly available at: https://github.com/An-Shunmin/DNIM.

Dual-COPE: A novel prior-based category-level object pose estimation network with dual Sim2Real unsupervised domain adaptation module

Category-level pose estimation offers the generalization ability to novel objects unseen during training, which has attracted increasing attention in recent years. Despite the advantage, annotating real-world data with pose label is intricate and laborious. Although using synthetic data with free annotations can greatly reduce training costs, the Synthetic-to-Real (Sim2Real) domain gap could result in a sharp performance decline on real-world test. In this paper, we propose Dual-COPE, a novel prior-based category-level object pose estimation method with dual Sim2Real domain adaptation to avoid expensive real pose annotations. First, we propose an estimation network featured with conjoined prior deformation and transformer-based matching to realize high-precision pose prediction. Upon that, an efficient dual Sim2Real domain adaptation module is further designed to reduce the feature distribution discrepancy between synthetic and real-world data both semantically and geometrically, thus maintaining superior performance on real-world test. Moreover, the adaptation module is loosely coupled with estimation network, allowing for easy integration with other methods without any additional inference overhead. Comprehensive experiments show that Dual-COPE outperforms existing unsupervised methods and achieves state-of-the-art precision under supervised settings.

A novel local feature fusion architecture for wind turbine pitch fault diagnosis with redundant feature screening

The safe and reliable operation of the pitch system is essential for the stable and efficient operation of a wind turbine (WT). The pitch fault data collected by supervisory control and data acquisition systems (SCADA) often contain a wide variety of variables, leading to redundant features that interfere with the accuracy of final diagnosis results, making it difficult to meet requirements. Also, the problem of extracting only local features while ignoring global information is present in the feature extraction process using the deep Convolutional Neural Network (CNN) model. To address these issues, the global average correlation coefficient is proposed in this article to measure the correlation between multiple variables in SCADA data. By considering the correlation among multiple variables comprehensively, redundant features are effectively eliminated, enhancing the accuracy of fault diagnosis. Furthermore, a new local amplification fusion architecture network (LAFA-Net) based on multi-head attention (MHA) is introduced. An efficient local feature extraction module, designed to enhance the model’s perception of detailed features while maintaining global context information, is first introduced. LAFA-Net integrates the advantages of CNN and MHA, efficiently extracting and fusing valuable features from filtered data for both local and global aspects. Experiments on real pitch fault data demonstrate that the global average correlation coefficient effectively screens out redundant features in the dataset that negatively impact fault diagnosis results, thereby improving diagnosis efficiency and accuracy. The LAFA-Net model, capable of accurately diagnosing multiple types of pitch faults, shows a superior classification effect and accuracy compared to several advanced models, along with a faster convergence speed.

Engineering of protein glutaminase for highly efficient modification of fish myofibrillar protein through structure-based and computational-aided strategy

Protein glutaminase (PG; EC 3.5.1.44) is a class of food-grade enzyme with the potential to significantly improve protein functionality. However, its low catalytic activity and stability greatly hindered industrial application. In this study, we employed structural-based engineering and computational-aided design strategies to target the engineering of protein glutaminase PG5, which led to the development of a combinatorial mutant, MT8, exhibiting a specific activity of 31.1 U/mg and a half-life of 216.2 min at 55 °C. The results indicated that the flexible region in MT8 shifted from the C-terminus to the N-terminus, with increased N-terminal flexibility positively correlating with its catalytic activity. Additionally, MT8 notably boosted fish myofibrillar proteins (MPs) solubility under the absence of NaCl conditions and enhanced their foaming and emulsifying properties. Key residues like Asp31, Ser72, Asn121, Asp471, and Glu485 were crucial for maintaining PG5-myosin interaction, with Ser72 and Asn121 making significant energy contributions.

A Survey of Visible-Light-Communication-Based Indoor Positioning Systems.

There is a growing demand for indoor positioning systems (IPSs) in a wide range of applications. However, traditional solutions such as GPS face many technical challenges. In recent years, a promising alternative has been emerging, the visible light communication (VLC)-based IPS, which offers a combination of high accuracy, low cost, and energy efficiency. This article presents a comprehensive review of VLC-based IPSs, providing a tutorial-like overview of the system. It begins by comparing various positioning systems and providing background information on their inherent limitations. Experimental results have demonstrated that VLC-based systems can achieve localization accuracy to within 10 cm in controlled environments. The mechanisms of VLC-based IPSs are then discussed, including a comprehensive examination of their performance metrics and underlying assumptions. The complexity, operating range, and efficiency of VLC-based IPSs are examined by analyzing factors such as channel modeling, signal processing, and localization algorithms. To optimize VLC-based IPSs, various strategies are explored, including the design of efficient modulation schemes, the development of advanced encoding and decoding algorithms, the implementation of adaptive power control, and the application of state-of-the-art localization algorithms. In addition, system parameters are carefully examined. These include LED placement, receiver sensitivity, and transmit power. Their impact on energy efficiency and localization accuracy is highlighted. Altogether, this paper serves as a comprehensive guide to VLC IPSs, providing in-depth insights into their vast potential and the challenges that they present.

Non-Isothermal Operation of Membrane Electrode Assembly-Based CO2 Electrolyzer for Enhanced Energy Efficiency

The utilization of membrane electrode assemblies (MEA) in carbon dioxide (CO2) electrochemical conversion showcases the promising potential for stable and scalable industrial applications in fuel production, attributed to efficient gas diffusion and minimized system resistance. The electrochemical performance of MEA-based CO2 electrolyzers relies on the chemical reactivity and product selectivity optimization which is dependent on three factors: CO2 mass transport, charge transport, and ionic transport. Elevating the overall system temperature benefits the performance of the oxygen evolution reaction (OER) at the anode. Furthermore, the elevated temperature enables higher conductivity of anion exchange membranes (AEM), thereby further diminishing cell voltage. However, the downside emerges as CO2 solubility decreases at higher temperatures, fostering a scenario where the catalytic reaction leans toward hydrogen evolution, thereby undermining product selectivity.Herein, we propose an innovative approach involving thermal management for separate temperature control for the anode and cathode, forming a non-isothermal MEA by maintaining an elevated anodic temperature while cooling the cathode. This approach simultaneously addresses the need for a high-temperature anode and a low-temperature cathode. This study exhibits a temperature gradient of approximately 20°C between the anodic and cathodic planes achieved through efficient thermal modulation of water cooling and heating within the electrode plates.We showcase the benefit of non-isothermal operation with an Ag-based cathode and IrO2-based anode using 1 M KOH as the anolyte for CO2 conversion into CO. The non-isothermal condition, i.e., maintaining an anodic temperature of 80°C and a cathode temperature of 60oC, demonstrated a 65% enhancement in CO partial current density (j co), 195.6 mA cm-2, compared to the isothermal 80°C condition, 118.6 mA cm-2. The optimal performance across all temperature regimes is found to be the non-isothermal condition with anode temperature at 60°C and cathode temperature at 40 oC, yielding a j co of 243.5 mA cm-2 at 3.25 V, marking a 13.3% improvement over the isothermal 60°C condition (j co = 214.9 mA cm-2). The increase in the j co can be attributed to the lower temperature at the cathode (40 oC for the non-isothermal condition vs. 60oC for the isothermal ccondition) resulting in higher CO faradaic efficiency.The CO faradaic efficiency remained above 80% (cell potential < 2.7V) for the non-isothermal 60°C condition, whereas it dropped below 80% throughout the entire region cell potential range for the isothermal 60°C condition. This study presents a novel non-isothermal operation for advanced thermal management for MEA-based CO2 electrolyzers for simultaneous enhancement in anode activity as well as cathode selectivity. We believe this thermal management strategy is promising for engineering high-performing MEA-based CO2 electrolyzers at industrial scales.

Resource utilization of emulsion evaporation modified aluminum industrial refractory waste in the preparation of composite modified asphalt

AbstractAluminum industrial refractory waste pose a huge risk to the environment, and hence a safe resource utilization is mandatory to build a green industrial construction industry. Herein, a thermoplastic polyester elastomers‐spent refractory material (TPEE‐SRM) modifier having a two‐layer structure of “protective layer‐functional layer” was developed. Scanning electron microscopy and surface area analysis revealed that Eps used in the protective layer formed a dense film layer (50% decrease in specific surface) with a 97.7% sequestration of fluoride. As the reaction occurred in an organic solution, fluoride posed no risk to the environment. TPEE functional layer itself melted and dissolved with asphalt under high‐temperature conditions, and hence it established a strong interfacial interaction with asphalt. This led to an increase of 99.73% in the complex modulus (G*) and 41.75% in the rutting‐resistance performance (Jnr) of the TPEE‐SRM composite modified asphalt than that of the pristine styrene‐butadiene‐styrene (SBS) modified asphalt. The performance of the TPEE‐SRM/SBS composite modified asphalt after 10 years of use was also evaluated by conducting high pressure aging simulation tests. The linear amplitude scanning test results showed a 27.1‐fold increase in Nf 5.0%. Owing to the efficient and green modification method, the newly designed TPEE‐SRM modifier poses great application prospects and feasibility for designing advanced functional modified asphalt for construction and highway industries alongside a suitable disposal aspect for SRM.

A multiscale feature fusion‐guided lightweight semantic segmentation network

AbstractSemantic segmentation, a task of assigning class labels to each pixel in an image, has found applications in various real‐world scenarios, including autonomous driving and scene understanding. However, its widespread use is hindered by the high computational burden. In this paper, we propose an efficient semantic segmentation method based on Feature Cascade Fusion Network (FCFNet) to address this challenge. FCFNet utilizes a dual‐path framework comprising the Spatial Information Path (SIP) and the Context Information Path (CIP). SIP is a shallow structure that captures the local dependencies of each pixel to improve the accuracy of detailed segmentation. CIP is the main branch with a deeper structure that captures sufficient contextual information from input features. Moreover, we design an Efficient Receptive Field Module (ERFM) to enlarge the receptive field in the SIP. Meanwhile, Attention Shuffled Refinement Module is used to refine feature maps from different stages. Finally, we present an Attention‐Guided Fusion Module to fuse the low‐ and high‐level feature maps effectively. Experimental results show that our proposed FCFNet achieves 70.7% mean intersection over union (mIoU) on the Cityscapes data set and 68.1% mIoU on the CamVid data set, respectively, with inference speeds of 110 and 100 frames per second (FPS), respectively. Additionally, we evaluated FCFNet on the Nvidia Jetson Xavier embedded device, which demonstrated competitive performance while significantly reducing power consumption.

Ag20 Nanoclusters with Surface Azides as an Easily Functionalized Platform for Diverse Chemical Applications.

Modifying atomically precise nanocluster surfaces while maintaining the cluster core remains a key challenge. Herein, the synthesis, structure, and properties of two targeted Ag20 nanoclusters (NCs) with eight surface azide moieties, [CO3@Ag20(StBu)10(m-N3-C6H4COO)8(DMF)4] (1-m) and [CO3@Ag20(StBu)10(p-N3-C6H4COO)8(DMF)4] (1-p) are reported, where DMF is N,N-dimethylformamide. These AgNCs are designed to undergo cluster surface strain-promoted azide-alkyne cycloaddition (CS-SPAAC) reactions, introducing new functionality to the cluster surface. Reactivity is screened using model strained cyclooctynes. Reaction products and parent clusters are characterized by UV-vis, FT-IR, and NMR spectroscopies. The structure of the parent clusters and presence of surface azides is confirmed by single crystal X-ray diffraction (SCXRD) analysis. Clusters 1-m and 1-p are found to be amenable to CS-SPAAC reactions with retention of the NC frameworks, opening new routes for efficient modification of AgNC for applications.

Efficient Reversible Upconversion Luminescence Modulation based on Photochromism of Lanthanides‐Doped BaMgSiO4 Glass Ceramics Toward Optical Storage Application

AbstractTransparent glass with photochromic and luminescent properties has recently garnered considerable interest in optical storage, while the photochromic transparent glass system is still limited. Herein, it is proposed that combining transparent glass matrix with photochromic nanocrystals is an efficient strategy to develop a broader range of photochromic luminescent glass systems. The Yb3+/Tb3+ co‐doped BaMgSiO4 glass ceramics are successfully prepared by a two‐step melt‐quenching approach followed by a thermal treatment in a reducing atmosphere. The glass ceramics exhibit high transparency and distinct coloration, showing a color change between colorless and pink by alternating irradiation between 365 nm ultraviolet light and 473 nm laser. The formation of color centers in the BaMgSiO4 nanoparticles drives the coloration of such glass ceramics. Based on their photochromic and photobleaching behavior, the efficient reversible upconversion luminescence modulation can be implemented with the maximum luminescence modulation rate of 79.83%. The excellent reproducibility and anti‐fatigue of such upconversion luminescence modulation demonstrate the promising application of Yb3+/Tb3+ co‐doped BaMgSiO4 glass ceramics as an optical storage medium.

Biologically Relevant Laminin-511 Moderates the Derivation and Proliferation of Human Lens Epithelial Stem/Progenitor-Like Cells.

The role of specific extracellular matrix (ECM) molecules in lens cell development and regeneration is poorly understood, as appropriate cellular models are lacking. Here, a laminin-based lens cell in vitro induction system was developed to study the role of laminin in human lens epithelial stem/progenitor cell (LES/PC) development. The human embryonic stem cell-based lens induction system followed a three-stage protocol. The expression profile of laminins during lens induction was screened, and laminin-511 (LN511) was tested as a candidate substitute. LN511 induction system cellular and molecular features, including induction efficiency, transcription factor expression related to different lens development stages, ECM alterations, and Hippo/YAP signaling, were evaluated. LAMA5, LAMB1, and LAMC1 were highly expressed around the time of LES/PC derivation. We chose LN511 (product of LAMA5, LAMB1, and LAMC1) and found that it considerably enhanced lens cell induction efficiency, compared to that in Matrigel-coated culture, as more and larger lentoid bodies were detected. Notably, LES/PC induction efficiency improved by promoting lens specification-related transcription factor expression and cell proliferation. Transcriptome analysis revealed that compared to those with Matrigel, ECM accumulation and cell adhesion were downregulated in the LN511 system. Hippo/YAP signaling was hypoactive during LES/P-like cell generation, and small molecule inhibitors of YAP/TAZ activity upregulated LES/PC marker expression and promoted the efficiency of LES/P-like cell derivation. The laminin isoform LN511 is a reliable substitute for the LES/P-like cell induction system, and LN511-YAP acted as efficient modulators of LES/PC derivation; this contributes to knowledge of the role of the ECM in human lens development.

CATNet: Cascaded attention transformer network for marine species image classification

Complex physicochemical environmental effects result in the underwater species images’ highly intricate and diverse backgrounds, which poses significant challenges for identifying marine species. Deep learning techniques are used extensively for image classification thanks to their exceptional feature extraction capabilities. To address the above issues, we present a cascaded attention transformer network for marine species image classification named CATNet. More specifically, we first utilize the efficient channel attention module with depthwise convolution, which can efficiently extract the essential features of the image and reduce the computational parameters of the traditional attention module. Subsequently, we design a stacked transformer module to focus on the different features among a large amount of feature information, thereby reducing redundancy and improving?the generalization performance of CATNet. To comprehensively exploit the features extracted from different modules, we integrate the efficient channel attention and stacked transformer modules to construct a feature-cascaded module. Meanwhile, we construct a large-scale Marine Species Image Dataset (MSID) including 8605 underwater images with different species. Concretely, it consists of 712 coral reef images, 1270 fish images, 1082 people images, 2414 sea urchin images, 1219 squid images, 837 sea cucumber images, and 1071 turtle images. Comprehensive experimentation on our built dataset underscores the proposed CATNet’s superiority over the existing state-of-the-art methods in marine species image classification. The data is available. https://www.researchgate.net/publication/381961074_2024MSID.

Ag2Se as a tougher alternative to n-type Bi2Te3 thermoelectrics

For half a century, only Bi2Te3-based thermoelectrics have been commercialized for near room temperature applications including both power generation and refrigeration. Because of the strong layered structure, Bi2Te3 in particular for n-type conduction has to be texturized to utilize its high in-plane thermoelectric performance, leaving a substantial challenge in toughness. This work presents the fabrication and performance evaluation of thermoelectric modules based on n-type Ag2Se paring with commercial p-Bi2Te3. Ag2Se mechanically allows an order of magnitude larger fracture strain and thermoelectrically secures the module efficiency quite competitive to that of commercial one for both refrigeration and power generation within ± 50 K of room temperature, enabling a demonstration of a significantly tougher alternative to n-type Bi2Te3 for practical applications.

Efficient Broad-Spectrum Cyanophage Function Module Mining.

Cyanobacterial harmful algal blooms (CyanoHABs) cause health and environmental effects worldwide. Cyanophage is a virus that exclusively infects cyanobacteria. Using cyanophages to control blooms is the latest biological control method. However, little research on the genomics of cyanophages and the presence of numerous proteins with unidentified functions in cyanophage genomes pose challenges for their practical application and comprehensive investigation. We selected the broad-spectrum and efficient cyanophage YongM for our study. On the one hand, through rational analysis, we analyze essential genes, establish the minimal cyanophage genome and single essential gene modules, and examine the impact of essential modules on growth. Additionally, we conducted ultraviolet mutagenesis on YongM to generate more efficient cyanophages' critical modules through random mutagenesis. Then, we sequenced and analyzed the functionality of the mutational gene modules. These findings highlight several gene modules that contribute to a deeper understanding of the functional components within cyanophage genomes.

Challenges and solutions in developing an objective and structured clinical examination for complementary and integrative medicine: A mixed-method approach.

This study explores the challenges encountered by developers when creating objective and structured clinical examination (OSCE) modules specifically for Korean medicine (KM). The complexity of developing pattern identification (PI) items, due to the lack of standardized materials and ambiguity in KM, was a primary focus. A mixed-method approach was utilized, including a survey, importance-performance analysis, and focus group interviews. Seven developers participated, creating a total of 21 OSCE modules. The main difficulties identified were in developing PI items, selecting appropriate cases, crafting realistic examination situations, determining scoring criteria, setting up checklists, and writing scenarios. Challenges were categorized into "case," "examination situation," "postexamination notes," "checklist," "scenario," "format," and "PI." The importance-performance analysis revealed improvements in module development capabilities with each iteration. For the future development of efficient OSCE modules, standardization of KM diagnostic methods and PI is essential. The study highlights the need for social and academic efforts, as well as support from the KM education community and schools, to address these challenges and enhance the development process.

Economic Potential of Solar Projects in India: Assessing Levelized Cost of Electricity andPayback Period withVariations in PV Module Efficiency and Solar Irradiation

India is endowed with vast solar resources, with about 5,000 trillion kWh per year of energy incident over India’s land area, with most parts receiving 4-7 kWh per sqm per day. The National Institute of Solar Energy (NISE) has assessed the country’s solar potential to be about 748 GW. The government is bullish on realizing this solar potential, setting out ambitious targets to install 500 GW of non-fossil fuel capacity by 2030, with Solar projects contributing a major chunk of it. Reports suggest that investments up to $294bn would be required to meet India’s solar and wind targets. However, this capital expenditure can be reduced or controlled by deploying our solar projects prudentially. This paper intends to quantify the effect of variations in Solar PV module efficiency due to varying technology and changes in solar irradiation according to differing geographies on the cost-effectiveness of Solar projects through the calculation of the Payback period and Levelized Cost of Electricity for Solar PV projects in different parts of India. Comparison of these techno-economic factors will aid policymakers and project developers in decision-making and deploying their resources to achieve maximum financial output. The results reveal that higher module efficiency and greater solar irradiation significantly bring down the cost of Solar projects, bringing it even below the global average LCOE of solar PV of 49$/MWh for higher efficiencies and reducing the payback period to as low as shorter than 5 years. Therefore, this paper aims to provide valuable insights to the policymakers and investors working in the solar energy sector in India to help build a clean, secure, and resilient energy future for India

Fabrication and performance evaluation of polyethersulfone membranes with varying compositions of polyvinylpyrrolidone and polyethylene glycol for textile wastewater treatment using MBR

Various industries polluting the water bodies by discharging untreated wastewater directly into the environment and conventional wastewater treatments are often insufficient for effectively treating the pollutants. However, membrane bioreactors (MBRs) offer a promising solution for wastewater treatment where membrane serving as the heart of the system. In this study, polyethersulfone (PES) was used as the membrane material and hydrophilicity of the membranes were tuned up by mixing with hydrophilic additives such as polyethylene glycol (PEG) and polyvinylpyrrolidone (PVP) and the membranes have shown promising results in treating wastewater, particularly in terms of chemical oxygen demand (COD), biochemical oxygen demand (BOD), and color removal. For example, PES-PEG membrane demonstrated COD, BOD, and color removal of 96 %, 94 %, and 92 %, respectively while those were 95 %, 94 %, and 92 %, respectively for PES-based commercial membrane. Although the performances of fabricated membranes were comparable to that of commercial membrane in COD, BOD, and color removal efficiencies, there is room for improvement in permeate yields. Notably, the average permeate efficiency for MBR modules produced with PES-3PEG and PES-5PVP membranes was recorded as 47 % (18 L/m2h) and 13 % (5 L/m2h) respectively of the commercial membrane (38 L/m2h). Despite the variance in permeate yields, the fabricated membranes also showcased significant efficacy in removing microorganisms, a crucial aspect of wastewater treatment. Their performance in this regard proved highly comparable to that of the commercial membrane, emphasizing the potential of these fabricated membranes in enhancing the wastewater treatment.

Experimental analysis of dust's impact on solar photovoltaic system efficiency in arid environments: a case study in Southern Algeria.

The purpose of this study is to explore the effects of accumulated dust and weather conditions on the energy generated by solar photovoltaic panels in Ouargla, Algeria, between May 3 and August 3, 2023. For this experiment, two monocrystalline panels with a power output of 390 W manufactured by Zergoune Green Energy Company, as well as data-logging equipment, were used. The first panel was perfectly cleaned before starting every test and the second panel remained uncleaned. On day 90, the cleaned panel maintained an average power of 193 W, while the dusty panel exhibited a lower average power of 139 W. The greatest average reduction in efficiency, approximately 36.32%, occurred after 3months of exposure to weather conditions. The scanning electron microscope (SEM) analysis demonstrates the existence of microscopic dust particles which prevent part of solar radiation away instead of being absorbed by the photovoltaic cells, leading to a drop in the efficiency of the PV module. The primary chemical elements found in dust are oxygen, silicon, aluminum, and magnesium.