Massachusetts Institute Of Technology Research Articles

“You Are MIT”: How a MIT Computer Scientist Used Institutional Authority to Frame Anti-Semitism as a Scientific Fact and Moral Academic Consensus

The academic performance-claim by scientist Megha M. Vemuri functions as a doctrinal assertion of institutional complicity in global harm. Framed within a ceremonial rite of passage, the speaker substitutes MIT’s institutional identity for her own, invoking its scientific and moral authority to declare complicity in genocide, demand divestment, and morally obligate future alumni. The act operates as a performative projection of moral doctrine under the guise of civic epistemic representation. The address is canonically classified as a Performance-as-Authoritative-Scientific-Claim-With-Public-Trust-and-Federal Grant Funds (PASC-PT-FGF), leveraging institutional branding, affective rhetoric, and ambient silence to elevate personal belief into perceived communal obligation. This archival record addresses the claims delivered by scientist Megha M. Vemuri on May 30, 2025 at Massachusetts Institute of Technology (MIT) which functioned structurally as her first act of academic publication. This publication is treated as an academic scientific claim under institutional endorsement and supervision, and intended for lasting impact. Accordingly, this response evaluates the publication as a public epistemic claim made by a junior academic author and scientist, and assesses it against standards of social epistemic trust, institutional legitimacy, and the protocols of justified knowledge, as well as any and all applicable credentialing authority laws and codes for academic misconduct claims. This poses the question of Institutional Wokeism: How a Federally Funded, Accredited University Let an Unverified Moral Doctrine Be Performed as Scientific Fact — Is This Epistemic Fraud?

Pricing Experimental Design: Causal Effect, Expected Revenue and Tail Risk

When launching a new product, historical sales data are often not available, leaving price as a crucial experimental instrument for sellers to gauge market response. When designing pricing experiments, there are three fundamental objectives: estimating the causal effect of price (i.e., price elasticity), maximizing the expected revenue through the experiment, and controlling the tail risk that, if not controlled, may lead to significant financial losses. In this paper, we reveal the relationship among such three objectives. Under a linear structural model, we investigate the trade-offs between causal effect estimation and expected revenue maximization, as well as between expected revenue maximization and tail risk control. Furthermore, we propose an optimal pricing experimental design, which can flexibly adapt to different desired levels of trade-offs. Through the optimal design, we also explore the relationship between causal effect estimation and tail risk control. Moreover, we establish an always-valid confidence sequence and a central limit theorem for the inference of the causal effect. Finally, we extend our results and the design to a misspecified setting, where the structural model is not necessarily linear but the seller still runs our design for linear structural models. The results demonstrate the robustness of our design and the wide existence of the relationships among the three objectives. This paper was accepted by George Shanthikumar, data science. Funding: The authors thank the Massachusetts Institute of Technology (MIT)-IBM partnership in Artificial Intelligence and the MIT Data Science Laboratory for support. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.03209 .

Bibliometric insights into breast cancer organoid chips: trends & emerging areas.

Breast cancer organoid microarray technology, as an emerging research tool, has been used to construct breast cancer organoid models with physiological functions in vitro by simulating the tumor microenvironment, providing powerful support for in-depth understanding of breast cancer pathogenesis, drug screening, personalized treatment, and prognosis assessment. The aim of this study is to delve into the research hotspots and prospective directions of breast cancer organoid microarrays, and to better understand the opportunities in the research hotspot areas. The Web of Science Core Collection database was used to search for articles published on the combination of organoid and microarray research in breast cancer, using bibliometric methods to analyze authors, research institutions, countries, journals, references and visualized by CiteSpace and VOSviewer. A total of 151 documents were included in this study, revealing a steady annual increase in publications with country China publishing the most articles with the highest citation (1667). Fudan University, Sun Yat-Sen University and China Medical University contributed the most research with 5 articles published. Nanyang Technological University and the Massachusetts Institute of Technology are the institutions with the highest connection strength, indicating that their cooperation and communication in this field play an important role. Over the past decade, the focus of breast cancer research has shifted from the optimization and standardization of organoid culture models to the simulation of the tumor microenvironment and the establishment of disease models. Subsequent research trends include improving the pathological representativeness of the models, drug screening and personalized therapy. This study provides an objective and comprehensive analytical perspective on the research prospects of breast cancer organoids, pointing out the way for future research.

A Comparative Analysis of Two Urban Building Energy Modelling Tools via the Case Study of an Italian Neighbourhood

Urban Building Energy Modelling (UBEM) represents a comprehensive approach to investigate the intricate interplay of the various factors impacting energy use of groups of buildings, offering invaluable insights for urban planners, architects, building engineers, and policymakers. Nonetheless, available UBEM tools are still “research tools” and lack a unified standard addressing input, output, nomenclature, and calculation approaches. In this context, this study aims to conduct a comprehensive comparative analysis of two of the most used UBEM tools: Integrated Computational Design (iCD), the commercial tool provided by the Integrated Environmental Solutions (IES) company, and Urban Modelling Interface (umi), developed by the Massachusetts Institute of Technology (MIT). The comparative analysis includes each step of the UBEM workflow: the creation of the model, the assignment of input data, energy simulation, and visualisation and exportation of results. The tools are tested through the simulation of a case study to provide insights on the rationale and informed use of the tools, highlighting the risks associated with use by modellers with different levels of expertise. Moreover, this study provides tool developers and the scientific community with suggestions for major areas of improvement and standardisation in the field of UBEM, since substantial differences are still reported with respect to output, input, nomenclature, and calculation approaches.

STFT Spectrogram for Epilepsy Seizure Detection and Channel Selection

Epilepsy is the chronic neurological disorder which affects more than 1% of the population in the world. More than 50 million individuals have been suffered and living under the condition, among them, approximately 30% of patients were suffer from intractable epilepsy which is hard to control with convulsant medication. (Fisher et al, 2005). Various factors induce epilepsy such as trauma, postoperative brain damage, acquired condition, or genetic factors but nothing may not be confirmed for sure to its reason. The main problem and challenging factor of epilepsy is unprovoked seizures which are unpredictable and challenging to detect. The research aims to visualize enhance the detection of seizure events to conduct prediction systems. 24 patients Scalpe EEG (Electroencephalogram) set from CHP-MIT(Children’s Hospital and MIT university in Boston) and 30 patients Scalpe EEG from MCG (Mediclub in Georgia) have been collected and used to conduct STFT (Shor Time Fourier Transform) Spectrogram to visualize EEG signal to be identified easily even for the non-professional doctors. Conducted STFT from raw EEG data will be able to use deep learning processing for future research to improve accuracy and efficiency.

Interpretation and Characterization of Seizure Detection In Epilepsy with Deep Learning Processing

Electroencephalography (EEG) plays a major role in detecting seizure and epilepsy. However, scanning and interpretation of EEG have been limited to the area of experts and professionals. The aim of this study is to suggest the possible method of EEG interpretation characterized by the deep learning process to reduce labor intensive work for professionals and enhance accurate and automated diagnosis. 54 patients scalp EEG data of CHPMIT (Children’s Hospital and MIT university in Boston) and MCG (Mediclub Hospital in Georgia) EEG dataset were used with 20 channels. CNN based Convolutional Neural Networks was conducted in ResNet and Efficientnet for EEG signal was conducted to classify and characterize seizure detection with increasing relative scores such as specificity, sensitivity, and accuracy.

Data-Driven Battery Remaining Life Prediction Based on ResNet with GA Optimization

As lithium batteries are widely used in mobile electronic devices and electric vehicles, accurately assessing the Remaining Useful Life (RUL) of lithium-ion batteries is crucial to ensure the stable operation of the devices, improve energy efficiency, and safeguard user safety. Although data-driven methods show good prediction potential without the need to understand the reaction mechanism inside the battery, they face the problems of high data demand and feature redundancy that may reduce the model learning accuracy in practical applications. To this end, this paper proposes a data-driven lithium-ion battery life prediction method based on residual network (ResNet) and genetic algorithm (GA) optimization, which is designed to screen the features of the lithium-ion battery training data in order to effectively reduce the redundant features and improve the prediction performance of the model. Fourteen health features were first extracted during the charging and discharging phases of the battery. In order to reduce the usage of the dataset, only the first 100 cycles of the battery data were used, and the prediction error was about 9%. Secondly, the deep feature extraction capability of ResNet is utilized to effectively capture the complex patterns and subtle changes during battery decline to alleviate the problem of gradient disappearance. Meanwhile, in order to reduce the influence of redundant information, GA is used for health feature selection and optimization to screen out the most representative health features and enhance the generalization ability of the model. The experimental results obtained using the test set provided by the Massachusetts Institute of Technology (MIT), demonstrated the effectiveness and superiority of the proposed method.

An Assessment of the Distinct Characteristics of the Top-Ranked Universities of the World

World University Rankings (WUR) are significant in the higher education ecosystem. Literature highlights several critiques and methodological differences adopted by renowned rankings such as the Times Higher Education World University Ranking (THE WUR), Quacquarelli Symonds (QS WUR), and the Academic Ranking of World Universities (ARWU). This paper identifies consistently high-ranked universities worldwide from 2014 to 2023, including Harvard University, Stanford University, and the Massachusetts Institute of Technology (MIT). An assessment and comparison of the performance of these three institutions across various ranking indicators such as teaching, research, and international outreach are conducted. Findings indicate that Stanford University ranked highest in THE WUR, MIT in QS WUR, and Harvard University in ARWU during the study period. The performance of different universities varied for similar criteria across the various rankings. Harvard University emerged as the best and most consistently ranked institution across all rankings for the decade. The analysis presented in the paper offers directions for improvement in performance for universities worldwide that aim to enhance the quality of teaching and research. Additionally, it provides policy imperatives and suggestions for stakeholders involved in improving the utility of the ranking system.

Allen E. Bergin (1934-2024).

Memorializes Allen E. Bergin, a leading pioneer in psychotherapy outcome research and cofounder of the Society of Psychotherapy Research, who died at home in St. George, Utah, on February 15, 2024. Bergin began his university education at Massachusetts Institute of Technology but soon became disenchanted with "tech." He transferred to Reed College in Portland, Oregon, and later to Brigham Young University in Provo, Utah, where he received BA (1956) and MA (1957) degrees in psychology. For his PhD in clinical psychology, Allen studied at Stanford under his beloved mentor Albert Bandura. He took his first academic position at Columbia University at the age of 26. In 1955 at Brigham Young University, he became a member of The Church of Jesus Christ of Latter-day Saints (Mormon), which motivated and informed much of his research and advocacy for integrating spirituality into the mental health professions. Allen earned many awards, including the American Psychological Association's Award for Distinguished Professional Contributions to Knowledge. He published prolifically and is perhaps best known for Bergin and Garfield's Handbook of Psychotherapy and Behavior Change, now in its seventh edition. The 50-year history of the Handbook speaks to its impact on the field and to the original editor's vision of psychotherapy as a science-based endeavor. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

R&D Technical Section Q&A: Quantum Computing—Are We Ready?

_ In this article, Gaurav Agrawal and T.S. Ramakrishnan of the SPE Research and Development Technical Section (RDTS) spoke with Madhava Syamlal of QubitSolve, a startup developing quantum solutions for computational fluid dynamics, about the status of quantum computing as part of the advanced computing roadmap. This series highlights innovative ideas and analysis shaping the future of energy, with a focus on emerging technologies and their roadmaps, potential, and impact. With these conversations, we hope to inspire dialogue and accelerate progress across new energy frontiers. Madhava Syamlal, CEO and founder of QubitSolve, was previously a senior Fellow for Computational Sciences and Engineering at the US Department of Energy’s National Energy Technology Lab and is credited for creating MFIX, a widely used multiphase CFD software. He holds a BTech in chemical engineering from the Indian Institute of Technology, Varanasi, and MS and PhD degrees from the Illinois Institute of Technology, Chicago. RDTS: Tell us, what is quantum computing? Syamlal: Quantum computing is based on quantum physics which has given us lasers and transistors, the foundation for common devices such as cell phones, computers, and televisions we all use. In 1985, David Deutsch at Oxford University described a quantum computer. Peter Shor, now at the Massachusetts Institute of Technology, published an efficient algorithm for factoring composite integers on a quantum computer in 1994. Shor’s algorithm shattered the belief that factoring large composite numbers is nearly impossible, challenging modern cryptography. This breakthrough sparked intense global interest in quantum computing. RDTS: How is quantum computing different from traditional computing? Syamlal: In traditional computing, information such as text or video is represented by a series of binary digits or bits, each of which is a 0 or 1. A computer processes the information by rapidly changing a programmable switch between the only two possible states, open (0) or closed (1). Qubit is the quantum computing equivalent of a bit. Unlike a classical bit, which can be only 0 or 1, a qubit can exist in a state known as superposition, in both the 0 and 1 states simultaneously. Qubits are created by manipulating and measuring quantum objects such as electrons, photons, or superconducting circuits. An N-qubit system can exist in a superposition of 2N states, represented by (2N-1) complex numbers called amplitudes. Storing these numbers would overwhelm the memory capacity of a supercomputer, even for N as low as 60. Thus, quantum computing offers significant computational power that relies on only a small number of qubits. However, quantum computer functioning is more complex than classical computers. Unlike the definitive binary states of 0 and 1, the measurement of quantum states results in a random state. The probability of obtaining a particular state is determined by the square of the magnitude of its amplitude, which can be manipulated through quantum interference. The most intriguing quantum phenomenon is quantum entanglement, wherein the measurement of a qubit instantly changes the state of all other qubits that are entangled with it.

Mapping Artificial Intelligence Research Trends in Critical Care Nursing: A Bibliometric Analysis.

Recent development in AI-driven predictive analytics have demonstrated potential to enhance critical care workflows, particularly in three areas, including continuous vital sign monitoring in the ICU, intelligent nursing process management, AI-powered early risk stratification. This bibliometric study analyzes trends of artificial intelligence research in critical care nursing between 2013 and 2023 and provides future research directions. The 1,346 relevant articles revealed a clear upward trajectory in research output related to AI in critical care nursing. The largest number of articles originated from the United States, followed by China, and the United Kingdom. Harvard University was the leading contributing institution, followed by the University of California and the Massachusetts Institute of Technology. Keyword clustering analysis generated seven representative cluster labels. Machine learning, AI, and deep learning were the major tools utilized during scholarly investigations of AI-driven research in critical care nursing. Our findings shed light on the opportunities for AI to transform critical care nursing practice, particularly in optimizing ICU workflow efficiency, precision patient monitoring, and evidence-based decision acceleration. We advocate for the expansion of this type of research, the facilitation of collaboration among research institutions, and further development of international research collaborations.

AI-driven discovery of synergistic drug combinations against pancreatic cancer

Pancreatic cancer treatment often relies on multi-drug regimens, but optimal combinations remain elusive. This study evaluates predictive approaches to identify synergistic drug combinations using a dataset from the National Center for Advancing Translational Sciences (NCATS). Screening 496 combinations of 32 anticancer compounds against the PANC-1 cells experimentally determined the degree of synergism and antagonism. Three research groups (NCATS, University of North Carolina, and Massachusetts Institute of Technology) leverage these data to apply machine learning (ML) approaches, predicting synergy across 1.6 million combinations. Of the 88 tested, 51 show synergy, with graph convolutional networks achieving the best hit rate and random forest the highest precision. Beyond highlighting the potential of ML, this work delivers 307 experimentally validated synergistic combinations, demonstrating its practical impact in treating pancreatic cancer.

Predicting Intraoperative Burst Suppression Using Preoperative EEG and Patient Characteristics.

Burst suppression (BS) is an electroencephalogram (EEG) pattern observed in patients undergoing general anesthesia. The occurrence of BS is associated with adverse outcomes such as postoperative delirium, extended recovery time, and increased postoperative mortality. The detection and prediction of BS can help expedite the evaluation of patient conditions, optimize anesthesia administration, and improve patient safety. This study explores the potential for automatic BS detection using intraoperative EEG and BS prediction using preoperative EEG signals and patient characteristics. A dataset comprising 287 patients who underwent carotid endarterectomy procedures at Maastricht University Medical Center+ was analyzed. An EEG toolbox developed by T. Zhan at the Massachusetts Institute of Technology was utilized for the automatic detection/annotation of BS, while five machine learning classifiers were employed to predict BS occurrence using preoperative data. Based on the 160 patients manually annotated by EEG experts (regarding the presence or absence of BS), the automatic detection tool demonstrated an accuracy of 0.75. For the BS prediction task, an initial subset of 120 patients was evaluated, showing modest performance, with the K-nearest neighbors ([Formula: see text]) classifier achieving the best results, with an accuracy of 0.72. Subsequent experiments indicated that increasing the number of patients (by using Zhan's Toolbox to annotate the unlabeled instances), applying SMOTE to balance the training set, and enriching the feature set was beneficial. The final experiment demonstrated a significant improvement, with Random Forest and Gradient Boosting outperforming other classifiers, achieving an accuracy of 0.86 and ROC-AUC of 0.94. Patient characteristics, including type of anesthetic agents, symptoms, age, mean absolute delta power, mean absolute theta power, and cognitive impairment, were identified by an xAI method as important features potentially indicating the predisposition to experience BS.

Deep learning models for segmenting phonocardiogram signals: a comparative study.

Cardiac auscultation requires the mechanical vibrations occurring on the body's surface, which carries a range of sound frequencies. These sounds are generated by the movement and pulsation of different cardiac structures as they facilitate blood circulation. Subsequently, these sounds are identified as phonocardiogram (PCG). In this research, deep learning models, namely gated recurrent neural Network (GRU), Bidirectional-GRU, and Bi-directional long-term memory (BILSTM) are applied separately to segment four specific regions within the PCG signal, namely S1 (lub sound), the systolic region, S2 (dub sound), and the diastolic region. These models are applied to three well-known datasets: PhysioNet/Computing in Cardiology Challenge 2016, Massachusetts Institute of Technology (MITHSDB), and CirCor DigiScope Phonocardiogram.The PCG signal underwent a series of pre-processing steps, including digital filtering and empirical mode decomposition, after then deep learning algorithms were applied to achieve the highest level of segmentation accuracy. Remarkably, the proposed approach achieved an accuracy of 97.2% for the PhysioNet dataset and 96.98% for the MITHSDB dataset. Notably, this paper represents the first investigation into the segmentation process of the CirCor DigiScop dataset, achieving an accuracy of 92.5%. This study compared the performance of various deep learning models using the aforementioned datasets, demonstrating its efficiency, accuracy, and reliability as a software tool in healthcare settings.

Leveraging Virtual Reality Technology in Saudi Arabia to Enhance Training: A New Paradigm for Developing Women Leaders in the Workplace.

Gender bias and workplace challenges continue to hinder the career advancement of women in leadership roles. As Saudi Arabia aligns with Vision 2030, empowering Saudi women leaders (SWLs) through innovative training methods has become a national priority. Virtual reality (VR) technology offers immersive, interactive environments that simulate workplace challenges, fostering skill development and bridging the gap between theory and practice, particularly beneficial for women leaders who can practice navigating leadership scenarios safely. This pilot study investigates the effectiveness of VR technology in enhancing the leadership competencies, digital communication skills, and workplace adaptability of SWLs. A total of 52 SWLs from public and private organizations in Riyadh participated in a two-week VR-based training program focused on real-life leadership scenarios and workplace challenges. Designed by Princess Nourah University's Women Leadership Center in collaboration with the Massachusetts Institute of Technology (MIT), which provided VR design expertise, the program addressed gender bias and leadership stereotypes. Data were collected using a System Usability Scale (SUS)-based questionnaire combined with leadership training evaluation items, which assessed both VR system usability and training effectiveness. Descriptive and inferential statistics (including Pearson correlation coefficients) were used to analyse the data. Participants reported high levels of satisfaction with the usability and immersive nature of VR training (mean scores ranging from 3.95 to 4.11 on a five-point scale). The VR system was rated as intuitive and engaging, with participants quickly adapting to its use (mean score: 4.11). The training facilitated interactive and stress-free learning experiences (mean score: 4.08), though some participants expressed uncertainty regarding applying VR skills to day-to-day leadership tasks such as conflict resolution and performance management (mean score: 3.05). VR technology, as indicated by mean usability scores around 4.0 on a five-point scale, is a promising tool for developing leadership competencies among SWLs. While participants demonstrated strong engagement and skill acquisition, further refinement is needed to align training scenarios with real-world tasks. Expanding carefully evaluated VR-based training programs can advance women's leadership development and support Saudi Vision 2030 objectives.

Student 5.0: immersive learning in next-gen Automation of Manufacturing Systems courses for the Industry 5.0 era

This manuscript introduces an educational approach by integrating Industry 4.0 and 5.0 technologies within a Smart Factory learning environment at Tecnologico de Monterrey (Tec) and through international collaboration with the Massachusetts Institute of Technology (MIT). It addresses the challenge of adapting engineering education to the demands of smart manufacturing, highlighting the importance of hands-on experience with cyberphysical systems, Additive Manufacturing (AM), Supervisory Control and Data Acquisition (SCADA) integration, and robotics. By integrating the curriculum with Sustainable Development Goals (SDGs), the initiative aims to train engineers capable of contributing to sustainable industrial practices and innovation. The Automation of Manufacturing Systems course provides immersive learning through direct engagement with advanced technologies and methodologies. Students develop technical skills in collaborative robots (cobots), human-machine interfaces (HMI), computer vision and Programmable Logic Controller (PLC) programming. This also develops methodological skills in analytical thinking problem-solving and other competences. The curricula further enhanced social and personal competencies, preparing students to lead in a technologically advanced and sustainable industrial environment. With the emergence of Industry 5.0, which emphasizes sustainability and worker wellbeing, this educational model integrates social behavior science with digital technologies in a student-centered approach. The presented case study highlights the effectiveness of redesigning the course to include immersive learning experiences focused on Industry 4.0 and 5.0 skills. A student survey conducted at the end of the course validated these findings, revealing high satisfaction levels (9.65/10) associated with developing new skills, exploring new concepts, generating new ideas, and promoting innovative thinking. Both the students and the teaching team offered recommendations to further scale and improve the course.

The effect of antiseizure medication on mortality in spontaneous aneurysmal subarachnoid hemorrhage.

Spontaneous aneurysmal subarachnoid hemorrhage (aSAH) is a major cause of morbidity and mortality in the United States. The efficacy of early antiseizure medication (ASM) is debated. Recent literature reports seizure rates ranging from 7.8% to 15.2% following spontaneous aSAH. Current guidelines recommend use of early ASM in patients with "high-risk features," but whether early ASM use decreases the rate of death associated with aSAH remains unclear. This study assessed whether early administration of early ASM impacts mortality rates after spontaneous aSAH. We conducted a retrospective cohort study using a publicly available dataset from the Massachusetts Institute of Technology, Medical Information Mart for Intensive Care-III (MIMIC) database of all patients over the age of 18 with spontaneous aSAH resulting in an intensive care unit (ICU) admission to a major United States trauma center from 2001 to 2012. The primary exposure was receiving early ASM and primary outcome of death within 7 days. Different regression models were created to explore the association between early ASM administration within 24 hours of admission and a composite outcome of seizure and/or death within 7 days of admission. Secondary outcomes included 30-day and one-year mortality. Of 253 patients with spontaneous aSAH, 148 received early ASM within 24 hours. Patients who did receive early ASM were less likely to die within 7 days of admission (adjusted odd ratio, [aOR]: 0.26 95% CI 0.10 to 0.68; P=0.006) but were more likely to have a seizure (aOR: 7.63 95% CI 2.07 to 28.17; P=0.002). Early ASM administration was associated with lower rates of death and composite death/seizure within 7 days of admission among patients who presented to an ICU with spontaneous aSAH. These findings suggest broader use of early ASM in patients who present with spontaneous aSAH may improve early mortality.

The Integration of AI and blockchain technologies for secure data management in cybersecurity

Introduction: This review examines the integration of artificial intelligence (AI) and blockchain technologies for secure data management in cybersecurity across the United States. Due to the growing instances of cyber threats today, organizations and government agencies are looking at new ways of technological integration to enhance the aspect of data protection. This paper aims to establish how AI and blockchain functions together to provide secure solutions to the current cybersecurity threats. Materials and Methods: This article adopts the secondary research approach that involves the use of surveys from peer-reviewed articles, industry reports, and analytical studies on AI-blockchain frameworks. The assumptions made are based on expert opinions from the interviews and case studies that have been compiled from reputed institutions such as the Cybersecurity and Infrastructure Security Agency (CISA), the Massachusetts Institute of Technology (MIT), and other Fortune 500 firms. From previous research, quantitative data is conducted and pertains to performance, the implementation rate, and sectorial outcomes in the financial service, health and the critical infrastructure sectors. Results: Based on the findings, it can be concluded that integrated AI-blockchain systems are 37% more effective in mitigating APT attacks as opposed to antecedent security models. The organizations that have adopted such technologies have showed improved statistics of reduced data breach incidents by 42% and unauthorized access attempts by 56%. As far as the adoption rate, there are several leaders – and they have proved to be the financial institutions in New York and California with health care providers in Massachusetts and Texas not far behind. The technical pros are better out of the ordinary detection (up to 89% accuracy rates) and cryptographic audited trails, which considerably improve the response time. Discussion: The review identifies four distinct implementation models emerging across different sectors and regions in the United States. Financial institutions primarily leverage these integrated technologies for transaction verification and fraud detection, while healthcare organizations focus on secure patient data management and access control. Government agencies, particularly in Virginia and Maryland, employ these systems for critical infrastructure protection and threat intelligence sharing. Key implementation factors include regulatory compliance requirements, organizational security maturity, and industry-specific threat landscapes. Conclusion: The integration of AI and blockchain technologies provide a new lea for viewer cybersecurity strategies in the United States. But all these need some considerations in form of rules and regulations, technical difficulties, and challenges of qualified human resource. The overall recommendations for increasing the level of adoption consist of sectoral best practices to be followed in implementing the discussed concepts and frameworks, cross-organizational collaboration models, and educational programs to foster the acquisition of adequate workforce competencies in these converging technologies.

Workshop on Noninvasive Glucose Monitoring 2024.

This first workshop on noninvasive glucose monitoring (NIGM) was held at the Massachusetts Institute of Technology (MIT) on October 30, 2024. Six invited speakers, representing industry, academia, and clinics, gave presentations that covered (1) an overview of the NIGM technologies, (2) the state of the art in NIGM technologies, such as near-infrared (NIR), mid-infrared (IR), photoacoustic, and Raman spectroscopies, (3) minimally invasive implantable continuous glucose monitoring (CGM) sensors, and (4) a clinician's perspective on the impact of the current CGM devices for patient care.

Fair and Reliable Reconnections for Temporary Disruptions in Electric Distribution Networks

Increasing reliability and reducing disruptions in supply networks are of increasing importance; for example, power outages in electricity distribution networks cost $35–$50 billion annually in the United States. Motivated by the operational constraints of such networks and their rapid adoption of decentralized paradigms and self-healing components, we introduce the minimum reconnection time (MRT) problem, which models reliability metrics such as the System Average Interruption Duration Index (SAIDI). MRT seeks to reduce outage time after network disruptions by programming reconnection times of different edges (i.e., switches), ensuring that the operating network is acyclic. We show that MRT is NP-hard and is a special case of the well-known (weighted) minimum sum set cover (MSSC) problem. We develop the theory of kernel-based randomized rounding approaches to give a tight polynomial-time approximation for MSSC, improving the state-of-the-art approximation factor for these instances. Further, motivated by the reliability incentive structure for utility companies and operational energy losses in distribution networks, we study minimizing energy losses and reliability metrics such as SAIDI and reconnection times simultaneously. Optimizing for any single objective at a time can create unfair duration of expected outage for industrial and residential areas. We, therefore, propose local search over spanning trees to balance these multiple objectives. We computationally validate our reconfiguration methods on the National Renewable Energy Laboratory Synthetic Models for Advanced, Realistic Testing: Distribution Systems and Scenarios Greensboro synthetic network and show that this improves equity by a factor of four across industrial and residential areas. History: Accepted by Erwin Pesch, Area Editor for Heuristic Search & Approximation Algorithms. Funding: C. Hettle and D. Molzahn's research was partially supported by the National Science Foundation [Grant NSF 2112533], received by the Georgia Institute of Technology. S. Gupta’s research is supported by the National Science Foundation CAREER [Grant 2239824], received by the Massachusetts Institute of Technology. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0295 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0295 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .