Miracle-dz/UniFAR-FTUs huggingface dataset:
Participatory Guarantee Systems, a more inclusive organic certification alternative? Unboxing certification costs and farm inspections in PGS based on a case study approach

The importance of Participatory Guarantee Systems (PGS) as an alternative organic certification to third-party certification (TPC) has grown remarkably in recent years. PGS are argued to be less costly than TPC, and adopt the exchange of advice and knowledge as a key element of farm inspections. For these reasons, PGS are promoted as being more accessible for smallholder farmers in low-income countries and as a tool for supporting organic conversion and standard compliance, and ultimately contributing to food system sustainability. PGS certification costs have not yet been studied thoroughly and empirical studies on PGS farm inspections are rare. This paper applies a mixed-methods case study approach in a Costa Rican PGS initiative and explores explicit and implicit PGS certification costs and PGS farm inspections. The framework for assessing actor participation in PGS and transaction cost theory were used as the theoretical foundation. Semi-structured interviews (n = 10), PGS member surveys (n = 17), and participant observations of PGS farm inspections (n = 11) were conducted and complemented with data from internal documents and informal interviews. The results showed that reduced explicit costs for PGS certification were accompanied by substantial implicit costs in terms of the time dedicated to the PGS certification process. These implicit costs were largely attributed to the legal requirements for PGS, and were borne by only a few members, resulting in differences in total certification costs between PGS members. Despite legal formalization, information exchange was an important part of PGS farm inspections. Against the backdrop of small audit teams and low participation in farm inspections, PGS members implementing farm inspections nurtured transparency and an exchange of experience within the PGS. The study provides novel insight into PGS, and defines cost components and PGS characteristics affecting PGS certification costs that may also be relevant to other PGS initiatives and related fields of action.
A regional map of mangrove extent for Myanmar, Thailand, and Cambodia shows losses of 44% by 1996

Southeast Asia is home to some of the planet’s most carbon-dense and biodiverse mangrove ecosystems. There is still much uncertainty with regards to the timing and magnitude of changes in mangrove cover over the past 50 years. While there are several regional to global maps of mangrove extent in Southeast Asia over the past two decades, data prior to the mid-1990s is limited due to the scarcity of Earth Observation (EO) data of sufficient quality and the historical limitations to publicly available EO. Due to this literature gap and research demand in Southeast Asia, we conducted a classification of mangrove extent using Landsat 1-2 MSS Tier 2 data from 1972 to 1977 for three Southeast Asian countries: Myanmar, Thailand, and Cambodia. Mangrove extent land cover maps were generated using a Random Forest machine learning algorithm that effectively mapped a total of 15,420.51 km2. Accuracy assessments indicated that the classification for the mangrove and non-mangrove class had a producer’s accuracy of 80% and 98% user’s accuracy of 90% and 96%, and an overall accuracy of 95%. We found a decline of 6,830 km2 between the 1970s and 2020, showing that 44% of the mangrove area in these countries has been lost in the past 48 years. Most of this loss occurred between the 1970s and 1996; rates of deforestation declined dramatically after 1996. This study also elaborated on the nature of mangrove change within the context of the social and political ecology of each case study country. We urge the remote sensing community to empathetically consider the local need of those who depend on mangrove resources when discussing mangrove loss drivers.
Total serum FGF-21 levels positively relate to visceral adiposity differently from its functional intact form

Objective Increased Fibroblast Growth Factor-21 (FGF-21) circulating levels have been described in obesity. In this observational study, we analysed a group of subjects with metabolic disorders to unravel the putative link between visceral adiposity and FGF-21 serum levels. Methods Total and intact serum FGF-21 concentration was measured with an ELISA assay respectively in 51 and 46 subjects, comparing FGF-21 levels in dysmetabolic conditions. We also tested Spearman’s correlations between FGF-21 serum levels and biochemical and clinical metabolic parameters. Results FGF-21 was not significantly increased in high-risk conditions such as visceral obesity, Metabolic Syndrome, diabetes, smoking, and atherosclerosis. Waist Circumference (WC), but not BMI, positively correlated with total FGF-21 levels (r=0.31, p <0.05), while HDL-cholesterol (r=-0.29, p <0.05) and 25-OH Vitamin D (r=-0.32, p <0.05) showed a significant negative correlation with total FGF-21. ROC analysis of FGF-21 in prediction of increased WC, showed that patients with total FGF-21 level over cut-off value of 161.47 pg/mL presented with impaired FPG. Conversely, serum levels of the intact form of FGF-21 did not correlate with WC and other metabolic biomarkers. Conclusion Our newly calculated cut-off for total FGF-21 according to visceral adiposity identified subjects with fasting hyperglycemia. However, waist circumference correlates with total FGF-21 serum levels but does not correlate with intact FGF-21, suggesting that functional FGF-21 does not necessarily relate with obesity and metabolic features.
New insights into hypothalamic neurogenesis disruption after acute and intense stress: implications for microglia and inflammation

In recent years, the hypothalamus has emerged as a new neurogenic area, capable of generating new neurons after development. Neurogenesis-dependent neuroplasticity seems to be critical to continuously adapt to internal and environmental changes. Stress is a potent environmental factor that can produce potent and enduring effects on brain structure and function. Acute and chronic stress is known to cause alterations in neurogenesis and microglia in classical adult neurogenic regions such as the hippocampus. The hypothalamus is one of the major brain regions implicated in homeostatic stress and emotional stress systems, but little is known about the effect of stress on the hypothalamus. Here, we studied the impact of acute and intense stress (water immersion and restrain stress, WIRS), which may be considered as an inducer of an animal model of posttraumatic stress disorder, on neurogenesis and neuroinflammation in the hypothalamus of adult male mice, focusing on three nuclei: PVN, VMN and ARC, and also in the periventricular area. Our data revealed that a unique stressor was sufficient to provoke a significant impact on hypothalamic neurogenesis by inducing a reduction in the proliferation and number of immature neurons identified as DCX+ cells. These differences were accompanied by marked microglial activation in the VMN and ARC, together with a concomitant increase in IL-6 levels, indicating that WIRS induced an inflammatory response. To investigate the possible molecular mechanisms responsible for neuroplastic and inflammatory changes, we tried to identify proteomic changes. The data revealed that WIRS induced changes in the hypothalamic proteome, modifying the abundance of three and four proteins after 1 h or 24 h of stress application, respectively. These changes were also accompanied by slight changes in the weight and food intake of the animals. These results are the first to show that even a short-term environmental stimulus such as acute and intense stress can have neuroplastic, inflammatory, functional and metabolic consequences on the adult hypothalamus.
A comparison of two group cognitive behavioral therapy protocols for anxiety in urban schools: appropriateness, child outcomes, and cost-effectiveness

Background Cognitive behavioral therapy (CBT) for pediatric anxiety is efficacious for reducing anxiety symptoms and improving functioning, but many children are unable to access CBT for anxiety in community settings. Schools are an important setting in which children access mental health care, including therapy for anxiety. In this setting, therapy is usually delivered by Masters-level therapists. Objectives Friends for Life (FRIENDS), a 12-session, manualized, group CBT program for anxiety has demonstrated effectiveness when implemented in schools. However, prior research has also found challenges regarding feasibility and cultural fit when delivering FRIENDS in the urban school context. To address these challenges, we adapted FRIENDS for implementation in the school setting so that it might be more feasible and culturally appropriate for low-income, urban schools in the United States, while maintaining the core components of treatment. The current study uses a mixed-method approach to compare the effectiveness, cost-effectiveness, and perceived appropriateness of FRIENDS and CATS when delivered by Masters-level therapists with train-the-trainer support. Materials and methods First, we compared change scores for student outcomes (i.e., child-report MASC-2 total score, parent-report MASC-2 total score, teacher-report Engagement and Disaffection subscale scores) from pre- to post- treatment between students receiving FRIENDS and students receiving CATS to assess whether the two conditions resulted in equivalent outcomes. Second, we compared the cost and cost-effectiveness between the groups. Finally, we used an applied thematic analysis to compare appropriateness of the interventions as perceived by therapists and supervisors. Results The mean change score for the child-reported MASC-2 was 1.9 (SE = 1.72) points in the FRIENDS condition and 2.9 (SE = 1.73) points in the CATS condition; results indicated that the conditions were similar in their treatment effects, and symptom reductions were small in both groups. The modified protocol, CATS, was shown to cost significantly less to implement compared to FRIENDS and showed greater cost-effectiveness. Finally, compared to therapists and supervisors in the CATS condition, therapists and supervisors in the FRIENDS condition more strongly described aspects of the intervention that were not appropriate for their context and in need of more extensive adaptations. Conclusion Relatively brief, group CBT for anxiety, with adaptations to improve cultural fit, is a promising approach to treat youth anxiety symptom when delivered by school-based therapists with train-the-trainer implementation support.
Risk assessment model for sleep disturbance based on gastrointestinal myoelectrical activity in middle-aged and elderly people

Background Sleep disturbance has become a considerable factor affecting the quality of life for middle-aged and elderly people; however, there are still many obstacles to screening sleep disturbance for those people. Given the growing awareness of the association between gastrointestinal function and sleep disturbance, our study aims to predict the risk of sleep disturbance using gastrointestinal electrophysiological signals. Methods The Pittsburgh Sleep Quality Index and gastrointestinal electrophysiological signals of 914 participants in western China were used to establish the model. Demographic characteristics and routine blood test were collected as covariates. Participants were randomly assigned into two sets with a 7:3 ratio for training and validation. In the training set, the least absolute shrinkage and selection operator (LASSO) regression and stepwise logistic regression were used, respectively for variables selection and optimization. To assess the model performance, receiver operator characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) were utilized. Then, validation was performed. Results Thirteen predictors were chosen from 46 variables by LASSO regression. Then, age, gender, percentage of normal slow wave and electrical spreading rate on the pre-meal gastric channel, dominant power ratio on the post-meal gastric channel, coupling percent and dominant frequency on the post-meal intestinal channel were the seven predictors reserved by logistic regression. The area under ROC curve was 0.65 in the training set and 0.63 in the validation set, both exhibited moderate predictive ability. Furthermore, by overlapping the DCA results of two data-sets, there might be clinical net benefit if 0.35 was used as reference threshold for high risk of sleep disturbance. Conclusion The model performs a worthy predictive potency for sleep disturbance, which not only provides clinical evidence for the association of gastrointestinal function with sleep disturbance, but also can be considered as an auxiliary assessment for screening sleep disturbance.
Relationships between microplastic pollution and land use in the Chongqing section of the Yangtze River

Microplastic (MP) pollution is an emerging environmental issue. While it is evident that human activities are the primary source of MP pollution, the relationship between land use and MP pollution in rivers requires further study. Using data from 26 MP monitoring sites in the Chongqing section of the Yangtze River, we analyzed MP data along with land use and construction data. We improved the model for the Land Use Intensity Composite Index (LUI) to obtain correlations between land use and MP pollution. We found: 1) The concentration of microplastics in surface water (MPsw) in the Chongqing section of the Yangtze River gradually declined from upstream (central urban section of Chongqing) to downstream (northeastern section of Chongqing). 2) From 2016 to 2020, MPsw in the Chongqing section of the Yangtze River decreased by about 7.5-fold. 3) MPsw in the Chongqing section of the Yangtze River was positively correlated with the amount of surrounding urban and garden land. When the buffer radius was 1 km, the correlation coefficient was 0.53 (p < 0.05). With increasing buffer radius, the correlation between land use and gradually weakened. 4) The LUI was also strongly correlated with MPsw. The correlation was strongest when the buffer radius was 1 km, with a correlation coefficient of 0.59 (p < 0.05). At all buffer scales, microplastics in sediments (MPss) was positively correlated with village land and arable land . Unlike MPsw, as the buffer radius increased, the strength of the correlations between land use types and MPss gradually increased. The LUI was strongly correlated with MPsw . As the buffer radius increases, the correlation between the two gradually weakens. These results provide information useful for remediation and reduction of MP in river systems.
Why don't politicians talk about meat? The political psychology of human-animal relations in elections

Building on literature from political science and psychology, I argue that political attention on animals and animal-friendly political candidates cause voter backlash. I test this using two different kinds of experiments with large, representative samples. I ask respondents to consider political candidates running for office in a U.S. presidential primary context. I find that, overall, political attention on the need to reduce meat consumption for environmental reasons caused voter backlash compared to both a control condition and attention on the need to reduce reliance on gasoline-powered vehicles (also for environmental reasons). But, the heterogeneous effects of partisan identification were strong: voter backlash was mainly driven by Republicans and Democrats were neutral. Surprisingly, candidates who put attention on farm animal rights during elections faced no voter backlash from Republicans or Democrats. Animal-friendly candidates, particularly Black women and Latinas, with attributes that demonstrate personal concern for farm animals and strong support for animal rights generally fared very well in elections, receiving large boosts in voter support. This work launches a research agenda in political psychology that “brings the animal in” to politics.
Enhanced Zero-Knowledge and Identity-Based Authentication with integrated Key Exchange for Internet of Things

Sensitive data have to be communicated via secure channels generally set up by using cryptography. This needs an authentic key exchange, which in turn requires an authentication process. However, the Internet of Things (IoT) includes its own challenges and security requirements. This paper introduces a novel signature algorithm and handshake protocol combining a zero-knowledge proof method being based on the graph isomorphism problem with an identity-based scheme to provide authentication with integrated key exchange while meeting the IoT challenges and security requirements. Our approach applies a way to parallelly perform multiple rounds usually needed by zero-knowledge proofs while retaining the same security level. Moreover, we present a graph compression algorithm providing a compression ratio of up to ca. 7:1. Our handshake protocol is resistant to active man in the middle attacks and does not require any public data pre-distribution or secret pre-sharing. Additionally, no third party has to hold any device-specific authentication data. Furthermore, our approach is application-independent and does not require any additional components or procedures. This paper also evaluates the high performance of our approach with regard to multiple affecting factors.
Investigating Author Research Relatedness through Crowdsourcing: A Replication Study on MTurk

Determining the relatedness of publications by detecting similarities and connections between researchers and their outputs can help science stakeholders worldwide to find areas of common interest and potential collaboration. To this end, many studies have tried to explore authorship attribution and research similarity detection through the use of automatic approaches. Nonetheless, inferring author research relatedness from imperfect data containing errors and multiple references to the same entities is a long-standing challenge. In a previous study, we conducted an experiment where a homogeneous crowd of volunteers contributed to a set of author name disambiguation tasks. The results demonstrated an overall accuracy higher than 75% and we also found important effects tied to the confidence level indicated by participants in correct answers. However, this study left many open questions regarding the comparative accuracy of a large heterogeneous crowd with monetary rewards involved. This paper seeks to address some of these unanswered questions by repeating the experiment with a crowd of 140 online paid workers recruited via MTurk’s microtask crowdsourcing platform. Our replication study shows high accuracy for name disambiguation tasks based on authorship-level information and content features. These findings can be of greater informative value since they also explore hints of crowd behavior activity in terms of time duration and mean proportion of clicks per worker with implications for interface and interaction design.
Comparing Stacking Ensemble and Deep Learning for Software Project Effort Estimation

This study focuses on improving the accuracy of effort estimation by employing ensemble, deep learning, and transfer learning techniques. An ensemble approach is utilized, incorporating XGBoost, Random Forest, and Histogram Gradient Boost as generators to enhance predictive capabilities. The performance of the ensemble method is compared against both the deep learning approach and the PFA-IFPUG technique. Statistical criteria including MAE, SA, MMRE, PRED(0.25), MBRE, MIBRE, and relevant information related to MMRE and PRED(0.25) are employed for evaluation. The results demonstrate that combining regression models with Random Forest as the final regressor and XGBoost and Histogram Gradient Boost as prior generators yields more accurate effort estimation than other combinations. Furthermore, the findings highlight the potential of transfer learning in the deep learning method, which exhibits superior performance over the ensemble approach. This approach leverages pre-trained models and continuously improves performance by training on new datasets, providing valuable insights for cross-company and cross-time effort estimation problems. The ISBSG dataset is used to build the pre-trained model, and the inductive transfer learning approach is verified based on the Desharnais, Albrecht, Kitchenham, and China datasets. The study underscores the significance of transfer learning and the integration of domain-specific knowledge from existing models to enhance the performance of new models, thereby improving accuracy, reducing errors, and enhancing predictive capabilities in effort estimation.
A Wideband and Low-Noise CMOS-Integrated X-Hall Current Sensor Operating in Current Mode

Power electronic circuits are moving toward higher switching frequencies, exploiting the capabilities of novel devices, so as to shrink the dimension of the passive components. This trend demands sensors able to operate at such high frequencies. This article aims to demonstrate the broadband capability of a fully integrated CMOS current sensor based on the X-Hall approach and configured in current mode, by alleviating the impact of stray capacitive loading at the probe–readout interface. Current-mode operation enables the usage of a transimpedance amplifier (TIA) as a readout circuit, offering better bandwidth, noise, and power performance than conventional instrumentation amplifiers (IAs). The system exploits a common-mode (CM) control system to operate the submodules at different supply voltages, respectively, 5 V for the X-Hall probe to achieve high sensitivity, and 1.2 V for the readout to exploit the high transition frequency of transistors with reduced oxide thickness. A chip-on-board mounting limits the parasitic inductive effects on the host printed circuit board (PCB). The developed prototype achieves a maximum acquisition bandwidth of 12 MHz. With a power consumption of 11.46 mW and a resolution of 39 mArms, it presents a sensitivity of 8% $T^{-1}$ and achieves an FoM of 569 MHz/ $\text{A}^{2}$ mW, which is significantly higher than the current state-of-the-art hybrid Hall/coil solutions. The prototype is implemented in a 90-nm microelectronic process from STMicroelectronics and occupies a silicon area of 2.4 mm2.
Constraining the Multiscale Structure of Geophysical Fields in Machine Learning: The Case of Precipitation

The use of deep-learning algorithms for estimating the value of geophysical variables from remotely sensed information is rapidly expanding. The typical objective function minimized in such algorithms is the mean square error (MSE), which is known to lead to smooth estimates with compressed dynamical range as compared to the true distribution of the variable of interest. Here, we introduce and evaluate alternative objective functions, focusing on the retrieval of precipitation rates from satellite passive microwave radiometric measurements using a deep convolutional neural network. For this testbed application, the results show that explicitly imposing the preservation of the statistical distribution and spatial wavelet power spectrum of the target variable allows to accurately reproduce extreme values and sharp gradients across multiple scales.