Implementation factors of non-communicable disease policies and programmes for children and youth in low-income and middle-income countries: a systematic review.

INTRODUCTION: Despite declared life-course principles in non-communicable disease (NCD) prevention and management, worldwide focus has been on older rather than younger populations. However, the burden from childhood NCDs has mounted; particularly in low-income and middle-income countries (LMICs). There is limited knowledge regarding the implementation of paediatric NCD policies and programmes in LMICs, despite their disproportionate burden of morbidity and mortality. We aimed to understand the barriers to and facilitators of paediatric NCD policy and programme implementation in LMICs. METHODS: We systematically searched medical databases, Web of Science and WHOLIS for studies on paediatric NCD policy and programme implementation in LMICs. Screening and quality assessment were performed independently by researchers, using consensus to resolve differences. Data extraction was conducted within the WHO health system building-blocks framework. Narrative thematic synthesis was conducted. RESULTS: 93 studies (1992-2020) were included, spanning 86 LMICs. Most were of moderate or high quality. 78% reported on paediatric NCDs outside the four major NCD categories contributing to the adult burden. Across the framework, more barriers than facilitators were identified. The most prevalently reported factors were related to health service delivery, with system fragmentation impeding the continuity of age-specific NCD care. A significant facilitator was intersectoral collaborations between health and education actors to deliver care in trusted community settings. Non-health factors were also important to paediatric NCD policies and programmes, such as community stakeholders, sociocultural support to caregivers and school disruptions. CONCLUSIONS: Multiple barriers prevent the optimal implementation of paediatric NCD policies and programmes in LMIC health systems. The low sociopolitical visibility of paediatric NCDs limits their prioritisation, resulting in fragmented service delivery and constraining the integration of programmes across key sectors impacting children, including health, education and social services. Implementation research is needed to understand specific contextual solutions to improve access to paediatric NCD services in diverse LMIC settings.

Emerging contaminants: A One Health perspective.

Environmental pollution is escalating due to rapid global development that often prioritizes human needs over planetary health. Despite global efforts to mitigate legacy pollutants, the continuous introduction of new substances remains a major threat to both people and the planet. In response, global initiatives are focusing on risk assessment and regulation of emerging contaminants, as demonstrated by the ongoing efforts to establish the UN's Intergovernmental Science-Policy Panel on Chemicals, Waste, and Pollution Prevention. This review identifies the sources and impacts of emerging contaminants on planetary health, emphasizing the importance of adopting a One Health approach. Strategies for monitoring and addressing these pollutants are discussed, underscoring the need for robust and socially equitable environmental policies at both regional and international levels. Urgent actions are needed to transition toward sustainable pollution management practices to safeguard our planet for future generations.

Harnessing a Dielectric/Plasma Photonic Crystal as an Optical Microwave Filter: Role of Defect Layers and External Magnetic Fields.

We investigate the transmittance spectrum of a multichannel filter composed of dielectric (A) and plasma (P) materials in the microwave region within the transfer matrix formalism. Two configurations of the proposed filter are studied under the influence of an applied magnetic field: (1) a periodic structure containing (A/P)N unit cells surrounded by air and (2) the introduction of a second dielectric material (D) acting as a defect layer to produce an (AP)N/2/D/(AP)N/2 structure. Our findings reveal that in the periodic case, the number of resonant states of the transmittance increases with number N; however, the observed blue and red shifts depend on the intensity and orientation of the applied magnetic field. We present contour plots of the transmission coefficients that show the effect of the incident angle on the shifts of the photonic band gaps. Furthermore, we find that the introduction of a defect layer generates additional resonant states and merges the central resonant peak into a miniband of resonances. Moreover, we show that the number of resonant peaks and their locations can be modulated by increasing the unit cell number, N, as well as increasing the width of the inserted defect layer. Our proposed structures enable the design of novel photonic filters using magnetized plasma materials operating in the microwave region.

Silicone cryogel skeletons enhance the survival and mechanical integrity of hydrogel-encapsulated cell therapies.

The transplantation of engineered cells that secrete therapeutic proteins presents a promising method for addressing a range of chronic diseases. However, hydrogels used to encase and protect non-autologous cells from immune rejection often suffer from poor mechanical properties, insufficient oxygenation, and fibrotic encapsulation. Here, we introduce a composite encapsulation system comprising an oxygen-permeable silicone cryogel skeleton, a hydrogel matrix, and a fibrosis-resistant polymer coating. Cryogel skeletons enhance the fracture toughness of conventional alginate hydrogels by 23-fold and oxygen diffusion by 2.8-fold, effectively mitigating both implant fracture and hypoxia of encapsulated cells. Composite implants containing xenogeneic cells engineered to secrete erythropoietin significantly outperform unsupported alginate implants in therapeutic delivery over 8 weeks in immunocompetent mice. By improving mechanical resiliency and sustaining denser cell populations, silicone cryogel skeletons enable more durable and miniaturized therapeutic implants.

Triboelectric Energy Harvesting from Highly Conjugated Fused Aromatic Ladder Structure Under Extreme Environmental Conditions.

Practical application of triboelectric nanogenerators (TENGs) has been challenging, particularly, under harsh environmental conditions. This work proposes a novel 3D-fused aromatic ladder (FAL) structure as a tribo-positive material for TENGs, to address these challenges. The 3D-FAL offers a unique materials engineering platform for tailored properties, such as high specific surface area and porosity, good thermal and mechanical stability, and tunable electronic properties. The fabricated 3D-FAL-based TENG reaches a maximum peak power density of 451.2 µW cm-2 at 5 Hz frequency. More importantly, the 3D-FAL-based TENG maintains stable output performance under harsh operating environments, over wide temperature (-45-100 °C) and humidity ranges (8.3-96.7% RH), representing the development of novel FAL for sustainable energy generation under challenging environmental conditions. Furthermore, the 3D-FAL-based TENG proves to be a promising device for a speed monitoring system engaging reconstruction in virtual reality in a snowy environment.

Beyond Conventional Density Functional Theory: Advanced Quantum Dynamical Methods for Understanding Degradation of Per- and Polyfluoroalkyl Substances.

Computational chemistry methods, such as density functional theory (DFT), have now become more common in environmental research, particularly for simulating the degradation of per- and polyfluoroalkyl substances (PFAS). However, the vast majority of PFAS computational studies have focused on conventional DFT approaches that only probe static, time-independent properties of PFAS near stationary points on the potential energy surface. To demonstrate the rich mechanistic information that can be obtained from time-dependent quantum dynamics calculations, we highlight recent studies using these advanced techniques for probing PFAS systems. We briefly discuss recent applications ranging from ab initio molecular dynamics to DFT-based metadynamics and real-time time-dependent DFT for probing PFAS degradation in various reactive environments. These quantum dynamical approaches provide critical mechanistic information that cannot be gleaned from conventional DFT calculations. We conclude with a perspective of promising research directions and recommend that these advanced quantum dynamics simulations be more widely used by the environmental research community to directly probe PFAS degradation dynamics and other environmental processes.

A predicted-loss based active learning approach for robust cancer pathology image analysis in the workplace.

BACKGROUND: Convolutional neural network-based image processing research is actively being conducted for pathology image analysis. As a convolutional neural network model requires a large amount of image data for training, active learning (AL) has been developed to produce efficient learning with a small amount of training data. However, existing studies have not specifically considered the characteristics of pathological data collected from the workplace. For various reasons, noisy patches can be selected instead of clean patches during AL, thereby reducing its efficiency. This study proposes an effective AL method for cancer pathology that works robustly on noisy datasets. METHODS: Our proposed method to develop a robust AL approach for noisy histopathology datasets consists of the following three steps: 1) training a loss prediction module, 2) collecting predicted loss values, and 3) sampling data for labeling. This proposed method calculates the amount of information in unlabeled data as predicted loss values and removes noisy data based on predicted loss values to reduce the rate at which noisy data are selected from the unlabeled dataset. We identified a suitable threshold for optimizing the efficiency of AL through sensitivity analysis. RESULTS: We compared the results obtained with the identified threshold with those of existing representative AL methods. In the final iteration, the proposed method achieved a performance of 91.7% on the noisy dataset and 92.4% on the clean dataset, resulting in a performance reduction of less than 1%. Concomitantly, the noise selection ratio averaged only 2.93% on each iteration. CONCLUSIONS: The proposed AL method showed robust performance on datasets containing noisy data by avoiding data selection in predictive loss intervals where noisy data are likely to be distributed. The proposed method contributes to medical image analysis by screening data and producing a robust and effective classification model tailored for cancer pathology image processing in the workplace.

Velocity-Gauge Real-Time Time-Dependent Density Functional Tight-Binding for Large-Scale Condensed Matter Systems.

We present a new velocity-gauge real-time, time-dependent density functional tight-binding (VG-rtTDDFTB) implementation in the open-source DFTB+ software package (https://dftbplus.org) for probing electronic excitations in large, condensed matter systems. Our VG-rtTDDFTB approach enables real-time electron dynamics simulations of large, periodic, condensed matter systems containing thousands of atoms with a favorable computational scaling as a function of system size. We provide computational details and benchmark calculations to demonstrate its accuracy and computational parallelizability on a variety of large material systems. As a representative example, we calculate laser-induced electron dynamics in a 512-atom amorphous silicon supercell to highlight the large periodic systems that can be examined with our implementation. Taken together, our VG-rtTDDFTB approach enables new electron dynamics simulations of complex systems that require large periodic supercells, such as crystal defects, complex surfaces, nanowires, and amorphous materials.

Harnessing Semi-Supervised Machine Learning to Automatically Predict Bioactivities of Per- and Polyfluoroalkyl Substances (PFASs).

Many per- and polyfluoroalkyl substances (PFASs) pose significant health hazards due to their bioactive and persistent bioaccumulative properties. However, assessing the bioactivities of PFASs is both time-consuming and costly due to the sheer number and expense of in vivo and in vitro biological experiments. To this end, we harnessed new unsupervised/semi-supervised machine learning models to automatically predict bioactivities of PFASs in various human biological targets, including enzymes, genes, proteins, and cell lines. Our semi-supervised metric learning models were used to predict the bioactivity of PFASs found in the recent Organisation of Economic Co-operation and Development (OECD) report list, which contains 4730 PFASs used in a broad range of industries and consumers. Our work provides the first semi-supervised machine learning study of structure-activity relationships for predicting possible bioactivities in a variety of PFAS species.

FLUID-GPT (Fast Learning to Understand and Investigate Dynamics with a Generative Pre-Trained Transformer): Efficient Predictions of Particle Trajectories and Erosion.

The deleterious impact of erosion due to high-velocity particle impingement adversely affects a variety of engineering and industrial systems, resulting in irreversible mechanical wear of materials/components. Brute force computational fluid dynamics (CFD) calculations are commonly used to predict surface erosion by directly solving the Navier-Stokes equations for fluid and particle dynamics; however, these numerical approaches often require significant computational resources. In contrast, recent data-driven approaches using machine learning (ML) have shown immense promise for more efficient and accurate predictions to sidestep computationally demanding CFD calculations. To this end, we have developed FLUID-GPT (Fast Learning to Understand and Investigate Dynamics with a Generative Pre-Trained Transformer), a new hybrid ML architecture for accurately predicting particle trajectories and erosion on an industrial-scale steam header geometry. Our FLUID-GPT approach utilizes a Generative Pre-Trained Transformer 2 (GPT-2) with a convolutional neural network (CNN) for the first time to predict surface erosion using only information from five initial conditions: particle size, main-inlet speed, main-inlet pressure, subinlet speed, and subinlet pressure. Compared to the bidirectional long- and short-term memory (BiLSTM) ML techniques used in previous work, our FLUID-GPT model is much more accurate (a 54% decrease in the mean squared error) and efficient (70% less training time). Our work demonstrates that FLUID-GPT is an accurate and efficient ML approach for predicting time-series trajectories and their subsequent spatial erosion patterns in these complex dynamic systems.

Hexaazatriphenylene-Based Two-Dimensional Conductive Covalent Organic Framework with Anisotropic Charge Transfer.

The development of covalent organic frameworks (COFs) with efficient charge transport is of immense interest for applications in optoelectronic devices. To enhance COF charge transport properties, electroactive building blocks and dopants can be used to induce extended conduction channels. However, understanding their intricate interplay remains challenging. We designed and synthesized a tailor-made COF structure with electroactive hexaazatriphenylene (HAT) core units and planar dioxin (D) linkages, denoted as HD-COF. With the support of theoretical calculations, we found that the HAT units in the HD-COF induce strong, eclipsed π-π stacking. The unique stacking of HAT units and the weak in-plane conjugation of dioxin linkages leads to efficient anisotropic charge transport. We fabricated HD-COF films to minimize the grain boundary effect of bulk COFs, which resulted in enhanced conductivity. As a result, the HD-COF films showed an electrical conductivity as high as 1.25 S cm-1 after doping with tris(4-bromophenyl)ammoniumyl hexachloroantimonate.

Electron/Hole Mobilities of Periodic DNA and Nucleobase Structures from Large-Scale DFT Calculations.

Electron/hole transfer mechanisms in DNA and polynucleotide structures continue to garner considerable interest as emerging charge-transport systems and molecular electronics. To shed mechanistic insight into these electronic properties, we carried out large-scale density functional theory (DFT) calculations (up to 650 atoms) to systematically analyze the structural and electron/hole transport properties of fully periodic single- and double-stranded DNA. We examined the performance of various exchange-correlation functionals (LDA, BLYP, B3LYP, and B3LYP-D) and found that single-stranded thymine (T) and cytosine (C) are predominantly hole conductors, whereas single-stranded adenine (A) and guanine (G) are better electron conductors. For double-stranded DNA structures, the periodic A-T and G-C electronic band structures undergo a significant renormalization, which causes hole transport to only occur on the A and G nucleobases. Our calculations (1) provide new benchmarks for periodic nucleobase structures using dispersion-corrected hybrid functionals with large basis sets and (2) highlight the importance of dispersion effects for obtaining accurate geometries and electron/hole mobilities in these extended systems.

Access to essential cancer medicines for children: a comparative mixed-methods analysis of availability, price, and health-system determinants in east Africa.

BACKGROUND: Access to essential childhood cancer medicines is a core determinant of childhood cancer outcomes. Available evidence, although scarce, suggests that access to these medicines is highly variable across countries, particularly in low-income and middle-income countries, where the burden of childhood cancer is greatest. To support evidence-informed national and regional policies for improved childhood cancer outcomes, we aimed to analyse access to essential childhood cancer medicines in four east African countries-Kenya, Rwanda, Tanzania, and Uganda-by determining the availability and price of these medicines and the health system determinants of access. METHODS: In this comparative analysis, we used prospective mixed-method analyses to track and analyse the availability and price of essential childhood cancer medicines, investigate contextual determinants of access to childhood cancer medicines within and across included countries, and assess the potential effects of medicine stockouts on treatment. Eight tertiary care hospitals were included, seven were public sites (Kenyatta National Hospital [KNH; Nairobi, Kenya], Jaramogi Oginga Odinga Referral and Teaching Hospital [JOORTH; Kisumu, Kenya], Moi University Teaching and Referral Hospital [MTRH; Eldoret, Kenya], Bugando Medical Centre [BMC; Mwanza, Tanzania], Muhimbili National Hospital [MNH; Dar es Salaam, Tanzania], Butaro Cancer Centre of Excellence [BCCE; Butaro Sector, Rwanda], and Uganda Cancer Institute [UCI; Kampala, Uganda]) and one was a private site (Aga Khan University Hospital [AKU; Nairobi, Kenya]). We catalogued prices and stockouts for 37 essential drugs from each of the eight study siteson the basis of 52 weeks of prospective data that was collected across sites from May 1, 2020, to Jan 31, 2022. We analysed determinants of medicine access using thematic analysis of academic literature, policy documents, and semi-structured interviews from a purposive sample of health system stakeholders. FINDINGS: Recurrent stockouts of a wide range of cytotoxic and supportive care medicines were observed across sites, with highest mean unavailability in Kenya (JOORTH; 48·5%), Rwanda (BCCE; 39·0%), and Tanzania (BMC; 32·2%). Drugs that had frequent stockouts across at least four sites included methotrexate, bleomycin, etoposide, ifosfamide, oral morphine, and allopurinol. Average median price ratio of medicines at each site was within WHO's internationally accepted threshold for efficient procurement (median price ratio ≤1·5). The effect of stockouts on treatment was noted across most sites, with the greatest potential for treatment interruptions in patients with Hodgkin lymphoma, retinoblastoma, and acute lymphocytic leukaemia. Policy prioritisation of childhood cancers, health financing and coverage, medicine procurement and supply chain management, and health system infrastructure emerged as four prominent determinants of access when the stratified purposive sample of key informants (n=64) across all four countries (Kenya n=19, Rwanda n=15, Tanzania n=13, and Uganda n=17) was interviewed. INTERPRETATION: Access to childhood cancer medicines across east Africa is marked by gaps in availability that have implications for effective treatment delivery for a range of childhood cancers. Our findings provide detailed evidence of barriers to access to childhood cancer medicine at multiple points in the pharmaceutical value chain. These data could inform national and regional policy makers to optimise cancer medicine availability and affordability as part of efforts to improve childhood cancer outcomes specific regions and internationally. FUNDING: American Childhood Cancer Organization, Childhood Cancer International, and the Friends of Cancer Patients Ameera Fund.

Degradation of Perfluorooctanoic Acid on Aluminum Oxide Surfaces: New Mechanisms from Ab Initio Molecular Dynamics Simulations.

Perfluorooctanoic acid (PFOA) is a part of a large group of anthropogenic, persistent, and bioaccumulative contaminants known as per- and polyfluoroalkyl substances (PFAS) that can be harmful to human health. In this work, we present the first ab initio molecular dynamics (AIMD) study of temperature-dependent degradation dynamics of PFOA on (100) and (110) surfaces of γ-Al2O3. Our results show that PFOA degradation does not occur on the pristine (100) surface, even when carried out at high temperatures. However, introducing an oxygen vacancy on the (100) surface facilitates an ultrafast (<100 fs) defluorination of C-F bonds in PFOA. We also examined degradation dynamics on the (110) surface and found that PFOA interacts strongly with Al(III) centers on the surface of γ-Al2O3, resulting in a stepwise breaking of C-F, C-C, and C-COO bonds. Most importantly, at the end of the degradation process, strong Al-F bonds are formed on the mineralized γ-Al2O3 surface, which prevents further dissociation of fluorine into the surrounding environment. Taken together, our AIMD simulations provide critical reaction mechanisms at a quantum level of detail and highlight the importance of temperature effects, defects, and surface facets for PFOA degradation on reactive surfaces, which have not been systematically explored or analyzed.

GPU-Enhanced DFTB Metadynamics for Efficiently Predicting Free Energies of Biochemical Systems.

Metadynamics calculations of large chemical systems with ab initio methods are computationally prohibitive due to the extensive sampling required to simulate the large degrees of freedom in these systems. To address this computational bottleneck, we utilized a GPU-enhanced density functional tight binding (DFTB) approach on a massively parallelized cloud computing platform to efficiently calculate the thermodynamics and metadynamics of biochemical systems. To first validate our approach, we calculated the free-energy surfaces of alanine dipeptide and showed that our GPU-enhanced DFTB calculations qualitatively agree with computationally-intensive hybrid DFT benchmarks, whereas classical force fields give significant errors. Most importantly, we show that our GPU-accelerated DFTB calculations are significantly faster than previous approaches by up to two orders of magnitude. To further extend our GPU-enhanced DFTB approach, we also carried out a 10 ns metadynamics simulation of remdesivir, which is prohibitively out of reach for routine DFT-based metadynamics calculations. We find that the free-energy surfaces of remdesivir obtained from DFTB and classical force fields differ significantly, where the latter overestimates the internal energy contribution of high free-energy states. Taken together, our benchmark tests, analyses, and extensions to large biochemical systems highlight the use of GPU-enhanced DFTB simulations for efficiently predicting the free-energy surfaces/thermodynamics of large biochemical systems.

Economic appraisal of prosthetic replacement of ocular surface ecosystem in Canada.

OBJECTIVE: To perform an economic appraisal of the Prosthetic Replacement of Ocular Surface Ecosystem (PROSE; BostonSight, Needham Heights, Mass.) lens in patients with a distorted corneal surface or ocular surface disease in Canada. DESIGN: Retrospective observational cohort study with cost, cost-utility, and benefit-cost analyses. PARTICIPANTS: Patients who received PROSE from the only PROSE clinic in Canada from 2018 to 2020. METHODS: Visual acuity (VA) outcomes of the participants were assessed. Benefits were defined as VA improvements that were converted into utilities and then quality-adjusted life years. Economic values were derived via government statements, clinic financial statements, and published literature. RESULTS: Average best-corrected VA (BCVA) improvement was -0.42 ± 0.41 logMAR (p = 2.68 × 10-13) or Snellen 20/53 for the overall cohort, -0.51 ± 0.48 (p = 5.42 × 10-8) or Snellen 20/65 for distorted corneal surface patients, and -0.31 ± 0.30 (p = 1.30 × 10-7) or Snellen 20/41 for ocular surface disease patients. This corresponded to discounted quality-adjusted life year gains of 0.51, 0.65, and 0.42, respectively, over an estimated 5-year PROSE device lifespan. Average cost to fit a patient with PROSE was USD$5 469.85 (CAD$7 087.28), of which USD$4 971.38 (CAD$6 441.42) was clinic cost and USD$498.47 (CAD$645.87) was patient cost. Cost-utility was USD$10 256.47 (CAD$13 289.31) for the overall cohort, USD$8 439.79 (CAD$10 935.44) for distorted corneal surface patients, and US$13 069.90 (CAD$16 934.67) for ocular surface disease patients. The benefit-cost ratio was 34.4 for all, 43.8 for distorted corneal surface patients, and 28.3 for ocular surface disease patients. CONCLUSIONS: Our economic appraisal demonstrated that PROSE treatment provides a significant, cost-effective benefit to Canadian patients with distorted corneal surfaces and ocular surface diseases. This indicates that PROSE clinics are an efficient investment.

Implementation of real-time TDDFT for periodic systems in the open-source PySCF software package.

We present a new implementation of real-time time-dependent density functional theory (RT-TDDFT) for calculating excited-state dynamics of periodic systems in the open-source Python-based PySCF software package. Our implementation uses Gaussian basis functions in a velocity gauge formalism and can be applied to periodic surfaces, condensed-phase, and molecular systems. As representative benchmark applications, we present optical absorption calculations of various molecular and bulk systems and a real-time simulation of field-induced dynamics of a (ZnO)4 molecular cluster on a periodic graphene sheet. We present representative calculations on optical response of solids to infinitesimal external fields as well as real-time charge-transfer dynamics induced by strong pulsed laser fields. Due to the widespread use of the Python language, our RT-TDDFT implementation can be easily modified and provides a new capability in the PySCF code for real-time excited-state calculations of chemical and material systems.

Retinal nerve fiber layer in frontotemporal lobar degeneration and amyotrophic lateral sclerosis.

Purpose: Tauopathy and transactive response DNA binding protein 43 (TDP-43) proteinopathy are associated with neurodegenerative diseases. These proteinopathies are difficult to detect in vivo. This study examined if spectral-domain optical coherence tomography (SD-OCT) can differentiate in vivo the difference in peripapillary retinal nerve fibre layer (pRNFL) thickness and macular retinal thickness between participants with presumed tauopathy (progressive supranuclear palsy) and those with presumed TDP-43 proteinopathy (amyotrophic lateral sclerosis and semantic variant primary progressive aphasia). Study design: Prospective, multi-centre, observational study. Materials and methods: pRNFL and macular SD-OCT images were acquired in both eyes of each participant using Heidelberg Spectralis SD-OCT. Global and pRNFL thickness in 6 sectors were analyzed, as well as macular thickness in a central 1 mm diameter zone and 4 surrounding sectors. Linear mixed model methods adjusting for baseline differences between groups were used to compare the two groups with respect to pRNFL and macular thickness. Results: A significant difference was found in mean pRNFL thickness between groups, with the TDP-43 group (n = 28 eyes) having a significantly thinner pRNFL in the temporal sector than the tauopathy group (n = 9 eyes; mean difference = 15.46 µm, SE = 6.98, p = 0.046), which was not significant after adjusting for multiple comparisons. No other significant differences were found between groups for pRNFL or macular thickness. Conclusion: The finding that the temporal pRNFL in the TDP-43 group was on average 15.46 µm thinner could potentially have clinical significance. Future work with larger sample sizes, longitudinal studies, and at the level of retinal sublayers will help to determine the utility of SD-OCT to differentiate between these two proteinopathies.

Harnessing deep reinforcement learning to construct time-dependent optimal fields for quantum control dynamics.

We present an efficient deep reinforcement learning (DRL) approach to automatically construct time-dependent optimal control fields that enable desired transitions in dynamical chemical systems. Our DRL approach gives impressive performance in constructing optimal control fields, even for cases that are difficult to converge with existing gradient-based approaches. We provide a detailed description of the algorithms and hyperparameters as well as performance metrics for our DRL-based approach. Our results demonstrate that DRL can be employed as an effective artificial intelligence approach to efficiently and autonomously design control fields in quantum dynamical chemical systems.