Search Results (8,190)

Background/Objectives: Developments in eye-tracking technology are opening up new possibilities for diagnosing patients in a state of minimal consciousness because they can provide information on visual behavior, and the movements of the eyeballs are correlated with the patients’ level of consciousness. The purpose of this study was to provide validation of a tool, based on eye tracking by comparing the results obtained with the assessment obtained using the Coma Recovery Scale-Revised (CRS-R). Methods: The mul-ti-center clinical trial was conducted in Poland in 2022–2023. The results of 46 patients who were not able to communicate verbally due to severe brain injury were analyzed in this study. The state of consciousness of patients was assessed using the Minimally Conscious State Detection test (MCSD), installed on an eye tracker and compared to CRS-R. The examinations consisted of performing the MCSD test on patients five times (T1–T5) within 14 days. Collected data were processed based on the FDA and GCP’s regulatory requirements. Depending on the nature of the data, the mean and standard deviation, median and lower and upper quartiles, and maximum and minimum values were calculated. Passing–Bablok regression analysis was used to assess the measurement equiva-lence of the methods used. Results: There was no difference between the MCSD and CRS-R in the raw change between T5 and T1 time points, as well as in the total % of points from all time points. The MCSD results from each time point show that at least the first two measurements serve to famil-iarize and adapt the patient to the measurement process, and the third and next measurement should be considered reliable. Conclusions: The results indicated a significant relationship be-tween the scores obtained with MCSD and CRS-R. The results suggest that it seems reasonable to introduce an assessment of the patient’s state of consciousness based on eye-tracking technology. The use of modern technology to assess a patient’s state of consciousness opens up the opportunity for greater objectivity, as well as a reduction in the workload of qualified personnel. Full article

Imaging of microscopic objects is of fundamental importance, especially in life sciences. Recent fast progress in electronic detection and control, numerical computation, and digital image processing, has been crucial in advancing modern microscopy. Digital holography is a new field in three-dimensional imaging. Digital reconstruction of a hologram offers the remarkable capability to refocus at different depths inside a transparent or semi-transparent object. Thus, this technique is very suitable for biological cell studies in vivo and could have many biomedical and biological applications. A comprehensive review of the research carried out in the area of digital holographic microscopy (DHM) for live-cell imaging is presented. The novel microscopic technique is non-destructive and label-free and offers unmatched imaging capabilities for biological and bio-medical applications. It is also suitable for imaging and modelling of key metabolic processes in living cells, microbial communities or multicellular plant tissues. Live-cell imaging by DHM allows investigation of the dynamic processes underlying the function and morphology of cells. Future applications of DHM can include real-time cell monitoring in response to clinically relevant compounds. The effect of drugs on migration, proliferation, and apoptosis of abnormal cells is an emerging field of this novel microscopic technique. Full article

Background/Objectives: Sport persistence is the embodiment of sports performance and mental toughness. It refers to our attempts concerning the performance plateau, failures, injuries, or even the resolution and processing of stressful situations associated with success and positive events. In our research, we used qualitative methods based on Bronfenbrenner’s socio-ecological model to investigate the factors influencing sport persistence among high school and university athletes. Methods: The research was based on semi-structured interviews with 133 athletes. ATLAS.ti software and the grounded theory methodology were applied for data analysis. Our analysis grouped the responses according to Bronfenbrenner’s categorisation system, highlighting motivational factors at the microsystem level. Our research question was as follows: What kind of factors dominate the development of sport persistence among adolescent (high school) and young adult (university) athletes along Bronfenbrenner’s dimension of the microsystem? Results: Regarding the microsystem, family, peers, and coaches were mentioned as influential factors. Concerning the family, general, person-specific, family value-related, future-oriented, introjected, and disadvantage-compensating motivational components were identified. General, individual, community and relational factors were identified concerning peers. Concerning the coach, general, individual, community, and coach personality-driven motivational segments were detected. Conclusions: By recognising the complex interplay of systemic factors, we can design interventions targeting these factors at various socio-ecological levels, promoting youth sports and increasing physical activity among young people. These findings instil hope and motivation for the future of sports and physical activity. Full article

Massive MIMO technology is recognized as a key enabler for beyond 5G (B5G) and next-generation wireless networks. By utilizing large-scale antenna arrays at the base station (BS), it significantly improves both system capacity and energy efficiency. Despite these advantages, the deployment of a high number of antennas at the BS presents considerable challenges, particularly in the design of signal detectors that can operate with low computational complexity. While the minimum mean square error (MMSE) detector offers optimal performance in these large-scale systems, it suffers from the computational burden that makes its practical implementation challenging. To mitigate this, various iterative methods and their improved versions have been introduced. However, these iterative methods often converge slowly and are less accurate. To address these challenges, this study introduces an improved variant of traditional accelerated over-relaxation (AOR), called optimized AOR (OAOR). AOR is an over-relaxation method, and its performance is highly dependent on its relaxation parameters. To find the optimal parameters, we have developed an innovative approach that integrates a nature-inspired meta-heuristic algorithm known as Particle Swarm Optimization (PSO). Specifically, we introduce a novel variant of PSO that improves upon basic PSO by enhancing the cognitive coefficients to optimize the relaxation parameters for OAOR. These key modifications to the standard PSO improve its ability to explore various solutions efficiently and help to find the optimal parameters more quickly for signal detection. It facilitates the OAOR with faster convergence towards the optimal solution by reducing the error rate, resulting in high detection accuracy and simultaneously decreasing computational complexity from $O\left(K^3\right)$ to $O\left(K^2\right)$ making it suitable for modern wireless communication systems. We conduct extensive simulations across various configurations of massive MIMO systems. The results indicate that our proposed method achieves better performance compared to existing techniques. This improvement is particularly evident in terms of both computational complexity and error rate. Full article

The paper aims to analyze the consumer joint choice behavior on fresh food purchase channels and terminal delivery services during major public health events, with the purpose of revealing the underlying influencing factors and behavioral characteristics. First, based on random utility maximization theory, the cross-nested logit model is formulated, which takes into account the influence of socioeconomic attribute factors, service attribute factors, risk perception attribute factors and trust perception attribute factors. Second, a questionnaire survey is conducted, and the obtained data are used to estimate the model parameters and perform an elasticity analysis of the utility variables. The parameter estimation results demonstrate that in the context of major public health events, consumers consider adjusting their attitudes toward e-commerce platforms first when the utility variables are altered, and fresh food purchase channels are easily replaced for consumers who choose unmanned equipment home delivery. The elasticity analysis results suggest that consumers are more willing to buy fresh food through community group-buying channels, are more sensitive to the convenience of the purchase process and are less concerned with delivery time. Although person-to-person contact increases the risk of infection, consumers still prefer attended terminal delivery services. Furthermore, consumers least agree with the effectiveness of body temperature detection methods in public places but feel that an effective way to increase consumer trust in enterprises is to strengthen personnel protection measures. Full article

Today’s environment demands that cybersecurity be given top priority because of the increase in cyberattacks and the development of quantum computing capabilities. Traditional security measures have relied on cryptographic techniques to safeguard information systems and networks. However, with the adaptation of artificial intelligence (AI), there is an opportunity to enhance cybersecurity through learning-based methods. IoT environments, in particular, work with lightweight systems that cannot handle the large data communications typically required by traditional intrusion detection systems (IDSs) to find anomalous patterns, making it a challenging problem. A deep learning-based framework is proposed in this study with various optimizations for automatically detecting and classifying cyberattacks. These optimizations involve dimensionality reduction, hyperparameter tuning, and feature engineering. Additionally, the framework utilizes an enhanced Convolutional Neural Network (CNN) variant called Intelligent Intrusion Detection Network (IIDNet) to detect and classify attacks efficiently. Layer optimization at the architectural level is used to improve detection performance in IIDNet using a Learning-Based Intelligent Intrusion Detection (LBIID) algorithm. The experimental study conducted in this paper uses a benchmark dataset known as UNSW-NB15 and demonstrated that IIDNet achieves an outstanding accuracy of 95.47% while significantly reducing training time and excellent scalability, outperforming many existing intrusion detection models. Full article

The gut microbiota plays a crucial role in the health and well-being of wildlife. However, its composition and diversity remain unexplored, particularly in threatened species such as the Siberian musk deer (SMD). This study aimed to elucidate the gut microbiota composition within different wild SMD communities for assessing their health status. We conducted the first comprehensive fecal microbiome analysis of wild SMD inhabiting three distinct locations in Gangwon Province, Republic of Korea (Korea). Fecal samples were collected non-invasively and 16S rRNA gene sequencing was performed for gut microbiota characterization. Consistent with previous research, Firmicutes and Bacteroidetes were the dominant phyla in the gut microbiota of wild SMD. Planctomycetota was a prevalent phylum in wild SMD gut microbiota, warranting further investigation of its ecological significance. While significant differences were observed in the gut microbiota richness among the three groups, no significant disparities were detected in the beta diversity. Additionally, certain genera exhibited distinct relative abundances among the groups, suggesting potential associations with geographic factors, gut disorders, and dietary habits. Our findings provide valuable insights into the gut microbiome of wild SMD and offer a foundation for future microbiome-based conservation efforts for this vulnerable species. Full article

The objective of the EU project Resilience for Dairy (R4D) is to develop and strengthen a self-sustainable Thematic Network on resilient dairy farms in 15 European countries. This article focusses on those solutions (practices and techniques) that are assessed contributing to a resilient dairy farming sector. The opinions of experts, farmers, and stakeholders were collected and scored through surveys and in a series of local workshops. Six key contributing knowledge fields are included: economic and social resilience, technical efficiency, environment, animal welfare and health, and societal perception. Assessing these knowledge fields proved to be a good predictor for measuring resilience. Only the impact fields of animal welfare and health and societal perception overlapped each other in response. This study shows differences in the choice of solutions across Europe. Experts from South and East Europe are more positive about the contribution of solutions to resilience than their colleagues from North and West Europe, except for social life items. Expert and farmer/stakeholder opinions differ regarding several of the solutions. Technical efficiency is a leading strategy. Priority topics of interest are communication with society, renewable energy production, strategic hoof trimming, early detection of diseases, monitoring fertility and health, and calf rearing. Besides resilience, attractiveness and readiness of the solutions were also assessed. Full article

Background/Objectives: MRI is the gold standard for detecting meniscal tears; however, ultrasound may readily detect meniscal changes, obviating the need for MRI. We aim to (1) determine ultrasound sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy in detecting meniscal changes, and (2) describe characteristic meniscal changes in US and their prevalence. Methods: A retrospective analysis of knee ultrasound scans for the presence of medial and lateral meniscal tears was conducted. Meniscal changes were characterized into five US appearances (cleft, diminutive, cyst, displaced fragment, and extrusion) by the consensus of two musculoskeletal radiologists. Ultrasound findings were then compared to MRI results. Results: In total, 249 patients were included. Ultrasound sensitivity, specificity, PPV, NPV, and accuracy for medial meniscal tears were 79%, 97.3%, 95.3%, 86.6%, and 90%, respectively, and for lateral meniscal tears the ultrasound sensitivity, specificity, PPV, NPV, and accuracy were 63%, 99.5%, 96%, 93%, and 93.6%, respectively. The false negative and false positive rates for medial meniscal tears were 13.4% and 4.7%, respectively, and for the lateral meniscus, the false negative and false positive rates were 6.7% and 3.8%, respectively. Meniscal clefts were the most prevalent appearance in the medial meniscus followed by extrusions. Meniscal extrusions were the most prevalent appearance in the lateral meniscus followed by clefts. Conclusions: Community-based US is highly accurate in the detection of meniscal tears when compared with MRI, making it a valuable diagnostic imaging tool for detecting meniscal tears in a community setting where accessibility to MRI is limited or if there are MRI contraindications. Full article

Environment plastic accumulation has been attracting the attention of both political and scientific communities, who wish to reduce global pollution. Plastic items have been detected everywhere, from oceans to the air, raising concerns about the fate of plastics within organisms. Leaked plastics are ingested by animals, entering the food chain and eventually reaching humans. Although a lot of studies focused on the evaluation of plastic particles in the environment and living organisms have already been published, the behavior of plastic at the cellular level is still missing. Here, we analyzed the bioaccumulation and extrusion trend of two differently sized plastic particles (1 and 2 µm), testing them on three human epithelial cell lines (liver, lung, and gut) that represent epithelial sites mainly exposed to plastic. A different behavior was detected, and the major plastic uptake was shown by liver cells, where the 1 µm beads accumulated with a dose-dependent profile. Moreover, a 60% reduction in the content of 1 µm particles in cells was evaluated after plastic removal. Finally, the viability and proliferation of the three human cell lines were not significantly affected by both the 1 and 2 µm beads, suggesting that cells might have a defense mechanism against plastic exposure risk. Full article

Background: Over the last five decades, a fair number of echocardiographic studies have evaluated the prevalence of mitral valve prolapse (MVP) in various cohorts of individuals, including heterogeneous study populations. The present systematic review has been primarily designed to summarize the main findings of these studies and to estimate the overall MVP prevalence in the general community. Methods: All echocardiographic studies assessing the MVP prevalence in various cohorts of individuals, selected from PubMed and EMBASE databases, were included. There was no limitation of time period. The risk of bias was assessed by using the National Institutes of Health (NIH) Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. Results: The full texts of 21 studies with 1354 MVP individuals out of 63,723 participants were analyzed. The overall pooled prevalence of MVP was 4.9% (range of 0.6–21%). When dividing the studies in two groups according to the echocardiographic criteria used for MVP diagnosis (less specific old criteria or more specific new criteria, respectively), the estimated pooled prevalence of MVP was 7.8% (range of 2–21%) for the older studies (performed between 1976 and 1998) and 2.2% (range of 0.6–4.2%) for the more recent ones (conducted between 1999 and 2021). Potential selection bias, hospital- or referral-based series, and the use of less specific echocardiographic criteria for MVP diagnosis have been indicated as the main reasons for the higher MVP prevalence detected by the older studies. MVP was commonly associated with a narrow antero-posterior thoracic diameter, isolated ventricular premature beats and nonspecific ST-T-wave abnormalities on a resting electrocardiogram, mild-to-moderate mitral regurgitation (MR), the reduced probability of obstructive coronary artery disease, and a low frequency of serious complications, such as severe MR, infective endocarditis, heart failure, stroke, and atrial fibrillation. Conclusions: MVP has a low prevalence in the general population, regardless of age, gender, and ethnicity, and is associated with a good outcome. Full article

Progressive climate change (CC) forces people—particularly in the Global South—to adapt to its consequences, some of which include droughts, flooding, and new diseases. This study investigates the determinants of behaviors for adapting to these threats in a population from the region of Cusco (Peru). Data were gathered via a cross-sectional interview-based survey in 2016, using random-route sampling. For up to 542 cases, we regressed a scale combining performed behaviors and intentions on psychological constructs, for the entire and sub-samples (n > 179, allowing to detect an R² of 10% with a power of 80% at p = 0.05). Behavior-specific evaluations—particularly perceived feasibility (β = 0.355), descriptive norms (β = 0.267), and cost-benefit evaluations (β = 0.235)—can explain most of the variance (44% with a total R² = 61%). Furthermore, trust in specific sources (β = 0.106), general trust (β = 0.098), and negative attitudes toward nature (β = 0.077) are positively related to adaptation, particularly regarding public behaviors (supporting community projects and policies). However, evaluations directly related to CC, such as risk perception (β = 0.010) or how much a behavior helps prevent damage (adaptation efficacy, β = −0.042)), do not explain adaptation, except for an effect of adaptation efficacy on changing daily behaviors. Experiences with and expectations of CC consequences are mostly unrelated to adaptation. However, worries about such events are correlated with adaptation (r between 0.097 and 0.360). We conclude that, to promote adaptation behaviors in this region, the focus should be on the characteristics of the behavior performance (e.g., its costs or feasibility), not on the expected risks of extreme events because of CC. Full article

Coralligenous bioconstructions are biogenic calcareous formations developing at low irradiance on littoral rocky cliffs or on the deeper sub-horizontal bottom in the Mediterranean Sea. Unusually shallow coralligenous banks on the sandy coast of Sinuessa (Mondragone City, Gulf of Gaeta, SW Italy) were investigated. Their communities and the surrounding biogenic detritus were characterized. Geophysical and acoustic data revealed the presence of coralligenous banks between 7.5 and 15 m depth, showing constant thickness and sub-horizontal geometry, incised by sub-perpendicular channels. Sediment deposits ranging from silty sands to bioclastic gravel occur in the area. The biogenic detritus of the soft bottom sampled around the coralligenous banks is highly heterogeneous. Through the thanatocoenosis analysis of macrozoobenthos, different biocenoses were detected, among which the coralligenous and photophilic habitats are mainly represented, followed by the well-calibrated fine sands and the relit sands. A total of 16 different species and 10 epimegabenthic morphological groups (MGs) were detected on the coralligenous banks, of which 4 are included in European regulation for threatened species. The density of epimegabenthic organisms has an average of 10.34 ± 5.46 individuals or colonies/100 m². Cladocora caespitosa is the dominant species, with a height of 17 ± 5 cm. This and other structuring species (SS) were larger in size in the sampled sites than in the literature data. Overall, coralligenous had a “medium” health status, with 52% of the individuals or colonies in healthy conditions, compared to 47% with epibiosis phenomena and 1% with entanglement. Longlines were the most common anthropogenic litter, with a density of 2/100 m². Ad hoc monitoring programs and conservation measures would be desirable to protect and guarantee the well-being of these sensitive and rare shallow bioconstructions. Full article

The creation and spreading of fake information can be carried out very easily through the internet community. This pervasive escalation of fake news and rumors has an extremely adverse effect on the nation and society. Detecting fake news on the social web is an emerging topic in research today. In this research, the authors review various characteristics of fake news and identify research gaps. In this research, the fake news dataset is modeled and tokenized by applying term frequency and inverse document frequency (TFIDF). Several machine-learning classification approaches are used to compute evaluation metrics. The authors proposed hybridizing SVMs and RF classification algorithms for improved accuracy, precision, recall, and F1-score. The authors also show the comparative analysis of different types of news categories using various machine-learning models and compare the performance of the hybrid RFSVM. Comparative studies of hybrid RFSVM with different algorithms such as Random Forest (RF), naïve Bayes (NB), SVMs, and XGBoost have shown better results of around 8% to 16% in terms of accuracy, precision, recall, and F1-score. Full article

In long-haul WDM (wavelength division multiplexing) optical communication systems utilizing the DP-16QAM modulation scheme, traditional methods for removing chaos have exhibited poor performance, resulting in a high bit error rate of ${10}^{-2}$ between the original signal and the removed chaos signal. To address this issue, we propose DeepChaos+, a machine learning-based approach for chaos removal in WDM transmission systems. Our framework comprises two key points: (1) DeepChaos+ automatically generates a dataset that accurately reflects the features of the original signals in the communication system, which eliminates the need for time-consuming data simulation, streamlining the process significantly; (2) it allows for the training of a lightweight model that provides fast prediction times while maintaining high accuracy. This allows for both efficient and reliable signal reconstruction. Through extensive experiments, we demonstrate that DeepChaos+ achieves accurate reconstruction of the original signal with a significantly reduced bit error rate of approximately ${10}^{-5}$. Additionally, DeepChaos+ exhibits high efficiency in terms of processing time, facilitating fast and reliable signal reconstruction. Our results underscore the effectiveness of DeepChaos+ in removing chaos from WDM transmission systems. By enhancing the reliability and efficiency of chaotic secure channels in optical fiber communication systems, DeepChaos+ holds the potential to improve data transmission in high-speed networks. Full article