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Precision peptide disruptors: the next generation of targeted therapeutics in oncology
Get PDFTherapeutically targeting the pathologically remodelled protein-protein interaction network in cancer with peptide disruptors increasingly represents a clinically attractive approach to treating recalcitrant cancers. In this review, we map the pre-clinical and clinical-stage peptide disruptor landscape within an oncology-specific context and discuss key clinical examples that are making significant impact to patients; demonstrating a key role for peptide disruptors in precision medicine as a next-generation targeted therapeutic
Probing Power and Minimum Rate Maximization for RIS-Aided ISAC System
No full textWith the growing demand for wireless resources, integrated sensing and communication (ISAC) technology has recently gained extensive attention from researchers. Current solutions fail to adapt to the dynamic needs of different users, leading to inefficiencies and resource waste. This paper proposes a scalable framework for real-time beamforming optimization in a reconfigurable intelligent surface (RIS) assisted ISAC system. Both active beamforming at the base station (BS) and passive beamforming at the RIS are optimized to enhance the minimum communication rate for users and increase probing power for sensing performance. Considering the working principles of different ISAC systems, we investigate two transmission techniques based on separated and shared antenna deployment at BS. During the optimization process, the weighted minimum mean-square error (WMMSE) method is employed for active beamforming, while fractional programming (FP) is used for passive beamforming to reformulate target functions into more tractable forms. Numerical results demonstrate that the algorithm is reliable and the RIS makes a 10% improvement in communication rate in both separated and shared deployment scenarios
Bindone-based polymer for colorimetric detection of volatile amines
Get PDFEffective detection and monitoring of volatile amines are crucial for protecting human health and the environment, particularly in areas such as disease diagnosis and food spoilage detection. Traditional gas sensors, including electrochemical, semiconductor, and photochemical types, often suffer from limited selectivity, sensitivity and require complex and expensive synthesis and detection equipment. Colorimetric sensors, which are easily interpreted through visible colour changes, have recently gained attention for their simplicity and real-time detection capabilities. In this study, we present a chemosensing system based on the bindone motif, both as a small molecule (Bin) and embedded in a polymer backbone (PBin), for the effective colorimetric detection of volatile amines. Our system exhibits high selectivity and sensitivity, with detection limits as low as 0.04 ppm for Bin and 1.57 ppm for PBin. The colour change, driven by amine-induced tautomerisation, was confirmed through UV-Vis spectroscopy, NMR spectroscopy, and TD-DFT calculations. pH dependent studies reveal the importance of basicity on the mechanism and selectivity. By incorporating the bindone moiety into the polymer backbone, its thermal stability was significantly enhanced. The versatility of the sensor was demonstrated in solution, and paper-based film formats, with successful application in detecting amines released during fish spoilage. This work highlights the potential of the bindone-based chemosensor as a cost-effective, portable, and efficient tool for monitoring food freshness and other applications requiring the detection of volatile amines
Towards fast quantum cascade laser spectrometers for high-throughput and cost-effective disease surveillance
Get PDFFourier transform infrared (FTIR) spectroscopy, coupled with machine learning (ML) analysis can be used for disease monitoring with high speed and accuracy, including the classification of mosquito samples by species, age and malaria detection. However, current FTIR instruments use low-brightness thermal light sources to generate infrared light, which limits their ability to measure complex biological samples, especially where high spatial resolution is necessary, such as for specific mosquito tissues. Moreover, these systems lack portability, which is essential for field applications. To overcome these issues, spectrometers using quantum cascade lasers (QCLs) have become an attractive alternative for building fast, and portable systems due to their high electrical-to-optical efficiency, small size, and potential for low-cost. Here, we present a QCL-based spectrometer prototype designed for large scale, low-cost, environmental field-based disease surveillance
Catalytic-enhanced thermal hydrogen-termination of diamond for electronic applications
Get PDFIn this work we demonstrate a new method to terminate the surface of single crystal (100) diamond with hydrogen using low hydrogen-concentration (5 % H2:95 % Ar) forming gas and platinum (Pt) and palladium (Pd) thin films which catalytically enhance the investigated thermal hydrogen termination process. The resultant hydrogen termination is verified by electrical characterization and contact wetting angle measurements. A sheet resistance of the diamond surface as low as 11.3 kΩ/□ is achieved due to transfer doping in air and a contact angle up to 90° is simultaneously achieved as associated with the hydrophobicity of hydrogen-terminated diamond. This work demonstrates a readily accessible processing method to hydrogen terminate the diamond surface to achieve high conductivity without the requirement for a pure hydrogen source or dedicated plasma equipment
Developing Transparent and Accountable Ethical AI Technologies to Build Trust in Higher Education
No full textJoin us for an insightful 50-minute online event that explores the practical and ethical application of Artificial Intelligence (AI) in higher education. This event provides participants with hands-on tips and strategies for integrating AI technologies transparently and accountably, benefiting both students and faculty. Four distinguished speakers, each with extensive expertise from diverse geographical and professional backgrounds, will lead the discussion
The office as a site of management control: an historical film elicitation study
Get PDFPurpose:
This study aims to elicit the characteristics of office management control work as presented by documentary filmmakers across four decades in the 20th century. To this end, it investigates office context and culture, office personnel control related activity, and representations of the role of the office.
Design/methodology/approach:
Departing from conventional textual and photographic evidence, it is a pioneer study in the accounting research literature that employs film elicitation method to investigate office characteristics, interpretations, and performance presentations by film-makers as an alternative window into the backstage office world as traditionally presented.
Findings:
Backstage office management control is brought to front stage view, presenting organisational rituals that both condition and reflect societal customs and audience expectations. The office emerges as a hub of interactive integrated management control activity. A shifting balance towards staff-machine interaction accompanies the visible central role of management control records. The key characteristic of control via records-in-action is seen to be both historically evident as well as relevant to today’s digital office.
Research limitations/implications:
The study illustrates the potential for additional insights into accounting and management control processes and their representation to be gained from application of film elicitation methods.
Practical implications:
This study contributes to a further understanding of the 20th century office management control characteristics that underpin the offices of today and can potentially inform office management control design and actions in the future.
Originality/value:
The study offers an alternative window into historical office management control operations that augments existing knowledge. It presents a pioneering film elicitation study in the accounting research literature and demonstrates the relevance and importance of understanding historical context for today’s emerging digital office context. Documentary filmmaker representations of the office world and accounting functions and their reflection of societal context are also demonstrated
A comprehensive review of double transition metal MXene (Mo2Ti2C3Tx) in energy storage, conversion, and harvesting
Get PDFMolybdenum titanium carbide (Mo2Ti2C3Tx), an emerging double-transition-metal (DTM) carbide MXene, has attracted considerable interest due to its exceptional physicochemical properties and diverse applications in energy-related technologies. This review provides a comprehensive overview of recent progress in Mo2Ti2C3Tx research, focusing on synthesis strategies, structural characteristics, and energy storage and conversion capabilities. The key aspects discussed include the intrinsic structural and electrochemical properties of the material, which contribute to its outstanding performance in supercapacitors, lithium-ion batteries, and electrocatalytic applications. Their high electrical conductivity, large surface area, and chemical stability underlie their efficiency in these applications. In addition, its thermoelectric potential, which is highlighted by its high Seebeck coefficient and power factor, makes it a promising energy-harvesting material. Despite these advantages, challenges remain in achieving scalable production, long-term operational stability, and comprehensive mechanistic understanding of interfacial processes and degradation pathways. Future research should prioritise hybrid architectures combining Mo2Ti2C3Tx with polymers, 2D materials, or metal oxides to intensify synergistic effects, as well as computational modelling to unravel the structure-property relationships. Addressing scalability through eco-friendly, high-yield synthesis routes is pivotal for the transition of laboratory-scale innovations into commercial applications. By summarising recent advancements and identifying critical research gaps, this review aims to facilitate further development and practical use of Mo2Ti2C3Tx MXene in next-generation energy technologies
SecureMind: a Framework for Benchmarking Large Language Models in Memory Bug Detection and Repair
Get PDFLarge language models (LLMs) hold great promise for automating software vulnerability detection and repair, but ensuring their correctness remains a challenge. While recent work has developed benchmarks for evaluating LLMs in bug detection and repair, existing studies rely on hand-crafted datasets that quickly become outdated. Moreover, systematic evaluation of advanced reasoning-based LLMs using chain-of-thought prompting for software security is lacking. We introduce SecureMind, an open-source framework for evaluating LLMs in vulnerability detection and repair, focusing on memory-related vulnerabilities. SecureMind provides a user-friendly Python interface for defining test plans, which automates data retrieval, preparation, and benchmarking across a wide range of metrics. Using SecureMind, we assess 10 representative LLMs, including 7 state-of-the-art reasoning models, on 16K test samples spanning 8 Common Weakness Enumeration (CWE) types related to memory safety violations. Our findings highlight the strengths and limitations of current LLMs in handling memory-related vulnerabilities
Deep transfer learning based on hybrid Swin transformers with LSTM for intrusion detection systems in IoT environment
Get PDFExtensive growth in the number of Internet Of Things (IoT) devices has significantly increased susceptibility to various cyber-attacks and hence emphasized the need for robust intrusion detection systems (IDS) for ensuring IoT network security. While deep learning (DL) methodologies have proven effective in the application of IDS, their success greatly depends on the availability of large datasets and significant computational resources during training. To overcome the limitations associated with this dependence on large datasets and significant computational capacity for training, the current work suggests employing the transfer learning (TL) mechanism by combining Swin Transformers with long short-term memory (LSTM) networks. Utilizing the beneficial properties of Swin Transformers in learning hierarchically structured data combined with the proficiency of LSTM in processing sequential dependencies, the hybrid model generates pre-trained weights in the first phase. These pre-trained weights are further transferred into another instance of the new model for subsequent fine-tuning. Experiments are carried out on several benchmarking datasets, namely NSL-KDD, ToN-IoT, BoTIoT, MQTTIoT, and CICIoT2023, which include both binary and multi-class classification scenarios. The proposed model outperforms state-of-the-art DL models, for example, the Autoencoders, ResNets, CNN, RNN, and LSTM models, and achieved an average of 98.97% in accuracy, of 98.97% in precision, of 99.02% in recall, of 98.97% in F1 score, across all datasets. Experimental results establish that the hybrid approach achieves better detection accuracy and better performance measures compared to the latest state-of-the-art methods, thus proving itself effective in increasing the scalability and adaptability of IDS in IoT
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