Holon Institute of Technology

In this short paper, we propose a new framework for obtaining basic aspects of quantum mechanics that originate from estimating the mean value of the position of a statistical system based on the generalized Bayes estimators. We show that while the first-order estimation leads to a classical system, the second-order estimation produces the time-independent Schrödinger equation. The Born rule describes the probabilistic nature of quantum particles, and Max Born postulated it independently from the Schrödinger equation. We show that under the proposed model, both the Schrödinger equation and the Born rule are captured organically; particularly, we show that the Born rule leads to the Schrödinger equation. Finally, we show how the proposed model deals with the transition from quantum mechanics into classical mechanics when dealing with macroscopic objects without external assumptions.

Significance Functional magnetic resonance imaging provides high spatial resolution but is limited by cost, infrastructure, and the constraints of an enclosed scanner. Portable methods such as functional near-infrared spectroscopy and electroencephalography improve accessibility but require physical contact with the scalp. Our speckle pattern imaging technique offers a remote, contactless, and low-cost alternative for monitoring cortical activity, enabling neuroimaging in environments where contact-based methods are impractical or MRI access is unfeasible. Aim We aim to develop a remote photonic technique for detecting human brain cortex activity by applying deep learning to the speckle pattern videos captured from specific brain cortex areas illuminated by a laser beam. Approach We enhance laser speckle pattern tracking with artificial intelligence (AI) to enable remote brain monitoring. In this study, a laser beam was projected onto Wernicke’s area to detect brain responses to a clear and incomprehensible speech. The speckle pattern videos were analyzed using a convolutional long short-term memory–based deep neural network classifier. Results The classifier distinguished brain responses to a clear and incomprehensible speech in unseen subjects, achieving a mean area under the receiver operating characteristic curve (area under the curve) of 0.94 for classifications based on at least 1 s of input. Conclusions This remote method for distinguishing brain responses has practical applications in brain function research, medical monitoring, sports, and real-life scenarios, particularly for individuals sensitive to scalp contact or headgear.

Variability is inherent in statistical, actuarial, and economic models, necessitating precise quantification for informed decision-making and risk management. Recently, Landsman and Shushi introduced the Location of Minimum Variance Squared Distance (LVS) risk functional, a novel variance-based measure of variability. We extend LVS to assess variability in regression models commonly used in actuarial analysis, enabling the construction of regression-type predictors in the Minimum Variance Squared Deviation (MVS) sense. We show that when the predicted vector Y follows a symmetric distribution, MVS aligns with the traditional Minimum Expected Squared Deviation (MES) functional. However, for non-symmetric distributions, MVS and MES diverge, with differences influenced by the joint third-moment matrix of distribution P and the covariance matrix of Y. We derive an analytical expression for MVS and explore a hybrid approach combining MVS and MES functionals. To illustrate the applicability of our approach, we present two numerical examples: (i) predicting three components of fire losses—buildings, contents, and profits—and (ii) forecasting returns for six market indices based on the returns of their dominant stocks.

Objective To develop and validate advanced machine learning (ML) models for predicting unplanned intrapartum cesarean deliveries in women with no previous cesarean delivery, using both static and dynamic clinical data. Methods A retrospective cohort study was conducted using nationwide data from a large integrated healthcare provider, including 262 632 women whose labor had started. Two ML models, logistic regression and decision tree algorithms, were employed to predict unplanned cesarean delivery. The models incorporated demographic, medical, and obstetric variables collected at multiple time points during labor. Model performance was evaluated based on accuracy, sensitivity, specificity, and the area under the receiver operating characteristics curve (AUC‐ROC). Results The logistic regression model demonstrated an accuracy of 95% with an AUC‐ROC of 0.92. The decision tree model showed adaptability in highly variable labor conditions, achieving an F1 score of 0.91 and excelling in real‐time prediction. Key predictors included maternal age, gestational age, body mass index, fetal heart rate patterns, and labor dynamics. Model performance remained robust across various demographic subgroups but was slightly reduced in nulliparous women. Conclusion These ML models provide an innovative approach to predicting unplanned cesarean delivery by integrating diverse clinical parameters, enhancing decision making, and optimizing labor management. Prospective validation and seamless integration into clinical workflows are required to establish their utility in broader obstetric practice.

An elementary, but very useful lemma due to Biernacki and Krzyż (1955) asserts that the ratio of two power series inherits monotonicity from that of the sequence of ratios of their respective coefficients. Over the last two decades it has been realized that, under some additional assumptions, similar claims hold for more general series ratios as well as for unimodality in place of monotonicity. This paper continues this line of research: we consider ratios of general functional series and integral transforms and furnish natural sufficiency conditions for preservation of unimodality by such ratios. Numerous series and integral transforms appearing in applications satisfy our sufficiency conditions, including Dirichlet, factorial and inverse factorial series, Laplace, Mellin and generalized Stieltjes transforms, among many others. Finally, we illustrate our general results by exhibiting certain statements on monotonicity patterns for ratios of some special functions. The key role in our considerations is played by the notion of sign regularity.

The compliance of health terminology servers with the FAIR principles is crucial for establishing an interoperable digital health ecosystem that facilitates health data exchange. These servers must maintain high-quality standards while providing persistent, multilingual, and cross-lingual management of health terminologies. Implementing the FAIR principles is vital for creating consistent connections within One Digital Health, leading to better global healthcare outcomes and decision support systems by supporting international collaboration and facilitating interactions between all the actors involved in health data usage. Thus, this paper focuses on aligning the Health Terminology/Ontology Portal (HeTOP) with the FAIR principles, hosting the One Digital Health - Unified Terminology (ODH-UT).

Precise analysis of electroencephalogram (EEG) signals is critical for advancing the understanding of neurological conditions and mapping brain activity. However, accurately visualizing brain regions and behavioral patterns from neural signals remains a significant challenge. The present study proposes a novel methodology for the automated calculation of the average power of EEG signals, with a particular focus on the beta frequency band which is known for its pronounced activity during cognitive tasks such as 2D content engagement. An optimization algorithm is employed to determine the most appropriate digital filter type and order for EEG signal processing, thereby enhancing both signal clarity and interpretability. To validate the proposed methodology, an experiment was conducted with 22 students, during which EEG data were recorded while participants engaged in cognitive tasks. The collected data were processed using MATLAB (version R2023a) and the EEGLAB toolbox (version 2022.1) to evaluate various filters, including finite impulse response (FIR) and infinite impulse response (IIR) Butterworth and IIR Chebyshev filters with a 0.5% passband ripple. Results indicate that the IIR Chebyshev filter, configured with a 0.5% passband ripple and a fourth-order design, outperformed the alternatives by effectively reducing average power while preserving signal fidelity. This optimized filtering approach significantly improves the accuracy of neural signal visualizations, thereby facilitating the creation of detailed brain activity maps. By refining the analysis of EEG signals, the proposed method enhances the detection of specific neural behaviors and deepens the understanding of functional brain regions. Moreover, it bolsters the reliability of real-time brain activity monitoring, potentially advancing neurological diagnostics and insights into cognitive processes. These findings suggest that the technique holds considerable promise for future applications in brain–computer interfaces and advanced neurological assessments, offering a valuable tool for both clinical practice and research exploration.

Overconfidence in deep learning models poses a significant risk in high-stakes medical imaging tasks, particularly in multi-label classification of chest X-rays, where multiple co-occurring pathologies must be detected simultaneously. This study introduces an uncertainty-aware framework for chest X-ray diagnosis based on a DenseNet-121 backbone, enhanced with two selective prediction mechanisms: entropy-based rejection and confidence interval-based rejection. Both methods enable the model to abstain from uncertain predictions, improving reliability by deferring ambiguous cases to clinical experts. A quantile-based calibration procedure is employed to tune rejection thresholds using either global or class-specific strategies. Experiments conducted on three large public datasets (PadChest, NIH ChestX-ray14, and MIMIC-CXR) demonstrate that selective rejection improves the trade-off between diagnostic accuracy and coverage, with entropy-based rejection yielding the highest average AuC across all pathologies. These results support the integration of selective prediction into AI-assisted diagnostic workflows, providing a practical step toward safer, uncertainty-aware deployment of deep learning in clinical settings.

Significance Stroke is a leading cause of disability worldwide, necessitating rapid and accurate diagnosis to limit irreversible brain damage. However, many advanced imaging modalities (computerized tomography, magnetic resonance imaging) remain inaccessible in remote or resource-constrained settings due to high costs and logistical barriers. Aim We aim to evaluate the feasibility of a laser speckle–based technique, coupled with deep learning, for detecting simulated stroke conditions in a tissue phantom. We investigate whether speckle patterns can be leveraged to differentiate healthy from restricted flow states in arteries of varying diameters and depths. Approach Artificial arteries (3 to 6 mm diameters) were embedded at different depths (0 to 10 mm) within a skin-covered chicken tissue, to mimic blood-flow scenarios ranging from no flow (full occlusion) to high flow. A high-speed camera captured the secondary speckle patterns generated by laser illumination. These video sequences were fed into a three-dimensional convolutional neural network (X3D_M) to classify four distinct flow conditions. Results The proposed method showed high classification accuracy, reaching 95% to 100% for larger vessels near the surface. Even for smaller or deeper arteries, detection remained robust (>80% in most conditions). The performance suggests that spatiotemporal features of speckle patterns can reliably distinguish varying blood-flow states. Conclusions Although tested on a tissue phantom, these findings highlight the potential of combining speckle imaging with deep learning for accessible, rapid stroke detection. Our next steps involve direct in vivo experiments targeting cerebral arteries, acknowledging that additional factors such as the skull’s optical properties and the likely need for near-infrared illumination must be addressed before achieving true intracranial applicability. We also note that examining the carotid artery in vivo remains a valuable and practical step, given its superficial location and direct relevance to stroke risk.

Fast and easily scalable solvent-free extrusion of polymer electrolytes would represent significant progress in the development of environmentally friendly solid alkali-ion and alkali-metal batteries. In this study, we compare cast and extruded solid polymer electrolytes composed of lithium bis(trifluoromethanesulfonyl)imide salt of varying content dissolved in host matrix of polyurethane and polyethylene oxide. The electrolytes were characterized by means of differential scanning calorimetry, infrared spectroscopy, AC impedance, broad-band spectroscopy, and one-dimensional ⁷Li and ¹⁹F nuclear magnetic resonance. Analysis of the FTIR spectra showed that both polymers can coordinate lithium cations by ethylene oxide (EO) and amine (NH) electron donating groups. The interactions between Li⁺ and each polymer affect phase transition behavior, conductivity, and ion self-diffusion coefficients. Contrary to our expectations, it was found that unidirectional annealing, which was assumed to be induced by the extrusion process, does not alter out-of-plane ionic conductivity. Moreover, the bulk conductivity of extruded blended polymer electrolyte with 1:15 Li:EO ratio is more than twice that of its cast counterpart over the entire temperature range studied.

Introduction: Integrating nursing informatics education into nursing is essential for advancing the digital transformation of nursing. The objective was to develop nursing informatics curricula tailored to the needs in Kosovo and Israel. Methodology: Reviewing 16 international guidelines, conducting interviews with 22 national stakeholders, and undertaking an international Delphi study. Results: The study identified key challenges and recommendations for nursing informatics education. It ranked a list of 40 nursing informatics topics and developed two national curricula. Discussion: Effective nursing informatics education on a national scale requires the integration of national stakeholders and international perspectives to address diverse needs and contexts and to advance the field.

Mind wandering is a common issue among schoolchildren and academic students, often undermining the quality of learning and teaching effectiveness. Current detection methods mainly rely on eye trackers and electrodermal activity (EDA) sensors, focusing on external indicators such as facial movements but neglecting voice detection. These methods are often cumbersome, uncomfortable for participants, and invasive, requiring specialized, expensive equipment that disrupts the natural learning environment. To overcome these challenges, a new algorithm has been developed to detect mind wandering during reading aloud. Based on external indicators like the blink rate, pitch frequency, and reading rate, the algorithm integrates these three criteria to ensure the accurate detection of mind wandering using only a standard computer camera and microphone, making it easy to implement and widely accessible. An experiment with ten participants validated this approach. Participants read aloud a text of 1304 words while the algorithm, incorporating the Viola–Jones model for face and eye detection and pitch-frequency analysis, monitored for signs of mind wandering. A voice activity detection (VAD) technique was also used to recognize human speech. The algorithm achieved 76% accuracy in predicting mind wandering during specific text segments, demonstrating the feasibility of using noninvasive physiological indicators. This method offers a practical, non-intrusive solution for detecting mind wandering through video and audio data, making it suitable for educational settings. Its ability to integrate seamlessly into classrooms holds promise for enhancing student concentration, improving the teacher–student dynamic, and boosting overall teaching effectiveness. By leveraging standard, accessible technology, this approach could pave the way for more personalized, technology-enhanced education systems.

This paper begins with a comprehensive review of the deliberate teaching practice literature related to generative AI training platforms. It then introduces a conceptual framework for a generative AI-powered system designed to simulate dynamic classroom environments, allowing teachers to engage in repeated, goal-oriented practice sessions. Leveraging recent advances in large language models (LLMs) and multiagent systems, the platform features virtual student agents configured to demonstrate varied learning styles, prior knowledge, and behavioral traits. In parallel, mentor agents—built upon the same generative AI technology—continuously provide feedback, enabling teachers to adapt their strategies in real time. By offering an accessible, controlled space for skill development, this framework addresses the challenge of scaling and personalizing teacher training. Grounded in pedagogical theory and supported by emerging AI capabilities, the proposed platform enables educators to refine teaching methods and adapt to diverse classroom contexts through iterative practice. A detailed outline of the system’s main components, including agent configuration, interaction workflows, and a deliberate practice feedback loop, sets the stage for more personalized, high-quality teacher training experiences, and contributes to the evolving field of AI-mediated learning environments.

Background Postoperative nausea and vomiting (PONV) is a frequently observed complication in patients undergoing surgery under general anesthesia. Moreover, it is a frequent cause of distress and dissatisfaction in the early postoperative period. Currently, the classical scores used for predicting PONV have not yielded satisfactory results. Therefore, prognostic models for the prediction of early and delayed PONV were developed in this study to achieve satisfactory predictive performance. Methods The retrospective data of inpatient adult patients admitted to the post-anesthesia care unit after undergoing surgical procedures under general anesthesia at the Sheba Medical Center, Israel, between September 1, 2018, and September 1, 2023, were used in this study. An ensemble model of machine-learning algorithms trained on the data of 35,003 patients was developed. The k-fold cross-validation method was used followed by splitting the data to train and test sets that optimally preserve the sociodemographic features of the patients. Results Among the 35,003 patients, early and delayed PONV were observed in 1,340 (3.82%) and 6,582 (18.80%) patients, respectively. The proposed PONV prediction models correctly predicted early and delayed PONV in 83.6% and 74.8% of cases, respectively, outperforming the second-best PONV prediction score (Koivuranta score) by 13.0% and 10.4%, respectively. Feature importance analysis revealed that the performance of the proposed prediction tools aligned with previous clinical knowledge, indicating their utility. Conclusions The machine learning-based models developed in this study enabled improved PONV prediction, thereby facilitating personalized care and improved patient outcomes.

Background: The role of adding chemotherapy to adjuvant radiation therapy in resectable Merkel cell carcinoma (MCC) remains controversial. Previous studies have shown conflicting results, and long-term outcome data are limited. Objectives: In this study, we aimed to evaluate the long-term survival outcomes of patients with resectable MCC treated with surgery followed by either radiation alone or combined chemoradiation. Methods: This retrospective multicenter cohort study analyzed 105 patients with resectable MCC treated between 1985 and 2023. Patients received either adjuvant radiation alone (n = 53) or chemoradiation (n = 52) following surgery. The primary endpoints were overall survival and disease-free survival. The secondary endpoints included an analysis of prognostic factors and treatment-related characteristics. The median follow-up was 12 years. Results: The 20-year overall survival rates were 53.4% for chemoradiation versus 30.7% for radiation alone (p = 0.324). Median survival in the chemoradiation groups was not reached during the follow-up period; in the radiation group, it was 8.8 years. Likewise, the twenty-year disease-free survival rates were not significantly different between the chemoradiation and radiation groups: 47% vs. 29.3%, respectively, p = 0.495. The chemoradiation group had significantly more advanced disease (88% vs. 28.3% stage III) but was younger (median 65.9 vs. 77.3 years, p = 0.002) and received higher radiation doses (median 50 Gy vs. 45 Gy, p = 0.002). After controlling for age, stage, and tumor location in a multivariable analysis, the survival differences were still not significantly different (hazard ratio (HR) = 1.36, 95% CI 0.61–3.00, p = 0.450). Conclusions: While the multivariate analysis did not indicate a survival advantage to adding chemotherapy to radiation, the comparable survival outcomes despite significantly more advanced disease in the chemoradiation group suggest a possible benefit in high-risk patients. Our results indicate the need for prospective studies with larger, stage-matched cohorts to definitively establish the role of adjuvant chemotherapy in high-risk resectable MCC.

Significance Alcohol consumption monitoring is essential for forensic and healthcare applications. While breath and blood alcohol concentration sensors are currently the most common methods, there is a growing need for faster, non-invasive, and more efficient assessment techniques. The rationale for our binary classification relates to law enforcement applications in countries with strict limits on alcohol consumption such as China, which seeks to prevent driving with even the smallest amount of alcohol in the bloodstream. Aim We propose a remote optical technique for assessing alcohol consumption using speckle pattern analysis, enhanced by machine learning for binary classification. This method offers remote and fast alcohol consumption evaluation without requiring before and after comparisons. Approach Our experimental setup includes a laser directed toward the subject’s radial artery, a camera capturing defocused speckle pattern images of the illuminated area, and a computer. Participants consumed alcohol and were tested periodically. We developed a machine learning classification model that performs automatic feature selection based on temporal analysis of the speckle patterns. The model was evaluated using various labeling schemes: classification with five labels, consolidation to three labels by merging similar labels, and three different binary classifications cases (“Alcohol” or “No alcohol”). Results Our classification models showed improving accuracy as we reduced the number of labels. The initial five-label model achieved 61% accuracy. When consolidated into three labels, the models achieved accuracies of 74% and 85% for the two cases. The binary classification models performed best, with model A achieving 91% accuracy and 97% specificity, model B achieving 83% accuracy, and model C achieving 88% accuracy with 99% sensitivity. Conclusions Our binary classification model C can successfully distinguish between pre- and post-alcohol consumption with high sensitivity and accuracy. This performance is particularly valuable for clinical and forensic applications, where minimizing false negatives is crucial.

Viral load (VL) in the respiratory tract is the leading proxy for assessing infectiousness potential. Understanding the dynamics of disease‐related VL within the host is of great importance, as it helps to determine different policies and health recommendations. However, normally the VL is measured on individuals only once, in order to confirm infection, and furthermore, the infection date is unknown. It is therefore necessary to develop statistical approaches to estimate the typical VL trajectory. We show here that, under plausible parametric assumptions, two measures of VL on infected individuals can be used to accurately estimate the VL mean function. Specifically, we consider a discrete‐time likelihood‐based approach to modeling and estimating partial observed longitudinal samples. We study a multivariate normal model for a function of the VL that accounts for possible correlation between measurements within individuals. We derive an expectation‐maximization (EM) algorithm which treats the unknown time origins and the missing measurements as latent variables. Our main motivation is the reconstruction of the daily mean VL, given measurements on patients whose VLs were measured multiple times on different days. Such data should and can be obtained at the beginning of a pandemic with the specific goal of estimating the VL dynamics. For demonstration purposes, the method is applied to SARS‐Cov‐2 cycle‐threshold‐value data collected in Israel.

The Stirling numbers of type B of the second kind count signed set partitions. In this paper, we provide new combinatorial and analytical identities regarding these numbers as well as Broder’s r-version of these numbers. Among these identities one can find recursions, explicit formulas based on the inclusion–exclusion principle, and also exponential generating functions. These Stirling numbers can be considered as members of a wider family of triangles of numbers that are characterized using results of Comtet and Lancaster. We generalize these theorems, which present equivalent conditions for a triangle of numbers to be a triangle of generalized Stirling numbers, to the case of the q, r-poly Stirling numbers, which are q-analogues of the restricted Stirling numbers defined by Broder and having a polynomial value appearing in their defining recursion. There are two ways to do this and these ways are related by a nice identity.

Social-emotional skills are vital for personal and academic growth in a Volatile, Uncertain, Complex, Ambiguous, and heterogenous world. Teachers play a key role in nurturing these skills in students. This study explores the alignment between self-reported social–emotional skills and actual behavior, focusing on the theory of ‘teachers as learners’. Using a mixed-method approach, this research analyzed self-report questionnaires and synchronous online meetings within a Teachers Professional Development program called ‘Fostering social–emotional skills in a diverse society’. Teachers were tasked with experiencing social-emotional skills by collaborating in heterogeneous groups to design programs for their students. Findings reveal a significant gap between self-reported and actual behavior. While teachers professed self-awareness, awareness of others, practical and emotional self-regulation, their reluctance to work in heterogeneous groups contradicted these claims. Most teachers preferred collaborating with familiar colleagues from the same school, exposing a lack of practical self-regulation and limited self-awareness. This study emphasizes the necessity for teachers to undergo an in-depth and ongoing process of personal development as ‘teachers as learners’ before effectively cultivating social and emotional skills in the classroom. It underscores the complexity of integrating these skills, emphasizing knowledge and tools but mostly experiential learning. It also advocates a shift toward assessing teachers’ behavior rather than relying solely on self-reports. Understanding the alignment between self-reported social-emotional skills and actual behavior is critical for effective teaching and fostering students’ social and emotional development in a rapidly changing educational landscape. This research provides valuable insights and calls for a more comprehensive, experiential approach to teacher training.