Search Results (1,558)

This work aims to solve the problem of product quality fluctuations caused by batch-to-batch variations in the adsorption capacity of activated carbon during the production of traditional Chinese medicine (TCM) injections. In this work, Shenqi Fuzheng injection was selected as an example. Diluted Shenqi Extract (DSE), an intermediate in the production process of Shenqi Fuzheng injection, was adsorbed with different batches of activated carbon. The adsorption capacities of adenine, adenosine, calycosin-7-glucoside, and astragaloside IV in DSE were selected as evaluation indices for activated carbon absorption. Characterization methods such as nitrogen adsorption, X-ray photoelectron spectrum (XPS), and Fourier transform infrared (FTIR) were chosen to explore the quantitative relationships between the properties of activated carbon (i.e., specific surface area, pore volume, surface elements, and spectrum) and the adsorption capacities of these four components. It was found that the characteristic wavelengths from FTIR characterization, i.e., 1560 cm⁻¹, 2325 cm⁻¹, 3050 cm⁻¹, and 3442 cm⁻¹, etc., showed the strongest correlation with the adsorption capacities of these four components. Prediction models based on the transmittance at characteristic wavelengths were successfully established via multiple linear regression. In validation experiments of models, the relative errors of predicted adsorption capacities of activated carbon were mostly within 5%, indicating good predictive ability of the models. The results of this work suggest that the prediction method of adsorption capacity based on the mid-infrared spectrum can provide a new way for the quality control of activated carbon. Full article

This study examined data from participants who completed the SLeep Education for Everyone Program (SLEEP) to explore how various demographic variables affected sleep outcomes and to determine which participant characteristics predicted success. A total of 104 individuals participated. The Sleep Hygiene Index (SHI) measured undesirable sleep behaviors; the Pittsburgh Sleep Quality Index (PSQI) assessed sleep quality and self-reported sleep duration. Participant demographic information was collected at baseline. A mixed ANOVA evaluated group differences, and a multiple linear regression model identified predictors of sleep improvements. Change in SHI scores from pre- to post-intervention demonstrated a significant time × group interaction between Black and white participants (p = 0.024); further analysis indicated Black participants improved more. Better baseline scores predicted more favorable post-intervention outcomes for SHI, PSQI, and sleep duration. Fewer chronic conditions predicted better post-intervention SHI and PSQI scores. Older age also predicted better SHI scores. More favorable initial scores, fewer chronic conditions, and older age were the strongest predictors of positive outcomes following SLEEP. Improved sleep hygiene, sleep quality, and sleep duration were observed over time within subjects across all groups. In summary, SLEEP appears to be effective. Further work exploring challenges experienced by younger participants or those with multiple co-morbidities is warranted. Full article

Solving competitive facility location problems can optimize market share or operational efficiency in environments where multiple firms compete for customer attention. In such contexts, facility attractiveness is shaped not only by geographic proximity but also by customer preference characteristics. This study presents a novel heuristic framework that integrates multi-view K-means clustering with customer behavior modeling reinforced by a co-regularization mechanism to align clustering results across heterogeneous data views. By jointly exploiting spatial and behavioral information, the framework clusters customers and facilities into meaningful market segments. Within each segment, a bilevel optimization model is applied to represent the sequential decision-making of competing entities—where a leader first selects facility locations, followed by a reactive follower. An empirical evaluation on a real-world dataset from San Francisco demonstrates that the proposed approach, using optimal co-regularization parameters, achieves a total runtime of approximately 4.00 s—representing a 99.34% reduction compared to the full CFLBP-CB model (608.58 s) and a 99.32% reduction compared to a genetic algorithm (585.20 s). Concurrently, it yields an overall profit of 16,104.17, which is an approximate 0.72% increase over the Direct CFLBP-CB profit of 15,988.27 and is only 0.21% lower than the genetic algorithm’s highest profit of 16,137.75. Moreover, comparative analysis reveals that the proposed multi-view clustering with co-regularization outperforms all single-view baselines, including K-means, spectral, and hierarchical methods. This superiority is evidenced by an approximate 5.21% increase in overall profit and a simultaneous reduction in optimization time, thereby demonstrating its effectiveness in capturing complementary spatial and behavioral structures for competitive facility location. Notably, the proposed two-stage approach achieves high-quality solutions with significantly shorter computation times, making it suitable for large-scale or time-sensitive competitive facility planning tasks. Full article

Refining the land use structure can boost land utilization efficiency and curtail regional carbon emissions. Nevertheless, prior research has predominantly concentrated on static linear planning analysis. It has failed to account for how future dynamic alterations in driving factors (such as GDP and population) affect simulation outcomes and how the land use spatial configuration impacts the attainment of the carbon-neutrality goal. In this research, 1 km spatial resolution LULC products were employed to meticulously simulate multiple land use scenarios across China at the national level from 2030 to 2060. This was performed by taking into account the dynamic changes in driving factors. Subsequently, an analysis was carried out on the low-carbon land use spatial structure required to reach the carbon-neutrality target. The findings are as follows: (1) When employing the PLUS (Patch—based Land Use Simulation) model to conduct simulations of various land use scenarios in China by taking into account the dynamic alterations in driving factors, a high degree of precision was attained across diverse scenarios. The sustainable development scenario demonstrated the best performance, with kappa, OA, and FoM values of 0.9101, 93.15%, and 0.3895, respectively. This implies that the simulation approach based on dynamic factors is highly suitable for national-scale applications. (2) The simulation accuracy of the PLUS and GeoSOS-FLUS (Systems for Geographical Modeling and Optimization, Simulation of Future Land Utilization) models was validated for six scenarios by extrapolating the trends of influencing factors. Moreover, a set of scenarios was added to each model as a control group without extrapolation. The present research demonstrated that projecting the trends of factors having an impact notably improved the simulation precision of both the PLUS and GeoSOS-FLUS models. When contrasted with the GeoSOS-FLUS model, the PLUS model attained superior simulation accuracy across all six scenarios. The highest precision indicators were observed in the sustainable development scenario, with kappa, OA, and FoM values reaching 0.9101, 93.15%, and 0.3895, respectively. The precise simulation method of the PLUS model, which considers the dynamic changes in influencing factors, is highly applicable at the national scale. (3) Under the sustainable development scenario, it is anticipated that China’s land use carbon emissions will reach their peak in 2030 and achieve the carbon-neutrality target by 2060. Net carbon emissions are expected to decline by 14.36% compared to the 2020 levels. From the perspective of dynamic changes in influencing factors, the PLUS model was used to accurately simulate China’s future land use. Based on these simulations, multi-scenario predictions of future carbon emissions were made, and the results uncover the spatiotemporal evolution characteristics of China’s carbon emissions. This study aims to offer a solid scientific basis for policy-making related to China’s low-carbon economy and high-quality development. It also intends to present Chinese solutions and key paths for achieving carbon peak and carbon neutrality. Full article

Background/Objectives: Accurate dietary assessment is crucial for nutritional epidemiology, but tools like 24 h recalls (24HRs) face challenges with missing or implausible data. The Automated Self-Administered 24 h Dietary Assessment Tool (ASA24) facilitates large-scale data collection, but its lack of interviewer input may lead to implausible dietary recalls (IDRs), affecting data integrity. Multiple imputation (MI) is commonly used to handle missing data, but its effectiveness in high-variability dietary data is uncertain. This study aims to assess MI’s accuracy in estimating nutrient intake under varying levels of missing data. Methods: Data from 24HRs completed by 743 adolescents (ages 13–18) in Ontario, Canada, were used. Implausible recalls were excluded based on nutrient thresholds, creating a cleaned reference dataset. Missing data were simulated at 10%, 20%, and 40% deletion rates. MI via chained equations was applied, incorporating demographic and psychosocial variables as predictors. Imputed values were compared to actual values using Spearman’s correlation and accuracy within ±10% of true values. Results: Spearman’s rho values between the imputed and actual nutrient intakes were weak (mean ρ ≈ 0.24). Accuracy within ±10% was low for most nutrients (typically < 25%), with no clear trend by missingness level. Diet quality scores showed slightly higher accuracy, but values were still under 30%. Conclusions: MI performed poorly in estimating individual nutrient intake in this adolescent sample. While MI may preserve sample characteristics, it is unreliable for accurate nutrient estimates and should be used cautiously. Future studies should focus on improving data quality and exploring better imputation methods. Full article

Most research studies have investigated the impact of pet ownership on the mental and physical well-being of elderly populations, supporting the beneficial effect that pets have on their owners. However, few researchers focused on the well-being of both owner and pet. The present study aimed to explore the well-being of pets owned by elderly individuals using an owner assessment tool and the relationship between elderly characteristics and the pet’s health-related quality of life based on the owner’s assessment of their pet’s well-being. Sixty elderly pet owners who made regular visits to veterinary clinics were selected to complete an electronic questionnaire about their pet’s health-related quality of life. The results identified a high agreement percentage on positive indicators related to the pet’s well-being such as “My pet wants to play and My pet responds to my presence” in the happiness domain, “My pet has more good days than bad days” in mental status, “My pet moves normally” in physical status and “My pet keeps him/herself clean” in hygiene which also resulted in a positive relationship with elderly age. Marital status influenced their responses to “My pet responds to my presence and My pet is as active as he/she has been”. The results also support the use of the applied questionnaire to help identify variables that contribute to a pet’s health-related quality of life. The correlation matrix revealed statistically significant positive associations (p < 0.001) among positively phrased items across all domains, as well as among negatively phrased items. These consistent alignments between direct and between reversed items suggest directional coherence and help mitigate potential response bias. Furthermore, the replication of these patterns across multiple domains reinforces the interpretation that the instrument captures a unified construct of pet well-being, In conclusion, based on subjective evaluation of pet-owner relationships, the ownership of pets by elderly individuals could be mutually beneficial to both elderly owners and their pets. Full article

Intrauterine growth retardation (IUGR) is highly prevalent in modern swine production, and many affected piglets survive past weaning and are raised for commercial pork production. This review summarizes the current understanding of the physiological challenges of IUGR piglets from a molecular perspective and evaluates recent advances in nutritional strategies aimed at mitigating their negative outcomes. Molecular approaches, including omics technologies and targeted analyses, have been employed to investigate the physiological characteristics of IUGR piglets. These approaches consistently show that IUGR piglets exhibit systemic dysfunction, including compromised gut health, increased inflammation and oxidative stress, and impaired function of multiple organs such as the intestine, liver, kidney, and immune-related tissues. Moreover, IUGR piglets often display poor muscle development and meat quality. The multifactorial nature of these issues suggests that targeting a single physiological parameter may be insufficient, and comprehensive interventions are needed to address the widespread effects of IUGR. Promising nutritional strategies such as supplementation with polyphenol-rich plant extracts, amino acids, and probiotics have demonstrated potential in improving gut integrity, beneficially modulating microbiota, and enhancing the overall health and performance of IUGR piglets. By supporting the systemic recovery of IUGR piglets, nutritional interventions could improve overall productivity in swine production systems. Full article

Background/Objectives: In late 2019, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) caused a pandemic called the ‘coronavirus disease 2019’ (COVID-19). After the acute SARS-CoV-2 infection, many individuals (up to 33%) complained of unexplained symptoms involving multiple organ systems and were diagnosed as having Long COVID-19 (LC-19). Currently, LC-19 is inadequately defined, requiring the formation of consistent diagnostic parameters to provide a foundation for ongoing and future studies of epidemiology, risk factors, clinical characteristics, and therapy. LC-19 represents a significant burden on multiple levels. The reduced ability of workers to return to work or compromised work efficiency has led to consequences at national, economic, and societal levels by increasing dependence on community services. On a personal scale, the isolation and helplessness caused by the disease and its subsequent impact on the patient’s mental health and quality of life are incalculable. Methods: In this paper, we used Walker and Avants’ eight-step approach to perform a concept analysis of the term “Long COVID-19” and define its impact across these parameters. Results: Using this methodology, we provide an improved definition of LC-19 by connecting the clinical symptomology with previously under-addressed factors, such as mental, psychological, economic, and social effects. This definition of LC-19 features can help improve diagnostic procedures and help plan relevant healthcare services. Conclusions: LC-19 represents a complex and pressing public health challenge with diverse symptomology, an unpredictable timeline, and complex pathophysiology. This concept analysis serves as a tool for improving LC-19 definition, but it remains a dynamic disease with evolving diagnostic and therapeutic approaches, requiring deeper investigation and understanding of its long-term effects. Full article

Flavor, as one of the primary factors that attracts consumers, has always been a crucial indicator for evaluating the quality of food. From processing to final consumption, the conditions that affect consumers’ perception of the aroma of aquatic products can be divided into three stages: aroma formation, release, and signal transmission. Currently, there are few reviews on the formation, release, and perception of aroma in aquatic products, which has affected the product development of aquatic products. This review summarizes aroma formation pathways, the effects of processing methods, characteristic volatile compounds, various identification techniques, aroma-release influencing factors, and the aroma perception mechanisms of aquatic products. The Maillard reaction and lipid oxidation are the main pathways for the formation of aromas in aquatic products. The extraction, identification, and quantitative analysis of volatile compounds reveal the odor changes in aquatic products. The composition of aquatic products and oral processing mainly influence the release of odorants. The characteristic odorants perceived from the nasal cavity should be given more attention. Moreover, the relationship between various olfactory receptors (ORs) and the composition of multiple aromatic compounds remains to be understood. It is necessary to clarify the relationship between nasal cavity metabolism and odor perception, reveal the binding and activation mode of ORs and odor molecules, and establish an accurate aroma prediction model. Full article

A scalable synthesis of two-dimensional titanium nitride chloride (TiNCl) via flash Joule heating (FJH) using titanium tetrachloride (TiCl₄) precursor has been developed. This single-step method overcomes traditional synthesis challenges, including high energy consumption, multi-step procedures, and hazardous reagent requirements. The structural and chemical properties of the synthesized TiNCl were characterized through multiple analytical techniques. X-ray diffraction (XRD) patterns confirmed the presence of TiNCl phase, while Raman spectroscopy data showed no detectable oxide impurities. Fourier transform infrared spectroscopy (FTIR) analysis revealed characteristic Ti–N stretching vibrations, further confirming successful titanium nitride synthesis. Transmission electron microscopy (TEM) imaging revealed thin, plate-like nanostructures with high electron transparency. These analyses confirmed the formation of highly crystalline TiNCl flakes with nanoscale dimensions and minimal structural defects. The material exhibits excellent structural integrity and phase purity, demonstrating potential for applications in photocatalysis, electronics, and energy storage. This work establishes FJH as a sustainable and scalable approach for producing MXenes with controlled properties, facilitating their integration into emerging technologies. Unlike conventional methods, FJH enables rapid, energy-efficient synthesis while maintaining material quality, providing a viable route for industrial-scale production of two-dimensional materials. Full article

This study explores the machinability of Incoloy 800HT (high temperature) under a sustainable lubrication approach, employing a twin-nozzle minimum quantity lubrication (MQL) system with groundnut oil as a green cutting fluid. The evaluation focuses on key performance indicators, including surface roughness, tool flank wear, power consumption, carbon emissions, and chip morphology. Groundnut oil, a biodegradable and nontoxic lubricant, was chosen to enhance environmental compatibility while maintaining effective cutting performance. The Taguchi L₁₆ orthogonal array (three factors and four levels) was utilized to conduct experimental trials to analyze machining characteristics. The best surface quality (surface roughness, Ra = 0.514 µm) was obtained at the lowest depth of cut (0.2 mm), modest feed (0.1 mm/rev), and moderate cutting speed (160 m/min). The higher ranges of flank wear are found under higher cutting speed conditions (320 and 240 m/min), while lower wear values (<0.09 mm) were observed under lower speed conditions (80 and 160 m/min). An entropy-integrated multi-response optimization using the MOORA (multi-objective optimization based on ratio analysis) method was employed to identify optimal machining parameters, considering the trade-offs among multiple conflicting objectives. The entropy method was used to assign weights to each response. The obtained optimal conditions are as follows: cutting speed = 160 m/min, feed = 0.1 mm/rev, and depth of cut = 0.2 mm. Optimized outcomes suggest that this green machining strategy offers a viable alternative for sustainable manufacturing of difficult-to-machine alloys like Incoloy 800 HT. Full article

Background: This paper aims to conduct an umbrella review of the effects of physical activity programmes for older adults (aged 70 and above). Methods: Comprehensive literature searches were conducted in MEDLINE, PubMed, EMBASE, PsychINFO, and Cochrane Library databases for English SRs 2024. Inclusion criteria were systematic reviews that included randomised controlled trials examining physical activity interventions in older adults. The data extracted were participant characteristics, physical activity interventions, and outcomes examined. A synthesis of results was conducted using the PRISMA guidelines, and the quality of the studies was assessed using the Assessment of Multiple Systematic Reviews-2 (AMSTAR-2). Results: Ten systematic reviews on 186 research articles were included. The AMSTAR-2 revealed that 4 out of 10 reviews were of high quality and 1 out of 10 were of moderate quality. The study samples in each systematic review ranged from 6 to 1254 participants. The total overall sample size for the 10 included studies was 22,652 participants. Across the included reviews, there was mixed evidence on whether physical activity interventions could improve outcomes in older adults across various settings. Conclusions: Sample sizes and findings in each included systematic review varied. The findings of this review emphasise the importance of physical activity as a vital component in maintaining and enhancing health, as well as combating poor health as we age. It also highlights the need for a deeper understanding of the specific physical activity requirements for those aged 70 and above. Future systematic reviews may focus on streamlined reporting of dosing of physical activity and specific intervention types, such as group versus single. Full article

Tomatoes are globally esteemed not only for their nutritional value but also for their complex and appealing aroma, a key determinant of consumer preference. The present study aimed to comprehensively characterise the volatilomic fingerprints of three tomato species—Solanum lycopersicum L., S. lycopersicum var. cerasiforme, and S. betaceum—encompassing six distinct varieties, through the application of headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME/GC-MS). A total of 55 volatile organic compounds (VOCs) spanning multiple chemical classes were identified, of which only 28 were ubiquitously present across all varieties examined. Carbonyl compounds constituted the predominant chemical family, with hexanal and (E)-2-hexenal emerging as putative key contributors to the characteristic green and fresh olfactory notes. Notably, esters were found to dominate the unique volatile fingerprint of cherry tomatoes, particularly methyl 2-hydroxybenzoate, while Kumato and Roma varieties exhibited elevated levels of furanic compounds. Multivariate statistical analyses, including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), demonstrated clear varietal discrimination and identified potential aroma-associated biomarkers such as phenylethyl alcohol, 3-methyl-1-butanol, hexanal, (E)-2-octenal, (E)-2-nonenal, and heptanal. Collectively, these findings underscore the utility of volatilomic fingerprint as a robust tool for varietal identification and quality control within the food industry. Full article

Background: Pediatric-onset multiple sclerosis (POMS) is a rare but often more aggressive form of multiple sclerosis, associated with early cognitive impairment and significant impact on quality of life. Multiple sclerosis and primary headaches, particularly migraine, are well established in adults, but data on pediatric populations remain limited. Methods: The purpose of this retrospective study was to examine 64 POMS patients, divided into groups with and without headaches, to determine potential correlations between headache presence, age at POMS onset, and MRI lesion burden. Results: Headaches were reported by 78% of patients, predominantly migraines (68%), with a significantly higher prevalence in females (74%). No significant differences were found in age at MS onset or lesion load on brain MRI between patients with and without headaches. Among those with headaches, migraines represented a higher frequency of attacks and a greater need for prophylactic treatment compared to other headache types. Headache characteristics, including pain location and associated symptoms, showed no correlation with age at MS onset or lesion burden. Conclusions: These findings indicate that while headaches are common in POMS and more frequent in females, their presence and features do not appear to directly influence the clinical or neuroradiological course of the disease. Further research with larger cohorts and longitudinal follow-up is warranted to better understand the underlying mechanisms and long-term impact of headaches in pediatric MS. Full article

To address the modeling complexity and multi-objective collaborative optimization challenges in multi-depot and multiple unmanned aerial vehicle (UAV) delivery task planning, this paper proposes a bi-layer planning framework, which comprehensively considers resource constraints, multi-depot coordination, and the coupling characteristics of path execution. The novelty of this work lies in the seamless integration of an enhanced genetic algorithm and tailored swarm optimization within a unified two-tier architecture. The upper layer tackles the task assignment problem by formulating a multi-objective optimization model aimed at minimizing economic costs, delivery delays, and the number of UAVs deployed. The Enhanced Non-Dominated Sorting Genetic Algorithm II (ENSGA-II) is developed, incorporating heuristic initialization, goal-oriented search operators, an adaptive mutation mechanism, and a staged evolution control strategy to improve solution feasibility and distribution quality. The main contributions are threefold: (1) a novel ENSGA-II design for efficient and well-distributed task allocation; (2) an improved PSO-based path planner with chaotic initialization and adaptive parameters; and (3) comprehensive validation demonstrating substantial gains over baseline methods. The lower layer addresses the path planning problem by establishing a multi-objective model that considers path length, flight risk, and altitude variation. An improved particle swarm optimization (PSO) algorithm is proposed by integrating chaotic initialization, linearly adjusted acceleration coefficients and maximum velocity, a stochastic disturbance-based position update mechanism, and an adaptively tuned inertia weight to enhance algorithmic performance and path generation quality. Simulation results under typical task scenarios demonstrate that the proposed model achieves an average reduction of 47.8% in economic costs and 71.4% in UAV deployment quantity while significantly reducing delivery window violations. The framework exhibits excellent capability in multi-objective collaborative optimization. The ENSGA-II algorithm outperforms baseline algorithms significantly across performance metrics, achieving a hypervolume (HV) value of 1.0771 (improving by 72.35% to 109.82%) and an average inverted generational distance (IGD) of 0.0295, markedly better than those of comparison algorithms (ranging from 0.0893 to 0.2714). The algorithm also demonstrates overwhelming superiority in the C-metric, indicating outstanding global optimization capability in terms of distribution, convergence, and the diversity of the solution set. Moreover, the proposed framework and algorithm are both effective and feasible, offering a novel approach to low-altitude urban logistics delivery problems. Full article