Search Results (29)

To address the issue of low fertilizer proportioning accuracy in irrigation and fertilization systems due to neglecting the influence of target ions in raw water, this study designed a high-precision online automatic water–fertilizer mixing device that can directly mix raw water (without water purification treatment) with fertilizer stock solution. This device is capable of preparing mixed fertilizer solutions containing N, K, and Ca elements. It employs ion-selective electrodes and flow meters for online detection and feedback of target ion concentrations in the fertilizer solution and flow rate information, and adopts an online fertilizer mixing control strategy that uses a constant raw water flow rate and a fuzzy PID control method to dynamically adjust the pulse frequency of metering pumps, thereby changing the injection volume of nutrient solution. Simulation and experimental analyses show that the piping system of the device is reasonably designed, ensuring stable and smooth fertilizer injection. The temperature-compensated concentration detection models for the three target ions in the fertilizer solution, constructed using a stepwise fitting method, achieve average relative detection errors of 1.94%, 1.18%, and 2.87% for K⁺, NO₃⁻, and Ca²⁺, respectively. When preparing single-element or mixed fertilizer solutions, the device achieves an average steady-state error of no more than 4% and an average steady-state time of approximately 40 s. Compared with deionized water, the average relative errors for potassium ions, nitrate ions, and calcium ions when preparing fertilizer solutions with raw water are 1.33%, 1.12%, and 1.19%, respectively. Compared with the theoretical errors of fertilizer preparation with raw water, the fertilizer proportioning errors of this device for potassium ions, nitrate ions, and calcium ions can be reduced by a maximum of 10.55%, 66.84%, and 62.71%, respectively, which is superior to the performance requirements for water–fertilizer integration equipment specified in the national industry standard DG/T 274-2024. Additionally, the device achieves accurate and stable fertilizer proportioning with safe and reliable operation during 6 h of continuous operation. This device significantly reduces the impact of raw water on fertilizer proportioning accuracy, improves the adaptability of the device to irrigation water sources, and provides theoretical basis and technical support for water-fertilizer integration systems in cost-sensitive agriculture. Full article

Our study aimed to identify the overall and cluster-specific characteristics of Czech beef cattle breeding systems. We used data from an online survey to ascertain farmers’ preferences in breeding objectives. Considering various evaluation criteria and clustering approaches in 41 farms, three beef systems were defined according to herd size, management, marketing, breeding strategies and structures, and farmer age. Breeding values and performance were jointly used as the primary information in all three systems. Cow temperament and calf viability, maternal fertility and longevity, and animal health were found to be the most important traits. Cluster 1 represents pure-breeding farms that specialize in producing breeding animals. Farms in clusters 2 and 3 combined pure- and crossbreeding strategies with production, which was partially (cluster 2) and fully (cluster 3) diversified for all beef categories. Farms also prioritized calving performance and calf growth (clusters 1 and 2) and exterior traits (cluster 3). Production type scores significantly (p < 0.05) differed in clusters 3 (4.12) and 2 (3.25). The proportion of production, functional, and exterior trait categories was 12:37:51, with low variability among clusters (±1 to 2 percentage points). The inter-cluster comparison showed that specific characteristics were compatible with certain breeding goal trait preferences. Full article

The online dynamic collection of irrigation and plant physiological information is crucial for the precise irrigation management of nutrient solutions and efficient crop cultivation in vegetable soilless substrate cultivation facilities. In this study, an intelligent weighing system was installed in a tomato substrate cultivation greenhouse. The monitored values from the intelligent weighing system’s pressure-type module were used to calculate irrigation start–stop times, frequency, volume, drainage volume, drainage rate, evapotranspiration, evapotranspiration rate, and stomatal conductance. In contrast, the monitored values of the suspension-type weighing module were used to calculate the amount of weight change in the plants, which supported the dynamic and quantitative characterization of substrate cultivation irrigation and crop growth based on an intelligent weighing system. The results showed that the monitoring curves of pressure and flow sensors based on the pressure-type module could accurately identify the irrigation start time and number of irrigations and calculate the irrigation volume, drainage volume, and drainage rate. The calculated irrigation amount was closely aligned with that determined by an integrated-water–fertilizer automatic control system (R² = 0.923; mean absolute error (MAE) = 0.105 mL; root-mean-square error (RMSE) = 0.132 mL). Furthermore, transpiration rate and leaf stomatal conductance were obtained through inversion, and the R², MAE, and RMSE of the extinction coefficient correction model were 0.820, 0.014 mol·m⁻²·s⁻¹, and 0.017 mol·m⁻²·s⁻¹, respectively. Compared to traditional estimation methods, the MAE and RMSE decreased by 12.5% and 15.0%, respectively. The measured values of fruit picking and leaf stripping linearly fitted with the calculated values of the suspended weighing module, and R², MAE, and RMSE were 0.958, 0.145 g, and 0.143 g, respectively. This indicated that data collection based on the suspension-type weighing module could allow for a dynamic analysis of plant weight changes and fruit yield. In summary, the intelligent weighing system could accurately analyze irrigation information and crop growth physiological indicators under the practical application conditions of facility vegetable substrate cultivation, providing technical support for the precise management of nutrient solutions. Full article

Background/Objectives: Currently, the type of population reproduction in China has changed to “low birth, low death, low growth”. Additionally, as the number of single people has increased and the number of women of childbearing age has decreased, the growth of the total population was bound to show a negative trend. Therefore, this study aimed to understand what factors influence fertility intentions from the perspective of single, unmarried women. Methods: We designed our own online questionnaire and collected information on fertility intentions from single, unmarried women of childbearing age (18–49 years). A total of 741 questionnaires were collected, with a valid response rate of 92.6%, and the Theory of Planned Behavior model was integrated into the questionnaire survey. Statistical analysis was performed via SPSS 24.0, and AMOS 24.0 software’s analysis of variance was used to determine whether there were differences in attitudes towards fertility intentions among respondents. Structural equation models were constructed via AMOS software for validation factor analysis and path analysis. Results: There was a significant difference (p < 0.05) in “monthly income”, “net family income”, “health status”, and “only-child status”. Structural equation modelling revealed that fertility attitudes (β₁ = 0.301, p < 0.05), subjective norms (β₂ = 0.295, p < 0.05), and perceived behavioural control (β₃ = 0.146, p < 0.05) positively influence fertility intentions. Conclusions: The fertility intentions of single, unmarried women of childbearing age in China are influenced by many factors. Therefore, society should pay more attention to the rights and interests of single, unmarried women and provide a better social environment for childbearing; in addition, it is possible to appropriately relax the fertility policy of single births to improve the low fertility rate in China. Full article

This Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) systematic review synthesized effects of background levels of per- and polyfluorylalkyl substance (PFAS) levels on reproductive health outcomes in the general public: fertility, preterm birth, miscarriage, ovarian health, menstruation, menopause, sperm health, and in utero fetal growth. The inclusion criteria included original research (or primary) studies, human subjects, and investigation of outcomes of interest following non-occupational exposures. It drew from four databases (Web of Science, PubMed, Embase and Health and Environmental Research Online (HERO)) using a standardized search string for all studies published between 1 January 2017 and 13 April 2022. Risk of bias was assessed by two independent reviewers. Data were extracted and reviewed by multiple reviewers. Each study was summarized under its outcome in terms of methodology and results and placed in context, with recommendations for future research. Of 1712 records identified, 30 were eligible, with a total of 27,901 participants (33 datasets, as three studies included multiple outcomes). There was no effect of background levels of PFAS on fertility. There were weakly to moderately increased odds of preterm birth with higher perfluorooctane sulfonic acid (PFOS) levels; the same for miscarriage with perfluorooctanoic acid (PFOA) levels. There was limited yet suggestive evidence for a link between PFAS and early menopause and primary ovarian insufficiency; menstrual cycle characteristics were inconsistent. PFAS moderately increased odds of PCOS- and endometriosis-related infertility, respectively. Sperm motility and DNA health were moderately impaired by multiple PFAS. Fetal growth findings were inconsistent. This review may be used to inform forthcoming drinking water standards and policy initiatives regarding PFAS compounds and drinking water. Future reviews would benefit from more recent studies. Larger studies in these areas are warranted. Future studies should plan large cohorts and open access data availability to capture small effects and serve the public. Funding: Great Lakes Water Authority (Detroit, MI), the Erb Family Foundation through Healthy Urban Waters at Wayne State University (Detroit, MI), and Wayne State University CLEAR Superfund Research (NIH P42ES030991). Full article

This systematic review underscores the significance of learning communities as fertile grounds for skill development across diverse contexts. Furthermore, it reviews and theoretically evaluates several commonly used strategies, methods, and supports for developing skills within learning communities by synthesizing the existing literature. We followed the procedure outlined by the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) to ensure a transparent, comprehensive, and standardized approach to conducting and reporting our systematic review, thereby enhancing the review’s credibility and reproducibility. Through an extensive analysis of the literature, we identified eleven strategies, methods, and supports (application of collaborative projects, mentorship programs, workshops and training sessions, online learning platforms, peer learning and feedback, problem-based learning, cross-collaboration initiatives, leadership development programs, inclusive learning environments, gamification and simulations, and social media and networking) that play pivotal roles in nurturing different types of skills. We describe each identified solution, its advantages and challenges, the types of skills targeted for development, and their overall contribution to skill enhancement. The findings emphasize the importance of fostering collaborative and interactive environments within learning communities to facilitate collective skill development and personal growth. Our systematic review faced some challenges (e.g., heterogeneity of studies and lack of longitudinal data) due to the overwhelming diversity of the literature on skill development across various disciplines and contexts. Overall, by synthesizing existing knowledge and identifying gaps in the literature, this review serves as a foundation for advancing theory, informing practice, and promoting continual improvement in skill development within learning communities. Full article

Online social networks (OSNs) are fertile ground for information sharing and public relationships. However, the uncontrolled dissemination of information poses a significant risk of the inadvertent disclosure of sensitive information. This poses a notable challenge to the information security of many organizations. Improving organizations’ ability to automatically identify data leaked within image-based content requires specialized techniques. In contrast to traditional vision-based tasks, detecting data leaked within images presents a unique challenge due to the context-dependent nature and sparsity of the target objects, as well as the possibility that these objects may appear in an image inadvertently as background or small elements rather than as the central focus of the image. In this paper, we investigated the ability of multiple state-of-the-art deep learning methods to detect censored objects in an image. We conducted a case study utilizing Instagram images published by members of a large organization. Six types of objects that were not intended for public exposure were detected with an average accuracy of 0.9454 and an average macro F1-score of 0.658. A further analysis of relevant OSN images revealed that many contained confidential information, exposing the organization and its members to security risks. Full article

Low fertility is a critical social problem worldwide, and infertility has a prevalence of 15%. This cross-sectional study aimed to understand the factors affecting the usage and satisfaction of Korean medicine (KM) in subfertile women. An online survey was conducted from 3 November to 8 November 2021. The survey collected basic information, KM treatment experience, and satisfaction from women who experienced poor pregnancy. The t-test and chi-square test (χ²-test) were used to determine the overall characteristics of the subjects and factors affecting the utilization and satisfaction of KM treatment. Of the total of 29,465 people, 4922 read the survey email, and 601 responded. After excluding 51 respondents with questionable response patterns, 550 respondents were included in the final analysis. Of these, 43.1% (n = 237) had experience with conventional treatment, and 16.5% (n = 91) had received KM treatment. The group that received both KM treatment and CM treatment (n = 59, 24.9%) was significantly more prevalent than the group that received KM treatment alone (n = 32, 10.2%) (p = 0.00). Women who had given birth more than once or held a master’s degree were significantly more willing to participate in the ‘KM Support Project for Subfertility’ program. Our findings suggest that subfertile patients prefer integrated treatment that combines KM and CM treatments. Further studies are needed to assess the status of integrative medicine treatment, satisfaction with each KM intervention, factors for low satisfaction, and patient requirements. Full article

The online detection of fertilizer solution information is a crucial link in the implementation of intelligent and precise variable fertilization techniques. However, achieving simultaneous rapid online detection of multiple fertilizer components is still challenging. Therefore, a rapid detection method based on spectrophotometry for qualitative and quantitative identification of four fertilizers (typical N, P, and K fertilizers: KNO₃, (NH₄)₂SO₄, KH₂PO₄, and K₂SO₄) was proposed in this work. Full-scan absorption spectra of fertilizer solutions at varying concentrations were obtained using a UV–visible/near-infrared spectrophotometer. By assessing the linear fit between fertilizer concentration and absorbance at each wavelength within the characteristic band, the characteristic wavelengths for KNO₃, (NH₄)₂SO₄, KH₂PO₄, and K₂SO₄ were identified as 214 nm, 410 nm, 712 nm, and 1708 nm, respectively. The identification method of fertilizer type and the prediction model of concentration were constructed based on characteristic wavelength and the Lambert–Beer law. Based on the above analysis, a four-channel photoelectric sensor was designed with four LEDs emitting wavelengths closely matched to characteristic wavelengths for fertilizer detection. A detection strategy of “qualitative analysis followed by quantitative detection” was proposed to realize the online detection of four fertilizer types and their concentrations. Evaluation of the sensor’s performance showed its high stability, with an accuracy of 81.5% in recognizing fertilizer types. Furthermore, the relative error of the sensor detection was substantially less than ±15% for the fertilizer concentrations not exceeding 80 mg/L. These results confirm the capability of the sensor to meet the practical requirements for online detection of four fertilizer types and concentrations in the field of agricultural engineering. Full article

Rates of infertility are rising, and informed decision making is an essential part of reproductive life planning with the knowledge that ART success decreases dramatically while a woman’s age increases and that high costs can often be incurred during fertility treatment. We aimed to determine the current knowledge of infertility and its treatments in the general public through an online survey. We received 360 complete responses. The average age of respondents was 35 years with most respondents being female (90%), heterosexual (88%), white (85%) and university educated (79%). Of the total, 49% had children and 23% had a condition that affects their fertility; 41% had concerns about future fertility and 78% knew someone who had had fertility treatment. Participants’ understanding of basic reproductive biology and causes of infertility varied with correct responses to questions ranging from 44% to 93%. Understanding of IVF outcomes was poorer with only 32% to 55% of responses being correct, and 76% of respondents felt that their education in fertility was inadequate. This survey highlights the inconsistencies in the general public’s understanding of infertility in this relatively educated population. With increasing demands on fertility services and limited public funds, better education is essential to ensure patients are fully informed with regard to their reproductive life planning. Full article

The online detection of fertilizer information is pivotal for precise and intelligent variable-rate fertilizer application. However, traditional methods face challenges such as the complex quantification of multiple components and sensor-induced cross-contamination. This study investigates integrating near-infrared principles with machine learning algorithms to identify fertilizer types and concentrations. We utilized near-infrared transmission spectroscopy and applied Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), and Back-Propagation Neural Network (BPNN) algorithms to analyze full spectrum data. The BPNN model, using S-G smoothing, demonstrated a superior classification performance for the nutrient ions of four fertilizer solutions: HPO₄²⁻, NH₄⁺, H₂PO₄⁻ and K⁺. Optimization using the competitive adaptive reweighted sampling (CARS) method yielded BPNN model RMSE values of 0.3201, 0.7160, 0.2036, and 0.0177 for HPO₄²⁻, NH₄⁺, H₂PO₄⁻, and K⁺, respectively. Building on this foundation, we designed a four-channel fertilizer detection device based on the Lambert–Beer law, enabling the real-time detection of fertilizer types and concentrations. The test results confirmed the device’s robust stability, achieving 93% accuracy in identifying fertilizer types and concentrations, with RMSE values ranging from 1.0034 to 2.4947, all within ±8.0% error margin. This study addresses the practical requirements for online fertilizer detection in agricultural engineering, laying the groundwork for efficient water–fertilizer integration technology aligned with sustainable development goals. Full article

Background and Objectives: Miscarriage is a complication that is influenced by many risk factors that have been reported in different studies and that vary among countries. Despite the influence of various known risk factors for miscarriage, 30% to 50% of miscarriages are from unidentified causes. The aim of this study is to determine the prevalence of miscarriages in Jordan and the associated risk factors. Materials and Methods: A cross-sectional online survey was conducted in Jordan among married women to investigate the prevalence of miscarriages and identify potential risk factors. Results: Women (n = 704) were surveyed, and 17.9% reported a history of miscarriage. The identified risk factors were being an active smoker during pregnancy, having more than four children, having a family history of miscarriage, having fertility problems, receiving medical assistance for conception, and traveling by air during pregnancy. Conclusions: The results suggest that there are both modifiable and non-modifiable risk factors for miscarriages in Jordan and that a proportion of these may be preventable. The findings can be used to enhance patient awareness and inform policy development to decrease the incidence of miscarriage in the country. Full article

As a new project associated with urban agriculture and community forestry in China, developing community gardens has been a concern of urban planners and managers. However, the response of urban residents to this project is still unknown, creating an awareness gap between communities and policy makers. This gap limits the successful implementation of the project. A combined method of on-site and online questionnaire was performed in eight sample residential areas in Wuhan. Results showed that the majority of the 496 respondents had favorable views, although the specific willingness varied. Lack of awareness about community gardens was considered the biggest obstacle restricting residents’ willingness, accounting for 69.15%. Other factors included confused relationships with neighbors (66.13%), imperfect policies (55.44%), and high planting costs (41.94%). The local government was highly expected to plan more land for agriculture use by 76.61% of the respondents. In addition, the respondents desired the government to conduct training on cultivating technologies (58.67%), address possible conflicts (57.46%), supply seeds and fertilizer (54.23%), and provide guidance in processing food products (34.48%). These findings will help local governments and managers draft scientific proposals closely related to the public’s perspectives in implementing community gardens in urban areas and provide informative references for other cities. Full article

Social media platforms are used for support and as resources by people from the endometriosis community who are seeking advice about diagnosis, education, and disease management. However, little is known about the scientific accuracy of information circulated on Instagram about the disease. To fill this gap, this study analysed the evidence-based nature of content on Instagram about endometriosis. A total of 515 Instagram posts published between February 2022 and April 2022 were gathered and analysed using a content analysis method, resulting in sixteen main content categories, including “educational”, which comprised eleven subcategories. Claims within educational posts were further analysed for their evidence-based accuracy, guided by a process which included fact-checking all claims against the current scientific evidence and research. Of the eleven educational subcategories, only four categories (cure, scientific article, symptoms, and fertility) comprised claims that were at least 50% or greater evidence-based. More commonly, claims comprised varying degrees of evidence-based, mixed, and non-evidence-based information, and some categories, such as surgery, were dominated by non-evidence-based information about the disease. This is concerning as social media can impact real-life decision-making and management for individuals with endometriosis. Therefore, this study suggests that health communicators, clinicians, scientists, educators, and community groups trying to engage with the endometriosis online community need to be aware of social media discourses about endometriosis, while also ensuring that accurate and translatable information is provided. Full article

With better survival rates for patients diagnosed with cancer, more attention has been focused on future risks, like fertility decline due to gonadotoxic treatment. In this regard, the emphasis during counselling regarding possible preservation options is often on the treatment itself, meaning that the medical and emotional needs of patients regarding counselling, treatment, and future fertility are often overlooked. This review focuses on patient-reported outcomes (PROs) and patient experiences regarding fertility preservation (FP)—among adolescents and young adults (AYAs) with cancer. A systematic review of the literature, with a systematic search of online databases, was performed, resulting in 61 selected articles. A quality assessment was performed by a mixed methods appraisal tool (MMAT). Based on this search, three important topics emerged: initiating discussion about the risk of fertility decline, acknowledging the importance of future fertility, and recognizing the need for more verbal and written patient-specific information. In addition, patients value follow-up care and the opportunity to rediscuss FP and their concerns about future fertility and use of stored material. A clear FP healthcare pathway can prevent delays in receiving a referral to a fertility specialist to discuss FP options and initiating FP treatment. This patient-centered approach will optimize FP experiences and help to establish a process to achieve long-term follow up after FP treatment. Full article