Efficient Approach Research Articles

#1166 ACTION3 pivotal Phase 3 & open-label extension study assessing DMX-200 in adult & paediatric patients with focal segmental glomerulosclerosis (FSGS)

Abstract Background and Aims Focal segmental glomerulosclerosis (FSGS) is a major cause of glomerular disease that affects both adults and children. FSGS can rapidly progress to end-stage kidney disease. Lack of safe and effective treatment represents a significant unmet need for patients with FSGS. Development of treatments has been hampered by complexities including the rare nature of the disease, varying drivers of pathogenesis, and the evolving diagnostic and clinical trial designs, necessitating innovative research approaches. DMX-200 (repagermanium) is a C-C motif chemokine receptor 2 (CCR2) pathway inhibitor. Activation of the angiotensin II type 1 receptor (AT1R) and CCR2 have been independently implicated in the pathogenesis and progression of FSGS. These receptors form a functional complex so inhibition of both receptors may provide an efficient approach to FSGS treatment. In vivo data shows co-administration of DMX-200 and the angiotensin receptor blocker (ARB) irbesartan demonstrated a synergistic reduction in proteinuria, macrophage infiltration, and podocyte loss compared with vehicle or either antagonist alone. A subsequent Phase 2a cross-over study (NCT03649152) in N = 8 patients with biopsy-proven primary FSGS showed that the combination of DMX-200 and irbesartan (compared to placebo) was well tolerated with most patients experiencing a reduction in proteinuria. Based on these data, the global Phase 3 study (ACTION3) was initiated to confirm the proteinuria lowering effects of DMX-200 and assess effects on eGFR decline. ACTION3 is a randomised, placebo-controlled, double-blind, long-duration study of FSGS patients and on completion will provide a resource for the wider nephrology community and advocacy groups on the natural history of FSGS patients and the effects of a CCR2-inhibitor on the background of an ARB. Here we present the baseline data from the first 90 patients enrolled in ACTION3 at 12th January 2024. Method The ACTION3 (NCT05183646) study is a pivotal Phase 3, multicentre study of the efficacy and safety of DMX-200, 120 mg twice daily compared with placebo in adult patients with biopsy-proven FSGS receiving maximal tolerated ARB, with persistent proteinuria >1.5g/ day and eGFR >25 ml/min/1.73 m2. The study plans to enroll approximately 286 patients with primary FSGS, genetic FSGS and FSGS of undetermined cause with the primary objective of evaluating the efficacy of DMX-200 in percent change in 24-hour urine PCR and eGFR slope. The initial 104-week randomized period will be extended in a 2-year open label extension study. A futility analysis by an Independent Data Monitoring Committee following the first 72 patients completing 35 weeks of treatment confirm that continuation of the study is non-futile. Results The study is actively recruiting at 71 sites in 11 countries listed in Table 1. Demographic data of randomised patients is listed in Table 2. Patients are predominantly male (65%), with an average age of 46.6 yrs. and mean eGFR 56.3 ± 27.6 (ml/min/1.73 m2). The study is expanding recruitment into additional countries in the Asia Pacific, Europe, and Latin American regions. Further, the study protocol has sought and received regulatory approval to enroll adolescent patients aged 12-18 in Argentina, Mexico, UK and the US. Conclusion DMX-200 (repagermanium) has shown encouraging signs of safety and efficacy in combination with an ARB for the treatment of FSGS. The contributing countries and baseline characteristics of the patients enrolled in ACTION3 are similar to the populations studied in other recent FSGS trials. No clinically significant safety concerns have been observed in patients enrolled to date in the ACTION3 Phase 3 study.

Rapid and sustainable production of nano and micro medicine crystals via freeze-dissolving technology

Modern pharmaceutical manufacturing emphasizes the need for sustainable technologies. Fine particles, including nano and micro-sized crystals, are increasingly important, particularly in the production of inhalation medicines. A novel application of freeze-dissolving technology has been demonstrated in the production of metronidazole, a model drug. This process involves creating frozen spherical particles by introducing a tert-butanol solution containing dissolved metronidazole into liquid nitrogen. Various antisolvents, such as n-hexane, n-heptane, ethanol, n-propanol, n-butanol, or n-pentanol, were employed to dissolve these frozen templates at temperatures ranging from 248.15 to 278.15 K. During this process, pre-formed metronidazole fine particles within the frozen template were released into the antisolvent solution. An alternative method involved placing these frozen particles into a vacuumed freeze dryer to extract the fine particles. The new freeze-dissolving technology can save 99% both energy and time compared to the traditional freeze-drying method, demonstrating a significantly more efficient and sustainable pharmaceutical manufacturing approach.

An Efficient Propagation Approach to Forcing Softwood Shoots from Epicormic Buds and Subsequent Rooting of Paulownia elongata S. Y. Hu.

The current research describes the multiplication of Paulownia elongata S. Y. Hu, a timber plant, through the forcing of softwood shoots from epicormic buds under glasshouse conditions in spring and fall seasons. Different growth media were used to compare their effect on the forcing potential of epicormic buds. For this, 25-30-cm-long and 1.2-2-cm-diameter stem segments taken from the lower juvenile portion of a mother plant were placed horizontally in flat trays containing media, i.e., sterilized well-moistened sand, peat moss, perlite, and vermiculite individually. Furthermore, 4-6-cm-long forced softwood shoots were detached and treated with various concentrations of IBA (indole-3-butyric acid) and NAA (α-naphthyl acetic acid) either individually or in combinations for subsequent rooting. The response of shoot forcing was better in spring as compared to fall in terms of shoot length (cm), and number of shoots or leaves; however, an earlier bud break was observed during fall after 30 days of the initial experiment. The use of peat moss and vermiculite proved to be equally suitable for early bud break in both seasons, whereas in terms of shoot and leaf number as well as the shoot length (cm), the best outcome was observed in sand. Best rooting was observed at 3 gL-1 IBA + 3 gL-1 NAA in terms of root number per shoot, root length (cm), and days to root initiation while using sand as the growth medium after 50 days of the rooting experiment. The successfully established plantlets were further shifted to soil at Botanical Garden, University of the Punjab, Lahore, Pakistan, exhibiting an 87.5% survival rate. On the basis of the results obtained, it may be concluded that reasonable softwood shoot forcing in P. elongata may further be exploited for its mass scale nursery propagation as well as use in future in vitro studies.

Rare Earth Nanoprobes for Targeted Delineation of Triple Negative Breast Cancer and Enhancement of Radioimmunotherapy.

Radiotherapy demonstrates a synergistic effect with immunotherapy by inducing a transformation of "immune cold" tumors into "immune hot" tumors in triple negative breast cancer (TNBC). Nevertheless, the effectiveness of immunotherapy is constrained by low expression of tumor-exposed antigens, inadequate inflammation, and insufficient tumor infiltrating lymphocyte (TILs). To address this predicament, novel lutecium-based rare earth nanoparticles (RENPs) are synthesized with the aim of amplifying radiation effect and tumor immune response. The nanoprobe is characterized by neodymium-based down-conversion fluorescence, demonstrating robust photostability, biocompatibility, and targetability. The conjugation of RENPs with a CXCR4 targeted drug enables precise delineation of breast tumors using a near-infrared imaging system and improves radiation efficacy via lutetium-based radio-sensitizer invivo. Furthermore, the study shows a notable enhancement of immune response through the induction of immunogenic cell death and recruitment of TILs, resulting in the inhibition of tumor progression both invitro and invivo models following the administration of nanoparticles. Hence, the novel multifunctional nanoprobes incorporating various lanthanide elements offer the potential for imaging-guided tumor delineation, radio-sensitization, and immune activation post-radiation, thus presenting an efficient radio-immunotherapeutic approach for TNBC.

High-throughput nanopore targeted sequencing for efficient drug resistance assay of Mycobacterium tuberculosis.

Drug-resistant tuberculosis (TB), especially multidrug-resistant tuberculosis (MDR-TB) and extensively drug-resistant tuberculosis (XDR-TB), is one of the urgent clinical problems and public health challenges. Culture-based phenotypic drug susceptibility testing (pDST) is time-consuming, and PCR-based assays are limited to hotspot mutations. In this study, we developed and validated a convenient and efficient approach based on high-throughput nanopore sequencing technology combined with multiplex PCR, namely nanopore targeted sequencing (NTS), to simultaneously sequence 18 genes associated with antibiotic resistance in Mycobacterium tuberculosis (MTB). The analytical performance of NTS was evaluated, and 99 clinical samples were collected to assess its clinical performance. The NTS results showed that MTB and its drug resistance were successfully identified in approximately 7.5 h. Furthermore, compared to the pDST and Xpert MTB/RIF assays, NTS provided much more drug resistance information, covering 14 anti-TB drugs, and it identified 20 clinical cases of drug-resistant MTB. The mutations underlying these drug-resistant cases were all verified using Sanger sequencing. Our approach for this TB drug resistance assay offers several advantages, including being culture-free, efficient, high-throughput, and highly accurate, which would be very helpful for clinical patient management and TB infection control.

A Concise Synthesis of (-)-Ambrox.

(-)-Ambrox, a highly prized and commercially significant component of ambergris, finds widespread application in perfumery, cigarettes, cosmetics, and the food industry. Despite considerable attention to this research area over the years, an environmentally friendly and practical method for synthesizing (-)-ambrox has remained elusive. This study presents a succinct and efficient approach to (-)-ambrox synthesis, involving two consecutive alkylations at C-6, followed by an acid-catalyzed cyclization to give bicyclic ketones starting from (R)-carvone. Subsequent reduction, Barton Vinyl Iodide synthesis, alkylation, and an acid-catalyzed cyclization collectively achieved the synthesis of (-)-ambrox with a satisfactory yield of 26.2 %.

Identification and Classification of Bilingual Language using Natural Language Processing

This paper presents an efficient approach to language detection and sentiment analysis using Naive Bayes classifiers. The research involves training classifiers on a diverse dataset of text samples in multiple languages. Our method employs vectorization techniques and separate classifiers for language detection and sentiment analysis tasks. Experimental results demonstrate the effectiveness of the classifiers, with high accuracy in identifying the language and sentiment of input text. The approach showcases notable efficiency in processing language detection and sentiment analysis, addressing the growing need for automated text processing solutions. The findings have implications for various domains, including customer feedback analysis, market research, and opinion Mining

Lightweight, Strong, and Transparent Wood Films Produced by Capillary Driven Self-Densification.

Wood delignification and densification enable the production of high strength and/or transparent wood materials with exceptional properties. However, processing needs to be more sustainable and besides the chemical delignification treatments, energy intense hot-pressing calls for alternative approaches. Here, this study shows that additional softening of delignified wood via a mild swelling process using an ionic liquid-water mixture enables the densification of tube-line wood cells into layer-by-layer sheet structures without hot-pressing. The natural capillary force induces self-densification in a simple drying process resulting in a transparent wood film. The as-prepared films with ≈150µm thickness possess an optical transmittance ≈70%, while maintaining optical haze >95%. Due to the densely packed sheet structure with a large interfacial area, the reassembled wood film is fivefold stronger and stiffer than the delignified wood in fiber direction. Owing to a low density, the specific tensile strength and elastic modulus are as high as 282MPacm3g-1 and 31GPacm3g-1. A facile and highly energy efficient wood nanotechnology approach are demonstrated toward more sustainable materials and processes by directly converting delignified wood into transparent wood omitting polymeric matrix infiltration or mechanical pressing.

A biogeography-based optimization algorithm with local search for large-scale heterogeneous distributed scheduling with multiple process plans

Since modern production mode has shifted from a single factory to a multi-factory production network, distributed scheduling has been derived. Distributed scheduling problem (DSP) is characterized by many varieties, large scale, redundant production factories, flexible production processes, and high-value products. Each factory in the heterogeneous DSP can be considered an individual entity, and there may be several production process plans. To address the heterogeneous DSP with multiple process plans, we consider minimizing the global makespan over all factories containing the transportation time delivering tasks from factories to their destinations and propose a biogeography-based optimization algorithm combined with local search based on heuristic rules (BBO-LH) to find the optimal production plan and enhance productivity. First, a new encoding scheme with three-segment representation has been developed to avoid illegal solutions and realize the information sharing between solutions selecting different process plans. Then two efficient local search approaches have been proposed based on different heuristic rules on sequence and equipment allocation respectively, to improve the search efficiency. Besides, BBO-LH has adopted a cosine migration rate model to replace the linear one to strengthen the ability to jump out of local optima. BBO-LH is compared with a genetic algorithm (GA_X), using generated examples to test the performance of the proposed strategies, and simulation results show the effectiveness of the proposed BBO-LH on large-scale heterogeneous DSP with multiple process plans.

Adaptive local neighborhood information based efficient fuzzy clustering approach

AbstractThe purpose of clustering is to partition data similar with each other into a same group and partition data dissimilar with each other into different groups. However, in most existing fuzzy clustering approaches, the membership degrees of an individual belonging to different clusters are relied on the different distances between the individual and different cluster centroids, the similarity between the individual and data in different clusters are ignored, besides, the outliers cannot be distinguished effectively. For improving the clustering performance and addressing the problems in most existing fuzzy clustering approaches, based on the concept that data close to each other should be grouped together and be assigned to a same cluster, in this paper, we present a novel efficient fuzzy clustering approach, in which the adaptive local neighborhood information of each data referring to different clusters is taken into consideration; an entirely new idea that the membership degree values of an individual referring to different clusters should not only depend on the distances between the individual and different cluster centers, but also rely on the distances between the individual and several nearest neighbor data in different clusters is put forward; a new scheme to search for the outliers is presented, a method for identifying the different importance of different features is introduced. Experiments on synthetic and publicly real-world datasets were conducted, the clustering results show that the approach put forward can yield consistently satisfying clustering performance and has significant advantages.

The Making of an Data Pipeline

This paper details the development and implementation of a data engineering pipeline designed for the extraction, transformation, and loading (ETL) of data from a web-based directory. The project involves using asynchronous web scraping techniques to gather user details from a local business directory, transforming the data into a structured format, and loading it into a storage solution. The pipeline utilises Python, the HTTPX library for asynchronous HTTP requests, BeautifulSoup for HTML parsing, and Amazon S3 for data storage. By leveraging these technologies, the pipeline demonstrates an efficient approach to handling large-scale web data extraction and processing, significantly reducing the time required to gather and organise data from multiple web pages. This paper provides insights into the architecture, implementation, and performance of the ETL pipeline, highlighting the benefits and challenges of using asynchronous programming in data engineering.

Ratio design of bimetallic Pd‐Rh nanoparticles on MoS2 nanosheets: Excellent electrocatalysts for hydrogen evolution reaction

The decrease in noble metal content presents an efficient approach to attain commercial, effective, and durable electrocatalysts for the hydrogen evolution reaction (HER) at a low fabrication cost. Nonetheless, achieving a proper balance between bimetallic loading ratios and HER performance remains challenging. In this study, a simple and environmentally friendly sonochemical method is employed to successfully synthesize bimetallic palladium–rhodium nanoparticles (Pd‐Rh) with varying ratios, confined within molybdenum disulfide (MoS2) nanosheets. Bimetallic Pd‐Rh/MoS2 composite with different ratios of Pd:Rh is synthesized by adjusting the feed ratio of Pd and Rh precursors (1:4, 1:1, and 4:1). The HER electrocatalytic activity of the bimetallic Pd1‐Rh1/MoS2 composite exhibits the lowest overpotential and a superior Tafel slope, closely rivaling the electrocatalytic activity of the commercial 20 wt% Pt/C. Furthermore, the bimetallic Pd1‐Rh1/MoS2 composite exhibits remarkable stability and durability, with almost negligible performance decay after 2000 cycles. These outstanding HER electrocatalytic properties of the bimetallic composite result from a higher number of active sites, a significantly larger electrochemically active surface area, reduced charge‐transfer resistance, and a larger double‐layer capacitance. These factors collectively facilitate faster adsorption and desorption of hydron on the surface of electrocatalyst.

Development of novel, green, efficient approach for the synthesis of indazole and its derivatives; insights into their pharmacological and molecular docking studies

Indazole is a potent heterocycle with wide range of biological applications. It is less naturally available alkaloid widely used in traditional medicine for the treatment of allergies, blood pressure, etc. In recent years due to the utility of indazoles as pharmacophores in drug discovery, it has been exemplified in recent research. The existing method of synthesis involves carbon-nitrogen bond formation using ccopper, costly Pd metal catalyst i.e.; Buchwald Hartwig coupling. Since it is the need of hour for simple method, we, report a new efficient and economically feasible, user friendly synthesis of indazoles by utilizing less hazardous raw materials, eliminating use of metal catalyst therefore coming up with a greener method. The sequence involves carbon-nitrogen bond formation via simple ipso substitution, by replacing copper and costly Pd, catalyst. This new green synthetic procedure includes two steps synthesis of 1H-indazole derivatives involving arylhydrazones as an intermediate. Further in order to better understand the biological significance of the novel Indazole and its derivatives, Molecular docking studies for antifungal activity were carried out against lanosterol 14 alpha-demethylase for all the newly synthesized molecules with reference to the standard drug fluconazole, in line with above docking studies the in-vitro anti-microbial, anti-oxidant and anti-inflammatory activity were conducted among which our synthesized moieties showed excellent results for antifungal activity. Thus, we report an efficient, eco-friendly strong synthetic method for the synthesis of novel Indazole and its derivatives.

The Elaborate Design of Multi‐Polarization Effect by Non‐Edge Defect Strategy for Ultra‐Broad Microwave Absorption

AbstractAnion defect engineering is proven to be an efficient approach to reconstruct the electronic configuration of carbon‐based magnetoelectric materials for targeted modulation of electromagnetic (EM) performance. However, traditional mono‐anionic doping suffers from low defect concentration and lacks diverse polarization mechanisms. In this work, multi‐anions (N/S/F) stepwise‐doped carbon/Fe3C magnetoelectric composites are elaborately constructed, wherein the predesigned N defects serve as activated sites for anomalously adopting S anions (Step I) and subsequent F anions (Step II) in non‐marginal areas of the carbon layer. It is found that S prefers to replace pyrrolic N defects while F tends to form dangling bonds with the C site adjacent to the pyridinic N. Intriguingly, besides the inherent polarized resonance of N defect at ≈15 GHz, customized S and F defects induce new polarization resonances at ≈10 GHz and ≈15+ GHz, respectively. Under a typical multi‐polarization effect with the synergetic magnetic response, the carbon/Fe3C composites with N/S/F defects harvest the broadest bandwidth of 8.28 GHz (9.72–18 GHz) at 2.55 mm, covering a wide frequency range almost from X to Ku bands. This work demonstrates the positive impact of localized multi‐defects customization and multi‐polarization effect on expanding microwave absorption bandwidth, providing valuable insights for the advanced design of ultra‐broadband absorbers.

A computational efficient approach for distributionally robust unit commitment with enhanced disjointed layered ambiguity set

AbstractTo achieve the sustainable development of the society, renewable energy dominated power systems are gradually formed. However, the uncertainty of renewable power poses challenges for power system operations, such as balancing the load and generation in day‐ahead unit commitment problems. To address this issue, an enhanced disjointed layered ambiguity set is proposed in this paper to effectively capture the uncertainty of renewable generation forecast errors. On this basis, a two‐stage distributionally robust unit commitment optimization problem is formulated, which is computational challenge. To this end, a novel reformation approach and a worst‐case support set identification method are proposed to derive the optimal solution in a computationally efficient way. Simulation results demonstrate that the proposed ambiguity set brings lower operation costs of unit commitment while maintaining similar reliability compared to existing methods. In detail, the proposed ambiguity set achieves a reduction in operating costs of approximately 10.01%. The proposed novel reformation approach and solution method are also shown to have higher computation efficiency than the existing Benders decomposition method, resulting in a reduction in computation time to less than 3.78% of the original duration.

Data-driven approach for air pollutant concentrations forecasting: A window-based multi-output GBRT approach

Air pollutant concentrations forecasting is essential for detrimental public exposure prevention, air quality enhancement, and effective environmental policy shaping. Recently, air pollutant concentrations forecasting based on machine learning has been considerably advanced with the development of data-driven approaches. However, multi-source data constraints and architectural complexity in machine learning approaches limit the use of machine learning in actual scenarios. Herein we present a window-based multi-output gradient boosting regression tree (WM-GBRT) approach to predict air pollutant concentrations for the next 48 h at each monitoring station, considering air pollutant monitoring data, meteorological monitoring data, NECP-GFS data, and GEOS-CF data. The key innovation of our approach lies in the flexible input window configuration and the implementation of an multi-step independent strategy. The experiments were conducted in China and compared to existing data-driven baselines, we show that our approach achieved the superior performance in forecasting concentrations of six air pollutants, with a considerable improvement of 6.94 % (PM2.5), 12.3 % (PM10), 3.84 % (O3), 30.6 % (SO2), 9.84 % (NO2), and 93.9 % (CO) when compared to the second-best baselines. This simple yet efficient approach can be easily applied, providing a reliable technical method for predicting air pollutant concentrations.

Utilizing the Optimal Auxiliary Function Method for the Approximation of a Nonlinear Long Wave System considering Caputo Fractional Order

In this article, we explore the utilization of the Caputo derivative and the Riemann–Liouville (R–L) fractional integral to analyze the optimal auxiliary function method for approximating fractional nonlinear long waves. Approximate long wave equation with a distinct dispersion relation offers the most accurate description of shallow water wave properties. Various methods, including the Adomian decomposition technique, the variational iteration method, the optimum homotopy asymptotic method, and the new iterative technique, have been employed and compared to those obtained using the fractional-order approximate long wave equation. The results of our study indicate that the optimal auxiliary function method is highly successful and practically simple, achieving better and more rapid convergence after just one repetition. This method is recognized as an efficient approach, demonstrating high precision in solving intriguing and intricate problems. Furthermore, it proves to be more time and resource efficient than other relevant analytical techniques, leading to significant savings in both volume and time. Compared to the Adomian decomposition technique, the new iterative technique, the variational iteration method, and the optimum homotopy asymptotic method, the suggested technique is extremely accurate computationally. It is also easy to analyze and solve fractionally linked nonlinear complex phenomena that arise in science and technology. We present the numerical and graphical findings that support these conclusions.

Innovative road distress detection (IR-DD): an efficient and scalable deep learning approach

In the rapidly evolving landscape of transportation infrastructure, the quality and condition of road networks play a pivotal role in societal progress and economic growth. In the realm of road distress detection, traditional methods have long grappled with manual intervention and high costs, requiring trained observers for time-consuming and expensive data collection processes. The limitations of these approaches are compounded by challenges in adapting to diverse road surfaces and handling low-resolution data, particularly in early automated distress survey technologies. This article addresses the critical need for efficient road distress detection, a key component of ensuring safe and reliable transportation systems. Effectively addressing these challenges is crucial for enhancing the efficiency, accuracy, and safety of road distress detection systems. Leveraging advancements in object detection, we introduce the Innovative Road Distress Detection (IR-DD), a novel framework that integrates the YOLOv8 algorithm to enhance the accuracy and real-time capabilities of road distress detection, catering to applications such as smart cities and autonomous vehicles. Our approach incorporates bidirectional feature pyramid network (BiFPN) recursive feature fusion and bidirectional connections to optimize the utilization of multi-scale features, addressing challenges related to information loss and gradients encountered in traditional methods. Comprehensive experimental analysis demonstrates the superior performance, efficiency, and robustness of our integrated approach, positioning it as a cost-effective and compelling alternative to conventional road distress detection methods. Our findings demonstrate the superior performance of our approach compared to other state-of-the-art methods across various evaluation metrics, including precision, recall, F1 score, and mean average precision (mAP) at different intersection over union (IoU) thresholds. Specifically, our method achieves notable results with a precision of 0.666, F1 score of 0.630, mAP@0.5 of 0.650, all while operating at a speed of 86 frames per second (FPS). These outcomes underscore the effectiveness of our approach in real-time road distress detection. This article contributes to the ongoing innovation in object detection techniques, emphasizing the practicality and effectiveness of our proposed solution in advancing the field of road distress detection.

Resolving Photoinduced Femtosecond Three-Dimensional Solute-Solvent Dynamics through Surface Hopping Simulations.

Photoinduced dynamics in solution is governed by mutual solute-solvent interactions, which give rise to phenomena like solvatochromism, the Stokes shift, dual fluorescence, or charge transfer. Understanding these phenomena requires simulating the solute's photoinduced dynamics and simultaneously resolving the three-dimensional solvent distribution dynamics. If using trajectory surface hopping (TSH) to this aim, thousands of trajectories are required to adequately sample the time-dependent three-dimensional solvent distribution functions, and thus resolve the solvent dynamics with sub-Ångstrom and femtosecond accuracy and sufficiently low noise levels. Unfortunately, simulating thousands of trajectories with TSH in the framework of hybrid quantum mechanical/molecular mechanical (QM/MM) can be prohibitively expensive when employing ab initio electronic structure methods. To tackle this challenge, we recently introduced a computationally efficient approach that combines efficient linear vibronic coupling models with molecular mechanics (LVC/MM) via electrostatic embedding [Polonius et al., JCTC 2023, 19, 7171-7186]. This method provides solvent-embedded, nonadiabatically coupled potential energy surfaces while scaling similarly to MM force fields. Here, we employ TSH with LVC/MM to unravel the photoinduced dynamics of two small thiocarbonyl compounds solvated in water. We describe how to estimate the number of trajectories required to produce nearly noise-free three-dimensional solvent distribution functions and present an analysis based on approximately 10,000 trajectories propagated for 3 ps. In the electronic ground state, both molecules exhibit in-plane hydrogen bonds to the sulfur atom. Shortly after excitation, these bonds are broken and reform perpendicular to the molecular plane on timescales that differ by an order of magnitude due to steric effects. We also show that the solvent relaxation dynamics is coupled to the electronic dynamics, including intersystem crossing. These findings are relevant to advance the understanding of the coupled solute-solvent dynamics of solvated photoexcited molecules, e.g., biologically relevant thio-nucleobases.

A reliability model for non-isothermal isotropic damages

This study introduces a novel lifetime distribution originating from the Neyman Type A distribution. We built a Neyman Type A counting process and developed a survival function. Some statistical properties of the new distribution were presented, such as the resulting humped hazard function and its convergence. An accelerated test model structure with Arrhenius law was specified, and the effects of different accelerating stresses were analyzed. The hazard function implied by the model is inversely proportional to the stress, which results in interesting features and provides an efficient approach to describe the lifespan phenomena of some engineering metals and bulbs under low temperatures. The estimation of parameters of the accelerated model by maximum likelihood, mean time to failure, and expected number of failures are discussed in the numerical experiments.