Multilayer Approach Research Articles

Multi-modal and multi-layer robustness analysis of the European rail and air networks.

The robustness of long-distance transport services is paramount for ensuring network connectivity under disruptions. We conduct a comparative analysis of the European rail and air networks of 124 main metropolitan areas, assessing their ability to withstand successive network degradation. We undertake a multi-modal and multi-layer approach in our analysis of the robustness of long-distance transport networks. In particular, we are interested in the role of individual nodes for both air and rail networks as well as for the integrated multi-modal network. Given the hierarchical nature of long-distance transport services, we adopt a multi-layer perspective by means of clustering nodes based on their criticality in order to identify common performance profiles. Original metrics are formulated to measure the impact of nodes on network fragmentation, both at the individual level and as cluster members. Additionally, a new metric is introduced to assess cities' reliance on air transportation, when considering the integrated multi-layer air-rail network during disruptions in air connections. Our findings indicate that the air network exhibits significantly greater robustness compared to rail, i.e. 10% versus 71% performance loss in the worst case scenario, respectively. Furthermore, primary sub-graph of nodes whose protection from attacks can greatly enhance network's overall robustness is identified. We discuss our findings in terms of the relationship between network structure, robustness, and the role of critical nodes as well as propose potential mitigation measures.

Recognition and Management of Postpartum Hemorrhage

Abstract Postpartum hemorrhage (PPH) is an obstetric emergency and refers to excessive blood loss after birth. Loss of blood volume and oxygen-carrying capacity may lead to maternal hypovolemia and hypotension resulting in tissue hypoxia, the onset of anaerobic metabolism, and multiorgan failure. If timely and appropriate action is not taken, cardiac arrest and maternal death may occur. If the amount of blood loss exceeds 500 mL following a vaginal birth or 1000 mL during or following a cesarean section, it is termed PPH. Similar to any other surgical hemorrhage, PPH is classified into primary PPH (occurs within 24 hours of birth) or secondary PPH (between 24 hours and 12 weeks postpartum). PPH is a major contributor to maternal deaths worldwide, and it is estimated that a person dies because of PPH approximately every 5 minutes. Therefore, measures should be directed at prevention and early detection of PPH with prompt management. The prevalence of PPH varies globally and is influenced by location, socioeconomic factors, and the availability and quality of health care. The World Health Organization reported that PPH accounts for a quarter of global maternal deaths. The Mothers and Babies Reducing Risks through Audits and Confidential Enquiries report from the United Kingdom (2023) highlighted that despite rare mortality due to hemorrhage, the number of people dying of obstetric hemorrhage is not decreasing, particularly among people with abnormally invasive placentation. Additionally, substandard care was found to be responsible for more than 50% of deaths due to PPH in the United Kingdom. Therefore, it is vital that adequate healthcare infrastructure, trained and competent healthcare professionals, and immediate access to resources, interventions, and multidisciplinary teams are essential both in well-resourced and resource-restrained healthcare systems. Healthcare professionals must identify the potential risk factors for PPH and initiate preventive measures whenever possible. Additionally, they must respond swiftly if PPH occurs and ensure a multidisciplinary, multilayered approach for a synchronized response to optimize outcomes. This review article emphasizes the etiopathogenesis, diagnosis, and management of PPH based on current scientific evidence as well as international best practice recommendations.

Harnessing Digital Strategies to Combat Cryptocurrency-Enabled Crimes: Addressing Money Laundering, Illicit Trade, and Cyber Threats

Purpose: This research explores how advanced digital strategies can be harnessed to combat cryptocurrency-enabled crimes, focusing on the use of blockchain analysis, artificial intelligence (AI), machine learning, and enhanced regulatory frameworks to detect, trace, and prevent illegal transactions on cryptocurrency platforms. Materials and Method: The study examines the challenges law enforcement and regulatory bodies face due to the pseudonymous nature of cryptocurrencies and their cross-border complexities. It analyses emerging digital tools that facilitate the de-anonymization of transactions and enable real-time monitoring of suspicious activities. Case studies from recent high-profile cryptocurrency crimes, such as the Silk Road shutdown and recent ransomware attacks, are utilized to highlight the effectiveness of these digital strategies. Findings: The findings indicate that a multi-layered approach, which combines technological innovations with global regulatory efforts, is essential for mitigating risks associated with cryptocurrency as a facilitator of cybercrime. Advanced analytics and regulatory techniques are identified as key resources for detecting and preventing illicit activities. Implications to Theory, Practice and Policy: The research demonstrates practical implications for law enforcement agencies in developing strategies that integrate advanced digital tools to improve their capabilities to manage and investigate cryptocurrency-related crimes. From a policy perspective, the study highlights the importance of creating adaptive regulatory frameworks that can evolve alongside cryptocurrency technology to effectively address the unique challenges it presents in combating cybercrime.

Data protection enhancement in smart grid communication: An efficient multi-layer encrypting approach based on chaotic techniques and steganography

In this study, a multi-layer encryption system is proposed through which sensitive and important data in smart grids can be secured from unauthorized persons as well as cyber threats. The suggested system is analyzed using fundamental performance assessment metrics say as average processing times across ten runs, peak signal-to-noise ratio (PSNR), and root mean square error (RMSE). The encryption process involves hiding the data encrypted by a digital dictionary and the advanced encryption standard (AES) algorithm in an image. In this way, AES encryption suggests rapid processing with a short average time per each block; similarly, the decryption technique, which includes the dictionary and AES decryption also renders the results in a justified computational time nominating it as a practical approach. It is worth noting that the small key era and dictionary construction instances provoke innovations in the general efficiency of the encryption mechanism. The conducted study, based on recorded values for PSNR, RMSE, UACI, and NPCR, demonstrates that the proposed multi-layer encryption system, which incorporates dictionary encryption and AES, is a robust and a fast method to shield important information and attaining a balance between protection and computational time.

Green Logistics 5.0: a review of sustainability-oriented innovation with foundation models in logistics

PurposeThe paper uses foundation models to integrate the green approach in Logistics 5.0. Such integration is innovative in logistics and leads to a more sustainable and prosperous future. By harnessing the power of foundation models and incorporating sustainable principles, this paper can systematize the logistics industry’s environmental framework, increase its social responsibility and ensure its long-term economic viability.Design/methodology/approachGeneralizing environmental sustainability goals requires a multi-layered innovation approach incorporating corporate philosophy, products, processes and business models. In this paper, this comprehensive approach is not just a strategy but a necessity in the current global context. This paper uses the sustainability-oriented innovation (SOI) method, crucial for achieving explicit environmental, social and economic impacts.FindingsArtificial intelligence, especially foundation models, can contribute to green logistics by optimizing routes, reducing packaging waste, improving warehouse layouts and other functions presented in the paper. At the same time, they can also consider social, economic and governance goals.Research limitations/implicationsArtificial intelligence algorithms present challenges such as high initial investment, regulatory compliance and technological integration.Practical implicationsThe paper contains implications for developing environmentally sustainable logistics, which is currently one of the most significant challenges. The framework presented can apply to logistics companies.Originality/valueThis paper fulfills an identified need to study sustainability in logistics. The framework is entirely original and not present in the literature. It is essential to help design and implement innovative logistics approaches.

Understanding Agility Unpacking Misconceptions and Embracing a Multi-Layered Approach in Modern Organizations

Purpose: This study aims to clarify the concept of agility in organizational contexts, especially in the wake of the Covid-19 pandemic. It seeks to address prevalent misconceptions and misunderstandings about agility, highlighting its multi-layered nature and its role as a comprehensive management and organizational philosophy. Methodology: The research employs a literature review and analysis of current organizational practices to explore how agility is defined and applied. It examines the three distinct layers of agility and investigates how these layers interact with the hierarchical structures of organizations. The study also considers the influence of labor market trends and generational expectations on the adoption of agile practices. Findings: The analysis reveals that agility is often misunderstood and misapplied due to its complex nature and the lack of clear guidelines for implementation. The study identifies three core layers of agility and shows how it functions as both a management strategy and a philosophical approach to organizational change. Additionally, it highlights the increasing pressure from the labor market and the preferences of Generation Z and Generation Alpha as key drivers for adopting agile methodologies. Implications for Theory and Practice: For theory, the study contributes to a deeper understanding of agility by delineating its components and addressing common misconceptions. Practically, it provides organizations with insights on effectively implementing agility, considering both hierarchical and cultural factors. The findings emphasize the need for clear communication and education about agility to overcome misunderstandings and leverage its benefits fully. Originality and Value: This research is original in its comprehensive examination of agility from both theoretical and practical perspectives. It adds value by offering a structured analysis of agility’s components and its impact on organizational practices in the context of evolving labor market demands and generational shifts. This provides a valuable resource for organizations seeking to navigate the complexities of agility in a post-pandemic world.

Cybersecurity in Critical Infrastructure: Defending Against Nation-State Groups

Cyber-attacks on critical infrastructure can be disastrous and undermine states' national security. Different groups execute these attacks for varying reasons; some may be state-sponsored, and their attack for geopolitical reasons or to achieve strategic national cyber objectives. Regardless of the nation-state actor, it is essential to identify the techniques used and defend critical infrastructure against these attacks. This study evaluated the attack methodology of five nation-state actors based on the MITRE ATT&CK ICS matrix and proposed a multi-layered defense architecture. A virtual organization with critical and enterprise infrastructure domains was created, and the proposed defense architecture and tooling were implemented there. Then, techniques of the nation-state adversaries were emulated against the infrastructure to evaluate the performance of the defense strategies. The results show that the multi-layered approach was sufficient to mitigate all the techniques of the nation-state actors.

Health IoT Threats: Survey of Risks and Vulnerabilities

The secure and efficient collection of patients’ vital information is a challenge faced by the healthcare industry. Through the adoption and application of Internet of Things (IoT), the healthcare industry has seen an improvement in the quality of delivered services and patient safety. However, IoT utilization in healthcare is challenging due to the sensitive nature of patients’ clinical information and communicating this across heterogeneous networks and among IoT devices. We conducted a semi-systematic literature review to provide an overview of IoT security and privacy challenges in the healthcare sector over time. We collected 279 studies from 5 scientific databases, of which 69 articles met the requirements for inclusion. We performed thematic and qualitative content analysis to extract trends and information. According to our analysis, the vulnerabilities in IoT in healthcare are classified into three main layers: perception, network, and application. We comprehensively reviewed IoT privacy and security threats on each layer. Different technological advancements were suggested to address the identified vulnerabilities in healthcare. This review has practical implications, emphasizing that healthcare organizations, software developers, and device manufacturers must prioritize healthcare IoT security and privacy. A comprehensive, multilayered security approach, security-by-design principles, and training for staff and end-users must be adopted. Regulators and policy makers must also establish and enforce standards and regulations that promote the security and privacy of healthcare IoT. Overall, this study underscores the importance of ensuring the security and privacy of healthcare IoT, with stakeholders’ coordinated efforts to address the complex and evolving security and privacy threats in this field. This can enhance healthcare IoT trust and reliability, reduce the risks of security and privacy issues and attacks, and ultimately improve healthcare delivery quality and safety.

Neural Network-Enabled Multiparametric Impedance Signal Templating for High throughput Single-Cell Deformability Cytometry Under Viscoelastic Extensional Flows.

Cellular biophysical metrics exhibit systematic alterations during processes, such as metastasis and immune cell activation, which can be used to identify and separate live cell subpopulations for targeting drug screening. Image-based biophysical cytometry under extensional flows can accurately quantify cell deformability based on cell shape alterations but needs extensive image reconstruction, which limits its inline utilization to activate cell sorting. Impedance cytometry can measure these cell shape alterations based on electric field screening, while its frequency response offers functional information on cell viability and interior structure, which are difficult to discern by imaging. Furthermore, 1-D temporal impedance signal trains exhibit characteristic shapes that can be rapidly templated in near real-time to extract single-cell biophysical metrics to activate sorting. We present a multilayer perceptron neural network signal templating approach that utilizes raw impedance signals from cells under extensional flow, alongside its training with image metrics from corresponding cells to derive net electrical anisotropy metrics that quantify cell deformability over wide anisotropy ranges and with minimal errors from cell size distributions. Deformability and electrical physiology metrics are applied in conjunction on the same cell for multiparametric classification of live pancreatic cancer cells versus cancer associated fibroblasts using the support vector machine model.

Toward a more comprehensive understanding of network centrality disruption in amnestic mild cognitive impairment: a MEG multilayer approach

BackgroundAlzheimer’s Disease (AD) is the most common form of dementia. Its early stage, amnestic Mild Cognitive Impairment (aMCI), is characterized by disrupted information flow in the brain. Previous studies have yielded inconsistent results when using electrophysiological techniques to investigate functional connectivity changes in AD, and a contributing factor may be the study of brain activity divided into frequencies.MethodsOur study aimed to address this issue by employing a cross-frequency approach to compare the functional networks of 172 healthy subjects and 105 aMCI patients. Using magnetoencephalography, we constructed source-based multilayer graphs considering both intra- and inter-frequency functional connectivity. We then assessed changes in network organization through three centrality measures, and combined them into a unified centrality score to provide a comprehensive assessment of centrality disruption in aMCI.ResultsThe results revealed a noteworthy shift in centrality distribution in aMCI patients, both in terms of spatial distribution and frequency. Posterior brain regions decrease synchrony between their high-frequency oscillations and other regions’ activity across all frequencies, while anterior regions increase synchrony between their low-frequency oscillations and other regions’ activity across all frequencies. Thus, posterior regions reduce their relative importance in favor of anterior regions.ConclusionsOur findings provide valuable insights into the intricate changes that occur in functional brain networks during the early stages of AD, demonstrating that considering the interplays between different frequency bands enhances our understanding of AD network dynamics and setting a precedent for the study of functional networks using a multilayer approach.

Balancing Efficiency and Accuracy: Enhanced Visual Simultaneous Localization and Mapping Incorporating Principal Direction Features

In visual Simultaneous Localization and Mapping (SLAM), operational efficiency and localization accuracy are equally crucial evaluation metrics. We propose an enhanced visual SLAM method to ensure stable localization accuracy while improving system efficiency. It can maintain localization accuracy even after reducing the number of feature pyramid levels by 50%. Firstly, we innovatively incorporate the principal direction error, which represents the global geometric features of feature points, into the error function for pose estimation, utilizing Pareto optimal solutions to improve the localization accuracy. Secondly, for loop-closure detection, we construct a feature matrix by integrating the grayscale and gradient direction of an image. This matrix is then dimensionally reduced through aggregation, and a multi-layer detection approach is employed to ensure both efficiency and accuracy. Finally, we optimize the feature extraction levels and integrate our method into the visual system to speed up the extraction process and mitigate the impact of the reduced levels. We comprehensively evaluate the proposed method on local and public datasets. Experiments show that the SLAM method maintained high localization accuracy after reducing the tracking time by 24% compared with ORB SLAM3. Additionally, the proposed loop-closure-detection method demonstrated superior computational efficiency and detection accuracy compared to the existing methods.

A multi-layer multi-configurational time-dependent Hartree approach to lattice models beyond one dimension.

The multi-layer multi-configurational time-dependent Hartree (MCTDH) approach is an efficient method to study quantum dynamics in real and imaginary time. The present work explores its potential to describe quantum fluids. The multi-layer MCTDH approach in second quantization representation is used to study lattice models beyond one dimension at finite temperatures. A scheme to map the lattice sites onto the MCTDH tree representation for multi-dimensional lattice models is proposed. A statistical sampling scheme previously used in MCTDH calculations is adapted to facilitate an efficient description of the thermal ensemble. As example, a two-dimensional hard-core Bose-Hubbard model is studied considering up to 64 × 64 lattice sites. The single particle function basis set size required to obtain converged results is found to not increase with the lattice size. The numerical results properly simulate the finite temperature Berezinskii-Kosterlitz-Thouless phase transition.

On morphology and roughness of upskin surfaces in laser powder-bed fusion additive manufacturing – Contouring strategy effects

The surface quality of inclined parts made by Laser powder-bed fusion (L-PBF) additive manufacturing is inferior to those made by conventional formative and subtractive manufacturing techniques. The inherited step edges formed during such layer-by-layer fabrications and partially melted or non-melted particles attached to the newly formed surface are the major causes of poor surface finish of L-PBF parts. This study is a part of a research framework, in which the effects of different contouring strategies and scan parameters on the surface morphology of inclined parts are investigated by both experimental and numerical methods. In particular, the effect of two different contour scan strategies, namely pre-contouring and post-contouring, on the upskin surface roughness of 30⁰ inclined parts is analyzed. The inclined specimens were fabricated with Ti6Al4V powder using an EOS M270 system and the upskin surfaces were measured by a white light interferometer. In addition, a multi-track multi-layer numerical approach using coupled discrete element method and thermo-fluid simulation was also performed to understand the inclined surface formation in L-PBF.The findings show that the pre-contouring strategy with high linear energy density (LED) (0.4 J/mm) results in average surface roughness (Sa) as low as 7.43 µm, whereas the lowest LED (0.05 J/mm) resulted in the highest Sa of 33.74 µm. Larger melt pool dimensions were obtained in the high LED pre-contouring simulations, which indicates a better material fusion resulting in a good surface finish. The numerical simulation showed that the upskin surface roughness is also affected by the raster scan, if the raster scanning LED is higher than that of pre-contour scans. For the post-contouring strategy studied, higher and lower LEDs were used in inner and outer contour scanning, respectively. It is indicated that the inner contour scanning with a high LED reduces the surface roughness because of a larger melt pool size, irrespective of outer contour scanning. The lowest Sa obtained was 8.24 µm for the highest LED inner contour (0.05 J/mm) case and the highest Sa was 19.95 µm, for the parts made with the lowest LED inner contour scan (0.075 J/mm) strategy. The coupled discrete-element and thermo-fluid-based simulations offer insights into the sloped surface formation and the results supported the experimental findings in qualitative manner.

High-performance CIGS solar cells using a double active layers approach – SCAPS 1D optoelectronic modeling approach

One of the properties of the CIGS solar cell is that it can have an adjustable band gap energy. The band gap varies in the range from 1.01 to 1.7 eV as a function of materials content. Enhancing the bandgap profile of the active layer, which is where the majority of charge carrier photo-generation occurs, should significantly improve cell performance. Superimposing absorber layers with different electrical characteristics is one way of refining the bandgap profile and achieving these results. It is possible to analyze the optoelectronic output properties and simulate this active multilayer approach using a reference cell thanks to the SCAPS numerical modeling tool. Considering this, we have successfully simulated the following structure: CIGS2/CIGS1/SDL/ZnS/ZnO. The SDL which stands for Surface Defect Layer, has been considered for the property discontinuity interface, mainly characterized by the formation of defect states. That enabled to achieve realistic-like device with an improvement of power conversion efficiency (PCE) up to 11.98 %, i.e. 29.22 % and a fill factor (FF) of 82 % employing a total thickness of active layer of 800 nm. All simulations were performed considering experimental environment parameters, an external temperature of 300 K, light illumination of AM1.5G and defects states were assumed in both the bulk and at interfaces.

SARS-CoV-2 Infection and Other Communicable Diseases Identified Among Evacuees From Afghanistan Arriving in Virginia and Pennsylvania, August to September 2021.

In 2021, the US government undertook Operation Allies Welcome, in which evacuees from Afghanistan arrived at 2 US ports of entry in Virginia and Pennsylvania. Because of the rapid evacuation process, the US government granted evacuees an exemption to a Centers for Disease Control and Prevention (CDC) requirement in place at that time-namely, that air passengers present a negative SARS-CoV-2 viral test result or documentation of recovery from COVID-19 before they boarded international flights bound for the United States. This study describes cases of SARS-CoV-2 infection detected among 65 068 evacuees who arrived at the 2 ports of entry in August and September 2021. Because evacuees were a population at increased risk for infection with diseases of public health concern, CDC staff helped coordinate on-site and on-arrival testing, visually observed evacuees for signs and symptoms of communicable disease, and referred evacuees for further evaluation and treatment as needed. CDC staff used antigen or nucleic acid amplification tests at the ports of entry to evaluate evacuees aged ≥2 years without documentation of recent SARS-CoV-2 infection. CDC staff isolated evacuees with confirmed SARS-CoV-2 infection and quarantined their close contacts, consistent with CDC guidance at the time, before evacuees rejoined the repatriation process. Of 65 068 evacuees, 214 (0.3%) were confirmed as having SARS-CoV-2 infection after port-of-entry testing. Cases of measles, varicella, pertussis, tuberculosis, hepatitis A, malaria, leishmaniasis, and diarrheal illness were also identified. Although the percentage of SARS-CoV-2 infection was low in this evacuated population, communicable disease detection at US ports of entry, along with vaccination efforts, was an important part of a multilayered approach to mitigate the transmission of disease in congregate housing facilities and into US communities.

Digital evolution and twin miracle of sugarcane breeding

ContextSugarcane, as an important economic crop, faces challenges such as long breeding cycles, low genetic improvement efficiency, and complex breeding operations. MethodIn order to address these challenges and improve the economic benefits of sugarcane breeding, this paper proposes an innovative smart sugarcane breeding system driven by artificial intelligence (AI), blockchain and digital twin technologies. ResultsThe system integrates these technologies within a Human-Cyber-Physical System framework to offer a more efficient, secure, and smart strategy for sugarcane breeding. Firstly, AI processes extensive genetic and phenotypic data to enable precise prediction and optimization of sugarcane traits, resulting in shortened breeding cycles and enhanced efficiency and accuracy in selecting elite sugarcane varieties. Secondly, blockchain technology ensures the security and traceability of breeding data, enhancing the reliability and integrity of the breeding process. Thirdly, digital twin technology enables the real-time circulation of lifelike representations of real-world data among breeding-related workers. The system architecture consists of three layers: a physical layer for data collection, a cyber layer responsible for data analysis, storage and circulation managed by AI, blockchain and digital twin, and a human layer comprised of breeders and stakeholders. This multi-layered approach allows for sophisticated interaction and collaboration between the physical and digital realms, enhancing decision-making and breeding outcomes. ConclusionTaken together, the system utilizes AI, blockchain, and digital twin technologies to support sugarcane breeding, offering a promising solution to overcome the limitations of traditional methods and establish a more sustainable and profitable sugarcane breeding system.

Detecting atmospheric oxidation in the PM2.5 and ozone multilayer complex network

Abstract Reducing the levels of Fine particulate matter (PM2.5) and ozone (O3) is essential for further improving air quality in China. The intricate and non-linear interactions between PM2.5 and O3 on a national scale over long-term records are not well understood. Here, we proposed a novel multi-layer network approach to bridge the gap. Our findings reveal that the variability of O3 precedes that of PM2.5 by one day. The degree, weighted degree, and edge distance follow a power-law decay distribution, suggesting that their relationship is not a random process. O3 concentration in the Yangtze River Delta (YRD) region and eastern Shandong Province significantly impacts the PM2.5 levels in surrounding areas. Conversely, PM2.5 concentrations in Shandong Province, Henan Province, YRD and Pearl River Delta (PRD) have a strong influence on O3 levels. Moreover, we quantified the seasonality of the interactions and elucidated the underlying reasons. The results revealed the interactions between PM2.5 and O3 are intricately tied to atmospheric oxidation processes. More specifically, in summer, the atmospheric oxidation has a strong impact on the interactions in key regions, such as the Beijing–Tianjin–Hebei,YRD region and Fenwei Plain. However, the PRD region experiences a more pronounced effect from atmospheric oxidation on this relationship in winter. These findings demonstrate that it is crucial to effectively regulate atmospheric oxidation to mitigate PM2.5 and O3. Our results also serve as a valuable methodological framework for understanding the characteristics of pollutants.

Towards Mine 4.0: A Proposed Multi-Layered Architecture for Real-Time Surveillance and Anomaly Detection in an Open-Pit Phosphate Mine

Given the global importance and limited availability of phosphate, optimizing the use of this critical resource and minimizing its wastage are of paramount importance. In this context, this paper proposes an innovative architecture for the implementation of an intelligent video surveillance system specifically designed for open-pit phosphate mines. The proposed architecture is designed to meet the overall functional requirements of a surveillance system in the challenging environment of open-pit mining, while aligning with the guidelines of the Mine 4.0 revolution. It incorporates advanced technologies that address the critical challenges of latency, data security, and transparency commonly encountered in traditional monitoring systems. By adopting a multi-layered approach that leverages edge, fog, and cloud computing, coupled with blockchain technology and expert collaboration, our architecture offers a comprehensive framework for efficient data processing at every stage, from initial data acquisition to real-time anomaly detection and decision-making.

Optimizing supply and production management through energy storage strategies: A solar cold production approach using artificial neural networks

The reliability of clean renewable energy hinges on robust energy systems, with storage serving a critical function. This paper investigates the influence of various storage types and configurations on thermal performance, with a focus on optimal sizing for economic and environmental cost reduction. To achieve this objective, we simulate a solar cooling facility with varied configurations of hot/cold storage installations. This study employs an ANN methodology with a multi-layer perceptron approach to forecast unit performance for each configuration based on data generated during the simulation process. In the pursuit of the most efficient and high-performance network, a comprehensive investigation is conducted on the number of neurons, activation functions, and training algorithms. Subsequently, the optimization process, conducted through a genetic algorithm, determines the Pareto fronts representing the best solution sets. The comparison shows that a system design with double hot and cold storage tanks shows superior techno-economic-environmental performance. Among possible optimum solution sets, a point with this specification is selected; flow rate ratio, minimum flow ratio, cooling capacity ratio, cold storage ratio, and hot storage ratio of 1.2, 0.4, 0.91, 3.4, and 3.8, respectively. This configuration anticipates a levelized cost of cooling at 341 USD/MWhr, representing a 13 % reduction compared to the benchmark.

Street photography and the flâneur: reflections on Australian city narratives

This original photographic work investigates the intersection of street photography and the concept of the flâneur within an Australian urban context. By transforming cityscapes into visual texts, the study captures urban narratives through the absence and reflection of inhabitants, rather than their direct representation. The photographic work employs window reflections as a key technique, introducing multiple visual dimensions to enrich street scenes and overall narratives. Drawing on Baudelaire's notion of the flâneur and Tomanić-Trivundža's three-stage journey of the flâneur (perambulating observation, critical reflection, and production of communicable texts), this work synthesises street photography with flâneur-like observation. It employs a multi-layered visual approach to city streets, representing a less-explored facet of lens-based practice in Australia. While the use of window reflections and the flâneur in street photography are well-established in European and North American contexts, their application in Australian settings remains less explored. This work aims to contribute to the street photography canon by translating these established techniques to Australian cities, offering insights into unique local urban characteristics. By exploring the interplay between global photographic traditions and local realities, this work seeks to provide a fresh perspective on Australian city life through the flâneur lens and reflective urban surfaces, thus expanding the geographical and cultural scope of this photographic tradition.