ethicalabs/Research-Intent-Collab huggingface dataset:
The impact of judicial constitutional court review of legislative omission on constitutional authoritarianism

This paper aims to provide an analysis of the impact of constitutional review on legislative omissions, their impact on constitutional authoritarianism by the Supreme Constitutional Court, and how constitutional review affects rights and freedoms, as well as the validity of legislation. The paper also aims to present the effects of constitutional review on the relationship between authorities (Al-Dughili, 2018). In light of the principle of separation of powers, and the potential for tension between the judiciary and the legislature. The paper employs an analytical approach to examine judicial rulings issued by the Supreme Constitutional Court, aiming to determine the criteria for ruling on the unconstitutionality of legislative omissions, the extent of constitutional judicial intervention in the legislative process, and the potential repercussions of such rulings on all aspects. The paper reached several conclusions, the most important of which is that the Supreme Constitutional Court, with its authority to exercise constitutional oversight over legislative actions, can maintain legal security. However, constitutional judicial oversight over legislative omissions has some adverse effects, which are represented by tension in the relationship between the constitutional judiciary and the legislative authority, which may lead to exposing the powers of the constitutional judiciary to constitutional judicial oversight over legislative omissions to danger. It may also contribute to legislative tyranny and the seizure of legislative authority by the constitutional judiciary.
Geology, fluid inclusion, and stable isotope systematics of the Dongyang epithermal gold deposit, Fujian Province, southeast China: Implications for ore genesis and mineral exploration

Abstract   The Dongyang gold deposit is a recently discovered large gold deposit located within the Dehua–Youxi–Yongtai mining camp in central–east Fujian Province, southeast China. The deposit contains two individual ore sections: the northwestern Jitou section and the southeastern Dadongkeng section. Most of the mineralization is hosted by rhyolite porphyry and volcanic rocks of the Nanyuan Formation. The locations of subvolcanic intrusions in this area are controlled by NE–SW and NW–SE trending faults that are also closely related to mineralization. Detailed drillhole logging and adit mapping indicates that the deposit contains massive, polymetallic quartz–sulfide veins, and cryptoexplosive breccia type ores. Ore minerals consist of gold, electrum, silver minerals, pyrite, marcasite, and arsenopyrite with lesser amounts of sphalerite and chalcopyrite. The mineralization is hosted by quartz, sericite, illite, chalcedony, and calcite gangue and is associated with sericite and argillic alteration as well as silicification. Fluid inclusion microthermometry indicates that the inclusions within quartz from auriferous quartz–sulfide veins formed during the main stage of mineralization and homogenize at temperatures (Th) of 172 °C–217 °C (average of 192 °C) and have salinities of 0.4–2.6 wt% NaCl equivalent. These low to moderate temperatures and low salinities suggest that the mineralization occurred in an epithermal environment. The precipitation of gold mineralization at Dongyang was most likely caused by the mixing of a large amount of meteoric fluid with a smaller amount of magmatic fluid. Quartz-hosted fluid inclusions have H and O isotope compositions (δDH2O = −63.1 to −54.2‰; δ18OH2O = −5.7‰ to −3.8‰) that are indicative of derivation from a predominantly meteoric source. The S isotope values of pyrite vary from a narrow range of −3.4‰ to 0.9‰, suggesting that the S within the deposit was derived from a homogeneous magmatic source. The mineralization and alteration, gangue and ore mineral assemblages, fluid inclusion and H–O–S isotope compositions indicate that the Dongyang gold deposit is a low-sulfidation epithermal-type deposit. The estimated ore formation depth of Donyang is nearly identical to its actual depth, indicating minor denudation in the region. These results imply a great prospecting potential for other epithermal mineralization at the Dehua–Youxi–Yongtai mining camp.
The importance of private land for biodiversity conservation is highlighted by rapid, broad‐scale sampling using citizen science

Abstract                   With the majority of terrestrial land privately owned and the Earth in a biodiversity extinction crisis, it is vital to improve our understanding of biodiversity on private lands. However, documenting biodiversity on private lands is challenging due to the time and financial constraints in gaining access, hindering our understanding of the value of private lands for conservation. In recent decades, citizen science has offered a unique opportunity to obtain biodiversity data on private lands through engaging landholders and encouraging participation in biodiversity documentation. To determine the value of citizen science for biodiversity sampling on private lands and enhance our understanding of the importance of private lands, we leveraged nearly 500,000 frog occurrence records from across New South Wales, Australia, from the Australian citizen science project, FrogID. Pairing this data with land tenure data, we found that private land was relatively better sampled than public land by citizen science data, with 86% of records from private lands. Private and public lands supported moderately different biodiversity, and frog species richness was predicted to likely be higher on private land once aridity and sampling density were accounted for. Citizen science offers a novel technique to supplement biodiversity data collection by rapidly amassing occurrence data on broad scales. Private lands are essential for global biodiversity conservation, encompassing the majority of land and providing vital habitat for both threatened and non‐threatened taxa.
Dissemination and participation in early warnings and disaster risk reduction in South Africa

Governments cannot effectively manage and handle disasters, particularly at the local community level, without actively engaging vulnerable people. The key to achieving sustainability in disaster recovery is community participation and information dissemination. The informal settlements’ lack of access to information and public engagement hampered their ability to recovery, thus prompting this study. Therefore, many cities and intervention partnerships faced information and participation gaps in disaster risk reduction (DRR). The study’s rationale was to determine the participation and communication of Khayalitjha household heads, regarding DRR information dissemination for sustainable human settlement, using a cross-sectional household survey of 295 household heads from Khayalitjha in situ informal settlement in the Free State provinces of South Africa. The security of dwelling unit tenure concept was an indirect indicator used to measure social resilience. The key findings revealed that community volunteers, ward committee members and most of the respondents, were responsible for initiating the DRR and disaster preparedness planning process. This indicated that local government needs to strengthen the human resource capacity building for DRR management information dissemination at a local level. The church, school, WhatsApp, Facebook, Twitter and Instagram were the preferred modes of communication for early warnings of disaster information.  Contribution: Despite advocating for a multidisciplinary stakeholder approach, urban DRR studies tend to ignore communities in high disaster-risk areas. Employing social resilience, it aims to extend the DRR information dissemination strategy to in situ informal settlements beyond the communication and public participation advocacy strategies of local municipal urban cities.
Continuous Hydrogen Production via Hydrothermal Gasification of Biodiesel Industry Wastewater: Experimental Optimization and Energy Integration Simulation

This study reports the continuous production of H2 from the wastewater effluent of the biodiesel industry in a medium containing water under supercritical conditions. The effects of temperature and feed flow rate on the generation of hydrogen were evaluated and optimized. At a temperature of 700 °C and a 17.5 mL/min feed flow, a total gas flow of 5541 NmL/min was achieved. Among all identified gases, hydrogen represented the highest molar fraction of 73%. Under optimized conditions, a H2 yield of 357 NmL/geffluent feed was observed. The experimental results indicate a significant increase in the H2 production at the highest experimented temperatures. On the other hand, the feed flow only slightly influenced the process within the assessed range but showed a tendency to increase the H2 production at the highest values. Finally, information on energy efficiency optimization and scale-up are presented, and at the same time, different designs for industrial implementation of the hydrothermal gasification process are proposed.
Recommender‐as‐a‐service with chatbot guided domain‐science knowledge discovery in a science gateway

SummaryScientists in disciplines such as neuroscience and bioinformatics are increasingly relying on science gateways for experimentation on voluminous data, as well as analysis and visualization in multiple perspectives. Though current science gateways provide easy access to computing resources, data sets and tools specific to the disciplines, scientists often use slow and tedious manual efforts to perform knowledge discovery to accomplish their research/education tasks. Recommender systems can provide expert guidance and can help them to navigate and discover relevant publications, tools, data sets, or even automate cloud resource configurations suitable for a given scientific task. To realize the potential of integration of recommenders in science gateways in order to spur research productivity, we present a novel “OnTimeRecommend” recommender system. The OnTimeRecommend comprises of several integrated recommender modules implemented as microservices that can be augmented to a science gateway in the form of a recommender‐as‐a‐service. The guidance for use of the recommender modules in a science gateway is aided by a chatbot plug‐in viz., Vidura Advisor. To validate our OnTimeRecommend, we integrate and show benefits for both novice and expert users in domain‐specific knowledge discovery within two exemplar science gateways, one in neuroscience (CyNeuro) and the other in bioinformatics (KBCommons).
Clinical Characteristics and Management of Patients with Secondary Hyperparathyroidism Undergoing Hemodialysis: A Feasibility Analysis of Electronic Health Records Using Natural Language Processing.

This study aimed to assess the feasibility of applying natural language processing (NLP) to analyze real-world data (RWD) and resolve clinical problems in patients with secondary hyperparathyroidism and chronic kidney disease undergoing hemodialysis (SHPT/CKD-HD). The primary objective was to evaluate how well the guideline-recommended analytical goals are achieved in a Spanish cohort of SHPT/CKD-HD patients based on RWD. Unstructured data in the electronic health records (EHRs) from 8 hospitals were retrospectively analyzed using the EHRead® technology, based on NLP and machine learning. Variables extracted from EHRs included demographics, CKD-related clinical characteristics, comorbidities and complications, mineral and bone disorder parameter levels, and treatments at baseline, 6-month, and 12-month follow-up. A total of 623 prevalent SHPT/CKD-HD patients were identified; of those, 282 fulfilled the inclusion criteria. They were predominantly elderly males with cardiovascular comorbidities, and the first cause of CKD was diabetic nephropathy. Diagnosis of SHPT was associated with an improvement in median values for PTH, calcium, and phosphate. However, the percentage of patients with normal PTH ranges remained stable during the study period (52.8-60.4%), while the percentage of patients with within-target range serum calcium or phosphate values showed an increasing trend (43.2-60% and 38.8-50%). At baseline, 74.1% of patients were using SHPT-related medication, including at least one vitamin D or analog (63.1%), phosphate binders (46.8%), and/or calcimimetics (9.6%). This study represents the first attempt to use clinical NLP to analyze SHPT/CKD-HD patients based on unstructured clinical data. This methodology is useful to address clinical problems based on RWD and identified a high rate of out-of-range mineral-bone analytical values in patients with HPT/CKD-HD and an increasing trend of out-of-range values for serum calcium and phosphate.
Analysis of the Role of Human Resource Information System (Hris) in Improving Work Efficiency and Effectiveness in Modern Organizations

This study aims to analyze the influence of the implementation of Human Resource Information System (HRIS) on work efficiency and effectiveness in modern organizations, considering the important role of HRIS in automating administrative processes and supporting data-driven decision-making. This study uses a quantitative approach with secondary data analysis methods in the form of administrative records, attendance, leave, and payroll systems that are processed comparatively between conditions before and after the implementation of HRIS. The results show that HRIS significantly improves work efficiency, with a reduction in data processing time of up to 80% and a reduction in data input errors to less than 2%, as well as accelerating real-time employee data access and supporting more objective performance evaluations. In addition, HRIS contributes to the effectiveness of organizations through more targeted promotion, training, and career development planning. Thus, the implementation of HRIS is an important instrument in improving the efficiency, effectiveness, and quality of human resource management in modern organizations
Influence of round-off errors on the reliability of numerical simulations of chaotic dynamic systems

We illustrate that, like the truncation error, the round-off error has a significant influence on the reliability of numerical simulations of chaotic dynamic systems. Due to the butterfly-effect, all numerical approaches in double precision cannot give a reliable long-term simulation of chaotic dynamic systems. So, in order to avoid man-made uncertainty of numerical simulations of chaos, we had to greatly decrease both of the truncation and round-off error to a small enough level, plus a verification of solution reliability by means of an additional computation using even smaller truncation and round-off errors.
The Impact of Autonomous Vehicles on P&amp;C Insurance: Preparing for the Next Frontier of Risk and Liability

T he arrival of autonomous vehicles (AVs) promises to revolutionize transportation with significant implications in the property and casualty (P&C) insurance business. Traditional paradigms for risk assessment and liability in auto insurance are under attack as self-driving technology evolves. This paper examines the emerging risks and opportunities that AVs have introduced to the P&C insurance world. On the key considerations side, product and manufacturer liability for human operator injuries, the increase in automation of insurance underwriting using advanced technologies like AI and machine learning and potentially decreased accident frequency but an increased severity in accidents caused by more advanced vehicle systems is all on the table
Learning nonequilibrium statistical mechanics and dynamical phase transitions

AbstractNonequilibrium statistical mechanics exhibit a variety of complex phenomena far from equilibrium. It inherits challenges of equilibrium, including accurately describing the joint distribution of a large number of configurations, and also poses new challenges as the distribution evolves over time. Characterizing dynamical phase transitions as an emergent behavior further requires tracking nonequilibrium systems under a control parameter. While a number of methods have been proposed, such as tensor networks for one-dimensional lattices, we lack a method for arbitrary time beyond the steady state and for higher dimensions. Here, we develop a general computational framework to study the time evolution of nonequilibrium systems in statistical mechanics by leveraging variational autoregressive networks, which offer an efficient computation on the dynamical partition function, a central quantity for discovering the phase transition. We apply the approach to prototype models of nonequilibrium statistical mechanics, including the kinetically constrained models of structural glasses up to three dimensions. The approach uncovers the active-inactive phase transition of spin flips, the dynamical phase diagram, as well as new scaling relations. The result highlights the potential of machine learning dynamical phase transitions in nonequilibrium systems.
Bridging the gap between science communication practice and theory: Reflecting on a decade of practitioner experience using polar outreach case studies to develop a new framework for public engagement design

AbstractThe International Polar Year 2007–2008 stimulated a wide range of education, outreach and communication (EOC) related to polar research, and catalysed enthusiasm and networks that persist ten years on. Using a multi-method approach that incorporates case studies, auto-ethnographic interviews, and survey data, we interrogate the opportunities and limitations of polar EOC activities and propose a new framework for practical, reflexive, engagement design. Our research suggests that EOC activities are under-valued and often designed based on personal instinct rather than strategic planning, but that there is also a lack of accessible tools that support a more strategic design process. We propose three foci for increasing the professionalisation of practitioner approaches to EOC: (1) improved articulation of goals and objectives; (2) acknowledgement of different drivers, voices and power structures; and (3) increased practical training, resources and reporting structures. We respond to this by proposing a framework for planning and design of public engagement that provides an opportunity to become more transparent and explicit about the real goals of an activity and what “success” looks like. This is critical to effectively evaluate, learn from our experiences, share them with peers, and ultimately deliver more thoughtfully designed, effective engagement.
Smart Sensor Network Based High Quality Air Pollution Monitoring System Using Labview

A ZigBee based wireless sensor network is implemented in this paper which is of low-cost solar-powered air quality monitoring system. The main objective of the proposed architecture is to interfacing various sensors to measure the sensor analog data and displayed in LabVIEW on the monitor using the graphical user interface (GUI).  The real time ambient air quality monitoring in smart cities is of greater significance for the health of people. The wireless network sensor nodes are placed at different traffic signals in the smart cities which collect and report real-time data on different gases which are present in the environment such as carbon monoxide (CO), nitrogen dioxide (NO2), methane (CH4) and humidity. The proposed system allows smart cities to monitor air quality conditions on a desktop/laptop computer through an application designed using graphical programming based LabVIEW software and provides an alert if the air quality characteristics exceed acceptable levels. The sensor network was successfully tested on the campus of the institute of aeronautical engineering, Hyderabad. The sensor data are indicated by different indicators on the front panel of LabVIEW and also different charts are plotted with respect to time and amplitude which explains the severity of polluted areas.
Agricultural Marketing and Sustainable Household Food Security in Sub-Saharan Africa

This paper assessed if agricultural marketing activities – assembling and grading, transportation, storage and distribution channels have made any appreciable impact on food security in the area of food availability, accessibility, utilization and stability in Sub-Saharan Africa using evidence from Nigeria.   In the explanatory research design, we adopted both descriptive and inferential statistics to answer the four research questions. Primary data was generated from a sample of 800 respondents selected across the six geopolitical zones of Nigeria using multiple sampling techniques. Results from logistic regression analysis model show that agricultural marketing activities have made significant impacts on food security in Nigeria.  The study concluded that any increase in the activities of Agricultural marketing affects food security directly and the common household either benefit or suffer for it. Hence, the study recommended that partnership between the government and private sector should ensure that enabling environment are created for sustainable agricultural marketing by improving on rural access road proper management of oil prices, provision of storage facilities as well as checking distribution channels.
Exploring persistent local homology in topological data analysis

Topological data analysis (TDA) has rapidly grown in popularity in recent years. One of the emerging tools is persistent local homology, which can be used to extract local structure from a dataset. In this paper, we provide a survey that explores this new tool, emphasizing its use in data analysis.
Score-based Causal Representation Learning: Linear and General Transformations

This paper addresses intervention-based causal representation learning (CRL) under a general nonparametric latent causal model and an unknown transformation that maps the latent variables to the observed variables. Linear and general transformations are investigated. The paper addresses both the identifiability and achievability aspects. Identifiability refers to determining algorithm-agnostic conditions that ensure the recovery of the true latent causal variables and the underlying latent causal graph. Achievability refers to the algorithmic aspects and addresses designing algorithms that achieve identifiability guarantees. By drawing novel connections between score functions (i.e., the gradients of the logarithm of density functions) and CRL, this paper designs a score-based class of algorithms that ensures both identifiability and achievability. First, the paper focuses on linear transformations and shows that one stochastic hard intervention per node suffices to guarantee identifiability. It also provides partial identifiability guarantees for soft interventions, including identifiability up to mixing with parents for general causal models and perfect recovery of the latent graph for sufficiently nonlinear causal models. Secondly, it focuses on general transformations and demonstrates that two stochastic hard interventions per node are sufficient for identifiability. This is achieved by defining a differentiable loss function whose global optima ensure identifiability for general CRL. Notably, one does not need to know which pair of interventional environments has the same node intervened. Finally, the theoretical results are empirically validated via experiments on structured synthetic data and image data.