Theranostic tools against lung and breast cancers : through the lens of mature gold nanoparticles and emerging graphene

In recent years, theranostic applications have emerged as promising tools in the fight against lung and breast cancers. This review aims to provide an in-depth exploration of the proof-of-concept theranostic applications of two cutting-edge nanomaterials: gold nanoparticles (AuNPs) and graphene. Nanotechnology plays a revolutionary role in cancer theranostics. AuNPs’ properties include high surface plasmon resonances, advantageous surface-to-volume ratio, remarkable photothermal conversion rates, and distinctive optical characteristics. Whereas graphene boasts high surface areas, optical transparency, and remarkable versatility in surface functionalisation. While AuNPs have long been recognised for their theranostic potential, this review spotlights the burgeoning role of graphene as a compelling choice for advancing theranostic applications in oncology with several exemplar studies. In fact, most recent advancements have witnessed the integration of AuNP-graphene nanocomposites in theranostic approaches targeting lung and breast cancers. Yet, there are still many intricate challenges that researchers face in harnessing the full potential of these nanomaterials in theranostics, from synthesis to clinical translation. This review provides valuable insights into both established and emerging nanomaterials. AuNPs show significant potential for diverse cancer theranostic applications, and graphene is rapidly evolving as a next-generation theranostic platform. The hybrid AuNP-graphene nanocomposite stands out as a promising candidate in the evolving landscape of cancer therapy, offering exciting prospects for future research and development

Meshfree simulation of dynamic brittle and quasi-brittle fracture using a local damage model based on lattice particle method

In this paper, the particle size-dependent issue of Lattice Particle Method (LPM) for simulating dynamic brittle and quasi-brittle fracture is addressed by coupling LPM with a local damage model based on fracture energy. The proposed model is simple and more robust than the Stress Intensity Factor (SIF)-based approach as it can model mixed-mode fracture with multiple cracks without any application-dependent tuning parameter. Numerical procedures for estimating the dissipative energy and the crack tip velocity for LPM simulation are also proposed. A series of benchmark problems involving dynamic fracture and crack branching are simulated using the proposed model. Good agreements are found against existing experimental observation and solutions from other numerical methods. Although a Cartesian-like (structured) lattice configuration is employed in the current LPM method, physically meaningful and accurate crack patterns can still be captured without any special numerical treatment

Personnel training for common facility management issues in Thermal-Energy-Storage chiller plant using a serious 3D game

Background. This study introduces an innovative personnel training method for facility management and maintenance of Thermal-Energy-Storage (TES) chiller plants using a serious 3D game. Training games can improve the decision making of personnel where they can learn to deal with management of TES chiller plants in the context of this study in an active learning approach. Intervention. The research aims to investigate the effectiveness of an immersive learning experience with computerized simulation and synthetic task environments as a training game for facility managers. The serious 3D game adopts a first-person perspective, allowing players to assume the role of a facility manager and actively learn how to address maintenance issues commonly encountered in chiller plants. Methods. The study implemented a first-person-based serious 3D game centered around a TES chiller plant. Participants, in the role of facility managers, engaged with the game and followed instructions to gain practical knowledge in managing maintenance issues within a controlled and simulated setting. Results. The findings demonstrate increased engagement and interest among personnel when learning how to manage chiller plant issues within a serious 3D game environment. Notably, personnel experienced reduced pressure compared to real-life scenarios, as they navigated the challenges without the presence of a supervisor. Limitations. The study’s limitations include a higher proportion of male participants in the qualitative content analysis, which may affect the generalizability of the findings. Additionally, the absence of a control group limits the ability to make direct comparisons with traditional training methods. Conclusion. The results suggest that serious 3D games hold potential as an effective training tool for facility management and maintenance personnel. By engaging in active training within simulated and synthetic task environments, personnel can enhance their skills and decision-making capabilities. However, further research with a more diverse participant sample and a control group is warranted to fully evaluate the effectiveness of this innovative personnel training approach

Effects of external weather on the water consumption of Thermal-Energy-Storage Air-Conditioning system

Thermal-Energy-Storage Air-Conditioning (TES-AC), a sustainable form of Air-Conditioning (AC) operates by storing thermal energy as chilled water when energy demand is low during nighttime. Later it uses the stored thermal energy during the daytime to cool the indoor air of the building the next day. However, the stored thermal energy in the form of water in the tanks of the chiller plant might be affected by external weather factors. It is essential to understand whether there is a relation between external weather conditions and water consumption in the TES-AC system. Without verifying the relation, applying computational intelligence for Thermal-Energy- Storage (TES) in Heating, Ventilation, and Air Conditioning (HVAC) would not be appropriate. However, not much research has focused on applying such techniques in HVAC for facility management and maintenance. Moreover, identifying these features by discovering the relation between weather and water consumption is a crucial part to apply computational intelligence such as machine learning techniques for predictive maintenance of this facility as it heavily relies on water volume for TES-AC charging. During warmer weather, the stored thermal energy might have an effectual loss due to evaporation which would mean more water consumption by TES-AC for cooling. Hence, this research investigates whether external weather data has any effect on the water consumption of TES-AC and discusses how external weather may affect the water consumption of TES-AC and if it is important to factor it in whilst utilizing computational intelligence for charging load prediction of TES-AC

“I felt sad then, I feel free now”: a case for examining the constructive resistance of opted-out mothers

Purpose While past research has explored how opting-out enables mothers to break free from masculinist organizational cultures, less attention has been given to how they resist disciplinary power that constitutes and governs their subjectivities. This paper aims to add to the discussion of opting-out as a site of power and resistance by proposing the concept of “constructive resistance” as a productive vantage point for investigating opted-out mothers' subversive practices of self-making. Design/methodology/approach This Malaysian case study brings together the notion of constructive resistance, critical narrative analysis and APPRAISAL theory to examine the reflective stories of eighteen mothers who exited formal employment. These accounts were collected through an open-ended questionnaire and semi-structured email interviews. Findings The mothers in the sample tend to construct themselves in two main ways, as (1) valuable mothers (capable, tireless, caring mothers who are key figures in their children's lives) and (2) competent professionals. These subjectivities are parasitic on gendered and neoliberal ideals but allow the mothers to undermine neoliberal capitalist work arrangements that were incongruent with their personal values and adversely impacted their well-being, as well as refuse organizational narratives that positioned them as “failed” workers. Originality/value Whereas power is primarily seen in previous opting-out scholarship as centralized and constraining, this case study illustrates how the lens of constructive resistance can be beneficial for examining opted-out mothers' struggles against a less direct form of power that governs through the production of truths and subjectivities

Numerical modelling of brittle fracture using lattice particle method with applications to fluid structure interaction problems via SPH coupling

This paper presents an improved failure model for simulating brittle fracture using the mesh-less Lattice Particle Method (LPM). By modelling the initial crack line using the Remove Bond (RB) approach as outlined in this paper, a new formulation is then developed for predicting the mode-I Stress Intensity Factor (SIF) near the crack tip. Compared to the conventional Remove Particle (RP) approach, it is found that the accuracy of the present SIF formulation based on the RB method is superior. A series of benchmark test cases are simulated to test the numerical accuracy and numerical convergence of the method. Finally, the LPM method is coupled with the Smoothed Particle Hydrodynamics (SPH) method for studying Fluid Structure Interaction (FSI) problems involving solid fracture and free surface

Charging water load prediction for a thermal-energy-storage air-conditioner of a commercial building with a multilayer perceptron

This research focuses on the development of a machine learning model for predicting the water volume that needs to be chilled in Thermal-Energy-Storage-Air-Conditioning (TES-AC) systems. TES-AC technology uses thermal energy storage tanks to store and distribute chilled water during peak hours, reducing the need for the continuous operation of chillers and resulting in significant cost savings and a reduction in carbon emissions. However, determining the optimal amount of chilled water to generate and store each day can be challenging. The aim of this research is to design a machine learning model that takes input variables about the next day’s weather, which day of the week it is, and occupancy data and outputs a predicted water volume that needs to be chilled. It utilizes a Multilayer Perceptron for charging water load prediction in TES-AC systems to assist facility managers in making informed decisions minimizing disruptions. By fine-tuning the hyperparameters of the deep learning model and evaluating different metrics, the model was trained sufficiently and optimized. The model provides a specific water range as a target output, giving facility managers a small set of ranges to choose from, minimizing errors, while the accuracy achieved was 93.4%. The developed model can be retrained for other TES-AC plants, without requiring specific sensor input that might not be available in different TES-AC systems. That makes the developed solution more flexible and can encourage more stakeholders to use TES-ACs which in turn would lead to greener buildings that would benefit the environment

A three-dimensional fluid-structure interaction model based on SPH and lattice-spring method for simulating complex hydroelastic problems

The present work revolves around the development of a 3D particle-based Fluid-Structure Interaction (FSI) solver to simulate hydroelastic problems that involve free surface. The three-dimensional Volume-Compensated Particle Method (VCPM) for modelling deformable solid bodies is developed within the open-source SPH software package DualSPHysics. Complex 3D FSI problems are readily simulated within a reasonable time frame thanks to the parallel scalability of DualSPHysics on both CPU and GPU. The Sequential Staggered (SS) scheme paired with a multiple time-stepping procedure is implemented in DualSPHysics for coupling the SPH and VCPM models. It is found that the SPH-VCPM method is computationally more efficient than the previously reported SPH-TLSPH method. Extensive validations have been performed based on some very recent 3D experimental setups that involve violent free surface and complex structural dynamics. Findings from this research highlight the capability of the 3D SPH-VCPM model to reproduce some of the physical observations that were not captured by previous 2D studies. Some preliminary 3D FSI results involving solid fracture are also demonstrated

“If your voice isn’t accepted, does it mean you stop talking?” exploring a woman leader’s reversal of postfeminist confidence discourses

This study offers a lens for exploring women leaders’ production of resistance through postfeminist discourses. Through the case study of Bozoma Saint John, a high-profile Black C-Suite executive, this study examines micro-acts of subversion and considers the extent they can promote feminist thinking in the corporate world and the implications for feminist theorising about women in leadership. Interviews with Saint John were collected from YouTube and examined using feminist critical discourse analysis informed by intersectionality, feminist poststructuralism and Foucault’s notion of “reverse discourse”. Saint John reproduces elements of the postfeminist confidence discourse to defy stereotypes of Black women, while simultaneously reversing the individualistic conception of confidence in favour of corporate and collective action. This has the potential to facilitate positive change, albeit within the boundaries of the confidence culture. Combining reverse discourse, intersectionality and feminist poststructuralism with a micro-level analysis of women leaders’ language use can help to capture the ways postfeminist concepts are given new subversive meanings. Whereas existing studies have focused on how elite women’s promotion of confidence sustains the status quo, this study shifts the research gaze to the resistance realised through rearticulations of confidence, illustrating how women-in-leadership research can advance feminist theorising without vilifying senior women even as they participate in postfeminist logics of success

Application of deep learning in facility management and maintenance for heating, ventilation, and air conditioning

Despite the promising results of deep learning research, construction industry applications are still limited. Facility Management (FM) in construction has yet to take full advantage of the efficiency of deep learning techniques in daily operations and maintenance. Heating, Ventilation, and Air Conditioning (HVAC) is a major part of Facility Management and Maintenance (FMM) operations, and an occasional HVAC malfunction can lead to a huge monetary loss. The application of deep learning techniques in FMM can optimize building performance, especially in predictive maintenance, by lowering energy consumption, scheduling maintenance, as well as monitoring equipment. This review covers 100 papers that show how neural networks have evolved in this area and summarizes deep learning applications in facility management. Furthermore, it discusses the current challenges and foresees how deep learning applications can be useful in this field for researchers developing specific deep learning models for FMM. The paper also highlights how establishing public datasets relevant to FM for predictive maintenance is crucial for the effectiveness of deep learning techniques. The utilization of deep learning methods for predictive maintenance on Thermal-Storage Air-Conditioning (TS-AC) in HVAC is necessary for environmental sustainability, as well as to improve the cost-efficiency of buildings