bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assessment Survey and evaluation of LGBT-Psychology in Nigeria: current state and recommendations
Authors: Olaseni Abayomi O. , Nel Juan A. 
Published: September 2024

AbstractThere is no gainsaying that individuals with diverse sexual orientations and gender identities are faced with serious socio-legal, and medical discrimination following the enactment of anti-homosexuality law in Nigeria. However, not much is known of the effort of an organized body of psychology in the country to ensure adequate knowledge and competence among Nigerian psychologists. This article, therefore, appraises the stance of Lesbian, Gay, Bisexual, and Transgender (LGBT) psychology in Nigeria in relation to the cardinal quadrants: Advocacy, Education, Research, and Practice. A multi-method design was adopted to sort for both primary and secondary data. Purposive sampling was adopted to involve 124 practicing psychologists. Findings revealed that the Nigerian psychology curriculum limits its scope to sexual and gender disorders (sexual dysfunction, gender dysphoria, and paraphilic disorders) while missing out on sexual and gender diversity content. Furthermore, the outcome shows that not much is documented on the contribution of the field of psychology to the knowledge of LGBT. Many of the participants had a history (and still) working with LGBT clients and did not have formal LGBT-affirmative training. The study concluded that the integration of LGBT psychology is essential for significant achievement in the space of advocacy, education, research, and professional practices.
Personalized federated learning for improving radar based precipitation nowcasting on heterogeneous areas
Authors: Sáinz-Pardo Díaz Judith , Castrillo María , Bartok Juraj , Cachá Ignacio Heredia , Ondík Irina Malkin , Martynovskyi Ivan , Alibabaei Khadijeh , Berberi Lisana , Kozlov Valentin , López García Álvaro 
Published: September 2024

AbstractThe increasing generation of data in different areas of life, such as the environment, highlights the need to explore new techniques for processing and exploiting data for useful purposes. In this context, artificial intelligence techniques, especially through deep learning models, are key tools to be used on the large amount of data that can be obtained, for example, from weather radars. In many cases, the information collected by these radars is not open, or belongs to different institutions, thus needing to deal with the distributed nature of this data. In this work, the applicability of a personalized federated learning architecture, which has been called adapFL, on distributed weather radar images is addressed. To this end, given a single available radar covering 400 km in diameter, the captured images are divided in such a way that they are disjointly distributed into four different federated clients. The results obtained with adapFL are analyzed in each zone, as well as in a central area covering part of the surface of each of the previously distributed areas. The ultimate goal of this work is to study the generalization capability of this type of learning technique for its extrapolation to use cases in which a representative number of radars is available, whose data can not be centralized due to technical, legal or administrative concerns. The results of this preliminary study indicate that the performance obtained in each zone with the adapFL approach allows improving the results of the federated learning approach, the individual deep learning models and the classical Continuity Tracking Radar Echoes by Correlation approach.
Optimizing Disease Surveillance Through Pooled Testing with Application to Infectious Diseases
Authors: Warasi Md S. , Das Kumer P. 
Published: September 2024

AbstractPooled (or group) testing has been widely used for the surveillance of infectious diseases of low prevalence. The potential benefits of pooled testing include savings in testing time and costs, reducing false positive tests, and estimating models or making predictions from limited observed data information (e.g., only initial pooled responses). However, realizing these benefits often critically depends on the pool size used. Statistical methods introduced in the literature for optimal pool size determination have been developed mainly to accommodate simpler pooling protocols or perfect diagnostic assays. In this article, we study these issues with the goal of presenting a general optimization technique. We evaluate the efficiency of the estimators of disease prevalence (i.e., the proportion of diseased individuals in a population) while accounting for testing costs. Then, we determine the optimal pool size by minimizing the measures of optimality, such as screening efficiency and estimation efficiency. Our findings are illustrated using data from an ongoing screening application at the Louisiana Department of Health. We show that when a pooling application is properly designed, substantial advantages can be realized. We provide an package and a software application to facilitate the implementation of our optimization techniques. Supplementary materials accompanying this paper appear online.
Multi-factor fuzzy sets decision system forecasting consumer insolvency risk
Authors: Korol Tomasz 
Published: September 2024

AbstractThe objective of this study is to develop a multi-factor decision system predicting insolvency risk for natural persons with the use of fuzzy sets. Considering that the financial situation of households is affected by various endogenous and exogenous factors, the main assumption of this study is that the system for predicting financial difficulties should not be limited to the use of only a few financial variables concerning consumers, but also include variables describing the environment. The author proposes a system consisting of three different forecasting models that connect the macroeconomic and microeconomic environments. It monitors the economic situation of households by also identifying those environmental variables, which may directly, or indirectly, endanger the consumer, such as unemployment rate (job market situation), inflation and interest rates, exchange rates, or economic situation in the country (GDP growth rate, the dynamics of retail sales, etc.). Moreover, the created cause-and-effect tool is in the form of a flexible application that can be easily adapted to changing economic conditions. Another unique feature of the study is the proposed use of newly developed ratios in household finance, similar to that in financial ratio analysis, which is commonly used in corporate finance. The proposed ratios demonstrated high predictive abilities. The paper also identifies the predictive capabilities of selected macroeconomic variables from the perspective of their impact on the risk of consumer insolvency. The research relies on four samples consisting of a total of 2400 consumers from Taiwan and Poland. The author created three forecasting models separately for the South-East Asian and Central European regions, and two multi-factor systems, each consisting of 1260 decision rules. The findings clearly showed that fuzzy logic is a significantly more effective method compared to traditional models based on classical logic.
Student-to-Student Connections in Postdigital Networked Learning
Authors: Wardak Dewa , Wilson Stephanie 
Published: September 2024

AbstractAnalysing learning situations as entanglements of humans and things in networked learning links to contemporary topics that are at the forefront of the postdigital challenge. The literature broadly conceptualises researching networked learning through four different understandings of networks, as a network of people, as situations or contexts, as infrastructure, and as actant. In this study, we explored the fourth way of understanding networked learning by examining how students described their connections with other students while learning online. Using mediated discourse analysis, we analysed 12 student focus groups to gain insights into what supported their connections with other students, paying particular attention to the nature of the networks they engaged in. This led to the identification of six enablers of student-to-student connections: attention, access, collaboration, proximity, timely interaction, and purpose. We demonstrate how the networks described by students when providing accounts of their connections with others can be best described as networks of human and non-human actants. While current research acknowledges that learning is a result of the socio-material entanglements of physical, virtual, and human actants, it is limited in demonstrating how and why such networks are supportive of learning. Our study contributes by showing how the identified enablers and their interrelationships influenced the extent to which networks were found to be supportive of learning. As such, it also supports a more tangible way of articulating the relationship between networked learning and postdigital education.
Quantifying the impact of trade liberalization on Lao imports (evidence from product-level data)
Authors: Chanthavong Sinthavanh 
Published: September 2024

AbstractIn pursuing economic development after market-oriented reforms, Lao PDR has actively liberalized imports through engagement in regional and multilateral trade agreements. However, a substantial research gap exists in assessing the impact of this liberalization, especially in terms of ex-post empirical studies, which are essential for nuanced policy evaluation. This study aims to thoroughly quantify the impact of tariff liberalization on Lao imports, specifically examining how the elimination of tariffs under regional trade and multilateral trade schemes affects Lao imports. In this study, a balanced gravity panel dataset at a product level from 2013 to 2022 was constructed using data from the Lao Official Import Management Database for a standard gravity model analysis. Results of this analysis reveal that tariff liberalization has significantly boosted Lao imports with an elasticity of -0.327. Counterfactual analysis strengthens this finding, indicating an approximate 6.7% increase in the average value of imports from 2018 to 2022.
The quest for optimal ketamine dosing formula in treatment-resistant major depressive disorder
Authors: Kwaśna Julia , Cubała Wiesław Jerzy , Kwaśny Aleksander , Wilkowska Alina 
Published: September 2024

Abstract
Background
Emerging evidence indicates that intravenous ketamine is effective in managing treatment-resistant unipolar and bipolar depression. Clinical studies highlight its favorable efficacy, safety, and tolerability profile within a dosage range of 0.5-1.0 mg/kg based on actual body weight. However, data on alternative dosage calculation methods, particularly in relation to body mass index (BMI) and therapeutic outcomes, remain limited.

Methods
This retrospective analysis of an open-label study aims to evaluate dose calculation strategies and their impact on treatment response among inpatients with treatment-resistant major depressive disorder (MDD) (n = 28). The study employed the Boer and Devine formulas to determine lean body mass (LBM) and ideal body weight (IBW), and the Mosteller formula to estimate body surface area (BSA). The calculated doses were then compared with the actual doses administered or converted to a dosage per square meter for both responders and non-responders.

Results
Regardless of treatment response, defined as a reduction of 50% in the Montgomery-Åsberg Depression Rating Scale, the use of alternative ketamine dosing formulas resulted in underdosing compared to the standardized dose of 0.5 mg/kg. Only two participants received higher doses (102.7% and 113.0%) when the Devine formula was applied.

Conclusions
This study suggests that ketamine dosing formulas, alternative to the standardized 0.5 mg/kg based on body weight, may lead to underdosing and potentially impact outcome interpretation. To enhance dosing accuracy, future studies should consider incorporating body impedance analysis and waist-to-hip ratio measurements, as this study did not account for body composition.
An Improved Adaptive Neuro-fuzzy Inference Framework for Lung Cancer Detection and Prediction on Internet of Medical Things Platform
Authors: Shabu S. L. Jany , Refonaa J. , Mallik Saurav , Dhamodaran D. , Grace L. K. Joshila , Ksibi Amel , Ayadi Manel , Alshalali Tagrid Abdullah N. 
Published: September 2024

AbstractIt has become increasingly difficult for medical practitioners to recognize illness in recent years due to the emergence of new diseases from their myriad causes on a daily basis. Due in large part to inadequate diagnostic and monitoring infrastructure, a substantial amount of illness and death are associated with lung cancer (LC). The aim of the paper is to find lung cancer early and help patients receive curative treatment. Quitting smoking or never starting is the best way to mitigate the potential for disease-related death. As a result, cutting-edge detection and monitoring technologies must be developed to enable rapid, accurate, and timely diagnosis. Fuzzy logic (FL) is one of the best approaches to modeling complex and uncertain systems; therefore, it helps us deal with these challenges. Fuzzy expert system for lung cancer [FES-LC] detection and prediction on Internet of medical things (IoMT) is employed to overcome the challenges. Hence, an enhanced adaptive neuro-fuzzy inference framework [ANF-IF] is proposed in the current research. The cloud-based application of an adaptive neuro-fuzzy inference system yields four risk categories: not at risk, slightly at risk, moderately at risk, and severely at risk. New methods and theoretical frameworks have made it possible to diagnose LC in its earliest stages with the help of magnetic nanoparticles (MNPs), which allow researchers to overcome the limitations of conventionally slow diagnostic efficiency. The proposed system exhibits a precision of 93.4%, accuracy of 95.1%, specificity of 90.6%, sensitivity of 92.8%, false positive rate of 0.22%, false negative ratio of 0.18%, and classification accuracy of 98.2%. The proposed method outperforms all methods and provides better lung cancer detection accuracy than others.
Research on marine oil spill identification based on laser-induced fluorescence LiDAR
Authors: Xue Qingsheng , Song Junhong , Lu Fengqin , Ma Jun , Gao Xinyu , Xu Jinfeng 
Published: September 2024

AbstractThe rise in oil extraction and transportation in marine environments has led to frequent oil spill incidents, posing a severe threat to marine ecosystems and becoming an urgent environmental issue. This paper presents a laser-induced fluorescence light detection and ranging (LiDAR) system specifically designed for monitoring marine oil spills. The system comprises a laser emission module, a receiving module, a data processing module, and a wireless transmission module. Through outdoor experiments, the system has demonstrated its effectiveness and reliability in detecting and identifying various oil types, including crude oil, diesel, heavy oil, gasoline, and lubricating oil. Additionally, a BP neural network model was employed to process the fluorescence spectral data collected by the LiDAR system. This model successfully predicted oil types with an accuracy of 96.58%. This research presents a new technological solution for marine oil spill monitoring, offering significant potential for practical applications and further research.
The effect of the universal two-child policy on the gender wage gap in urban China
Authors: Huang Qian , Jin Xiaofei , Fan Ziang 
Published: September 2024

AbstractOn 1 January 2016, China further relaxed its family planning policy and adopted the universal two-child policy, which allows any Chinese couple to have two children to address the country’s increasingly severe ageing problems and low fertility. With this shift comes a direct and profound impact on society, especially women; this paper evaluates the effect of the universal two-child policy on the gender wage gap and its mechanism. Several major conclusions emerge from this analysis. The policy significantly expands the urban gender wage gap by 12.86% in the low-policy-fertility-rate (PFR) provinces versus high-PFR provinces. Evidently, it increases the gap among younger or lower-educated people. Moreover, the severity of gender discrimination in the labour market after the implementation of the universal two-child policy is rising, and deserves further attention.
Conspiracy! Or, when bad things happen to good litigants in person
Authors: Leader Kate 
Published: September 2024

AbstractThis paper considers the relationship between litigants in person (LiPs) and conspiracy theories and seeks to answer two questions: how, and why, do some LiPs come to be conspiracy theorists? The majority of LiPs, of course, do not become conspiracy-minded. There is also no evidence that LiPs are more likely than anyone else in legal proceedings to be conspiracists, only, perhaps, that it is more obvious when they are. But there continue to be individuals who have conspiracist explanations for difficulties or failures they experience throughout legal proceedings. And while it is widely held that some LiPs hold eccentric beliefs about the law, there has been little attempt to understand how and why LiPs may come to acquire or articulate these beliefs. This is presumably because it has not been considered important to interrogate the views of people already often assumed to be ‘difficult’ or eccentric. This paper contends, however, that trying to understand how and why these conspiracist beliefs are acquired matters very much. This is because conspiracy theories can give us a critical insight into how negative experiences of litigation can result in a loss of faith or trust in legal institutions.
Divine contradiction: fascinating but unpersuasive
Authors: Kilby Karen 
Published: September 2024

AbstractThis article, offered from the point of view of a non-analytic, systematic theologian, admires the freshness, clarity, and simplicity of the proposal at the heart of Beall's Divine Contradiction, while raising three objections. The first is to the style in which the book is written: I suggest that it remains far too technical to reach large parts of its intended audience. The second is to the tendency to speak of God as ‘portion’ or ‘fragment’ of reality. The third, more substantive objection is to the proposal that the denial of the divinity of each of the Persons of the Trinity can be part of the Christian faith: I argue that Beall's position that only the failure to affirm a truth, and not its denial, counts as a real heresy, is under-argued and unpersuasive.
Divine Contradiction: some snippets
Authors: Beall Jc 
Published: September 2024

AbstractTwo doctrines (or axioms) of christian theology sharply distinguish christian monotheism from its traditional monotheistic siblings (viz. jewish and islamic monotheism): the incarnation of God and the triunity of God. Both doctrines, as many have long observed, face a conspicuous so-called logical problem – namely, apparent contradiction. How should the strong appearance of such fundamental contradiction be explained? Beall's answer: the incarnation and trinity appear to be contradictory because God is a contradictory being – a being of whom some contradictions are true. The full truth of God is expressed only via contradiction, which is why the fundamental axioms of christian theology have long appeared to be contradictory. Divine Contradiction presents the target contradictory account of the trinity; its predecessor The Contradictory Christ presents the contradictory account of the incarnation.
Enhancing Adaptive Capacity by Engaging and Empowering Frontline Workers: A Case Study From a Water Utility
Authors: Gonzales Katerina R. , Roop Heidi A. , Rozance Mary Ann , Grodnik‐Nagle Ann , Purnell Danielle , Rack Marieke , Branam Easton 
Published: September 2024

AbstractWater utility crews, including those who perform system maintenance and operations at drinking water, wastewater, and stormwater utilities, are experiencing the impacts of changing rainfall characteristics first‐hand. These frontline crews have relevant, experiential knowledge about changing climate‐related risks and adaptation needs that, to date, are not included in utilities' strategic climate planning or implementation efforts, resulting in critical knowledge systems and action gaps. In this co‐production case study with Seattle Public Utilities (SPU), a municipal public water utility in Washington State, we use surveys and interviews to document barriers and opportunities for frontline crew engagement to enhance SPU's climate adaptation strategies to extreme rainfall. We find that 66% of crew workers perceive changes to rainfall intensity over the past decade and 59% report responding more to rainfall‐related issues. These frontline crews are coping with changes in extreme precipitation by increasing preventative maintenance, working overtime during major storm events, and doing more pre‐storm planning and preparation. Crews identified several aspects limiting their ability to respond to and prepare for extreme rainfall. To enhance their adaptive capacity, crews documented needs for infrastructure improvement, workforce facilities improvement, increased access to appropriate personal protective equipment, improved communication and coordination across the utility, and enhanced workforce capacity, including increased staff numbers. Findings suggest that for more effective, equitable, and responsive climate adaptation, water utilities should explicitly solicit and include crews' knowledge and lived experiences in adaptation planning while prioritizing strategies that enhance frontline crews' capacity and working conditions as a form of reciprocal action.
Phenological Shifts in Lake Ice Cover Across the Northern Hemisphere: A Glimpse Into the Past, Present, and the Future of Lake Ice Phenology
Authors: Basu A. , Culpepper J. , Blagrave K. , Sharma S. 
Published: September 2024

AbstractLong‐term ice phenology records quantify the effects of climate change on Northern Hemisphere lakes. This study uses lake ice phenological records across a gradient of lake sizes (0.1–31,967.8 km2 in lake surface area) obtained from community science networks. We compiled in situ ice phenological records for 2,499 lakes across 15 countries for an average of 30 years. These data revealed that for the last 50 years (1971–2020), the annual mean duration of lake ice cover decreased at a rate of 9 days per decade, with a regime shift in lake ice phenology in the late 1980s. We projected that at the end of the century (2070–2099), ice duration will decrease by an average of 10 days when compared to the historical time period (1971–2000) for the shared socioeconomic pathway (SSP) 1–2.6 climate scenario (SSP126), 23 days for SSP370, and 28 days for the SSP585. Impending human development can enhance or attenuate lake ice loss, as adaptation strategies can accelerate fossil fuel use, result in conflict, or seek strategies apart from fossil fuel development. These future pathways have critical implications for the future preservation of lake ice cover.