Innovative Method Research Articles

Real-world implementation of a cloud-based MPC for HVAC control in educational buildings

In their quest to enhance energy efficiency and carbon footprint management, many enterprises are turning their attention towards optimizing their facilities through amplified data monitoring and innovative control methods. In this context, this research highlights a practical solution in a commercial building located in the United States. It explains the creation, implementation, and costs of a universally applicable model predictive control (MPC) framework for the building’s heating, ventilation, and air conditioning (HVAC) system. The paper elaborates on both the hardware and software used to accumulate pertinent data and the formulation of the MPC system. This approach leverages cloud-based microservices and can be seamlessly integrated into current building management systems. In this regard, this paper presents three innovative strategies: Proportional–integral (PI)+Preheating, MPC, and occupancy-based control. These scenarios were designed to improve energy efficiency and thermal comfort significantly by reducing temperature fluctuations and adhering more closely to the setpoint temperature. The implementation of these strategies resulted in substantial energy savings, with reductions in energy consumption of 12.83%, 19.21%, and 14.98%, respectively.

Multi-view graph pooling with coarsened graph disentanglement

Multi-view graph pooling utilizes information from multiple perspectives to generate a coarsened graph, exhibiting superior performance in graph-level tasks. However, existing methods mainly focus on the types of multi-view information to improve graph pooling operations, lacking explicit control over the pooling process and theoretical analysis of the relationships between views. In this paper, we rethink the current paradigm of multi-view graph pooling from an information theory perspective, subsequently introducing GDMGP, an innovative method for multi-view graph pooling derived from the principles of graph disentanglement. This approach effectively simplifies the original graph into a more structured, disentangled coarsened graph, enhancing the clarity and utility of the graph representation. Our approach begins with the design of a novel view mapper that dynamically integrates the node and topology information of the original graph. This integration enhances its information sufficiency. Next, we introduce a view fusion mechanism based on conditional entropy to accurately regulate the task-relevant information in the views, aiming to minimize information loss in the pooling process. Finally, to further enhance the expressiveness of the coarsened graph, we disentangle the fused view into task-relevant and task-irrelevant subgraphs through mutual information minimization, retaining the task-relevant subgraph for downstream tasks. We theoretically demonstrate that the performance of the coarsened graph generated by our GDMGP is superior to that of any single input view. The effectiveness of GDMGP is further validated by experimental results on seven public datasets.

Modeling and Simulation of Hydroxyapatite Recovery in the Desliming Circuit of the Tapira Industrial Plant, Brazil

The modeling and simulation of industrial mineral processing operations are traditionally used for cyclone sizing and optimizations of industrial operations. However, the main models used are based on the total population of particles in the pulp, thus not distinguishing the individual minerals. This article presents the results of an innovative method that investigated the optimization of the metallurgical recovery of P2O5 in the desliming circuit of a phosphate ore processing plant in Brazil. A survey campaign was carried out in the existing industrial circuit, followed by determining the partition curves for the overall particles and specifically for the hydroxyapatite particles. The results were used to calibrate the Narasimha–Mainza cyclone model. From a Base Case determined with reference to the industrial survey, three optimization scenarios were simulated through cyclone geometries and respective operating conditions changes. Simulated scenarios indicated the possibility of P2O5 metallurgical recovery increasing from 9.4% to 12.7% compared to the Base Case.

Cultivation Of Spirulina Platensis for carbon dioxide bio sequestration in hybrid photobioreactor with real-time monitoring system

Innovative methods for effectively sequestering significant amounts of carbon dioxide (CO2) are required to address the ongoing problem of greenhouse gas emissions, which have increased in tandem with industrial development. Optimisation of the performance of the photobioreactor remains a challenge, despite the potential of using photobioreactors and cyanobacteria for bio sequestration. A potential solution lies in the development of a hybrid photobioreactor design. Thus, this study aims to develop a hybrid design of the photobioreactor equipped with tailor-made real-time monitoring system known as FCB2022 for cultivating Spirulina platensis. The performance is evaluated by monitoring real-time data such as pH, dissolved oxygen (DO), and temperature, alongside assessing the growth kinetic of cyanobacterium, CO2 sequestration, oxygen release rate and mass carbon balance. These assessments are conducted under varying photon flux densities (PFD) of 100µmol/ (m2.s), 200µmol/ (m2.s), and 300µmol/ (m2.s). The result demonstrates that FCB2022 exhibited high hydrodynamic performance with parameters like mixing time, circulation time, Reynold number, and empty bed residence time showing favourable values. The optimal ranges for temperature, pH, and dissolved oxygen are determined to be between 23.56 and 29.11 ⁰C, 9.03–9.58, and 6.57–6.89 mgO2/L, respectively. Under the conditions of 15% CO2 and PFD of 300µmol/ (m2.s), the specific growth rates reach 0.36±0.004 /day and maximum biomass concentration attains 1.52±0.03 kg/m3. Notably, the PFD of 300µmol/ (m2.s) yields the highest conversion of carbon into biomass, ranging from 1.09×10−02 kg to 1.26×10−02 kg, representing the yield of 31–83% in each CO2 injection treatment.

Sweet Pepper Leaf Area Estimation Using Semantic 3D Point Clouds Based on Semantic Segmentation Neural Network

In the field of agriculture, measuring the leaf area is crucial for the management of crops. Various techniques exist for this measurement, ranging from direct to indirect approaches and destructive to non-destructive techniques. The non-destructive approach is favored because it preserves the plant's integrity. Among these, several methods utilize leaf dimensions, such as width and length, to estimate leaf areas based on specific models that consider the unique shapes of leaves. Although this approach does not damage plants, it is labor-intensive, requiring manual measurements of leaf dimensions. In contrast, some indirect non-destructive techniques leveraging convolutional neural networks can predict leaf areas more swiftly and autonomously. In this paper, we propose a new direct method using 3D point clouds constructed by semantic RGB-D (Red Green Blue and Depth) images generated by a semantic segmentation neural network and RGB-D images. The key idea is that the leaf area is quantified by the count of points depicting the leaves. This method demonstrates high accuracy, with an R2 value of 0.98 and a RMSE (Root Mean Square Error) value of 3.05 cm2. Here, the neural network's role is to segregate leaves from other plant parts to accurately measure the leaf area represented by the point clouds, rather than predicting the total leaf area of the plant. This method is direct, precise, and non-invasive to sweet pepper plants, offering easy leaf area calculation. It can be implemented on laptops for manual use or integrated into robots for automated periodic leaf area assessments. This innovative method holds promise for advancing our understanding of plant responses to environmental changes. We verified the method's reliability and superior performance through experiments on individual leaves and whole plants.

Swimming behavior as a potential metric to detect satiation levels of European seabass in marine cages

Aquaculture is anticipated to contribute to two-thirds of the world’s fish consumption by 2030, emphasizing the need for innovative methods to optimize practices for economic viability, social responsibility, and environmental sustainability. Feeding practices play a pivotal role in aquaculture success and the feeding requirements are dynamic, influenced by factors like fish size, environmental conditions, and health status necessitating ongoing improvements in feeding practices. This study addresses a critical gap in feeding control systems in sea cages. It introduces a continuous, real-time monitoring system for analyzing the feeding behavior of European seabass, employing advanced AI models (YOLO and DEEPSORT) and computer vision techniques. The investigation focuses on key parameters, including speed and the newly defined feeding behavior index (FBI), to evaluate swimming responses under varying feeding scenarios exploring meal frequency, feeding time, and feeding quantity. The findings reveal a sensitivity of fish speed and the feeding behavior index (FBI) to different feeding scenarios, elucidating distinct behavioral patterns in response to varying frequencies, times, and quantities of feeding, such as increased activity in the morning relative to later times and the emergence of asymmetric activity patterns when fish are underfed or overfed. Notably, this study is one of the few in the field, presenting the development of a continuous, real-time monitoring system for feeding control in sea cages. Simultaneously, it explores reference curves and threshold values to enhance the overall efficacy of feeding control measures.

Intermolecular Interaction Between BODIPY and TPE Enhances Phototherapy

AbstractFluoroborodipyrrole (BODIPY) and Tetraphenylethylene (TPE) are well‐known molecules in the realm of optical materials, celebrated for their exceptional properties. Recent research increasingly focuses on their combined use. In this study, a novel non‐synthetic method is taken to harness the synergistic therapeutic potential stemming from intermolecular interactions by employing nanoparticle encapsulation and core–shell structure. In vitro and in vivo experiments results show that the nanoparticles encapsulating BODIPY and TPE exhibit low cytotoxicity, efficient antitumor properties, and excellent visualization, achieving remarkable 1+1>2 effects. This innovative method not only provides high therapeutic efficacy, but also reduces time and economic costs, providing a novel perspective for exploring the combination of BODIPY and TPE as well as similar molecules in the field of optical materials.

Enhancing gust load alleviation performance in an optimized composite wing using passive wingtip devices: Folding and Twist approaches

This paper introduces an innovative numerical method for the design and optimization of high-aspect-ratio composite wings equipped with passive control systems, specifically, Folding WingTip (FWT) and Twist WingTip (TWT) devices. The aim is to enhance Gust Load Alleviation (GLA) performance in the baseline wing. Recent numerical studies have indicated that the inclusion of spring devices and wingtip modifications can offer additional benefits in alleviating gust loads during flight. The baseline wing is designed using a comprehensive multi-disciplinary optimization framework, taking into account aerostructural constraints and exploiting the anisotropic properties of composite materials. The proposed methodology integrates Finite Element (FE) software, an in-house Reduced Order Model (ROM) framework for nonlinear aeroelastic analyses, and Particle Swarm Optimization (PSO). This method, implemented in the Nonlinear Aeroelastic Simulation Software (NAS2) package, facilitated the streamlined design of composite wings with optimized aeroelastic and structural performance. The paper is divided into two main parts. Part 1 introduces a Multidisciplinary Design Optimization (MDO) approach for high-aspect-ratio composite wings, leading to the development of a baseline wing model. Part 2 evaluates the effectiveness of the FWT and TWT devices in alleviating gust loads on the baseline wing, with a focus on the Root Bending Moment (RBM) as a critical criterion for comparison. In wingtip modeling, geometrical nonlinearity is incorporated, and elastic trim is adjusted in each iteration to accommodate shape changes under load and aerodynamic panel movement is synchronized with structural adjustments.

Studies on magnetorheological properties of carbonyl iron/Fe3O4 powder based soft-magnetic fluids with artificial intelligence for industrial applications

For mechanical devices such as haptic gadgets, mechanical dampers, and brake systems to function properly, magnetorheological fluids (MRF)—also known as smart fluids—are crucial. An innovative method for optimizing the preparation process of magnetorheological fluid that is both cost-effective and data-driven in nature is investigated in this article. In this MRF preparation experiment, silicone oil is the carrier liquid and carbonyl iron and Fe3O4 powders are carefully selected as magnetic particles to prepare soft magnetic fluids. Sodium dodecyl benzene sulfonate, oleic acid, polyethylene glycol, graphite, and diatomite were added to improve the MRF's physical qualities. Then, the MRF properties were examined with artificial intelligence method and an enhanced grey wolf optimization algorithm is developed for improving the hyperparameters of the least square support vector machine. In addition, a new procedure for updating individual positions is suggested to better preserve population variety and increase solution quality, and a better updating technique is created for the convergence coefficient vector to avoid local optima. The results show that the suggested modified least square support vector machine is the best algorithm to predict the MRF properties.

Integration of a facile sustainable resonance Rayleigh scattering switchable-based system for feasible determination of centrophenoxine, a nootropic and antioxidant agent; application to crude materials and dosage forms

The proposed research adheres to a certain methodology to ensure that the technique used for analyzing the centrophenoxine drug is sustainable and green. It is important to highlight that several tools that have been recently developed were utilized as potential indicators of environmental sustainability and applicability. The present research presents a novel and entirely innovative method utilizing ultrasensitive spectrofluorimetry for the detection of centrophenoxine (CPX) drug. The employed methodology in this study involved the utilization of one-step, one-pot, and direct spectrofluorimetric technique, which was found to be both efficient and environmentally sustainable in the validation and assessment of the drug. Simply, when CPX and erythrosine B reagent were combined in an acidic environment, the highly resonance Rayleigh scattering product was immediately produced. The sensitivity limits were observed to be within the range of 15–47 ng mL−1, whereas the linearity was assessed to be in the range of 50–2000 ng mL−1. The optimal settings for all modifiable parameters of the system were ascertained through an analysis of centrophenoxine-erythrosine B complexes. Moreover, the system demonstrated compliance with International Council for Harmonization (ICH) specifications without encountering any issues. The suggested process was then rated on different recent environmental safety measuring metrics to see how good it was for the environment. Fortunately, the WAC standards that combine ecological and functional elements utilizing the Green/Red/Blue (RGB 12) design also acclaimed the current analytical technique as a white one. Additionally, a new applicability evaluation tool (BAGI) was employed to estimate the practicability of the planned method in the analytical chemistry field.

Building Stock Models for Embodied Carbon Emissions—A Review of a Nascent Field

Building stock modeling emerges as a critical tool in the strategic reduction of embodied carbon emissions, which is pivotal in reshaping the evolving construction sector. This review provides an overall view of modern methodologies in building stock modeling, homing in on the nuances of embodied carbon analysis in construction. Examining 23 seminal papers, our study delineates two primary modeling paradigms—top-down and bottom-up—each further compartmentalized into five innovative methods. This study points out the challenges of data scarcity and computational demands, advocating for methodological advancements that promise to refine the precision of building stock models. A groundbreaking trend in recent research is the incorporation of machine learning algorithms, which have demonstrated remarkable capacity, improving stock classification accuracy by 25% and urban material quantification by 40%. Furthermore, the application of remote sensing has revolutionized data acquisition, enhancing data richness by a factor of five. This review offers a critical examination of current practices and charts a course toward an environmentally prudent future. It underscores the transformative impact of building stock modeling in driving ecological stewardship in the construction industry, positioning it as a cornerstone in the quest for sustainability and its significant contribution toward the grand vision of an eco-efficient built environment.

Differentiating localized autoimmune pancreatitis and pancreatic ductal adenocarcinoma using endoscopic ultrasound images with deep learning.

Localized autoimmune pancreatitis is difficult to differentiate from pancreatic ductal adenocarcinoma on endoscopic ultrasound images. In recent years, deep learning methods have improved the diagnosis of diseases. Hence, we developed a special cross-validation framework to search for effective methodologies of deep learning in distinguishing autoimmune pancreatitis from pancreatic ductal adenocarcinoma on endoscopic ultrasound images. Data from 24 patients diagnosed with localized autoimmune pancreatitis(8751 images) and 61 patients diagnosed with pancreatic ductal adenocarcinoma (20,584 images) were collected from 2016 to 2022. We applied transfer learning to a convolutional neural network called ResNet152, together with our innovative imaging method contributing to data augmentation and temporal data process. We divided patients into five groups according to different factors for 5-fold cross-validation, where the ordered and balanced datasets were created for the performance evaluations. ResNet152 surpassed the endoscopists in all evaluation metrics with almost all datasets. Interestingly, when the dataset is balanced according to the factor of the endoscopists' diagnostic accuracy, the area under the receiver operating characteristic curve and accuracy were highest at 0.85 and 0.80, respectively. It is deduced that image features useful for ResNet152 correlate with those used by endoscopists for their diagnoses. This finding may contribute to sample-efficient dataset preparation to train convolutional neural networks for endoscopic ultrasonography-imaging diagnosis.

Real-time quantitative detection of H2S for assessing fish freshness using a gas-controlled micro-drainage device

Hydrogen sulfide (H2S), a potential indicator of fish freshness, necessitates the development of rapid quantitative detection methods for ensuring food safety. Nevertheless, most of the current methods for H2S detection cannot simultaneously meet the requirements of ease-of-use and quantifiability. In this study, we introduce an innovative quantitative detection method of H2S that employs a gas-controlled micro-drainage device with an electronic balance as readout. When H2S, a product of fish spoilage, is present, it interacts with Platinum nanoparticles (PtNPs)-catalase nanozyme, affecting their catalytic activity and, subsequently, the weight of overflowed water. This allows for the conversion of H2S concentration into a measurable weight change signal. The sensor demonstrates an inverse correlation between the mass of overflowed water and H2S concentration (0–50 μM) with a detection limit of 0.43 μM. This device enables sensitive, real-time, and quantitative weighing analysis of H2S to assess fish freshness during storage, offering an effective solution to traditional limitations.

New perspectives on ‘Breathomics’: metabolomic profiling of non-volatile organic compounds in exhaled breath using DI-FT-ICR-MS

Breath analysis offers tremendous potential for diagnostic approaches, since it allows for easy and non-invasive sample collection. “Breathomics” as one major research field comprehensively analyses the metabolomic profile of exhaled breath providing insights into various (patho)physiological processes. Recent research, however, primarily focuses on volatile compounds. This is the first study that evaluates the non-volatile organic compounds (nVOCs) in breath following an untargeted metabolomic approach. Herein, we developed an innovative method utilizing a filter-based device for metabolite extraction. Breath samples of 101 healthy volunteers (female n = 50) were analysed using DI-FT-ICR-MS and biostatistically evaluated. The characterisation of the non-volatile core breathome identified more than 1100 metabolites including various amino acids, organic and fatty acids and conjugates thereof, carbohydrates as well as diverse hydrophilic and lipophilic nVOCs. The data shows gender-specific differences in metabolic patterns with 570 significant metabolites. Male and female metabolomic profiles of breath were distinguished by a random forest approach with an out-of-bag error of 0.0099. Additionally, the study examines how oral contraceptives and various lifestyle factors, like alcohol consumption, affect the non-volatile breathome. In conclusion, the successful application of a filter-based device combined with metabolomics-analyses delineate a non-volatile breathprint laying the foundation for discovering clinical biomarkers in exhaled breath.

Understanding mobile use behavior, stigma and associated needs among female sex workers in Nepal: a qualitative study

Female sex workers (FSWs) in Nepal continue to disproportionately experience increased HIV risk alongside individual, social, and structural barriers to accessing and using health services. Innovative methods are needed to provide improved HIV prevention information and other health services for FSWs. Mobile health (mHealth) is a mechanism that can overcome structural and social barriers. Studies show that FSWs are increasingly using mobile phones to solicit clients. Hence, this exploratory study using the Uses and Gratifications (U&G) framework aimed to understand Nepali FSWs' mobile use behavior, including the purposes they use it for. We conducted four focus group discussions. Results showed that FSWs in Nepal used mobile phones to gratify their needs to: (1) manage their profession, (2) maintain social connection, (3) to keep their sex work information hidden (4) have access to information in a stigma-free environment. The study's findings can be instrumental in developing and designing innovative health interventions to reduce HIV and STI incidents in this population.

Starch – carrageenan based low-cost membrane permeability characteristic and its application for yeast microbial fuel cells

Microbial fuel cells (MFCs) are an innovative method that generates sustainable electricity by exploiting the metabolic processes of microorganisms. The membrane that divides the anode and cathode chambers is an important component of MFCs. Commercially available membranes, such as Nafion, are both costly, not sustainable, and harmful to the environment. In this study, a low-cost alternative membrane for MFCs based on a starch-carrageenan blend (SCB-LCM) was synthesized. The SCB-LCM membrane was created by combining starch and carrageenan and demonstrated a high dehydration rate of 98.87 % over six hours. SEM analysis revealed a smooth surface morphology with no pores on the membrane surface. The performance of SCB-LCM membrane-based MFCs was evaluated and compared to that of other membranes, including Nafion 117 and Nafion 212. All membranes tested over 25 hours lost significant weight, with SCB-LCM losing the least. The maximum power density (MPD) of the SCB-LCM MFCs was 15.77 ± 4.34 mW/m2, indicating comparable performance to commercial membranes. Moreover, the cost-to-power ratio for MFCs employing SCB-LCM was the lowest (0.03 USD.m2/mW) when compared to other membranes, indicating that SCB-LCM might be a viable and cost-effective alternative to Nafion in MFCs. These SCB-LCM findings lay the groundwork for future research into low-cost and sustainable membrane for MFC technologies.

Endoscopic resection of a huge tumor located at an anatomically challenging position using an innovative extra snare traction method.

Endoscopic resection of a huge tumor located at an anatomically challenging position using an innovative extra snare traction method.

Effect of temperature on the ductile-to-brittle transition in laser-assisted mechanical ruling of silicon

This paper introduces an innovative hybrid processing method that combines in-situ laser assisted with mechanical ruling. The innovative diamond ruling tool can facilitate laser incidence from both the A and B faces. Additionally, this paper establishes a thermal-mechanical coupled finite element simulation model for in-situ laser-assisted mechanical ruling of single-crystal silicon with varying cutting depths. It aims to analyze the material removal process and the effect of temperature on the ductile-to-brittle transition. In the process, an in-situ laser assisted mechanical ruling setup is designed to investigate the impact of different laser powers on the ductile-to-brittle transition. The experimental results align with the simulation outcomes. When the laser power was set to 30W, the critical ductile-to-brittle transition depth reached 320.69 nm, successfully suppressing brittle fracture, validating that the proposed in-situ laser assisted mechanical ruling hybrid processing method can enhance the critical depth of ductile-to-brittle transition and improve the surface quality of microgrooves.

Temporal-aware structure-semantic-coupled graph network for traffic forecasting

The spatial–temporal graph neural networks have been a critical approach to capturing the complicated spatial–temporal dependencies inherent in traffic series for more accurate forecasting. However, the issue of graph indistinguishability demands further attention, as graphs learned by existing methods tend to converge to implicit and indistinguishable representations, deviating from the genuine distribution. This issue can be attributed to the lack of three primary factors within graphs: the intrinsic graph features, the temporal-distinct features, and the node-distinct features. Aiming to address this problem, we propose a Temporal-Aware Structure-Semantic-Coupled Graph Network (TASSGN) in this paper. Firstly, we design a novel graph learning block to simultaneously learn the structural and semantic aspects of graphs, thereby capturing inherent graph features. Secondly, we propose an innovative Self-Sampling method to sample the relevant history series and present a Temporal-Aware Graphs Encoder to explicitly incorporate temporal information into graph learning and capture temporal-distinct features. Thirdly, sparse graphs are intentionally generated to capture node-distinct features. By combining these three key components together, our method is capable of overcoming the problem of graph indistinguishability and achieving state-of-the-art performances in traffic forecasting.

The Emerging Role of Cell Membrane-coated Nanomaterials in Cancer Therapy.

This review investigates the revolutionary application of cell membrane-coated nanoparticles (CMNPs) as a promising avenue for cancer therapy within the embryonic landscape of nanotechnology. Nanoparticles, pivotal in cancer treatment, are systematically examined for their diverse physicochemical structures, categorized as organic (lipid-based, protein-based, and polymer-assisted) and inorganic (carbon-based and metal) varieties. A significant focus is placed on CMNPs, which serve as an innovative drug delivery vehicle, overcoming limitations associated with conventional nanoparticle therapies. This manuscript accurately explores the advantages and challenges of various cell membranes, including those derived from cancer cells, red blood cells, platelets, stem cells, and white blood cells. Importance is placed on their roles in enhancing drug delivery precision, immune system circumvention, and targeted recognition. Detailed insights into the crafting of CMNPs are provided, elucidating membrane extraction and fusion techniques, such as sonication, extrusion, co-extrusion, and microfluidic electroporation. Maintaining membrane integrity during extraction and the benefits of coating techniques in augmenting biocompatibility and targeted drug delivery are underscored. This comprehensive resource consolidates the latest advancements in targeted drug delivery, positioning itself at the forefront of nanotechnology and biomedicine research. Encapsulating various methodologies like membrane extrusion, electrospray, and chemical conjugation, this manuscript showcases the expanding toolbox available to researchers in this dynamic field. Focusing on the unique characteristics of CMNPs, this review explores their multifaceted applications in biomedical research, particularly in tumour therapy. It provides an indepth analysis of the biocompatibility of CMNPs, their stability, immune evasion capabilities, targeted drug delivery precision, increased payload capacity, and retained biological functionality. The manuscript outlines current applications and future prospects of CMNPs in targeted chemotherapy, photothermal and photodynamic therapy, immunotherapy, gene therapy, and innovative therapeutic methods. It concludes by highlighting the advantages of CMNPs in tumour therapy and their transformative potential in reshaping the landscape of cancer treatment.