Emily A. Carter

Achieving chemical accuracy for molecular simulations remains a central challenge in computational chemistry. Here, we present an embedded correlated wavefunction transfer learning (ECW-TL) framework for accurately simulating molecular dynamics in the condensed phase. ECW-TL incorporates high-level electron exchange and correlation effects in ECW theory while preserving training and computational efficiency of machine learned interatomic potentials. We demonstrate the framework on Ca2+-CO32- ion pairing in aqueous solution, a key process underlying CO2 mineralization in seawater. As proof of principle, we first show that finetuning a DFT-revPBE-D3(BJ) baseline model with embedded-DFT-SCAN data reproduces the DFT-SCAN free-energy surface within 1 kcal/mol across all solvation states. Extending the framework to embedded MP2 and localized natural-orbital CCSD(T) further refines the free-energy profile, revealing the crucial role of exact electron exchange and correlation in determining ion-pair stability and structure. ECW-TL thus provides a general, data-efficient route for transferring CW accuracy to large-scale simulations of complex aqueous and interfacial chemical processes.

Achieving chemical accuracy for molecular simulations remains a central challenge in computational chemistry. Here, we present an embedded correlated wavefunction transfer learning (ECW-TL) framework for accurately simulating molecular dynamics in the condensed phase. ECW-TL incorporates high-level electron exchange and correlation effects in ECW theory while preserving training and computational efficiency of machine learned interatomic potentials. We demonstrate the framework on Ca2+-CO32- ion pairing in aqueous solution, a key process underlying CO2 mineralization in seawater. As proof of principle, we first show that finetuning a DFT-revPBE-D3(BJ) baseline model with embedded-DFT-SCAN data reproduces the DFT-SCAN free-energy surface within 1 kcal/mol across all solvation states. Extending the framework to embedded MP2 and localized natural-orbital CCSD(T) further refines the free-energy profile, revealing the crucial role of exact electron exchange and correlation in determining ion-pair stability and structure. ECW-TL thus provides a general, data-efficient route for transferring CW accuracy to large-scale simulations of complex aqueous and interfacial chemical processes.

High Resolution Image Download MS PowerPoint Slide Carbon–carbon (C–C) bond formation is necessary for hydrocarbon (and oxygenate) synthesis beyond methane (and formate/formic acid) during electrochemical CO and CO 2 reduction (ECOR and ECO 2 R). Cu has notable ability to form hydrocarbons compared to other pure metals. In particular, the (100) facet of face-centered cubic Cu forms ethylene competitively with H 2 and methane during both ECOR and ECO 2 R. Past simulations based on density functional theory (DFT) with standard exchange-correlation functional approximations predict fast nonelectroactive C–C bond formation channels involving adsorbed (*) CO together with another *CO, formyl (*CHO), or hydroxymethylidyne (*COH), forming OC*–*CO, OC*–CHO*, and OC*–*COH, respectively. Such simulations support the prevailing hypothesis that emergence of C 2 products is kinetically determined at the early stages of the reduction chemistry. Here we show, via simulations with more accurate many-body, i.e., “correlated”, wavefunction theory (enabled by an embedding scheme), that the coupling of *CO with a *CO or a *COH (previously predicted at the same level of theory to kinetically dominate over *CHO as the one-electron reduction product of *CO) is highly activated (kinetically impeded), with free energy barriers >1 eV, in contradiction to previous DFT-based simulations. Intriguingly, we find that the coupling of two adjacent *COHs incurs only a small barrier (<0.3 eV) and is exoergic (< –1 eV); however, given the predicted low surface mobility of *COH, the emergence of HOC*–*COH is also improbable, at least at low *COH coverages. We therefore conclude that it is highly unlikely for *CO to participate in nonelectroactive C–C bond formation on pristine Cu(100), contrary to conventional wisdom, and that the energetically favorable *COH dimerization may occur only after substantial buildup of *COH on the surface.

Formation of hydrocarbons containing two or more carbon atoms (C₂₊) during heterogeneous electrochemical CO and CO₂ reduction (ECOR and ECO₂R) only occurs, among pure metals, on Cu electrodes. Moreover, the activity and selectivity is facet dependent, with Cu(100) generally preferentially forming ethylene over methane. Previously, we found via quantum-mechanics-based modeling that, unlike standard density functional theory, more accurate correlated wavefunction methods predict that non-electroactive coupling pathways involving two adsorbed COs (*CO) or a *CO and a *COH to form C–C bonds on Cu(100) are kinetically inhibited, with the former also thermodynamically unfavorable. Here, we extend that embedded complete active space second order perturbation theory (ECASPT2) study, further showing that electrochemical coupling of two *COs to form an anionic dimer [OC*–*CO]^(1+δ)–, followed by protonation to form [OC*–*COH]^δ−, is not kinetically competitive with the reduction of *CO to *COH at relevant ECO/CO₂R potentials. Our simulations therefore suggest that the ability of Cu(100) to electrochemically synthesize C₂₊ molecules from CO and CO₂ is unlikely to be via *CO, at least on pristine Cu(100). Instead, hydrogenated CO species (*COH, *CH_<i>x</i>OH, or *CH_<i>x</i>) are most likely to be the key intermediates in C–C bond formation.

High Resolution Image Download MS PowerPoint Slide Single-atom alloys (SAAs) are an increasingly popular platform for heterogeneous catalysis because of their distinct electronic structures and ability to break catalytic linear scaling relationships. This popularity has led to a proliferation of computational studies probing SAA reactivity at the density functional theory (DFT) level. However, some phenomena such as photo- and electrocatalysis require use of electronic structure methods beyond DFT; such studies are both rare and fundamentally challenging. Density functional embedding theory (DFET)/embedded correlated wavefunction (ECW) studies of reactions on metal surfaces have been shown to provide a reliable way to correct for DFT-related errors. DFET/ECW studies of chemistry involving SAAs, however, could require active spaces beyond the capabilities of traditional multireference methods when transition-metal dopants give rise to many degenerate states. To overcome this limitation, we combined our DFET/ECW methodology with the density matrix renormalization group (DMRG) complete active space self-consistent field (DMRGSCF) and DMRG N -electron valence state second-order perturbation theory (DMRG-NEVPT2) methods in the PySCF code. Using embedded DMRGSCF and embedded DMRG-NEVPT2, we analyze CO adsorption on Ni-, Rh-, Pd-, and Pt-doped Ag(100) with different active spaces. We show that the active spaces approachable with conventional multireference methods lead to overbinding of CO due to an inability to treat all of the dopant d-orbitals on equal footing. Larger active spaces, which are easily treated by both DMRGSCF and DMRG-NEVPT2, yield much more reasonable adsorption free energies. Our findings suggest that future multireference calculations of these systems should similarly employ active spaces containing all of the dopant d-orbitals along with sp-band orbitals of the host metal near the Fermi level. Emb-DMRG-NEVPT2 is a method that can be broadly applied to study catalytic reactions on metal surfaces when large active spaces are required.

High Resolution Image Download MS PowerPoint Slide Formation of hydrocarbons containing two or more carbon atoms (C 2+ ) during heterogeneous electrochemical CO and CO 2 reduction (ECOR and ECO 2 R) only occurs, among pure metals, on Cu electrodes. Moreover, the activity and selectivity is facet dependent, with Cu(100) generally preferentially forming ethylene over methane. Previously, we found via quantum-mechanics-based modeling that, unlike standard density functional theory, more accurate correlated wavefunction methods predict that non-electroactive coupling pathways involving two adsorbed COs (*CO) or a *CO and a *COH to form C–C bonds on Cu(100) are kinetically inhibited, with the former also thermodynamically unfavorable. Here, we extend that embedded complete active space second order perturbation theory (ECASPT2) study, further showing that electrochemical coupling of two *COs to form an anionic dimer [OC*–*CO] (1+δ)–, followed by protonation to form [OC*–*COH] δ−, is not kinetically competitive with the reduction of *CO to *COH at relevant ECO/CO 2 R potentials. Our simulations therefore suggest that the ability of Cu(100) to electrochemically synthesize C 2+ molecules from CO and CO 2 is unlikely to be via *CO, at least on pristine Cu(100). Instead, hydrogenated CO species (*COH, *CH x OH, or *CH x ) are most likely to be the key intermediates in C–C bond formation.

Understanding how movable objects affect navigability is critical for embodied agents operating in realistic environments. This study proposes a learning-based approach to infer traversable scene structures under object mobility constraints. A neural graph encoder is trained to predict passability relations between spatial regions conditioned on object states, using RGB-D observations and interaction feedback. The model is trained on 15,000 simulated navigation trajectories generated in rearranged indoor scenes. Quantitative evaluation shows that the learned scene structure reduces navigation failure due to blocked paths by 28.4% and improves average navigation efficiency by 16.7% compared with static scene graph representations.

Magnetic resonance imaging (MRI) is an indispensable diagnostic tool, yet the confined bore and acoustic noise can evoke considerable anxiety and claustrophobic reactions. High anxiety leads to motion artifacts, incomplete scans and reliance on pharmacological sedation. MIRAGE (Mixed Reality Anxiety Guidance Environment) harnesses the latest mixed reality (MR) hardware to prepare patients for MRI through immersive virtual reality (VR) and markerless augmented reality (AR) registration. In this paper, we extend our previous work by providing a comprehensive review of related research, detailing the system architecture, and exploring metrics for patient and clinician experience. We also present considerations for clinical deployment of MR systems within hospital workflows. Our results indicate that depth-based registration achieves sub-centimeter accuracy with minimal setup, while the immersive coaching environment reduces patient anxiety and yields favourable usability scores.

OBJECTIVES We assessed the impact of implementing gamification via mHealth in psychotherapies treating late-life depression on promoting adherence to between-session homework and improving depressive symptoms. We also assessed the relationship between adherence and depressive symptoms. DESIGN & SETTING We compared a gamified phase (Phase II) and a nongamified phase (Phase I) of three reward exposure-based psychotherapies treating late-life depression at the Weill Cornell ALACRITY Research Center. Each phase recruited its own cohort of participants. PARTICIPANTS Middle-aged to older adults (Mean [SD] age 69.4 [8.04]) with major depression (N = 102). MEASUREMENTS Adherence was indicated using daily self-reported homework completion, and depressive symptoms were measured four times over the 12-week study using the Montgomery Åsberg Depression Rating Scale (MADRS). RESULTS Participants who received gamified psychotherapy had higher odds (aOR = 8.20 [95% CI: 2.68-25.04]; p <0.001) of completing daily homework and greater reduction in MADRS (9.58 vs. 4.73, Δ=4.85 [95% CI: 2.23-7.47], p <0.001) compared to those who received a nongamified version. In the gamified version, a 20% increase in homework completion rate was associated with a 2.97-point reduction (95% CI: 1.31-4.63; p <0.001) in MADRS at the following assessment. CONCLUSIONS Using gamification in mHealth psychotherapies treating late-life depression was associated with higher homework adherence and reduced depressive symptoms. Higher adherence in gamified psychotherapies was associated with a greater reduction in depressive symptoms. This suggests that gamification could enhance the effectiveness of mHealth psychotherapies and promote the wider adoption among older adults, helping to address the growing demand for mental health care among this population.

Trauma-informed care (TIC) is widely recognised as essential for improving health outcomes and reducing retraumatisation. While most studies have focussed on clinical implementation, embedding TIC principles at the system and policy levels is increasingly considered necessary for sustainable, equitable change in healthcare. To map the existing literature on system-level trauma-informed policies in healthcare settings and identify key domains, implementation strategies and reported outcomes. This scoping review followed Arksey and O’Malley’s six-stage framework, with enhancements from Levac et al. , and was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines. A systematic search was conducted across the PubMed, MEDLINE, CINAHL, PsycINFO and Scopus databases for studies published between January 2000 and June 2025. Eligible studies described trauma-informed organisational or system-level policies in healthcare and included empirical data, frameworks and implementation reports. Twelve studies focussing on mental health and primary care, mainly from the United States and Canada, were included. Five recurring domains have emerged: workforce development and training, physical and environmental safety, leadership and governance commitment, patient and staff empowerment and language and procedural changes. Implementation strategies included staff education, advisory board involvement, policy redesign and leadership engagement. Outcomes were inconsistently reported but included improvements in staff attitudes, patient satisfaction and a reduction in coercive practices. Trauma-informed system-level policies hold significant promise for promoting equity, safety and cultural transformation in healthcare. However, current evidence remains limited and concentrated in specific settings. Future research should prioritise evaluation, diverse context applications and co-leadership with people with lived experience to advance trauma-informed system change.

Energy efficiency is a critical factor in reducing the environmental impact and operational costs of chemical manufacturing processes. Innovative technologies and methodologies are continually being developed to improve energy utilization, minimize waste, and optimize chemical production. This article explores the latest innovations in energy-efficient chemical manufacturing, including advancements in process integration, energy recovery systems, and renewable energy applications. The paper highlights key strategies for improving energy efficiency in chemical plants and discusses the potential for these innovations to drive sustainability and cost savings in the chemical industry.

Density functional embedding theory (DFET) enables use of electronic structure methods with higher accuracy than density functional theory in a local region, with applications thus far ranging from (photo/electro)catalysis to reactions in solution. DFET partitions a large collection of atoms into smaller groups that interact via a shared embedding (interaction) potential V emb, determined via functional optimization. The optimized effective potential (OEP) process used to optimize V emb is time-consuming and becomes a computational bottleneck due to sharp, oscillating features of V emb near nuclei. Here, similar to pseudopotential theory, by reconstructing electron densities used in the OEP process from smoother pseudo-valence-only (PVO) electron densities as proxies for total densities of the full system and subsystems, we can retain accuracy in the embedded electronic structure calculations while potentially reducing the overhead of V emb construction, within the projector augmented-wave (PAW) formalism. We explore three different chemical reactions as exemplars to test PVO–DFET, namely, H 2 dissociative adsorption on a Cu(111) surface, H 2 O adsorption on a Pt(111) surface, and aqueous [Ca 2+ –SO 4 2– ] ion-pair formation. The PVO approximation works well for all three systems with minimal loss of accuracy (∼10–70 meV error relative to the original exact-derivative (ED) approach) while accelerating V emb generation for the Cu and Pt systems respectively by 20× and 5×. Given proper numerical convergence parameters, the spatial distributions of differences between PVO- and ED-based V emb outside the core regions are small, explaining the exceptional agreement between the two approaches. We anticipate that this more efficient PVO–DFET approximation will be useful whenever computation of V emb is much more expensive than subsequent embedded high-level electron correlation calculations.

BACKGROUND: Residents and fellows in graduate medical education (GME) programs across the USA often complete training during childbearing years, presenting challenges for pregnant and parenting trainees balancing work and family. Institutional policies must better support these trainees. Previous studies show supported trainees experience reduced burnout, better health, and improved patient outcomes. OBJECTIVE: This study assessed the experiences and unmet needs of pregnant and parenting GME trainees and presents their recommendations for improved support. METHODS: Using a sequential explanatory mixed-methods design, we examined the unmet needs and challenges of pregnant and parenting trainees, and their recommendations for improvement. We distributed a survey to all GME trainees across all specialties at Oregon Health and Science University (OHSU) in 2023. Ninety-eight out of 160 eligible participants completed the survey (~60% response rate). RESULTS: Despite existing policies, trainees at Oregon Health and Science University faced persistent challenges. We identified three themes and related recommendations from our analysis of quantitative and open-ended survey data: (1) Leave and coverage-barriers to adequate parental leave and inconsistent enforcement of GME policies; [2] Lactation-meeting breast/chest-feeding goals required immense effort due to limited resources; and [3] Health and childcare-existing policies negatively impacted fertility, childcare access, and mental health. Respondents recommended standardized, flexible leave policies; transparent processes for work adjustments and planning; improved access to private, well-equipped lactation spaces; and tailored mental health and wellness programs to support the perinatal period. CONCLUSIONS: Barriers persist for trainees starting families. Institutional leaders have actionable opportunities to improve equity and institutional support of parenting trainees. Key messages What is already known on this topic: GME trainees face significant challenges during childbearing years, including inconsistent parental leave policies, limited institutional support, and increased risk of stress and burnout, which negatively impact their health and patient-care outcomes. What this study adds: This study contributes trainees' recommendations for institutional reforms necessary to address persistent gaps in support for parenting trainees, such as insufficient parental leave, inadequate lactation accommodations, and barriers to mental health care. How this study might affect research, practice, or policy: Incorporating trainee perspectives is crucial to developing effective interventions. Institutions and national standards should prioritize equitable parental leave, flexible scheduling, and comprehensive supports to foster a culture that aligns with trainees' personal and professional goals. Research Questions How do Accreditation Council for Graduate Medical Education requirements and institutional policies reinforce or mitigate structural inequities that impact parent medical trainees? What systemic barriers and supports shape access to perinatal health and mental health resources for parent medical trainees? How might medical trainees' perspectives influence the development and implementation of Accreditation Council for Graduate Medical Education requirements and institutional policies that better support medical trainees ability to balance professional training and family responsibilities?

The random phase approximation (RPA), a method for treating electron correlation, has been shown to be superior to standard density functional theory (DFT) approximations in numerous cases. However, the RPA's computational cost is substantially higher than that of DFT, particularly restricting its application to extended surfaces. The recently introduced embedded RPA (emb-RPA) approach [Wei et al., J. Chem. Phys. 159(19), 194108 (2023)] reduces this computational cost by approximately two orders of magnitude. While previous applications of emb-RPA focused on non-spin-polarized systems, here we extend the approach to ferromagnetic ones. Unlike other embedded correlated wavefunction methods, such as embedded complete active space self-consistent field theory, emb-RPA is advantageous for spin-polarized systems because the RPA is compatible with unrestricted DFT solutions, which are eigenfunctions of the spin angular momentum operator Sz but not the total spin-squared operator S2. By applying emb-RPA with specific magnetization constraints, we achieved a speedup of two to three orders of magnitude (one order when accounting for the one-time embedding potential optimization cost) with only small errors (∼50 meV) compared to full periodic RPA. Moreover, emb-RPA significantly reduces the over-binding errors of DFT approximations. We anticipate that the acceleration enabled by the spin-polarized emb-RPA approach will broaden the applicability of RPA to magnetic materials.

As cities continue to grow and urbanize, sustainable transportation infrastructure has become essential for reducing carbon emissions, promoting environmental health, and improving urban mobility. Innovative approaches to sustainable transportation infrastructure are transforming the way cities plan and design transportation systems. This article explores the latest innovations in sustainable transportation infrastructure, focusing on green mobility solutions, energy-efficient public transit systems, and smart urban planning. It highlights the role of sustainable transportation in mitigating climate change, improving air quality, and promoting equitable access to transportation options for all city residents.

Financial institutions are increasingly leveraging---such as text, audio, and images---to gain insights and competitive advantage. Deep learning (DL) has emerged as a powerful paradigm for analyzing these complex data types, transforming tasks like financial news analysis, earnings call interpretation, and document parsing. This paper provides a comprehensive academic review of deep learning techniques for unstructured financial data. We present a taxonomy of data types and DL methods, including natural language processing models, speech and audio processing frameworks, multimodal fusion approaches, and transformer-based architectures. We survey key applications ranging from sentiment analysis and market prediction to fraud detection, credit risk assessment, and beyond, highlighting recent advancements in each domain. Additionally, we discuss major challenges unique to financial settings, such as data scarcity and annotation cost, model interpretability and regulatory compliance, and the dynamic, non-stationary nature of financial data. We enumerate prominent datasets and benchmarks that have accelerated research, and identify research gaps and future directions. The review emphasizes the latest developments up to 2025, including the rise of large pre-trained models and multimodal learning, and outlines how these innovations are shaping the next generation of financial analytics.

Objectives: High rates of depression and suicidal ideation among elder abuse (EA) victims have been documented, but few studies have examined post-traumatic stress disorder (PTSD) symptoms in this population. This study assessed PTSD symptom rates and presentation among EA victims with depression and evaluated PTSD symptom trajectories throughout PROTECT, a brief behavioral intervention. Design: Depressed (PHQ-9 ≥ 10) EA victims with no cognitive impairment (Mini MoCA ≥ 11) were referred by partner agencies and consented by research staff. Eligible participants received nine weeks of PROTECT and were assessed at baseline, weeks 6 and 9. The study was approved by the Weill Cornell IRB (Protocol #19-09020854). Setting: EA victims with depression in NYC were referred to study team for 9 weeks of psychotherapy in NIMH funded trial P50 MH113838. Participants: 40 elder abuse victims with depression and no cognitive impairment. Intervention: PROTECT is a brief behavioral psychotherapy delivered remotely in nine weekly 45-minute sessions. Measurements: PTSD Checklist for DSM-5 (PCL-5) and Life Events Checklist (LEC) assessed trauma symptoms and history; MADRS assessed depression severity. Mixed-effects models examined presence and change in PTSD symptoms across treatment, adjusting for covariates. Results: 60% of participants met the criteria for probable PTSD (PCL-5 ≥ 31) at baseline. These individuals had significantly higher baseline depression scores. PTSD symptoms significantly decreased across treatment, even after adjusting for demographic or trauma-related variables. Conclusions: PTSD symptoms are highly prevalent among depressed EA victims. Participants who received the PROTECT psychotherapy intervention demonstrated reductions in PTSD symptoms. Results underscore the potential utility of brief, remotely delivered psychotherapy in addressing trauma-related distress among EA victims and highlight the importance of integrating PTSD-focused care into services for this high-risk population.

This article explores Philip Rieff’s critical analysis of modern society’s shift from traditional sacred orders to the dominance of therapeutic culture. Rieff argues that the erosion of communal sacred frameworks has led to the rise of individualism and self-fulfillment as primary social values, undermining collective authority and shared meaning. The paper examines how this transition impacts the cohesion and identity of communities, highlighting the consequences of therapeutic culture’s focus on personal well-being over social responsibility. By revisiting Rieff’s critique, the article provides insight into the challenges facing contemporary community life and the potential paths toward reconstituting social order.

Background: As healthcare systems grow increasingly complex and technology-driven, there is a pressing need to equip future physicians with problem-solving skills, systems thinking and design principles that define engineering disciplines. Although isolated programmes have integrated engineering into medical training, there is a limited synthesis of how these approaches are operationalised and evaluated. Objective: This narrative review explores the integration of engineering principles into medical education and identifies models, thematic trends and opportunities for interdisciplinary innovation. Methods: Relevant literature was identified through a comprehensive search of PubMed, Scopus, IEEE Xplore and grey literature sources using keywords related to engineering mindsets, medical education and interdisciplinary training. A flexible and exploratory approach was used to include peer-reviewed and conceptual articles that addressed the application of systems thinking, simulation, quality improvement (QI) and design in medical training contexts. Results: Fifteen articles spanning North America and Europe were included in the study. Four themes emerged: (1) engineering-based curricula and dual-degree programmes; (2) simulation, modelling and systems thinking; (3) healthcare systems engineering and QI and (4) interdisciplinary collaboration and innovation. Programmes such as EnMED and MEDTEC exemplify dual-degree initiatives that foster innovation capacity. Simulation tools enhance experiential learning of physiology and pharmacokinetics. Lean methodology, human factors and system optimisation are embedded in QI education. Interdisciplinary programmes, such as healthcare hackathons and design thinking challenges, cultivate real-world problem-solving and team-based innovation. Conclusion: Integrating engineering principles into medical education enhances learners’ ability to navigate complex technology-intensive healthcare systems. Embracing systems thinking, process design and interdisciplinary collaboration represents curricular enhancement and a fundamental evolution in preparing future physician-innovators.

Optical Character Recognition (OCR) is a crucial technology for converting images of text into editable and searchable data. The increasing demand for efficient OCR systems in various fields, such as document digitization and text mining, highlights the significance of optimizing OCR processes. However, existing OCR methods often face challenges in accurately recognizing characters from distorted or low-quality images, limiting their practical applicability. In this context, this paper proposes a novel approach for efficient OCR based on Radial Basis Function (RBF) networks. By leveraging the capabilities of RBF networks in nonlinear mapping and pattern recognition, our method aims to enhance the accuracy and efficiency of character recognition tasks. The innovative framework introduced in this study combines the robustness of RBF networks with advanced image processing techniques to improve OCR performance, particularly in challenging image conditions. This research contributes to the optimization of OCR systems, offering a promising solution for enhancing the effectiveness of character recognition processes in real-world applications.

BACKGROUND: The combination of antidepressant and antipsychotic medication is an effective treatment for major depressive disorder with psychotic features ('psychotic depression'). The present study aims to identify sociodemographic and clinical predictors of remission of psychotic depression treated with combination pharmacotherapy and determine the accuracy of prediction models. METHODS: Two hundred and sixty-nine participants aged 18 to 85 years with psychotic depression were acutely treated with protocolized sertraline plus olanzapine for up to 12 weeks. Three cross-validated machine learning models were implemented to predict remission based on 74 sociodemographic and clinical variables measured at acute baseline. The optimal model for each method was selected by the average fold C-index. Based on the performance of each method, grouped elastic net (cox) regression was chosen to examine the association of each predictor with remission of psychotic depression. RESULTS: Of the 269 participants, 145 (53.9 %) experienced full remission of the depressive episode and psychotic features. Multivariable models had 65.1 % to 67.4 % accuracy in predicting remission. In the grouped elastic net (cox) regression model, longer duration of index episode, somatic or tactile hallucinations, higher burden of comorbid physical problems, and single or divorced marital status were independent predictors of longer time to remission. A higher number of lifetime depressive episodes and peripheral vascular or cardiovascular disease were predictors of shorter time to remission. CONCLUSIONS: Future research needs to determine whether the addition of biomarkers to clinical and sociodemographic variables can improve model accuracy in predicting remission of pharmacologically-treated psychotic depression.

Ammonia (NH 3 ) is an energy-rich molecule that is routinely synthesized from nitrogen (N 2 ) and hydrogen (H 2 ). NH 3 ’s more favorable physical properties compared to H 2 suggests it may offer a way to more conveniently store, transport, and, when needed, extract H 2 via thermal decomposition. However, the high kinetic barrier and endoergicity to decompose to H 2 and N 2 require high temperatures. The standard reaction free energy indicates nearly 100% thermodynamic conversion to the diatomic molecules only at ∼673 K and higher. However, even at these temperatures, a catalyst, e.g., iron (Fe), is needed for favorable kinetic conversion. Here, we explore via density functional theory the kinetics of NH 3 decomposition on the most stable facet of body-centered cubic Fe, namely, (110), under typical high-temperature and finite-pressure operando conditions. We predict coverage-dependent energetics of elementary surface reactions, often neglected in atomic-scale modeling. From these models, we find the recombinative desorption of adsorbed N as N 2 is rate-determining at 573.15–773.15 K and even at an extreme case of 1173.15 K. From microkinetic modeling, we find that the steady-state turnover frequencies (TOFs) for N 2 and H 2 generation rates ( r H 2 ) depend exponentially on temperature. The catalyst achieves a steady-state TOF of 36.4 s –1 and an r H 2 of 0.107 μmol cm –2 s –1 for a feed of 1.8 bar NH 3 with 0.2 bar H 2 at 1173.15 K. However, at 773.15 K, with the same feed composition and velocity, the steady-state TOF and r H 2 decrease to 0.14 s –1 and 4.10 × 10 –4 μmol cm –2 s –1, respectively, as the process is significantly hindered by slow N 2 desorption. Although at first glance counterintuitive, our simulations suggest that surface modifications that reduce Fe’s reactivity toward NH x species should enhance its overall NH 3 decomposition activity.

• Ambient pressure X-ray photoelectron spectroscopy of GaP in CH 3 OH atmosphere. • DFT enables identification of CH 3 OH dehydrogenation products on GaP surface. • At room temperature, CH 3 O, CH 2 O, and hydrogen bonded CH 3 OH-CH 3 O are present. • Availability of surface phosphorus sites limits the formation of adsorbed CH 2 O. • Above 400 K, concentration of CH x species increases, suggesting C-O bond cleavage. Ambient pressure X-ray photoelectron spectroscopy (AP-XPS) was used to investigate methanol (CH 3 OH) adsorption and reaction on the GaP(110) surface. Exposure of CH 3 OH to GaP(110) at room temperature led to the formation of at least four different surface species as indicated by analysis of C 1s and O 1s XPS features. By combining AP-XPS data with density functional theory calculations, the surface species were identified as methoxy (CH 3 O*), formaldehyde (CH 2 O*), and paired methanol (p-CH 3 O*H) and methoxy (p-CH 3 O*) species, where “paired” means that they belong to a hydrogen-bonded methoxy-methanol complex. Asterisk * here indicates an adsite. The formation of CH 2 O* via the dehydrogenation of CH 3 O* was shown to be limited by the availability of vacant phosphorus (P) sites on GaP(110). With an increase in CH 3 OH pressure, the fractional coverage of CH 3 O* species reached 0.55, and the surface P sites were completely saturated with hydrogen. Under a constant CH 3 OH pressure of 0.5 Torr, the surface concentration of the paired species and of CH 2 O* remained constant until 400 K. At higher temperatures, thermally driven reactions led to a significant increase in the concentration of surface CH x * species, which suggests that C-O bond cleavage of the CH 3 O group is the dominant decomposition mechanism on GaP(110). Based on the reactivity of GaP(110) toward CH 3 OH dehydrogenation, elevated temperatures and CH 3 OH pressures may be used to functionalize this surface.

As power densities in modern electronics increase, efficient thermal management is essential. Conventional heat sink designs often fail to balance heat dissipation, airflow resistance, and manufacturability. This study proposes an AI-driven optimization framework, integrating deep reinforcement learning (DRL) and multi-objective genetic algorithms (MOGA), to refine fin geometries while ensuring fabrication feasibility. Unlike conventional methods, this approach incorporates additive manufacturing constraints, bridging the gap between computational optimization and real-world implementation. Validated through computational fluid dynamics (CFD) simulations and experimental fabrication, the optimized design achieved a 14.3% reduction in maximum temperature and a 32.8% decrease in thermal resistance, ensuring a more uniform temperature distribution. It also maintained stable cooling performance across airflow variations, confirming its adaptability. Manufacturability analysis revealed height deviations of up to 0.4 mm, which could affect airflow, while thickness deviations remained within ± 0.05 mm, indicating high precision. These results highlight the importance of integrating fabrication constraints early in the design process to ensure optimization benefits translate into practical performance. This study shows that AI-driven optimization can enhance heat sink efficiency and reliability, offering a scalable approach for high-power electronics. Future work should refine manufacturing compensation models and transient thermal analysis to further improve real-world applicability.

The ability to analyze consumer behavior and market trends has become a crucial aspect of business strategy in the digital age. Machine learning (ML) techniques, powered by big data analytics, enable businesses to gain deeper insights into customer preferences, predict market trends, and personalize marketing efforts. This paper explores the role of machine learning in analyzing consumer behavior, covering key applications such as AI-driven recommendation systems, sentiment analysis, demand forecasting, and customer segmentation. Additionally, challenges such as data privacy, bias in algorithms, and real-time processing constraints are discussed, along with emerging trends in AI-powered market analytics.

Ammonia (NH 3 ) is one of the most important industrial chemicals. The conventional NH 3 synthesis method─the Haber–Bosch process─converts atmospheric nitrogen (N 2 ) into NH 3 using H 2 with an iron (Fe) catalyst. However, this process requires high pressures (100–200 atm) and temperatures (700–800 K) near thermal equilibrium. Recently, Fe-based nanocatalysts have been reported to produce promising NH 3 yields under atmospheric pressures and temperature-modulated nonequilibrium conditions. Understanding the mechanism of nonequilibrium catalysis with programmed temperature variation could help to optimize this fully electrified and less energy-intensive process. Although reactive molecular dynamics (RMD) simulations can be a useful tool to model nonequilibrium catalytic processes, they require the development of accurate force fields (i.e., interatomic potentials). Here, we present a machine-learned (ML) force field within the Deep Potential MD (DPMD) framework, trained using periodic density functional theory (DFT) calculations, to model NH 3 synthesis on Fe catalysts with various surface adsorbates such as *N, *H, *N 2, *H 2, *NH, *NH 2, and *NH 3 . We generated the DFT data from static models of elementary reactions on the most stable (110) surface of body-centered cubic Fe, which then were augmented by data from constant number of particles–volume–temperature (NVT) DFT-MD trajectories at various temperatures. Finally, we utilized the fully optimized ML force field to investigate reaction dynamics at an Fe(110) surface at linearly increasing temperatures using NVT-DPMD simulations. Our simulations indicate that pulsed temperature ramping could prove favorable for NH 3 synthesis. For example, we conducted ramping under multiple sets of conditions: (i) from 900 to 1200 K over periods of 0.1–0.3 ns for Fe surfaces precovered with N or NH along with H; and (ii) from 300 to 600 K over 0.1–0.3 ns for Fe surfaces precovered with NH 3 . While our simulations so far are limited to short time scales (very rapid heating), these observations shed light on the mechanism of the high NH 3 synthesis rate achieved in a novel temperature-modulated nonequilibrium catalytic reactor using pulsed heating and cooling.

Energy consumption prediction is a crucial area of research due to its significant impact on energy efficiency and sustainability. Current research on this topic faces challenges in accurately forecasting energy usage patterns, limiting the effectiveness of energy management systems. This paper proposes a novel approach utilizing Support Vector Regression (SVR) to improve the accuracy of energy consumption prediction models. The study explores the integration of SVR with historical energy data and external factors to enhance the predictive capabilities of the model. The innovative methodology presented in this paper aims to address the limitations of existing prediction techniques and contribute to the advancement of energy forecasting technology.

Aging is associated with marked declines in episodic memory corresponding with decreased volume in studies of morphology and reduced network response in studies of functional connectomics. Furthermore, recent research has demonstrated that reductions in resting state network connectivity are related to declines in episodic memory, specifically in the default mode and frontoparietal cortical networks. Additionally, the interactive effects of aging and traumatic brain injury (TBI) are associated with increased risk for neurodegeneration and episodic memory impairments. However, there is a gap in the literature examining episodic memory and hippocampal-subcortical resting state connectivity differences related to aging with and without TBI. The current work aims to investigate episodic memory differences between older adults with TBI (N = 45) and older adults with no history of TBI (N = 28) and how that relates to hippocampal-subcortical network differences at rest. We demonstrate a positive relationship between default mode and frontoparietal network connectivity and memory performance differentially between those aging with and without moderate-severe TBI (msTBI). Additionally, we demonstrate that reliability in the strength of resting state functional connectivity between parcellations is weakest among connections to the hippocampus compared to other cortical connections but is generally reliable across other connections.

The global energy sector is undergoing a significant transformation with the adoption of renewable energy technologies. Advancements in solar, wind, hydro, and bioenergy are shaping the future of sustainable power, reducing dependency on fossil fuels and mitigating climate change. Emerging technologies such as perovskite solar cells, floating wind farms, and next-generation battery storage systems are enhancing the efficiency and reliability of renewable energy. Despite these breakthroughs, challenges such as energy intermittency, infrastructure costs, and policy barriers persist. This paper explores recent advancements in renewable energy, their impact on the global energy landscape, and future directions for achieving a sustainable energy transition. Data analysis includes statistical trends in renewable energy adoption and investment patterns worldwide.

This article explores how post-truth politics undermines legal epistemology and erodes the role of legal facts in polarized democratic societies. Using a scientific narrative review and descriptive analysis method, this study synthesizes interdisciplinary literature from legal theory, political philosophy, media studies, and epistemology published between 2020 and 2024. Relevant peer-reviewed journal articles, legal commentaries, and philosophical texts were selected to examine the influence of post-truth dynamics on legal reasoning. The analysis focused on conceptual clarification, comparative case studies from countries such as the United States, Brazil, Hungary, and the United Kingdom, and critical reflection on mechanisms such as judicial politicization, manipulation of evidence, expert delegitimization, and the impact of algorithmic media systems. The findings reveal that legal systems in polarized democracies are increasingly susceptible to epistemic fragmentation, where multiple conflicting narratives replace a shared understanding of legal facts. The ideal of objectivity in judicial reasoning is collapsing under political pressure, and proof standards are becoming inconsistent due to ideological polarization and cognitive biases. Media trials and disinformation campaigns further distort public understanding of legal outcomes. These dynamics collectively weaken public trust in legal institutions and challenge the ability of courts to function as neutral, evidence-based arbiters. However, the study also identifies pathways for institutional and educational reform, including reinforcing judicial independence, integrating media literacy and epistemology into legal education, enhancing procedural safeguards, and promoting transnational cooperation against disinformation. The post-truth era presents a serious threat to the epistemological foundations of law. To protect the integrity of legal systems, democratic societies must adopt comprehensive strategies that reaffirm truth-seeking as a central value in legal practice and discourse.

Solutions and minerals containing sulfate (SO42-), and Ca2+ and Mg2+ cations, are ubiquitous throughout the lithosphere and are significant components of seawater, thus presenting a prototypical system for the study of strong electrolytes and crystal nucleation mechanisms. However, despite their relative abundance, key questions remain unanswered about the most fundamental atomic-level steps of their mineralization pathways and aqueous dynamics. Here, we carry out enhanced sampling multi-level molecular dynamics (MD) embedded correlated wavefunction theory simulations to elucidate ion-pairing mechanisms for Mg-SO4 and Ca-SO4 in concentrated aqueous solution, accurately capturing effects arising from both structural dynamics and electron exchange-correlation. We predict contact-ion-pair formation to be barrierless and highly exoergic for Ca-SO4, in agreement with its minimal solubility, whereas for Mg-SO4, solvent-shared and contact ion pairs have similar free energies, qualitatively consistent with its higher solubility. Finally, we demonstrate that brief high-temperature pre-equilibration may be utilized to accelerate convergence of free energies in blue-moon-ensemble enhanced-sampling MD.

<h3>Background</h3> Estimates suggested that the hospice reached 46% of those who could benefit from its care. In January 2022 the hospice launched a five year strategy. A new clinical model to create a sustainable foundation for patients/carers and meet projected increases in demand and complexity was central to the strategy. <h3>Aims</h3> To make better use of existing workforce model, facilities and workforce. To reach all who could benefit from our services through an inclusive and collaborative approach to delivery while maintaining quality and financial balance. <h3>Methodology</h3> The programme took a Managing Successful Programmes (MSP) approach, tailored to suit the hospice’s needs. The programme was broken into five phases across two years covering six processes of the MSP approach. New, expanded and improved services included: New central admin team coordinating all services. New rapid response service. Expansion of care coordination service. Relaunched enhanced outpatient and bereavement programme. New Compassionate Neighbours project. <h3>Results</h3> Hospice community Rapid Response team launched July 2023: Over 1,400 interventions to support community patients in crisis and prevent hospital admissions. We doubled the number of community patients who died in their PPD. Reduced pressure on our community nursing team and reached 104 more patients with non–urgent care. Care Coordination Service expanded across catchment area, July 2023. Outpatient and enhanced bereavement service launched May 2023. Compassionate Neighbours launched January 2023. <h3>Conclusion</h3> Overall, the programme management approach – customised MSP and Prince 2 methodologies – was effective; however, timelines and workplans could have been better managed to ensure the right pace of change. The inclusive approach to the development and implementation of services has played an instrumental role in collaborating between key teams. Improved patient flow should continue to increase the overall number of patients the hospice can care for over time.

Recent advancements in mobile health (mHealth) technology and the ubiquity of wearable devices and smartphones have expanded a market for digital health and have emerged as innovative tools for data collection on individualized behavior. Heterogeneous levels of device usage across users and across days within a single user may result in different degrees of underestimation in passive sensing data, subsequently introducing biases if analyzed without addressing this issue. In this work, we propose an unsupervised 2-Stage Pre-processing Algorithm for Passively Sensed mHealth Data (2SpamH) algorithm that uses device usage variables to infer the quality of passive sensing data from mobile devices. This article provides a series of simulation studies to show the utility of the proposed algorithm compared to existing methods. Application to a real clinical dataset is also illustrated.

The integration of data science and smart technologies is revolutionizing urban sustainability by optimizing resource management, reducing environmental impact, and improving the quality of life for city dwellers. By leveraging big data analytics, Internet of Things (IoT) sensors, and AI-driven insights, policymakers can make data-informed decisions to enhance energy efficiency, waste management, air quality monitoring, and transportation systems. This paper explores data-driven approaches to urban sustainability, covering key applications such as smart city infrastructure, predictive environmental modeling, and real-time sustainability monitoring. Additionally, challenges such as data privacy, infrastructure costs, and equitable urban development are discussed, along with emerging trends in AI-powered sustainable urban planning.

Abstract Background Treatment of depressive symptoms in older adults is a growing public health concern. Collecting patient-reported outcomes (PROs) may facilitate efficiently scaling psychotherapy for older adults but user-specific tailoring is needed to improve completion. Objectives This study investigates (1) the effect of updating PRO collection tools for middle-aged and older adults with depressive symptoms through a user-centered design process on user completion of PRO questions, (2) what sociodemographic factors correspond with participant completion, and (3) how completion of PRO questions change during the course of a psychotherapy intervention. Methods Analysis was conducted on 139 middle-aged and older adults with depressive symptoms from three clinical trials at the Weill Cornell ALACRITY Center. Overall response percentages to daily PRO questionnaires were compared before and after the implementation of findings from a multiphase user-centered design process. Grouped least absolute shrinkage and selection operator (LASSO) was employed to examine which baseline factors correspond with patient completion and linear regression was conducted to explore the association. Changes in daily dichotomized completion over time were analyzed with mixed-effect logistic regression. Results After user-centered updates, there was a significantly higher (p &lt; 0.001) percentage of completion (mean [standard deviation (SD)] percentage, 67.0 [35.6]%) than before (mean [SD] percentage, 24.9 [28.9]%). Additional years of education, age, and total annual household income greater than $25,000 were significant with completion percentage. Mixed-effects logistic regression showed that the odds of high completion increased each day (OR = 1.019 [95% CI: 1.014, 1.023; p &lt; 0.001]). Conclusion This study has shown that user-centered technology tailoring may be associated with increased PRO completion among middle-aged and older adults with depressive symptoms. PRO-supported psychotherapies are promising for middle-aged and older adults with depressive symptoms. Likewise, this study has demonstrated the potential benefits of employing a rigorous user-centered design process with PRO technology.

Based on the survey of the basic situation and causes of sports injuries among college students, a prevention and treatment plan for sports injuries was formulated in conjunction with new media intervention methods. The study found that: after three months of intervention experiments conducted through three terminals of the internet platform and two concentrated online classes, there was a certain degree of improvement in college students' ability to prevent and treat sports injuries.