Related Data Research Articles

AI will change EA practice – but are we ready for it? A call for discussion based on developments in collecting and processing biodiversity data

ABSTRACT The opportunities and potential of advanced digitalisation involving the application of Artificial Intelligence (AI) in Environmental Assessment (EA) are often mentioned across international studies. However, it is essential for us in EA research and practice to comprehensively grasp the implications of this transformation and proactively prepare for the imminent changes. In this context and drawing on insights from biological sciences, this letter examines the established use, prospects and risks of these technological advances in the field of species, habitat and biodiversity related data and its analysis. We aim to initiate a thought-provoking dialogue across diverse groups of EA actors regarding the practical implications of AI for EA, highlighting new roles and evolving skills needed to guarantee quality and legal compliance. Central to this discussion is the origination of data, alongside the distribution of responsibilities across actors/stakeholders involved in EA with regard to data collection, sharing and interpretation. Key considerations regard the quality and integrity of AI-supported and systematically collected data and the prevention of potential manipulation. We emphasise the need to re-evaluate education and training programs, adapt practices, and enhance decision-making processes as initial steps toward establishing a focused research agenda.

A Novel Leak-Proof Thermal Conduction Slot Battery Thermal Management System Coupled with Phase Change Materials and Liquid-Cooling Strategies

Electric vehicles (EVs) are experiencing explosive developments due to their advantages in energy conservation and environmental protection. As a pivotal component of EVs, the safety performance of lithium-ion batteries directly affects driving miles and even safety; hence, a battery thermal management system (BTMS) is especially important. To improve the thermal safety performance of power battery modules, first, a new leak-proof phase change material (PCM)-coupled liquid-cooled composite BTMS for large-scale battery modules is proposed in this research. Second, the numerical simulation analysis method was utilized to analyze the influences of the fluid flow channel shape, working fluid inlet temperature, inlet velocity, and reverse flow conditions on the BTMS. Eventually, the abovementioned performances were compared with the traditional PCM-coupled liquid-cooling strategy. The relative data indicated that the Tmax was reduced by 17.5% and the ΔTmax was decreased by 19.5% compared to the liquid-cooling approach. Further, compared with conventionally designed PCM composite liquid cooling, the ΔTmax was reduced by 34.9%. The corresponding data showed that, when using the e-type flow channel, reverse flow II, the inlet flow velocity was 0.001–0.005 m/s, and the inlet temperature was the ambient temperature of the working condition. The thermal performance of the anti-leakage system with a thermal conduction slot PCM-coupled liquid-cooling composite BTMS reached optimal thermal performance. The outcome proved the superiority of the proposed BTMS regarding temperature control and temperature equalization capabilities. It also further reduced the demand for liquid-cooling components, avoided the problem of the easy leakage of the PCM, and decreased energy consumption.

Limits to the employer in managing employee data in labor relations

Despite the creation of legal standards for data protection in both the international and Brazilian spheres, the matter needs further elaboration regarding the limitations that must be established for the employer in the management of employee data in labor relations both in pre-contractual phase, both in the maintenance of labor contracts and in the dismissal of employees. Principles such as Dignity of the Human Person, Freedom of Profession, Non-Discrimination, Proportionality, Purpose, Adequacy and Necessity must be observed. To this end, the role of data protection supervisory agents in the labor sphere must be highlighted.

Development of an Arduino-based Multi-Channel Temperature and Relative Humidity Data Acquisition System for Domestic and Industrial Applications

Losses incurred during postharvest processes of food and other agricultural products in the rural and some urban areas of West African countries have been attributed to poor thermal conditions of the indoor air. The quality of these products at storage or after thermal processes such as drying, cooking, frying, etc. has been identified to be majorly dependent on the temperature and humidity of the air. The objective of this study was therefore to develop a low cost Arduino based multi-channel temperature and humidity data acquisition system for domestic and industrial applications. It comprises an ATmega 328P controller type, fourteen (14) DS18B20s temperature sensors, and six (6) DHT22 humidity sensors. The system was configured and programmed to log in readings every 30 minutes (30 min) on the SD card or through the Bluetooth module in Excel format directly into the computer. The results from the experiments conducted with this data acquisition system demonstrated reliability because they were within the reasonable and acceptable range for commercial logging devices.

Blockchain and public service delivery: a lifetime cross-referenced model for e-government

ABSTRACT The article presents the results of field studies, analysing the perspectives of blockchain developers on decentralised service delivery and elaborating on unique algorithms for lifetime ledgers to reliably and safely record e-government transactions in an intrinsically cross-referenced manner. New interesting technological niches of service delivery and emerging models of related data management in the industry were proposed and further elaborated such as the generation of unique lifetime personal data profiles, blockchain-driven cross-referencing of e-government metadata, parallel maintenance of serviceable ledgers for data identifiers and phenomena of blockchain ‘black holes’ to ensure reliable protection of important public, corporate and civic information.

Data Bodies

Abstract This essay examines two recent books that each take up the vexed topic bodies of data in relation to literature and race in the US: Richard Jean So’s Redlining Culture: A Data History of Racial Inequality and Postwar Fiction and Elizabeth Rodrigues’s Collecting Lives: Critical Data Narrative as Modernist Aesthetic in Early Twentieth-Century U.S. Literatures. Each book, in its own way, asks literary scholars to take the data corpus seriously as both an object and a method of study. While acknowledging that the collection of bodies of data has long been used to efface, dehumanize, and racialize, So and Rodrigues ask readers not to dismiss reactively the data corpus’s potential to also be an unexpected tool for refuting those same naturalized and totalizing narratives. Contemplating, as they do, the unique temporality and spatiality of the accretion of singular points into a collective data, the books explore what a data corpus looks, feels, and sounds like and what it might tell us about the relationship between bodies, books in twentieth-century US literature, and the stories that have and could be told about both.Thinking in different ways about the vexed relationship between bodies of data, literature, and race in the US, Rodrigues and So each demonstrate that, while data cannot speak for itself, there are some stories we can neither hear, see, nor tell without its help.

The Sydney Declaration – An unique opportunity for Africa

The Sydney Declaration (SD) has the inherent virtue of shifting the focus from the tools used in forensic science to the fundamental characteristics of appropriate forensic practice analysis of a situation and all related trace data for the purpose of resolving a case. Though several differences might be observed between countries regarding the technologies used, the fundamental principle of forensic logical reasoning are universally applicable to all contexts and environments and not only restricted to sophisticated, well-resourced, established forensic science laboratories based in countries that offer adequate resources, strong networks and legal frameworks. In Africa, several countries lack resources to train practitioners and to develop forensic science institutes, laboratories and other relevant institutions. The SD can serve as departure point to attain appropriate forensic science outcomes. Upholding its sound guidance can help forensic science practitioners remain faithful to their science, acknowledge their limits, give expert opinions based solely on facts and scientific analysis and interpretation, whilst also enhancing communication with their stakeholders. As forensic science develops in Africa, the SD presents a timely opportunity for us to proceed with a common understanding and a solid foundation through which our field can grow.

Reconstruction of palaeoenvironmental conditions that led to the formation of Eocene sub-bituminous coal seams in the Hungarian Paleogene Basin

Although in the second half of the Eocene, thick sub-bituminous coal seams were deposited in the Hungarian Paleogene Basin, it is not known under what climatic conditions these strata were formed. To investigate this, the paleoenvironmental conditions were reconstructed for the upper Lutetian-middle Priabonian period. The reconstruction of paleoclimatic conditions was based on the Coexistence Approach, for which the climatic data of the extant relatives of 15 fossil plant genera provided the basis of the analysis. Most (87%) of the extant relatives of the studied fossil plant genera can be found most frequently in tropical climate regions. Plotting the reconstructed average mean annual temperature (23.4 ± 3.9 °C) and precipitation (2375 mm ± 964 mm) values to the Whittaker Plot and Peltier's plots it was found that the region had a tropical monsoon climate during the upper Lutetian- middle Priabonian period. At the same time, there was a great difference between the precipitation sums of the wettest and driest months (409 ± 178 mm and 59 ± 56 mm), indicating a tropical monsoon-like climate with strong chemical weathering conditions. Currently, in climatically similar regions, tropical rainforest and tropical moist deciduous forest vegetation grow under tropical rainforest and tropical monsoon climates, and lateritic soils are formed. Furthermore, relatively rich mangrove plant associations flourish along the coasts of these regions, indicating that a part of the peat may have been deposited in mangroves. The contemporary lateritic soils, which later became the protoliths of bauxites, provided a poor nutrient supply for coastal vegetation, likely due to peat accumulation.

Heart & Homoeopathy In COVID 19

Recently, the Indian Council of Medical Research (ICMR) through studies confirmed that there is no link between the COVID-19 vaccines & heart issues. The larger issue is that there is an established link between COVID-19 disease & heart issues. The current article deals with the details of the process of COVID-19 & heart problems. The patho-physiology, symptoms pattern, epidemiology of the entire phenomenon is touched upon. To deal with the COVID related heart issues, the article proposes the integration of Homoeopathy of AYUSH. In the article, it details out a suggested treatment protocol based on homoeopathic therapeutics for COVID related heart issues. Taking into account the risk factors involved in these heart issues, the percentage of individuals who have high blood sugar & high blood pressure levels in India are considered at high risk. The related data from National Family Health Survey 5th round is cited to enumerate the risk involved at the national level. Finally, the article banks on the useful properties of homoeopathy. These are cost effectiveness, clinical effectiveness & zero side effects. The article also enumerates its role in the mass coverage at the national level.

Correlation enhanced distribution adaptation for prediction of fall risk

With technological advancements in diagnostic imaging, smart sensing, and wearables, a multitude of heterogeneous sources or modalities are available to proactively monitor the health of the elderly. Due to the increasing risks of falls among older adults, an early diagnosis tool is crucial to prevent future falls. However, during the early stage of diagnosis, there is often limited or no labeled data (expert-confirmed diagnostic information) available in the target domain (new cohort) to determine the proper treatment for older adults. Instead, there are multiple related but non-identical domain data with labels from the existing cohort or different institutions. Integrating different data sources with labeled and unlabeled samples to predict a patient's condition poses a significant challenge. Traditional machine learning models assume that data for new patients follow a similar distribution. If the data does not satisfy this assumption, the trained models do not achieve the expected accuracy, leading to potential misdiagnosing risks. To address this issue, we utilize domain adaptation (DA) techniques, which employ labeled data from one or more related source domains. These DA techniques promise to tackle discrepancies in multiple data sources and achieve a robust diagnosis for new patients. In our research, we have developed an unsupervised DA model to align two domains by creating a domain-invariant feature representation. Subsequently, we have built a robust fall-risk prediction model based on these new feature representations. The results from simulation studies and real-world applications demonstrate that our proposed approach outperforms existing models.

Prinsip Non-Refoulement Penanganan Pengungsi dan Relevansinya dalam Perspektif Kebijakan Selektif Keimigrasian

Immigration is a matter of the movement of people into and out of Indonesian territory which is motivated by several factors such as socio-cultural, political and economic which are the reasons why foreigners migrate. The Immigration Office is under the Directorate General of Immigration and is the responsibility of the Ministry of Law and Human Rights and has the authority to regulate the presence and activities of foreigners while they are in Indonesian territory. Immigration adheres to a selective policy system, which means that only useful and useful foreigners are allowed to enter Indonesian territory and do not disturb the country's sovereignty. International law applies the principle of non-refoulement, namely prohibiting the expulsion or refusal of foreigners to return to their country of origin for certain reasons. This principle aims to protect the human rights of refugees. National interests are not linear with international legal interests. Therefore, refugees cannot be separated from Immigration's responsibility in carrying out supervision and providing enforcement of immigration law. This research uses a normative-empirical research method which refers to the provisions in primary and secondary legal studies. Researchers collect related data from legal principles, legal conceptions, views, legal doctrines, statutory regulations and the legal system as the main focus.

Six dilemmas for stakeholders inherently affecting data sharing during a zoonotic (re-)emerging infectious disease outbreak response

BackgroundTimely access to outbreak related data, particularly in the early events of a spillover, is important to support evidence based control measures in response to outbreaks of zoonotic Emerging Infectious Diseases (EID). Yet, this is impeded by several barriers that need to be understood to promote timely sharing of data. Using the MERS epidemic as a model for a zoonotic EID outbreak, this study sought to provide an in-depth understanding of data sharing practices.MethodsSemi-structured interviews with 25 experts were conducted, along with Focus Group Discussions with 15 additional experts. A root-cause analysis was performed to examine the causal relationships between barriers. Enablers were mapped to the root-cause analysis to understand their influence on the barriers. Finally, root causes were placed in context of core dilemmas identified from the qualitative analysis.FindingsEight barriers to data sharing were identified, related to collaboration, technical preparedness, regulations, and (conflict of) interests, and placed in the context of six dilemmas inherent to the multi-stakeholder collaboration required for a zoonotic outbreak response. Fourteen identified enablers showed the willingness of stakeholders to overcome or circumvent these barriers, but also indicated the inherent trial and error nature of implementing such enablers.InterpretationAddressing the barriers requires solutions that must consider the complexity and interconnectedness of the root causes underlying them, and should consider the distinct scopes and interests of the different stakeholders. Insights provided by this study can be used to encourage data sharing practices for future outbreaksFundingWellcome Trust and UK Aid; EU-H2020 Societal Challenges (grant agreement no. 643476), Nederlandse Organisatie voor Wetenschappelijk Onderzoek (VI.Veni.201S.044)

Bibliometric analysis of antimalarial drug resistance.

Malaria has always been a serious infectious disease prevalent in the world. Antimalarial drugs such as chloroquine and artemisinin have been the main compounds used to treat malaria. However, the massive use of this type of drugs accelerates the evolution and spread of malaria parasites, leading to the development of resistance. A large number of related data have been published by researchers in recent years. CiteSpace software has gained popularity among us researchers in recent years, because of its ability to help us obtain the core information we want in a mass of articles. In order to analyze the hotspots and develop trends in this field through visual analysis, this study used CiteSpace software to summarize the available data in the literature to provide insights. Relevant literature was collected from the Web of Science Core Collection (WOSCC) from 1 January 2015 to 29 March 2023. CiteSpace software and Microsoft Excel were used to analyze and present the data, respectively. A total of 2,561 literatures were retrieved and 2,559 literatures were included in the analysis after the removal of duplicates. An irrefutable witness of the ever-growing interest in the topic of antimalarial drug resistance could be expressed by the exponentially increased number of publications and related citations from 2015 to 2022, and its sustained growth trend by 2023. During the past 7 years, USA, Oxford University, and David A Fidock are the country, institution, and author with the most publications in this field of research, respectively. We focused on the references and keywords from literature and found that the research and development of new drugs is the newest hotspot in this field. A growing number of scientists are devoted to finding new antimalarial drugs. This study is the first visual metrological analysis of antimalarial drug resistance, using bibliometric methods. As a baseline information, it is important to analyze research output published globally on antimalarial drug resistance. In order to better understand the current research situation and future research plan agenda, such baseline data are needed accordingly.

Development of a survey tool to assess the environmental determinants of health-enabling food retail practice in Aboriginal and Torres Strait Islander communities of remote Australia

BackgroundEnvironmental factors can impact the ability of food retail businesses to implement best practice health-enabling food retail.MethodsWe co-designed a short-item survey on factors influencing food retail health-enabling practice in a remote Australian setting. Publicly available submissions to an Australian Parliamentary Inquiry into food pricing and food security in remote Indigenous communities were coded using an existing remote community food systems assessment tool and thematically analysed. Themes informed survey questions that were then prioritised, refined and pre-tested with expert stakeholder input.ResultsOne-hundred and eleven submissions were coded, and 100 themes identified. Supply chain related data produced the most themes (n = 25). The resulting 26-item survey comprised questions to assess the perceived impact of environmental factors on a store’s health-enabling practice (n = 20) and frequency of occurrence (n = 6).ConclusionsThe application of this evidence-informed, co-designed survey will provide a first-time cross-sectional analysis and the potential for ongoing longitudinal data and advocacy on how environmental factors affect the operations of remote stores.

Flood Detection with SAR: A Review of Techniques and Datasets

Floods are among the most severe and impacting natural disasters. Their occurrence rate and intensity have been significantly increasing worldwide in the last years due to climate change and urbanization, bringing unprecedented effects on human lives and activities. Hence, providing a prompt response to flooding events is of crucial relevance for humanitarian, social and economic reasons. Satellite remote sensing using synthetic aperture radar (SAR) offers a great deal of support in facing flood events and mitigating their effects on a global scale. As opposed to multi-spectral sensors, SAR offers important advantages, as it enables Earth’s surface imaging regardless of weather and sunlight illumination conditions. In the last decade, the increasing availability of SAR data, even at no cost, thanks to the efforts of international and national space agencies, has been deeply stimulating research activities in every Earth observation field, including flood mapping and monitoring, where advanced processing paradigms, e.g., fuzzy logic, machine learning, data fusion, have been applied, demonstrating their superiority with respect to traditional classification strategies. However, a fair assessment of the performance and reliability of flood mapping techniques is of key importance for an efficient disasters response and, hence, should be addressed carefully and on a quantitative basis trough synthetic quality metrics and high-quality reference data. To this end, the recent development of open SAR datasets specifically covering flood events with related ground-truth reference data can support thorough and objective validation as well as reproducibility of results. Notwithstanding, SAR-based flood monitoring still suffers from severe limitations, especially in vegetated and urban areas, where complex scattering mechanisms can impair an accurate extraction of water regions. All such aspects, including classification methodologies, SAR datasets, validation strategies, challenges and future perspectives for SAR-based flood mapping are described and discussed.

Multi-response optimisation during face milling of polyoxymethylene copolymer using grey relational analysis and data envelopment analysis based ranking coupled with the Taguchi approach

ABSTRACT The present work aims to evaluate the effect of cutting factors, namely cutting speed (Vc), feed per tooth (fz), and depth of cut (ap) on surface quality (average arithmetic roughness Ra and total roughness Rt) and productivity (material removal rate MRR) when face milling polyoxymethylene (POM C). In this context, the experiments were planned according to the standard orthogonal network of Taguchi L16 (4^3). An analysis of variance (ANOVA) was performed to study the influence of each input factor on the output parameters. The processing of the results made it possible to propose prediction models for the surface. Finally, a mono and multi-objective optimisation was performed using the grey relational analysis (GRA) approach and ranking based on data envelopment analysis (DEAR) coupled with the Taguchi method based on the signal-to-noise ratio (S/N). ANOVA results revealed that feed per tooth (fz) has the greatest influence on (Ra, Rt, and MRR), with successive contributions of (97.69, 97.05, and 66.26)%. On the other hand, GRA and DEAR resulted in the same cutting regime (Vc = 251.2 m/min, fz = 0.004 mm/tooth, and ap = 6 mm), leading to optimised responses (Ra = 1.67 µm and MRR = 11.51 cm3/min).

Variable Pivot Gait Based a Novel Dynamics Correction Method for Human Lower Limbs Model.

The rationality of gait analysis directly affects the dynamics of human lower limbs in the sagittal plane, and recent studies on gait stage redivision lack the stage when both feet are not in complete contact with the ground. This paper proposes a novel variable pivot gait, which includes the stage when the heel of one foot and the toe of the other are in contact with the ground and a dynamics correction method based on this gait. First, the relative motion data between the foot and the ground are measured by motion capture experiments, and then a variable pivot gait is proposed in terms of the pivot transformation between the foot and the ground. Second, the dynamics modeling is conducted based on the principle of mechanisms of human lower limbs in each stage of the variable pivot gait. Third, a dynamics correction method is proposed to correct the foot dynamics when the foot is not in complete contact with the ground. The experiment and simulation show that the variable pivot gait is consistent with the actual motion of the foot relative to the ground. The effectiveness of the dynamics correction method is proved by comparing dynamics results (hip, knee, and ankle moments) with previous studies. The variable pivot gait and the dynamics correction method can be applied to the human lower limbs and lower-limb robots, providing a new avenue.

Correlations of SDF-1ɑ and XRCC1 gene polymorphisms with the risk of renal cancer development and bioinformatics studies of SDF-1α and XRCC1 and the prognosis of renal cancer

To study the relationships between stromal cell-derived factor-1 (SDF-1ɑ) and renal cell carcinoma (RCC) susceptibility and the presence of single nucleotide polymorphisms in the human X-ray cross-complementary repair gene (XRCC1). Compare SDF-1 based on RCC related data in the TCGA database α, The expression difference of XRCC1 between RCC tissue and normal tissue; Collect 166 newly diagnosed RCC cases and 166 healthy individuals who underwent physical examinations during the same period, and detect genotype using iMLDR method. The results The rs1801157 locus (C:T) of the SDF-1α gene was not significantly associated with the pathohistological type, the rs1799782 locus (G:A) of the XRCC1 gene was associated with the pathohistological type of RCC, and there were interactions between rs1799782 and smoking, alcohol consumption, pesticide exposure, hair dye, and urine holding. The rs1799782 locus of the XRCC1 gene may be a key factor in the pathogenesis and pathological development of RCC. High SDF-1ɑ expression is a protective factor for the overall survival of patients with RCC, and SDF-1ɑ and XRCC1 may be important for the treatment of RCC.

Uranium Dissemination with Phosphate Fertilizers Globally: A Systematic Review with Focus on East Africa

Growing concern has been expressed about uranium (U) accumulation in agricultural soils caused by the long-term application of mineral fertilizers. More than 80% of naturally occurring U transfers from phosphate rock (PR), the raw material used in mineral fertilizer production, to phosphorus (P) fertilizers. These fertilizers are then distributed on agricultural soils, where the U could accumulate over time and become a risk to the environment. The objective of this work was to review the reported content of U in P fertilizers, its potential dispersion in soils, and its uptake by plants in different countries in the world as reported in the literature. The articles for this systematic review were selected from the Scopus database published between 2003 and 2022. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol were used. A total of 54 articles were assessed based on the standard inclusion and exclusion criteria. U concentrations in P fertilizers, agricultural soil dissemination, and plant uptake for available data were obtained and assessed. In order to compare a set of related data from the collected articles, box and whisker plots showing the distribution of U in P fertilizers are presented by region. The results from the reviewed articles show that the U concentrations in P fertilizer were in the range of 0.1–653 mg kg−1. Interestingly, Minjingu P fertilizers from Tanzania, which are used in six East African countries, showed the highest U concentrations (159 to 653 mg kg−1, average 390 mg kg−1). The reported U concentrations for these fertilizers are, in fact, comparable to those of conventional low-grade uranium deposits mined in Namibia and elsewhere. Additionally, approximately 96% of the reviewed articles indicate that fertilized soil has higher U concentrations than non-fertilized soils, hinting at a measurable effect of mineral fertilizer use. The review recommends U extraction during mineral fertilizer production so that potential environmental risks can be reduced and U resources that would otherwise be lost can be recovered and used to substitute conventional U mining elsewhere.

A Novel Piecewise Cubic Hermite Interpolating Polynomial-Enhanced Convolutional Gated Recurrent Method under Multiple Sensor Feature Fusion for Tool Wear Prediction.

The monitoring of the lifetime of cutting tools often faces problems such as life data loss, drift, and distortion. The prediction of the lifetime in this situation is greatly compromised with respect to the accuracy. The recent rise of deep learning, such as Gated Recurrent Unit Units (GRUs), Hidden Markov Models (HMMs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Attention networks, and Transformers, has dramatically improved the data problems in tool lifetime prediction, substantially enhancing the accuracy of tool wear prediction. In this paper, we introduce a novel approach known as PCHIP-Enhanced ConvGRU (PECG), which leverages multiple-feature fusion for tool wear prediction. When compared to traditional models such as CNNs, the CNN Block, and GRUs, our method consistently outperformed them across all key performance metrics, with a primary focus on the accuracy. PECG addresses the challenge of missing tool wear measurement data in relation to sensor data. By employing PCHIP interpolation to fill in the gaps in the wear values, we have developed a model that combines the strengths of both CNNs and GRUs with data augmentation. The experimental results demonstrate that our proposed method achieved an exceptional relative accuracy of 0.8522, while also exhibiting a Pearson's Correlation Coefficient (PCC) exceeding 0.95. This innovative approach not only predicts tool wear with remarkable precision, but also offers enhanced stability.