Optimization Approach Research Articles

Modeling and optimization of culture media for recombinant Helicobacter pylori vaccine antigen HpaA.

H. pylori (Helicobacter pylori) infection represents a significant global health concern, exacerbated by the emergence of drug-resistant strains resulting from conventional antibiotic treatments. Consequently, the development of vaccines with both preventive and therapeutic properties has become crucial in addressing H. pylori infections. The H. pylori adhesin protein HpaA has demonstrated strong immunogenicity across various adjuvants and dosage forms, positioning it as a key candidate antigen for recombinant subunit vaccines against H. pylori. Optimizing fermentation culture conditions is an effective strategy to enhance product yield and lower production costs. However, to date, there has been no systematic investigation into methods for improving the fermentation yield of HpaA. Enhancing the fermentation medium to increase HpaA yield holds significant potential for application and economic benefits in the prevention and detection of H. pylori infection. To achieve a stable and high-yielding H. pylori vaccine antigen HpaA, this study constructed recombinant Escherichia coli expressing HpaA. The impact of fermentation medium components on the rHpaA yield was assessed using a one-factor-at-a-time approach alongside Plackett-Burman factorial experiments. Optimal conditions were effectively identified through response surface methodology (RSM) and artificial neural network (ANN) statistical computational models. The antigenicity and immunogenicity of the purified rHpaA were validated through immunization of mice, followed by Western Blot analysis and serum IgG ELISA quantification. Glucose, yeast extract, yeast peptone, NH4Cl and CaCl2 all contributed to the production of rHpaA, with glucose, yeast extract, and NH4Cl demonstrating particularly significant effects. The artificial neural network linked genetic algorithm (ANN-GA) model exhibited superior predictive accuracy, achieving a rHpaA yield of 0.61g/L, which represents a 93.2% increase compared to the initial medium. Animal immunization experiments confirmed that rHpaA possesses good antigenicity and immunogenicity. This study pioneers the statistical optimization of culture media to enhance rHpaA production, thereby supporting its large-scale application in H. pylori vaccines. Additionally, it highlights the advantages of the ANN-GA approach in bioprocess optimization.

APoX-M: Accelerate deep point cloud analysis via adaptive graph construction

Graph-based deep learning point cloud processing has gained increasing popularity but its performance is dragged by the dominating graph construction (GC) phase with irregular computation and memory access. Existing works that accelerate GC by tailoring architecture for a single GC algorithm fail to maintain efficiency because they neglect the best GC algorithm variation incurred by the point-cloud density variation in changing scenarios. Therefore, we propose APoX-M, a unified architecture with an adaptive GC scheme that can identify the optimum GC approach according to the point cloud variation. We also provide better memory management and scheduling optimizations for better performance. Experiments indicate that APoX-M achieves higher performance and energy efficiency over existing accelerators.

Ergonomic concern in determination of optimum teaching approach in a primary school system – A systematic Review

Ergonomic concern in determination of optimum teaching approach in a primary school system – A systematic Review

A novel approach for optimisation of bioenergy supply chain: Integrating mathematical programming, Geographic Information System, and Analytic Hierarchy Process

The crucial role of sustainable energy in addressing environmental challenges and using agricultural waste for bioenergy supports a circular bioeconomy. This paper presents a four-stage approach to optimise a biorefinery supply chain for Ireland’s agricultural waste sector. The spatial availability of agricultural waste was assessed, followed by evaluating land suitability for biorefinery development under economic, environmental, and social constraints. A hybrid method using GIS and AHP was employed to rank suitable areas, and a mathematical model was used for supply chain optimisation. Results showed only 12 % of the study area is highly suitable for biorefineries, with an average transportation distance of 39 km, leading to reduced costs and emissions. The framework’s efficiency is highlighted by a transportation cost of only 4 %, compared to 16 % in previous studies. This research fills a gap in bioenergy supply chain management by demonstrating advanced tools for optimising sustainability and site suitability.

Novel curcumin derivatives as potential anticancer agents: design, synthesis and biological evaluation

Curcumin, originally isolated as natural product from the rhizome of Curcuma longa L., is widely known for its anticancer properties. However, the clinical application of curcumin is still limited due to its poor absorption and rapid metabolism. In this study, structural modification of curcumin by introducing active small organic acids into its pyrazole ring intermediate, was employed to yield five curcumin derivatives 5a-5e. All the target compounds were characterised by 1H NMR,13C NMR and ESI-MS. Biological evaluation through the CCK-8 method indicated that nearly all these derivatives displayed higher proliferation inhibitory effect on A549 cells than that of curcumin. Among them, 5d bearing biotin moiety exhibited even stronger cytotoxicity action (2.25 μmol/L) than the positive control drug Doxorubicin (3.99 μmol/L) and therefore became the most promising lead compound for further investigation. Our findings provided a potential approach for the structural optimisation of curcumin derivatization in cancer treatment.

A deep reinforcement learning approach for online and concurrent 3D bin packing optimisation with bin replacement strategies

In the realm of robotic palletisation, the quest for optimal space utilization remains vital but also presents a critical challenge, particularly due to the constraints of decision complexity and the need for real-time decision-making without complete prior information. The widely adopted rule-based heuristics approaches were ease to use, but failed to adapt dynamically to the complex and changing landscape of online 3D bin packing. This study is motivated by the need for a system that is both more agile and intelligent, capable of managing the intricacies of dual-bin scenarios and the variable inflow of items. This study introduces a novel deep reinforcement learning (DRL) optimiser, employing a double deep Q-network (DDQN) to obtain optimal packing policies in an online environment with two proposed bin replacement strategies. This approach surpasses the limitations of previous methods by facilitating the simultaneous management of multiple bins and enabling on-the-fly adjustments to decisions based on limited prior knowledge. In a case study involving a logistics company, the proposed optimizer demonstrated a significant improvement in average space utilization across various lookahead scenarios, outperforming traditional heuristics in simulation experiments. The proposed optimiser contributes significantly to the economic and environmental sustainability of robotic warehouses, positioning itself as a cornerstone for the future of smart logistics.

Simulation and optimisation of vacuum (pressure) swing adsorption with simultaneous consideration of real vacuum pump data and bed fluidisation

Pressure swing adsorption (PSA) is a promising technology for gas separation and purification, receiving considerable attention in the past decades. Existing studies on simulating PSA involving a vacuum pump, the vacuum (pressure) swing adsorption [V(P)SA] process, typically estimate vacuum pump energy consumption using an approximate constant energy efficiency or an empirical energy efficiency correlation, leading to inaccurate representation of realistic vacuum pump performance. In this work, we propose an enhanced computational approach for simulation and optimisation of V(P)SA processes through simultaneous incorporation of realistic vacuum pump performance prediction models and adsorption bed fluidisation limits. The vacuum pump performance prediction models are developed using data-driven modelling techniques with realistic vacuum pump performance data. The enhanced computational approach more accurately accounts for the V(P)SA performance, without relying on an estimated vacuum pump efficiency and assumed pressure/flow rate boundary conditions at the vacuum pump end of the adsorption bed. Furthermore, the impact of bed fluidisation on optimal PSA design is addressed. The computational results show that the developed prediction models accurately represent the actual performance curves of the vacuum pump. It is also demonstrated that incorporating the vacuum pump performance models and fluidisation constraints in PSA optimisation leads to significantly different optimal solutions compared to when these factors are not considered.

Direct vegetation response to recent CO2 rise shows limited effect on global streamflow

Global streamflow, crucial for ecology, agriculture, and human activities, can be influenced by elevated atmospheric CO2 (eCO2) though direct regulation of vegetation physiology and structure, which can either decrease or increase streamflow. Despite a 21.8% rise in CO2 over 40 years, its impact on streamflow is not obvious and remains highly debated. Using a full differential approach at the catchment scale and an optimum finger approach globally, both constrained by observed streamflow, here, we find that vegetation responses to eCO2 in 1981–2020 has limited impact on streamflow via direct regulation. The median eCO2 contribution approaches zero across 1116 unimpacted catchments, and global streamflow changes cannot be solely attributed to eCO2. These results offer key insights into the intricate dynamics of CO2 and other factors shaping streamflow changes over the past four decades. Such understanding is vital for attributing current streamflow changes under eCO2 conditions.

Functionalized iminodiacetic acid pectin/montmorillonite hydrogel for enhanced adsorption of 4-nitrophenol from simulated water: An integrated approach of response surface optimization and non-linear modeling

This research focuses on the modification of pectin through an amination reaction, followed by the development of a novel iminodiacetic acid-functionalized pectin/montmorillonite hydrogel nanocomposite (IMAP/MMT) using gamma radiation. This composite was investigated as an adsorbent for 4-nitrophenol removal from aqueous solutions. Central composite design was utilized for numerical optimization, identifying the maximum adsorption capacity under optimal conditions [0.8 g/L; 52 min;4 pH, 73 mg/L). A decrease in Qm (330.19 to 273.96 mg/g) with increasing temperature indicated an exothermic adsorption process, supported by the calculated ΔH° value of −28.39 kJ/mol. Analysis of pH, FTIR, and XPS results revealed that the primary mechanisms for 4-nitrophenol uptake include hydrogen bonding, π-π interactions, and n-π interactions. Additionally, the Temkin and D-R isotherms, along with ΔH° values, confirmed the physical nature of the adsorption process, as evidenced by bT (0.70–0.86 kJ/mol) and E (0.74–0.82 kJ/mol) values. The IMAP/MMT composite exhibited superior adsorption selectivity, efficient performance in real water, and good reusability, with a rate of 79.4 % maintained for up to five cycles, highlighting its potential for practical applications as an effective adsorbent. Overall, the findings suggest that IMAP/MMT can serve as an environmentally and economically sustainable material for the removal of 4-nitrophenol from wastewater.

Balanced-risk analysis in the engineering design of complex systems with extreme conditions

The engineering design of components and systems in a fusion power plant, an example of a complex system with extreme conditions, requires careful consideration and balance of the numerous associated risks. Key risks are observed from several perspectives and categorised as failure modes, situational and quality risks. Several methods exist for the separate consideration of these risks at the various design phases. However, an optimum approach is to consider all risks and balance the effects during the early design phase. The paper introduces a novel methodology to consider all associated risks at the conceptual design phase by combining a generalised Π-theorem and dimensional analysis for qualitative physics reasoning. The method is applied to a Balance of Plant components as a case study. Initial results show positive concurrent consideration and analysis of all risks in the conceptual stage.

Fast Charging System of Electric Vehicle Using Optimized Isolated Multi-Port DC-DC Converter based on Modified Coati Optimization Algorithm

Once clean, renewable energy sources are used to charge the batteries in electric vehicles (EVs), the vehicles can produce zero gas emissions, greatly improving the environment. EVs and other distributed energy storage devices can be used in a smart microgrid to deliver energy to the loads throughout highest times, reducing the impact of load shading and improving the quality of the electricity. To achieve these goals of energy balance between EVs, the grid, and renewable energy sources, an isolated hybrid multiport converter is required. This paper develops an optimized isolated multi-port DC-DC converter for controlling power flow in multiple directions in an EV. This converter contains a dc-dc unidirectional converter, a bidirectional dc-dc converter, a triple active bridge (TAB), and a multi-port dual active bridge converter. Additionally, the Optimal Controller (OC) is developed to manage the power between the EV and the battery. The power flow is achieved by using the Modified Coati Optimization Algorithm (MCOA). In the MCOA, the optimal gain parameter of the converter is selected. In the planned technique, a bidirectional DC-DC converter can be considered to interconnect the EV battery towards deliver bidirectional power flow ability with the battery. The proposed approach is executed in MATLAB, and presentations are assessed by considering performance measures. Moreover, it is contrasted with the traditional approaches of particle swarm optimization (PSO) and grey wolf optimization (GWO).

Meta-Analysis of Randomised Controlled Trials Comparing Bypass and Endovascular Revascularisation for Peripheral Artery Disease.

Peripheral artery disease affects approximately 250 million people globally. Multiple randomised controlled trials have compared bypass and endovascular interventions but the optimum revascularisation approach remains unclear. The recently published BEST-CLI and BASIL-2 trials provide current and robust data addressing this question, however their findings are not concordant. This systematic review and meta-analysis provides an overview of the worldwide randomised evidence comparing bypass surgery and endovascular revascularisation in lower limb peripheral artery disease. A comprehensive literature search of MEDLINE, Embase and CENTRAL databases was performed of all time periods up to 7 May 2023 to identify randomised controlled trials comparing bypass and endovascular revascularisation for treating lower limb peripheral artery disease. The primary outcome was major amputation. Secondary outcomes were mortality, re-intervention, 30-day adverse events and 30-day mortality. Odds ratios were calculated and pooled using the random-effects model. Risk of bias was assessed using the Cochrane risk of bias 2 tool. Fourteen cohorts were identified across thirteen studies, enrolling 3840 patients. There was no significant difference in major amputation (OR 1.12; 95% CI 0.80-1.57) or mortality (OR 0.96; 95% CI 0.79-1.17) between the bypass and endovascular groups. Bypass was associated with a significant reduction in re-intervention compared with endovascular treatment (OR 0.57, 95% CI 0.40-0.82). These findings suggest that rates of major amputation and mortality are similar following bypass and endovascular interventions. Patients who undergo bypass surgery have a significantly lower re-intervention rate post-operatively.

Dealing with uncertainty in cybersecurity decision support

The mathematical modeling of cybersecurity decision-making heavily relies on cybersecurity metrics. However, achieving precision in these metrics is notoriously challenging, and their inaccuracies can significantly influence model outcomes. This paper explores resilience to uncertainties in the effectiveness of security controls. We employ probabilistic attack graphs to model threats and introduce two resilient models: minmax regret and min-product of risks, comparing their performance.Building on previous Stackelberg game models for cybersecurity, our approach leverages totally unimodular matrices and linear programming (LP) duality to provide efficient solutions. While minmax regret is a well-known approach in robust optimization, our extensive simulations indicate that, in this context, the lesser-known min-product of risks offers superior resilience.To demonstrate the practical utility and robustness of our framework, we include a multi-dimensional decision support case study focused on home IoT cybersecurity investments, highlighting specific insights and outcomes. This study illustrates the framework’s effectiveness in real-world settings.

Neural‑network‑driven approach in optimization of municipal solid waste collection integrated with geo-spatial techniques

<p class="AbstractText"><span lang="EN-US">Municipal solid waste (MSW) management is the most important as well as difficult tasking the present context of rapid urbanization. In this study, a comparison has been done using Geographical information system(GIS) and artificial neural network(ANN) to determine optimum route and time for the collection of solid waste during each single trip by each type of collection vehicle; this will reduce the cost involved in collection phase of Municipal Solid Waste (MSW). The area selected for the study was Erode city which is located in north west of Tamil Nadu, India. The optimum route analysis have been carried out using both GIS and ANN tools. Multiple structures of ANN have been analyzed by modifying the hidden layers. Based on the results, the optimized structure of the network was found. The suggested model is validated by the  high value of the coefficient of regression, minimum value of mean square error. Based on these performance parameters, it is found that the ANN model gives optimum results. Implementing these optimum routes will leads to a cost-effective solid waste management system.</span></p>

Split-Level Evolutionary Neural Architecture Search With Elite Weight Inheritance.

Neural architecture search (NAS) has recently gained extensive interest in the deep learning community because of its great potential in automating the construction process of deep models. Among a variety of NAS approaches, evolutionary computation (EC) plays a pivotal role with its merit of gradient-free search ability. However, a massive number of the current EC-based NAS approaches evolve neural architectures in an absolutely discrete manner, which makes it tough to flexibly handle the number of filters for each layer, since they often reduce it to a limit set rather than searching for all possible values. Moreover, EC-based NAS methods are often criticized for their inefficiency in performance evaluation, which usually requires laborious full training for hundreds of candidate architectures generated. To address the inflexible search issue on the number of filters, this work proposes a split-level particle swarm optimization (PSO) approach. Each dimension of the particle is subdivided into an integer part and a fractional part, encoding the configurations of the corresponding layer, and the number of filters within a large range, respectively. In addition, the evaluation time is greatly saved by a novel elite weight inheritance method based on an online updating weight pool, and a customized fitness function considering multiple objectives is developed to well control the complexity of the searched candidate architectures. The proposed method, termed split-level evolutionary NAS (SLE-NAS), is computationally efficient, and outperforms many state-of-the-art peer competitors at much lower complexity across three popular image classification benchmark datasets.

Cross-Modal Clustering With Deep Correlated Information Bottleneck Method.

Cross-modal clustering (CMC) intends to improve the clustering accuracy (ACC) by exploiting the correlations across modalities. Although recent research has made impressive advances, it remains a challenge to sufficiently capture the correlations across modalities due to the high-dimensional nonlinear characteristics of individual modalities and the conflicts in heterogeneous modalities. In addition, the meaningless modality-private information in each modality might become dominant in the process of correlation mining, which also interferes with the clustering performance. To tackle these challenges, we devise a novel deep correlated information bottleneck (DCIB) method, which aims at exploring the correlation information between multiple modalities while eliminating the modality-private information in each modality in an end-to-end manner. Specifically, DCIB treats the CMC task as a two-stage data compression procedure, in which the modality-private information in each modality is eliminated under the guidance of the shared representation of multiple modalities. Meanwhile, the correlations between multiple modalities are preserved from the aspects of feature distributions and clustering assignments simultaneously. Finally, the objective of DCIB is formulated as an objective function based on a mutual information measurement, in which a variational optimization approach is proposed to ensure its convergence. Experimental results on four cross-modal datasets validate the superiority of the DCIB. Code is released at https://github.com/Xiaoqiang-Yan/DCIB.

A comparison of efficacy of erector spinae plane block versus serratus anterior plane block plus subcostal transversus abdominus plane block for bariatric laparoscopic sleeve gastrectomy surgery: study protocol for a randomised clinical trial

BackgroundObesity is a rapidly growing global health concern. Limited long-term success of diet, behavioural modification and medical therapy have led to the increased performance of bariatric surgery. Laparoscopic sleeve gastrectomy, which permanently reduces the size of the stomach, has been shown to cause considerable weight loss, as well as improving or even eliminating obesity related medical comorbidities such as diabetes, obstructive sleep apnoea and hypertension. Unfortunately, this surgery can also result in significant postoperative pain which, when combined with the dangers of perioperative opioid administration for bariatric patients, can lead to a significantly reduced quality of recovery. Opioid-sparing analgesia has been widely recommended for perioperative bariatric patients, but research into the optimum regional analgesia approach for this surgery is lacking, with no trials to date comparing different regional analgesic techniques. This study protocol describes a randomised clinical trial aimed at answering this question, comparing the quality of recovery after laparoscopic sleeve gastrectomy for patients who receive erector spinae plane block, versus those who receive serratus anterior plane block plus subcostal TAP block.MethodsWe propose a prospective, randomised, blinded (investigator) clinical trial in a tertiary hospital in Ireland. Seventy patients presenting for laparoscopic sleeve gastrectomy will be randomised to two study groups—group A will receive bilateral erector spinae blockade; group B will receive left sided serratus anterior plane block plus subcostal TAP blocks. Both groups will receive the same dose of the same local anaesthetic and the different regional technique performed will be the only difference in their care. The primary outcome will be QoR-15 scores at 24 h postoperatively, a validated international tool for assessing a patient’s overall postoperative recovery.DiscussionRegional analgesia should be a mainstay of perioperative opioid-sparing analgesia where possible. This is especially important in the bariatric cohort who are particularly susceptible to the complications of perioperative opioid administration. To the best of our knowledge, this trial will be the first to compare efficacy of two different regional analgesia techniques for bariatric patients undergoing laparoscopic sleeve gastrectomy surgery.Trial registrationThis trial was pre-registered on clinicaltrials.gov, registration number NCT05839704, on March 5, 2023. All items from the World Health Organisation Trial Registration Data Set have been included.

Nonconvex Factorization and Manifold Formulations Are Almost Equivalent in Low-Rank Matrix Optimization

In this paper, we consider the geometric landscape connection of the widely studied manifold and factorization formulations in low-rank positive semidefinite (PSD) and general matrix optimization. We establish a sandwich relation on the spectrum of Riemannian and Euclidean Hessians at first-order stationary points (FOSPs). As a result of that, we obtain an equivalence on the set of FOSPs, second-order stationary points, and strict saddles between the manifold and factorization formulations. In addition, we show that the sandwich relation can be used to transfer more quantitative geometric properties from one formulation to another. Similarities and differences in the landscape connection under the PSD case and the general case are discussed. To the best of our knowledge, this is the first geometric landscape connection between the manifold and factorization formulations for handling rank constraints, and it provides a geometric explanation for the similar empirical performance of factorization and manifold approaches in low-rank matrix optimization observed in the literature. In the general low-rank matrix optimization, the landscape connection of two factorization formulations (unregularized and regularized ones) is also provided. By applying these geometric landscape connections (in particular, the sandwich relation), we are able to solve unanswered questions in the literature and establish stronger results in the applications on geometric analysis of phase retrieval, well-conditioned low-rank matrix optimization, and the role of regularization in factorization arising from machine learning and signal processing. Funding: This work was supported by the National Key R&D Program of China [Grants 2020YFA0711900 and 2020YFA0711901], the National Natural Science Foundation of China [Grants 12271107 and 62141407], and the Shanghai Science and Technology Program [Grant 21JC1400600]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoo.2022.0030 .

Bus travel feature inference with small samples based on multi-clustering topic model over Internet of Things

With the widespread application of Internet of Things (IoT) technology, there has been a shift from a broad-brush to a more refined approach in traffic optimization. An increasing amount of IoT data is being utilized in trajectory mining and inference, offering more precise characteristic information for optimizing public transportation. Services that optimize public transit based on inferred travel characteristics can enhance the appeal of public transport, increase its likelihood as a travel choice, alleviate traffic congestion, and reduce carbon emissions. However, the inherent complexities of disorganized and unstructured public transportation data pose significant challenges to extracting travel features. This study explores the enhancement of bus travel by integrating advanced technologies like positioning systems, IoT, and AI to infer features in public transportation data. It introduces the MK-LDA (MeanShift Kmeans Latent Dirichlet Allocation), a novel thematic modeling technique for deducing characteristics of public transit travel using limited travel trajectory data. The model employs a segmented inference methodology, initially leveraging the Mean-shift clustering algorithm to create POI seeds, followed by the P-K-means algorithm for discerning patterns in user travel behavior and extracting travel modalities. Additionally, a P-LDA (POI-Latent Dirichlet Allocation) inference algorithm is proposed to examine the interplay between travel characteristics and behaviors, specifically targeting attributes significantly correlated with public transit usage, including age, occupation, gender, activity levels, cost, safety, and personality traits. Empirical validation highlights the efficacy of this thematic modeling-based inference technique in identifying and predicting travel characteristics and patterns, boasting enhanced interpretability and outperforming conventional benchmarks.

Effective Patient Scheduling For Public Health Events Based On Deep Reinforcement Learning

Amidst the current worldwide pandemic of the novel coronavirus, there are still issues about the efficient allocation of emergency supplies and shortcomings in recovery protocols. To enhance individuals' health and overall welfare, it is imperative to enhance and refine the emergency patient scheduling framework for health emergencies while upholding the idea of a society with an inherent future for humanity. The paper proposed an Efficient Patient Scheduling for Public Health Emergencies (EPS-PHE) using Multiagent Reinforcement Learning (MRL). Originally intended to solve the PHE and improve EPS, the technology was conceived as a distributed multiagent system. MRL has proposed a decentralized incentive system evaluation technique for the EPS-PHE input. An incentive type called a Direct Incentive (DI) is used to show the relationships between individuals or details about two closely connected events. The quality of data transmission in EPS-PHE or the efficacy of the emergency network is measured using a Collective Incentive (CI). Simulation research has shown the value of the Deep Learning (DL) - MRL approach in EPS-PHE framework optimization.