ethicalabs/Research-Intent-Collab huggingface dataset:
Reconstruction of the Ethics of Artificial Intelligence Development in Islamic Philosophy and Muhammadiyah Thought

This study aims to analyze the perspectives of al-Islam and Muhammadiyah on the development of Artificial Intelligence (AI) within the framework of ethics, epistemology, and the concept of blessing (barakah). The research employs a qualitative approach using a library research method, through the analysis of literature on Islamic philosophy, Muhammadiyah thought, and studies on technology ethics and AI. The data were analyzed using content analysis and hermeneutic techniques to identify normative principles relevant to responding to AI development. The findings indicate that Islam views technology as an instrument of the human mandate of khalifah (vicegerency), which must be directed toward public welfare (maslahah), justice, and balance between worldly life and the hereafter. The concept of Islamic ethics including tawhid, adl, moral character (akhlaq), and social responsibility serves as the normative foundation for evaluating and utilizing AI. Knowledge (ilm) is understood as a religious obligation that is not morally neutral; therefore, AI development must be oriented toward truth and benefit. Meanwhile, the concept of blessing (barakah) emphasizes sustainability and the spiritual dimension in the use of technology. From the Muhammadiyah perspective, the integration of religion and science, the strengthening of education, and community empowerment constitute the primary principles in AI development. AI is positioned as a means of civilizational renewal that must be guided by ethical values to prevent injustice or dehumanization. Thus, al-Islam and Muhammadiyah offer an integrative and normative philosophical framework for directing AI development in a responsible, just, and spiritually meaningful manner.
Automatic detection of hardhats worn by construction personnel: A deep learning approach and benchmark dataset

Abstract   Hardhats play an essential role in protecting construction individuals from accidents. However, wearing hardhats is not strictly enforced among workers due to all kinds of reasons. To enhance construction sites safety, the majority of existing works monitor the presence and proper use of hardhats through multi-stage data processing, which come with limitations on adaption and generalizability. In this paper, a one-stage system based on convolutional neural network is proposed to automatically monitor whether construction personnel are wearing hardhats and identify the corresponding colors. To facilitate the study, this work constructs a new and publicly available hardhat wearing detection benchmark dataset, which consists of 3174 images covering various on-site conditions. Then, features from different layers with different scales are fused discriminately by the proposed reverse progressive attention to generate a new feature pyramid, which will be fed into the Single Shot Multibox Detector (SSD) to predict the final detection results. The proposed system is trained by an end-to-end scheme. The experimental results demonstrate that the proposed system is effective under all kinds of on-site conditions, which can achieve 83.89% mAP (mean average precision) with the input size 512 × 512.
Public Communication about Science in 68 Countries: Global Evidence on How People Encounter and Engage with Information about Science

This 68-country survey (n = 71,922) examines science information diets and communication behavior, identifies cross-country differences, and tests how such differences are associated with sociopolitical and economic conditions. We find that social media are the most used sources of science information in most countries, except those with democratic-corporatist media systems where news media tend to be used more widely. People in collectivist societies are less outspoken about science in daily life, whereas lower education is associated with higher outspokenness. Limited access to digital media is correlated with participation in public protests on science matters. We discuss implications for future research, policy, and practice.
A Deep Dive into CI/CD Pipelines Tailored for Telecom: Orchestrating Cloud-Native 5G Services with DevOps and Infrastructure Automation

The deployment of 5G services is challenging since it entails deploying multiple Virtual Network Functions (VNFs) across a diverse set of compute and networking infrastructure. This deployment typically relies on Command Line Interface (CLI) automation tooling, which leads to an error-prone process that is difficult to control and maintain. The problem is exacerbated in multi-site deployments where orchestration and synchronization between sites become necessary. Continuous Integration (CI)/Continuous Deployment (CD) methodologies from the software world can bring significant benefits to telecom, easing the deployment of 5G services while maintaining quality and control. These problems and solutions are rooted in practical experience in deploying and managing 5G services for private networks. A workflow enabling telecom CI/CD is presented, along with a proof of concept developed in this context to illustrate the approach. This proof of concept deploys the Open 5G core based on the CORD project onto on-premise OpenStack infrastructure. The holistic approach followed in designing this solution makes it extensible to other services, infrastructures, and cloud platforms.  The Open architecture developed by the O-RAN Alliance is moving cellular networks toward disaggregated, softwarized, programmable intelligent systems. This evolution brings a different networking paradigm with new opportunities, but also important challenges. By breaking radio, transport, and core functionalities into individual parallel processes, disaggregation and softwarization can break the current vendor lock-in and open the cellular ecosystem to a larger number of players, facilitating innovation in the cellular market . Such innovation could encompass the development of new RAN functionalities, the use of advanced AI-based analytics to assess, predict, and improve the performance of the system, and the creation of new applications and services that use network telemetry. By exposing key parameters to this programmatic intelligence, cellular systems can be made more efficient, adaptable, and less costly, while exposing media at the most abstract level to facilitate a higher level of automation, synchronization, and coordination with the edge and the cloud. This vision of programmable intelligence can be achieved only if the necessary analogy with the computer ecosystem is implemented.
An Appraisal of the Practice of Indigenous Conflict Resolution Mechanisms in Building a Culture of Peace in Bale Zones: Learning From the Past, Building a Better Future

Research don by College of Social Sciences and Humanities June, 2021
The role of educational technology, artificial intelligence, and gamification in improving education and students’ cognitive and social-emotional development

Αποτελεί αναμφισβήτητο γεγονός πως οι σημερινοί μαθητές δεν είναι πλέον τα ίδια άτομα για τα οποία το εκπαιδευτικό σύστημα είχε σχεδιαστεί να εκπαιδεύσει. Καθώς οι μαθητές μεγαλώνουν και ωριμάζουν σε σύγχρονες και ευέλικτες κοινωνίες χρησιμοποιώντας όλο και πιο συχνά τεχνολογικές εφαρμογές στην καθημερινή τους ζωή, οι εκπαιδευτικές τους ανάγκες για αποτελεσματικότερη και ουσιαστικότερη μάθηση έχουν αλλάξει δραστικά. Επομένως, η αναθεώρηση και ο μετασχηματισμός της εκπαίδευσης και της παιδαγωγικής στα πλαίσια του 21ου αιώνα κρίνονται απαραίτητα. Η ενσωμάτωση νέων τεχνολογιών και προσεγγίσεων στην εκπαιδευτική διαδικασία μπορεί να βοηθήσει ουσιαστικά στην κάλυψη των νέων απαιτήσεων και στην προσαρμογή στις νέες συνθήκες. Ο βασικός σκοπός αυτής της διδακτορικής διατριβής είναι να διερευνήσει εάν, σε ποιο βαθμό, και με ποιον τρόπο η εκπαιδευτική τεχνολογία και συγκεκριμένα η εφαρμογή των τεχνολογιών της εκτεταμένης πραγματικότητας (επαυξημένη πραγματικότητα, εικονική πραγματικότητα και μικτή πραγματικότητα), της αναλυτικής εκπαιδευτικών δεδομένων και της τεχνητής νοημοσύνης, καθώς και η χρήση καινοτόμων εκπαιδευτικών προσεγγίσεων, όπως η παιχνιδοποίηση, τα ψηφιακά παιχνίδια και τα παιχνίδια σοβαρού σκοπού μπορούν συνδυαστικά να επηρεάσουν τον εμπλουτισμό της εκπαιδευτικής διαδικασίας και την προαγωγή της γνωστικής και κοινωνικο-συναισθηματικής ανάπτυξης των μαθητών που με τη σειρά τους θα τους οδηγήσουν στην επίτευξη ατομικής και κοινωνικής ευημερίας. Στην παρούσα διδακτορική διατριβή εκτός από το θεωρητικό υπόβαθρο, παρουσιάζεται η διαδικασία και τα αποτελέσματα τριών μελετών και παρέχονται συμπεράσματα σύμφωνα με τα ευρήματα. Η πρώτη μελέτη περιλαμβάνει μια εκτενή συστηματική βιβλιογραφική επισκόπηση και τη διερεύνηση 15 ερευνητικών ερωτημάτων (ΕΕ). Η δεύτερη μελέτη περιλαμβάνει την ανάλυση μεγάλων δεδομένων εξετάζοντας 6 ΕΕ και χρησιμοποιώντας πάνω από 17 εκατομμύρια δεδομένων. Η τρίτη μελέτη εξετάζει 10 EE και περιλαμβάνει το σχεδιασμό και την ανάπτυξη εκπαιδευτικών εφαρμογών και παιχνιδιών σοβαρού σκοπού χρησιμοποιώντας τις προαναφερθείσες τεχνολογίες και προσεγγίσεις για τη δημιουργία ευφυών, εμβυθιστικών και διαδραστικών περιβαλλόντων μάθησης, τη δημιουργία ενός εργαλείου αξιολόγησης και τη διεξαγωγή ενός πειράματος στο οποίο συμμετείχαν φοιτητές/τριες τριτοβάθμιας εκπαίδευσης. Συνεπώς, η παρούσα διδακτορική διατριβή συμβάλλει στην ανάπτυξη των θεωρητικών και πρακτικών πτυχών του εξελισσόμενου αυτού πεδίου μελέτης στο πλαίσιο των εκπαιδευτικών τεχνολογιών, ενισχύοντας, έτσι, την κατανόησή μας και εμπλουτίζοντας τις γνώσεις μας για αυτό τον αναδυόμενο τομέα και τις εφαρμογές του.
Sustainable management of floodplain fisheries through aquaculture in Nigeria

Fish and fish products are important sources of valuable protein and also provide essential nutrients for balanced human diets. The production of animal protein is insufficient to meet demand in many developing countries of the world. The extent to which Nigeria, a developing country faces protein shortage is alarming. This situation underscores the need to exploit all avenues of protein supply including aquaculture, in the floodplains which has been estimated to cover about 21,300km2. Fish production in the floodplains has been an integral part of domestic fish supply in Nigeria over the years. However, the integrated fish farming during the dry season in floodplains and wetlands “fadama” is gaining more popularity among the local fishing communities. This is not only boosting fish production, but also contributes to sustainability of floodplain fisheries resources. This paper therefore, reviews fisheries resources and various forms of aquaculture in the floodplains. The strategies to improve and sustain the fish production are also provided.Key words: Floodplain, fish production, sustainable management, Nigeria
NILE: Fast Natural Language Processing for Electronic Health Records

Objective: Narrative text in Electronic health records (EHR) contain rich information for medical and data science studies. This paper introduces the design and performance of Narrative Information Linear Extraction (NILE), a natural language processing (NLP) package for EHR analysis that we share with the medical informatics community. Methods: NILE uses a modified prefix-tree search algorithm for named entity recognition, which can detect prefix and suffix sharing. The semantic analyses are implemented as rule-based finite state machines. Analyses include negation, location, modification, family history, and ignoring. Result: The processing speed of NILE is hundreds to thousands times faster than existing NLP software for medical text. The accuracy of presence analysis of NILE is on par with the best performing models on the 2010 i2b2/VA NLP challenge data. Conclusion: The speed, accuracy, and being able to operate via API make NILE a valuable addition to the NLP software for medical informatics and data science.
A Commentary on Statistical Assessment of Violence Recidivism Risk

Increasing integration and availability of data on large groups of persons has been accompanied by proliferation of statistical and other algorithmic prediction tools in banking, insurance, marketiNg, medicine, and other FIelds (see e.g., Steyerberg (2009a;b)). Controversy may ensue when such tools are introduced to fields traditionally reliant on individual clinical evaluations. Such controversy has arisen about actuarial assessments of violence recidivism risk, i.e., the probability that someone found to have committed a violent act will commit another during a specified period. Recently Hart et al. (2007a) and subsequent papers from these authors in several reputable journals have claimed to demonstrate that statistical assessments of such risks are inherently too imprecise to be useful, using arguments that would seem to apply to statistical risk prediction quite broadly. This commentary examines these arguments from a technical statistical perspective, and finds them seriously mistaken in many particulars. They should play no role in reasoned discussions of violence recidivism risk assessment.
Higher-distance commuting varieties

The commuting variety of matrices over a given field is a well-studied object in linear algebra and algebraic geometry. As a set, it consists of all pairs of square matrices with entries in that field that commute with one another. In this paper we generalise the commuting variety by using the commuting distance of matrices. We show that over an algebraically closed field, each of our sets does indeed form a variety. We compute the dimension of the distance-$2$ commuting variety and characterize its irreducible components. We also work over other fields, showing that the distance-$2$ commuting set is a variety but that the higher distance commuting sets may or may not be varieties, depending on the field and on the size of the matrices.
Research on Rights Ownership of Artificial Intelligence-Generated Content

The AI-generated content that has emerged in recent years is at least formally original compared to traditional works. If such generated content is allowed to exile the market, its potential market effect and industrial interests make it difficult for us to ignore the existence of such generated content. From the perspective of copyright incentive, on the basis of the impersonal nature of artificial intelligence creations and the two-element theory of copyright nature in China, we separate the property rights of works, protect them as property rights alone, and allocate the property rights of works around investors.
Empagliflozin and Dapagliflozin in Improving Ejection Fraction: A Prospective Observational Comparison of Clinical Outcomes and Patient Reported Quality of Life

Abstract                 Background  SGLT2 inhibitors have transformed heart failure treatment, especially empagliflozin and dapagliflozin. Although both have proven cardiovascular benefits in clinical trials, there is little data comparing them in real-world settings. This study aims to address that by comparing their effects on heart function, lab tests, echocardiography, and quality of life using routine clinical data and the Kansas City Cardiomyopathy Questionnaire-12. Methods  We conducted a prospective observational study with 70 heart failure patients, evenly divided between those taking empagliflozin 10 mg daily and those on dapagliflozin 10 mg daily as part of their usual care. We collected vital signs, blood tests, echocardiographic measures, including left ventricular ejection fraction, and KCCQ-12 scores. Statistical analysis included t-tests, Mann-Whitney U, Chi-square, ANOVA, Shapiro-Wilk for normality, Levene’s test for equal variances, and Pearson correlation. Results with p-values under 0.005 were considered significant. Results  The groups were similar in age, gender, rates of hypertension, HbA1c, and lipid levels. However, more patients in the dapagliflozin group had diabetes (68.6% vs. 31.4%, p = 0.007). Both drugs significantly lowered systolic blood pressure and improved ejection fraction. Dapagliflozin caused a larger drop in sodium levels (2.51 vs 0.83 mEq/L, p = 0.007). Among diabetic patients, empagliflozin reduced blood glucose by a greater amount (-75.00 vs -42.33 mg/dL). Patients on empagliflozin also had higher overall KCCQ-12 scores (66.22 vs. 56.06), with a significant difference in symptom frequency (p = 0.033). Improvements in ejection fraction, kidney function, hemoglobin, and lipids were similar for both drugs. Conclusions  Empagliflozin and dapagliflozin showed similar effects in heart failure patients, though some important differences emerged. Empagliflozin was associated with greater reductions in KCCQ-12 symptom frequency scores and blood sugar levels in patients with diabetes, whereas dapagliflozin was associated with a larger drop in serum sodium levels. These results suggest that although both drugs are generally equivalent, individual differences might guide personalized treatment choices.
(Battery Division Technology Award Address) Integrating Materials Science, Electrochemistry and Engineering in Energy Storage Technologies

Electrochemical energy storage has been a very hot topic in the last decade. Many efforts have been pursued in identifying new battery materials or technologies for a broad range of applications such as grid energy storage, vehicle electrification, mobile electronics and so on. This talk will highlight the importance of interdisciplinary research but also emphasize the urgent need to identify and address real challenges at relevant scales for battery systems. Three examples will be discussed: (1) Single crystal Ni-rich cathode for advanced Li-ion batteries, (2) next-generation of rechargeable lithium metal battery and (3) microbattery technologies for miniaturized sensors to encourage the integration of materials chemistry, electrochemistry and engineering to overcome the roadblocks for developing advanced battery technologies.
Enhancing Phenotype Discovery in Electronic Health Records through Prior Knowledge-Guided Unsupervised Learning

Objectives: Unsupervised learning with electronic health record (EHR) data has shown promise for phenotype discovery, but approaches typically disregard existing clinical information, limiting interpretability. We operationalize a Bayesian latent class framework for phenotyping that incorporates domain-specific knowledge to improve clinical meaningfulness of EHR-derived phenotypes and illustrate its utility by identifying an asthma sub-phenotype informed by features of Type 2 (T2) inflammation.   Materials and methods: We illustrate a framework for incorporating clinical knowledge into a Bayesian latent class model via informative priors to guide unsupervised clustering toward clinically relevant subgroups. This approach models missingness, accounting for potential missing-not-at-random patterns, and provides patient-level probabilities for phenotype assignment with uncertainty. Using reusable and flexible code, we applied the model to a large asthma EHR cohort, specifying informative priors for T2 inflammation-related features and weakly informative priors for other clinical variables, allowing the data to inform posterior distributions.   Results and Conclusion: Using encounter data from January 2017 to February 2024 for 44,642 adult asthma patients, we found a bimodal posterior distribution of phenotype assignment, indicating clear class separation. The T2 inflammation-informed class (38.7%) was characterized by elevated eosinophil levels and allergy markers, plus high healthcare utilization and medication use, despite weakly informative priors on the latter variables. These patterns suggest an "uncontrolled T2-high" sub-phenotype. This demonstrates how our Bayesian latent class modeling approach supports hypothesis generation and cohort identification in EHR-based studies of heterogeneous diseases without well-established phenotype definitions.
Regulation of chromatin epigenetic modification by the nucleosomal kinase VRK1, implications for new cancer therapies

[Introduction]: Epigenetic alterations are one of the main mechanisms driving different human diseases, including many cancer types and neurological diseases. Histone posttranslational modifications (PTMs) are changes in the amino acid residues of the histone tail which regulate gene expression. The coordination of PTMs requires the strictly regulation of multiple epigenetic enzymes, termed writer and eraser proteins. Chromatin active states are characterized by PTMs such as H3K4me3 and H3K27ac, whereas repressive states are enrichment in H3K9me3 and H3K27me3 among others. Thus, it could be crucial to understand the mechanisms that mediate the coordination of such enzymes. VRK1 (Vaccinia-related kinase 1) is a nuclear kinase implicated in different cellular process like transcription or DNA damage response. In this context, we studied the potential role of VRK1 in the regulation of those epigenetic modifiers. [Material and Methods]: We compared the transient VRK1 silencing with the effect of modulators of various epigenetic enzymes. In our study, we evaluated the expression levels of different PTMs (H3K4me3, H3K9ac/me3, H3K27ac/me3, and H4K16ac), by immunodetection in two different human cancer cell lines, lung adenocarcinoma A549 and osteosarcoma U2OS. We studied the interaction of VRK1 with the histonemodifying enzymes of this PTMs by co-immunoprecipitation. [Results and Discussions]: Our results showed that VRK1 depletion caused an alteration of post-translational histone marks in cells. We observed that VRK1 depletion promotes a decrease of chromatin active marks (H3K4me3, H3K9ac, H3K27ac, and H4K16ac), and an increase of H3K9me3 and H3K27me3. Treatments with drugs targeting histone-modifying enzymes also remodel the acetylation and methylation pattern. HAT (C646 and MG149) and KDM (JMJD2i and ORY-1001) inhibitors mimic the effect of VRK1 depletion. HDAC (Vorinostat, Entinostat, Panobinostat, Selisistat, Thiomyristoil, AGK2, and AK7) and KMT (Chaetocin and Tazemetostat) inhibitors mimic the effect of VRK1 on histone marks. VRK1 forms a stable protein complex with KDM1a, KDM3a, KDM4a, HDAC1, Sirt1/2, and SETDB1. [Conclusion]: Our findings revealed that permissive chromatin marks decreased (H3K4me3, H3K9ac, H3K27ac, H4K16ac) while repressive marks (H3K9me3, H3K27me3) increased in VRK1-depleted cells. Therefore, the loss of VRK1 expression in the cells had the same effect than the inhibition of these enzymes, suggesting that VRK1 might be a regulator of those epigenetic enzymes and used as new target in synthetic lethality strategies
Continuous particle swarm optimization for model updating of structures from experimental modal analysis

Abstract. The aim of this work is to present a structural model updating procedure based on a recent variant of Particle Swarm Optimization, called Continuous Particle Swarm Optimization. In order to validate the proposed model, the modal parameters of a real scale large electrical device have been identified by measuring the dynamical response of the system subjected to a measured input. Numerical results confirm the robustness and reliability of the proposed method.
Enhancing Federated Learning Privacy with QUBO

8 pages, 9 figures
Radio emission of extensive air showers as a method for detecting ultra-high energy cosmic rays

Recording radio emission from extensive air showers (EASs) is considered now as a new promising method for detecting ultra-high energy (E                         0 > 5 × 1016 eV) cosmic rays. The results of calculation of EAS radio emission at frequencies from 40 to 80 MHz in the EAS energy range E                         0 = 1014–1017 eV are reported here, and the possibilities of determining EAS parameters from the radio emission lateral distribution are discussed.
Environmental and engineering geophysical studies for sinkhole problems using seismic reflection, refraction tomography, electrical resistivity imaging, and joint inversions

[no abstract]