ethicalabs/Research-Intent-Collab huggingface dataset:
Smartphone digital phenotyping, surveys, and cognitive assessments for global mental health: Initial data and clinical correlations from an international first episode psychosis study

Objective                     To examine feasibility and acceptability of smartphone mental health app use for symptom, cognitive, and digital phenotyping monitoring among people with schizophrenia in India and the United States.                                                           Methods                     Participants in Boston, USA and Bhopal and Bangalore, India used a smartphone app to monitor symptoms, play cognitive games, access relaxation and psychoeducation resources and for one month, with an initial clinical and cognitive assessment and a one-month follow-up clinical assessment. Engagement with the app was compared between study sites, by clinical symptom severity and by cognitive functioning. Digital phenotyping data collection was also compared between three sites.                                                           Results                     By Kruskal-Wallis rank-sum test, we found no difference between app activities completed or digital phenotyping data collected across the three study sites. App use also did not correlate to clinical or cognitive assessment scores. When using the app for symptom monitoring, preliminary findings suggest app-based assessment correlate with standard cognitive and clinical assessments.                                                           Conclusions                     Smartphone app for symptom monitoring and digital phenotyping for individuals with schizophrenia appears feasible and acceptable in a global context. Clinical utility of this app for real-time assessments is promising, but further research is necessary to determine the long-term efficacy and generalizability for serious mental illness.
Data Quality Management and Risk Assessment of Dairy Farming with Feed Behaviour Analysis Using Big Data Analytics with YOLOv5 Algorithm

Dairy farming is crucial for global food security, providing essential products like milk and cheese. However, challenges such as animal health, economic instability, and environmental issues threaten the industry’s sustainability. This study utilizes big data analytics and machine learning, including YOLOv5 and Cascade Feedforward Neural Networks, to enhance feeding strategies, improve data quality management, and predict ketosis risks, ultimately improving cow health and preventing metabolic disorders. The study employs a combination of Apache Spark HDFS for handling large-scale data and YOLOv5 for real-time feed behaviour detection. Physiological data like rumination time, body temperature, and activity levels are collected, along with behavioural data from YOLOv5. These data types are integrated into a unified training pipeline, with the Cascade Feedforward Neural Network [CSFEM] for ketosis prediction. A Butterfly Optimization Algorithm [BOA]-guided stacking ensemble is applied to optimize model performance. The approach was implemented for efficient data processing and risk assessment. The proposed system achieved 99.8% accuracy, 99.2% precision, and 99.4% recall, effectively predicting ketosis and mastitis risks, showcasing the power of big data and machine learning in dairy farming. Future research could enhance model generalizability by incorporating diverse datasets, real-time monitoring, environmental sensors, and genetic data, and refining YOLOv5 for better real-world adaptability.
The role of Menzuma in conflict resolution and peace building in Wollo

This research explores the role of Menzuma, a form of religious music, in conflict resolution and peace building in Wollo, Ethiopia. The research was conducted within the indigenous practices of the Wollo community in Ethiopia. It highlights the significance of music as a medium for expressing feelings, thoughts, and cultures, and its potential in promoting peace and healing. Key findings reveal that the local community actively engages in Menzuma during cultural ceremonies, evoking strong emotional responses that facilitate introspection and foster a sense of belonging. Menzuma promotes values such as unity, forgiveness, and mutual respect, serving as a medium for dialogue and helping conflicting parties find common ground. However, concerns about its sustainability are raised, emphasizing the need for formal systems of transmission amid modern influences. This study addresses gaps in existing literature on Menzuma's role in conflict resolution within ethnomusicology. Its academic significance lies in serving as a reference for future researchers studying similar topics, while its practical implications inform policymakers about conflict resolution mechanisms related to Menzuma and identify factors that hinder its effectiveness, thereby contributing to its strengthening and alignment with other peace building efforts.
Reorientation processes and surface phase transitions—I. A statistical mechanical treatment

Abstract   A statistical mechanical treatment of the critical and transition properties of monolayers absorbed on electrode surfaces is presented. The monolayer has a lattice structure and consists of solvent molecules occupying one adsorption site and adsorbate molecules present in two distinct states occupying r1 and r2 adsorption sites, respectively. Therefore, the adsorbate molecules can reorientate during the adsorption. General expressions for the adsorption isotherms and equation of state are obtained by means of the Bragg-Williams approximation. It is shown that when nearest-neighbour interactions prevail among similar species a phase separation process takes place. This process is favoured by the asymmetry of the adsorbate molecules and thus it occurs at higher temperatures with increasing deviations of the ratio r1/r2 from unity. At a certain electrode potential the coexisting equilibrium phases are characterized by different surface coverages of the adsorbate and its adsorbed states. The thermodynamic properties of the monolayer expressed via the plots of surface coverage vs. electrode potential and adsorbate chemical potential, charge density vs. electrode potential, surface pressure vs. adsorbate chemical potential and charge density vs. surface coverage and adsorbate chemical potential are also presented and discussed.
Digital Treasure Hunt: Discovering Central Bank Digital Currencies

Central Bank Digital Currencies (CBDCs) represent a significant shift in the financial landscape, merging traditional banking with digital technology. As central banks explore CBDCs, understanding their foundational aspects is crucial. CBDCs, issued and regulated by central banks, enhance financial inclusion, increase payment efficiency, maintain monetary sovereignty, and improve cross-border transactions. This study comprehensively examines these motivations, technical designs, challenges, and impacts. This study demonstrates that while CBDCs offer significant benefits, they also pose technological, financial, societal, and legal challenges. The paper&apos;s detailed analysis, including a table of current CBDC projects, provides valuable insights for future research and implementation. These findings are vital for central banks, policymakers, technologists, economists, and researchers in the evolving landscape of digital currencies.
Association between after death caregiver reported quality of dying and patient reported quality of life during end-of-life in dyads attending to outpatient palliative care at a public hospital in Santiago, Chile

Context: Improving quality of dying (QOD) is key in Palliative Care (PC). It is unknown whether family caregivers (FCs) after-death assessment is associated with patient reported quality of life (QOL) during end-of-life.Objectives: To assess the relationship between family caregivers’ evaluation of the dying experience with the overall QOL that patients with advanced cancer reported during the time close to death in a Latino community.Methods: In this longitudinal study, dyads of advanced cancer patients and their FCs were enrolled from a PC Unit in Santiago, Chile. Dyads completed a baseline questionnaire and were followed-up until patients’ death. FCs were contacted to complete questionnaires regarding to death and dying experience. Simple and multivariate analysis were performed to evaluate associations of interest.Results: Two hundred eight dyads were enrolled during the study period. Of the total, seventy-seven dyads were analyzed. FCs were on average 48 years old and 70% female, while patients’ median age was 65 years, 51% being males, and the most common cancer was gastrointestinal. In the univariate analysis, FCs reported QOD was associated with patients’ QOL and with caregiver satisfaction with care. In multivariate analysis, the association between QOD and QOL remained significant.Conclusion: QOD is independently associated with patient QOL during the last month of life, when adjusted by caregiver satisfaction and caregiver depression and anxiety. Therefore, QOD could be used as a surrogate for patient QOL.
Organizational Career Management as a Developmental System: Collective Leadership Behaviors and the Enactment of Career Support

Career development spans higher education through post-entry adaptation, retention, and transition. Organizational career management (OCM) links human resource management, career attitudes, and employability, yet lacks a coherent account of how organizational provisions become concrete developmental experience in daily work. This article re-specifies OCM as a developmental system comprising four layers: OCM as superordinate architecture, developmental HR practices as implementation infrastructure, developmental networks as a relational access layer, and proactive career behaviors/career self-management (CSM) as self-regulatory behaviors conditioned by institutional and relational support. The central contribution is proposing collective leadership behaviors (CLB) as a candidate for specifying the missing workplace-practice layer. Developmental networks explain who employees turn to for support; CLB explains how support is enacted in team interaction so that organizational provision becomes developmentally usable. CLB is treated not as shared leadership or a substitute for supervisor support, but as enacted workplace practice once institutional provision and relational access are in place. Because empirical studies linking CLB to career development remain limited, this framework advances as a theory-building integrative review: developmental networks matter most when the bottleneck is access to heterogeneous support, whereas CLB matters most when support exists but is not yet enacted as usable developmental experience.
TECRR: a benchmark dataset of radiological reports for BI-RADS classification with machine learning, deep learning, and large language model baselines

Abstract                 Background                 Recently, machine learning (ML), deep learning (DL), and natural language processing (NLP) have provided promising results in the free-form radiological reports’ classification in the respective medical domain. In order to classify radiological reports properly, a high-quality annotated and curated dataset is required. Currently, no publicly available breast imaging-based radiological dataset exists for the classification of Breast Imaging Reporting and Data System (BI-RADS) categories and breast density scores, as characterized by the American College of Radiology (ACR). To tackle this problem, we construct and annotate a breast imaging-based radiological reports dataset and its benchmark results.                  The dataset was originally in Spanish. Board-certified radiologists collected and annotated it according to the BI-RADS lexicon and categories at the Breast Radiology department, TecSalud Hospitals Monterrey, Mexico. Initially, it was translated into English language using Google Translate. Afterwards, it was preprocessed by removing duplicates and missing values. After preprocessing, the final dataset consists of 5046 unique reports from 5046 patients with an average age of 53 years and 100% women. Furthermore, we used word-level NLP-based embedding techniques, term frequency-inverse document frequency (TF-IDF) and word2vec to extract semantic and syntactic information. We also compared the performance of ML, DL and large language models (LLMs) classifiers for BI-RADS category classification.                                Results                 The final breast imaging-based radiological reports dataset contains 5046 unique reports. We compared K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient-Boosting (GB), Extreme Gradient Boosting (XGB), Long Short-Term Memory (LSTM), Bidirectional Encoder Representations from Transformers (BERT) and Biomedical Generative Pre-trained Transformer (BioGPT) classifiers. It is observed that the BioGPT classifier with preprocessed data performed 6% better with a mean sensitivity of 0.60 (95% confidence interval (CI), 0.391-0.812) compared to the second best performing classifier BERT, which achieved mean sensitivity of 0.54 (95% CI, 0.477-0.607).                                Conclusion                 In this work, we propose a curated and annotated benchmark dataset that can be used for BI-RADS and breast density category classification. We also provide baseline results of most ML, DL and LLMs models for BI-RADS classification that can be used as a starting point for future investigation. The main objective of this investigation is to provide a repository for the investigators who wish to enter the field to push the boundaries further.
On Topological Data Analysis Techniques for stability of functions in persistent homology

Describing topological and geometric properties in point set topological spaces and metric spaces has remained inherently difficult over a long period of time to mathematicians. Studies in topological data analysis (TDA) have revolved around the question that asks: Is there a possibility of finding a robust estimate of the properties of geometry and topology of Big data sets within a global setting that preserves its structure? This question further posses the problem, that the estimates can only remain of use provided that we could determine the stability of constants given certain variations within the data inputs are unchanged. In this regard, this study seeks to characterize stability of functions in persistent homology (PH) and their applications in cyber-threat detection and cyber-attack prediction within the cybersecurity data sets, using persistent homology.
STUDY OF CASTE DISCRIMINATION IN MULK RAJ ANAND’S UNTOUCHABLE

This present paper focuses on the caste discrimination that is projected in the novel, Untouchable. Mulk Raj Anand narrated the lives of the impoverished and oppressed in traditional Indian society. The novel, Untouchable illuminates the atrocities that still exist in India. The narrative illustrates the tense and troubled interactions between upper-caste Hindus, Muslims, Christians and untouchables oppressed in colonial India. Bakha is a metaphor for the oppression and exploitation that have been untouchables like him. Bakha is an extremely skilled worker and passers-by frequently admired his prowess and briefly wondered whether he belonged to cleaning public restrooms. The main character, Bakha, is a life-size character who effectively conveys the agony of an oppressed, disadvantaged, and fated human being for no other reason than being an outcast. Mulk Raj Anand has painted a true and accurate picture of traditional Brahminical India, when the low caste population's plight was truly deplorable and pitiful.
Synthesis and Electrochemistry of Layered Li<sub>1.2</sub>(Ni<sub>0.4</sub>Mn<sub>0.4</sub>)<sub>1-x-y</sub>(Co<sub>0.4</sub>Mn<sub>0.4</sub>)<sub>x</sub>(Ni<sub>0.2</sub>Mn<sub>0.6</sub>)<sub>y</sub>O<sub>2</sub> (0≤=x+y≤=1) Cathode Materials for Li Rechargeable Battery

Abstract not Available.
Making Differentiable Architecture Search less local

Neural architecture search (NAS) is a recent methodology for automating the design of neural network architectures. Differentiable neural architecture search (DARTS) is a promising NAS approach that dramatically increases search efficiency. However, it has been shown to suffer from performance collapse, where the search often leads to detrimental architectures. Many recent works try to address this issue of DARTS by identifying indicators for early stopping, regularising the search objective to reduce the dominance of some operations, or changing the parameterisation of the search problem. In this work, we hypothesise that performance collapses can arise from poor local optima around typical initial architectures and weights. We address this issue by developing a more global optimisation scheme that is able to better explore the space without changing the DARTS problem formulation. Our experiments show that our changes in the search algorithm allow the discovery of architectures with both better test performance and fewer parameters.
Compiling Combinatorial Genetic Circuits with Semantic Inference

A central strategy of synthetic biology is to understand the basic processes of living creatures through engineering organisms using the same building blocks. Biological machines described in terms of parts can be studied by computer simulation in any of several languages or robotically assembled in vitro. In this paper we present a language, the Genetic Circuit Description Language (GCDL) and a compiler, the Genetic Circuit Compiler (GCC). This language describes genetic circuits at a level of granularity appropriate both for automated assembly in the laboratory and deriving simulation code. The GCDL follows Semantic Web practice and the compiler makes novel use of the logical inference facilities that are therefore available. We present the GCDL and compiler structure as a study of a tool for generating $κ$-language simulations from semantic descriptions of genetic circuits.
Sampling Theory of Jointly Bandlimited Time-vertex Graph Signals

Time-vertex graph signal (TVGS) models describe time-varying data with irregular structures. The bandlimitedness in the joint time-vertex Fourier spectral domain reflects smoothness in both temporal and graph topology. In this paper, we study the critical sampling of three types of TVGS including continuous-time signals, infinite-length sequences, and finite-length sequences in the time domain for each vertex on the graph. For a jointly bandlimited TVGS, we prove a lower bound on sampling density or sampling ratio, which depends on the measure of the spectral support in the joint time-vertex Fourier spectral domain. We also provide a lower bound on the sampling density or sampling ratio of each vertex on sampling sets for perfect recovery. To demonstrate that critical sampling is achievable, we propose the sampling and reconstruction procedures for the different types of TVGS. Finally, we show how the proposed sampling schemes can be applied to numerical as well as real datasets.
A node pruning algorithm for feedforward neural network based on neural complexity

In this paper, a hidden node pruning algorithm based on the neural complexity is proposed, the entropy of neural network can be calculated by the standard covariance matrix of the neural network's connection matrix in the training stage, and the neural complexity can be acquired. In ensuring the information processing capacity of neural network is not reduced, select and delete the least important hidden node, and the simpler neural network architecture is achieved. It is not necessary to train the cost function of the neural network to a local minimal, and the pre-processing neural network weights is avoided before neural network architecture adjustment. The simulation results of the non-linear function approximation shows that the performance of the approximation is ensured and at the same time a simple architecture of neural networks can be achieved.
Mistake of Law at the International Criminal Court and Other International Criminal Tribunals

Abstract                The version of mistake of law adopted by the International Criminal Court (ICC) Statute is the ‘denial of mens rea’ one, due to a precise drafting choice. The concept of mistake of law as an excuse was deliberately excluded from the Statute. The language adopted in the ICC Elements of Crimes restricts resort to mistake of law even further. Thus, the ICC Statute allows punishment of defendants who did not know—and sometimes could not have known—the relevant law. However, the ICC approach towards mistake of law is more lenient than the approach of other international criminal tribunals. Several offences under the ICC jurisdiction, including war crimes and crimes against humanity, contain legal elements about which the defendant must have knowledge: an honest mistake on those elements would exclude responsibility. Moreover, Article 33 expands the range of exonerating mistakes by introducing the possibility of invoking the defence of superior orders.
A novel artificial condensed matter lattice and a new platform for one-dimensional topological phases

Topologically protected electron states arranged artificially in real space form a highly tunable emergent atomic chain.
Prediction of the Yield Per Hectare of the Crop of Chili Pepper (Capsicumchinense), by Means of a Simulation Model with GIS. A Case Study in Santo Domingo - San Jacinto Del Bua

The present investigation refers to the application of a model of prediction of the yield per hectare of the pepper crop (Capsicumchinense), with the help of GIS tools. Results are delivered in maps which are easy to interpret. The main independent variables will be number, weight and fruit diameters. In addition, the Normalized Difference Vegetation Index (NDVI) will be included as a variable. This index will be extracted from an aerial image captured by a drone with multispectral camera. The aim is to observe the statistical correlation that exists between the Performance and the NDVI. Also a validation of the model is made comparing the estimated performance vs. the real performance. In case there is a deficit in production, corrective measures will be recommended. The methodology begins with the processing of the NDVI index. It will be treated and reclassified. Later regression will be applied in the ArcGis software. The results show that the prediction achieved a visual similarity which is very close to reality, offering ranges of (4125 to 580 kg/Ha) Estimated yield (3594 to 694 kg/Ha) Real yield and a difference of (1039 to −353 Kh/Ha); the R2 in general gave as a result 0.9 in the multivariate regression; the exponential regression between the NDVI and the real yield gave R2 of 0.068, so there is a linear dispersion of data. In general the prediction was very close to reality, and it was very similar in visual terms between the resulting images.