Search Results (37)

Monitoring parturient cattle during calving is crucial for reducing cow and calf mortality, enhancing reproductive and production performance, and minimizing labor costs. Traditional monitoring methods include direct animal inspection or the use of specialized sensors. These methods can be effective, but impractical in large-scale ranching operations due to time, cost, and logistical constraints. To address this challenge, a network of low-power and long-range IoT sensors combining the Global Navigation Satellite System (GNSS) and tri-axial accelerometers was deployed to monitor in real-time 15 parturient Brangus cows on a 700-hectare pasture at the Chihuahuan Desert Rangeland Research Center (CDRRC). A two-stage machine learning approach was tested. In the first stage, a fully connected autoencoder with time encoding was used for unsupervised detection of anomalous behavior. In the second stage, a Random Forest classifier was applied to distinguish calving events from other detected anomalies. A 5-fold cross-validation, using 12 cows for training and 3 cows for testing, was applied at each iteration. While 100% of the calving events were successfully detected by the autoencoder, the Random Forest model failed to classify the calving events of two cows and misidentified the onset of calving for a third cow by 46 h. The proposed framework demonstrates the value of combining unsupervised and supervised machine learning techniques for detecting calving events in rangeland cattle under extensive management conditions. The real-time application of the proposed AI-driven monitoring system has the potential to enhance animal welfare and productivity, improve operational efficiency, and reduce labor demands in large-scale ranching. Future advancements in multi-sensor platforms and model refinements could further boost detection accuracy, making this approach increasingly adaptable across diverse management systems, herd structures, and environmental conditions. Full article

Farm household pluriactivity has become increasingly prevalent in China; however, its influence on crop structure remains insufficiently explored. This study examines the impact of farm household pluriactivity on crop structure in China, focusing on factor input mechanisms. Based on survey data from 473 farm households in Sichuan Province, this study employs ordinary least squares (OLS), two-stage least squares (2SLS), and mediation analyses to systematically assess the impact of pluriactivity on crop structure through factor input mechanisms. The analysis reveals three key findings. First, rather than reducing the grain planting area, an increase in part-time farming is associated with a significant rise in the proportion of grain cultivation. Second, factor inputs partially mediate this relationship: while pluriactivity tends to reduce staple crop cultivation through mechanisms such as cultivated land transfer-out, land abandonment, and increased non-agricultural labor input, it simultaneously promotes staple crop expansion via enhanced agricultural technical services. Third, heterogeneity tests indicate that the positive effect of pluriactivity on staple crop cultivation is especially pronounced among households in hilly areas and those that have adopted agricultural insurance. These findings provide valuable policy insights for fostering sustainable agricultural transitions and enhancing food security in developing regions. Full article

Real-time monitoring of cotton moisture status and determination of appropriate irrigation thresholds are essential for achieving precision irrigation. Currently employed diagnostic methods based on physiological indicators, remote sensing, or soil moisture measurements typically present limitations including cumbersome procedures, high labor intensity, requirements for specialized technical expertise, and delayed results. To address these challenges, this study investigated the relationship between plant water content and leaf RGB color values (red, green, and blue color values measured using LScolor technology) during the bud, flowering, and boll development stages, with the objective of establishing a predictive model for rapid, real-time moisture status monitoring. Given that leaf position and color values (R, G, and B) of different functional leaves may influence the relationship between leaf color and plant water content, and this relationship varies across different temporal periods, a two-year experiment was conducted. In 2023, leaf color data from the top five functional leaves were measured at five time points daily throughout the irrigation cycle. In 2024, the following four irrigation treatments were established: one conventional irrigation control treatment (CK) and three irrigation treatments at 72% (T1), 70% (T2), and 68% (T3) plant water content thresholds. Results demonstrated that the following: (1) plant water content initially declined during the day and subsequently showed slight recovery, indicating cotton’s particular susceptibility to water stress between 2:30 p.m. and 7:00 p.m.; (2) plant water content continuously decreased across five measurement periods following irrigation during the bud, flowering, and boll development stages, with R and G color values of the five functional leaves showing declining trends between 2:30 p.m. and 7:00 p.m., while B color values exhibited no consistent pattern; (3) correlation analysis revealed significant positive correlations between plant water content and R and G color values of the five functional leaves during the 2:30 p.m. to 5:00 p.m. period, with highly significant correlations observed for the third and fourth leaves from the apex; (4) univariate and bivariate linear regression models were successfully established between cotton water content and R and G color values of the third and fourth leaves from the top; and (5) under 72% plant water content conditions, cotton achieved the highest yield and Irrigation Water Use Efficiency, indicating that 72% represents the optimal irrigation threshold. In conclusion, integrating leaf color–plant water content relationships with the 72% irrigation threshold enables rapid, non-destructive, large-scale diagnosis of cotton moisture status, providing a robust foundation for implementing effective precision irrigation strategies. Full article

Sleep occupies about one-third of human life and is crucial for health, but traditional sleep staging relies on experts manually performing polysomnography (PSG), a process that is time-consuming, labor-intensive, and susceptible to subjective differences between evaluators. With the development of deep learning technologies, particularly the application of convolutional neural networks and recurrent neural networks, significant progress has been made in automatic sleep staging. However, existing methods still face challenges in feature extraction and cross-modal data fusion. This paper introduces an innovative deep learning architecture, MultiSEss, aimed at solving key issues in automatic sleep stage classification. The MultiSEss architecture utilizes a multi-scale convolution module to capture signal features from different frequency bands and incorporates a Squeeze-and-Excitation attention mechanism to enhance the learning of channel feature weights. Furthermore, the architecture discards complex attention mechanisms or encoder–decoder structures in favor of a state–space sequence coupling module, which more accurately captures and integrates correlations between multi-modal data. Experiments show that MultiSEss achieved accuracy results of 83.84% and 82.30% in five-fold cross-subject testing on the Sleep-EDF-20 and Sleep-EDF-78 datasets. MultiSEss demonstrates its potential in improving sleep stage accuracy, which is significant for enhancing the diagnosis and treatment of sleep disorders. Full article

Cortisol is an important marker of hypothalamic-pituitary-adrenal function and follows robust circadian and diurnal rhythms. However, biomarker sampling protocols can be labor-intensive and cost-prohibitive. Objectives: Explore analytical approaches that can handle differing biological sampling frequencies to maximize these data in more detailed and time-dependent analyses. Methods: Healthy adult males [N = 8; 26.1 (±3.1) years; 176.4 (±8.6) cm; 73.1 (±12.0) kg)] completed two 24 h admissions: one at rest and one including a high-intensity exercise session on the cycle ergometer. Serum and salivary cortisol were sampled every 60 and 120 min, respectively. Six alternative sampling profiles were defined by downsampling from the observed data and creating two intermittent sampling profiles. A polynomial (1–6 degrees) validation process was performed, and interpolation was conducted to match the observed data. Model fit and performance were assessed using the coefficient of determination (R²) and the root mean square error (RMSE), as well as an examination of the equivalence, via two one-sided t-tests (TOST), of 24 h cortisol output between the observed and interpolated data. Results: Mean serum cortisol output was higher than salivary cortisol (p < 0.001), and no effect was observed for condition (p = 0.61). Second- and third-degree polynomial regressions were determined to be the optimal models for fitting salivary. TOST tests determined that serum data and estimated 24 h output from these models (with interpolation) provided statistically similar estimates to the observed data (p < 0.05). Conclusions: Second- and third-degree polynomial fits of salivary and serum cortisol provide a reasonable means for interpolation without introducing bias into estimates of 24 h output. This allows researchers to sample biomarkers at biologically relevant frequencies and subsequently match necessary sampling frequencies during the data processing stage of various machine learning workflows. Full article

Background: Obstetric hemorrhage is the leading cause of maternal death worldwide. Obstetric hemorrhage accounts for 27.1% of all maternal death worldwide. Of all obstetric hemorrhages, postpartum hemorrhage (PPH) accounts for 72%. The physiological management of the third stage of labor is a growing area as a preventive measure to control postpartum blood loss. Immediate kangaroo mother care (KMC) is suggested as one of the physiological management methods of the third stage of labor to reduce postpartum blood loss. The duration of the third stage of labor, uterine involution, and amount of postpartum blood loss are the physiological parameters of effective management of the third stage of labor. Examining the absolute effects of immediate KMC on maternal physiological parameters is needed in different populations with different settings. Thus, this study aimed to examine the effects of immediate KMC on uterine involution and postpartum blood loss. Methods: A quasi-experimental comparative design was conducted in the labor and delivery room at Maternity and Children Hospital, Makkah, Saudi Arabia. A sample of 80 women was divided into two equal groups: a treatment group that underwent immediate KMC and a control group that received routine care. Instrument: A questionnaire developed by the researchers was used to collect the data. Results: The effects of immediate KMC were significant concerning uterine involution and regarding the uterine position immediately after placenta separation (70% at umbilicus, χ² = 8.5, p < 0.01), postpartum blood loss (χ² = 76.098, p < 0.00), the heaviness of lochia (χ² = 44.679, p = 0.00), and the number of pads used in the first 24 h (p < 0.001). Full article

Background: The optimum model of perinatal care for low-risk pregnancies has been a topic of debate. Obstetrician-led care tends to perform unnecessary interventions, whereas the quality of midwife-led care has been subject to debate. This review aimed to assess whether midwife-led care reduces childbirth intervention and whether this comes at the expense of maternal and neonatal wellbeing. Methods: PubMed, Scopus, Cochrane Library, and Web of Science were systematically searched for relevant studies. Studies were checked for eligibility by screening the titles, abstracts, and full texts. We performed meta-analyses using the inverse variance method using RevMan software version 5.3. We pooled data using the risk ratio and mean difference with the 95% confidence interval. Results: This review included 44 studies with 1,397,320 women enrolled. Midwife-led care carried a lower risk of unplanned cesarean and instrumental vaginal deliveries, augmentation of labor, epidural/spinal analgesia, episiotomy, and active management of labor third stage. Women who received midwife-led care had shorter hospital stays and lower risks of infection, manual removal of the placenta, blood transfusion, and intensive care unit (ICU) admission. Furthermore, neonates delivered under midwife-led care had lower risks of acidosis, asphyxia, transfer to specialist care, and ICU admission. Postpartum hemorrhage, perineal tears, APGAR score < 7, and other outcomes were comparable between the two models of management. Conclusions: Midwife-led care reduced childbirth interventions with favorable maternal and neonatal outcomes in most cases. We recommend assigning low-risk pregnancies to midwife-led perinatal care in health systems with infrastructure allowing for smooth transfer when complications arise. Further research is needed to reflect the situation in low-resource countries. Full article

The conservation of agricultural heritage systems (AHSs) has played a pivotal role in fostering the sustainable development of agriculture and safeguarding farmers’ livelihoods and food security worldwide. This significance is particularly evident in the case of tea AHSs, due to the economic and nutritional value of tea products. Taking the Anxi Tieguanyin Tea Culture System (ATTCS) and Fuding White Tea Culture System (FWTCS) in Fujian Province as examples, this study uses statistical analyses and a multinomial logistic regression model to assess and compare farmer livelihood and food security at the tea AHS sites. The main findings are as follows. First, as the tea industries are at different stages of development, compared with agricultural and non-agricultural part-time households, the welfare level of pure agricultural households is lowest in the ATTCS, while welfare is the highest in the FWTCS. Second, factors such as the area of tea gardens and the number of laborers significantly affect farmers’ livelihood strategies transformation from pure agricultural households to agricultural part-time households in the ATTCS and FWTCS. Third, the high commodity rate of tea products, combined with compound cultivation in tea gardens, provides local people with essential sources of income, food, and nutrients, so as to improve food security in the ATTCS and FWTCS. These findings are essential for designing policies to ensure farmers’ livelihoods and food security through AHSs and other sustainable agriculture. Full article

Objective: This study aimed to clarify the relationship between fluctuations in uterine stiffness during the third stage of labor and blood loss upon placenta delivery using shear wave elastography. Methods: This prospective cohort study enrolled consecutive singleton pregnant women above 37 weeks of gestation who delivered infants transvaginally at a single perinatal center. Shear wave velocities (SWV) were continuously measured during the third stage of transvaginal labor using transabdominal ultrasound and these values were compared between groups with large (≥500 g) and small amounts of bleeding during this stage. Results: In total, 8 cases of large bleeding and 47 cases of small bleeding were compared. The large amount of bleeding group had a significantly lower median of minimum SWV values (0.97 [0.52–1.01] m/s than the small amount of bleeding group (1.25 [1.04–1.48] m/s p = 0.02). However, no significant differences were observed between the two groups in terms of median, mean, and maximum SWV values. The time from delivery of the infant to placental delivery was significantly longer in the large amount of bleeding group (median time: 370.5 s vs. 274 s, p < 0.05). Conclusion: Ultrasound quantification of uterine stiffness using shear wave elastography demonstrated that uterine contractions may influence the biological hemostasis of the uterus during the third stage of labor. Baseline uterine stiffness was weak and a longer duration of placental separation might be associated with cases of large amounts of bleeding during this stage. Full article

The use of hydrotherapy during childbirth has gained relevance due to the demand for natural childbirth and greater respect for the woman’s choice. Studies have shown benefits such as less use of epidural analgesia, increased ability to cope with pain, shorter labor, and a better overall birth experience. Objective: The main objective of this study was to generate further evidence on maternal and birth outcomes associated with the use of hydrotherapy during labor, specifically aiming to describe the effects of water immersion during all stages of labor (first, second, and third) on women. Methodology: A retrospective cohort study was carried out on a random sample of women who gave birth at the Costa del Sol Hospital between January 2010 and December 2020. The calculated sample size was 377 women and the data were extracted from their partograms. After data extraction, two groups were formed: one group used hydrotherapy during childbirth (n = 124), while the other group included women who did not use hydrotherapy during the childbirth process (n = 253). Results: The results highlight significant differences in pain perception, analgesia use, types of labor, and delivery times between the two groups. Women who did not use hydrotherapy reported higher pain perception, with a median (IQR) of 8 (7–9) on a numerical scale, compared to a median (IQR) of 6 (5–7) for the hydrotherapy group. Furthermore, the group without hydrotherapy required epidural analgesia in 40% of cases, while in the hydrotherapy group, it was only necessary in 20%. In terms of the type of delivery, the hydrotherapy group had more spontaneous vaginal deliveries compared to the non-hydrotherapy group, which had more operative vaginal deliveries. The overall duration of labor was longer in the hydrotherapy group, especially in women who arrived at the hospital late in labor. Conclusions: Hydrotherapy is associated with a longer time to delivery. Women with a higher pain tolerance tend to opt for hydrotherapy instead of epidural analgesia. Full article

(1) Background: This challenge is exacerbated by the aging of the rural population, leading to a scarcity of available manpower. To address this issue, the automation and mechanization of outdoor vegetable cultivation are imperative. Therefore, developing an automated cultivation platform that reduces labor requirements and improves yield by efficiently performing all the cultivation activities related to field vegetables, particularly onions and garlic, is essential. In this study, we propose methods to identify onion and garlic plants with the best growth status and accurately predict their live bulb weight by regularly photographing their growth status using a multispectral camera mounted on a drone. (2) Methods: This study was conducted in four stages. First, two pilot blocks with a total of 16 experimental units, four horizontals, and four verticals were installed for both onions and garlic. Overall, a total of 32 experimental units were prepared for both onion and garlic. Second, multispectral image data were collected using a multispectral camera repeating a total of seven times for each area in 32 experimental units prepared for both onions and garlic. Simultaneously, growth data and live bulb weight at the corresponding points were recorded manually. Third, correlation analysis was conducted to determine the relationship between various vegetation indexes extracted from multispectral images and the manually measured growth data and live bulb weights. Fourth, based on the vegetation indexes extracted from multispectral images and previously collected growth data, a method to predict the live bulb weight of onions and garlic in real time during the cultivation period, using functional regression models and machine learning methods, was examined. (3) Results: The experimental results revealed that the Functional Concurrence Regression (FCR) model exhibited the most robust prediction performance both when using growth factors and when using vegetation indexes. Following closely, with a slight distinction, Gaussian Process Functional Data Analysis (GPFDA), Random Forest Regression (RFR), and AdaBoost demonstrated the next-best predictive power. However, a Support Vector Machine (SVM) and Deep Neural Network (DNN) displayed comparatively poorer predictive power. Notably, when employing growth factors as explanatory variables, all prediction models exhibited a slightly improved performance compared to that when using vegetation indexes. (4) Discussion: This study explores predicting onion and garlic bulb weights in real-time using multispectral imaging and machine learning, filling a gap in research where previous studies primarily focused on utilizing artificial intelligence and machine learning for productivity enhancement, disease management, and crop monitoring. (5) Conclusions: In this study, we developed an automated method to predict the growth trajectory of onion and garlic bulb weights throughout the growing season by utilizing multispectral images, growth factors, and live bulb weight data, revealing that the FCR model demonstrated the most robust predictive performance among six artificial intelligence models tested. Full article

There is a growing interest in wearable inertial sensors to monitor and analyze the movements of pregnant women. The noninvasive and discrete nature of these sensors, integrated into devices accumulating large datasets, offers a unique opportunity to study the dynamic changes in movement patterns during the rapid physical transformations induced by pregnancy. However, the final cut of the third trimester of pregnancy, particularly the first stage of labor up to delivery, remains underexplored. The growing popularity of “walking epidural”, a neuraxial analgesia method allowing motor function preservation, ambulation, and free movement throughout labor and during delivery, opens new opportunities to study the biomechanics of labor using inertial sensors. Critical research gaps exist in parturient fall prediction and detection during walking epidural and understanding pain dynamics during labor, particularly in the presence of pelvic girdle pain. The analysis of fetal descent, upright positions, and their relationship with dynamic pelvic movements facilitated by walking during labor is another area where inertial sensors can play an interesting role. Moreover, as contemporary obstetrics advocate for less restricted or non-restricted movements during labor, the role of inertial sensors in objectively measuring the quantity and quality of women’s movements becomes increasingly important. This includes studying the impact of epidural analgesia on maternal mobility, walking patterns, and associated obstetrical outcomes. In this paper, the potential use of wearable inertial sensors for gait analysis in the first stage of labor is discussed. Full article

Background and Objectives: Accurate diagnosis of labor progress is crucial for making well-informed decisions regarding timely and appropriate interventions to optimize outcomes for both the mother and the fetus. The aim of this study was to assess the progress of the second stage of labor using intrapartum ultrasound. Material and methods: This was a prospective study (December 2022–December 2023) conducted at the Third Department of Obstetrics and Gynecology, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece. Maternal–fetal and labor characteristics were recorded, and two ultrasound parameters were measured: the angle of progression (AoP) and the head–perineum distance (HPD). The correlation between the two ultrasonographic values and the maternal–fetal characteristics was investigated. Multinomial regression analysis was also conducted to investigate any potential predictors of the mode of delivery. Results: A total of 82 women at the second stage of labor were clinically and sonographically assessed. The mean duration of the second stage of labor differed between vaginal and cesarean deliveries (65.3 vs. 160 min; p-value < 0.001) and between cesarean and operative vaginal deliveries (160 vs. 88.6 min; p-value = 0.015). The occiput anterior position was associated with an increased likelihood of vaginal delivery (OR: 24.167; 95% CI: 3.8–152.5; p-value < 0.001). No significant differences were identified in the AoP among the three different modes of delivery (vaginal: 145.7° vs. operative vaginal: 139.9° vs. cesarean: 132.1°; p-value = 0.289). The mean HPD differed significantly between vaginal and cesarean deliveries (28.6 vs. 41.4 mm; p-value < 0.001) and between cesarean and operative vaginal deliveries (41.4 vs. 26.9 mm; p-value = 0.002); it was correlated significantly with maternal BMI (r = 0.268; p-value = 0.024) and the duration of the second stage of labor (r = 0.256; p-value = 0.031). Low parity (OR: 12.024; 95% CI: 6.320–22.876; p-value < 0.001) and high HPD (OR: 1.23; 95% CI: 1.05–1.43; p-value = 0.007) were found to be significant predictors of cesarean delivery. Conclusions: The use of intrapartum ultrasound as an adjunctive technique to the standard clinical evaluation may enhance the diagnostic approach to an abnormal labor progress and predict the need for operative vaginal or cesarean delivery. Full article

Most preterm births occur in the late preterm period. While prematurity-related adverse outcomes are significantly diminished when birth occurs during this period, these infants are still at increased risk of complications. Parity affects the incidence of obstetric complications. The purpose of this study was to determine whether parity impacts the risk of spontaneous late preterm birth (SLPTB) and associated complications. A retrospective observational cohort study was conducted. Patients were divided into three study groups according to parity. The primary outcome was the rate of SLPTB in each group. Secondary outcomes were unplanned cesarean delivery (UCD), prolonged third stage of labor respiratory distress syndrome (RDS), transient tachypnea of the newborn (TTN), intraventricular hemorrhage (IVH), neonatal hypoglycemia, duration of NICU admission, neonatal death, and composite adverse neonatal outcome (CANO). Primiparas were more likely to have SLPTB, UCD, and CANO compared to multiparas (2.6% vs. 1.9% OR 1.5 [1.3–1.7] p < 0.01) (4.1% vs. 1.3% OR 2.7 [1.2, 5.9] p < 0.01) (8.5% vs. 4.2 OR 2.1 [1.3–3.5] p = 0.002) and grandmultiparas (2.6% vs. 1.7% OR 1.4 [1.2–1.5] p < 0.001) 8.5% vs. 4.4% OR 2.0 [1.1, 3.8], p = 0.01) but no difference in UCD compared to grandmultiparas (4.1% vs. 3.3% OR 1.2 [0.6–2.7] p = 0.28). Primiparas are at increased risk of SLPTB and UCD, and this is accompanied by an increased risk of adverse neonatal outcomes. Full article

Angular pregnancies are rare and difficult to diagnose. Evidence suggests they are associated with a higher risk of intrauterine growth restriction and abnormal third stage of labor due to a retained placenta. The lack of standardized AP diagnostic criteria impacts on their correct identification and makes the treatment of potential complications challenging. We present a case of the successful conservative surgical management of a retained placenta after a term AP also complicated by intrauterine growth restriction. Moreover, to identify the best evidence regarding AP diagnostic criteria and retained placenta therapeutic approaches, we have realized an expert literature review. Full article