Search Results (1,297)

The quality of bicycle path surfaces significantly influences the comfort of cyclists. This study evaluates the effectiveness of smartphone sensor data and smart bicycle lights data in assessing the roughness of bicycle paths. The research was conducted in Hasselt, Belgium, where various bicycle path pavement types, such as asphalt, cobblestone, concrete, and paving tiles, were analyzed across selected streets. A smartphone application (Physics Toolbox Sensor Suite) and SEE.SENSE smart bicycle lights were used to collect GPS and vertical acceleration data on the bicycle paths. The Dynamic Comfort Index (DCI) and Root Mean Square (RMS) values from the data collected through the Physics Toolbox Sensor Suite were calculated to quantify the vibrational comfort experienced by cyclists. In addition, the data collected from the SEE.SENSE smart bicycle light, DCI, and RMS computed results were categorized for a statistical comparison. The findings of the statistical tests revealed no significant difference in the comfort assessment among DCI, RMS, and SEE.SENSE. The study highlights the potential of integrating smartphone sensors and smart bicycle lights for efficient, large-scale assessments of bicycle infrastructure, contributing to more informed urban planning and improved cycling conditions. It also provides a low-cost solution for the city authorities to continuously assess and monitor the quality of their cycling paths. Full article

Background: Neglected tropical diseases (NTDs) are a diverse group of twenty diseases that occur in tropical and subtropical regions that particularly affect vulnerable and often marginalised populations. Five of these are classified as “preventive chemotherapy” (PC) diseases such as trachoma, onchocerciasis, geo-helminthiasis, lymphatic filariasis, and schistosomiasis. This study aimed to describe the knowledge, attitudes, and practices of healthcare providers in the Forecariah health district with respect to PC-NTDs in Guinea in 2022. Methods: A descriptive cross-sectional study was conducted from 7 to 22 November 2022 among healthcare providers in the health district of Forécariah in Guinea. Data on participants’ socio-demographic characteristics and knowledge of and attitudes and practices regarding PC-NTDs were collected using an electronic (KoboToolbox) semi-structured questionnaire and analysed using descriptive statistics. Results: Among the 86 healthcare providers who participated in this study, nurses (44.2%) and young adults aged between 25 and 49 years (81.4%) were mostly represented. The majority of respondents declared having already heard about onchocerciasis (70.7%) and lymphatic filariasis (60.0%) but only the minority declared having already heard about geo-helminthiasis (30.7%), schistosomiasis (21.3%), and trachoma (9.3%). Only a few respondents knew how to prevent PC-NTDs (onchocerciasis 26.7%, lymphatic filariasis 26.7%, geo-helminthiasis 29.3%, and schistosomiasis 17.3%). Many healthcare providers reported they would refer cases of onchocerciasis (50.6%), lymphatic filariasis (58.7%), and schistosomiasis (46.7%) to a management centre. Conclusions: This study highlights the varying levels of knowledge, attitudes, and practices among healthcare providers in dealing with PC-NTDs, suggesting areas for improvement in training and resource allocation. Full article

In this study, we developed a MATLAB 2024a toolbox that performs advanced algebraic calculations in the algebra of elliptic numbers and elliptic quaternions. Additionally, we introduce color image processing methods, such as principal component analysis, image compression, image restoration, and watermarking, based on singular-value decomposition theory for elliptic quaternion matrices; we added these to the newly developed toolbox. The experimental results demonstrate that elliptic quaternionic methods yield better image analysis and processing performance compared to other hypercomplex number-based methods. Full article

Multibeam echosounders provide ideal data for the semi-automated seabed feature extraction and accurate morphometric measurements. In this study, bathymetric and raw backscatter data were initially used to manually delimit the reef morphologies found in an insular semi-enclosed gulf in the northern Aegean Sea (Gera Gulf, Lesvos Island, Greece). The complexity of this environment makes it an ideal area to “crash test” (test to the limit) and compare the results of the delineation methods. A large number of (more than 7000) small but prominent reefs were detected, which made manual mapping extremely time-consuming. Three semi-automated tools were also employed to map the reefs: the Benthic Terrain Modeler (BTM), Confined Morphologies Mapping (CoMMa), and eCognition Multiresolution Segmentation. BTM did not function properly with irregular reef footprints, but by modifying both the bathymetry and slope, the outcome was improved, producing accurate results that appeared to exceed the accuracy of manual mapping. CoMMa, a new GIS morphometric toolbox, was a “one-stop shop” that, besides generating satisfactory reef delineation results (i.e., detecting the same total reef area as the manual method), was also used to extract the morphometric characteristics of the polygons resulting from all the methods. Lastly, the Multiresolution Segmentation also gave satisfactory results with the highest precision. To compare the final maps with the distribution of the reefs, mapcurves were created to estimate the goodness-of-fit (GOF) with the Precision, Recall, and F1 Scores producing values higher than 0.78, suggesting a good detection accuracy for the semi-automated methods. The analysis reveals that the semi-automated methods provided more efficient results in comparison with the time-consuming manual mapping. Overall, for this case study, the modification of the bathymetry and slope enabled the results’ accuracy to be further enhanced. This study asserts that the use of semi-automated mapping is an effective method for delineating the geomorphometry of intricate relief and serves as a powerful tool for habitat mapping and decision-making. Full article

Objective: The development of adjacent segment disease or the progression of spondylosis following the surgical treatment of spinal stenosis and spondylolisthesis is well documented and can lead to subsequent functional decline after a successful index surgery. The early detection of negative inflection points during patients’ functional recovery can improve timely intervention. In this study, we developed machine learning (ML) models to predict the occurrence of post-operative decline in patient mobility. Methods: Patients receiving spine surgery for degenerative spinal stenosis or spondylolisthesis were retroactively consented and enrolled. Activity data (steps-per-day) previously recorded across a 4-year peri-operative were collected alongside relevant clinical and demographic variables. Logistic regression (LR), random forest (RF), and extreme gradient boosting (XGBoost) ML models were constructed and trained on 80% of the dataset and validated using the remaining 20%. The study’s primary endpoint was the models’ ability to predict post-operative decline in patient mobility. Results: A total of 75 patients were included. Following training, RF and XGBoost models achieved accuracy values of 86.7% (sensitivity 80%, specificity 90%) and 80% (sensitivity 60%, specificity 90%), respectively, in predicting post-operative functional decline. The LR model was the least effective with an accuracy of 73.3% (sensitivity 50%, specificity 88.8%). Receiver operating characteristic curves showed an area under the curve of 0.80 for RF, 0.70 for XGBoost, and 0.69 for LR. Conclusions: ML models trained on activity data collected from smartphones successfully forecast functional decline in post-operative activity following spine surgery. These results lay the groundwork for the future integration of ML into the surgeon’s toolbox for prognostication and surgical planning. Full article

Ene-reductases (ERs) are enzymes known for catalyzing the asymmetric hydrogenation of activated alkenes. Among these, old yellow enzyme (OYE) ERs have been the most extensively studied for biocatalytic applications due to their dependence on NADH or NADPH as electron donors. These flavin-containing enzymes are highly enantio- and stereoselective, making them attractive biocatalysts for industrial use. To discover novel thermostable OYE-type ERs, we explored genomes of thermophilic fungi. Five genes encoding ERs were selected and expressed in Escherichia coli, namely AtOYE (from Aspergillus thermomutatus), CtOYE (from Chaetomium thermophilum), LtOYE (from Lachancea thermotolerans), OpOYE (from Ogatae polymorpha), and TtOYE (from Thermothielavioides terrestris). Each enzyme was purified as a soluble FMN-containing protein, allowing detailed characterization. All ERs exhibited a preference for NADPH, with AtOYE showing the broadest substrate range. Moreover, all the enzymes showed activity toward maleimide and p-benzoquinone, with TtOYE presenting the highest catalytic efficiency. The optimal pH for enzyme activity was between 6 and 7 and the enzymes displayed notable solvent tolerance and thermostability, with CtOYE and OpOYE showing the highest stability (T_m > 60 °C). Additionally, all enzymes converted R-carvone into (R,R)-dihydrocarvone. In summary, this study contributes to expanding the toolbox of robust ERs. Full article

Background: Speech production is a possible way to monitor bulbar and respiratory functions in patients with amyotrophic lateral sclerosis (ALS). Moreover, the emergence of smartphone-based data collection offers a promising approach to reduce frequent hospital visits and enhance patient outcomes. Here, we studied the relationship between bulbar and respiratory functions with voice characteristics of ALS patients, alongside a speech therapist’s evaluation, at the convenience of using a simple smartphone. Methods: For voice assessment, we considered a speech therapist’s standardized tool—consensus auditory-perceptual evaluation of voice (CAPE-V); and an acoustic analysis toolbox. The bulbar sub-score of the revised ALS functional rating scale (ALSFRS-R) was used, and pulmonary function measurements included forced vital capacity (FVC%), maximum expiratory pressure (MEP%), and maximum inspiratory pressure (MIP%). Correlation coefficients and both linear and logistic regression models were applied. Results: A total of 27 ALS patients (12 males; 61 years mean age; 28 months median disease duration) were included. Patients with significant bulbar dysfunction revealed greater CAPE-V scores in overall severity, roughness, strain, pitch, and loudness. They also presented slower speaking rates, longer pauses, and higher jitter values in acoustic analysis (all p < 0.05). The CAPE-V’s overall severity and sub-scores for pitch and loudness demonstrated significant correlations with MIP% and MEP% (all p < 0.05). In contrast, acoustic metrics (speaking rate, absolute energy, shimmer, and harmonic-to-noise ratio) significantly correlated with FVC% (all p < 0.05). Conclusions: The results provide supporting evidence for the use of smartphone-based recordings in ALS patients for CAPE-V and acoustic analysis as reliable correlates of bulbar and respiratory function. Full article

The detection and mitigation of stiction are crucial for maintaining control system performance. This paper proposes the comparison of two preprocessing methods for detecting stiction in control valves via pattern recognition via an artificial neural network (ANN). This method utilizes process variables (PVs) and controller outputs (OPs) to accurately identify stiction within control loops. The ANN was comprehensively trained using data from a data-driven model after processing them. Validation and testing were conducted with real industrial data from the International Stiction Database (ISDB), ensuring a practical assessment framework. This study evaluated the impact of two preprocessing methods on fault detection accuracy, namely, the D-value and principal component analysis (PCA) methods, where the D-value method achieved a commendable overall accuracy of 76%, with 86% precision in stiction prediction and a 66% success rate in nonstiction scenarios. This signifies that feature reduction leads to a degraded stiction detection. The data-driven model was implemented in SIMULINK, and the ANN was trained in MATLAB with the Pattern Recognition Toolbox. These promising results highlight the method’s reliability in diagnosing stiction in industrial settings. Integrating this technique into existing control systems is expected to enhance maintenance protocols, reduce operational downtime, and improve efficiency. Future research should aim to expand this method’s applicability to a wider range of control systems and operational conditions, further solidifying its industrial value. Full article

Genome editing (GE) technologies have the potential to completely transform breeding and biotechnology applied to crop species, contributing to the advancement of modern agriculture and influencing the market structure. To date, the GE-toolboxes include several distinct platforms able to induce site-specific and predetermined genomic modifications, introducing changes within the existing genetic blueprint of an organism. For these reasons, the GE-derived approaches are considered like new plant breeding methods, known also as New Breeding Techniques (NBTs). Particularly, the GE-based on CRISPR/Cas technology represents a considerable improvement forward biotech-related techniques, being highly sensitive, precise/accurate, and straightforward for targeted gene editing in a reliable and reproducible way, with numerous applications in food-related plants. Furthermore, numerous examples of CRISPR/Cas system exploitation for non-editing purposes, ranging from cell imaging to gene expression regulation and DNA assembly, are also increasing, together with recent engagements in target and multiple chemical detection. This manuscript aims, after providing a general overview, to focus attention on the main advances of CRISPR/Cas-based systems into new frontiers of non-editing, presenting and discussing the associated implications and their relative impacts on molecular traceability, an aspect closely related to food safety, which increasingly arouses general interest within public opinion and the scientific community. Full article

The chief financial officer (CFO) is a crucial executive position in an organisation, responsible for overseeing the financial operations and strategy of the company. Despite rising interest among academics and practitioners, the literature corpus on CFO research remains largely fragmented, which warrants the unpacking of the underlying intellectual knowledge structure of the domain. In response, this study aims to provide a concise overview of the trends and science relating to CFO research, comprehend potential gaps in the literature, and highlight crucial future research pathways. A quantitative bibliometric overview of 669 research articles from 1982 to 2022 provides a spectrum of intellectual clout that helps decipher performance trends and delineates six significant clusters of knowledge in CFO research. We selectively discuss the empirical findings and theoretical and conceptual advancements within each cluster. This study offers recommendations for future research, emphasising the growing role of CFOs in leadership and addressing the fragmentation in current research. The findings and contributions of this study could further elevate CFOs’ importance in the C-suite. Full article

Background: BCR::ABL1 fusion is mostly derived from a reciprocal translocation t(9;22)(q34.1;q11.2) and is rarely caused by insertion. Various methods have been used for the detection of t(9;22)/BCR::ABL1, such as G-banded chromosomal analysis, fluorescence in situ hybridization (FISH), quantitative real-time reverse transcription-polymerase chain reaction (RT-PCR) and optical genome mapping (OGM). Understanding the strengths and limitations of each method is essential for the selection of appropriate method(s) of disease diagnosis and/or during the follow-up. Methods: We compared the results of OGM, chromosomal analysis, FISH, and/or RT-PCR in 12 cases with BCR::ABL1. Results: BCR:ABL1 was detected by FISH and RT-PCR in all 12 cases. One case with ins(22;9)/BCR::ABL1 was cryptic by chromosomal analysis and nearly missed by OGM. Atypical FISH signal patterns were observed in five cases, suggesting additional chromosomal aberrations involving chromosomes 9 and/or 22. RT-PCR identified the transcript isoforms p210 and p190 in seven and five cases, respectively. Chromosomal analysis revealed additional chromosomal aberrations in seven cases. OGM identified extra cytogenomic abnormalities in 10 cases, including chromoanagenesis and IKZF1 deletion, which were only detected by OGM. Conclusions: FISH offers rapid and definitive detection of BCR::ABL1 fusion, while OGM provides a comprehensive cytogenomic analysis. In scenarios where OGM is feasible, chromosomal analysis and RT-PCR may not offer additional diagnostic value. Full article

The use of artificial intelligence in diverse diagnosis areas has significantly increased in the past few years because of the advantages it represents in clinical routine. Among the diverse diagnostic techniques, the use of ultrasounds is often preferred because of their simplicity, low cost, non-invasiveness, and non-ionizing characteristic. However, obtaining an adequate number of patients and data for training and testing machine learning models is challenging. To overcome this limitation, a novel approach is proposed for simulating data produced by ultrasonic diagnostic devices. The implemented method was based on a clinical prototype for eye cataract diagnosis, although the method can be extended to other applications as well. The proposed model encompasses the electric-to-acoustic signal conversion in the ultrasonic transducer, the wave propagation through the biological medium, and the subsequent acoustic-to-electric signal conversion in the transducer. Electrical modelling of the transducer was performed using a two-port network approach, while the acoustic wave propagation was modelled by using the k-Wave MATLAB toolbox. It was verified that the holistic modelling approach enabled the generation of synthetic data augmentation, presenting high similarity with real data. Full article

This paper presents two intelligent robust control strategies applied to manage the dynamics of a DC-DC bidirectional buck–boost converter, which is used in conjunction with a supercapacitor as an auxiliary energy system (AES) for regenerative braking in electric vehicles. The Neural Inverse Optimal Controller (NIOC) and the Neural Sliding Mode Controller (NSMC) utilize identifiers based on Recurrent High-Order Neural Networks (RHONNs) trained with the Extended Kalman Filter (EKF) to track voltage and current references from the converter circuit. Additionally, a driving cycle test tailored specifically for typical urban driving in electric vehicles (EVs) is implemented to validate the efficacy of the proposed controller and energy improvement strategy. The proposed NSMC and NIOC are compared with a PI controller; furthermore, an induction motor and its corresponding three-phase inverter are incorporated into the EV control scheme which is implemented in Matlab/Simulink using the “Simscape Electrical” toolbox. The Mean Squared Error (MSE) is computed to validate the performance of the neural controllers. Additionally, the improvement in the State of Charge (SOC) for an electric vehicle battery through the control of buck–boost converter dynamics is addressed. Finally, several robustness tests against parameter changes in the converter are conducted, along with their corresponding performance indices. Full article

As the global energy structure undergoes transformation and the low-carbon development process continues to advance, integrated energy systems have progressively emerged as crucial technical support for achieving sustainable development. In this paper, a joint-day optimal scheduling model is put forward considering the existence of dispatchable resources in community integrated energy systems (CIES). The aim is to cut down the system operation cost and enhance energy utilization efficiency. This model is founded on the concept of energy hubs and combines the shiftable, transferable, and reducible characteristics of demand-side flexible loads. It includes gas turbine power generation systems, energy storage, as well as wind and solar renewable resources. System operation cost and carbon trading cost are comprehensively taken into account, and ultimately, the CIES low-carbon economic dispatch model with the lowest total cost as the optimization objective is established. The Yalmip toolbox and Cplex solver are employed to solve the model. The optimization results of flexible electric and thermal loads participating in dispatching under different scenarios are analyzed through simulation. The economic benefits of electric and thermal independent dispatching are compared and analyzed, and the economic benefits of electric and thermal coupled dispatching are verified. The study reveals that the rational scheduling of user-side flexible loads can notably reduce operating costs, lower the load peak-to-valley difference and carbon emissions, and boost the comprehensive economic and environmental benefits of the system. Full article

Gastric cancer (GC) is an asymptomatic malignancy in early stages, with an invasive and cost-ineffective diagnostic toolbox that contributes to severe global mortality rates on an annual basis. Ectopic expression of the lineage survival transcription factors (LS-TFs) GATA4 and 6 promotes stomach oncogenesis. However, LS-TFs also govern important physiological roles, hindering their direct therapeutic targeting. Therefore, their downstream target genes are particularly interesting for developing cancer-specific molecular biomarkers or therapeutic agents. In this work, we couple inducible knockdown systems with chromatin immunoprecipitation and RNA-seq to thoroughly detect and characterize direct targets of GATA-mediated transcriptional regulation in gastric cancer cells. Our experimental and computational strategy provides evidence that both factors regulate the expression of several coding and non-coding RNAs that in turn mediate for their cancer-promoting phenotypes, including but not limited to cell cycle, apoptosis, ferroptosis, and oxidative stress response. Finally, the diagnostic and prognostic potential of four metagene signatures consisting of selected GATA4/6 target transcripts is evaluated in a multi-cancer panel of ~7000 biopsies from nineteen tumor types, revealing elevated specificity for gastrointestinal tumors. In conclusion, our integrated strategy uncovers the landscape of GATA-mediated coding and non-coding transcriptional regulation, providing insights regarding their molecular and clinical function in gastric cancer. Full article