Editor’s Choice Articles

Recent advancements in carbon nanotubes and graphene have driven significant research into other low-dimensional materials, with phosphorus-based materials emerging as a notable area of interest. Phosphorus nanowires and thin sheets show promise for applications in devices such as batteries, photodetectors, and field-effect transistors. However, the presence of multiple allotropes of phosphorus complicates their characterization. Accurate identification of these allotropes is essential for understanding their physical, optical, and electronic properties, which influence their potential applications. Researchers frequently encounter difficulties in consolidating literature for the confirmation of the structure of their materials, a process that can be time-consuming. This minireview addresses this issue by providing a comprehensive, side-by-side comparison of Raman and X-ray diffraction characteristic peaks, as well as electron microscopic images and lattice spacings, for the various phosphorus allotropes. To our knowledge, this is the first compilation to integrate all major structural fingerprints into unified summary tables, enabling rapid cross-referencing. This resource aims to support researchers in accurately identifying phosphorus phases during synthesis and device fabrication workflows. For example, distinguishing between red phosphorus polymorphs is crucial for optimizing anode materials in sodium-ion batteries, where electrochemical performance is phase-dependent. Full article

The aviation industry prioritizes safety and has embraced innovative approaches for both reactive and proactive safety measures. Machine learning (ML) has emerged as a useful tool for aviation safety. This systematic literature review explores ML applications for safety within the aviation industry over the past 25 years. Through a comprehensive search on Scopus and backward reference searches via Google Scholar, 87 of the most relevant papers were identified. The investigation focused on the application context, ML techniques employed, data sources, and the implications of contextual nuances for safety analysis outcomes. ML techniques have been effective for post-accident analysis, predictive, and real-time incident detection across diverse aviation scenarios. Supervised, unsupervised, and semi-supervised learning methods, including neural networks, decision trees, support vector machines, and deep learning models, have all been applied for analyzing accidents, identifying patterns, and forecasting potential incidents. Notably, data sources such as the Aviation Safety Reporting System (ASRS) and the National Transportation Safety Board (NTSB) datasets were the most used. Transparency, fairness, and bias mitigation emerge as critical factors that shape the credibility and acceptance of ML-based safety research in aviation. The review revealed seven recommended future research directions: (1) interpretable AI; (2) real-time prediction; (3) hybrid models; (4) handling of unbalanced datasets; (5) privacy and data security; (6) human–machine interface for safety professionals; (7) regulatory implications. These directions provide a blueprint for further ML-based aviation safety research. This review underscores the role of ML applications in shaping aviation safety practices, thereby enhancing safety for all stakeholders. It serves as a constructive and cautionary guide for researchers, practitioners, and decision-makers, emphasizing the value of ML when used appropriately to transform aviation safety to be more data-driven and proactive. Full article

In this work, ZnO nanorods were grown on vertically aligned and randomly aligned silica nanosprings using the hydrothermal method. The initial step was the deposition of a ZnO seed layer by atomic layer deposition to promote nucleation. For hydrothermal growth, equimolar (0.2 M) solutions of Zinc nitrate hexahydrate and hexamethylene tetraamine prepared in DI water were used. The ZnO NR grown on the VANS were flower-like clusters, while for the RANS, the ZnO NR grew radially outward from the individual nanosprings. The lengths and diameters of ZnO NR grown on VANS and RANS were 175 and 650 nm, and 35 and 250 nm, respectively. Scanning electron microscopy confirmed the formation of ZnO nanorods, while X-ray diffraction and Raman spectroscopy verified that they have a hexagonal wurtzite crystal structure with preferential growth along the c-axis. X-ray photoelectron spectroscopy, in conjunction with in vacuo annealing, was used to examine the surface electronic structure of ZnO nanorods and defect healing. Photoluminescence of the ZnO nanorods indicates high crystal quality, as inferred from the weak defect band relative to strong excitonic band edge emission. Full article

The rise in multidrug-resistant bacterial strains and persistent infections such as Lyme disease caused by Borrelia burgdorferi highlights the need for novel antimicrobial agents. The present study explores the antioxidant, antibacterial, and cytotoxic properties of extracts from submerged mycelial biomass of Fomitopsis pinicola, cultivated in synthetic and lignocellulosic media. Four extracts were obtained using hot water and 80% ethanol. The provided analysis of extracts confirmed the presence of various bioactive compounds, including flavonoids, alkaloids, and polyphenols. All extracts showed dose-dependent antioxidant activity (IC₅₀: 1.9–6.7 mg/mL). Antibacterial tests revealed that Klebsiella pneumoniae was most sensitive, with the L2 extract producing the largest inhibition zone (15.33 ± 0.47 mm), while the strongest bactericidal effect was observed against Acinetobacter baumannii (MBC as low as 0.5 mg/mL for L1). Notably, all extracts significantly reduced the viability of stationary-phase B. burgdorferi cells, with L2 reducing viability to 42 ± 2% at 5 mg/mL, and decreased biofilm mass, especially with S2. Cytotoxicity assays showed minimal effects on NIH 3T3 cells, with slight toxicity in HEK 293 cells for S2 and L1. These results suggest that F. pinicola extracts, particularly ethanolic L2 and S2, may offer promising natural antimicrobial and antioxidant agents for managing resistant infections. Full article

Antioxidants, such as stilbenes, anthocyanidins, coumarins, tannins and flavonoids, are often based on oxygen-containing redox systems and tend to feature several hydroxyl groups in their chemical structures. From a synthetic perspective, oxygen atoms are prone to bioisosteric replacement with sulfur and, notably, selenium. The main objective of this narrative literature review is to explore if and how bioisosteric substitution of oxygen with sulfur or selenium can enhance the biological activity of natural products. This replacement boosts the biological activity of the resulting molecules considerably as they now combine the redox and antioxidant properties of the original flavonoids and other natural products with the specific redox behavior of sulfur and selenium. Besides sequestering free radicals and peroxides, they may, for instance, also catalyze the removal of oxidative stressors, capture free metal ions and even provide scope for selenium supplementation. Since these molecules resemble their natural counterparts, they also exhibit considerable selectivity inside the body and a good pharmacokinetic profile. Still, the synthesis of such hybrid molecules integrating sulfur and selenium into flavonoids and other natural products is a challenge and requires innovative synthetic strategies and approaches. Full article

This study proposes an optimized energy management strategy for alternating current microgrids, integrating wind generation, battery energy storage systems (BESSs), and distribution static synchronous compensators (D-STATCOMs). The objective is to minimize operational costs, including grid electricity purchases (grid-connected mode), diesel generation costs (islanded mode), and maintenance expenses of distributed energy resources while ensuring voltage limits, maximum line currents, and power balance. A master–slave optimization approach is employed, where the Gray Wolf Optimizer (GWO) determines the optimal dispatch of energy resources, and successive approximations (SAs) perform power flow analysis. The methodology was validated on a 33-node microgrid, considering variable wind generation and demand profiles from a Colombian region under grid-connected and islanded conditions. To assess performance, 100 independent runs per method were conducted, comparing GWO against particle swarm optimization (PSO) and genetic algorithms (GAs). Statistical analysis confirmed that GWO achieved the lowest operational costs (USD 3299.39 in grid-connected mode and USD 11,367.76 in islanded mode), the highest solution stability (0.19% standard deviation), and superior voltage regulation. The results demonstrate that GWO with SA provides the best trade-off between cost efficiency, system stability, and computational performance, making it an optimal approach for microgrid energy management. Full article

Eye-gaze writing technology holds significant promise but faces several limitations. Existing eye-gaze-based systems often suffer from slow performance, particularly under challenging conditions such as low-light environments, user fatigue, or excessive head movement and blinking. These factors negatively impact the accuracy and reliability of eye-tracking technology, limiting the user’s ability to control the cursor or make selections. To address these challenges and enhance accessibility, we created a comprehensive dataset by integrating multiple publicly available datasets, including the Eyes Dataset, Dataset-Pupil, Pupil Detection Computer Vision Project, Pupils Computer Vision Project, and MPIIGaze dataset. This combined dataset provides diverse training data for eye images under various conditions, including open and closed eyes and diverse lighting environments. Using this dataset, we evaluated the performance of several computer vision algorithms across three key areas. For object detection, we implemented YOLOv8, SSD, and Faster R-CNN. For image segmentation, we employed DeepLab and U-Net. Finally, for self-supervised learning, we utilized the SimCLR algorithm. Our results indicate that the Haar classifier achieves the highest accuracy (0.85) with a model size of 97.358 KB, while YOLOv8 demonstrates competitive accuracy (0.83) alongside an exceptional processing speed and the smallest model size (6.083 KB), making it particularly suitable for cost-effective real-time eye-gaze applications. Full article

Microbial enzymes have revolutionised the textile industry by replacing harmful chemicals with eco-friendly alternatives, enhancing processes such as desizing, scouring, dyeing, finishing, and promoting water conservation while reducing pollution. This review explores the role of enzymes like amylases, pectinases, cellulases, catalases, laccases, and peroxidases in sustainable textile processing, focusing on their ability to mitigate environmental pollution from textile effluents. The review also examines the types and characteristics of hazardous textile waste and evaluates traditional waste treatment methods, highlighting sustainable alternatives such as microbial enzyme treatments for effluent treatment. Recent advancements in recombinant enzyme technology, including enzyme engineering and immobilisation techniques to enhance stability, reusability, and catalytic performance, are also explored. Additionally, the potential of extremozymes in textile processing and effluent treatment is explored, emphasising their stability under harsh industrial conditions. Strategies for reducing textile waste through enzyme-based processes are presented, focusing on principles of the circular economy. The review also addresses challenges such as scalability, cost, and process optimisation, while proposing potential solutions and outlining future directions for the widespread adoption of microbial enzymes in sustainable textile production and waste management. This review underscores the transformative potential of microbial enzymes in achieving greener textile manufacturing practices. Full article

Estimating the amplitude of a sinewave from a set of data points is a common procedure in various applications. This is typically achieved using a least squares method that provides closed-form estimators. The sampling process itself is often affected by different non-ideal phenomena like additive noise, phase noise, or sampling jitter, for example. Here, the precision of the estimation of a sinewave amplitude when the samples are affected by phase noise or sampling jitter is studied in the case of coherent sampling. The mathematical expression derived is useful in obtaining the confidence intervals for the estimated sinusoidal amplitude. It is also valuable at the time of choosing the proper number of samples to acquire from a signal in order to reach a certain desired level of sinewave amplitude estimation precision. The analytical expression presented is validated using both numerically generated data and experimental data. Various non-ideal factors, such as a fixed, uncontrollable amount of jitter in the setup, additive noise, analog-to-digital converter non-linearity, and limited signal bandwidth, are observed and discussed. Additionally, this work presents an exhaustive overview of the technical aspects involved in the experimental validation, including the implementation of the Monte Carlo type procedure, instrument interface, programming language, and the general development of automated measurement systems, which may be useful to other engineers. Full article

The technological developments in the Internet of Things (IoT), data science, artificial intelligence, wearable sensors, remote monitoring, decision support systems, fog, and edge systems have transformed digital healthcare. Especially after the pandemic, there has been a rapid transformation of healthcare infrastructure from a conventional to a digital approach. Now, specifically, technologies such as the Internet of Things play a vital role in the transformation of the healthcare system. In this paper, an effort has been made to encompass the transformation of healthcare with a focus on the Internet of Medical Things (IoMT). In particular, it provides a detailed overview of the Internet of Medical Things whilst discussing the design goals and challenges, the resource constraints and limitations of the complex healthcare systems. The paper also provides a detailed account of the research initiatives as well as off-the-shelf wireless motes, internet-enabled sensors and open-source platforms. A thorough account of the next-generation digital healthcare technologies and future research opportunities is provided. This work not only covers the state-of-the-art but also offers critical insight into the digital healthcare challenges. The work attempts to summarise the extensive literature in the domain and present a new perspective on the internet of medical things, affiliate technologies and their role in healthcare. Full article

Inverse Distance Weighting (IDW) is a common method for spatial interpolation. Still, its accuracy decreases when there is a cluster of measurement stations or when some measuring stations are hidden behind others. This paper introduces Clusters Unifying Through Hiding Interpolation (CUTHI), a simple approach to enhance IDW accuracy. CUTHI calculates a weight for each station that considers its visibility from the interpolation point, reducing the influence of clustered or hidden stations. The method is tested in three cases: elevation data, rainfall measurements, and a mathematical function. Results demonstrate that CUTHI consistently outperforms traditional IDW, especially in areas with clustered measurement stations. CUTHI also treats the bull’s eye problem. This improved accuracy makes CUTHI a valuable tool for various applications requiring precise spatial interpolation. Full article

The inherent challenges of financial time series forecasting demand advanced modeling techniques for reliable predictions. Effective financial time series forecasting is crucial for financial risk management and the formulation of investment decisions. The accurate prediction of stock prices is a subject of study in the domains of investing and national policy. This problem appears to be challenging due to the presence of multi-noise, nonlinearity, volatility, and the chaotic nature of stocks. This paper proposes a novel financial time series forecasting model based on the deep learning ensemble model LSTM-mTrans-MLP, which integrates the long short-term memory (LSTM) network, a modified Transformer network, and a multilayered perception (MLP). By integrating LSTM, the modified Transformer, and the MLP, the suggested model demonstrates exceptional performance in terms of forecasting capabilities, robustness, and enhanced sensitivity. Extensive experiments are conducted on multiple financial datasets, such as Bitcoin, the Shanghai Composite Index, China Unicom, CSI 300, Google, and the Amazon Stock Market. The experimental results verify the effectiveness and robustness of the proposed LSTM-mTrans-MLP network model compared with the benchmark and SOTA models, providing important inferences for investors and decision-makers. Full article

(1) Background: Microbiota could be related to tumorigenesis through the persistence of an inflammatory state, also at the endometrial level. Inflammation, in fact, is involved in the promotion of genetic instability and in a favorable microenvironment for tumor growth. One pathway could be the disruption of the epithelial/mucosal barrier, with the activation of cytokines. The microbiota also seem to favor other involved patterns, such as insulin resistance and increased adipose tissue. (2) Methods: The online search for this review was based on keywords such as “endometrial cancer” and “microbiota” on the main online scientific database. Our objective is a narrative up-to-date review of the current literature on gynecological microbiota; we analyze the possible correlations with known modifying and promoting oncological factors (i.e., Body Mass Index- BMI, menopause, pH), with particular attention to vaginal and uterine microorganisms respective to the development of endometrial cancer in comparison to healthy women. (3) Results: Various species and distributions of bacteria could be related to tumorigenesis and induce alterations in cell signaling and cycle pathways, including those in the gynecological field. (4) Conclusions: In the literature, the different composition of uterine and vaginal microbiota has been analyzed in the past years, and their diversity and actions seem to correlate with possible oncological effects. Full article

Although linseed oil (LO) has been used in wood protection for centuries, research continues to develop new and more effective formulations and treatment approaches. In the future, growing interest in LO use could be expected due to its cost and environmental friendliness. This review summarizes recent research (from 2000 onwards) on the use of LO in wood protection, published in peer-reviewed scientific journals and included in the online publication databases Scopus or Web of Science. The studies cover surface and impregnation treatments of various wood substrates using different LO formulations, including chemically modified LO and the use of LO as a base for the development of biofinish and as a medium for thermal modification of wood, as well as research into the mechanisms behind the changes in wood properties due to treatment methods and interaction with LO formulations. Although the improvement of wood hydrophobicity and biodurability dominates, other aspects such as weathering and color stability, adhesion, and environmental safety are included in these studies. In general, almost all of the studies show a greater or lesser potency of the proposed approaches to provide benefits in wood protection; however, the level of innovation and practical feasibility varies. Full article

This study investigates the potential health risks associated with consuming mussels (Mytilus galloprovincialis Lamarck, 1819) from the Romanian Black Sea coast between 2001 and 2023. The research focuses on heavy metal (copper, cadmium, lead, nickel, and chromium) bioaccumulation in mussels and the associated human health hazards. While most metals fell within safe limits, lead and cadmium exceeded the maximum admissible concentrations set by the European Commission in a small percentage of samples (10% for cadmium, 14% for lead). To assess human health risks, we calculated dietary intake estimates and hazard quotients. These calculations suggested that current metal concentrations in the mussels are unlikely to cause adverse health effects at typical consumption levels. Although current metal concentrations seem safe based on estimated intake and hazard quotients, we emphasize the need for continuous monitoring of pollutants in seafood to ensure consumer safety. Future research should consider the cumulative effects of various contaminants and how individual factors like age and health conditions might influence risk. Public health protection requires continuous monitoring, comprehensive risk assessments, and transparent communication between scientists, policymakers, and the public to establish safe consumption guidelines. Full article

Energy transformation powers change in the universe. In physical systems, maximal power (rate of energy input or output) may occur only at submaximal efficiency (output/input), or conversely, maximal efficiency may occur only at submaximal power. My review of power and efficiency in living systems at various levels of biological organization reveals that (1) trade-offs (negative correlations) between power and efficiency, as expected in physical systems, chiefly occur for resource-supply systems; (2) synergy (positive correlations) between power and efficiency chiefly occurs for resource use systems, which may result from (a) increasing energy allocation to production versus maintenance as production rate increases and (b) natural selection eliminating organisms that exceed a maximal power limit because of deleterious speed-related effects; (3) productive power indicates species-wide ‘fitness’, whereas efficiency of resource acquisition for production indicates local ‘adaptiveness’, as viewed along a body size spectrum and within clades of related species; (4) covariation of the power and efficiency of living systems occurs across space and time at many scales; (5) the energetic power/efficiency of living systems relates to the rates and efficiencies/effectiveness of nutrient/water uptake/use, the functional performance of various activities, and information acquisition/processing; and (6) a power/efficiency approach has many useful theoretical and practical applications deserving more study. Full article

Audio classification using deep learning models, which is essential for applications like voice assistants and music analysis, faces challenges when deployed on edge devices due to their limited computational resources and memory. Achieving a balance between performance, efficiency, and accuracy is a significant obstacle to optimizing these models for such constrained environments. In this investigation, we evaluate diverse deep learning architectures, including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), for audio classification tasks on the ESC 50, UrbanSound8k, and Audio Set datasets. Our empirical findings indicate that Mel spectrograms outperform raw audio data, attributing this enhancement to their synergistic alignment with advanced image classification algorithms and their congruence with human auditory perception. To address the constraints of model size, we apply model-compression techniques, notably magnitude pruning, Taylor pruning, and 8-bit quantization. The research demonstrates that a hybrid pruned model achieves a commendable accuracy rate of 89 percent, which, although marginally lower than the 92 percent accuracy of the uncompressed CNN, strikingly illustrates an equilibrium between efficiency and performance. Subsequently, we deploy the optimized model on the Raspberry Pi 4 and NVIDIA Jetson Nano platforms for audio classification tasks. These findings highlight the significant potential of model-compression strategies in enabling effective deep learning applications on resource-limited devices, with minimal compromise on accuracy. Full article

Petrochemical feedstocks are experiencing a fast growth in demand, which will further expand their market in the coming years. This is due to an increase in the demand for petrochemical-based materials that are used in households, hospitals, transportation, electronics, and telecommunications. Consequently, petrochemical industries rely heavily on olefins, namely propylene, ethylene, and butene, as fundamental components for their manufacturing processes. Presently, there is a growing interest among refineries in prioritising their operations towards the production of fuels, specifically gasoline, diesel, and light olefins. The cost-effectiveness and availability of petrochemical primary feedstocks, such as propylene and butene, can be enhanced through the direct conversion of crude oil into light olefins using fluid catalytic cracking (FCC). To achieve this objective, the FCC technology, process optimisation, and catalyst modifications may need to be redesigned. It is helpful to know that there are several documented methods of modifying traditional FCC catalysts’ physicochemical characteristics to enhance their selectivity toward light olefins’ production, since the direct cracking of crude oil to olefins is still in its infancy. Based on a review of the existing zeolite catalysts, this work focuses on the factors that need to be optimized and the approaches to modifying FCC catalysts to maximize light olefin production from crude oil conversion via FCC. Several viewpoints have been combined as a result of this research, and recommendations have been made for future work in the areas of optimising the yield of light olefins by engineering the pore structure of zeolite catalysts, reducing deactivation by adding dopants, and conducting technoeconomic analyses of direct crude oil cracking to produce light olefins. Full article

The significant advancements in applying artificial intelligence (AI) to healthcare decision-making, medical diagnosis, and other domains have simultaneously raised concerns about the fairness and bias of AI systems. This is particularly critical in areas like healthcare, employment, criminal justice, credit scoring, and increasingly, in generative AI models (GenAI) that produce synthetic media. Such systems can lead to unfair outcomes and perpetuate existing inequalities, including generative biases that affect the representation of individuals in synthetic data. This survey study offers a succinct, comprehensive overview of fairness and bias in AI, addressing their sources, impacts, and mitigation strategies. We review sources of bias, such as data, algorithm, and human decision biases—highlighting the emergent issue of generative AI bias, where models may reproduce and amplify societal stereotypes. We assess the societal impact of biased AI systems, focusing on perpetuating inequalities and reinforcing harmful stereotypes, especially as generative AI becomes more prevalent in creating content that influences public perception. We explore various proposed mitigation strategies, discuss the ethical considerations of their implementation, and emphasize the need for interdisciplinary collaboration to ensure effectiveness. Through a systematic literature review spanning multiple academic disciplines, we present definitions of AI bias and its different types, including a detailed look at generative AI bias. We discuss the negative impacts of AI bias on individuals and society and provide an overview of current approaches to mitigate AI bias, including data pre-processing, model selection, and post-processing. We emphasize the unique challenges presented by generative AI models and the importance of strategies specifically tailored to address these. Addressing bias in AI requires a holistic approach involving diverse and representative datasets, enhanced transparency and accountability in AI systems, and the exploration of alternative AI paradigms that prioritize fairness and ethical considerations. This survey contributes to the ongoing discussion on developing fair and unbiased AI systems by providing an overview of the sources, impacts, and mitigation strategies related to AI bias, with a particular focus on the emerging field of generative AI. Full article

In recent years, generative transformers have become increasingly prevalent in the field of artificial intelligence, especially within the scope of natural language processing. This paper provides a comprehensive overview of these models, beginning with the foundational theories introduced by Alan Turing and extending to contemporary generative transformer architectures. The manuscript serves as a review, historical account, and tutorial, aiming to offer a thorough understanding of the models’ importance, underlying principles, and wide-ranging applications. The tutorial section includes a practical guide for constructing a basic generative transformer model. Additionally, the paper addresses the challenges, ethical implications, and future directions in the study of generative models. Full article

Blockchain offers a cutting-edge solution for storing medical data, carrying out medical transactions, and establishing trust for medical data integration and exchange in a decentralized open healthcare network setting. While blockchain in healthcare has garnered considerable attention, privacy and security concerns remain at the center of the debate when adopting blockchain for information exchange in healthcare. This paper presents research on the subject of blockchain’s privacy and security in healthcare from 2017 to 2022. In light of the existing literature, this critical evaluation assesses the current state of affairs, with a particular emphasis on papers that deal with practical applications and difficulties. By providing a critical evaluation, this review provides insight into prospective future study directions and advances. Full article

This article depicts the role of the Digital Calibration Certificate (DCC) for an end-to-end digital quality infrastructure and as the basis for developments that are designated by the keyword “Industry 4.0”. Furthermore, it describes the impact the DCC has on increasing productivity in the manufacturing of products and in global trade. The DCC project is international in its scope. Calibration certificates document the measurement capability of a measurement system. They do this independently and by providing traceability to measurement standards. Therefore, they do not only play an important role in the world of metrology, but they also make it possible for manufacturing and commercial enterprises to exchange measurement values reliably and correctly at the national and at the international level. Thus, a DCC concept is urgently needed for the end-to-end digitalization of industry for the era of Industry 4.0 and for Medicine 4.0. A DCC brings about important advantages for issuers and for users. The DCC leads to the stringent, end-to-end, traceable and process-oriented organization of manufacturing and trading. Digitalization is thus a key factor in the field of calibration as it enables significant improvements in product and process quality. The reason for this is that the transmission of errors will be prevented, and consequently, costs will be saved as the time needed for distributing and disseminating the DCCs and the respective calibration objects will be reduced. Furthermore, it will no longer be necessary for the test equipment administration staff to update the data manually, which is a time-consuming, tedious and error-prone process. Full article