ethicalabs/Research-Intent-Collab huggingface dataset:
Transition-Metal-Doped M-Li<sub>8</sub>ZrO<sub>6</sub> (M = Mn, Fe, Co, Ni, Cu, Ce) as High-Specific-Capacity Li-Ion Battery Cathode Materials: Synthesis, Electrochemistry, and Quantum Mechanical Characterization

Lithium-ion batteries (LIBs) are promising devices for high capacity, rechargeable electrical energy storage; however, LIBs are currently limited by the low specific capacity of the cathode compared to the anode. In previous work, our group demonstrated the viability of a novel cathode material, Li8ZrO6 (LZO), through computational and experimental results. Here we report a general synthesis for transition-metal-doped LZO, and we study the effects of doping on electrochemical delithiation and relithiation. A synthesis using transition-metal (M) doped ZrO2 nanoparticle/carbon black composites as precursors produces doped M-LZO with grain sizes between 35 and 67 nm. The materials were tested as electrode materials. Specific capacities of the doped materials depend on the transition metal and on the Li:Zr ratio used in the synthesis, but they are generally higher than in similarly prepared undoped LZO. In this set of cathode materials, Fe3+-doped LZO/C composites showed the highest specific capacities, with ...
The Database Preparation and Analysis of Spatial Data with Geographic Information Systems (GIS) for General Solid Waste Management: A Case Study of Tha Kham Subdistrict, Bang Pakong District, Municipality Chachoengsao Province

Journal of Information Science, 40, 2, 67-94
Business ethics and corporate social responsibility comparative approach between capitalism, socialism and Islamic economy

The purpose of this study is to present contemporary trends in the field of business law through comparative analysis by presenting the similarities and differences regarding issues related to business ethics and corporate social responsibility (CSR) within the three economic systems, capitalism, socialism, and Islamic economics. The concept of the capitalist economy is based on capital and has the welfare of large corporations as a priority. Socialist economics prioritizes the interests of the state, while Islamic economics is human-centered and it prioritizes the well-being of the individual (Migdad, 2022). Business ethics is a very important factor that should be present in all segments of society. The importance of business ethics is particularly evident in modern society, which is burdened by numerous crises and contradictions, both globally and locally. The need for good corporate governance is important as it helps companies to meet their capital needs beyond traditional sources of financing, in order to expand and be internationally competitive. In a globalized economy, as we live today, companies in countries with weak corporate governance systems are less competitive in the market. Good corporate governance serves as a defense mechanism against corporate scandals and potential corporate tortious and criminal liability (Aziri, 2020).
COVID-19 vaccine uptake, confidence and hesitancy in rural KwaZulu-Natal, South Africa between April 2021 and April 2022: A continuous cross-sectional surveillance study

High COVID-19 vaccine hesitancy in South Africa limits protection against future epidemic waves. We evaluated how vaccine hesitancy and its correlates evolved April 2021-April 2022 in a well-characterized rural KwaZulu-Natal setting. All residents aged &gt;15 in the Africa Health Research Institute’s surveillance area were invited to complete a home-based, in-person interview. We described vaccine uptake and hesitancy trends, then evaluated associations with pre-existing personal factors, dynamic environmental context, and cues to action using ordinal logistic regression. Among 10,011 respondents, vaccine uptake rose as age-cohorts became vaccine-eligible before levelling off three months post-eligibility; younger age-groups had slower uptake and plateaued faster. Lifetime receipt of any COVID-19 vaccine rose from 3.0% in April-July 2021 to 32.9% in January-April 2022. Among 7,445 unvaccinated respondents, 47.7% said they would definitely take a free vaccine today in the first quarter of the study time period, falling to 32.0% in the last. By March/April 2022 only 48.0% of respondents were vaccinated or said they would definitely would take a vaccine. Predictors of lower vaccine hesitancy included being male (adjusted odds ratio [aOR]: 0.70, 95% confidence interval [CI]: 0.65–0.76), living with vaccinated household members (aOR:0.65, 95%CI: 0.59–0.71) and knowing someone who had had COVID-19 (aOR: 0.69, 95%CI: 0.59–0.80). Mistrust in government predicted greater hesitancy (aOR: 1.47, 95%CI: 1.42–1.53). Despite several COVID-19 waves, vaccine hesitancy was common in rural South Africa, rising over time and closely tied to mistrust in government. However, interpersonal experiences countered hesitancy and may be entry-points for interventions.
Exploring Depth Contribution for Camouflaged Object Detection

Camouflaged object detection (COD) aims to segment camouflaged objects hiding in the environment, which is challenging due to the similar appearance of camouflaged objects and their surroundings. Research in biology suggests depth can provide useful object localization cues for camouflaged object discovery. In this paper, we study the depth contribution for camouflaged object detection, where the depth maps are generated with existing monocular depth estimation (MDE) methods. Due to the domain gap between the MDE dataset and our COD dataset, the generated depth maps are not accurate enough to be directly used. We then introduce two solutions to avoid the noisy depth maps from dominating the training process. Firstly, we present an auxiliary depth estimation branch (ADE), aiming to regress the depth maps. We find that ADE is especially necessary for our "generated depth" scenario. Secondly, we introduce a multi-modal confidence-aware loss function via a generative adversarial network to weigh the contribution of depth for camouflaged object detection. Our extensive experiments on various camouflaged object detection datasets explain that the existing "sensor depth" based RGB-D segmentation techniques work poorly with "generated depth", and our proposed two solutions work cooperatively, achieving effective depth contribution exploration for camouflaged object detection.
Analysis of the Carrying Capacity of Agricultural Land in Supporting Sustainable Food Security in Tolitoli Regency, Central Sulawesi Province

Abstract                The increase in the population growth rate of 1.05 percent per year in Tolitoli Regency is certainly positively proportional to the demand for food, but whether the availability of food can meet the rate of population growth every year, while the availability of agricultural land cannot be ensured to be available in increasing food production. For this reason, it is necessary to know whether the available land resource capacity is able or not to support a large number of basic needs of the community. The purpose of this study is to analyze the carrying capacity of agricultural land in supporting sustainable food security in Tolitoli Regency, Central Sulawesi Province. Based on proportional sampling, 250 respondents were obtained using the formula proposed by Toro Yamane, from the village sample, for the village sample, determined based on the multistage random sampling technique. The analysis of the carrying capacity of agricultural land put forward by Muta’ali was used to answer the research objectives, based on secondary data on land area, population population, minimum physical needs and average land production per hectare and field surveys, the results of the analysis showed that the carrying capacity of agricultural land in Tolitoli Regency in aggregate was at an indicator above the value of 1 or 28.97 which means that Tolitoli Regency is able to achieve food self-sufficiency, or in other words, the number of people is below the optimal population.
What future for biodiversity in agricultural areas in a context of climate change? : from evaluation of conservation measures to scenarios of land use

Les changements climatiques et d'usages des sols sont des facteurs clés affectant la biodiversité. Par ailleurs, la nécessité de concilier production agricole et biodiversité, en améliorant les mesures de conservation existantes et en intégrant les considérations environnementales dans les futures politiques agricoles, est devenue une préoccupation majeure. L'objectif de la thèse était d'étudier comment les changements environnementaux (climatiques et d'usages des sols agricoles) et les mesures de conservation mises en œuvre en réponse à ces pressions environnementales, sont susceptibles d'affecter les communautés d'oiseaux nicheurs. Une première partie de mes travaux a porté sur l'évaluation des effets des mesures agro-environnementales (MAE) récemment mises en œuvre en France. Ces travaux mettent en évidence l'efficacité mitigée des MAE dans leur ensemble, bien que certaines semblent pouvoir améliorer la dynamique des populations en déclin. Dans une seconde partie, j'ai cherché à prédire les impacts potentiels des changements environnementaux futurs sur les communautés d'oiseaux. Les prédictions des impacts potentiels de différents scénarios PAC à l'horizon 2020 m'ont tout d'abord permis de mettre en évidence la nécessité de développer des scénarios à fine échelle spatiale, tenant compte de la diversité des systèmes agricoles en France. J'ai donc développé différents scénarios de changements d'assolements à l'échelle des petites régions agricoles, certains étant déclinés à deux échelles de prise de décision politique : nationale et régionale. Les prédictions des impacts de ces scénarios combinés à différents scénarios de changements climatiques et d'usage des sols, nous permettent notamment de mettre en évidence que la régionalisation de politiques d'extensification en fonction des agrosystèmes, pourrait permettre de maximiser la conservation des communautés d'oiseaux communs en milieu agricole. En conclusion, le développement de telles approches prédictives est utile pour prédire les impacts des changements potentiels du climat et de l'utilisation des terres, et est par conséquent un réel outil d'aide à la décision pour élaborer les futures politiques agricoles
A Comparative Readability Analysis of Artificial Intelligence-Generated and Evidence-based Medical Content on the Diagnosis and Management of Multiple Sclerosis

Abstract                                        Introduction:                     Multiple sclerosis (MS) is a complex neurological disorder requiring effective patient education. With the increasing use of artificial intelligence (AI) in healthcare, evaluating the readability of AI-generated content compared to evidence-based resources is essential to ensure patient accessibility.                                                           Aims:                     This study aims to compare the readability of patient education guides on the diagnosis and management of MS generated by the AI tool Google Gemini with a standard clinical reference, UpToDate.                                                           Methodology:                     Content was analyzed using the metrics including word count, sentence count, difficult word count/percentage, flesch reading ease (FRE), Flesch-Kincaid Grade Level (FKGL), and simple measure of gobbledygook (SMOG) index. The Wilcoxon signed–rank test was used for statistical comparison.                                                           Results:                                            Statistically significant differences (                       P                       &lt; 0.05) were found for word count, sentence count, and difficult word count. UpToDate had a significantly higher median word count (6332.5 vs. 800.5) and sentence count (199.5 vs. 44.0) than Google Gemini, indicating it was more verbose and complex. Google Gemini produced content that was relatively easier to read based on FRE, FKGL, and SMOG Index, although these differences were not statistically significant (                       P                       &gt; 0.05). Importantly, neither source met the recommended 6                       th                       to 8                       th                       -grade readability levels for patient health materials.                                                                                Conclusions:                     The overall readability scores (FRE, FKGL, and SMOG) were similar, and neither platform delivers information at the comprehension level recommended for the average patient. AI tools such as Google Gemini may serve as a useful adjunct for brief, patient-oriented information, but further refinement is needed to improve accessibility.
Central Bank Digital Currencies: Benefits, Risks and the Role of Blockchain Technology

Abstract Blockchain technology can be the basis for future currencies. Numerous central banks are experimenting with this distributed ledger technology to map currencies on a blockchain basis in the future (central bank digital currencies, CBDCs). In this article, we explore motives for the introduction of CBDCs. The main motives are higher financial stability, higher security and efficiency of payment systems and a higher degree of automation of business processes. Furthermore, the risks of CBDCs are presented and the current state of development at the European Central Bank (ECB) is discussed.
Innovative AI Regional Economic Development Driven by Information System Innovation: Opportunities and Challenges - A Boosting Factor from Digitalization, Business Intelligence Analytics

This investigation looks at the intricate relationships between business intelligence (BI) analytics, digitalization, cutting-edge artificial intelligence (AI), and information system innovation, as well as how they affect local economic growth. The study emphasizes the transformative potential of AI as a powerful accelerator for regional growth by stimulating innovation, enhancing efficiency, and expediting decision-making processes. It does this by applying map analysis and spatial econometric model regression to a dataset covering Chinese regions from 2010 to 2022. The study highlights the critical need for the development of sophisticated digitalization frameworks and information systems to adapt to the ever-evolving dynamics of regional economies and advocates for the collabourative efforts of researchers and local governments in tailoring AI-driven solutions that cater to the unique demands of individual localities. Although the research highlights the initial benefits provided by BI analytics, it also strongly emphasizes the need for ongoing innovation to maintain long-term success, encouraging academics to improve and streamline current BI analytics programmes. The paper also highlights the crucial importance of computing sustainability, effective administration of massive datasets, seamless human-AI collabouration, geographic computing, and the ethical considerations connected with AI adoption. The study emphasizes the value of utilizing AI's revolutionary potential while proactively tackling the issues it presents by offering a series of doable recommendations for policymakers, practitioners, and researchers. In the end, this study makes a significant contribution to the body of knowledge already available about the role of AI in regional economic development, providing priceless insights for those involved in directing the course of regional economies.
Protoplanetary disks and planet formation around brown dwarfs and very low‐mass stars

AbstractBrown dwarfs and very low‐mass stars are very common in the Galaxy, yet we know little about the planetary systems they may host. Here we review observational evidence emerging from comparative studies of disks around brown dwarfs and sun‐like stars. These studies show that very young brown dwarfs and very low mass stars have disks as frequently as sun‐like stars do, arguing for the same formation processes. There are indications, but no conclusive evidence yet, for a longer disk lifetime around the lowest‐mass stars and brown dwarfs. At the same time, evidence for faster dust processing and more strongly reduced disk scale heights is found, demonstrating that the first steps of planet formation also take place around brown dwarfs. With increasingly sensitive infrared instruments a new window is opening on gas‐phase chemistry in these disks and the first surveys indicate a different gas‐phase chemistry and, perhaps, a suppressed nitrogen chemistry. Sub‐millimeter surveys reveal disk masses of a few Jupiter mass, which core accretion models show is enough to form few Earth‐mass and smaller planets, but not gas giant planets. (© 2013 WILEY‐VCH Verlag GmbH &amp; Co. KGaA, Weinheim)
Stochastic Fault Tree Analysis With Self-Loop Basic Events

This paper presents an analytical approach for performing fault tree analysis (FTA) with stochastic self-loop events. The proposed approach uses the flow-graph concept, and moment generating function (MGF) to develop a new stochastic FTA model for computing the probability, mean time to occurrence, and standard deviation time to occurrence of the top event. The application of the method is demonstrated by solving one example.
Finite-Dimensional Representations of Hyper Loop Algebras Over Non-Algebraically Closed Fields

We study finite-dimensional representations of hyper loop algebras over non-algebraically closed fields. The main results concern the classification of the irreducible representations, the construction of the Weyl modules, base change, tensor products of irreducible and Weyl modules, and the block decomposition of the underlying abelian category. Several results are interestingly related to the study of irreducible representations of polynomial algebras and Galois theory.
High Resolution Overtone Spectroscopy of HNC$^+$ and HCN$^+$

Rotationally resolved spectra of the HNC$^+$ and HCN$^+$ molecular ions have been recorded in the spectral range between 6200 and 6800 \rcm\ using a cryogenic ion trap instrument. The rovibrational transitions were probed using two different action spectroscopy schemes, namely laser-induced reaction (LIR) and leak-out spectroscopy (LOS). Various vibrational bands of HNC$^+$ and HCN$^+$ were measured with high resolution for the first time. For HNC$^+$, the $\text{X}~^2Σ^+~(20^00)-(00^00)$ overtone band was recorded using LIR, while LOS was used to probe the $\text{X}~^2Π~(000)^1-(210)^0μ$ combination band and the $\text{X}~^2Π~(000)^1-\text{A}~^2Σ^+~(10^00)$ vibronic band of HCN$^+$. Spectroscopic constants, band origins and radiative lifetimes for the observed states have been determined. The effective fit for the HCN$^+$ spectra revealed the presence of strong vibrational couplings leading to perturbations of the rovibrational levels of the excited states. The two action spectroscopy schemes are compared and their potential use to explore ion-molecule interactions is discussed.
Detecting the Presence of Vehicles and Equipment in SAR Imagery Using Image Texture Features

In this work, we present a methodology for monitoring man-made, construction-like activities in low-resolution SAR imagery. Our source of data is the European Space Agency Sentinel-l satellite which provides global coverage at a 12-day revisit rate. Despite limitations in resolution, our methodology enables us to monitor activity levels (i.e. presence of vehicles, equipment) of a pre-defined location by analyzing the texture of detected SAR imagery. Using an exploratory dataset, we trained a support vector machine (SVM), a random binary forest, and a fully-connected neural network for classification. We use Haralick texture features in the VV and VH polarization channels as the input features to our classifiers. Each classifier showed promising results in being able to distinguish between two possible types of construction-site activity levels. This paper documents a case study that is centered around monitoring the construction process for oil and gas fracking wells.
PROGNOSIS OF NEURODEGENERATIVE DISEASES: ARTIFICIAL INTELLIGENCE IN THE DIAGNOSIS OF ALZHEIMER'S, PARKINSON'S AND MULTIPLE SCLEROSIS

Introduction: Neurodegenerative diseases, such as Alzheimer's, Parkinson's, and Multiple Sclerosis, cause progressive loss of neurons and cognitive function. Alzheimer's Disease is the most common, primarily affecting the elderly, while Parkinson's causes motor problems and affects all ethnicities. Multiple Sclerosis has a survival rate of up to 30 years, with age being the primary risk factor. Early diagnosis is challenging due to the high cost of tests, but the use of artificial intelligence in neuroimaging can improve diagnosis and prognosis, offering a better quality of life for patients. Objective: Given the increasing number of neurodegenerative diseases, especially Alzheimer's Disease, this study aimed to evaluate the impact of using artificial intelligence for the diagnosis and prognosis of future patients. Method: This is a literature review on the use of artificial intelligence in neuroimaging for early diagnosis and improved prognosis. Databases such as PubMed and Arxiv were used, with keywords like Alzheimer's, Parkinson's, Multiple Sclerosis, deep learning, and artificial intelligence, covering the years 2014 to 2024. Results: Based on the analysis of selected studies, it was observed that Alzheimer's Disease is the most prevalent pathology in the current population, and artificial intelligence, combined with neuroimaging, can facilitate a more effective and early diagnosis. Conclusion: It was concluded that, although promising, the use of artificial intelligence in neuroimaging still requires extensive research and advancements in this broad field.
Augmented Contrastive Self-Supervised Learning for Audio Invariant Representations

Improving generalization is a major challenge in audio classification due to labeled data scarcity. Self-supervised learning (SSL) methods tackle this by leveraging unlabeled data to learn useful features for downstream classification tasks. In this work, we propose an augmented contrastive SSL framework to learn invariant representations from unlabeled data. Our method applies various perturbations to the unlabeled input data and utilizes contrastive learning to learn representations robust to such perturbations. Experimental results on the Audioset and DESED datasets show that our framework significantly outperforms state-of-the-art SSL and supervised learning methods on sound/event classification tasks.
Epigenetic Regulation of Chromatin States in<i>Schizosaccharomyces pombe</i>

This article discusses the advances made in epigenetic research using the model organism fission yeast Schizosaccharomyces pombe. S. pombe has been used for epigenetic research since the discovery of position effect variegation (PEV). This is a phenomenon in which a transgene inserted within heterochromatin is variably expressed, but can be stably inherited in subsequent cell generations. PEV occurs at centromeres, telomeres, ribosomal DNA (rDNA) loci, and mating-type regions of S. pombe chromosomes. Heterochromatin assembly in these regions requires enzymes that modify histones and the RNA interference (RNAi) machinery. One of the key histone-modifying enzymes is the lysine methyltransferase Clr4, which methylates histone H3 on lysine 9 (H3K9), a classic hallmark of heterochromatin. The kinetochore is assembled on specialized chromatin in which histone H3 is replaced by the variant CENP-A. Studies in fission yeast have contributed to our understanding of the establishment and maintenance of CENP-A chromatin and the epigenetic activation and inactivation of centromeres.