Center Operators Research Articles

Diagnostics as a Key Tool in Providing Organizational and Scientific-Methodical Conditions for the Creation of Stem Centers

The article reveals the importance of diagnostics in the context of the creation of STEM centers, which are an integral part of the introduction of STEM education into the educational process. The concept of "diagnostics" in the article is considered as a process of analysis and assessment of existing organizational and scientific-methodical conditions for the creation of STEM centers. The article adds scientific knowledge about the STEM center, highlights the purpose of its activity and directions of work. Some key criteria have been identified by which the state of providing organizational conditions for the creation of STEM centers can be determined, namely: strategic planning, financial support, infrastructure, human resources, partnerships, marketing and public relations, organizational culture and management efficiency. It is proposed to evaluate the scientific and methodological conditions for the creation of STEM centers according to such key criteria as: development of STEM courses and curricula, educational materials and resources, methodical support for teachers, evaluation and analysis of learning results, research work, support of scientific consultants and experts, development cooperation networks. It was found that the diagnosis allows to assess the availability of the necessary premises, equipment, personnel support and infrastructure for the creation of STEM centers. Various methods of diagnosis make it possible to assess the availability of relevant training programs, methodological materials and other components necessary for the effective operation of the STEM center. It has been determined that such diagnostic methods as questionnaires, observations, interviews and document analysis can help in providing specific recommendations on how to improve the organizational and scientific-methodical conditions for the successful creation of a STEM center. Examples of successful innovative educational projects and experiments at the all-Ukrainian level in the field of STEM, which were implemented and are being implemented now, taking into account the results of diagnostics that helped them achieve success, are given.

Resource demands in telco data centers

Telecommunication (telco) cloud services have emerged as crucial components in the modern digital landscape, offering extensive capabilities for data management, connectivity, and service provision. However, research on telco clouds lacks comprehensive data on the characteristics of production workloads, which is fundamental for designing effective resource management systems, such as workload schedulers and power management mechanisms. To this end, this paper addresses a substantial gap in telco cloud research by creating a comprehensive dataset that encapsulates crucial information regarding the pattern demands of applications within telco data centers. In addition, the proposed dataset contributes to the field by enabling strategic network configuration, optimizing data center sizing, facilitating proactive decision-making for data center operations, but its applicability extends beyond these cases. These examples illustrate the practical value of the dataset in enhancing efficiency, reducing operational costs, and ensuring optimal performance within telecommunication data centers.

Decentralized energy market integrating carbon allowance trade and uncertainty balance in energy communities

With sustained attention to carbon neutrality, the personal carbon trading (PCT) scheme has been embraced as an auspicious paradigm for scaling down carbon emissions. To facilitate the simultaneous clearance of energy and carbon allowance inside the energy community while hedging against uncertainty, a joint trading framework is proposed in this article. The energy trading is implemented in a peer-to-peer (P2P) manner without the intervention of a central operator, and the uncertainty trading is materialized through procuring reserve of conventional generators and flexibility of users. Under the PCT scheme, carbon allowance is transacted via a sharing mechanism. Possible excessive carbon emissions due to uncertainty balance are tackled by obliging renewable agents to procure sufficient carbon allowances, following the consumption responsibility principle. A two-stage iterative method consisting of tightening McCormick envelope and alternating direction method of multipliers (ADMM) is devised to transform the model into a mixed-integer second-order cone program (MISOCP) and to allow for a fully decentralized market-clearing procedure. Numerical results have validated the effectiveness of the proposed market model.

Discovery of Cloud Applications from Logs

Continuous discovery and update of applications or their boundaries running in cloud environments in an automatic way is a highly required function of modern data center operation solutions. Prior attempts to address this problem within various products or projects were/are applying rule-driven approaches or machine learning techniques on specific types of data–network traffic as well as property/configuration data of infrastructure objects, which all have their drawbacks in effectively identifying roles of those resources. The current proposal (ADLog) leverages log data of sources, which contain incomparably richer contextual information, and demonstrates a reliable way of discriminating application objects. Specifically, using native constructs of VMware Aria Operations for Logs in terms of event types and their distributions, we group those entities, which then can be potentially enriched with indicative tags automatically and recommended for further management tasks and policies. Our methods differentiate not only diverse kinds of applications, but also their specific deployments, thus providing hierarchical representation of the applications in time and topology. For several applications under Aria Ops management in our experimental test bed, we discover those in terms of similarity behavior of their components with a high accuracy. The validation of the proposal paves the path for an AI-driven solution in cloud management scenarios.

Discrete Approximations of Gaussian Smoothing and Gaussian Derivatives

This paper develops an in-depth treatment concerning the problem of approximating the Gaussian smoothing and the Gaussian derivative computations in scale-space theory for application on discrete data. With close connections to previous axiomatic treatments of continuous and discrete scale-space theory, we consider three main ways of discretizing these scale-space operations in terms of explicit discrete convolutions, based on either (i) sampling the Gaussian kernels and the Gaussian derivative kernels, (ii) locally integrating the Gaussian kernels and the Gaussian derivative kernels over each pixel support region, to aim at suppressing some of the severe artefacts of sampled Gaussian kernels and sampled Gaussian derivatives at very fine scales, or (iii) basing the scale-space analysis on the discrete analogue of the Gaussian kernel, and then computing derivative approximations by applying small-support central difference operators to the spatially smoothed image data.We study the properties of these three main discretization methods both theoretically and experimentally and characterize their performance by quantitative measures, including the results they give rise to with respect to the task of scale selection, investigated for four different use cases, and with emphasis on the behaviour at fine scales. The results show that the sampled Gaussian kernels and the sampled Gaussian derivatives as well as the integrated Gaussian kernels and the integrated Gaussian derivatives perform very poorly at very fine scales. At very fine scales, the discrete analogue of the Gaussian kernel with its corresponding discrete derivative approximations performs substantially better. The sampled Gaussian kernel and the sampled Gaussian derivatives do, on the other hand, lead to numerically very good approximations of the corresponding continuous results, when the scale parameter is sufficiently large, in most of the experiments presented in the paper, when the scale parameter is greater than a value of about 1, in units of the grid spacing. Below a standard deviation of about 0.75, the derivative estimates obtained from convolutions with the sampled Gaussian derivative kernels are, however, not numerically accurate or consistent, while the results obtained from the discrete analogue of the Gaussian kernel, with its associated central difference operators applied to the spatially smoothed image data, are then a much better choice.

A Wideband Circularly Polarized Dipole Antenna with Compact Size and Low-Pass Filtering Response.

This paper presents a compact wideband circularly polarized cross-dipole antenna with a low-pass filter response. It consists of two pairs of folded cross-dipole arms printed separately on both sides of the top substrate, and the two dipole arms on the same surface are connected by an annular phase-shifting delay line to generate circular polarization. A bent metal square ring and four small metal square rings around the cross-dipoles are employed to introduce new resonant frequencies, effectively extending the impedance and axial-ratio bandwidth. Four square patches printed on the middle substrate are connected to the ground plane by the vertical metal plates in order to reduce the antenna height. Thus, a compact wideband circularly polarized antenna is realized. In addition, a transmission zero can be introduced at the upper frequency stopband by the bent metal square rings, without using extra filter circuits. For verification, the proposed model is implemented and tested. The overall size of the model is 90mm×90mm×33mm (0.37λ0×0.37λ0×0.14λ0; λ0 denotes the center operating frequency). The measured impedance bandwidth and 3 dB axial-ratio (AR) bandwidth are 53.3% and 41%, respectively. An upper-band radiation suppression level greater than 15 dB is realized, indicating a good low-pass filter response.

HEAL: Performance Troubleshooting Deep inside Data Center Hosts

This study demonstrates the salient facts and challenges of host failure operations in hyperscale data centers. A host incident can involve hundreds of distinct host-level metrics. The faulting mechanism inside the host connects these heterogeneous metrics through direct and indirect correlation, making it extremely difficult to sort out the propagation procedures and the root cause from these intertwined indicators. To deeply understand the failure mechanism inside the host, we develop HEAL -- a novel host metrics analysis toolkit. HEAL discovers dynamic causality in sparse heterogeneous host metrics by combining the strengths of both time series and random variable analysis. It also extracts causal directional hints from causality's asymmetry and historical knowledge, which finally help HEAL produce accurate results given undesirable inputs. Evaluations in our production environment verify that HEAL provides significantly better result accuracy and full-process interpretability than the SOTA baselines. With these advantages, HEAL successfully serves our data center and worldwide product operations and impressively contributes to many other workflows.

Bridge effect on charge transfer and energy transfer in fullerene-chromophore dyads.

Fullerene-chromophore dyads have attracted a great deal of research interest because these complexes can be potentially designed as nanoscale artificial photosynthetic centers, in which the chromophore and fullerene function as the electron donor and acceptor, respectively. The basic operation of this dyad-type artificial reaction center is photoinduced electron transfer from the donor to the acceptor. The fullerene and chromophore are usually covalently linked so that sufficient electronic coupling between these two moieties can facilitate the electron transfer. However, other deactivation pathways for the chromophore excited state, such as energy transfer to the fullerene, may reduce the quantum yield of the photoinduced electron transfer. Here, a series of C60-perylene dyads is exploited to interrogate the effect of the linkage on deactivation mechanisms of the chromophore excited state. For the C60-perylene dyads with a single or double bond bridge, we find that the decay of the singlet state of the chromophore is dominated by the electron transfer, and the corresponding time constant is determined to be 45ps. On the other hand, for the dyad with a triple bond bridge, the singlet state of the chromophore is quickly quenched through energy transfer to fullerene, and the time constant is as short as 7.9ps. Our finding suggests that the bond order of the bridge in the fullerene-chromophore dyads can be utilized to control the deactivation pathways of the excited state.

Effect of war on radiotherapy in Ukraine and how to help.

1513 Background: The full-scale Russian invasion of Ukraine on February 24th, 2022 brought the largest humanitarian disaster to the heart of Europe since the World War II. In this work, we describe the impact of the full-scale Russian invasion on the radiation therapy (RT) services in Ukraine and suggest how to help. Methods: We collected the information on operation of RT centers in Ukraine during the war. In addition, in 2023 an annual survey was conducted by Grigoriev Institute for Medical Radiology in Kharkiv for the IAEA DIRAC database. The survey was sent to 41 RT centers in Ukraine requesting information on equipment, staffing, and patient volume. Due to the war conditions the data provided in the survey is limited to the un-occupied territories. Results: After the initial Russian invasion of Ukraine in 2014 and occupation of Crimea and parts of Donbas, Ukraine lost access to 7 RT centers with 18 external beam radiotherapy (EBRT) machines (17% of total). Just before the full-scale invasion in 2022, there were a total of 44 Co-60 machines (57%) and 33 linear accelerators (43%) in the unoccupied territories. As a result of the full-scale invasion of Ukraine in 2022, almost all RT centers suspended operation in the first weeks of war. The most challenging period for RT in Ukraine was in March when Kyiv (where 20 EBRT machines are located) was partially surrounded by the occupiers. Based on the DIRAC survey, compared to 2021 data, in 2022 the number of radiation oncologists decreased on average by 2.9%, medical physicists by 6.7%, and therapists by 6.5% but there was a large variation in staffing levels depending on the region and its proximity to war. The workload in the RT centers close to the combat zone almost halved, while increasing by 10 - 60% in the western and central Ukraine. The total number of patients treated with EBRT decreased by 11% and brachytherapy by 21%. Brachytherapy services sustained the most interruption (47% decrease in LDR and 18% decrease in HDR patient volume) due to inability to perform source exchanges. Overall, approximately 5,500 less patients were treated in 2022 compared to 2021. Despite the ongoing war, Ukrainian cancer centers installed 8 new linear accelerators, with 5 linear accelerators pending installation. In addition, the Ministry of Heath of Ukraine has recently purchased 15 linear accelerators to replace the aging Co-60 machine fleet as the future source exchanges might not be possible. There is an acute need in helping Ukraine transition from Co-60 to modern IMRT/VMAT linear accelerators. Conclusions: Radiotherapy in Ukraine sustained significant damages since the 2014 Russian invasion, which was exacerbated further by the full-scale invasion in 2022. To support the vitality and absorptive capacity of RT services in Ukraine, the democratic world can help by donating equipment, hardware, software, and participating in training of Ukrainian RT professionals as they transition to modern and high-quality RT care for Ukrainian cancer patients.

Systematic solution for multi-model control approaches in nonlinear dynamic systems based on the s-gap metric

This paper proposes a systematic approach for optimizing the distribution of local models in multi-model control systems (MMCS) to enhance overall robustness. While existing literature discusses this method for linear parameter varying (LPV) and uncertain linear time-invariant (LTI) systems, significant limitations persist in addressing nonlinear dynamic systems. Robust control tools like the gap metric and generalized stability margin (GSM) have limited effectiveness in analyzing the robustness of nonlinear feedback systems. To address these challenges, novel concepts of the gap metric and GSM are introduced to determine central operating points (COPs) within local operating areas (LOAs) across the total operating area (TOA). These COPs guide the extraction of affine disturbance local models (ADLMs). Additionally, an optimization problem based on the s-gap metric and GSM is presented to optimize COPs placement and LOAs boundaries. Challenges such as non-monotonic behavior of the cost function and complexity arising from the s-gap metric formulation necessitate novel solution methods. To address these, constraints are applied to the cost function, and a novel discrete optimization approach is introduced. Finally, theoretical findings are applied to the Duffing system, pH neutralization process, and continuous stirred tank reactor (CSTR) plant to evaluate the proposed method's effectiveness. This comprehensive validation across different systems underscores the versatility and practical utility of the proposed approach.

Pilot study to evaluate automated laboratory data transfer into Medidata Rave for NCI-supported pediatric cooperative group trials in the Pediatric Early Phase Clinical Trials Network (PEP-CTN) and Pediatric Brain Tumor Consortium (PBTC).

e23017 Background: Electronic data capture (EDC) has the potential to improve data accuracy and efficiency. As an initial step toward improvement in the accuracy and completeness of data acquisition for clinical trials, PEP-CTN and PBTC conducted a pilot project to assess the feasibility of automated transfer of laboratory data from participating institutions’ electronic health records to the NCI Medidata Rave EDC that is housed within each consortium’s Uniform Resource Locator (URL). The pilot aimed to assess feasibility of automated laboratory data extraction and transfer and to evaluate discrepancies between laboratory data obtained retrospectively through automated extraction and data manually ascertained during the original conduct of the study. Methods: Data from patients treated on 7 completed PEP-CTN or PBTC trials at 7 sites were included. Each consortium created a Rave EDC study to receive data. Sites used the ExtractEHR R package or local methods to map and extract laboratory data for designated patients. Post-extraction data were uploaded via a file transfer protocol (FTP) periodically polled by the Rave Batch Uploader (BU) that uploaded data into Rave EDC. After successful upload, extracted data (EHR) were compared to laboratory data that had been manually reported when patients were enrolled on the trials (MANUAL) and discrepancies were identified. Discrepancy types were categorized and 2 sites reviewed discrepancies at their sites to identify reasons. Results: Preparation and support for automated extraction and upload required significant central operations and local site effort. All sites successfully extracted specified laboratory data and had at least one complete upload via BU to Rave EDC. There were more laboratory data in the EHR datasets than MANUAL datasets across most sites. These results were due to multiple EHR results on a given day or from additional days in the study window than specified in MANUAL. Sites verified that laboratory data in MANUAL and not EHR were stored in un-extractable formats (e.g. PDFs) or outside laboratories. Most discrepant results in both EHR and MANUAL were mismatched values or units. Conclusions: This pilot demonstrated the feasibility of implementing automated laboratory data extraction and transfer in the cooperative group setting. Automated data transfer requires significant central operational support as well as detailed protocols, data specifications, and case report forms and engagement of site technical teams to be successful, but also permits collection of more comprehensive data with reduced manual data entry. Clinical trial information: NCT05020951 .

Big data processing and analysis platform based on deep neural network model

Users are increasingly turning to big data processing systems to extract valuable information from massive datasets as the field of big data grows. Data analytics platforms are used by e-commerce enterprises to improve product suggestions and model business processes. In order to meet the needs of large-scale data center operation and maintenance management, Internet companies often use Flink to process log data. This paper takes the big data processing and analysis platforms built by Internet financial companies and large banks as examples, and implants a stock prediction model based on Deep Neural Network (DNN). In this context, this paper completes the following work: 1) The research status of big data processing and analysis platforms at home and abroad is introduced. 2) Drawing on the modular design idea, the commercial bank big data platform is designed and the functions of each sub-module are introduced. Then the basic principle and structure of Convolutional Neural Networks (CNN) are expounded. 3) The optimal parameters of Convolutional Neural Networks are selected through experiments, and then the trained model is used for experiments. It can be seen that the stock prediction model proposed in this article has a higher prediction accuracy compared to existing models, which also verifies the validity of the proposed model. Input the data and compare the obtained results with the actual results, and finally show that the model in this paper has a good performance on stock prediction.

BLOOD BANK MANAGEMENT STSTEM

The Blood Bank Management System (BBMS) is a comprehensive software solution designed to streamline and automate the operations of blood banks and transfusion centers. This system provides a centralized platform for managing blood inventory, donor information, blood testing, and distribution processes, thereby enhancing operational efficiency and ensuring timely access to safe blood products for patients.

Spatio-temporal data fusion techniques for modeling digital twin City

ABSTRACT The digital twin city technique maps the massive city environmental and social data on a three-dimensional virtual model. It presents the operational status of physical world and supports intelligent city governance. However, the inefficient utilization of distributed data resources, and the lack of sharing and collaboration among multiple departments restrict the data formulation of digital twin city construction. This research proposes a new cross-domain spatio-temporal data fusion framework for supporting complex urban governance. It integrates the heterogeneous urban information generated and stored by different government departments using multiple-information techniques. A specified geographic base reflecting the real city status is established, using geographical entities with unified address as identifiers to encapsulate the urban elements information. We introduce a comprehensive urban spatio-temporal data center construction process, which has already supported multiple urban governance projects. The two distinct advantages in using this data fusion system are: 1) The proposed Bert+PtrNet+ESIM-based address mapping method associates the urban elements information to their corresponding geographic entities with 99.3% F1-Score on real-world dataset. 2) The Wuhan spatio-temporal data center operation illustrates the capability of our framework for complex urban governance, which significantly improves the efficiency of urban management and services. This integrated system engineering provides reference and inspiration for further spatio-temporal data management, which contributes to the future social governance in digital twin city platform.

The Role of Virtual Libraries in the Contemporary World an Analytical Study

Virtual libraries play a crucial role in the development of libraries and information profession, as well as the advancement of working methods and service levels in cultural institutions such as museums and archives. These libraries are a global phenomenon resulting from the information and communications technology revolution, facilitated by the World Wide Web. Furthermore, virtual libraries are not just a cultural, educational, and scientific necessity, but also serve as a vital means of providing accurate and relevant information to users. This study aims to address the impact of artificial intelligence technology on the operations of libraries and documentation centers. Through examples of its implementation in libraries, study explore how these technologies can enhance the provision of information. Additionally, the study discusses the current state and challenges faced by virtual libraries, while elucidating their key features and user utilization.

Ni-Co Complex Ionic Synergistically Modifying Octahedron Lattice of Cordierite Ceramics for the Application of 5G Microwave-Millimeter-wave Antennas.

In this work, phase-pure Mg1.8(Ni1-xCox)0.2Al4Si5O18 (0 ≤ x ≤ 1) ceramics were synthesized by a high-temperature solid-state method. On the basis of Rietveld refinement data of X-ray powder diffraction and Phillips-Vechten-Levine theory, the atomic ionicity, lattice energy, and bond energy of the compound were calculated to explore their influence on the microwave dielectric properties of ceramics. The Mg1.8Ni0.1Co0.1Al4Si5O18 (x = 0.5) ceramic exhibited the best microwave dielectric properties: εr = 4.44, Qf = 73 539 GHz@13 GHz, and τf = -23.9 ppm/°C. (Ni1-xCox)2+ complex ionic doping, compared with only Ni2+ or Co2+, is beneficial for improving the symmetry of [Si4Al2O18] hexagonal rings and reducing distortion. Subsequently, 8 wt % TiO2 was added to Mg1.8Ni0.1Co0.1Al4Si5O18, resulting in a near-zero τf and high Qf values for the composite ceramic, with εr = 5.22, Qf = 58 449 GHz@13 GHz, and τf = -2.06 ppm/°C. Finally, a 5G millimeter-wave antenna with a central operating frequency of 25.52 GHz was designed and fabricated using the Mg1.8Ni0.1Co0.1Al4Si5O18-8 wt % TiO2 ceramics. Operating in the 24.7-26.0 GHz range, it demonstrated favorable radiation characteristics with a simulated efficiency of 85.2% and a gain of 4.58 dBi. The antenna's performance confirms the high potential of the cordierite composite for application in 5G communication systems.

Variational quantum circuit learning-enabled robust optimization for AI data center energy control and decarbonization

As the demand for artificial intelligence (AI) models and applications continues to grow, data centers that handle AI workloads are experiencing a rise in energy consumption and associated carbon footprint. This work proposes a variational quantum computing-based robust optimization (VQC-RO) framework for control and energy management in large-scale data centers to address the computational challenges and overcome limitations of conventional model-based and model-free strategies. The VQC-RO framework integrates variational quantum circuits (VQCs) with classical optimization to enable efficient and uncertainty-aware control of energy systems in AI data centers. Quantum algorithms executed on noisy intermediate-scale quantum (NISQ) devices are used for value function estimation trained with Q-learning, leading to the formulation of a robust optimization problem with uncertain coefficients. The quantum computing-based robust control strategy is designed to address uncertainties associated with weather conditions and renewable energy generation while optimizing energy consumption in AI data centers. This work also outlines the computational experiments conducted at various AI data center locations in the United States to analyze the reduction in power consumption and carbon emission levels associated with the proposed quantum computing-based robust control framework. This work contributes a novel approach to energy-efficient and sustainable data center operation, promising to reduce carbon emissions and energy consumption in large-scale data centers handling AI workloads by 9.8 % and 12.5 %, respectively.

Effects and optimization of airflow on the thermal environment in a data center

In this research, the escalating energy consumption challenges in data centers are addressed by optimizing airflow organization designs. Through the use of computational fluid dynamics (CFD) simulations, three different airflow strategies were evaluated and improved: underfloor precision air conditioning, inter-column air conditioning, and backplane air conditioning. These cooling systems, which are usually considered in isolation, were compared in a comprehensive manner to get a full picture of their efficiency and effectiveness. The findings reveal that the implementation of cold aisle containment (CAC) or hot aisle containment (HAC) significantly improves air supply efficiency (ASE) and reduces the supply heat index (SHI), leading to a more uniform temperature distribution and enhanced cooling performance. Specifically, the ASE increased from 65.69% to 85.57% and 90.25% for underfloor precision air conditioning and from 71.29% to 92.16% and 92.17% for inter-column air conditioning, with corresponding reductions in SHI. The backplane cooling system offered consistent ambient temperatures throughout the room, eliminating thermal hotspots without the need for aisle containment. This study offers a comparative analysis of different airflow organization schemes, highlighting the benefits of aisle containment in precision and inter-column air conditioning and the suitability of backplane air conditioning for high-density cooling without the need for traditional aisle separation. The results are crucial for informing energy-efficient cooling strategies in data center design and operation.

Estimating restaurants’ unconstrained demand: a systematic approach to reducing structural bias in forecast accuracy measures

研究目的技术从其他行业的传播以及厨房设备的创新推动了餐饮业内的结构变化。然而, 这种变化直接影响了评估需求预测准确性。本研究探讨了在餐饮业结构改变后，评估至关重要的需求预测准确性时所面临的令人独特和复杂性。研究方法本文自研了一个数学模型来描述和探讨评估需求预测准确性中的结构性偏差的本质。然后, 使用数值模拟构建一个市场示例, 以更好地了解上述偏差的特征。最后, 将这种预测准确性评估的系统性偏差与其他传统的餐饮业需求预测情境进行对比。研究发现本文概述了中央厨房运营中需求预测是动态的, 因此产生了结构性偏差的理论基础。更具体地说, 在使用中央厨房并集中订单的情境下, 本文发现需求预测直接设定了容量限制, 因此产生了在需求预测准确度衡量中的结构性偏差。依赖这样的预测准确性度量可能产生严重的负面商业结果。研究创新这项研究首次表明, 在中央厨房运营的独特的新环境中, 由于新的设定即每日菜品需求预测直接决定每日容量水平, 需求预测准确度衡量标准有着严重偏差, 长期来讲准确性可能下降, 从而导致次优的商业决策。本研究的主要理论贡献是提供一个餐饮企业在新运营环境中解释和描述需求预测准确度中结构性偏差的全新分析模型。

Operating Room Waste Generated From Corneal and Conjunctival Surgeries.

The purposes of this study were to bring awareness to the surgical waste generated from corneal and conjunctival surgeries and to compare those findings with the waste generated from cataract surgeries. This was an observational prospective pilot cohort study at a tertiary corneal/anterior segment private practice. All waste related to cataract, cornea, and conjunctival surgical procedures (including anesthesia waste and corneal tissue storage) was weighed. The primary outcome was total waste generated while other outcomes included surgical setting (ambulatory surgical center, hospital, and minor operating room) and comparison of corneal/conjunctival surgeries with cataract surgery. Surgical waste data were collected from 119 surgeries (82 corneal/conjunctival surgeries and 37 cataract surgeries). Hospital surgeries produced more waste than ambulatory surgical center and minor operating room surgeries. Penetrating keratoplasty (2.22 kg, P = 0.483) and Descemet stripping only (2.11 kg, P = 0.326) procedures generated comparable mean waste with cataract surgery (2.07 kg) while endothelial keratoplasties produced more ( P < 0.001, 0.002). (Deep) anterior lamellar keratoplasty results depended on the surgical setting. All conjunctival surgeries produced less waste than cataract surgery. In comparison with cataract surgery, keratoplasties overall produced comparable or more waste while conjunctival surgeries produced less waste. The surgical setting and type of anesthesia played a substantial role in the amount of waste generated. Assessing waste production from different ophthalmic surgeries may increase awareness of the negative environmental impact of surgical waste and promote practice or legal changes to improve environmental sustainability.