Miracle-dz/UniFAR-FTUs huggingface dataset:
Low Fasting Oxytocin Levels Are Associated With Psychopathology in Weight-Recovered Anorexia Nervosa

Anorexia nervosa (AN), a psychiatric disorder characterized by restriction of food intake despite severe weight loss, is associated with increased comorbid anxiety and depression. Secretion of oxytocin, an appetite-regulating neurohormone with anxiolytic and antidepressant properties, is abnormal in AN. The link between oxytocin levels and psychopathology in AN has not been well explored. We performed a cross-sectional study of 80 women ages 18-45 years old [(20 AN, 26 weight recovered AN (ANWR) and 34 healthy controls (HC)] investigating the relationship between basal oxytocin levels and disordered eating psychopathology, anxiety, and depressive symptoms. Fasting serum oxytocin levels were obtained and the following self-report measures were used to assess psychopathology: Eating Disorder Examination Questionnaire, State Trait Anxiety Inventory, and Beck Depression Inventory – II. We found that fasting oxytocin levels were low in ANWR compared to HC (p=0.0004). In ANWR but not AN, oxytocin was negatively associated with disordered eating psychopathology (r=-0.39, p=0.0496) and anxiety symptoms (state anxiety: r=-0.53, p=0.006; trait anxiety: r=-0.49, p=0.01). Furthermore, ANWR with significant disordered eating psychopathology, anxiety symptoms, or depressive symptoms had lower oxytocin levels compared to those with minimal or no symptoms. We conclude that dysregulation of oxytocin pathways may contribute to persistent psychopathology after weight recovery from anorexia nervosa.
Trends and progress in studying butter ﬂ y migration

Several hundred butter ﬂ y species show some form of migratory behaviour. Here we identify how the methodologies available for studying butter ﬂ y migration have changed over time, and document geographic and taxonomic foci in the study of butter ﬂ y migration. We review publications on butter ﬂ y migration published in six languages (English, Simpli ﬁ ed Chinese, Traditional Chinese, Japanese, Korean, and Spanish), summarise how migration in butter ﬂ ies has been studied, explore geographic and taxonomic patterns in the knowledge base, and outline key future research directions. Using English search keywords, we found only 58 studies from Asia; however, after searching in local languages, we found an additional 98 relevant studies. Overall, butter ﬂ y migration studies are mostly from North America and Europe. Most studies focus on three species: monarch ( Danaus plexippus ), painted lady ( Vanessa cardui ) and red admiral ( Vanessa atalanta ). About 62% of publications are focused on the monarch, with nearly 50% of migratory butter ﬂ y species mentioned in only a single paper. Several research methods have been applied to ascribe migratory status and to study the physiology, neurobiology, and ecology of migration; however, virtually all this research is on a handful of species. There remain hundreds of species for which we do not understand the comprehensive seasonal pattern of movement, ﬂ ight destinations, wintering, or breeding grounds. A better understanding of movement ecology and migratory connectivity is needed to effectively conserve migratory butter ﬂ ies. It is essential that research becomes more geographically and linguistically representative since migrants frequently cross political borders and international cooperation is necessary for their conservation.
Abundance, distribution and conservation threats of African wild dog (Lycaon pictus) in the Loliondo Game Controlled Area, Tanzania

Abstract. Masenga E, Hasan SN, Japhet K. 2018. Abundance, distribution, and conservation threats of African wild dog (Lycaon pictus) in the Loliondo Game Controlled Area, Tanzania. Asian J For 2: 31-41. Assessment of abundance, distribution, and conservation threats to African wild dogs was conducted in Loliondo Game Controlled Area (LGCA), northern Tanzania. Specifically, the study focused on determining population size and structure, spatial distribution, attitudes of local people towards wild dogs and wild dog conservation and threats impacting the species. Semi-structured interviews, diurnal random searches, internal and external examinations of wild dogs carcasses examined, and night transect surveys were employed. Eight packs with a total of 132 recognized individuals at an average pack size of 16.50 ± SD 7.50 individuals were recorded. Pack sizes 3 individuals were reported to be sighted mostly and of all respondents (n= 210), only 26% were able to recognize wild dog sexes. The density of both known and unknown wild dogs was 0.19 animals/km2, higher compared to other carnivores. In terms of distribution, most of the packs were concentrated in the northern part as compared to the central and southern parts of LGCA. The species was observed to occur most in woodland type of vegetation. Interestingly, 55.30% of respondents showed a positive attitude towards wild dogs and wild dog conservation despite that 52.90% of respondents dismissed lack of any conservation action or strategy in place towards conserving the species. However, poisoning and Canine Distemper Virus were identified as the main threats. Therefore, conserving African wild dogs in LGCA requires mult-approach conservation efforts (i.e. awareness rising to community, fitting radio telemetry to the dogs and threats management interventions) due to nature of the species.
Transdisciplinary weed research new leverage on challenging weed problems?

situation and renegotiation of further actions. Notably, ‘purposive’ disciplines (design, humanities and arts) have central, critical and recurrent roles in this process, as do integrative analyses of relevant multidimensional and multilevel factors, via multiple natural and social science disciplines. We exemplify this process in prospect and retrospect. Importantly TWR is not a replacement for current weed research; rather, the intent is to powerfully leverage current efforts.
Multi-Modal Inverse Constrained Reinforcement Learning from a Mixture of Demonstrations

Inverse Constraint Reinforcement Learning (ICRL) aims to recover the underlying constraints respected by expert agents in a data-driven manner. Existing ICRL algorithms typically assume that the demonstration data is generated by a single type of expert. However, in practice, demonstrations often comprise a mixture of trajectories collected from various expert agents respecting different constraints, making it challenging to explain expert behaviors with a unified constraint function. To tackle this issue, we propose a Multi-Modal Inverse Constrained Reinforcement Learning (MMICRL) algorithm for simultaneously estimating multiple constraints corresponding to different types of experts. MMICRL constructs a flow-based density estimator that enables unsupervised expert identification from demonstrations, so as to infer the agent-specific constraints. Following these constraints, MMI-CRL imitates expert policies with a novel multi-modal constrained policy optimization objective that minimizes the agent-conditioned policy entropy and maximizes the unconditioned one. To enhance robustness, we incorporate this objective into the contrastive learning framework. This approach enables imitation policies to capture the diversity of behaviors among expert agents. Extensive experiments in both discrete and continuous environments show that MMICRL outperforms other base-lines in terms of constraint recovery and control performance. Our implementation is available at: https://github.com/qiaoguanren/Multi-Modal-Inverse-Constrained-Reinforcement-Learning.
Fast Hexahedral Mesh Extraction from Locally Injective Integer-Grid Maps

Integer-Grid Maps as boundary-aligned volume parametrizations of tetrahedral meshes onto a voxel grid have proven to be a valuable approach in the field of hex-meshing as they can induce highly structured hexahedral meshes. The decomposition of the map into multiple cell charts due to the presence of singularities and non-identity transitions between adjacent charts makes the process of extracting a hex-mesh from an IGM non-trivial. Additionally, inverted and degenerate charts in the parametrization may cause inconsistencies that need to be fixed in a postprocessing step. As most current methods work without such defects in the IGM, we present a specialized hex-extraction algorithm that expects all cell charts to have a strictly positive volume and aims to be as fast as possible. To achieve this, we apply common rasterization techniques to efficiently extract the hex-mesh’s geometry and use a specifically designed data structure called propeller to extract its topology.
My Baby, My Move+: Feasibility of a Community Prenatal My Baby, My Move+: Feasibility of a Community Prenatal Wellbeing Intervention Wellbeing Intervention

Background Excessive gestational weight gain (EGWG), insufficient prenatal physical activity and sleep, and poor psychological wellbeing independently increase risks for adverse maternal and infant outcomes. A novel approach to mitigate these risks is utilizing peer support in a community-based prenatal intervention. This study assessed the feasibility (acceptability, demand, implementation, and practicality) of a remotely delivered prenatal physical activity intervention called My Baby, My Move + (MBMM +) that aims to increase prenatal physical activity,
SYNTHESIS AND ANTIMICROBIAL ACTIVITY OF SCHIFF’S AND N-MANNICH BASES OF ISATIN AND ITS DERIVATIVES WITH 4-AMINO-N-CARBAMIMIDOYL BENZENE SULFONAMIDE

Isatin and substituted Isatin were reacted with 4-amino-N-carbamimidoyl benzene sulfonamide to form a series of Schiff’s bases. The Mannich bases of these compounds were synthesized by reacting them with formaldehyde and secondary amine (piperidine). All the compounds were characterized by means of their IR, 1H NMR spectroscopic data and elemental analysis. The antimicrobial activity of the synthesized compounds was evaluated by tube dilution method. The synthesized compounds showed better antibacterial activity than the reference drugs.
Investigating the Consumer Behavior of E-Commerce Product and Its Impact on Purchase Intention in Indonesia

E-commerce has changed the pattern of people’s habits in general, which in the past the buying and selling process was only carried out directly, but with the existence of e-commerce, the buying and selling process can be done online. The purpose of this study is to analyze consumer behavior on e-commerce product and its impact on purchasing decisions. The method used in this study is quantitative, using a questionnaire and data analysis calculated with the Partial Least Square - Structural Equation Model (PLS-SEM). The study sample was 394 respondents from nine provinces in Indonesia. Based on the analysis, the beneﬁts provided by e-commerce, convenience, and fast service have a signiﬁcant effect on consumers’ purchasing intentions of e-commerce products. Meanwhile, trust and reviews have no relationship with the intensity of e-commerce purchases. In main-taingsustainablee-commercegrowth,digitaleconomicandbusinessactorsshould considerthatsocietieswillpurchasee-commerceproductifonlythee-commerce platformprovidebeneﬁt,convenienceandfastresponsetotheirconsumer.The existanceofafﬁliatororinﬂuencerhasnocorrelationwiththeconsumer’sdecision topurchase.
Endogenous Entry, Product Variety, and Business Cycles

This paper builds a framework for the analysis of macroeconomic fluctuations that incorporates the endogenous determination of the number of producers and products over the business cycle. Economic expansions induce higher entry rates by prospective entrants subject to sunk investment costs. The sluggish response of the number of producers generates a new and potentially important endogenous propagation mechanism for business cycle models. The return to investment determines household saving decisions, producer entry, and the allocation of labor across sectors. Our framework replicates several features of business cycles and predicts procyclical profits even for preference specifications that imply countercyclical markups.
A Fabrication and Simulation Recipe for Untethering Soft-Rigid Robots with Cable-Driven Stiffness Modulation

We explore the idea of robotic mechanisms that can shift between soft and rigid states, with the long-term goal of creating robots that marry the flexibility and robustness of soft robots with the strength and precision of rigid robots. We present a simple yet effective method to achieve large and rapid stiffness variations by compressing and relaxing a flexure using cables. Next, we provide a differentiable modeling framework that can be used for motion planning, which simultaneously reasons about the modulated stiffness joints, tendons, rigid joints, and basic hydrodynamics. We apply this stiffness tuning and simulation recipe to create SoRiTu, an untethered soft-rigid robotic sea turtle capable of various swimming maneuvers.
Aligning Bone Tumour Radiology and Histology Data - How Could We Close the Loopƒ

In this study, we identify requirements for a clinically feasible approach to register histology with radiology data which is suitable for bone tumours. Orthopaedic oncology as the main treatment for bone tumours has benefited from developments in patient-specific surgical systems, equipment, and computation. This surgical technology relies upon clinical radiology. However, the accuracy of identifying tumour margins in clinical radiology is difficult to validate. Three-dimensional registration of histology with pre-operative radiology in a clinical setting could be used to improve the identification of bone tumour margins. Preliminary investigations evaluate four proposed approaches against ranking criteria. The approaches considered are; a double scanning approach, cutting jig guides, a fiducial vector technique, and fast-setting resin fixation. The double scanning approach was identified in canine trials as the most versatile and robust for the variable morphology of bone tumours. Within a clinical workflow, a histology-radiology framework will enable quantitative validation of presurgical image interpretation and planning. This will allow improvements to surgical planning approaches with better-informed resection decisions.
Classification of Hoax News Using Machine Learning and Neural Networks with BERT Embeddings

In the digital era, hoax news poses a serious threat, especially during elections, impacting unity and causing conflicts. With 8.7 million fake news articles circulating, public susceptibility to hoaxes, often fueled by emotional language and lack of source verification, adds to the risk. Deep learning offers a potential solution by utilizing advanced neural networks for in-depth text analysis, effectively distinguishing valid news from hoaxes based on content patterns. This approach holds promise in eradicating hoax news, raising awareness, and safeguarding the nation’ s unity. This study focuses on detecting hoax news using machine learning and deep learning techniques. The researchers used a general dataset containing 44,898 news articles, with 23,481 labeled as hoaxes and 21,417 as factual. Preprocessing involved tokenization, stopword removal, and lemmatization/stemming before converting text into vectors using TF-IDF. For deep learning, the BERT method was employed to extract contextual embeddings. The models were evaluated using different combinations of news titles and content. Among the machine learning models, Support Vector Machine (SVM) performed best, with an F1-score of 94% when using only news titles. However, the decision tree method outperformed others with a 99.6% F1 score when using titles and content. Nevertheless, all machine learning models were outperformed by BERT + NN, achieving an F1-score of 98.72% with titles only and 99.9% with both titles and content.
Driver-TRN: An Approach to Driver Behavior Detection Enhanced SOTIF in Automated Vehicles

Detecting and understanding driver behaviors in automated vehicles are critical for reducing the risk of misuse and ensuring the Safety Of The Intended Functionality (SOTIF). Using a Temporal Recurrent Network (TRN), the paper proposes the driver-TRN model to detect and predict driver behaviors based on real-time vehicular data, such as in-car video, road camera, and sensor data, which addresses intended risks resulting from the interaction between drivers and automated systems that may be overlooked. This paper improves the performance of the driver-TRN with multiple data sources and multi-task loss fine-tuning. The experiments based on the Brain4Cars dataset show that the driver-TRN can enhance the safety and reliability of automated vehicles by providing a more comprehensive detection of driver behaviors.
On the Validity of Single Regression Strategy for Granger Causality Assessment in Cardiovascular and Cardiorespiratory Control Studies

Granger causality (GC) analysis is based on the comparison between prediction error variances computed over the full and restricted models after identifying the coefficients of appropriate vector regressions. GC markers can be computed via a double regression (DR) approach identifying two separate, independent models and a single regression (SR) strategy optimizing the description of the dynamics of the target over the full model and, then, reusing some parts of it in the restricted model. The present study compares the SR and DR strategies over heart period (HP), systolic arterial pressure (SAP) and respiration (R) beat-to-beat series collected during a graded orthostatic challenge induced by head-up tilt in 17 healthy individuals (age: 21-36 yrs; median: 29 yrs; 9 females and 8 males). We found that the DR approach was more powerful than the SR one in detecting the expected stronger involvement of the baroreflex during the challenge, while the expected weaker cardiorespiratory coupling was identified by both SR and DR strategies. The less powerful ability of the SR approach was the result of the greater variance of GC markers compared to the DR strategy. We conclude that, contrary to the suggestions present in literature, the SR approach is not necessarily associated with a smaller dispersion of GC markers. Moreover, we suggest that additional factors, such as the strategy utilized to build embedding spaces and metric utilized to compare prediction error variances, might play an important role in differentiating SR and DR approaches.
‘The Mind’ promotes brain synchronization: an ecological evaluation of brain synchronization in co-operative tasks

This paper presents an ecologically valid approach for using EEG hyperscanning methods to assess levels of interbrain synchrony (IBS) in teams during co-operative tasks. We employ a card-based task in an out-of-the-lab setting to evaluate levels of neural synchrony between team members completing a co-operative task. We also examine the interplay between the recorded synchronization levels and the collective performance of the team.Clinical Relevance— This study provides a simplistic and ecologically valid setup with potential to bring a better understanding of brain synchronization in clinical settings where co-operation would improve outcomes, such as home care facilities and memory clinics.
Effect of Physical Characteristics on Artificial Neural Network Error Reduction for Indoor Propagation Modeling

In this paper, we examine the effect of physics-based input data in artificial neural network (ANN) for indoor propagation. Our prior knowledge of electromagnetic propagation forms, enables us to predict path loss for a specified geometry. There is a good agreement between ANN predicted output and ray-tracing results for a simple room and the error reduces notably compared to related works. Additionally, we explore feature extraction for more complex scenarios.
Semantic map construction based on LIDAR and vision fusion

When Internet of Things (IoT) based unmanned ground vehicles need to perform tasks in complex scenes, traditional geometric maps are difficult to meet the requirements due to insufficient information. Therefore, it is necessary to construct semantic maps. In this paper, we introduce a method to construct 3D semantic maps by integrating LiDAR and camera data. LIDAR is responsible for localization and mapping, while the camera is responsible for high-precision semantic segmentation. Through external parameter calibration between the sensors, a point cloud is projected onto the image, which facilitates the construction of semantic maps by associating each pixel with the corresponding semantic information in the point cloud. Meanwhile, the semantic details in the point cloud are utilized to enhance feature point matching and support back-end SLAM optimization for improved pose estimation. This helps to improve the robustness and accuracy of the whole system. IoT technology can also support remote updating and upgrading of maps so that ground vehicles can access the latest semantic information in a timely manner and adapt to changing environments. In conclusion combining IoT technology with this approach to achieve smarter ground vehicle mission execution and environment awareness.
SMART-Degradation: A Dataset for LiDAR Degradation Evaluation in Rain

Sensor degradation is one of the major challenges for autonomous driving. During the rain, the interference from raindrops can negatively influence LiDAR measurements. For example, valid measurements could be reduced during the rain, and some measurements may become noisy. Unreliable measurements can lead to potential safety issues if autonomous driving systems are unaware of these changes. In this work, we will release a naturalistic driving dataset to advance the research in studying LiDAR degradation. Our dataset consists of 3D LiDAR scans collected by a data collection vehicle under various rainy conditions. Besides these raw scans, we also release LiDAR scan pairs (each pair consists of one scan from rainy weather and one scan from clear weather at the same location). These LiDAR pairs are developed to help researchers identify LiDAR degradation. Finally, we will release a toolbox integrated with mapping, localization, and scan synthesis functions used to create this dataset. This toolbox can facilitate dataset creation for studying degradation in other harsh weather conditions. More information can be found at https://smart-rain-dataset.github. io/.