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Physiological and psychological responses to five-day fasting
The objective of this study was to examine the variations in adipokines, myokines, inflammation indicators, glucose, insulin, and ketones in the body over a 5-day fasting period. Additionally, the study aimed to investigate the underlying factors contributing to changes in body mass index (BMI) and fat mass. These factors included blood markers, participants’ healthy lifestyle habits, emotional intelligence, personality traits, impulsivity, overall well-being, and subjective happiness. The study involved 42 women with an average age of 49.8 years (± 9.3 years). The following indicators were measured: leptin, adiponectin, TNF-alpha, BDNF, irisin, IL-6, insulin, glucose levels, and ketone bodies. Various assessments were utilized, including the Physical Activity Questionnaire, the Brunel Mood Scale (BRUMS-LTU), the Schutte Self-Report Emotional Intelligence Test, the 10-item Perceived Stress Scale, and the Big Five personality traits. The results showed that fasting led to substantial reductions in body mass, waist circumference, leptin levels, glucose, and insulin levels, while simultaneously increasing ketone bodies. Basal energy expenditure decreased, but participants experienced improvements in mood, with increased vigor and reduced tension. Although markers of inflammation rose, the concentration of irisin declined, while levels of BDNF and adiponectin remained unchanged. Moreover, a greater reduction in fat mass was associated with higher pre-fasting well-being, emotional intelligence, and lower levels of tension and impulsivity. Conversely, the loss of lean mass was linked to neuroticism and higher levels of impulsivity, particularly concerning pre-fasting tension levels.These findings suggest that psychological factors may impact fasting outcomes, emphasizing the need for personalized fasting strategies.
Classification of spinopelvic balance in ambulatory adolescents and adults with cerebral palsy: a cross-sectional study
A Mallows-like criterion for anomaly detection with random forest implementation
Anomaly detection plays a crucial role in fields such as information security and industrial production. It relies on the identification of rare instances that deviate significantly from expected patterns. Reliance on a single model can introduce uncertainty, as it may not adequately capture the complexity and variability inherent in real-world datasets. Under the framework of model averaging, this paper proposes a criterion for the selection of weights in the aggregation of multiple models, employing a focal loss function with Mallows’ form to assign weights to the base models. This strategy is integrated into a random forest algorithm by replacing the conventional voting method. Empirical evaluations conducted on multiple benchmark datasets demonstrate that the proposed method outperforms classical anomaly detection algorithms while surpassing conventional model averaging techniques based on minimizing standard loss functions. These results highlight a notable enhancement in both accuracy and robustness, indicating that model averaging methods can effectively mitigate the challenges posed by data imbalance.
Enhancing electric vehicle powertrain energy efficiency using robust nonlinear control approaches
Abstract This paper addresses the issue of controlling the drivetrain of electric vehicles. Taking into account both internal and external system disturbances, including the vehicle’s mass, rotational friction of the shafts, wind speed, vehicle aerodynamics, road type, and slope constraints, the controller’s task is to ensure robustness in vehicle behavior. The significant dynamics of these disturbances and uncertainties in vehicle parameters have a substantial impact on vehicle performance. To overcome these challenges, a nonlinear model of the entire controlled system is developed. Subsequently, a robust nonlinear controller is designed using the damping function version of the backstepping design technique to compensate for all uncertain terms. Within this framework, two primary control loops are established. Firstly, a speed control loop is implemented to achieve precise tracking of the driver’s speed reference. Secondly, the machine current is optimized to generate maximum torque. A formal analysis based on Lyapunov stability is conducted to describe the control system’s performance. Despite parameter uncertainties, it is demonstrated that all control objectives are asymptotically achieved. Ultimately, all control objectives are validated through simulation results using Matlab/Simulink, showcasing the efficiency and robustness of the proposed control technique.
Blockchain-based zero trust networks with federated transfer learning for IoT security in industry 5.0
The rise of Industry 5.0 focuses on merging advanced intelligence, automation, and human-centered teamwork in industrial settings. However, keeping interconnected IoT networks secure is still a challenging problem. This paper proposes a new security framework that combines Blockchain, Federated Transfer Learning, and zero trust network (ZTN) principles to improve IoT security in Industry 5.0. Blockchain is a decentralized ledger that ensures secure data sharing and protects model updates. Federated Transfer Learning allows model training across distributed IoT devices to keep data private. The ZTN approach enforces strict access rules, assuming that no entity is trusted by default. The proposed framework offers a scalable and resilient solution to protect next-generation industrial IoT networks, using Blockchain for data security, transfer learning for adaptability, and ZTN for strict access control. The ZTN architecture strengthens security by checking every access request and keeping the IoT system safe. The experimental results show good performance of the proposed method, with better accuracy, precision, recall, and F1 scores. The model achieved an accuracy of 0.85, 0.88, and 0.87 for learning rates of 0.01, 0.001, and 0.0001, respectively, at 100 epochs. The precision values reached 0.84, 0.87, and 0.86, while the recall scores were 0.82, 0.86, and 0.85, respectively. The F1-scores were recorded at 0.83, 0.86, and 0.85, which confirms the robustness of our model.
Characterization of glycoside hydrolases involved in xyloglucan degradation in the thermophilic bacterium Thermotoga maritima
Toward resilient cities: Mapping the interconnected factors shaping urbanization in a dual analysis framework
Utilizing the Push and Pull theory, this study examines the impact of socio-economic disparities and natural disasters on migration and urbanization. With a global significance, the shift of population from rural to urban areas carries profound implications for societies and economies. In the specific context of Pakistan, the research delves into the driving forces behind the rapid urbanization in Karachi and Quetta. Employing a mixed-methods approach, combining quantitative data and qualitative Geographic Information System (GIS) analysis, the study surveyed 1120 migrants. Results indicate a significant positive correlation between socio-economic disparities, natural disasters, and migration, highlighting the interplay of rural push factors and urban pull factors. GIS and satellite images reveal noticeable expansion in covered areas in both cities. The study underscores the importance of effective disaster management and resilient infrastructure to mitigate the impact of natural disasters on migration and urbanization. The findings offer valuable insights for policymakers and academics, discussed in the later sections of the study.
Optimized breast cancer diagnosis using self-adaptive quantum metaheuristic feature selection
Abstract Breast cancer is a leading cause of mortality among women and is increasing rapidly around the world. For early diagnosis of breast cancer, precise classification, and finding the best subset for cancer identification, evolutionary-based feature selection methods play a vital role in effective treatment. Previous studies have shown that existing evolutionary methods are complicated in correctly differentiating BC disease subtypes with high consistency, which seriously affects the performance of classification methods. To prevent diagnostic errors with hostile implications for patient health, in this study, we develop a new evolutionary method called SeQTLBOGA that incorporates the learner quantization before the search capability of the feature space to prevent premature falls into the local optima. In the SeQTLBOGA algorithm, quantum theory and a self-adaptive mechanism are employed to update the Teaching Learning-based Optimization (TLBO) rule to enhance convergence search capabilities. Most importantly, a self-adaptive genetic algorithm (GA) is also incorporated into TLBO to tradeoff between exploration and exploitation to handle slow convergence and exploitation competence, and simultaneously optimizing parameters of support vector machines (SVM) and the best features subset is our primary objective. Comparative results based on optimal computing time and performance are also offered to empirically analyze the traditional algorithms. Therefore, this paper aims to evaluate the most recent quantum-inspired metaheuristic algorithms in WBCD and WDBC databases, emphasizing their advantages and disadvantages.
Global burden and future trends of inguinal, femoral, and abdominal hernia in older adults: A systematic analysis from the Global Burden of Disease Study 2021
Objective This study aims to comprehensively evaluate the global, regional, and national burden, trends, and health inequalities of inguinal, femoral, and abdominal hernias among older adults from 1990 to 2021, conduct predictive analyses, and provide insights to inform future public health strategies. Methods A secondary analysis was conducted using the Global Burden of Disease 2021, focusing on the temporal trends, health inequality, and predictive development of inguinal, femoral, and abdominal hernia burden among older adults. Results Globally, the number of incident cases of inguinal, femoral, and abdominal hernias among older adults continuously increased from 1990 to 2021, along with the decline in age-standardized rates, prevalence, and Disability-Adjusted Life Years (DALYs). Older males exhibited higher incidence rates, prevalence, and DALYs for hernias relative to females. In terms of the Socio-Demographic Index (SDI) from 1990 to 2021, the Age-Standardized Prevalence Rate (ASPR) and Age-Standardized Rate of Disability-Adjusted Life Years (ASDR) remained the highest in low-middle and low SDI regions, while the Age-Standardized Incidence Rate (ASIR) was the highest in high SDI regions. At the national level, 10 countries experienced a significant increase in ASDR and ASPR, and 15 countries in ASIR. Among these, the highest increase was observed for ASIR in China, ASPR in Georgia, and ASDR in American Samoa. The projections to the year 2035 indicate an increase in the incidence and prevalence of hernias, with older males remaining predominant. However, the DALY rate is expected a declining trend. Conclusions In spite of the progress in reducing the burden of inguinal, femoral, and abdominal hernias in older adults, the overall burden tends to rise. In particular, countries such as India, China, and Georgia are experiencing an increasing burden. It is crucial to implement targeted medical interventions, especially for older males in these regions.
Speed and accuracy tradeoff in whole body movement during vertical jumps under varying landing constraints
Abstract The speed-accuracy trade-off, described by Fitts’ law, has been well studied in fine motor tasks but remains insufficiently explored in whole-body movements, such as jumping. This gap limits our ability to identify universal motor control principles applicable to fine and gross motor tasks. To address this, we investigated the influence of landing accuracy constraints on vertical jump performance. Twelve participants performed jumps under four conditions: no accuracy constraints and progressively smaller target areas (100%, 65%, and 36% of the force-plate surface). Stricter accuracy demands a reduced jump height and systematic adjustments in the magnitude and direction of the take-off velocity. Notably, these trade-offs occurred despite the participants’ inability to continuously monitor the target during the jump, relying instead on the initial recognition of accuracy constraints. Entropy analysis revealed decreased variability in landing positions, reflecting precise motor adaptations to meet the task requirements. These findings suggest that principles similar to Fitts’ law govern speed-accuracy trade-offs in whole-body movements. This study provides valuable insights for sports, rehabilitation, and robotics applications by illustrating how accuracy constraints shape dynamic full-body movements.
Instability of estimation results based on caliper matching with propensity scores
Caliper matching is often used to adjust for confounding biases in observational studies. This method with random order matching allows for the cherry-picking of the analysis results to suit the analyst’s convenience. Random order matching can also result in large fluctuations in the analysis results due to small additions and/or changes in data. These “instability problems” might compromise the reproducibility of the study results. Some studies have discussed instability issues, but the conditions are limited, and there is no knowledge of which alternative order method should be used instead of the random order method. We evaluate the instability problem by calculating the extent to which the results can vary within a single study dataset and provide guidelines for choosing the best alternative matching order method based on simulations and a case study. From simulation studies, instability might be serious when the sample size was small, the true odds ratio was large, the proportion for the treatment group was large, and the c-statistic for the propensity score model was large. We recommend not using random order matching and instead using lowest to highest score order matching or the median of multiple random order matching results. We also recommend pre-specifying the matching order method.
A secure medical image encryption technique based on DNA cryptography with elliptic curves
The “Dogs’ Catching Mice” conjecture in Chinese phonogram processing
In Chinese phonogram processing studies, it is not strange that phonetic radicals contribute phonologically to phonograms’ phonological recognition. The present study, however, based on previous findings of phonetic radicals’ proneness to semantic activation, as well as free-standing phonetic radicals’ possession of triadic interconnections of orthography, phonology, and semantics at the lexical level, proposed that phonetic radicals may contribute semantically to the host phonograms’ phonological recognition. We label this speculation as the “Dogs’ Catching Mice” Conjecture. To examine this conjecture, three experiments were conducted. Experiment 1 was designed to confirm whether phonetic radicals, when embedded in phonograms, can contribute semantically to their host phonograms’ phonological recognition. Experiment 2 was intended to show that the embedded phonetic radicals employed in Experiment 1 were truly semantically activated. Experiment 3, on top of the first two experiments, was devoted to demonstrating that the semantically activated phonetic radicals, when used as independent characters, can truly contribute semantically to their phonological recognition. Results from the three experiments combine to confirm the conjecture. The implication drawn is that phonetic radicals may have forged two paths in contributing to the host phonograms’ phonological recognition: one is the regular “Cats’ Catching Mice” Path, the other is the novel “Dogs’ Catching Mice” Path.
Optimizing the synthesis of nanostructured SiO2 from Ethiopian pumice for use in rubber reinforcement
Prevalence of influenza A and B and respiratory syncytial virus infections before and during COVID-19 pandemic in the pediatric population in Lebanon: A retrospective study
Seasonal influenza and RSV outbreaks cause considerable morbidity and mortality in the pediatric population worldwide. The COVID-19 pandemic has changed virus epidemiology. Until today, it is still unclear how this pandemic affected the transmission of common respiratory viruses. The present study aimed at comparing the prevalence of RSV and influenza A/B infection before and during the COVID-19 pandemic in the Lebanese pediatric population. A multicenter retrospective cross-sectional study was performed from September 2018 to December 2022 at the Lebanese American University Medical Center – Rizk Hospital and Abou Jaoude Hospital in Lebanon. Included were children (0–18 years) tested for Influenza A and B and RSV by Rapid Influenza Diagnostic Test and Rapid Antigen Testing, respectively, taken by nasopharyngeal swab at both hospitals where the study was conducted. Data collection was retrieved from the medical records of the patients. The statistical analysis was performed using SPSS software, version 30.0. This study has considered all ethical measures. Among a total of 1069 children tested, 19.7% tested positive for influenza A, 11.9% for influenza B, and 13.8% for RSV. The study found that young infants were significantly less susceptible to contracting these viruses compared to older children and adolescents (p < 0.001). A statistically significant difference in the odds of testing positive was observed between the two hospitals (p = 0.011), and a significant temporal trend in influenza circulation was noted (p < 0.05). The prevalence of co-infection was low with no statistically significant differences (p = 0.779). The COVID-19 pandemic and its associated preventive measures led to a significant decrease in the spread of influenza A and B and RSV among the Lebanese pediatric population. Conversely, the post-lockdown period saw a notable resurgence of these infections, with low coinfection occurrences. These results have important implications for public health strategies aimed at controlling respiratory virus infections.
Micromagnetic simulation and optimization of spin-wave transducers
Abstract The increasing demand for higher data volume and faster transmission in modern wireless telecommunication systems has elevated requirements for 5G high-band RF hardware. Spin-Wave technology offers a promising solution, but its adoption is hindered by significant insertion loss stemming from the low efficiency of magnonic transducers. This work introduces a micromagnetic simulation method for directly computing the spin-wave resistance, the real part of spin-wave impedance, which is crucial for optimizing magnonic transducers. By integrating into finite-difference micromagnetic simulations, this approach extends analytical models to arbitrary transducer geometries. We demonstrate its effectiveness through parameter studies on transducer design and waveguide properties, identifying key strategies to enhance the overall transducer efficiency. Our studies show that by varying single parameters of the transducer geometry or the YIG thickness, the spin-wave efficiency, the parameter describing the efficiency of the transfer of electromagnetic energy to the spin wave, can reach values up to 0.75. The developed numerical model allows further fine-tuning of the transducers to achieve even higher efficiencies.
How trustworthy and applicable is the evidence from systematic reviews of depression treatments: Protocol for systematic examination
Background Depression is a common mental disorder significantly impacting daily functioning. Standard treatments include drugs, psychotherapies, or a combination of both. Treatment selection relies on scientific evidence, though the trustworthiness and applicability of this evidence can vary. Objectives This protocol presents a method to evaluate evidence from systematic reviews for pharmacological and psychological treatments for depression, focusing on trustworthiness and applicability structured into five components: quality of conduct and reporting, risk of bias, spin in abstract conclusions, robustness of meta-analytical results, heterogeneity and clinical diversity. Methods We will conduct a systematic search of systematic reviews in MEDLINE, Embase, PsycInfo, and Cochrane Database of Systematic Reviews. Our focus will be on systematic reviews of first-line treatments for depression in adults, including antidepressants, psychotherapy, or combined treatments, compared to either active or inactive comparators. We will extract information needed for a comprehensive methodological evaluation using qualitative tools, including AMSTAR 2, ROBIS, Conflict-of-Interest assessment, Referencing Framework for SRs, Spin Measure, and heterogeneity exploration assessment. For quantitative analyses, such as Fragility Index, Ellipse of Insignificance, Region of Attainable Redaction, GRIM test, Leave-N-Out analysis, and prediction intervals, we will select and recalculate two meta-analyses per review. We define a set of outcomes to enable practical and intuitive interpretation of these analyses’ results. Descriptive statistics, non-parametric statistical tests, and narrative summaries will be used to synthesize and compare outcomes across several pre-specified subgroups. Expected outcomes We expect these analyses to provide an enhanced perspective on the practice of evidence synthesis in the field of mental health, offer methodological guidance for future systematic reviews and meta-analyses, and contribute to improved informed decision-making by clinicians and patients. OSF registration osf.io/7f9cj and osf.io/ynejs
Incidence and risk factors of new clinical disorders in patients with COVID-19 hyperinflammatory syndrome
Abstract This study investigated new incident clinical disorders in patients with COVID-19-related hyperinflammatory syndrome (cHIS) 3.5 years post infection. We analyzed 14,335 patients hospitalized with COVID-19 from March-2020 to July-2023. cHIS was defined based on a point system that included elevated body temperature, macrophage activation, hematological dysfunction, coagulopathy, and hepatic enzyme. Outcomes were newly diagnosed disorders of hypertension, diabetes, cardiovascular diseases, chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD), and asthma post COVID-19. Cumulative incidences and hazard ratios were computed. Compared to non-cHIS patients, cHIS patients were older, fewer female, more Blacks, higher prevalence of pre-existing comorbidities. Patients with cHIS had higher risk of developing cardiovascular disease (HR = 1.24 [1.04,1.47] p < 0.05), CKD (1.24 [1.01, 1.53] p < 0.05), and obesity (1.61 [1.31,1.98], p < 0.001) but not hypertension, diabetes, COPD, and asthma. Cumulative incidence analysis showed that patients ≥ 50 years old showed markedly higher new incidences of individual new disorders compared to patients < 50 years old. COVID-19 related hyperinflammatory syndrome confers a significantly higher risk for developing new common clinical disorders. Identifying risks for developing new clinical disorders in patients with COVID-19 related hyperinflammatory syndrome may encourage diligent follow-up of high-risk individuals.
Efficacy and safety of Tuina (Chinese Therapeutic Massage) for chronic ankle instability: A systematic review and meta-analysis of randomized controlled trials
Objective The efficacy of tuina in treating chronic ankle instability (CAI) arouses controversy. Therefore, the present study adopted the meta-analysis to evaluate the effectiveness and safety of tuina in treating CAI and aims to provide high-quality evidence for this promising treatment. Methods We searched eight databases from inception to April 1st, 2024 for randomized controlled trials (RCTs) of tuina treatment for CAI, including PubMed, Web of Science, Embase, Cochrane Library, Wanfang database, China National Knowledge Infrastructure database, and VIP Chinese Science and Technique Journals database. Information was independently extracted and bias risks were evaluated by two researchers. To assess the quality of the studies, we utilized Cochrane Collaboration’s tool and the GRADE evaluation system. Meta-analysis was performed using the RevMan5.4 software. Results Thirteen RCTs involving 984 patients were included in this study. The overall methodological quality of the studies was low. The meta-analysis revealed the followings: (1) the clinical effective rate was higher in the treatment group compared to the control group (OR = 6.51, 95% CI [3.76, 11.28]); (2) the treatment group performed better in reducing the Visual Analogue Scale score (MD = −1.59, 95% CI [−2.59, −0.59]); (3) the Baird-Jackson Ankle Score was superior in the treatment group (MD = 8.20, 95% CI [6.37, 10.04]); (4) the improvement in the AOFAS Ankle Hindfoot Scale was greater in the treatment group (MD = 14.52, 95% CI [9.81, 19.23]). All differences were statistically significant. Regarding adverse events, there were no significant differences in incidence rates between the groups. Conclusions Tuina is an effective and safe treatment option for CAI, the conclusions are limited by the methodological quality of the included trials. Further high-quality research is needed to confirm these findings and guide clinical practice.