Components Of System Research Articles

Optical system characterization in Fourier ptychographic microscopy

Fourier ptychographic microscopy (FPM) is a recent technique to overcome the diffraction limit of a low numerical aperture (NA) objective lens by algorithmic post-processing of several low-resolution images. It can increase the space-bandwidth product of an optical system by computationally combining images captured under different illumination conditions. Vignetting determines the spatial extent of the bright and dark regions in the captured images. State-of-the-art analyses treat vignetting as a nuisance that needs to be reduced or excluded from algorithmic consideration using ad hoc decision rules. In contrast, this work investigates vignetting effects as a tool to infer a range of properties of the optical system. Generally, the goal of the FPM reconstruction algorithm is to achieve results that closely resemble the actual specimen at the highest resolution possible. However, as the optimization process does not necessarily guarantee a unique solution, we identify system properties that support alignment between computational predictions and empirical observations, potentially leading to a more accurate and reliable analysis. To achieve this, we characterize the individual system components of the experimental setup and compare experimental data to both, geometrical and wave optical simulations. We demonstrate that using vignetting as an analytical tool enables the modeling of the geometric and coherence properties of the optical system as evidenced by the good agreement between our simulation and experiment.

DREAM: Debugging and Repairing AutoML Pipelines

Deep Learning models have become an integrated component of modern software systems. In response to the challenge of model design, researchers proposed Automated Machine Learning (AutoML) systems, which automatically search for model architecture and hyperparameters for a given task. Like other software systems, existing AutoML systems have shortcomings in their design. We identify two common and severe shortcomings in AutoML, performance issue (i.e., searching for the desired model takes an unreasonably long time) and ineffective search issue (i.e., AutoML systems are not able to find an accurate enough model). After analyzing the workflow of AutoML, we observe that existing AutoML systems overlook potential opportunities in search space, search method, and search feedback, which results in performance and ineffective search issues. Based on our analysis, we design and implement DREAM, an automatic and general-purpose tool to alleviate and repair the shortcomings of AutoML pipelines and conduct effective model searches for diverse tasks. It monitors the process of AutoML to collect detailed feedback and automatically repairs shortcomings by expanding search space and leveraging a feedback-driven search strategy. Our evaluation results show that DREAM can be applied on two state-of-the-art AutoML pipelines and effectively and efficiently repair their shortcomings.

Congruent Glass Composite Scintillator for Efficient High-Energy Ray Detection.

The scintillator is the most crucial component in the high-energy ray detection system. The available scintillators suffer from the insurmountable drawbacks including poor shaping ability for single crystal and ceramic and low efficiency for glass. Here, a glass composite scintillator is proposed from a congruent crystallization system which possesses both excellent processability and high scintillating yield. It can be fabricated into diverse shapes and sizes including the bulk and tiny fiber. Benefitting from the unique compositing combination with a high crystallization ratio, the glass composite exhibits a giant scintillating light yield of ≈26,000 photons per MeV. The practical application for X-ray imaging is demonstrated and a high spatial resolution of 12 lpmm-1 is achieved. Furthermore, the fiber derived detector is built and the remote and micro-area detection is realized. These findings not only represent a novel design concept for the development of glass composite but also suggest a great step for expanding the scope of scintillators.

Data management practice of health extension workers and associated factors in Central Gondar Zone, northwest Ethiopia

IntroductionGenerating quality data for decision-making at all levels of a health system is a global imperative. The assessment of the Ethiopian National Health Information System revealed that health information system resources, data management, dissemination, and their use were rated as “not adequate” among the six major components of the health system. Health extension workers are the frontline health workforce where baseline health data are generated in the Ethiopian health system. However, the data collected, compiled, and reported by health extension workers are unreliable and of low quality. Despite huge problems in data management practices, there is a lack of sound evidence on how to overcome these health data management challenges, particularly among health extension workers. Thus, this study aimed to assess data management practices and their associated factors among health extension workers in the Central Gondar Zone.MethodAn institution-based cross-sectional study was conducted among 383 health extension workers. A simple random sampling method was used to select districts, all health extension workers were surveyed in the selected districts, and a structured self-administered questionnaire was used for data collection. The data was entered using EpiData version 4.6 and analyzed using STATA, version 16. Bivariable and multivariable binary logistic regression analyses were executed. An odds ratio with a 95% confidence interval and a p-value of <0.05 was calculated to determine the strength of the association and to evaluate statistical significance, respectively.ResultsOf the 383 health extension workers enrolled, all responded to the questionnaire with a response rate of 100%. Furthermore, 54.7% of the respondents had good data management practices. In the multivariable logistic regression analysis, being a married woman, having good data management knowledge, having a good attitude toward data management, having 1–5 years of working experience, and having a salary ranging from 5,358 to 8,013 Ethiopian Birr were the factors significantly associated with good data management practices among health extension workers. The overall data management practice was poor with only five health extension workers out of ten having good data management practices.

BSN: The First Photometric Analysis of Contact Binary Systems V1961 Cyg and V0890 Lyr

Abstract We presented the first photometric analysis of the V1961 Cyg and V0890 Lyr binary systems. We observed and analyzed these systems at an observatory in France as part of the Binary Systems of South and North (BSN) Project. We extracted and collected the times of minima from the observations and literature and presented a new ephemeris for each system. Due to the few observations about these systems over the years, both O-C diagrams were fitted linearly. The PHysics Of Eclipsing BinariEs (PHOEBE) Python code and the Markov Chain Monte Carlo (MCMC) method were used to light curve solutions. The light curve solution required a cold starspot on the hotter component in the V1961 Cyg binary system. We compared and have close agreements between our mass ratios' results from the light curve analysis processes and a new method based on the light curve derivative. We estimated the absolute parameters using an empirical relationship between the semi-major axis and orbital period for contact binary systems. The results show that V1961 Cyg and V0890 Lyr are W-type contact binary systems. We displayed stars and systems' positions in the $M-L$, $M-R$, and $logM_{tot}-logJ_0$ diagrams. We also presented a new relationship between mass ratio and luminosity ratio.

Ensuring the performance of external communications: An informal classification of target groups

The increasing influence of the investment community on business sustainability highlights the need for a compre hensive understanding and accurate segmentation of this audience. The research aims to identify consistent criteria for intra group differentiation in the perception of financial reporting information among members of various stakeholder groups in or der to improve the performance of external communications management. Methodologically, the study rests upon stakeholder theory, which is supported by expert assessments, structural-logical analysis, and multidimensional discriminant analysis. The study draws on authentic data of a survey on the quality of corporate reporting information conducted in the fourth quarter of 2020 among 52 representatives of the Russian business. The research identifies motivationally-rooted statistically significant discrepancies in the probability of correlating some survey participants with their functional group formed on a formal basis. We discuss the relevant criteria for an informal division of respondents into groups based on subjective judgments about qualitative characteristics of corporate financial reports. These criteria include variations in estimates of complexity of reporting standards and functionality of cost indicators based on the concept of fair market value. The revealed criteria for motivational and func tional identification within stable subgroups of financial statement users formed within functional groups represents the scien tific novelty of this study. The results contribute to a deeper understanding of the relevant criteria for the group structure of the investment community, which constitutes the target audience for corporate reporting. These findings can be utilized to refine the formal classification of users of reporting information, as well as to adjust other components of corporate management systems with the aim of enhancing the effectiveness of external communications.

Advancements in Nanowire-Based Devices for Neuromorphic Computing: A Review.

Neuromorphic computing, inspired by the highly interconnected and energy-efficient way the human brain processes information, has emerged as a promising technology for post-Moore's law era. This emerging technology can emulate the structures and the functions of the human brain and is expected to overcome the fundamental limitation of the current von Neumann computing architecture. Neuromorphic devices stand out as the key components of future electronic systems, exhibiting potential in shaping the landscape of neuromorphic computing. Especially, nanowire (NW)-based neuromorphic devices, with their advantages of high integration, high-speed computing, and low power consumption, have recently emerged as candidates for neuromorphic computing technology. Here, a critical overview of the current development and relevant research in the field of NW-based neuromorphic devices are provided. Neuromorphic devices based on different NW materials are comprehensively discussed, including Ag NW-based, organic NW-based, metal oxide NW-based, and semiconductor NW-based devices. Finally, as a foresight perspective, the potentials and the challenges of these NW-based neuromorphic devices for use as future brain-like electronics are discussed.

Systematizing cellular complexity: A Hilbertian approach to biological problems

Examining individual components of cellular systems has been successful in uncovering molecular reactions and interactions. However, the challenge lies in integrating these components into a comprehensive system-scale map. This difficulty arises due to factors such as missing links (unknown variables), overlooked nonlinearities in high-dimensional parameter space, downplayed natural noisiness and stochasticity, and a lack of focus on causal influence and temporal dynamics. Composite static and phenomenological descriptions, while appearing complicated, lack the essence of what makes the biological systems truly “complex.” The formalization of system-level problems is therefore important in constructing a meta-theory of biology. Addressing fundamental aspects of cellular regulation, adaptability, and noise management is vital for understanding the robustness and functionality of biological systems. These aspects encapsulate the challenges that cells face in maintaining stability, responding to environmental changes, and harnessing noise for functionality. This work examines these key problems that cells must solve, serving as a template for such formalization and as a step towards the axiomatization of biological investigations. Through a detailed exploration of cellular mechanisms, particularly homeostatic configuration, ion channels and harnessing noise, this paper aims to illustrate complex concepts and theories in a tangible context, providing a bridge between abstract theoretical frameworks and concrete biological phenomena.

Identifying Performance Issues in Cloud Service Systems Based on Relational-Temporal Features

Cloud systems, typically comprised of various components ( e.g. , microservices), are susceptible to performance issues, which may cause service-level agreement violations and financial losses. Identifying performance issues is thus of paramount importance for cloud vendors. In current practice, crucial metrics, i.e. , key performance indicators (KPIs), are monitored periodically to provide insight into the operational status of components. Identifying performance issues is often formulated as an anomaly detection problem, which is tackled by analyzing each metric independently. However, this approach overlooks the complex dependencies existing among cloud components. Some graph neural network-based methods take both temporal and relational information into account, however, the correlation violations in the metrics that serve as indicators of underlying performance issues are difficult for them to identify. Furthermore, a large volume of components in a cloud system results in a vast array of noisy metrics. This complexity renders it impractical for engineers to fully comprehend the correlations, making it challenging to identify performance issues accurately. To address these limitations, we propose Identifying Performance Issues based on Relational-Temporal Features (ISOLATE ), a learning-based approach that leverages both the relational and temporal features of metrics to identify performance issues. In particular, it adopts a graph neural network with attention to characterizing the relations among metrics and extracts long-term and multi-scale temporal patterns using a GRU and a convolution network, respectively. The learned graph attention weights can be further used to localize the correlation-violated metrics. Moreover, to relieve the impact of noisy data, ISOLATE utilizes a positive unlabeled learning strategy that tags pseudo labels based on a small portion of confirmed negative examples. Extensive evaluation on both public and industrial datasets shows that ISOLATE outperforms all baseline models with 0.945 F1-score and 0.920 Hit rate@3. The ablation study also proves the effectiveness of the relational-temporal features and the PU-learning strategy. Furthermore, we share the success stories of leveraging ISOLATE to identify performance issues in Huawei Cloud, which demonstrates its superiority in practice.

The role of Pharmaceutical Marketing in Prescribing Decisions in a Developing Country: A Comprehensive Study Exploiting Theory of Planned Behaviour

Background: Prescription medicines have become a major component of health systems in both developed and developing countries contributing to, may be, more than half of the total health care expenditure in the developing countries. Although literature directly attributing rise in medicines expenditure to prescribers’ decision, but still only a few researches had employed studies with sound theoretical foundations to comprehensively examine prescribing decision behaviours. Our objective was to investigate potentially modifiable factors, which may influence prescribing decisions of physicians employing theory of planned behaviour. Method: A quantitative survey approach was used to investigate the proposed hypotheses. Population was physicians in outpatient clinics in both private and governmental hospitals in Sana’a, capital city of Yemen. A structured questionnaire was designed to measure the influence of drug-promoting marketing techniques on physicians’ prescribing decisions. Data was collected through an anonymous survey questionnaire. The hypotheses were tested by multiple regressions analysis. Ethical approval was obtained from the Ethical Committee of the Ministry of Public Health and Population (MoPHP), and participants provided written consent before enrolling in the study. Result: Physicians admitted the presence of promotional influence on their prescribing decision. The study revealed that receiving low-value promotional items, belief of physicians that it is appropriate to accept both low- and high-value promotional items, physicians’ exposure to company-direct sources of information, and drug promotion-related factor all are positively and significantly related to physicians’ prescribing decisions (? = 0.438, p <0.001; ? = 0.089, p =0.027; ? = 0.157, p <0.001; ? = 0.093, p =0.032; and ?= 0.118, p = 0.007 respectively). Conclusion: This research clarified the role played by pharmaceutical companies to influence prescribing decisions of physicians. Therefore, could provide theoretical framework for policymakers in Yemen, and other countries with similar conditions, to develop a suitable policy and strategy in terms of drug promotion.

Automated control complex of agrotechnology in precision farming

The paper presents a project of an automated complex for managing agricultural technology. The complex consists of two inseparable components, a control unit and an executive robotic technological machine. The control unit implements the modern theory of controlling a complex dynamic system, which is an agricultural field with sowing crops. The main distinctive feature of the proposed management theory is that the object of management is an agrocenoses, which includes sowing crops and weeds. This feature is transferred to the executive technological machine, through which the simultaneous application of mineral fertilizers and herbicide treatment is carried out. At the same time, the formation of optimal technological operations is carried out on the basis of Earth remote sensing data. Based on these data, the parameters of the state of the agrocenoses are assessed, and the resulting estimates are system-wide feedback through which agricultural technology is managed. The presented complex is of interest to specialists developing individual components of modern precision farming systems.

Synthesis of Benzoic Acids from Electrochemically Reduced CO2 Using Heterogeneous Catalysts.

A method for the synthesis of benzoic acids from aryl iodides using two of the most abundant and sustainable feedstocks, carbon dioxide (CO2) and water, is disclosed. Central to this method is an effective and selective electrochemical reduction of CO2 (eCO2RR) to CO, which mitigates unwanted dehalogenation reactions occurring when H2 is produced via the hydrogen evolution reaction (HER). In a 3-compartment set-up, CO2 was reduced to CO electrochemically by using a surface-modified silver electrode in aqueous electrolyte. The ex-situ generated CO further underwent hydroxycarbonylation of aryl iodides by MOF-supported palladium catalyst in excellent yields at room temperature. The method avoids the direct handling of hazardous CO gas and gives a wide range of benzoic acid derivatives. Both components of the tandem system can be recycled for several consecutive runs while keeping a high catalytic activity.

Rolling Bearing Fault Diagnosis Model Based on External Attention Integrated Convolutional Neural Network under Imbalanced Data Conditions

Abstract Rolling bearings are essential components in numerous mechanical systems, and their failure can result in considerable downtime and expensive repairs. Therefore, accurate and timely fault diagnosis is vital for effective predictive maintenance and overall reliability. Traditional diagnostic methods often struggle with complex and non-stationary signals, compounded by issues of data imbalance in 
 realworld scenarios. A method for diagnosing rolling bearing faults has been developed in this paper utilizing External Attention (EA), Convolutional Neural Networks (CNN), and Continuous Wavelet Transform (CWT), specifically addressing the challenge of imbalanced sample data. This approach offers significant advantages, including a reduction in complexity by eliminating the need for data augmentation and leveraging external attention for enhanced feature extraction from samples. Compared to other attention mechanisms, this method demonstrates outstanding performance on both training and testing sets with imbalanced samples, exhibiting minimal overfitting tendencies. The proposed CWT-EACNN method effectively addresses the challenge of imbalanced sample data in rolling bearing fault diagnosis, demonstrating exceptional performance and reduced complexity.

Control of the Motion of an Inverted Spherical Pendulum on a Moving Base. Hybrid Impact Approach

A new hybrid method for the construction of control actions of a linear control system with constant coefficients is considered in this paper. It is assumed in this paper that a part of the discussed system meets some conditions. Some states of the main system are considered to be control actions for a subsystem for which and LQR stabilizer is acquired. Then, those control actions of the subsystem are used to construct the control actions for the main system. In the problem of controlling the motion of a complex linear system of an inverted spherical pendulum on a moving base, a new approach to the construction of control actions (hybrid action method) was used. It is assumed that a component of the complex system under discussion satisfies certain conditions. The inertial forces at the center of mass of the base of the composite system are considered to be the controlling influences on the inversion of the pendulum, for which the LQR stabilizer was purchased. The determined internal control actions on the inverted pendulum are then used to construct external control actions on the base of the composite system. In the end, a numerical analysis was carried out.

Milk contamination in Europe under anticipated climate change scenarios

Transforming the food system while addressing climate change requires proactive measures based on quantitative projections of anticipated future conditions. A key component of the food system that must be considered during this transformation is food safety, which is the focus of this paper. Milk safety has been selected as a case study. Future milk contamination levels in Europe, in terms of total bacterial counts, are evaluated under various climate change scenarios. Projections from multiple climate models are integrated into a data-driven milk contamination model, validated using data from Malta, Spain, and Belgium. The modeling framework accounts for variability among dairy farms and the inherent uncertainties in climate projections. Results are presented through geographical heatmaps, highlighting coastal and southern areas such as Portugal, Western Spain, Southern Italy, and Western France as regions expected to face the highest bacterial counts. The analysis underlines the significant roles of humidity and wind speed, alongside temperature. It also examines compliance with the regulatory threshold for raw milk, revealing an increased frequency of summer weeks exceeding the threshold of 100,000 colony-forming units. Based on this analysis, regions are classified into low-risk, high-risk, and emerging-risk categories. This classification can guide the selection of farm strategies aimed at meeting future food safety standards. By informing these decisions with the anticipated impacts of climate change, the food system can be future-proofed.

Light curve modeling of the eclipsing binary systems V0876 Lyr, V3660 Oph, and V0988 Mon

Abstract Photometric study and light curve modeling for the systems V0876 Lyr, V3660 Oph, and V0988 Mon were carried out by means of their first charge-coupled device observations and the Wilson–Devinney code based on model atmospheres by Kurucz (1993. In: Milone, E. (Ed.), Light curve modeling of eclipsing binary stars. New York: Springer-Verlag. p. 93). The accepted models reveal physical parameters that show that all the primary components of the studied systems are more massive and hotter than the secondary ones. The systems V0876 Lyr and V0988 Mon were classified as over contact, with mass ratios of 0.8940 and 0.3904, respectively, while the system V366Oph was classified as detached, with a mass ratio of 0.1181. The evolutionary state of the system components was investigated, and their spectral types were adopted. Both components of the system V0876 Lyr and secondary components of the systems V3660 Oph and V0988 Mon show lower radii and luminosities than expected for Zero Age Main Sequence (ZAMS). Secondary components of the system, V3660 Oph and V0988 Mon, deviated from ZAMS and the Terminal-age Main Sequence (TAMS) with a higher radius and luminosity than expected for ZAMS and TAMS.

Monetary work-incentives within the Austrian tax and benefit system

AbstractThis paper analyses incentives to take up work or to increase working hours within the Austrian tax and benefit system. We analyze the monetary work incentives for a variety of family constellations (singles, single parents, families with children) with different incomes from dependent employment, when receiving unemployment benefits, and in the system of means-tested minimum income. Moreover, the effect of different earning ceilings (childcare and unemployment) and childcare costs is additionally investigated. Insufficient and, therefore, privately provided childcare is viewed as a missing component of the benefit system. The Austrian tax and benefit system is designed to be incentive-compatible for singles. Only marginal employment without deductions in the event of unemployment creates a negative incentive to expand employment beyond this extent. However, raising children creates negative monetary incentives. On the one hand, through the upper limits on additional earnings during times of childcare allowance, but especially when childcare costs arise.

Mapping Soil Surface Moisture of an Agrophytocenosis via a Neural Network Based on Synchronized Radar and Multispectral Optoelectronic Data of SENTINEL-1,2—Case Study on Test Sites in the Lower Volga Region

In this article, the authors developed a novel method for the moisture mapping of the soil surface of agrophytocenosis using a neural network based on synchronized radar and multispectral optoelectronic data from Sentinel-1,2. The significance of this research lies in its potential to enhance precision farming practices, which are increasingly vital in addressing global agricultural challenges such as water scarcity and the need for sustainable resource management. To verify the developed method, data from two experimental plots were utilized. These plots were located on irrigated soybean crops, with the first plot situated on the right bank (plot No. 1) and the second on the left bank (plot No. 2) of the lower Volga River. Two experimental soil moisture geodatasets were created through measurements and geo-referencing points using the gravimetric method (for plot No. 1) and the proximal sensing method (for plot No. 2) employing the Soil Moisture Sensor ML3-KIT (THETAKIT, Delta). The soil moisture retrieval algorithm was based on the use of a neural network to predict the reflection coefficient of an electro-magnetic wave from the soil surface, followed by inversion into soil moisture using a dielectric model that takes into account the soil texture. The input parameter of the neural network was the ratio of the microwave radar vegetation index (calculated based on Sentinel-1 data) to the index (calculated based on the data of multispectral optoelectronic channels 8 and 11 of Sentinel-2). The retrieved soil moisture values were compared with in situ measurements, showing a determination coefficient of 0.44–0.65 and a standard deviation of 2.4–4.2% for plot No. 1 and similar metrics for plot No. 2. The conducted research laid the groundwork for developing a new technology for remote sensing of soil moisture content in agrophytocenosis, serving as a crucial component of precision farming systems and agroecology. The integration of this technology promotes sustainable agricultural practices by minimizing water consumption while maximizing crop productivity. This aligns with broader environmental goals of conserving natural resources and reducing agricultural runoff. On a larger scale, data derived from such studies can inform policy decisions related to water resource management, guiding regulations that promote efficient water use in agriculture.

Are asymmetric designs of tibial components superior to their symmetric counterparts for constrained condylar total knee arthroplasty using metal block augmentation?

PurposeIn total knee arthroplasty (TKA), asymmetric tibial components have been developed alongside symmetric tibial components to enhance bony coverage at the tibia. In primary TKA and revision TKA for patients with significant bone defects, augmentation is employed to fill the bone defect. However, there have been no reports on bony coverage of the tibial component of the revision system in the cases of bone defects. Therefore, we simulated bone defects using CT and compared the bony coverage of asymmetric and symmetric tibial components in the revision TKA system.MethodsThis study included 45 patients (50 knees involved) with medial osteoarthritis. Preoperative CT scans were used to simulate placement using ZedKnee. Three models were evaluated: Persona Revision PCCK (Zimmer) for the asymmetric component, NexGen LCCK (Zimmer) for the symmetric component, and the ATTUNE revision system (Depuy-Synthes). A 130-mm stem extension was utilized. Augmentations of each thickness were placed to simulate bone defects of 5, 10, and 15 mm. The coverage, overhang, and underhang rates were measured for each slice and compared among the models.ResultsIn terms of coverage, the rate was greater for PCCK at 0 mm, and only ATTUNE exhibited a significantly lower coverage at 5 and 10 mm. There was no significant difference in coverage at 15 mm. At 0 mm, PCCK demonstrated less posterior underhangs. At 5 and 10 mm, PCCK showed less anterior overhang but more anterior underhang. At 15 mm, PCCK had a less anterior overhang, with an overhang in the posterior region but less underhang. When overhang and underhang were combined and compared, the asymmetric component generally yielded superior results.ConclusionIn the cases of bone defects, asymmetric components demonstrated reduced anterior overhang and decreased posterior underhang, resulting in greater bone coverage. This may contribute to improved long-term outcomes in the revision TKA system.

A novel acid-free combined technology to achieve the full recovery of crystalline silicon photovoltaic waste

To achieve the global carbon neutrality commitment, photovoltaics, a clean and renewable source of energy, is increasingly deployed. Photovoltaic panels are core components of photovoltaic systems. As these panels reach the end of their life, recovering the photovoltaic waste becomes crucial. Currently, strong acid reagents are commonly used in the recovery of silver from crystalline silicon photovoltaic waste, posing environmental risks and restricting the industrialization of their recycling. In this study, a novel acid-free technology to achieve the full recovery of crystalline silicon photovoltaic waste was proposed. A pyrolysis process was first conducted for decapsulation, with carbon dioxide being the main gas component at 60.64 %. Next, bioleaching technology was employed to leach silver from waste crystalline silicon photovoltaic cells. The silver leaching rate in a single leaching cycle reached 44.7 %. Meanwhile, the mechanism of silver leaching was further analyzed. Finally, high-velocity fluid frictional separation, a technique that uses high-speed fluid flow to separate material, was chosen to obtain silicon wafers. The reclaimed silicon samples had a total thickness variation of 6.64 µm to11.62 µm, with average carrier lifetimes exceeding 4.9 µs, higher than that obtained by wet etching. This study is expected to advance the industrialization of the recovery of photovoltaic waste, which is also beneficial for the sustainable development of the photovoltaic supply chain.