bluuebunny/crossref_metadata_2025 huggingface dataset:
The role of betatrophin at onset of the Iraqi type II Diabetes Mellitus patients
Authors: Mustafa Saleam Khalaf  , Sadik A Abdullah  , Ghufran Saad Nsaif
Published: October 2024

Background: Type II diabetes mellitus It is one of the most important metabolic disorders that the body suffers from, which affects glucose metabolism. It begins first with insulin resistance and then develops into type II diabetes mellitus disorder. One of the most famous pathological complications of type II diabetes mellitus disorder is the inhibition of insulin secretion and hyperlipidemia. Betatrophin which is a protein hormone produced at the beginning of infection with type II diabetes mellitus disorder. It works as a protective factor as it activates the growth and development of pancreatic beta cells and also has a role in regulating lipid metabolism at the beginning of infection with the disorder. Materials and methods: The present study included the selection of 28 individuals suffering from type II diabetes mellitus disorder, and 28 healthy individuals as control. The study individuals were of both sexes and their ages ranged between 38-55 years. Many of biochemical markers were measured for all study individuals include Insulin hormone, random blood sugar, glycated hemoglobin, Betatrophin hormone and C-peptide, where special kits from the best international companies were used for this. On the other hand, the t-test method was used for statistical analysis to compare the two groups. Results: After comparing the control group with the patients group with type II diabetes mellitus disorder, it was found that there was elevate in the levels of random blood sugar, glycated hemoglobin, Betatrophin hormone and C-peptide in the group of patients compared to the control group, except insulin hormone which was not affected. The values ​​of mean ± standard deviation (SD) and p-value were relied upon to determine the clinically significant values. Conclusion: The current study results conclude that the hormone Betatrophin begins to rise as a protective factor for the body at the onset of insulin resistance, which causes with type II diabetes mellitus disorder This increase in the level of the hormone Betatrophin is an attempt by the body to rebuild parts of the pancreas to enhance insulin secretion and also to regulate the metabolism of fat in the body, which is considered a complication of with type II diabetes mellitus disorder
Aquifer delineation in parts of Akwa-Ibom State, Southern Nigeria using Resistivity Survey
Authors: Christopher Chijioke Ukwuani  , Godwin Jeremiah Udom  , Ferdinand Dumbari Giadom
Published: October 2024

In line with Sustainable Development Goals (SDG 6.6), aquifer delineation in parts of Akwa-Ibom State, Southern Nigeria was carried out using resistivity survey. The areas covered in the study included Uyo, Itu and Ibiono Ibom. Electrical resistivity technique was applied to obtain data from the subsurface layers. The study utilized a Signal Averaging System (SAS) 1000 Terrameter, four stainless steel electrodes, four reels of cable, geologic hammer, a measuring tape and a Global Positioning System (GPS). A SAS 1000 Terrameter was used in measuring the resistivity of the materials within the subsurface. Electric current was transmitted into the ground from the Terrameter using current electrodes while the potential difference between the current electrodes was measured. A total of four (4) stainless steel electrodes were used: two (2) current electrodes which transmitted electric current into the ground and two (2) potential electrodes from which the potential difference was measured between the current electrodes. Four reels of cable were used to link the electrodes and the Terrameter. The cable served as conductor of electric current into the ground. A total of four geologic hammers were used in sending the electrodes into the ground and in making them to be firmly fixed. A 100-m measuring tape was used to determine the intervals between the current electrode and potential electrode. A total of nine (9) ERT data points were obtained, processed and interpreted. The data obtained from the field were processed and also interpreted from curve matching techniques together with computer-aided software called Interplex 1-D. Deeper aquifers free from surface contamination and suitable for groundwater development are encountered at depth ranges of 90 m to 120 m, 70 m to 130 m, 80m to 160m and 70 m at Uyo, Itu, Ibiono Ibom and Ibesikpo respectively. The depths to aquifer, thicknesses and resistivity values were 1.086-445m, 1.086-248m and 0.114-8,132 Ωm, and 1.45-378m, 1.45-348m and 203-5,229Ωm, and 1.345-863.8m, 1.345-422.5m and 202.5-445.3Ωm, and 1.464-66.61m, 1.464-41.99m and 90.85-695.4Ωm in Uyo, Itu, Ibiono Ibom and Ibesikpo Asutan respectively. Borehole drilling within the locations where survey was carried out should target the depths of the delineated aquifers is recommended.
Spatial Variation of Physico-chemical and Microbial Parameters in Groundwater of Selected LGAs of Akwa-Ibom State, Nigeria
Authors: Christopher Chijioke Ukwuani  , Godwin Jeremiah Udom  , Ferdin Dumbari Giadom
Published: October 2024

Physico-chemical and microbial parameters of groundwater are useful indicators in monitoring the progress of Goal 6.3 of Sustainable Development Goals (SDGs). The study evaluated the spatial variation of physico-chemical and microbial parameters in groundwater of selected LGAs of Akwa-Ibom State, Nigeria in line with SDGs. A total of twenty (20) groundwater samples were collected from boreholes at different locations within Uyo, Itu, Ibiono Ibom and Ibesikpo Asutan Local Government Areas of Akwa-Ibom State. The boreholes were selected at random. Sterilized and pre-labelled water sampling bottles were used in collecting representative water samples to prevent contamination. Physico-chemical parameters sensitive to environmental changes such as temperature, pH and electrical conductivity were measured in-situ. Sampling and analysis followed guidelines specified by the American Public Health Association, (2012). Chemical analysis was carried out at the Geospectra Laboratory in Port Harcourt, Rivers State of Nigeria. Results showed that the groundwater condition ranged from acidic and slightly weakly acidic in Uyo and Ibiono Ibom but slightly alkaline in Itu. All the heavy metals analysed were within the permissible limits specified by WHO (2018) and NSDWQ (2015) in drinking water. The average values of total dissolved solids (TDS), biochemical oxygen demand (BOD), chemical oxygen demand (COD), faecal coliform and total coliform in Uyo, Itu, Ibiono and Ibesikpo were 57.4 mg/L, 40.1 mg/L, 97.0 mg/L, 1032 and 1542.3 MPN/100ml, 54.3 mg/L, 51.6 mg/L, 110 and 47, 20.5 mg/L, 35.7 mg/L, 78.5 mg/L, 28.8 and 82.5 MPN/100ml, and 184 mg/L, 42.8 mg/L, 94.3 mg/L, 130 and 280 MPN/100ml respectively. The values of pH, BOD, COD, faecal and total coliforms were found to be above the permissible limits. Faecal and total coliforms, which indicate recent contamination, are high in Uyo and could be attributed to urbanisation with its consequent sewage management issues and increased animal droppings. There is a need for proper sewage management within the city.
Mapping data-driven strategies in improving health care and patient satisfaction
Authors: Stefanos Karakolias
Published: October 2024

Health services involve data analytics, central to healthcare service delivery, research, and planning. Based on the literature, this specific article seeks to enlighten the reader about data analytics in health services by discussing its advantages, shortcomings, and issues that come with it. Section one mapping outlines how data analytics optimizes the results of health service delivery through risk factor definition, disease detection, and case-by-case management. It helps inform disease trajectory, determine drug interaction, monitor disease, support decision-making, and enhance service delivery quality. The research also examines data analysis in health services, medicine, and pharmaceuticals. Several big data analytics applications have been identified as relevant to drug development, personalized medication, and specialized trials. Evaluations that rely on biomarkers differentiate between patients who are convenient for specific therapies, improving the patient's status and developing health care services. The third section also stresses the role of data analytics in enhancing efficiency and profitability. They involve detecting fraud, abuse, and unnecessary medical activities, creating savings and better financial returns. Data analytics also leads to identifying high-cost patients and ways of managing healthcare costs, increasing revenues and profitability. The fourth section discusses how big data complements traditional public health surveillance and outbreak platforms. It facilitates collecting data, analyzing different sources, and identifying special patterns or outliers to detect the outbreak early enough, contain it, or allocate resources to control it. It anticipates and addresses the dangers to the public's well-being and effectively controls episodes of infectious diseases. However, some key issues are associated with data analytics implementation, such as data quality, governance, privacy, bias, integration, expertise, and ethics, among others. Therefore, solving these challenges to achieve the optimum shifting of health service paradigms through data analytics in inpatient treatment, organizational management, and affordability is imperative.
Antiplasmodial activities of n-hexane and water fractions of Dennettia tripetala leaf extract in Wistar rats
Authors: Mba Ogbonnaya  , Chibueze P. Ihekwereme  , Uchechukwu H. Orji  , Aziakpono M. Omoirri
Published: October 2024

Many people die from malaria, a global health crisis. Most global malaria cases and deaths occur in sub-Saharan Africa. The rising resistance of Plasmodium spp. to antimalarial medications is hindering efforts to eradicate the illness. This study tested Dennettia tripetala leaf extract, N-hexane, and water fractions for antimalarial activity in rats. We used standard methods to screen Dennettia tripetala solvent (N-hexane and water) for phytochemical components. We tested the antimalarial potential of rats using Peters' 4-day suppressive, Rane's curative, and prophylactic tests. After conducting an exhaustive investigation, it was discovered that both fractions contained varying amounts of flavonoids, alkaloids, terpenoids, and saponins. There was a significant amount of antimalarial activity demonstrated by both fractions across all test models (P<0.05). Compared to the water fraction, Dennettia tripetala's n-hexane fraction was highly chemosuppressive and curative. Dennettia tripetala n-hexane has chemoprophylactic, curative, and chemosuppressive effects. The curative effect was 62.3% to 72.4%, and the chemosuppressive effect was 51.2% to 88.5%. Chemoprophylactic activity was 32.1%–61.2%. At 250–1500 mg/kg, Combretum nigricans butanol suppressed 40.3%, 54.1%, and 69.1%, respectively. Therapeutic effects were 26.2%, 36.9%, and 34.5%, while chemoprophylaxis was 48.4%, 70.0%, and 87.4%. Both Dennettia tripetala solvent fractions have antimalarial activity, suggesting they may be effective in different malaria therapy stages.
Software data objects application integrity modeling in medication dispensing errors predictions using machine learning algorithms in workflow-based pharmacy software systems
Authors: Siva Nagarajan
Published: October 2024

The study focuses on using the software objects created at each stage of the workflow-based systems (and in our article we can focus on the pharmacy-based medication dispensing software systems) to predict the anomalies or potential issues in that software object constructed at each stage of the workflow using the historical issues recorded in that specific workflow stage using linear regression, decision trees, and neural network algorithms. By utilizing historical data and integrity metrics, the research aims to forecast potential failures and maintain the reliability of software applications. The focus is on employing algorithms such as linear regression, decision trees, and neural networks to enhance predictive accuracy and facilitate informed decisions or recommendations to the workflow users (pharmacists) about the potential medication dispensing error the software objects constructed at that state or workflow could cause and advise to take necessary corrections.
The metaphorical conceptualization of economic relationships in the mass media discourse related to the economic and financial uncertainty (based on the English-language articles on debt ceiling crisis in the USA in 2023)
Authors: Popova Vera Vladimirovna
Published: October 2024

The article examines the metaphorical conceptualization of a crisis economic situation in mass media discourse. The article looks at the metaphorical representation of economic relationships in the period of financial uncertainty caused by the inflation and the debt ceiling crisis in the USA in 2023. The aim of the research is to determine and describe the most relevant metaphorical models depicting the economic phenomena in the given historical period. The scientific novelty lies in describing the ways of metaphorical conceptualization of economic relationships in mass media discourse brought to life by new empirical data of the debt ceiling crisis. The research findings include describing the most relevant metaphorical models that depict the economic phenomena in the articles of The New York Times, The Washington Post, The Economist; determining the nominative field of the target concept ECONOMY; extending the corpus of metaphors from crisis discourse and identifying the primary functions of the metaphors used in the texts under the study.
Stability of Adaptive Cruise Control of Automated Vehicle Platoon under Constant Time Headway Policy
Authors: Luu Lich , Phan Thanh Long , Pham Huu Truyen , Hoang Thang , Le Minh-tien
Published: October 2024

Traffic congestion is becoming more prevalent as the number of vehicles on the roads continues to rise.To shorten travel times and enhance driver comfort, a range of Advanced Driver Assistance Systems (ADAS) has been developed to assist drivers in urban areas and on highways. The demand for increasing road capacity has introduced the concept of vehicle platooning, where the use of the Adaptive Cruise Control (ACC) system is a key function within the advanced driver assistance (ADAS) technology, this technology manages the vehicle's longitu-dinal control in specific driving conditions. Cars equipped with ACC can efficiently maintain a set distance from the vehicle ahead, easing the driver’s workload while offering advantages such as improved road capacity, lower fuel consumption, and reduced pollution emissions. However, they can be susceptible to string instability, resulting in the amplification of oscillations caused by speed variations along the platoon's rear. This paper presents a string stability analysis of car platoons equipped with ACC system based on a heuristic method by choosing of the constant time headway policy (CTHP). The constant time headway policy selection for string stability is based on the Nyquist diagram of the transfer function of the spacing errors between two cars. A platoon operated by using distance-based ACC control structures is implemented. These structures employ a linear quadratic regulator (LQR) using a dual integrator. The simulation results were acquired by modeling and simulating the studied platoon within Matlab/Simulink.
Deep learning based classification for alzheimer's disease detection using MRI images
Authors: Polater Seda Nur , Sevli Onur
Published: October 2024

Alzheimer's disease is a common type of dementia that can cause serious problems in cognitive functions and activities of daily living. Although there is no definitive cure for Alzheimer's disease today, early diagnosis is important to slow down the adverse conditions that may arise and to improve the quality of life. As a result of the development of artificial intelligence technologies and their consistent application in different fields, machine learning techniques have the potential to play an important role in the detection of Alzheimer's disease. In particular, deep learning-based methods, which have the ability to automatically extract patterns from complex patterns, are promising in this field. Recent studies show that the use of deep learning models for Alzheimer's detection on images is becoming widespread. In addition to contributing to the early diagnosis of the disease, these models also show potential in detecting different stages of the disease by analyzing the symptoms in magnetic resonance images. These developments enable the development of more effective treatment methods for patients. However, more studies are needed to evaluate the efficacy and safety of these technologies in clinical applications. In this study, classification studies were performed using MobileNetV2, InceptionV3, Xception, Vgg16 and Vgg19 models for the diagnosis of the disease on a publicly shared Alzheimer's dataset consisting of 6400 different samples and 4 different classes. An accuracy of 99.92% was calculated for the MobileNetV2 model. The performances of the models used in this study were compared with similar studies in the literature and their performances were reported in terms of different metrics. Among the five different models used, the highest accuracy value of 99.92% was obtained with MobileNetV2. It was concluded that the architectures used in the experimental studies produced generally better results than similar studies in the literature.
Modeling Electricity Generation and Consumption in Cameroon
Authors: Fombuwing Blaise , Tekbıyık Ersoy Neyre
Published: October 2024

Currently, there is a significant gap between electricity generation and consumption in Cameroon. Research has shown that electricity consumption in the country is estimated to increase by 965.7 GWh in five years, from 2020 to 2024 due to demographic and economic growth. Hence, this study aims to find methods that can be useful in developing strategies to balance the energy supply and demand in the country. This is done by developing models that can predict future electrical power consumption and generation. Correlation analysis and regression analysis were performed by using data obtained from various databases, and related models were developed accordingly. The model parameters were carbon dioxide emissions, electricity consumption per capita, final consumption expenditures, electricity installed capacity, fossil fuel installed capacity, labor force, and GDP. The models' results demonstrated excellent performance coefficients with RMSE of 0.17041, 0.23893, 0.27571, and 0.2465 for hydroelectricity generation, fossil fuel electricity generation, net electricity generation, and net electricity consumption respectively. Also, hydroelectricity generation, net electricity generation, and net electricity consumption models showed very good RRMSE performance indicating that the models can make predictions with only 4.26%, 5.26%, and 5.77% deviation from the mean values of hydroelectricity generation, net electricity generation, and net electricity consumption, respectively.
Interpretable AI analysis of chaos systems distribution in time series data from industrial robotics
Authors: Özkurt Cem
Published: October 2024

In this study, the generalizability and distributivity of three different chaotic systems within an industrial robotics time series dataset are explored using an annotated artificial intelligence algorithm. A time series dataset derived from industrial robotics processes was constructed and transformed into the Runge-Kutta system, comprising fourth-order differential equations for normalization. Among the processed data, variables related to x-y-z positions underwent chaotic transformations through Lorenz, Chen, and Rossler chaos systems. The x variable and angle variables from the transformed x-y-z data were inputted into the InterpretML model, an annotated artificial intelligence model, to elucidate the effects of angle variables on the x position variable. As a result of this analysis, InterpretML Local analysis revealed a sensitivity of 0.05 for the Rossler chaos system, 0.15 for Chen, and 0.25 for Lorenz. Furthermore, global analysis indicated precision rates of 0.17 for Rossler, 0.255 for Chen, and 0.35 for Lorenz chaos systems. These sensitivity results suggest that the Rossler chaos system consistently provides more accurate results in both InterpretML local and global analyses compared to other chaotic systems. This study contributes significantly to the literature by analyzing the distributive and generalization properties of chaos systems and enhancing understanding of these systems.
Editorial
Authors: Haatainen Outi , Vuorio Emmi , Pernaa Johannes
Published: October 2024

In the face of rapid societal and cultural changes influenced by the global challenges related to sustainability, the concept of sustainable transition has become paramount. The sustainability transition involves swift transformations that impact our economy, society, culture, and every individual, aiming to secure the resilience of our environment. Each of us is a participant in this transition. Understanding the interdependence between humans and nature, and how we, as members of society, can influence and support sustainability within the constraints of natural systems, requires research-based knowledge, sustainability skills, and the courage to experiment and make an impact. These elements are integral in education for sustainability that has understandably been incorporated in curricula across the world. Therefore, educational sector is a powerful change agent in promoting sustainability skills throughout the world. This special issue addresses the challenges of chemistry education for sustainability through three international research articles that focus on teacher education and provide tools for teaching chemistry for sustainability.
Leveraging Social Engineering Techniques for Ethical Purposes: An Approach to Develop Fake Android App for Collecting Valuable Data Discreetly
Authors: Alsharya Hussein Abdulkhaleq Saleh
Published: October 2024

Social engineering techniques are often viewed negatively due to their association with deceptive practices. However, these techniques can also be utilized ethically, as many cybersecurity professionals do, particularly when evaluating vulnerabilities and testing security defenses. This paper presents EDC (Ethical Data Collector), an Android application that utilizes social engineering techniques to discreetly collect valuable data from an Android device for ethical purposes. EDC employs deception through a simulated UI (fake) to engage the target for a period, while secretly collecting data such as device information, active phone number, and images in the background, then sending them to a designated server via the internet. The researcher argues that EDC could help identify inexperienced cybercriminals or extortionists without complex efforts or significant cost, provided that its capabilities are judiciously utilized and subject to proper controls and oversight. EDC's development methodology emphasizes understanding the target's personality, predilections, and preferences to tailor the app experience as required for attracting the target to install and run the application. The paper describes the core functions and workflows for collecting and sending data. Additionally, permissions handling has been addressed as being critical for enabling EDC to collect the required information. Testing on Android emulators demonstrated that the EDC's APK file size is 4 MB, and data collection and transmission processes functioned as intended across various Android versions. The minimum SDK version required to run EDC is level 16. The total estimated time to complete the fake UI process (cumulative user engagement time) is 57-60 seconds, where each activity takes 8 seconds based on the adopted development approach.
Texture Analysis and Classification using Local Binary Patterns and Statistical Features
Authors: Maher Hasan
Published: October 2024

Texture analysis and classification are crucial in many applications such as medical applications, satellite imagery analysis, and so on. This paper presents a new technique that incorporates the LBP with the HOG to improve the texture analysis and classification. LBP is famous for finding out the local texture descriptors by comparing the pixel intensities it deals with and can challenge light variations and image transformations. On the other hand, there is HOG which emphasizes edge and gradient information to detect the orientation and shape of a texture in a given image. While LBP describes the local micro-patterns HOG gives the perspective of the global gradient orientation. The combined use of local and global texture information is helpful in the presented problem when prioritizing the recognition of complex texture patterns that cannot be deciphered by a single algorithm. To demonstrate the efficiency of the proposed hybrid method, several experiments are performed on several texture databases. According to the findings, the proposed LBP-HOG strategy proves more beneficial than basic LBP and HOG techniques concerning the classification rate and computing time. The combination method shows improved performance in terms of agreement with the reference of fine and coarse level of texture descriptions along with providing large-scale description of the texture which results in the improved discriminatory power of the texture classes.
A reductive explanation of consciousness
Authors: Wolstencroft Martin
Published: October 2024

The origins of consciousness and conscious experience have been regarded as a mystery for millennia. Philosophical exploration remains divided on physical or non-physical origins. Research in psychology and neuroscience are revealing the physical construction of the brain and illuminating features of the mind in operation. However, current theories of consciousness and intelligence have not quite crystalised into a workable model that fully explains the emergence of consciousness. Here I propose a logical model for the origin of consciousness. The paper describes step by step how consciousness emerges using only memory plus the capabilities of neurons to distil and match patterns. The model shows that consciousness is inevitable in active creatures. Using the model, the paper addresses the ‘hard problem’ of consciousness and describes a mechanism for ‘attention’. The distillation mechanism, along with the layering of pattern matching, is central to the subsequent development of further features of mind by allowing the brain to learn increasingly complex patterns. Extension of the logical model will yield a general theory of intelligence. As a logical model it can be applied in a non-biological substrate.
Beyond the Mean: How Thinking About The Distribution of Public Opinions Reduces Politicians' Perceptual Errors
Authors: Dias Nicholas C. , Lucas Jack , Sheffer Lior
Published: October 2024

Recent studies find that elected politicians regularly over-estimate the conservatism of their constituents’ preferences. While these findings have potentially concern- ing implications for democratic representation, they depend on the magnitude and sources of this ‘conservative over-estimation,’ neither of which is well-understood. Here, we show that a novel approach to measuring politicians’ perceptions—whereby politicians draw the distribution of their constituents’ positions, rather than pro- vide a point estimate—clarifies the magnitude and sources of politicians’ perceptual errors. While the vast majority of politicians in our sample exhibit a conservative bias, our ‘perceived-distribution’ task cuts the size of this bias in half. Exploring how politicians build out public-opinion distributions, we further show that conser- vative over-estimation is counterbalanced by projection effects among politicians on the left, but that projection among politicians on the right reinforces it. Our re- sults raise questions about existing accounts of elite misperception and help identify cognitive sources of the conservative over-estimation.
Παραγωγή Προσαρμοσμένου Εκπαιδευτικού Υλικού Τεχνολογίας στα Etwinning projects: Μια Μελέτη Περίπτωσης για Μαθητές με Μαθησιακές δυσκολίες
Authors: Σκαρβελάκης Βασίλης , Emmanouil Katergiannakis
Published: October 2024

Το άρθρο αυτό αναπτύσσει ως μελέτη περίπτωσης τη δημιουργία και υλοποίηση ενός έργου etwinning για το μάθημα της τεχνολογίας για μαθητές με μαθησιακές δυσκολίες
Continuous and discrete operation of potential flow networks with dissimilar sink potentials
Authors: Das Tarak , Kurian Varghese , Narasimhan Sridharakumar
Published: October 2024

Nodal demand satisfaction in potential flow networks often requires a scheduled manipulation of edge resistances. The actuators used to implement the resistance variations can broadly be classified into (i) discrete (edge fully OPEN or CLOSED) and (ii) continuous (continuously vary edge resistance). Previously, we presented theoretical bounds on the ratio of time required to transport a given quantum of material with either type of actuators assuming that that all sinks are at the same potential. In the present work, we extend the results to a class of networks with demand nodes at different potentials. Based on our findings, we conjecture that the bounds remain the same irrespective of whether the sinks are at the same potentials or not.
Structure-Trait-Identity Relation (STIR): An Integrative Framework for Understanding Redistribution Decisions in Economic Games
Authors: Yang Yi , Smith David Victor
Published: October 2024

As a social species, making redistribution decisions about how to allocate or reallocate resources across different individuals or groups is crucial for human’s survival and well-being at both micro (e.g., disaster relief distribution, charity donation, household income budget) and macro levels (e.g., national budget allocation, public support to wealth redistribution policies) of human society. Dictator games, among other economic games, have been used intensively to study such decisions regarding resource (re)allocation. In a typical dictator game, there are two players: a dictator and a recipient. The dictator decides how to redistribute an endowment, which the recipient cannot reject. Various factors, including dictator personality traits, recipient identity, and endowment transfer actions, have been found to influence dictators' decisions. Several models have been proposed to explain why dictators make certain redistribution decisions in dictator games. While insightful, existing models often oversimplify complex factors, such as task economic structure (e.g., source of endowment, price of transferring endowment between dictators and recipients) and partner identity (e.g, group membership, social closeness), hindering a holistic understanding of redistribution decisions. In this narrative review, we present an integrative framework categorizing factors into four main categories: task structure, dictator trait (trait), recipient identity (identity), and framing. We discuss potential interactions between task economic structure, dictator trait, and recipient identity. We also discuss how framing may influence redistribution decisions through multiple domains. This Structure-Trait-Identity Relation (STIR) framework provides a systematic approach to analyzing decision dynamics and resolving empirical inconsistencies. Future directions are also discussed.
Therapists’ Work Conditions as Patients’ Treatment Conditions: A Qualitative Study of Unionized Public Mental Health Therapists
Authors: Last Briana Shiri , Adams Danielle , Mirhashem Rebecca
Published: October 2024

As mental health treatment seeking has increased recently, U.S. public mental health therapists have been asked to do more with less. Facing growing work demands, many have left the workforce, exacerbating existing mental health workforce shortages and mental health inequities. However, a growing share of therapists have sought to improve their working conditions through labor organizing. This qualitative study explored how unionized public mental health therapists in New York City and Chicago experience and seek to address their workplace challenges. Participants (N = 30) were interviewed for 60-90 minutes to gain their in-depth perspectives; interviews were analyzed using thematic analysis. The research team identified four main themes. First, public mental health therapists described that their under-resourced and diverse patients have complex social service and clinical needs —needs therapists are highly motivated to address. Second, therapists described significant workplace challenges (e.g., staffing shortages, demanding workloads, low pay, insufficient clinical support). Third, therapists described that these workplace challenges negatively impact them emotionally and negatively impact the care their under-resourced patients receive. Fourth, therapists described reasons for and benefits to labor organizing including improving their work conditions and the quality of services. Findings revealed that public mental health therapists are intrinsically motivated to support their under-resourced patients, but that their labor conditions often hinder them from offering the high-quality care they feel their patients deserve. Public mental health therapists across both cities view labor unions as a vehicle through which they can improve their working conditions and the treatment conditions of their patients.
Characterizing Inhibitory Control Challenges Among Autistic Adults: An Examination of Demographic and Psychiatric Moderators and Associations with Anxiety Symptomatology
Authors: O'Brien Meghan , McQuaid Goldie , McNulty Jessica , Wallace Gregory , Lee Nancy Raitano
Published: October 2024

PurposeAutism spectrum disorder is associated with elevated rates of mental health difficulties and executive function challenges. Emerging evidence links executive function to mental health in autistic individuals. However, less is known about a) everyday inhibitory control difficulties among autistic adults; b) the influence of sex assigned at birth and co-occurring attention deficit/hyperactivity disorder (ADHD) features on inhibition challenges; and c) relations between inhibition challenges and anxiety symptoms.MethodsDrawing upon data from 732 autistic adults aged 18-83, this online study examined self-reported inhibitory control in autistic adults and the influence of assigned sex and ADHD screening status (based on a positive screening score on a self-report tool) on the degree of inhibitory control challenges experienced. In addition, this research examined relations between inhibitory control challenges and anxiety symptoms, and the moderating role of assigned sex and ADHD screening status in this relationship.ResultsAutistic adults endorsed significantly more inhibitory control challenges relative to published norms. Participants assigned female reported more difficulties in inhibitory control relative to sex-adjusted normative expectations than participants assigned male. Participants who screened positive for ADHD reported more inhibitory control challenges than those who screened negative. Greater endorsement of inhibitory control challenges was associated with greater anxiety symptomatology; this relationship was moderated by ADHD screening status, but not by assigned sex.ConclusionInhibitory control is an area of difficulty in autistic adults and is associated with anxiety symptomatology, suggesting that inhibitory control may be a valuable intervention target to improve emotional well-being in autistic adults.
Increasing the Effectiveness of Psychotherapy in Routine Care through Transdiagnostic Online Modules? Randomized Controlled Trial Investigating Blended Care
Authors: Schaeuffele Carmen , Mutak Augustin , Behr Solveig , Fenski Friederike , Hammelrath Leona , Puetz Marie , Pohl Steffi , Knaevelsrud Christine , Boettcher Johanna
Published: October 2024

Objective: Blended Care (BC), the integration of Internet-based interventions into psychotherapy (PT), is thought of as a promising approach to enhance PT’s effectiveness and efficiency. This randomized controlled trial aimed to investigate the usage, implementation, and effectiveness of BC with transdiagnostic online modules compared to PT in routine care.Method: Psychotherapists in routine care recruited 1159 patients who were randomized to BC or PT. Outcomes included self-reported anxiety, depression, satisfaction with life and level of functioning, as well as therapist-rated severity and changes. Outcomes were measured at baseline, 6 weeks, 12 weeks, and 6 months. We examined whether BC and PT groups changed differently over time using linear mixed models. We also investigated differences in sessions and terminations and report usage metrics of the BC platform.Results: Contrary to our hypotheses, we did not find differences between BC and PT in outcomes, including anxiety, depression, satisfaction with life, level of functioning, therapist-rated severity / changes, and satisfaction with treatment at 6-months post-randomization (all p > 0.05). BC and PT did not differ in the number of sessions or terminations. Regarding usage of the BC platform, 534 patients (91.6%) received at least one online chapter, with M = 7.26 (SD = 7.01) online chapters assigned on average. Conclusions: In real-world applications of BC, therapists have considerable flexibility in integrating IBIs with sessions. Our findings suggest that the benefits observed in more controlled BC settings may not fully translate to routine care, possibly due to variations in implementation and adherence to BC.
Crossing in the Dark: The Effect of Vehicle Kinematics and eHMI on Older Pedestrians’ Crossing Behaviour
Authors: Lee Yee Mun , Madigan Ruth , Wang Yueyang , Qin Hao , Srinivasan Aravinda , Markkula Gustav , Merat Natasha
Published: October 2024

With higher levels of Automated Vehicles (AVs) entering the traffic system in the future, it is important to investigate pedestrians’ crossing behaviour and experience while interacting with AVs, which lack a human driver. There has been a surge in interest within the last few years in evaluating new forms of communication for AVs, namely external human-machine interfaces (eHMIs). However, much of the research to date has focused on younger pedestrians, and on daytime conditions with optimum visibility. Given that the AVs will interact with pedestrians of all ages, and at all times, there are still key knowledge gaps that need to be addressed. Using the Highly Immersive Kinematic Experimental Research (HIKER) cave-based pedestrian lab, this study investigated the effect of AV kinematics (i.e., deceleration, speed, time gaps) and eHMI presence (a Slow Pulsing Light Band) on the crossing behaviour of younger (18-35 years old) and older pedestrians (64-77 years old), in both daytime and nighttime virtual environments. Results showed that older pedestrians adopted a different crossing strategy to younger pedestrians. If they decided to cross in the non-deceleration trials, they compensated for their longer crossing duration by initiating their crossing earlier than younger pedestrians. However, if they decided to wait until the deceleration was more prominent, before making a crossing decision, they waited longer than the younger pedestrians. Generally, pedestrians reported feeling less safe and behaved more cautiously during nighttime crossings (i.e., less likely to cross, longer crossing initiation time (CIT) when there was no eHMI). The lower perceived safety ratings were more prominent for older pedestrians, especially when crossing at 3-second time gaps and in the nighttime environment. eHMI decreased CIT for both age groups. However, different interpretations of eHMI were observed between younger and older pedestrians, showing that eHMI might have been used differently across age groups. Finally, an eHMI failure trial mainly affected younger pedestrians, whose collision rate increased from 0.43% in non-deceleration trials without eHMI, to 1.72% when the vehicle displayed the eHMI but did not decelerate. This study demonstrated that pedestrians are likely to behave differently during daytime and nighttime, and that older pedestrians are likely to have different requirements from AVs than younger pedestrians. This knowledge should inform the design of effective communication for AVs.
Unfollowing hyperpartisan social media influencers durably reduces out-party animosity
Authors: Rathje Steve , Pretus Clara , He James Kunling , Harjani Trisha , Roozenbeek Jon , Gray Kurt , Linden Sander van der , Van Bavel Jay Joseph
Published: October 2024

There is considerable debate over whether and how social media contributes to polarization. Research suggests that a small number of hyperpartisan “influencers,” or highly followed accounts, produce the vast majority of misinformation and toxic content. Yet, little is known about the long-term causal effects of exposure to these influencers. In a correlational study (n1 = 1,447) and two digital field experiments (n2 = 494, n3 = 1,133), we examined whether (un)following hyperpartisan social media influencers contributes to polarization and misinformation sharing. We found that incentivizing Twitter/X users to unfollow hyperpartisan social media influencers improved their recent feelings toward the out-party by 23.5% compared to the control group, with effects persisting for at least six months. Unfollowing also led participants to engage with more accurate news accounts, increased satisfaction with their Twitter/X feeds, and reduced the amount of political content they reported seeing a full year later—without reducing engagement. By contrast, incentivizing users to follow accounts that tweeted about science improved well-being. Additionally, we found that, after Elon’s Musk purchased Twitter/X and made several platform changes, participants used Twitter/X less frequently, viewed their feeds as less reliable, and posted lower quality news. Our results demonstrate the long-term, causal impact of repeated exposure to hyper-partisan influencers on attitudes and behavior. They also illustrate that the behavior and experience of Twitter/X users changed substantially after Elon Musk’s purchase of the platform, revealing the potential impact of social media design changes. Our work has implications for interventions that can be made by platforms or by individuals seeking to curate their social media experience. Unlike other social media reduction interventions, unfollowing is a targeted approach: like a scalpel, it surgically removes a few harmful parts of one’s feed, allowing the beneficial aspects to remain.
Language and/or Memory: How to Slice the Domain-Cake?
Authors: Piai Vitória , Madan Christopher R , Francken Jolien
Published: October 2024

Historically, memory and language have been seen as separate cognitive functions and studied in isolation. To date, it remains an open question to what extent these cognitive domains are related. The central question in this paper is how we should see the relationship between the domains of language and memory. We discuss relevant empirical evidence from the fields of cognitive psychology, cognitive neuropsychology, and cognitive neuroscience and take a philosophical perspective on this central question, considering issues such as how to weigh different types of evidence and how to conceptualize the relationship between language and memory. We conclude that elucidating questions about the nature of the relationship between language and memory requires not only more empirical data, but also parallel conceptual development.
Promoting the transfer of learning among school staff following an online asynchronous training on dating violence prevention: A mixed-methods study
Authors: Brodeur Geneviève , Hébert Martine , Fernet Mylène , Guyon Roxanne
Published: October 2024

The SPARX team responded to the scarcity of training available to school staff concerning dating violence prevention by developing an innovative asynchronous online training program as one facet of their broader initiative. This mixed-methods study aimed to fill the existing gap in the literature regarding the factors influencing the transfer of learning, with two primary objectives. First, it sought to investigate how transfer culture and perceived social support from colleagues and school principals influenced school staff’s intention to transfer learning post-online training. Second, it aimed to uncover the barriers and facilitators associated with learning transfer six months post-training. Engaging 101 school staff members, the research utilized an online questionnaire to evaluate their intentions to transfer learning from the SPARX program’s School Staff component. Regression analysis was used to identify variables predicting intention to transfer, complemented by a qualitative multiple-case study approach focusing on the experiences of a teacher, school counsellor, and school administrator. Findings highlighted that perceived support from colleagues and opportunities to implement new skills significantly predicted a greater intention to transfer learning. In contrast, mutual support for collective learning predicted a lower intention to transfer. Key barriers included perceptions of sole responsibility for implementation and resource constraints, whereas facilitators encompassed access to human, financial, and material resources. Results underscore the pivotal role of environmental factors in promoting learning transfer within school settings, emphasizing the importance of perceived support from colleagues and advocating for a transfer culture characterized by delegated responsibilities, turnkey activities, and dedicated time for learning transfer.
Riesgo de preeclampsia en primigestas adolescentes en un hospital de la región de Junín en la sierra peruana
Authors: Pacheco Ramos Estefany , Vásquez Ávila Jimmy , Galdos-Bejar Marcelo , Yhuri Carreazo Nilton
Published: October 2024

Introducción. La preeclampsia representa una de las principales causas demorbimortalidad en el mundo y el Perú. El rol del embarazo adolescente en eldesarrollo de preeclampsia no es claro. Objetivos. Analizar el riesgo de preeclampsiaen primigestas adolescentes embarazadas en un hospital materno infantil de lasierra peruana. Métodos. Estudio analítico de cohorte retrospectivo. Se incluyerongestantes atendidas entre el 2018 y 2021 en el Hospital Regional Docente MaternoInfantil “El Carmen”, en Junín, Perú. Se utilizó un modelo de regresión de Poissonajustado por criterio teórico y epidemiológico, calculando el riesgo relativo.Resultados. Se incluyeron 504 primigestas, 35,9% fueron adolescentes de 10 a 19 añosy 64,1% adultas. Se observó una mayor incidencia no significativa de preeclampsiaen adolescentes (7,2%) que en gestantes adultas (3,4%) (RR 1,77; IC 95% 0,76 a 4,14;p 0,19). Conclusión. No se encontró una asociación entre el embarazo adolescente yel desarrollo de preeclampsia en un hospital materno infantil de la sierra peruana.
Reducción fetal en un embarazo múltiple y parto asincrónico del segundo gemelo
Authors: Quispe Prado José , Quispe Almandoz Patricia , Quispe Almandoz Angela , Tami Gambetta Derek
Published: October 2024

El parto diferido o parto asincrónico es poco frecuente en los embarazos múltiples.Cuando el nacimiento del primer gemelo ocurre antes de las 20 a 26 semanas, el partodiferido del segundo gemelo es una opción terapéutica que permite incrementar laedad gestacional para alcanzar la viabilidad, madurez pulmonar y la supervivencia.Se presenta un caso clínico de parto diferido del segundo gemelo, en el que el primerparto fue a las 24+1/7 semanas y el segundo a las 34+2/7 semanas de gestación,prolongando el embarazo por 73 días y teniendo como resultado un recién nacidopretérmino de 2,150 g.