Sequential Process Research Articles

Boosting Microfluidic Enzymatic Cascade Reactions with pH-Responsive Polymersomes by Spatio-Chemical Activity Control.

Microfluidic flow reactors permit the implementation of sensitive biocatalysts in polymeric environments (e.g., hydrogel dots), mimicking nature through the use of diverse microstructures within defined confinements. However, establishing complex hybrid structures to mimic biological processes and functions under continuous flow with optimal utilization of all components involved in the reaction process represents a significant scientific challenge. To achieve spatial, chemical, and temporal control for any microfluidic application, compartmentalization is required, as well as the unification of different sensitive compartments in the reaction chamber for the microfluidic flow design. This study presents a self-regulating microfluidic system fabricated by a sequential photostructuring process with an intermediate chemical process step to realize pH-sensitive hybrid structures for the fabrication of a microfluidic double chamber reactor for controlled enzymatic cascade reaction (ECR). The key point is the adaptation and retention of the function of pH-responsive horseradish peroxidase-loaded polymersomes in a microfluidic chip under continuous flow. ECR is successfully triggered and controlled by an interplay between glucose oxidase-converted glucose, the membrane state of pH-responsive polymersomes, and other parameters (e.g., flow rate and fluid composition). This study establishes a promising noninvasive regulatory platform for extended spatio-chemical control of current and future ECR and other cascade reaction systems.

Bi-SeqCNN: A Novel Light-weight Bi-directional CNN Architecture for Protein Function Prediction.

Deep learning approaches, such as convolution neural networks (CNNs) and deep recurrent neural networks (RNNs), have been the backbone for predicting protein function, with promising state-of-the-art (SOTA) results. RNNs with an in-built ability (i) focus on past information, (ii) collect both short-and-long range dependency information, and (iii) bi-directional processing offers a strong sequential processing mechanism. CNNs, however, are confined to focusing on short-term information from both the past and the future, although they offer parallelism. Therefore, a novel bi-directional CNN that strictly complies with the sequential processing mechanism of RNNs is introduced and is used for developing a protein function prediction framework, Bi-SeqCNN. This is a sub-sequence-based framework. Further, Bi-SeqCNN + is an ensemble approach to better the prediction results. To our knowledge, this is the first time bi-directional CNNs are employed for general temporal data analysis and not just for protein sequences. The proposed architecture produces improvements up to +5.5% over contemporary SOTA methods on three benchmark protein sequence datasets. Moreover, it is substantially lighter and attain these results with (0.50-0.70 times) fewer parameters than the SOTA methods.

Gradient-free training of recurrent neural networks using random perturbations.

Recurrent neural networks (RNNs) hold immense potential for computations due to their Turing completeness and sequential processing capabilities, yet existing methods for their training encounter efficiency challenges. Backpropagation through time (BPTT), the prevailing method, extends the backpropagation (BP) algorithm by unrolling the RNN over time. However, this approach suffers from significant drawbacks, including the need to interleave forward and backward phases and store exact gradient information. Furthermore, BPTT has been shown to struggle to propagate gradient information for long sequences, leading to vanishing gradients. An alternative strategy to using gradient-based methods like BPTT involves stochastically approximating gradients through perturbation-based methods. This learning approach is exceptionally simple, necessitating only forward passes in the network and a global reinforcement signal as feedback. Despite its simplicity, the random nature of its updates typically leads to inefficient optimization, limiting its effectiveness in training neural networks. In this study, we present a new approach to perturbation-based learning in RNNs whose performance is competitive with BPTT, while maintaining the inherent advantages over gradient-based learning. To this end, we extend the recently introduced activity-based node perturbation (ANP) method to operate in the time domain, leading to more efficient learning and generalization. We subsequently conduct a range of experiments to validate our approach. Our results show similar performance, convergence time and scalability when compared to BPTT, strongly outperforming standard node perturbation and weight perturbation methods. These findings suggest that perturbation-based learning methods offer a versatile alternative to gradient-based methods for training RNNs which can be ideally suited for neuromorphic computing applications.

Rate Lifting for Stochastic Process Algebra by Transition Context Augmentation

This article presents an algorithm for determining the unknown rates in the sequential processes of a Stochastic Process Algebra (SPA) model, provided that the rates in the combined flat model are given. Such a rate lifting is useful for model reverse engineering and model repair. Technically, the algorithm works by solving systems of nonlinear equations and—if necessary—adjusting the model’s synchronisation structure, without changing its transition system. The adjustments cause an augmentation of a transition’s context and thus enable additional control over the transition rate. The complete pseudo-code of the rate lifting algorithm is included and discussed in the article, and its practical usefulness is demonstrated by two case studies. The approach taken by the algorithm exploits some structural and behavioural properties of SPA systems, which are formulated here for the first time and could be very beneficial also in other contexts, such as compositional system verification.

Phenolic Composition and Bioactivities of Invasive Ailanthus altissima (Mill.) Swingle Leaf Extracts Obtained by Two-Step Sequential Extraction.

Ailanthus altissima, a highly invasive species, contains valuable compounds in different plant parts, indicating great practical potential. This paper proposes the use of non-polar (n-hexane) and polar (ethanol) solvents for the extraction of antioxidant compounds from A. altissima (family Simaroubaceae) leaves in a sequential two-step process. Fresh and dried leaves were examined for their microstructure by scanning electron microscopy, and for color changes in the CIELAB color space co-ordinates. An investigation of the harvesting season, processing (freezing and drying), and solvent indicates ethanol can be used for the highly efficient extraction of phenolics, flavonoids, tannins, and carotenoids. Statistically significant differences were found between the autumn and summer samples for phenolic content, and between dried and frozen samples for tannin content. The HPLC phenolic profile indicates more phenolics (nine polyphenols) in dried leaves harvested in both seasons compared to those in frozen ones (five to six polyphenols). Frozen leaves showed a higher antioxidant activity in a ferric-reducing antioxidant power assay than that of the dried samples, which exhibited a higher antioxidant activity using the 1, 1-diphenyl-2-picryl-hydrazyl assay, but it was not statistically significant. The phenolic, flavonoid, and carotenoid contents significantly influenced the antioxidant activities. Among the ethanolic extracts, those from dried leaves showed better antibacterial activity, in particular, on Staphylococcus aureus and Enterococcus faecalis. The high bioactive content and activity of A. altissima leaves make them suitable natural raw materials for various applications.

Cultivation of Limnospira maxima under extreme environmental conditions in Saudi Arabia: Salinity adaptation and scaling-up from laboratory culture to large-scale production

Limnospira maxima has been adapted to grow in high salinity and in an economically alternative medium using industrial-grade fertilizers under harsh environmental conditions in Saudi Arabia. A sequence of scaling-up processes, from the laboratory to large-scale open raceways, was conducted along with gradual adaptation to environmental stress (salinity, light, temperature, pH). High biomass concentration at harvest point and areal productivity were achieved during the harsh summer season (1.122 g L−1 and 60.35 g m−2 day−1, respectively). The average protein content was found to be above 40 % of dry weight. Changes in the color and morphological appearance of the L. maxima culture were observed after direct exposure to sunlight in the outdoor raceways. These results demonstrate a successful and robust adaptation method for algal cultivation at outdoor large-scale in harsh environment (desert conditions) and also prove the feasibility of using hypersaline seawater (42 g kg−1) as an algal growth medium.

Slow 7Be and the primordial 7Li abundance

The cross sections of neutron-induced nuclear reactions are commonly large at low energies. Given this feature, the 7Be(n, p)7Li process with slow 7Be is studied in connection with the cosmological lithium problem. An idea is tested whether slow (non-Maxwellian) 7Be born in the radiative capture 3He(α, γ)7Be reaction can boost the 7Be(n, p)7Li(p, α)α mechanism of 7Li burn-up in the early universe, reducing the amount of primordial 7Li. The rate of this sequential process is examined and its effect on the 7Li abundance is clarified. In the absence of 7Be thermalization in the primordial plasma, the above mechanism can decrease 7Li/H by several percent, leaving unchanged the abundances of D and 4He which are consistent with observations. However, taking into account the 7Be thermalization due to scattering off electrons, positrons, nuclei, and photons radically suppresses the effect. It is found that the typical thermalization time of 7Be is less than 10−5 s, while the fraction of non-Maxwellian particles in the 7Be component is at most 2 × 10−7. Under this condition, the corresponding impact on 7Li/H is estimated to be no more than 10−5%.

Deciphering the Thermal Stability of Bacteriophage MS2-Derived Virus-like Particle and Its Engineered Variant.

RNA bacteriophage MS2-derived virus-like particles (VLPs) have been widely used in biomedical research as model systems to study virus assembly, structure-function relationships, vaccine development, and drug delivery. Considering the diverse utility of these VLPs, a systemic engineering approach has been utilized to generate smaller particles with optimal serum stability and tissue penetrance. Additionally, it is crucial to demonstrate the overall stability of these mini MS2 VLPs, ensuring cargo protection until they reach their target cell/organ. However, no detailed analysis of the thermal stability and heat-induced disassembly of MS2 VLPs has yet been attempted. In this work, we investigated the thermal stability of both wild-type (WT) MS2 VLP and its "mini" variant containing S37P mutation (mini MS2 VLP). The mini MS2 VLP exhibits a higher capsid melting temperature (Tm) when compared to its WT MS2 VLP counterpart, possibly attributed to its smaller interdimer angle. Our study presents that the thermal unfolding of MS2 VLPs follows a sequential process involving particle destabilization, nucleic acid exposure/melting, and disassembly of VLP. This observation underscores the disruption of cooperative intersubunit interactions and protein-nucleic acid interactions, shedding light on the mechanism of heat-induced VLP disassembly.

Improve Parallel Resistance of Hashcash Tree

Denial of Service (DoS) attacks remain a persistent threat to online systems, necessitating continual innovation in defense mechanisms. In this work, we present an improved algorithm for mitigating DoS attacks through the augmentation of client puzzle protocols. Building upon the foundation of hashcash trees, a recently proposed data structure combining hashcash and Merkle trees, we introduce a new version of the data structure that enhances resistance against parallel computation (a common tactic employed by attackers). By incorporating the labels of children and the next node in a breadth-first traversal into the hash function, we establish a sequential processing order that inhibits parallel node evaluation. The added dependency on the next node significantly elevates the complexity of constructing hashcash trees, introducing a linear number of synchronization points and fortifying resilience against potential attacks. Empirical evaluation demonstrates the efficacy of our approach, showcasing its ability to accurately control puzzle difficulty while bolstering system security against DoS threats.

Comparative life cycle assessment of sequential chemical and electrochemical processes for the treatment of industrial textile wastewater

AbstractFenton-based processes, chemical and electrochemical, have attracted the interest of industrial and academic researchers for wastewater treatment. However, the deficiency of rigorous comparison between different methods, including assessment of their impact on the environment, has hindered their large-scale application. This study reports for the first time on the sustainability of raw textile wastewater treatment through two sequential processes, Coagulation-Flocculation-Fenton-Neutralization (CF-F-N) and Coagulation-Flocculation-Electro-Fenton-Neutralization (CF-EF-N), based on Life Cycle Assessment (LCA) approach. The CF-F-N and CF-EF-N were optimized at laboratory scale and compared through LCA, using the IPCC-2013 and ReCiPe-2016 midpoint and endpoint methods. The highest CO2 emissions relied on the wastewater primary treatment by CF. This due to the high amount of hazardous sludge generated and the technology necessary for its disposal (i.e., 16.89 kg CO2-Eq/FU for underground deposit in security cells or 47.52 kg CO2-Eq/FU for incineration) as well as the consumption of reagents required for the treatment (alum, 7.72 kg CO2-Eq/FU; and slaked lime, 5.56 kg CO2-Eq/FU). Regarding the sequential processes, the EF-N presented lower carbon footprint (CFP) than the F-N (14.74 kg CO2-Eq/FU vs. 20.74 kg CO2-Eq/FU). Electricity (87.02% of the total CFP) and reagents (88.63% of the total CFP) denoted the main environmental hotspot during the EF-N and F-N, respectively. The EF-N, compared to the F-N, had an inferior incidence in 14 of the 18 impact categories analyzed using the ReCiPe-2016 method at the midpoint level. This is the result of low consumption of reagents and auxiliary chemicals. The electricity was also found as main environmental hotspot of the EF-N. The ReCiPe-2016 method at the endpoint level showed that the EF-N resulted in lower environmental load in all impact categories. The economic performance (11.91 USD/m3 for CF-EF-N vs. 13.66 USD/m3 for CF-F-N) and LCA demonstrated the competitiveness of the electrochemical sequential process compared to the chemical one. The CF-EF-N can be considered more environmentally sustainable technology.

On the value of a time-inconsistent mean-field zero-sum Dynkin game

We study a mean-field zero-sum Dynkin game (MF-ZSDG) with time-inconsistent performance functionals adapted to the Brownian filtration. Despite the time-inconsistency of the MF-ZSDG, we show that it admits a value and that the pair of first times the value process hits the upper and lower obstacles, respectively, is a saddle point for the game. We solve the problem by approximating the associated lower and upper value processes with a sequence of value processes of interacting time-consistent zero-sum Dynkin games for which the saddle point of each of the value processes is the pair of first times each of those value processes hits the associated upper and lower obstacles, respectively. Under mild assumptions, we show that this sequence of saddle points converges in probability to the pair of first hitting times of the value process of the upper and lower obstacles, respectively, and that the limit is a saddle point for the time-inconsistent MF-ZSDG.

P-251 Denudation time is crucial to increase the proportion of top-quality embryos in egg donation cycles

Abstract Study question How do different denudation times affect in the efficiency of the egg donation cycles? Summary answer Undetected in vitro matured oocytes, resulting from delayed denudations, contribute to a decreased top-quality blastocyst ratio. What is known already While the timeframe for sperm microinjection in ICSI cycles is widely agreed upon (2-6 hours post-oocyte retrieval), no definitive denudation time preference has been established. Published studies diverge on the advantages of early or late denudation, assessing the impact of trigger-to-denudation time (T-D time) on oocyte maturation. Advocates of early denudation contend it prevents oocyte aging, reducing in vitro culture and enhancing outcomes. These studies, often using data from cycles with own oocytes, exhibit high variability and inconsistent conclusions. The present work introduces an alternative hypothesis on the benefits of early denudation, employing a more stable study model. Study design, size, duration This retrospective study encompasses oocyte donation cycles, employing a short antagonist protocol and ovum pick-up 35.5 hours post-trigger, conducted from January 2019 to July 2023 including 1,559 stimulations. Additionally, 1,275 ICSI cycles without detected sperm pathologies derived from these stimulations were examined to evaluate the influence of T-D time on the reproductive outcomes. Six comparable groups were established by segmenting the sample according to T-D time, annotated in 30-minute intervals (36.5h to 39h). Participants/materials, setting, methods Rates of MII, MI, and nonviable oocytes were examined among 1,559 stimulations resulting in 29,671 oocytes (36.5h:187-stimulations/3,178-oocytes; 37h:226/3,836; 37.5h:614/11,921; 38h:329/6,884; 38.5h:119/2,322; 39h:84/1,530). Additionally, in 1,275 ICSI cycles involving 11,333 MII-oocytes (36.5h:117-ICSI cycles/1,027-MII; 37h:130/1,133; 37.5h:502/4,426; 38h:316/2,925; 38.5h:120/1,041; 39h:90/781), comparisons were made for fertilization, abnormal fertilization, ICSI degeneration, useful day 5/6 blastocyst (transferred or cryopreserved; >3BB-top and ≤3BB-good quality by Gardner score), pregnancy, and live birth rates. Statistics were ANOVA, Chi-square test and Pearson’s correlation. Main results and the role of chance Oocyte maturation profile assessment reveals that groups with later denudation exhibit a higher proportion of MII-oocytes (36.5h:80.71%; 37h:81.04%; 37.5h:81.26%; 38h:80.83%; 38.5h:84.84%; 39h:85.29%; r:0.8475; p < 0.05), reaching significance from 38.5h onward (p < 0.05). This is complemented by a strong negative correlation between T-D time and MI rate (36.5h:5.51%; 37h:5.32%; 37.5h:4.64%; 38h:4.17%; 38.5%h:3.27%; 39h:2.94%; r:-0.9881; p < 0.05), with significant declines at 38h and 38.5h (p < 0.05). No correlation between useless oocytes proportion and T-D time was observed (r:-0.5983; p = 0.21). This illustrates that certain MI-collected oocytes progressively mature during in vitro culture and subsequently, upon denudation, are classified as MII. This overestimates in vivo-matured oocytes. No differences attributed to T-D time were observed regarding fertilization, abnormal fertilization, or ICSI degeneration rates. While time does not affect the rate of useful embryos achieved (36.5h:49.4%; 37h:46.4%; 37.5h:44.6%; 38h:48.0%; 38.5h:50.6%; 39h:47.2%; r:0.1342; p = 0.80), it has an impact on the blastocysts quality. The proportion of top-quality blastocysts (>BB) progressively decreases with T-D time (36.5h:86.2%; 37h:85.2%; 37.5h:85.4%; 38h:81.2%; 38,5h:80.5%; 39h:80.5%; r:-0.925; p < 0.05). Out of 1,794 single embryo transfers, 1,477 have conclusive outcome. No implication of T-D time was detected in pregnancy rate (36.5h:59.88%; 37h:64.07%; 37.5h:61.54%; 38h:65.02%; 38.5h:59.55%; 39h:62.26%; r:0.0489; p = 0.93) or live birth rate (36.5h:40.31%; 37h:51.85%; 37.5h:39.12%; 38h:45.04%; 38.5h:41.83%; 39h:37.96%; r:-0.375; p = 0.46). Limitations, reasons for caution This study has inherent limitations as a retrospective analysis. Additionally, the potential effects of a denudation earlier than 36.5 hours post-trigger, not yet standardized in the laboratory, were not assessed. Wider implications of the findings Delayed denudation may benefit autologous IVF by fostering in vitro oocyte maturation. In egg donation cycles, early denudation is preferable for stringent selection of in vivo matured oocytes, yielding superior results. Additionally, shortening or eliminating incubation periods would enhance the automated sequence of diverse processes during an IVF cycle. Trial registration number not applicable

P-242 ICSI, the sooner, the better

Abstract Study question What is the impact of different ICSI timing on the efficiency of egg reception cycles concerning both denudation and trigger events? Summary answer The interval between denudation and ICSI significantly compromises cycle efficiency, particularly blastocyst quality. Delayed ICSI impairs this impact relative to the trigger event. What is known already The established consensus on the optimal ICSI timeframe, spanning 2-6 hours post-oocyte retrieval, has been extensively documented. However, the acknowledged variability within this window has prompted a critical review. Recent studies highlight the need for a more nuanced understanding, suggesting that phenomena affecting ICSI outcomes can be better elucidated by adopting stable models such as oocyte reception cycles. By removing the oocyte factor, these models offer a more precise perspective, addressing the temporal intricacies integral to successful ICSI procedures. The present research underscores the imperative of refining the temporal dimension for optimal reproductive results. Study design, size, duration This retrospective study encompassed 1,275 egg reception ICSI-cycles conducted between January 2019 and July 2023. Donors underwent stimulation using a short antagonist protocol, and oocytes were collected 35.5h post-trigger. The impact of both, the time between denudation-ICSI (D-ICSI time) and the time between trigger-ICSI (T-ICSI time) on reproductive outcomes were analysed. Six comparable groups were established by 30-minute intervals, segmenting the sample based on D-ICSI time (0.5h to 3h) and T-ICSI time (38h to 40.5h). Participants/materials, setting, methods A total of 1,275 cycles, involving 11,333 MII-oocytes, were stratified based on D-ICSI time (0.5h:89-cycles/761-MII; 1h:128/1,186; 1.5h:269/2,385; 2h:270/2,415; 2.5h:340/3,064; 3h:179/1,522) and T-ICSI time (38h:183/1,631; 38.5h:131/1,158; 39h:246/2,205; 39.5h:219/1,952; 40h:173/1,534; 40.5h:323/2,853). Comparative analyses were conducted for fertilization, abnormal fertilization, ICSI degeneration, and useful day 5/6 blastocyst outcomes (transferred or cryopreserved; >3BB-top and ≤3BB-good quality by Gardner score), as well as pregnancy and live birth rates (1,594 SET). Statistical methods employed included ANOVA, Chi-square tests, and Pearson’s correlation. Main results and the role of chance Several parameters showed stability across different time intervals of D-ICSI time: fertilization (0.5h:74.6%; 1h:72.6%; 1.5h:74.0%; 2h:75.9%; 2.5h:74.8%; 3h:74.4%; r = 0.3707; p = 0.469), blastocyst (0.5h:65.3%; 1h:62.6%; 1.5h:61.7%; 2h:60.1%; 2.5h:64.8%; 3h:63.8%; r= -0.068; p = 0.898), and live birth rates (0.5h:39.5%; 1h:35.9%; 1.5h:36.6%; 2h:40.2%; 2.5h:39.9%; 3h:39.7%; r = 0.474; p = 0.342). However, an extended interval significantly correlated with increased oocyte ICSI-degeneration (0.5h:5.5%; 1h:6.7%; 1.5h:6.7%; 2h:6.5%; 2.5h:7.4%; 3h:8.5%; r = 0.903; p < 0.05) and a decreased proportion of top-quality blastocysts (0.5h:86.8%; 1h:89.2%; 1.5h:86.4; 2h:85.3%; 2.5h:81.2%; 3h:75.2%; r= -0.879; p < 0.05). Extended culture of cumulus cell-free oocytes may promote oocyte aging and, consequently, compromise optimal development (consistent with findings in other studies). In evaluating T-ICSI time, similar trends to those observed in D-ICSI time persist in fertilization (r = 0.275, p = 0.598), blastocyst (r = 0.233, p = 0.657), live birth (r = 0.516, p = 0.295), and ICSI-degeneration rates (r = 0.864, p < 0.05). The negative correlation in the quality of generated useful blastocysts significantly intensifies (38h:90.6%; 38.5h:87.5%; 39h:84.7%; 39.5h:82.4%; 40h:80.1%; 40.5h:79.7%; r= -0.982, p < 0.05). In a separate analysis, we observed that undetected in vitro matured oocytes, stemming from delayed denudations, lead to a reduction in the top-quality blastocyst ratio. This concept, along with D-ICSI time, seems to synergize, emphasizing the adverse trend when evaluating blastocyst qualities concerning T-ICSI times. Limitations, reasons for caution This study has inherent limitations as a retrospective analysis. Additionally, the potential effects of ICSI earlier than 0.5 hours post denudation and 38 hours post-trigger, not yet standardized in the laboratory, were not assessed. Wider implications of the findings Extended incubation of denuded and non-injected oocytes fosters oocyte aging. Consequently, cycle efficiency is compromised with prolonged incubation times. The reduction of these times holds promise for improving the efficiency of IVF treatments. Additionally, shortening or eliminating incubation periods would enhance the automated sequence of diverse processes during an IVF. Trial registration number not applicable

Influence of sequential melting and sintering process on morphology development and performance properties of ultra‐high molecular weight polyethylene/low‐density polyethylene blends

AbstractBlending ultra‐high molecular weight polyethylene (UHMWPE) with a low molecular weight polyolefin is a well‐established technique to improve its processability. The binary blends of UHMWPE and low‐density polyethylene (LDPE) were prepared with varying LDPE content (10%, 20%, 30%, 40%, and 50% by wt.) leading to the formation of a single polymer composite system (SPCS) as melting and sintering followed preferential stages during mixing. Interestingly, the sequence of either “melting followed by sintering” or “simultaneous sintering and melting” depends on the LDPE content in the blend, resulting in unique blend morphology. Separate melting peaks for the blend's constituents were observed in thermal characterization, showing the blend's immiscibility, while intermediate peaks showed moderate miscibility in the melt state. Due to the high entanglement density, UHMWPE retained a solid‐like structure and showed an intense viscous nature even above the melting point in rheological characterization. Typically, the UHMWPE samples are fabricated by boundary fusion between particles called sintering. At LDPE content higher than 30 wt.%, the sea island structure was visible in morphological analysis, and the preexisting cavities reduced the tensile modulus by 54% in the blend, along with decreased elongation at failure (250%–100%). Similarly, the flexural modulus was reduced by 34.7% in the blends, along with the decreased flexural strength. Interestingly, the decrease in performance properties occurred in two steps over the range of LDPE content. Thus, it was clear that 30 wt.% of LDPE in the UHMWPE/LDPE blend represented the critical concentration for controlling melting and sintering sequence to develop specified morphology and performance properties. These blends have the potential for biomedical application as well as for the development of foam products, which is the further scope of the present study.

Epidermal growth factor-loaded, dehydrated physical microgel-formed adhesive hydrogel enables integrated care of wet wounds

Integrated wound care, a sequential process of promoting wound hemostasis, sealing, and healing, is of great clinical significance. However, the wet environment of wounds poses formidable challenges for integrated care. Herein, we developed an epidermal growth factor (EGF)-loaded, dehydrated physical microgel (DPM)-formed adhesive hydrogel for the integrated care of wet wounds. The DPMs were designed using the rational combination of hygroscopicity and reversible crosslinking of physical hydrogels. Unlike regular bioadhesives, which consider interfacial water as a barrier to adhesion, DPMs utilize water to form desirable adhesive structures. The hygroscopicity allowed the DPMs to absorb interfacial water and subsequently, the interfacial adhesion was realized by the interactions between tissue and DPMs. The reversible crosslinks further enabled DPMs to integrate into hydrogels (DPM-Gels), thus achieving wet adhesion. Importantly, the water-absorbing gelation mode of DPMs enabled facile loading of biologically active EGF to promote wound healing. We demonstrated that the DPM-Gels possessed wet tissue adhesive performance, with about 40 times the wet adhesive strength of fibrin glue and about 4 times the burst pressure of human blood pressure. Upon application at the injury site, the EGF-loaded DPM-Gels sequentially promoted efficient wound hemostasis, stable sealing, and quick healing, achieving integrated care of wet wounds.

A Lightweight Sequential Convolutional Neural Network for Smart Grid Stability Analysis

A Smart grid stability analysis is essential for ensuring modern power systems' reliable and secure operation. This approach helps identify potential instabilities and disturbances that can lead to blackouts or equipment failures. By analyzing the stability of the grid, operators can take proactive measures to maintain a stable and resilient power infrastructure. Monitoring smart grid data from various sources and analyzing how to control the stability of the grid are challenging tasks. A convolutional neural network (CNN) can effectively capture spatial dependencies and patterns from grid data and can help in enabling accurate prediction and classification of stability-related events in a power system. However, developing a CNN that has fewer learnable parameters and provides high accuracy is challenging. This paper presents a sequential CNN architecture to detect the stability of the Decentral Smart Grid Control (DSGC) system. A mathematical model of the 4-node start architecture of a smart grid was presented. Later, 12 parameter-based grid datasets from the UCI repository were used to validate the proposed network. The proposed CNN accepts sequential data to capture temporal dependencies in the data. The sequential process in a single dimension offers fewer learnable parameters, making the network more compact and computationally efficient. The proposed 11-layered CNN has a total of 12.7K learnable parameters. The detailed analysis of the proposed CNN using ambiguity and the t-SNE score suggested that the model can identify discriminative features for classifying data into stable and unstable classes. A comparison analysis of the quantitative parameters revealed that the model performed well, with 98.82%, 98.55%, 98.88%, and 98.77% accuracy, precision, recall, and F1 score, respectively.

The impact of structural analysis of works of fine art on enhancing the creativity of artists and interior designers

This article explores the impact of structural analysis of a work of art on enhancing the creativity of designers and artists. The results of the study showed that consistent adherence to all stages of the artistic and compositional aspect of the analysis of an art object can have a significant impact on increasing creative capabilities in the development of creative thinking, as well as obtaining the necessary instrumental skills for creating various types of internal spaces using objects of fine art. As part of this study, recommendations were also drawn up for the practical use of associative components to create the desired emotional atmosphere in various types of interiors. The results of the study allowed us to propose a methodological approach based on a sequential process, which includes art tasks for students and recommendations for integrating the creative process from the field of art into art education. The proposed approach can also be applied to improve creative skills through analysis in other types of fine arts and will provide an opportunity to replenish and expand the knowledge base in this area.

Optimizing anterior urethral stricture assessment: leveraging AI-assisted three-dimensional sonourethrography in clinical practice.

This investigation sought to validate the clinical precision and practical applicability of AI-enhanced three-dimensional sonographic imaging for the identification of anterior urethral stricture. The study enrolled 63 male patients with diagnosed anterior urethral strictures alongside 10 healthy volunteers to serve as controls. The imaging protocol utilized a high-frequency 3D ultrasound system combined with a linear stepper motor, which enabled precise and rapid image acquisition. For image analysis, an advanced AI-based segmentation process using a modified U-net algorithm was implemented to perform real-time, high-resolution segmentation and three-dimensional reconstruction of the urethra. A comparative analysis was performed against the surgically measured stricture lengths. Spearman's correlation analysis was executed to assess the findings. The AI model completed the entire processing sequence, encompassing recognition, segmentation, and reconstruction, within approximately 5min. The mean intraoperative length of urethral stricture was determined to be 14.4 ± 8.4mm. Notably, the mean lengths of the urethral strictures reconstructed by manual and AI models were 13.1 ± 7.5mm and 13.4 ± 7.2mm, respectively. Interestingly, no statistically significant disparity in urethral stricture length between manually reconstructed and AI-reconstructed images was observed. Spearman's correlation analysis underscored a more robust association of AI-reconstructed images with intraoperative urethral stricture length than manually reconstructed 3D images (0.870 vs. 0.820). Furthermore, AI-reconstructed images provided detailed views of the corpus spongiosum fibrosis from multiple perspectives. The research heralds the inception of an innovative, efficient AI-driven sonographic approach for three-dimensional visualization of urethral strictures, substantiating its viability and superiority in clinical application.

An efficient two-stage optimization algorithm for a flexible job shop scheduling problem with worker shift arrangement

Due to the involvement of workers, scheduling production jobs necessitates consideration of worker shifts in most production activities. In this study, we address a flexible job shop scheduling problem with worker shift arrangement, considering constraints such as job priority, limited resources, and resource unavailability. To minimize the overdue days of low-priority jobs and ensure the timely delivery of high-priority jobs, we establish a mixed-integer programming model to allocate production resources, process sequencing, and schedule worker shifts. An improved differential evolution algorithm is proposed and designed such that overdue days and worker overtime of all jobs are calculated. Furthermore, we develop a two-stage intelligent optimization algorithm. First, we design a two-segment chromosome encoding and decoding method. Then, we propose generation strategies that follow the urgency of the priority rule to generate high-quality initial chromosomes. In adaptive worker shift adjustment, we prioritize high-priority jobs to align with delivery times. We conducted experiments to validate our model and algorithm by comparing them against four well-known intelligent optimization algorithms. Our improved algorithm proves to be highly beneficial in job and worker scheduling as it effectively minimizes overdue days and arranges worker overtime.

Validation of the Elekta Synergy Platform Linac at 6 MV Photon Beam using PRIMO Monte Carlo Software

Aims: The objective was to validate the initial beam parameters of the Davao Doctors Hospital’s 6 MV Elekta Synergy Platform linac, which performs to the specification of the commissioning data per our records using the gamma-index analysis toolkit integrated inside PRIMO software. Materials and Methods: In PRIMO, a sequence of optimization processes is performed, in which the measured and simulated percent depth dose (PDD) and lateral beam profiles at various depths are compared, using the stringent gamma-index passing rate at 1%/1 mm criteria (GPR11). Using four fields of sizes 3 cm × 3 cm, 4 cm × 4 cm, 5 cm × 5 cm, and 10 cm × 10 cm, the dose is calculated on a water phantom measuring 16.2 cm × 16.2 cm × 30.0 cm. In addition, one field of size 20 cm × 20 cm is used on a 50.0 cm × 50.0 cm × 30.0 cm water phantom with a bin size of 0.2 cm × 0.2 cm × 0.2 cm at a source-surface distance of 100.0 cm. Results: For PDD and beam profiles comparison at the largest field size, the 6.5 MeV initial electron beam energy, 0.25 MeV full-width-half-maximum energy, 0.20 cm focal spot size, and 3° beam divergence tuned configuration yield GPR11 values of 94.0% and 97.7% (PRIMO PDD and lateral beam profile at 200 mm scan depth, respectively) with a statistical uncertainty of 2.9%. For lower field sizes, the GPR11 values are consistent at more than 90% for the PDD, whereas GPR11 values of 80.3% and 70.6% for the lateral beam profiles (at 15 mm and 200 mm scan depths) at 10 cm × 10 cm and 5 cm × 5 cm, respectively. The percentage difference between the measured and simulated PDD 20,10 ratios of not more than 2.45% is observed in all field settings. Conclusions: These tuned beam parameters are remarkably in agreement with the suggested beam parameters listed on the PRIMO website for the 6 MV Elekta linac which was optimized with a different set of measurements.