bluuebunny/crossref_metadata_2025_split huggingface dataset:
Climate-Resilient Agriculture and Migration Dynamics in Maharashtra: A Comparative Analysis
Authors: Lord Hannah 
Published: August 2023

Under the context of global climate change patterns and trends, rural India faces heightened vulnerability to changing weather conditions, leading to various shocks and stresses that can forcibly displace a growing number of its residents. This displacement exacerbates issues of poverty, food insecurity, and marginalization in these populations. A recent strategy to address this challenge is climate resilient agriculture (CRA), an approach aimed at improving community and individual resilience within the context of climate change by sustainably utilizing natural resources through crop and livestock production systems.
This research project, titled the "Climate-Resilient Agriculture and Migration Dynamics in Maharashtra: A Comparative Analysis," seeks to analyze the impacts of CRA programs on migration dynamics in rural India. The primary goal is to assess the diverse designs of CRA initiatives, their implementation and governance mechanisms, and their relative effectiveness in building resilient rural development and mitigating forced displacement due to climate change.
The study will entail an extensive literature review of case studies examining the effects of climate-resilient agriculture in rural India. Additionally, field studies conducted by the project supervisor, Dr. Marcus Taylor, the Department Head and Professor of Global Development at Queen’s University with expertise in agriculture, livelihoods, and anti-poverty policies in rural India and other regions, will complement the research.
By identifying more sustainable adaptation approaches to address the challenges of forced migration for vulnerable populations, this research aims to contribute to improved policy responses, particularly in the Global South. The study's timeliness is emphasized by recent agro-ecological impacts in India due to extreme climate variability. Understanding the relationship between climate-induced migration and climate-resilient agriculture could have significant implications for international organizations such as the United Nations and the World Health Organization in shaping effective climate change mitigation and adaptation strategies. Ultimately, this research strives to advance knowledge in the realm of climate resilience and its impact on migration dynamics, paving the way for enhanced socio-economic outcomes and improved climate change response mechanisms.
Bioprocess Modelling, Intensification, and Control
Authors: McQueen Gabriel , Hudon Nicolas 
Published: August 2023

Chemical bio-process intensification, driven by the need for sustainability and cost-effectiveness, has gained significant attention. This study explores dynamic intensification (DI), a strategy to enhance efficiency by modifying system dynamics, operation, and control. Two key approaches are investigated: intensification by design, involving deliberate changes to physical and chemical aspects, and intensification by control, optimizing process parameters and feedback loops.
The research focuses on the Optimal Periodic Control (OPC) method, dating back to the 1960s but now revisited with modern technology. To investigate the feasibility and effectiveness of dynamic intensification, a comprehensive methodology encompassing modeling, optimization, linearization, dynamic intensification, and control was designed. This methodology ensures systematic and standardized testing of DI in various bioprocesses within the industry.
Through rigorous case studies, such as Activated Sludge Model No. 1 (ASM1) and an antibiotic perfusion bioreactor, the study demonstrates the practical benefits of dynamic intensification, including enhanced process efficiency and control. Applying model predictive control (MPC) further improves process efficiency, and the intensified systems are thoroughly compared to their respective base cases.
The insights gained from this research contribute to advancements in bioprocessing technologies across industries, paving the way for sustainable and efficient engineering practices. Engineers can leverage this knowledge and the developed methodology to optimize chemical bioprocesses through innovative intensification strategies, ultimately promoting more eco-friendly and economical bio-processes. The study's comprehensive methodology offers valuable insights and understanding for bioprocess engineers seeking to enhance system efficiency and contribute to a greener future.
Leveraging Dual-Generative Adversarial Networks for Adversarial Malware Detection via Ensemble Learning
Authors: Gordon Lucas 
Published: August 2023

In the expanding realm of cybersecurity, machine learning-based malware detection has emerged as a vital line of defense. However, the growing sophistication of malware attacks poses formidable challenges to conventional detection systems. To address this, this paper uses a Generative Adversarial Network that utilizes dual generators for adversarial learning on malware, designed to enhance the detection of harmful Portable Executable (PE) files. Our model employs a two-tiered generator system within the GAN architecture, where the secondary generator intervenes when the primary generator yields a malware PE executable dismissed by the detector.
The detection unit leverages ensemble learning techniques to analyze the PE software feature vector, capitalizing on the synergy of multiple learning models for improved performance and generalization. This setup empowers the system to generate a broader range of adversarial examples and respond to them effectively, enhancing the robustness of the detector against previously unseen or variable malware types.
Personal Construct Theory and Parents’ Perceptions of Developing Girls Through Sport
Authors: Posel Amy , Pinkerton Elena , Murata Alex , Côté Jean 
Published: August 2023

Despite increased participation across all sporting contexts, many sports are still thought to be more or less appropriate for female athletes to engage with based on biologically or socially constructed characteristics (Plaza et al., 2017; Abadi & Gill, 2020). Further, the persistence of gender stereotypes continues to impact the participation levels of girls in sport; particularly in traditionally masculine sports (Tucker Center, 2018; Staurowsky, 2016). Given the influence parents can have in dictating their children’s participation in sport (Dorsch et al., 2020), the objective of the present study was to explore how parents perceive their daughters’ participation in traditionally boy-dominated sport. Semi-structured interviews were conducted with eight parents of daughters currently engaged in traditionally boy-dominated sports at a competitive level. A laddering interview technique—based on the personal construct theory—was used as a basis for the interview guide (Bourne & Jenkins, 2005). This technique allowed the interviewer to elicit participants’ higher-level constructs, revealing their overarching values more effectively. Three themes were constructed through the assessment of the interviews, these being that parents believe participation in non-traditional girls’ sport provides young female athletes with (a) the capability to identify gender inequality more easily, (b) opportunities to face unique challenges and shed the perceived limitations imposed upon girls, and (c) opportunities to express less stereotypically feminine traits and, therefore, to grow. These results suggest that parents of daughters who participate in traditionally boy-dominated sports perceive these sports to contribute to building several positive aspects of their daughters’ development.
Characterization of the Bakelite Prototype Sensor for NEWS-G
Authors: Moring Mackenzie 
Published: August 2023

The New Experiments With Spheres - Gas (NEWS-G) group at Queen's University uses Spherical Proportional Counters (SPCs) in the search for dark matter. Incoming radiation interacts with the gas inside the SPC through ionization, producing electron/ion pairs [1]. These primary electrons drift towards the central anode under the influence of an electric field [1]. A secondary ionization, termed avalanche, occurs at distances proportional to the size of the anode resulting in the multiplication of charges [1]. The signal that is produced is proportional to the number of secondary charges created during the avalanche [1]. A 30 cm-diameter sphere was used to characterize a six anode, spherical Bakelite prototype sensor. The sensor is divided into north and south three-anode read-out channels. To study the detector's response, an Fe55 source was placed inside the sphere, which was then filled with 1 bar of 40Ar+CH4 2% gas. The source was moved around the sphere with thirty-second data-taking runs taken at each point and high voltages applied to each channel. Cuts were applied to the event properties to isolate the 5.9 keV Fe55 peak. The peak was plotted as a histogram and fit with a Gaussian function with two additional linear terms. The gain and resolution of the sensor were extracted from the fit parameters. The channel dominance change angle was calculated by interpolating the number of events recorded by each channel at each point on the sphere. The gain, number of events and the channel dominance change angle were mapped on the sphere between 20 and 40 cm arc length in the theta direction, and all the way around the sphere in the phi direction to improve the understanding of the Bakelite sensor.
References
[1] G. Savvidis, "Sensor Characterization and Fiducial Volume Studies for the NEWS-G Dark Matter Experiment," Queen's University, Kingston, 2023.
Investigating Gene Expression of Bone-Marrow Derived Macrophages with GM-CSF Stimulation in vitro
Authors: Philippi Alecco , Basta Sam 
Published: August 2023

Macrophages are specialized immune cells that play a crucial role in engulfing and destroying harmful pathogens and debris within the body. When stimulated with specific cytokines, macrophages transition into subtypes with specialized functions. For example, macrophages can differentiate into classically activated macrophages (M1) when stimulated with interferon-γ (IFN-γ ) and lipopolysaccharide (LPS). M1 macrophages are a subtype of activated macrophages that exhibit pro-inflammatory properties and are essential for combating infections and promoting tissue inflammation and repair. When macrophages are stimulated with another cytokine, known as Granulocyte-Macrophage Colony-Stimulating Factor (GM-CSF), they tend to develop an M1-like phenotype. The altered gene expression that occurs when macrophages are cultured in GM-CSF remains a relatively unexplored area, with few studies having profiled this modified genotype of bone marrow-derived macrophages (BMDMs). This study aims to characterize the genotype of GM-CSF macrophages using an in vitro model as well as a genetic database. Expression data from M-CSF or GM-CSF BMDM was retrieved using microarrays, and differential gene analysis was performed using R studio. The analysis suggested an increase in pro-inflammatory gene expression in GM-CSF BMDMs compared to M-CSF-induced macrophages. The findings are reinforced by RT-PCR and qPCR analyses of specific key pro-inflammatory genes. For instance, interleukin-6 (IL-6), IL-1, as well as NLR family pyrin containing domain 3 (NLRP3) were all significantly upregulated in GM-CSF BMDMs. This project seeks to comprehensively characterize GM-CSF-induced macrophages as pro-inflammatory agents. Moreover, the cytokine's propensity to induce a pro-inflammatory response raises concerns about its applicability in treatment plans.
Natural Language Processing of Radiology Reports: Predicting Metastatic Progression from Text Data
Authors: Assad Lola 
Published: August 2023

The project’s goal is to extract tumour measurement data from oncological radiology reports with equal, or improved accuracy of a human radiologist. The purpose is to streamline and improve the efficiency of cancer diagnosis, accomplishing this through methods in artificial intelligence.

In this experiment, a collection of 85,218 colorectal, and lung radiology reports were used. After loading reports into a data frame, and a BioBERT (bidirectional encoder representations from transformers pre-trained on biomedical corpora) model into a virtual environment, a question is set to be answered by the model where the context of the question is each radiology report’s findings section of the specified organ. These inputs are tokenized and embedded into numerical values to map sentences to vectors of real numbers. Vectors are fed into the model, and an answer is output in natural language for human readability. Answers are stored in the data frame in their corresponding row from which they were derived.

The model successfully answered questions about measurements of tumours written in free text in reports where tumours were present, and successfully ignored or did not report in cases where tumours were not present, or measurements were unchanged. In cases with multiple tumours, the model reported exclusively first listed measurements. In an updated version of this model, context will be run through sentence-wise to ensure equal attention to the context entirely.

This project is evidence that using one question and selecting the findings portion of a radiology report for one organ as context in a question-answering model built using BioBERT is effective, and efficient in collecting measurements from radiology reports. This algorithm can be applied to other areas of medicine, or other fields entirely with a few model alterations. This project is a step forward in improving cancer diagnosis efficiency and improving medical AI.
Emotional gradients in autobiographical memories across the lifespan
Authors: Li Freda , Poppenk Jordan 
Published: August 2023

Autobiographical memory is the mental re-experiencing of complex events from one’s life (Grilli and Sheldon, 2019). Memories are not perfect reproductions – rather, they are reconstructed to serve one’s goals, identity, and social communication. Emotions are known to modulate memory. In this study, we investigated changes in the emotional phenomenology of autobiographical memories across different life periods.
65 participants (ages 22-35) completed the Autobiographical Interview (Levine et al., 2002), in which they verbally recalled memories from childhood, adolescence, adulthood, and the past year; they also recalled a negative memory (from any period) and imagined a future memory. Recall occurred in random order across two sessions to control for state effects. Memory emotionality was scored in multiple ways. Participants self-reported the vividness and emotion (valence, arousal, and dominance) within their memory. Raters evaluated the emotion of each memory on a 1-9 Likert scale (intraclass correlation coefficient = 0.90). Finally, transcripts were analyzed with LIWC-22 to determine the average emotion of words within each memory.
Across scoring methods, we found participants consistently reported greater vividness in their memory from adulthood compared to childhood. We observed gradients in emotion across the lifetime: childhood memories were more negative and less dominant, while adulthood memories were more positive and more dominant. Childhood memories contained more words associated with negative emotions, especially anger. Imagined future memories were more positive compared to childhood memories. Our results extend previous findings of a negative-to-positive temporal gradient in self-selected memories (Williamson et al., 2019) by showing this gradient to apply not only when emotional memories are prompted, but also when memories are freely retrieved without special instruction. We also show the pattern to extend to future, imagined memories. We speculate that this gradient may explain why self-appraisal is typically more positive and negative for future and past selves, respectively.
Adaptive Vibration Control of Smart Structure Using Deep Reinforcement Learning
Authors: Honda Shinya , Imura Yuta , Sasaki Katsuhiko , Takeda Ryo 
Published: August 2023

In this research, the authors developed an adaptive control method using deep reinforcement learning which is a kind of machine learning to suppress the vibration of smart structures. This method just requires information about the control response and input, and don’t require numerical models for the controlled object to design the controller. We experimented to verify the effectiveness of this method. In this experiment, a smart structure fabricated by an aluminum plate and a piezoelectric actuator was used as a controlled object. Three kinds of reinforcement learning algorithms are employed, Deep Q Network (DQN), Deep Deterministic Policy Gradient (DDPG), and Twin Delayed DDPG (TD3), and the control performance is compared. As a result, we succeeded in reducing the norm of the frequency response to impulse disturbance by up to about 40 dB compared to the uncontrolled case. This demonstrates the applicability of the control method using deep reinforcement learning to adaptive vibration control.
STRATEGI PEMBANGUNAN CITRA MELALUI KONTEN PRODUK LOKAL TIKTOK PADA DCT AGENCY
Authors: Nurizki Akbar 
Published: August 2023

DCT Agency is an agency with services that accommodate brand image creation for local products through TikTok by involving content creators. The demand for brand image creation for local products and the role of TikTok content creators create an urgency for research on the topic. This research uses qualitative methods through interview and observation on DCT Agency. Data on this research is based on corporate identity, corporate communication, corporate image & reputation, and competitive advantage as analysis units and analysed through data reduction, data display, and verification. The result for this research explains DCT Agency's branding strategy through local product content on TikTok by considering the right targets, content that fits the brand identity, and relationships with the audience.
Keywords: branding, DCT Agency, local product content, TikTok
ESPEN PRACTICAL GUIDELINE: CLINICAL NUTRITION IN CANCER
Authors: Muscaritoli Maurizio , Arends Jann , Bachmann Patrick , Baracos Vickie , Barthelemy Nicole , Bertz Hartmut , Bozzetti Federico , Hutterer Elisabeth , Isenring Elizabeth , Kaasa Stein 
Published: August 2023

Довідкова інформація. Ці практичні настанови засновані на сучасних наукових настановах ESPEN з харчування онкологічних пацієнтів.
Методи. Для спрощення використання у клінічній практиці настанови ESPEN були скорочені та переведені в блок-схеми. Практичні настанови призначені для всіх фахівців, зокрема лікарів, дієтологів, нутриціологів і медичних сестер, які працюють з онкологічними пацієнтами.
Результати. В цілому представлено 43 рекомендації з короткими коментарями щодо лікування нутритивних і мета- болічних розладів у пацієнтів з онкологічними захворюваннями. Рекомендаціям, що надаються у зв’язку із захво- рюванням, передують загальні рекомендації з оцінки нутритивного статусу онкологічних хворих.
Висновок. Ця практична настанова надає рекомендації медичним працівникам, які беруть участь у лікуванні онко- логічних хворих, для забезпечення оптимального харчування.
Predictori ai evoluţiei clinice a schizofreniei
Authors: Crişan Cătălina-Angela , Pop Răzvan 
Published: August 2023

Introduction. Schizophrenia is a serious disorder that influences all aspects of the patients’ life. The most important goals in schizophrenia are remission, recovery, and improving psychosocial functioning and quality of life. Unfortunately, there are important barriers to achieving optimal long-term outcomes in this disease due to patient-related factors and treatment-related factors. Lack of insight is considered the main reason for partial/non-adherence and, therefore, for the long-term outcome.
Aim. The aim of this study was to identify the predictive factors involved in long-term schizophrenia, and especially the role of the awareness of illness.
Materials and method. Eighty patients (44 males and 36 females) recruited from the First and Second Psychiatric Clinic from Cluj-Napoca, diagnosed according to ICD-10 criteria with schizophrenia, participated in this study. A semi-structured interview collected the demographical data. Psychotic symptoms were evaluated using Positive and Negative Syndrome Scale (PANSS), severity of the disease using Clinical Global Impression (CGI) and insight using the Scale for the Assessment of Unawareness of Mental Disorder (SUMD), the Schedule for Assessment of Insight-Expanded version (SAI-E) and Beck Cognitive Insight Scale (BCIS).
Results. Our results showed that, among potential predictors of psychotic symptoms change measured in this research, those significantly correlated with changes are the level of insight measured with SUMD scale (r=-0.41; p<0.01), presence of family history (r=0.24; p<0.05), and belonging to urban areas (r=0.23; p<0.05). The level of insight, as one predictor, with PANSS initially controlled, explained 16% of variance of improving psychotic symptoms during hospitalization. Also, patients from urban areas seem to have a slight tendency towards higher magnitudes of the changes (low correlation, but statistically significant). Again, the square of the correlation obtained (coefficient of determination) indicates that urban membership explaines 5% of variance when symptoms improve. Also, it seems that the family history is positively associated with the magnitude of symptoms’ change, but the level of this relationship is also reduced.
Conclusions. The awareness of illness is one of the predictive factors for long-term schizophrenia, and the best predictive model of disease progression is composed of variables SUMD total and PANSS total on admission.
Religious Liberty and Atheism: The Case of Richard Dawkins
Authors: Kristiatmo Thomas 
Published: August 2023

Through the Second Vatican Council, the Catholic Church asserts the import of religious liberty for every individual and believes that one is entitled to live out one’s faith. Thus, through Dignitatis Humanae, the Church has revealed her intention to create inter-religion dialogues and to encourage respect for the positive values of different religions. This raises a question as to whether such dialogues and respect as voiced in Dignitatis Humanae are applicable when it comes to the issue of atheism, a pressing issue faced by the Church today. This article explores the engagement of such document with atheism. Since atheism covers various views coming from a number of authors, this current article focuses only on the atheism as promoted by Richard Dawkins. Building his atheism upon science, Dawkins’ views are worthy of our attention in today’s world, which is characterised by unending scientific revolutions.
New Advances in Social Cognitive Neuroscience
Authors: Li Xue 
Published: August 2023

Social cognitive neuroscience, as an important branch of the field of neuroscience, studies the cognitive, emotional and interactive processes of individuals in social environments. In recent years, with the continuous development of neuroscience, social cognitive research has been progressing steadily. This paper discusses the latest research progress in the field of social cognitive neuroscience from the perspective of neuroscience, covering the mechanism of emotional disorders and cognitive decision making, and brain imaging of social cognitive neuroscience. These advances not only deepen our understanding of social cognition, but also provide new ideas for the intervention and treatment of social cognitive disorders.