Current Work Research Articles

Energy efficiency investment in Swiss industry: Analysis of target agreements

Slowing down the energy efficiency (EE) improvement rate over the past half a decade and relatively low EE-related investments highlight a need for a more targeted approach to encourage EE improvement investment in the Swiss manufacturing sector. The current work is based on an analysis of already implemented energy efficiency measures (EEMs) via target agreements. Four segments of the Swiss manufacturing sector are identified using the K-means clustering based on the energy consumption profiles of the establishments. The EEMs are categorized through text analysis with the help of a machine learning algorithm. The EEMs are ranked using two indicators that can inform different stakeholders: i) EEMs with Internal rates of return well above the reported risk-adjusted hurdle rates are identified for each segment to help industrial decision-makers take ad-hoc, profit-based investment decisions on EE improvement. ii) Levelized costs of saved energy are presented as value ranges for the EEMs that have the largest impact on annual total final energy (TFE) reduction for each segment to help policymakers design a more targeted approach for the promotion of high-impact EEMs. EE system improvements such as insulation of steam systems and process optimization, as well as EE equipment improvements like process equipment insulation, installation of variable frequency drives (VFDs), smart controls, and IE3 motors, were found to be the most profitable options across all clusters. For the electricity cluster, CHP was found to be the most impactful way of EE improvement (high energy savings) but not cost-effective. For fuel-consuming clusters, waste heat recovery measures were found to be the most cost-effective and among the most impactful options for EE improvement, along with fuel substitution measures with varying degrees of cost-effectiveness.

A Complete Sojourn of Current Trends in Gastro-retentive Drug Delivery System: Recent Advances and Patent Survey.

The current work aims to provide a complete sojourn on gastro-retentive drug delivery system (GRDDS) along with formulation methods, polymer selection, and in vitro/ in vivo challenges with finished dosage forms. Ideally, a biopharmaceutical-hindered drug has a rapid clearance and erratic bioavailability due to its low aqueous solubility and permeability. Additionally, it also suffers from high first-pass metabolism and pre-systemic gut wall clearance. Gastro-retentive drug delivery systems have become an emerging technology where newer methodologies and scientific approaches have been used to provide the controlled release of drugs and provide a protective mechanism in the stomach. By the virtue of utilizing GRDDS as a dosage form, these formulations increase Gastroretention time (GRT) which prolongs the controlled release of the drug in the dosage form. GRDDS contribute to increased drug bioavailability and targeting at a site of action, which enhances therapeutic action and offers significant patient compliance. Furthermore, the present work also highlighted the critical role of polymers in favoring drug retention across GIT with the mechanism of gastro-retention and recommended concentration ranges. The emerging technology is also highlighted by the approved drug products and patented formulations in the recent decade which is depicted in a justified manner. GRDDS formulations have demonstrated clinical efficacy, which is supported by a compilation of patents for cutting-edge innovations in dosage forms that can be held in the stomach for an extended period of time.

An Exceptional Valorization of CuO Nanoparticles in Ionic Liquids as an Efficient Medium for the Electrophilic Substitution of Indole Towards the Formation of Bis(indolyl)methanes

Aim: Ionic liquids are promising green solvents with simple but unique structure-related physical properties such as negligible vapour pressure, exceptional thermal conductivity, remarkable thermal stability and their suitability and inertness towards a broad range of catalytic applications. CuO NPs have been addressed as a cost-effective and a reagent of a choice that necessitates only mild reaction conditions to offer a high yield of the desired products with exceptional selectivity in a short duration of time. Therefore, in the present work, attempts have been made to explore the catalytic potentials of CuO NPs in an ionic liquid medium to synthesize biologically important bis(indolyl)methanes. Background: Catalytic explorations of metal oxide nanoparticles in ionic liquids offers a cooperative effect that has a significant impact on the kinetics as well as on the outcome of the reaction. Therefore, such catalytic systems in the present times have seized the scientific community's interest from the perspectives of sustainable development in synthetic organic chemistry. The combination of metal oxide nanoparticles with highly tunable ionic liquids is not only used to synthesize simple organic molecules but also explored in the synthesis of complex organic molecules of high commercial and biological relevance. Objectives: The current work offers a rapid and robust protocol for synthesizing bis(indolyl)methanes via electrophilic substitution reaction between indole and various aldehydes in the presence of a CuO nanoparticles-ionic liquid system. The discussion focuses on the high tolerance of different functionalities by the catalytic system leading to the synthesis of bis(indolyl)methanes. Methods: CuO NPs have been synthesized via the co-precipitation method using ionic liquid. The applicability of metal oxide nanoparticles-IL matrix was further investigated in synthesizing bis(indolyl)methanes. Results: The FT-IR absorption below 600 cm-1 and the XRD pattern showing all the peaks in the diffraction diagram revealed the formation of CuO NPs. FESEM images show the flake-shaped morphology of CuO NPs and are found to be separated from the agglomerated clusters Conclusion: Ionic liquid-CuO NPs matrix reveals good to exceptional catalytic properties, and their advancements as a catalytic system at room temperature open new avenues for synthetic organic chemists.

Managerial approaches to blockchain technology adoption in the rail freight sector: An emerging market perspective

Modern supply chains demand adaptability driven by the globalization of production and markets, the fourth industrial revolution (4IR) and recent events such as the unexpected closure of the Suez Canal, the Russia-Ukraine war, and the Covid-19 pandemic. Investing in digital supply chain technologies boosts end-to-end visibility, mitigates risk, and improves the resilience of supply chains. Many disruptive technologies are currently changing the business landscape in Africa. However, Africa has traditionally been an adopter rather than a pioneer of technologies for business development. Although there has been an increase in the use of blockchain technology in various industries, there is still a challenge of understanding how the technology can be applied in the South African rail supply chain context. Thus, it is crucial to investigate how managers perceive the technology and how this understanding affects the decision to use the technology in the rail freight Container Corridor. A gap exists between the understanding, organizational support and benefits of the technology and its application within the corridor. A case study approach was utilized to collect primary data obtained through a structured self-administered survey questionnaire. The main findings of the study were that the understanding of blockchain influenced managers perceptions and willingness to adopt the technology. While the railway operator was committed to implementing blockchain, other operational constraints and inefficiencies still present on the corridor were impediments to further investigation. Furthermore, potential benefits and challenges, costs and resources required as well as the impact on current operations and workings were highlighted by management as important decision-making factors. The study provides valuable insights into disruptive technologies applications within the transport supply chains. It substantiates the significance of how managers view blockchain as a technology to address various challenges as well as enhance operations within the South African freight rail sector.

Hydrogel Breakthroughs in Biomedicine: Recent Advances and Implications.

The objective of this review is to present a succinct summary of the latest advancements in the utilization of hydrogels for diverse biomedical applications, with a particular focus on their revolutionary impact in augmenting the delivery of drugs, tissue engineering, along with diagnostic methodologies. Using a meticulous examination of current literary works, this review systematically scrutinizes the nascent patterns in applying hydrogels for biomedical progress, condensing crucial discoveries to offer a comprehensive outlook on their ever-changing importance. The analysis presents compelling evidence regarding the growing importance of hydrogels in biomedicine. It highlights their potential to significantly enhance drug delivery accuracy, redefine tissue engineering strategies, and advance diagnostic techniques. This substantiates their position as a fundamental element in the progress of modern medicine. In summary, the constantly evolving advancement of hydrogel applications in biomedicine calls for ongoing investigation and resources, given their diverse contributions that can revolutionize therapeutic approaches and diagnostic methods, thereby paving the way for improved patient well-being.

Testing the role of associative learning in evidence-based treatments for anorexia nervosa.

Treatments for anorexia nervosa (AN) remain ineffective for many patients. Processes that can account for differential treatment outcomes remain mostly unknown. We propose that the field test the role of associative learning in current psychological treatments. We hold that this line of research could yield actionable information for understanding non-response and improving long-term outcomes. To make this argument, we define associative learning and outline its proposed role in understanding psychiatric disorders and their treatment. We then briefly review data exploring associative learning in AN. We argue that associative learning processes are implicitly implicated in existing treatments; by this rationale, baseline differences in learning may interfere with treatment response. Finally, we outline future research to test our hypotheses. Altogether, future research aimed at better understanding how associative learning may contribute to AN symptom persistence has the potential to inform novel directions in intervention research. PUBLIC SIGNIFICANCE: There is a pressing need to improve outcomes in treatments for anorexia nervosa (AN). We propose that individual differences in associative learning-the ability to form and update associations between cues, contexts, behaviors, and outcomes-may account for differential response to existing treatments. Undertaking this research could provide an understanding of how current treatments work and inform new approaches for those who may be at risk of poor outcomes.

Redefining Counterfactual Explanations for Reinforcement Learning: Overview, Challenges and Opportunities

While AI algorithms have shown remarkable success in various fields, their lack of transparency hinders their application to real-life tasks. Although explanations targeted at non-experts are necessary for user trust and human-AI collaboration, the majority of explanation methods for AI are focused on developers and expert users. Counterfactual explanations are local explanations that offer users advice on what can be changed in the input for the output of the black-box model to change. Counterfactuals are user-friendly and provide actionable advice for achieving the desired output from the AI system. While extensively researched in supervised learning, there are few methods applying them to reinforcement learning (RL). In this work, we explore the reasons for the underrepresentation of a powerful explanation method in RL. We start by reviewing the current work in counterfactual explanations in supervised learning. Additionally, we explore the differences between counterfactual explanations in supervised learning and RL and identify the main challenges that prevent the adoption of methods from supervised in reinforcement learning. Finally, we redefine counterfactuals for RL and propose research directions for implementing counterfactuals in RL.

Secure deep learning-based energy efficient routing with intrusion detection system for wireless sensor networks

In general, wireless sensor networks are used in various industries, including environmental monitoring, military applications, and queue tracking. To support vital applications, it is crucial to ensure effectiveness and security. To prolong the network lifetime, most current works either introduce energy-preserving and dynamic clustering strategies to maintain the optimal energy level or attempt to address intrusion detection to fix attacks. In addition, some strategies use routing algorithms to secure the network from one or two attacks to meet this requirement, but many fewer solutions can withstand multiple types of attacks. So, this paper proposes a secure deep learning-based energy-efficient routing (SDLEER) mechanism for WSNs that comes with an intrusion detection system for detecting attacks in the network. The proposed system overcomes the existing solutions’ drawbacks by including energy-efficient intrusion detection and prevention mechanisms in a single network. The system transfers the network’s data in an energy-aware manner and detects various kinds of network attacks in WSNs. The proposed system mainly comprises two phases, such as optimal cluster-based energy-aware routing and deep learning-based intrusion detection system. Initially, the cluster of sensor nodes is formed using the density peak k-mean clustering algorithm. After that, the proposed system applies an improved pelican optimization approach to select the cluster heads optimally. The data are transmitted to the base station via the chosen optimal cluster heads. Next, in the attack detection phase, the preprocessing operations, such as missing value imputation and normalization, are done on the gathered dataset. Next, the proposed system applies principal component analysis to reduce the dimensionality of the dataset. Finally, intrusion classification is performed by Smish activation included recurrent neural networks. The proposed system uses the NSL-KDD dataset to train and test it. The proposed one consumes a minimum energy of 49.67 mJ, achieves a better delivery rate of 99.92%, takes less lifetime of 5902 rounds, 0.057 s delay, and achieves a higher throughput of 0.99 Mbps when considering a maximum of 500 nodes in the network. Also, the proposed one achieves 99.76% accuracy for the intrusion detection. Thus, the simulation outcomes prove the superiority of the proposed SDLEER system over the existing schemes for routing and attack detection.

3D Monte Carlo geometry inversion using gravity data

Diverse Monte Carlo methods have gained widespread use across a broad range of applications. However, the challenge of 3D Monte Carlo sampling remains due to the curse of dimensionality. To date, only a few works have been published regarding 3D Monte Carlo sampling. This study aims to develop an efficient 3D transdimensional Monte Carlo framework for reconstructing the spatial geometry of an anomalous body using gravity data. Our framework also can quantify the uncertainty of the shape of an anomalous body recovered from geophysical measurements. To improve the computational efficiency of 3D Monte Carlo sampling, we develop a sparse geometry parameterization strategy. This approach adequately approximates the shape of a complex 3D anomalous body using a set of simple geometries, such as ellipsoids. Each ellipsoid can be characterized by a few parameters, such as the centroid, axes, and orientations, significantly reducing the number of parameters to be sampled. During the sampling, we randomly perturb the number, locations, sizes, and orientations of the ellipsoids. To impose prior structural constraints from other geophysical methods, such as seismic imaging, we design a new method by placing a fixed layer oriented along the top boundary of the anomalous body. The fixed layer is then connected to the randomly sampled ellipsoids using an alpha shape, allowing us to estimate the geometry of the anomalous source body. The current work focuses on the reconstruction of salt bodies. We start with a synthetic spherical salt model and then conduct a more realistic study using a simplified 3D SEG/EAGE salt model, which has a much more complex geometry than the synthetic spherical model. Finally, we apply our method to the Galveston Island salt dome, offshore Texas. The numerical results demonstrate that our framework can effectively recover the shape of an anomalous body and quantify the uncertainty of the reconstructed geometry.

Xiaoyu Hu: Let the data guide you.

Xiaoyu Hu is a professor and director of the Institute for Immunology at Tsinghua University. Her lab is interested in understanding the molecular mechanisms underlying the transcriptional, translational, and metabolic mechanisms of the key cell types involved in host defense and inflammatory responses: macrophages and intestinal epithelial cells (IECs). We recently spoke with Xiaoyu about her current work, the benefits of a multidisciplinary team, and how being part of journal editorial boards brings her intellectual stimulation and inspiration for her own lab.

EnDL-HemoLyt: Ensemble Deep Learning-based Tool for Identifying Therapeutic Peptides with Low Hemolytic Activity.

Low hemolytic therapeutic peptides have gained an edge over small molecule-based medicines. However, finding low hemolytic peptides in laboratory is time-consuming, costly and necessitates the use of mammalian red blood cells. Therefore, wet-lab researchers often perform in-silico prediction to select low hemolytic peptides before proceeding with in-vitro testing. The in-silico tools available for this purpose have following limitations: (i) They do not provide predictions for peptides having N/C terminal modifications. (ii) Data is food for AI; however, datasets used to create existing tools do not contain peptide data generated over past eight years. (iii) Performance of available tools is also low. Therefore, a novel framework has been proposed in current work. Proposed framework utilizes recent dataset and uses ensemble learning technique to combine the decisions produced by bidirectional long short-term memory, bidirectional temporal convolutional network, and 1-dimensional convolutional neural network deep learning algorithms. Deep learning algorithms are capable of extracting features themselves from data. However, instead of relying solely on deep learning-based features (DLF), handcrafted features (HCF) were also provided so that deep learning algorithms can learn features that are missing from HCF, and a better feature vector can be constructed by concatenating HCF and DLF. Additionally, ablation studies were carried out to understand the roles of an ensemble algorithm, HCF, and DLF in the proposed framework. Ablation studies found that the ensemble algorithm, HCF and DLF are crucial components of proposed framework, and there is a decrease in performance on eliminating any of them. Mean value of performance metrics, namely Acc, Sn, Pr, Fs, Sp, Ba, and Mcc obtained by proposed framework for test data is ≈ 87, 85, 86, 86, 88, 87, and 73, respectively. To aid scientific community, model developed from proposed framework has been deployed as a web server at https://endl-hemolyt.anvil.app/.

Artificial intelligence-based model for predicting the minimum inhibitory concentration of antibacterial peptides against ESKAPEE pathogens.

In response to environmental threats, pathogens make several changes in their genome, leading to antimicrobial resistance (AMR). Due to AMR, the pathogens do not respond to antibiotics. Amongst drug-resistant pathogens, the ESKAPEE group of bacteria poses a major threat to humans, and therefore World Health Organization has given them the highest priority status. Antibacterial peptides (ABPs) are a family of peptides found in nature that play a crucial role in the innate immune systems of organisms. These ABPs offer several advantages over widely used antibiotics. As a result, they have recently received a lot of attention as potential replacements for currently available antibiotics. But it is expensive and time-consuming to identify ABPs from natural sources. Thus, wet lab researchers employ various tools to screen promising ABPs rapidly. However, the main limitation of the existing tools is that they do not provide the minimum inhibitory concentration values against the ESKAPEE pathogens for the identified ABP. To address this, in the current work, we developed ESKAPEE-MICpred, a two-input model that utilizes transfer learning and ensemble learning techniques. The concept of ensemble learning was realized by combining the decisions provided by deep learning algorithms, whereas the concept of transfer learning was realized by utilizing pretrained amino acid embeddings. The proposed model has been deployed as a web server at https://eskapee-micpred.anvil.app/ to aid the scientific community.

NRMG: News Recommendation With Multiview Graph Convolutional Networks

The emergence of a news recommendation system can effectively improve the news reading experience of users. The most important task of the system is how to learn news and user representations accurately. In the process of learning news representations, most of the current research works do not fully utilize the news features, which makes it difficult to learn more comprehensive news representations. Most research work only learns user representations from a single perspective, which may not be sufficient to learn diverse and dynamic user representations. Therefore, we propose a news recommendation system with a multiview graph convolutional network (NRMG). It contains two parts: news representation and user representation. The knowledge–content collaboration network is adapted to learn news representations from news content and entities, while the multiview graph convolutional network (GCN) is utilized to learn user representations from the user’s click history. The advantage of the NRMG system is that we not only expand the available features by constructing a subclass knowledge graph (KG), but also effectively improve the ability of the news recommendation system to accurately learn news and user representations. Experimental results on the real dataset MIcrosoft News Dataset (MIND) show that the NRMG achieves a 2.25% improvement in area under the receiver operating characteristic (ROC) curve (AUC) value compared with state-of-the-art methods.

Scheduling of Continuous Annealing With a Multi-Objective Differential Evolution Algorithm Based on Deep Reinforcement Learning

This paper studies a multi-objective scheduling problem in a continuous annealing operation of the steel industry, which is to simultaneously determine the production batch sizes for coils and their schedules so as to minimize the cost of setup, earliness, and tardiness. A large batch can reduce the setup costs but lead to a cost increase in earliness and tardiness. Thus, the conflict objectives of minimizing setup cost, earliness, and tardiness can be formulated separately. In this paper, we formulate a multi-objective optimization model for the problem and develop an adaptive multi-objective differential evolutionary based on deep reinforcement learning (AMODE-DRL) for effectively obtaining the Pareto solutions. In the proposed AMODE-RDL, DRL is integrated into the MODE algorithm as a controller, which can adaptively select mutation operators and parameters according to different search domains. Computational results on randomly generated instances and the practical problem instances show that DRL can effectively guide MODE to select mutation operators and parameters. The proposed algorithm can obtain better solutions compared to other powerful multi-objective evolutionary and adaptive MODE algorithms. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Setup usually leads to a decrease in production capacity and an increase in production costs. Similarly, earliness and tardiness costs are also crucial. These costs correspond to production, customer demand, and inventory, which are common objectives in production systems. However, the three objectives are generally conflicting, and it is very hard for practitioners to make appropriate decisions with manual experience. The multi-objective optimization methods can provide different scheduling for practitioners who may choose the suitable one according to current working conditions. Accordingly, the proposed model and algorithm can extend to other fields with similar characteristic problems.

HME-assisted formulation of taste-masked dispersible tablets of cefpodoxime proxetil and roxithromycin

ObjectivesAntibiotics are the most commonly administered medications among pediatric patients. However most of the time, accurate dose administration to children becomes a problem due to the extremely bitter taste. Cefpodoxime proxetil (CP) and roxithromycin (ROX) are antibiotics often prescribed to the pediatric population and have a bitter taste. Marketed formulations of these drugs are dry suspension and/or tablets. The lyophilization method involves various steps and thus is time consuming and expensive. The objective of this study was to mask the bitter taste of CP and ROX without compromising the solubility and drug release profile compared to marketed formulations, as well as to overcome the disadvantages associated with the currently used lyophilization technique. MethodsHot melt extrusion (HME) technology was used to process CP and ROX individually with Eudragit E PO polymer. The extrudates obtained were characterized by Fourier transform infrared spectroscopy, powder X-ray diffraction, and differential scanning calorimetry. The powdered extrudates were formulated as dispersible tablets and evaluated for in vitro and in vivo taste-masking efficiency. ResultsThe tablets prepared in this study showed comparable dissolution profiles but the taste-masking efficiency was significantly enhanced compared to the marketed tablets of CP and ROX. The results of in vivo human taste-masking evaluation were also in agreement with the in vitro taste-masking studies. ConclusionThe current work presents solvent-free, scalable, and continuous HME technology for addressing the bitter taste issues of CP and ROX. The disadvantages associated with the currently used lyophilization technique were overcome by developing the formulations using HME technology.

Computational Approaches Molecular Docking and MD Simulation Establishes the Potential COVID-19 Main Protease Inhibitors from Natural Products

Aim: COVID-19 was classified as a pandemic by the World Health Organization (WHO) on March 11, 2020. No reliable cure, however, was found. To prevent viral replication, complementary therapy with antiviral and antimalarial medications were used. However, due to their synthetic origin, they have a lot of side effects. To overcome this bane natural origin drugs were repositioned. Background: As repositioned drugs do not undergo a pro-long process of pre-clinical trial, hence, they play an excellent role in the spillover of pathogens. The main protease (6LU7) enzyme found in severe acute respiratory syndrome coronavirus-2 (SAR-CoV-2) is essential for viral replication. Thus, it acts as a hotspot in drug discovery. Objective: A molecular docking computational approach was used to determine the ability of the binding contract between the selected 3D-models of COVID-19 protease target and proposed natural compounds pristimerin, amazoquinone, kendomycin, celastrol, 20-epi-isoguesterinol, phenanthrenequinone, taxodione, maytenoquinone, hippeastrine, ammothamnine, 28-hydroxy isoiguesterin, hemanthamine, alisol-B, stigmasterol, β-pinene,and β-sitosterol through Autodock v.1.5.6 software. Method: The present study is designed to perform in-silico studies using molecular docking (Autodock tool v.1.5.6), Discovery Studio 2017 R2 client, Patch dock, SWISS-ADME prediction, and molecular simulation (Desmond simulation package of Schrodinger) between 6LU7 and natural origin compounds. Result: The results of docking study performed between 6LU7 and compounds pristimerin, amazoquinone, kendomycin, celastrol, 20-epi-isoguesterinol, phenanthrenequinone, taxodione, maytenoquinone, hippeastrine, ammothamnine, 28-hydroxy isoiguesterin, hemanthamine, alisol-B, stigmasterol, β-pinene, and β-sitosterol, showed binding energy as -9.68, -7.34, -5.34, -4.63, -4.24, -4.13, -4.08, -3.85, -3.83, -3.7, -3.6, -3.57, -3.54, -3.39, -3.18, and -3.03 Kcal/mol, respectively. It can be shown that the Pristimerin-6LU7 protein complex was maintained throughout the simulation since the ligand RMSDs varied with a maximum value of 4.2Å during the first 10 ns, followed by more stable interactions for the remaining time of the simulation. Conclusion: The goal of the current work was to find inhibitors for both prophylactic and therapeutic usage in COVID-19 patients.

Review of Systemic Mock Circulation Loops for Evaluation of Implantable Cardiovascular Devices and Biological Tissues.

On needs-based ex vivo monitoring of implantable devices or tissues/organs in cardiovascular simulators provides new insights and paves new paths for device prototypes. The insights gained could not only support the needs of patients, but also inform engineers, scientists and clinicians about undiscovered aspects of diseases (during routine monitoring). We analyze seminal and current work and highlight a variety of opportunities for developing preclinical tools that would improve strategies for future implantable devices. Holistically, mock circulation loop studies can bridge the gap between in vivo and in vitro approaches, as well as clinical and laboratory settings, in a mutually beneficial manner.

Abstract 2240: An exploratory study of financial toxicity intervention components preferred by cancer survivors

Abstract Background: Despite growing research on financial toxicity (FT) in cancer survivors, few studies have examined cancer survivors’ preferences for FT interventions. Understanding cancer survivors’ preferences for specific intervention components may facilitate the development and implementation of such interventions, potentially reducing the financial burden of cancer treatment. Purpose: The current work is an exploratory study of cancer survivors’ FT intervention component preferences. Methods: Adult survivors (N=105) of colorectal cancer (CRC; n=55) or non-Hodgkin lymphoma (NHL; n=50) from three tertiary care centers self-reported demographic characteristics (gender, race, income, employment status, insurance), clinical characteristics (comorbidities), psychosocial characteristics (PROMIS Global Health scale for mental health, Brief Health Literacy Screen scale, eHealth Literacy Scale), financial impact of cancer (the 11-item comprehensive score for financial toxicity COST scale, and life-altering and care-altering coping strategy scales), and preferences for FT intervention components. Intervention preferences were assessed through survey items on intervention timing and specific content. We employed descriptive statistics to assess overall preferences, and unadjusted linear regression and ANOVAs to identify demographic, clinical, psychosocial and financial predictors of intervention preferences. Results: In terms of timing, 79% of participants favored initiating a FT intervention before treatment compared to during or after treatment. The majority of participants (57%) were interested in at least one FT intervention component. In terms of specific components, participants were most interested in help understanding out-of-pocket costs and insurance (49%), help applying for financial assistance (39%), financial education (32%), and help communicating with the treatment team (32%). Participants were less interested in online tools to manage cancer-related expenses (27%), finance-related stress management (25%), and budgeting (20%). Predictors of overall interest (i.e., number of components endorsed) were: having high FT (B=.413, p=&amp;lt;.001), adopting life-altering coping behaviors (B=.374, p=&amp;lt;.001), using care-altering coping behaviors (B=.241, p=.014), being a non-White survivor (B=.219, p=.024), and having poorer mental health (B=-.280, p=.004). Conclusions: Cancer survivors may prefer FT interventions offered before treatment, and providing guidance on out-of-pocket costs and insurance. FT interventions may be particularly desired by those who experience greater financial toxicity, life and care-altering costs, are a racial/ethnic minority, or have poorer mental health, suggesting potential priority groups and target outcomes for intervention development. Citation Format: Maisha Huq, Claire Conley, Heather Derry-Vick, Amanda Khoudary, Lia Sorgen, Osairys Billini, Thomas Gunning, Conor Luck, Shreya Kaushik, Vanessa Hurley, John Marshall, Benjamin Weinberg, Anteneh Tesfaye, Andrew Ip, Arnold Potosky, Marc D. Schwartz. An exploratory study of financial toxicity intervention components preferred by cancer survivors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2240.

Work Preferences, Control, and Independence among Midlife and Older Adults in Aotearoa/New Zealand.

In 2023, current life expectancy for adults aged 65 and over in Aotearoa/New Zealand ranged from 84.6 to 89.2 years. Mandatory retirement has been abolished, but pension age eligibility remains at age 65. However, some older adults prefer to continue working, often experiencing a mismatch between current and preferred work statuses. The current study used data drawn from the 2020 wave of the New Zealand Health, Work and Retirement study (HWR; N = 3,916; Mage = 66.84) to examine associations between work preferences and perceptions of control and independence. We found that those not working but who preferred to work reported the lowest levels of perceived control and independence. Conversely, work preference match and higher levels of perceived control and independence were positively related to life satisfaction. The current study adds to our understanding of the centrality of work status, perceived control, and perceived independence among midlife and older adults, particularly in Aotearoa/New Zealand.

Tailoring thin film composite membranes for clean water production: A study on structural variations and predictive insights using machine learning

This study focuses on the fabrication of three thin film composite membranes by interfacial polymerization on polyacrylonitrile support. The objective is to explore the influence of different monomers on structural and functional features of resultant membranes for treating saline water and removing micropollutants. A new linear aliphatic amine 4A–2E was used as an aqueous monomer for the first time. Trimesoyl chloride, isophthaloyl chloride, and terephthaloyl chloride were used as organic phase crosslinkers. The resulting membranes exhibited distinctive structural features such as varied roughness and hydrophilicity, which correlated with their functional performance. The membrane roughness for 4A–2E-TPC@PAN/PET reached the highest value of Ra = 56 nm among the fabricated membranes. Moreover, permeate flux of 32, 56, and 49 L m−2 h−1 at 25 bar was measured for 4A–2E-TPC@PAN/PET, 4A–2E-IPC@PAN/PET, and 4A–2E-TMC@PAN/PET, respectively. Notably, 4A–2E-TPC@PAN/PET membrane exhibited 98% rejection of MgCl2 among the tested divalent salts. Furthermore, the membrane displayed high rejections of micropollutants, with 85% rejection for Amitriptyline. Upon chlorine exposure, the 4A–2E-TPC@PAN/PET membrane proved more resistant than both 4A–2E-IPC@PAN/PET and 4A–2E-TMC@PAN/PET membranes. Machine learning predictive insight was developed using Matérn Gaussian Process Regression model to simulate flux before and after chlorination of membranes. The results in both training and testing proved reliable predictive insight with 99% accuracy in terms of Nash Sutcliffe Efficiency. Hence, current work highlights the use of a linear amine 4A–2E and TPC crosslinker to fabricate a dense polyamide membrane and the application of ML algorithms to desalination data makes it possible to predict the membrane performance.