Explore the vast range of titles available.

Humans are fundamentally interested in monitoring and understanding interactions that occur in and around our bodies. Biological interactions within the body determine our physical condition and can be used to improve medical treatments and develop new drugs. Daily life involves contact with numerous chemicals, ranging from household elements, naturally occurring scents from common plants and animals, and industrial agents. Many chemicals cause adverse health and environmental effects and require regulation to prevent pollution. Chemical detection is critically important for food and environmental quality control efforts, medical diagnostics, and detection of explosives. Thus, sensitive devices are needed for detecting and discriminating chemical and biological samples. Compared to other sensing devices, the Quartz Crystal Microbalance (QCM) is well-established and has been considered and sufficiently sensitive for detecting molecules, chemicals, polymers, and biological assemblies. Due to its simplicity and low cost, the QCM sensor has potential applications in analytical chemistry, surface chemistry, biochemistry, environmental science, and other disciplines. QCM detection measures resonate frequency changes generated by the quartz crystal sensor when covered with a thin film or liquid. The quartz crystal is sandwiched between two metal (typically gold) electrodes. Functionalizing the electrode’s surface further enhances frequency change detection through to interactions between the sensor and the targeted material. These sensors are sensitive to high frequencies and can recognize ultrasmall masses. This review will cover advancements in QCM sensor technologies, highlighting in-sensor and real-time analysis. QCM-based sensor function is dictated by the coating material. We present various high-sensitivity coating techniques that use this novel sensor design. Then, we briefly review available measurement parameters and technological interventions that will inform future QCM research. Lastly, we examine QCM’s theory and application to enhance our understanding of relevant electrical components and concepts.

In this review paper, we present a comprehensive summary of the different organic solar cell (OSC) families. Pure and doped conjugated polymers are described. The band structure, electronic properties, and charge separation process in conjugated polymers are briefly described. Various techniques for the preparation of conjugated polymers are presented in detail. The applications of conductive polymers for organic light emitting diodes (OLEDs), organic field effect transistors (OFETs), and organic photovoltaics (OPVs) are explained thoroughly. The architecture of organic polymer solar cells including single layer, bilayer planar heterojunction, and bulk heterojunction (BHJ) are described. Moreover, designing conjugated polymers for photovoltaic applications and optimizations of highest occupied molecular orbital (HOMO)–lowest unoccupied molecular orbital (LUMO) energy levels are discussed. Principles of bulk heterojunction polymer solar cells are addressed. Finally, strategies for band gap tuning and characteristics of solar cell are presented. In this article, several processing parameters such as the choice of solvent(s) for spin casting film, thermal and solvent annealing, solvent additive, and blend composition that affect the nano-morphology of the photoactive layer are reviewed.

Many governments seek to impose gender equality on boards, but the consequences of doing so are not clear and could harm firms and economies. We shed light on this topic by conceptualizing the relationships as firm-and board-specific and embedded within specific contexts. The theory is developed with reference to emerging markets, and tested on Malaysian firms. We find that female directors create value for some firms and decrease it for others. The impact varies across different performance indicators, firms' ownership, and boards' structure. The findings call for nuanced responses in relation to women's nominations from both governments and firms.

Deep learning-based medical image analysis has shown strong potential in disease categorization, segmentation, detection, and even prediction. However, in high-stakes and complex domains like healthcare, the opaque nature of these models makes it challenging to trust predictions, particularly in uncertain cases. This sort of uncertainty can be crucial in medical image analysis; diabetic retinopathy is an example where even slight errors without an indication of confidence can have adverse impacts. Traditional deep learning models rely on single-point predictions, limiting their ability to provide uncertainty measures essential for robust clinical decision-making. To solve this issue, Bayesian approximation approaches have evolved and are gaining market traction. In this work, we implemented a transfer learning approach, building upon the DenseNet-121 convolutional neural network to detect diabetic retinopathy, followed by Bayesian extensions to the trained model. Bayesian approximation techniques, including Monte Carlo Dropout, Mean Field Variational Inference, and Deterministic Inference, were applied to represent the posterior predictive distribution, allowing us to evaluate uncertainty in model predictions. Our experiments on a combined dataset (APTOS 2019 + DDR) with pre-processed images showed that the Bayesian-augmented DenseNet-121 outperforms state-of-the-art models in test accuracy, achieving 97.68% for the Monte Carlo Dropout model, 94.23% for Mean Field Variational Inference, and 91.44% for the Deterministic model. We also measure how certain the predictions are, using an entropy and a standard deviation metric for each approach. We also evaluated the model using both AUC and accuracy scores at multiple data retention levels. In addition to overall performance boosts, these results highlight that Bayesian deep learning does not only improve classification accuracy in the detection of diabetic retinopathy but also reveals beneficial insights about how uncertainty estimation can help build more trustworthy clinical decision-making solutions.

Crowd counting in aerial images presents unique challenges due to varying altitudes, angles, and cluttered backgrounds. Additionally, the small size of targets, often occupying only a few pixels in high-resolution images, further complicates the problem. Current crowd counting models struggle in these complex scenarios, leading to inaccurate counts, which are crucial for crowd management. Moreover, these regression-based models only provide the total count without indicating the location or distribution of people within the environment, limiting their practical utility. While YOLOv8 has achieved significant success in detecting small targets within aerial imagery, it faces challenges when directly applied to crowd counting tasks in such contexts. To overcome these challenges, we propose an improved framework based on YOLOv8, incorporating a context enrichment module (CEM) to capture multiscale contextual information. This enhancement improves the model’s ability to detect and localize tiny targets in complex aerial images. We assess the effectiveness of the proposed framework on the challenging VisDrone-CC2021 dataset, and our experimental results demonstrate the effectiveness of this approach.

Constructing visible-light-active Z-scheme heterojunctions has proven fruitful in enhancing the photocatalytic activity of photocatalysts for superior water clean-up. Herein, we report the fabrication of a CoFe2O4@Bi2O3/NiO (CBN) Z-scheme nanoheterojunction. The obtained CBN heterojunction was used for visible-light-assisted degradation of ofloxacin (OFL) in water. The OFL degradation efficiency achieved by the CBN heterojunction was 95.2% in 90 min with a rate constant of kapp = 0.03316 min−1, which was about eight times that of NiO and thirty times that of CoFe2O4. The photocatalytic activity of a Bi2O3/NiO Z-scheme heterojunction was greatly enhanced by the visible activity and redox mediator effect of the cobalt ferrite co-catalyst. Higher charge-carrier separation, more visible-light capture, and the Z-scheme mechanism in the Z-scheme system were the important reasons for the high performance of CBN. The scavenging experiments suggested ●O2− as an active species for superior OFL degradation. The possible OFL degradation pathway was predicted based on LC-MS findings of degradation intermediate products. The magnetic nature of the CBN helped in the recovery of the catalyst after reuse for six cycles. This work provides new insights into designing oxide-based heterojunctions with high visible-light activity, magnetic character, and high redox capabilities for potential practical applications in environmental treatment.

Background Given the high rates globally of Type 2 Diabetes Mellitus (T2DM), there is a clear need to target health behaviours through person-centred interventions. Health coaching is one strategy that has been widely recognised as a tool to foster positive behaviour change. However, it has been used inconsistently and has produced mixed results. This systematic review sought to explore the use of behaviour change techniques (BCTs) in health coaching interventions and identify which BCTs are linked with increased effectiveness in relation to HbA1C reductions. Methods In line with the PICO framework, the review focused on people with T2DM, who received health coaching and were compared with a usual care or active control group on HbA1c levels. Studies were systematically identified through different databases including Medline, Web of science, and PsycINFO searches for relevant randomised controlled trials (RCTs) in papers published between January 1950 and April 2022. The Cochrane collaboration tool was used to evaluate the quality of the studies. Included papers were screened on the reported use of BCTs based on the BCT taxonomy. The effect sizes obtained in included interventions were assessed by using Cohen’s d and meta-analysis was used to estimate sample-weighted average effect sizes (Hedges’ g). Results Twenty RCTs with a total sample size of 3222 were identified. Random effects meta-analysis estimated a small-sized statistically significant effect of health coaching interventions on HbA1c reduction ( g +  = 0.29, 95% CI: 0.18 to 0.40). A clinically significant HbA1c decrease of ≥5 mmol/mol was seen in eight studies. Twenty-three unique BCTs were identified in the reported interventions, with a mean of 4.5 (SD = 2.4) BCTs used in each study. Of these, Goal setting (behaviour) and Problem solving were the most frequently identified BCTs. The number of BCTs used was not related to intervention effectiveness. In addition, there was little evidence to link the use of specific BCTs to larger reductions in HbA1c across the studies included in the review; instead, the use of Credible source and Social reward in interventions were associated with smaller reductions in HbA1c. Conclusion A relatively small number of BCTs have been used in RCTs of health coaching interventions for T2DM. Inadequate, imprecise descriptions of interventions and the lack of theory were the main limitations of the studies included in this review. Moreover, other possible BCTs directly related to the theoretical underpinnings of health coaching were absent. It is recommended that key BCTs are identified at an early stage of intervention development, although further research is needed to examine the most effective BCTs to use in health coaching interventions. Trial registration https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021228567 .

Developments in the synthesis and scalable manufacturing of carbon nanomaterials like carbon nanotubes (CNTs) have been widely used in the polymer material industry over the last few decades, resulting in a series of fascinating multifunctional composites used in fields ranging from portable electronic devices, entertainment and sports to the military, aerospace, and automotive sectors. CNTs offer good thermal and electrical properties, as well as a low density and a high Young’s modulus, making them suitable nanofillers for polymer composites. As mechanical reinforcements for structural applications CNTs are unique due to their nano-dimensions and size, as well as their incredible strength. Although a large number of studies have been conducted on these novel materials, there have only been a few reviews published on their mechanical performance in polymer composites. As a result, in this review we have covered some of the key application factors as well as the mechanical properties of CNTs-reinforced polymer composites. Finally, the potential uses of CNTs hybridised with polymer composites reinforced with natural fibres such as kenaf fibre, oil palm empty fruit bunch (OPEFB) fibre, bamboo fibre, and sugar palm fibre have been highlighted.

A pipeline, railway or telecommunication cable (referred to as victim line) sharing a common corridor with ac power transmission or distribution lines captures a portion of the electromagnetic field energy surrounding the power lines in the air and soil. This captured energy, often designated as ac interference, can result in an electrical shock hazard for people touching the victim lines or metallic structures connected to them. Furthermore, excessive stress voltages across rails, telephone pairs or pipe walls and coating surfaces can result in degradation or damage to equipment and puncture of pipe coating, leading to accelerated corrosion and can damage insulation flanges and rectifiers. The main objective for this study is to analyse proximity effect from a proposed double circuit transmission lines in different scenarios, which are steady-state, fault conditions and lightning strike. Induced voltage and currents on a gas pipeline to be determined using Current Distribution, Electromagnetics and Soil Structure Analysis (CDEGS) software.

It has been suggested that for objects identifiable by their corners, every triangle formed by these corner points can serve as a reference for detecting other corner points. This approach enables effective rigid object detection, including partial matches. However, when there are many corner points, the implementation becomes impractical due to excessive memory requirements. To overcome this, we propose a new algorithm that leverages Delaunay triangulation, considering only the triangles generated by the Delaunay triangulation to reduce the complexity of the original approach. Our algorithm is significantly faster and requires significantly less memory, offering a viable solution for large problem instances. Moreover, it excels at identifying all matches of a queried object in an image when visible triangles of the object are present. A triangle formed by an object’s vertices is considered visible if a matching triangle is detected, and no vertices from any other object lie within its circumcircle. Recent AI-based methods have revolutionized rigid object matching, providing impressive accuracy with deep learning techniques. However, these methods require extensive training and specialized hardware like GPUs. In contrast, our approach requires no training or specialized hardware, making it a lightweight and efficient solution that maintains strong matching capabilities without the overhead of AI-based methods. Our study of the geometric features, combined with Delaunay triangulation, offers new mathematical insights.