In this investigation, the chemical composition of the hydro-distilled essential oil (HD-EO), obtained from the fresh aerial parts (inflorescence heads (Inf), leaves (L), and stems (St)) of Conyza canadensis growing wild in Jordan was determined by GC/MS. Additionally, the methanolic extract obtained from the whole aerial parts of C. canadensis (CCM) was examined for its total phenolic content (TPC), total flavonoids content (TFC), DPPH radical scavenging activity, iron chelating activity and was then analyzed with LC-MS/MS for the presence of certain selected phenolic compounds and flavonoids. The GC/MS analysis of CCHD-EOs obtained from the different aerial parts revealed the presence of (2E, 8Z)-matricaria ester as the main component, amounting to 15.4% (Inf), 60.7% (L), and 31.6% (St) of the total content. Oxygenated monoterpenes were the main class of volatile compounds detected in the Inf-CCHD-EO. However, oils obtained from the leaves and stems were rich in polyacetylene derivatives. The evaluation of the CCM extract showed a richness in phenolic content (95.59 ± 0.40 mg GAE/g extract), flavonoids contents (467.0 ± 10.5 mg QE/ g extract), moderate DPPH radical scavenging power (IC₅₀ of 23.75 ± 0.86 µg/mL) and low iron chelating activity (IC₅₀ = 5396.07 ± 15.05 µg/mL). The LC-MS/MS profiling of the CCM extract allowed for the detection of twenty-five phenolic compounds and flavonoids. Results revealed that the CCM extract contained high concentration levels of rosmarinic acid (1441.1 mg/kg plant), in addition to caffeic acid phenethyl ester (231.8 mg/kg plant). An antimicrobial activity assessment of the CCM extract against a set of Gram-positive and Gram-negative bacteria, in addition to two other fungal species including Candida and Cryptococcus, showed significant antibacterial activity of the extract against S. aureus with MIC value of 3.125 µg/mL. The current study is the first phytochemical screening for the essential oil and methanolic extract composition of C. canadensis growing in Jordan, its antioxidant and antimicrobial activity. Full article

Electricity load forecasting is a crucial undertaking within all the deregulated markets globally. Among the research challenges on a global scale, the investigation of deep transfer learning (DTL) in the field of electricity load forecasting represents a fundamental effort that can inform artificial intelligence applications in general. In this paper, a comprehensive study is reported regarding day-ahead electricity load forecasting. For this purpose, three sequence-to-sequence (Seq2seq) deep learning (DL) models are used, namely the multilayer perceptron (MLP), the convolutional neural network (CNN) and the ensemble learning model (ELM), which consists of the weighted combination of the outputs of MLP and CNN models. Also, the study focuses on the development of different forecasting strategies based on DTL, emphasizing the way the datasets are trained and fine-tuned for higher forecasting accuracy. In order to implement the forecasting strategies using deep learning models, load datasets from three Greek islands, Rhodes, Lesvos, and Chios, are used. The main purpose is to apply DTL for day-ahead predictions (1–24 h) for each month of the year for the Chios dataset after training and fine-tuning the models using the datasets of the three islands in various combinations. Four DTL strategies are illustrated. In the first strategy (DTL Case 1), each of the three DL models is trained using only the Lesvos dataset, while fine-tuning is performed on the dataset of Chios island, in order to create day-ahead predictions for the Chios load. In the second strategy (DTL Case 2), data from both Lesvos and Rhodes concurrently are used for the DL model training period, and fine-tuning is performed on the data from Chios. The third DTL strategy (DTL Case 3) involves the training of the DL models using the Lesvos dataset, and the testing period is performed directly on the Chios dataset without fine-tuning. The fourth strategy is a multi-task deep learning (MTDL) approach, which has been extensively studied in recent years. In MTDL, the three DL models are trained simultaneously on all three datasets and the final predictions are made on the unknown part of the dataset of Chios. The results obtained demonstrate that DTL can be applied with high efficiency for day-ahead load forecasting. Specifically, DTL Case 1 and 2 outperformed MTDL in terms of load prediction accuracy. Regarding the DL models, all three exhibit very high prediction accuracy, especially in the two cases with fine-tuning. The ELM excels compared to the single models. More specifically, for conducting day-ahead predictions, it is concluded that the MLP model presents the best monthly forecasts with MAPE values of 6.24% and 6.01% for the first two cases, the CNN model presents the best monthly forecasts with MAPE values of 5.57% and 5.60%, respectively, and the ELM model achieves the best monthly forecasts with MAPE values of 5.29% and 5.31%, respectively, indicating the very high accuracy it can achieve. Full article

Insectivores (Eulypotiphla) are a substantial component of Russian forest ecosystems. The parasites of these animals also form an important part of natural biocenoses and act as one of the factors in the formation of biodiversity. The Mordovia Nature Reserve and National Park “Smolny” are large, forested areas located in the center of European Russia. We studied the helminth fauna of insectivores in these protected areas in 2018–2022. In total, using the method of complete helminthological necropsy, we examined 478 individuals of shrews, moles, and hedgehogs and recorded 34 species of parasitic worms, i.e., 8 trematode, 7 cestode, 1 acanthocephalan, and 18 nematode species. The most diverse helminth fauna was found in Sorex araneus (22 species). The composition of helminths in Sorex isidon (12), Neomys fodiens (9), Sorex minutus, and Erinaceus roumanicus (8 species each) turned out to be less diverse. The lowest species diversity of helminths was observed in Neomys milleri (3) and Talpae europaea (2 species). Taking into account the newly obtained data, we conducted a review of the helminth diversity in shrews, hedgehogs, and moles in the Middle Volga region. According to our literature data, the helminth fauna of insectivores in this region consists of 52 species, including 14 cestodes, 13 trematodes, 22 nematodes, and 3 acanthocephalans. Most of them belong to the Palearctic faunal complex (36 species). The helminth fauna of insectivores in the studied protected areas was compared with the helminth fauna of micromammals in other areas of the Middle Volga region. Our comparative analysis showed a high and average degree of similarity in the helminth fauna within individual species and genera of Eulipotyphla. Full article

A high-sensitivity silicon microring (Si MRR) optical biosensor for detecting the nucleocapsid protein of SARS-CoV-2 is proposed and demonstrated. In the proposed biosensor, the surface of a Si MRR waveguide is modified with antibodies, and the target protein is detected by measuring a resonant wavelength shift of the MRR caused by the selective adsorption of the protein to the surface of the waveguide. A Si MRR is fabricated on a silicon-on-insulator substrate using a CMOS-compatible fabrication process. The quality factor of the MRR is approximately 20,000. The resonant wavelength shift of the MRR and the detection limit for the environmental refractive index change are evaluated to be 89 nm/refractive index unit (RIU) and 10⁻⁴ RIU, respectively. The sensing characteristics are examined using a polydimethylsiloxane flow channel after the surface of the Si MRR waveguide is modified with the IgG antibodies through the Si-tagged protein. First, the selective detection of the protein by the MRR sensor is experimentally demonstrated by the detection of bovine serum albumin and human serum albumin. Next, various concentrations of nucleocapsid protein solutions are measured by the MRR, in which the waveguide surface is modified with the IgG antibodies through the Si-tagged protein. Although the experimental results are very preliminary, they show that the proposed sensor has a potential nucleocapsid sensitivity in the order of 10 pg/mL, which is comparable to the sensitivity of current antigen tests. The detection time is less than 10 min, which is much shorter than those of other antigen tests. Full article

Predators significantly influence amphibian larval development. Predator-induced plasticity is often studied independently from conspecific density effects, but these environmental factors may interact. We conducted two-factor factorial experimental design to manipulate conspecific density and predator cues, aiming to investigate the independently or interactive impacts of these two factors on the development of Chinhai spiny newt larvae (Echinotriton chinhaiensis). Our findings reveal that both high and low conspecific densities constrain spiny newt larval growth and predators also limit growth. Interestingly, high conspecific density restricts predator-induced growth plasticity without interacting effects. Only lower density groups exhibit slower growth responses to predators. Our study investigates how density mediates predator-induced plasticity in the endangered Chinhai spiny newt larvae, providing insights into their intricate life history. These results contribute to the understanding of predator-induced plasticity in amphibians and provide insights into the adaptive strategies of endangered species like Chinhai spiny newt. Such knowledge informs the development of effective conservation strategies for their protection. Full article

The development of materials with self-healing capabilities has garnered considerable attention due to their potential to enhance the durability and longevity of various engineering and structural applications. In this review, we provide an overview of recent advances in materials with self-healing properties, encompassing polymers, ceramics, metals, and composites. We outline future research directions and potential applications of self-healing materials (SHMs) in diverse fields. This review aims to provide insights into the current state-of-the-art in SHM research and guide future efforts towards the development of innovative and sustainable materials with enhanced self-repair capabilities. Each material type showcases unique self-repair mechanisms tailored to address specific challenges. Furthermore, this review investigates crack healing processes, shedding light on the latest developments in this critical aspect of self-healing materials. Through an extensive exploration of these topics, this review aims to provide a comprehensive understanding of the current landscape and future directions in self-healing materials research. Full article

Background: Coronavirus 19 (COVID-19) has created complex pressures and challenges for healthcare systems worldwide; however, little is known about the impacts COVID-19 has had on regional/rural healthcare workers. The Loddon Mallee Healthcare Worker COVID-19 Study (LMHCWCS) cohort was established to explore and describe the immediate and long-term impacts of the COVID-19 pandemic on regional and rural healthcare workers. Methods: Eligible healthcare workers employed within 23 different healthcare organisations located in the Loddon Mallee region of Victoria, Australia, were included. In this cohort study, a total of 1313 participants were recruited from November 2020–May 2021. Symptoms of depression, anxiety, post-traumatic stress, and burnout were measured using the Patient Health Questionnaire-9 (PHQ-9), Generalised Anxiety Disorder-7 (GAD-7), Impact of Events Scale-6 (IES-6), and Copenhagen Burnout Inventory (CBI), respectively. Resilience and optimism were measured using the Brief Resilience Scale and Life Orientation Test—Revised (LOT-R), respectively. Subjective fear of COVID-19 was measured using the Fear of COVID-19 Scale. Results: These cross-sectional baseline findings demonstrate that regional/rural healthcare workers were experiencing moderate/severe depressive symptoms (n = 211, 16.1%), moderate to severe anxiety symptoms (n = 193, 14.7%), and high personal or patient/client burnout with median total scores of 46.4 (IQR = 28.6) and 25.0 (IQR = 29.2), respectively. There was a moderate degree of COVID-19-related fear. However, most participants demonstrated a normal/high degree of resilience (n = 854, 65.0%). Based on self-reporting, 15.4% had a BMI from 18.5 to 24.9 kgm² and 37.0% have a BMI of 25 kgm² or over. Overall, 7.3% of participants reported they were current smokers and 20.6% reported alcohol consumption that is considered moderate/high-risk drinking. Only 21.2% of the sample reported consuming four or more serves of vegetables daily and 37.8% reported consuming two or more serves of fruit daily. There were 48.0% the sample who reported having poor sleep quality measured using the Pittsburgh Sleep Quality Index (PSQI). Conclusion: Regional/rural healthcare workers in Victoria, Australia, were experiencing a moderate to high degree of psychological distress during the early stages of the pandemic. However, most participants demonstrated a normal/high degree of resilience. Findings will be used to inform policy options to support healthcare workers in responding to future pandemics. Full article

The development height of the water-conducting fracture zone (WCFZ) is crucial for the safe production of coal mines. The back-propagation neural network (BP-NN) can be utilized to forecast the WCFZ height, aiding coal mines in water hazard prevention and control efforts. However, the stochastic generation of initial weights and thresholds in BP-NN usually leads to local optima, which might reduce the prediction accuracy. This study thus invokes the excellent global optimization capability of the Improved Radial Movement Optimization (IRMO) algorithm to optimize BP-NN. The influences of mining thickness, coal seam depth, working width, and hard rock lithology proportion coefficient on the height of WCFZ are investigated through 75 groups of in situ data of WCFZ heights measured in the Jurassic coalfield of the Ordos Basin. Consequently, an IRMO-BP-NN model for predicting WCFZ height in the Jurassic coalfield of the Ordos Basin was constructed. The proposed IRMO-BP-NN model was validated through monitoring data from the 4⁻²216 working faces of Jianbei Coal Mine, followed by a comparative analysis with empirical formulas and conventional BP-NN models. The relative error of the IRMO-BP-NN prediction model is 4.93%, outperforming both the BP-NN prediction model, the SVR prediction model, and empirical formulas. The results demonstrate that the IRMO-BP-NN model enhances the accuracy of predicting WCFZ height, providing an application foundation for predicting such heights in the Jurassic coalfield of the Ordos Basin and protecting the ecological environment of Ordos Basin mining areas. Full article

Entomopathogenic fungi, often acknowledged primarily for their insecticidal properties, fulfill diverse roles within ecosystems. These roles encompass endophytism, antagonism against plant diseases, promotion of the growth of plants, and inhabitation of the rhizosphere, occurring both naturally and upon artificial inoculation, as substantiated by a growing body of contemporary research. Numerous studies have highlighted the beneficial aspects of endophytic colonization. This review aims to systematically organize information concerning the direct (nutrient acquisition and production of phytohormones) and indirect (resistance induction, antibiotic and secondary metabolite production, siderophore production, and mitigation of abiotic and biotic stresses) implications of endophytic colonization. Furthermore, a thorough discussion of these mechanisms is provided. Several challenges, including isolation complexities, classification of novel strains, and the impact of terrestrial location, vegetation type, and anthropogenic reluctance to use fungal entomopathogens, have been recognized as hurdles. However, recent advancements in biotechnology within microbial research hold promising solutions to many of these challenges. Ultimately, the current constraints delineate potential future avenues for leveraging endophytic fungal entomopathogens as dual microbial control agents. Full article

3D point cloud registration is a crucial technology for 3D scene reconstruction and has been successfully applied in various domains, such as smart healthcare and intelligent transportation. With theoretical analysis, we find that geometric structural relationships are essential for 3D point cloud registration. The 3D point cloud registration method achieves excellent performance only when fusing local and global features with geometric structure information. Based on these discoveries, we propose a 3D point cloud registration method based on geometric st ructure embedding into the attention mechanism (GraM), which can extract the local features of the non-critical point and global features of the corresponding point containing geometric structure information. According to the local and global features, the simple regression operation can obtain the transformation matrix of point cloud pairs, thereby eliminating the semantics that ignores the geometric structure relationship. GraM surpasses the state-of-the-art results by 0.548° and 0.915° regarding the relative rotation error on ModelNet40 and LowModelNet40, respectively. Full article

Over the past two decades, environmental sustainability has become a key corporate and organisational issue. Today, firms are increasingly turning to existing and emerging digital technologies to help ensure that they meet the medium and long-term needs and expectations of customers and other stakeholders with respect to sustainability performance. This raises the important question of which digitisation factors most significantly impact environmental sustainability performance, as well as the mediating factor of sustainability innovation balance (the ability of a firm to balance the exploration of new innovations with the exploitation of existing innovations). A comprehensive survey instrument was developed and refined through expert feedback and a pilot study, leading to data collection from 374 professionals in the Freight and Logistics industry in Saudi Arabia, all of whom held senior positions in areas such as business development, IT, and Environmental, Social, and Governance (ESG) departments. This data was then analysed using structural equation modelling (SEM). The results of this analysis showed that the key factors impacting sustainability performance were digital competence, strategy alignment, digital adaptability, innovation exploitation and innovation exploration. These findings contribute to the current literature by expanding our understanding of the real-world drivers of sustainability performance. In practical terms, the study will help managers improve sustainability performance by enhancing resource efficiency, streamlining, and supply chain management, as well as improving employee engagement and training, fostering a culture of sustainability within the organisation. Full article

Background: People with Parkinson’s disease (pwPD) present alterations of spatiotemporal gait parameters that impact walking ability. While preliminary studies suggested that dual-task gait training improves spatiotemporal gait parameters, it remains unclear whether dual-task gait training specifically improves dual-task gait performance compared to single-task gait training. The aim of this review is to assess the effect of dual-task training relative to single-task gait training on specific gait parameters during dual-task tests in pwPD. Methods: We conducted a systematic review and meta-analysis of randomized controlled trials (RCTs), searching three electronic databases. Two reviewers independently selected RCTs, extracted data, and applied the Cochrane risk-of-bias tool for randomized trials (Version 2) and the GRADE framework for assessing the certainty of evidence. The primary outcomes were dual-task gait speed, stride length, and cadence. Secondary outcomes included dual-task costs on gait speed, balance confidence, and quality of life. Results: We included 14 RCTs (548 patients). Meta-analyses showed effects favoring dual-task training over single-task training in improving dual-task gait speed (standardized mean difference [SMD] = 0.48, 95% confidence interval [CI] = 0.20–0.77; 11 studies; low certainty evidence), stride length (mean difference [MD] = 0.09 m, 95% CI = 0.04–0.14; 4 studies; very low certainty evidence), and cadence (MD = 5.45 steps/min, 95% CI = 3.59–7.31; 5 studies; very low certainty evidence). We also found a significant effect of dual-task training over single-task training on dual-task cost and quality of life, but not on balance confidence. Conclusions: Our findings support the use of dual-task training relative to single-task training to improve dual-task spatiotemporal gait parameters in pwPD. Further studies are encouraged to better define the features of dual-task training and the clinical characteristics of pwPD to identify better responders. Full article

One of the priorities in family and community care is the epidemiological surveillance of the care needs and dysfunctionality present in populations of highly complex chronic patients (HCCPs) using standardised nursing languages. The aim of this study is to establish the prevalence of care needs and dysfunctionality among HCCPs in a specific health area by municipalities and geographical areas (metropolitan, north, and south) while verifying correlations with sociodemographic, financial, and health characteristics. This is an epidemiological, observational, descriptive, cross-sectional study carried out with a sample of 51,374 HCCPs, whose data were grouped into 31 municipalities. Data were collected on the following variables: sociodemographic, financial, health, functional status (health patterns), and care needs (nursing diagnoses). The mean age of the HCCPs was 73.41 (1.45) years, of which 56.18 (2.86)% were women. The municipalities in the northern area have a significantly higher proportion of older patients, HCCPs, lower incomes, and higher unemployment rates. The southern area had higher proportions of non-Spanish nationals and professionals in the hotel and catering industry, and the metropolitan area had a higher proportion of employed individuals and higher levels of education. Northern municipalities had a higher prevalence of illnesses and anxiolytic and anti-psychotic treatments. Dysfunctionality frequencies did not differ significantly by area. However, a higher prevalence of 13 nursing diagnoses was observed in the north. A high number of correlations were observed between population characteristics, dysfunctionality, and prevalent diagnoses. Finally, the frequencies of dysfunctionality in the population and the most common care needs were mapped by municipality. This research sought to ascertain whether there was an unequal distribution of these two aspects among HCCPs in order to gain a deeper epidemiological understanding of them from a family and community perspective using standardised nursing languages. This study was not registered. Full article

This study investigates the structural and adsorption characteristics of channel- and cage-type zeolites obtained through lithium extraction. Through XRD, FT-IR spectroscopy, and adsorption isotherm analyses, distinct adsorption behaviours of CH₄ and CO₂ were observed in both zeolite types. Cage-type zeolites exhibited higher adsorption capacities attributed to their structural advantages, highlighting the importance of structural framework selection in determining adsorbent efficacy. The presence of structural defects and an amorphous phase influenced adsorption behaviours, while thermodynamic data underscored the role of adsorbate properties. Kinetics studies revealed the influence of the structural framework on CH₄ adsorption and CO₂ adsorption kinetics. Analysis of adsorbate–adsorbent interactions demonstrated robust interactions, particularly with LPM16-Y. These findings offer insights into the potential applications of zeolites in gas adsorption processes, emphasising the importance of structural properties and adsorbate characteristics in determining adsorption performance. Full article

MicroRNAs (miRNAs) are small non-coding RNA molecules that regulate gene expression by binding to target messenger RNAs (mRNAs). miRNAs have been implicated in a variety of cardiovascular and neurological diseases, such as myocardial infarction, cardiomyopathies of various geneses, rhythmological diseases, neurodegenerative illnesses and strokes. Numerous studies have focused on the expression of miRNA patterns with respect to atrial fibrillation (AF) or acute ischemic stroke (AIS) However, only a few studies have addressed the expression pattern of miRNAs in patients with AF and AIS in order to provide not only preventive information but also to identify therapeutic potentials. Therefore, the aim of this review is to summarize 18 existing manuscripts that have dealt with this combined topic of AF and associated AIS in detail and to shed light on the most frequently mentioned miRNAs-1, -19, -21, -145 and -146 with regard to their molecular mechanisms and targets on both the heart and the brain. From this, possible diagnostic and therapeutic consequences for the future could be derived. Full article

Cigarette smoke is one of the main factors in Chronic Obstructive Pulmonary Disease (COPD), a respiratory syndrome marked by persistent respiratory symptoms and increasing airway obstruction. Perturbed NAD+/NADH levels may play a role in various diseases, including lung disorders like COPD. In our study, we investigated the preventive effect of NADH supplementation in an experimental model of COPD induced by cigarette smoke extract (CSE). N = 64 mice randomly distributed in eight groups were injected with NADH (two doses of 100 mg/kg or 200 mg/kg) or dexamethasone (2 mg/kg) before being exposed to CSE for up to 9 weeks. Additionally, NADH supplementation preserved lung antioxidant defenses by preventing the functional loss of key enzymes such as superoxide dismutase (SOD), glutathione peroxidase (GPX), catalase, and the expression levels of glutathione (GSH) (n = 4, p < 0.001). It also reduced oxidative damage markers, such as malondialdehyde (MDA) and nitrites (n = 4, p < 0.001). A marked increase in tissue myeloperoxidase activity was assessed (MPO), confirming neutrophils implication in the inflammatory process. The latter was significantly ameliorated in the NADH-treated groups (p < 0.001). Finally, NADH prevented the CSE-induced secretion of cytokines such as Tumor Necrosis Factor alpha (TNF-α), IL-17, and IFN-y (n = 4, p < 0.001). Our study shows, for the first time, the clinical potential of NADH supplementation in preventing key features of COPD via its unique anti-inflammatory and antioxidant properties. Full article

Inspections of concrete bridges across the United States represent a significant commitment of resources, given their biannual mandate for many structures. With a notable number of aging bridges, there is an imperative need to enhance the efficiency of these inspections. This study harnessed the power of computer vision to streamline the inspection process. Our experiment examined the efficacy of a state-of-the-art Visual Transformer (ViT) model combined with distinct image enhancement detector algorithms. We benchmarked against a deep learning Convolutional Neural Network (CNN) model. These models were applied to over 20,000 high-quality images from the Concrete Images for Classification dataset. Traditional crack detection methods often fall short due to their heavy reliance on time and resources. This research pioneers bridge inspection by integrating ViT with diverse image enhancement detectors, significantly improving concrete crack detection accuracy. Notably, a custom-built CNN achieves over 99% accuracy with substantially lower training time than ViT, making it an efficient solution for enhancing safety and resource conservation in infrastructure management. These advancements enhance safety by enabling reliable detection and timely maintenance, but they also align with Industry 4.0 objectives, automating manual inspections, reducing costs, and advancing technological integration in public infrastructure management. Full article

In the domain of the Internet of Things (IoT), Optical Camera Communication (OCC) has garnered significant attention. This wireless technology employs solid-state lamps as transmitters and image sensors as receivers, offering a promising avenue for reducing energy costs and simplifying electronics. Moreover, image sensors are prevalent in various applications today, enabling dual functionality: recording and communication. However, a challenge arises when optical transmitters are not in close proximity to the camera, leading to sub-pixel projections on the image sensor and introducing strong channel dependence. Previous approaches, such as modifying camera optics or adjusting image sensor parameters, not only limited the camera’s utility for purposes beyond communication but also made it challenging to accommodate multiple transmitters. In this paper, a novel sub-pixel optical transmitter discovery algorithm that overcomes these limitations is presented. This algorithm enables the use of OCC in scenarios with static transmitters and receivers without the need for camera modifications. This allows increasing the number of transmitters in a given scenario and alleviates the proximity and size limitations of the transmitters. Implemented in Python with multiprocessing programming schemes for efficiency, the algorithm achieved a 100% detection rate in nighttime scenarios, while there was a 89% detection rate indoors and a 72% rate outdoors during daylight. Detection rates were strongly influenced by varying transmitter types and lighting conditions. False positives remained minimal, and processing times were consistently under 1 s. With these results, the algorithm is considered suitable for export as a web service or as an intermediary component for data conversion into other network technologies. Full article

Osteosarcoma (OSA) is the most common type of primary bone malignancy in people and dogs. Our previous molecular comparisons of canine OSA against healthy bone resulted in the identification of differentially expressed protein-expressing genes (forkhead box protein O4 (FOXO4), interferon regulatory factor 8 (IRF8), and lymphoid enhancer binding factor 1 (LEF1)). Immunohistochemistry (IHC) and H-scoring provided semi-quantitative assessment of nuclear and cytoplasmic staining alongside qualitative data to contextualise staining (n = 26 patients). FOXO4 was expressed predominantly in the cytoplasm with significantly lower nuclear H-scores. IRF8 H-scores ranged from 0 to 3 throughout the cohort in the nucleus and cytoplasm. LEF1 was expressed in all patients with significantly lower cytoplasmic staining compared to nuclear. No sex or anatomical location differences were observed. While reduced levels of FOXO4 might indicate malignancy, the weak or absent protein expression limits its primary use as diagnostic tumour marker. IRF8 and LEF1 have more potential for prognostic and diagnostic uses and facilitate further understanding of their roles within their respective molecular pathways, including Wnt/beta-catenin/LEF1 signalling and differential regulation of tumour suppressor genes. Deeper understanding of the mechanisms involved in OSA are essential contributions towards the development of novel diagnostic, prognostic, and treatment options in human and veterinary medicine contexts. Full article

With recent advances in vehicle technologies, in-vehicle networks (IVNs) and wiring harnesses are becoming increasingly complex. To solve these challenges, the automotive industry has adopted a new zonal-based IVN architecture (ZIA) that connects electronic control units (ECUs) according to their physical locations. In this paper, we evaluate how the number of zones in the ZIA affects the end-to-end (E2E) delay and the characteristics of the wiring harnesses. We evaluate the impact of the number of zones on E2E delay through the OMNeT++ network simulator. In addition, we theoretically predict and analyze the impact of the number of zones on the wiring harnesses. Specifically, we use an asymptotic approach to analyze the total length and weight evolution of the wiring harnesses in ZIAs with 2, 4, 6, 8, and 10 zones by incrementally increasing the number of ECUs. We find that as the number of zones increases, the E2E delay increases, but the total length and weight of the wiring harnesses decreases. These results confirm that the ZIA effectively uses the wiring harnesses and mitigates network complexity within the vehicle. Full article

In this research, we consider cyber risk in insurance using a quantum approach, with a focus on the differences between reported cyber claims and the number of cyber attacks that caused them. Unlike the traditional probabilistic approach, quantum modeling makes it possible to deal with non-commutative event paths. We investigate the classification of cyber claims according to different cyber risk behaviors to enable more precise analysis and management of cyber risks. Additionally, we examine how historical cyber claims can be utilized through the application of copula functions for dependent insurance claims. We also discuss classification, likelihood estimation, and risk-loss calculation within the context of dependent insurance claim data. Full article