Who Do You Choose to Date: Demographics, Tinder Use, Attachment Styles, Personality and Physical Attractiveness as predictors of Partner Preferences - A Quantitative Study

This study investigates how demographics, Tinder use, attachment styles, personality, and physical attractiveness influence partner preferences. Data was collected online via Prolific from 953 single UK residents. The independent variables included demographics (gender, age, child responsibilities, education, income, and socioeconomic status), Tinder use, personality (the Dirty Dozen and the HEXACO Personality Inventory), attachment styles (the Adult Attachment Questionnaire), and physical attractiveness. The dependent variables comprised ideal partner preferences in terms of warmth-trustworthiness, vitality- attractiveness, social status, and confidence-humor. The study revealed distinct gender patterns in partner preferences. For men, education, Extraversion, and avoidant attachment were negatively associated with preferences for warmth and trustworthiness in a partner. For women, child responsibilities and Honesty-Humility positively influenced the preferences for warmth and trustworthiness, while physical attractiveness had a negative impact on their preferences for these traits. The preferences for vitality and attractiveness in a partner was positively associated with age and physical attractiveness for men, and negatively related to Honesty-Humility for women. For men, both older age and Openness to Experience reduced the importance placed on social status in a partner, while higher education, Narcissism, Extraversion, and avoidant attachment increased the preference for that dependent variable. For women, higher socioeconomic status increased the emphasis put on the preference for social status in a partner. Extroverted men valued confidence and humor in a partner more, while those prioritizing physical attractiveness in a partner valued these dependent variables less. Women with greater child responsibilities also placed less emphasis on confidence and humor when choosing a partner.Hovedoppgave psykologprogrammetPROPSY317PRPSY

Towards Liberation: Enhancing BCI Communication in Locked-in Syndrome with RGB-Evoked EEG Signal Classification Using Ensemble Learning Techniques

Denne avhandlingen undersøker gjennomførbarheten av å utvikle et sanntids BCI-kommunikasjonssystem for pasienter med LIS ved bruk av RGB framkalte EEG-signaler. Arbeidet sammenligner klassifiseringsnøyaktighet av ulike nevrale nettverksmodeller på to datasett, der EAV ble brukt som en teknikk for å øke ytelsen. Datasett A består av data fra 21 forsøkspersoner samlet i Helsinki i 2021, med en 58-kanals EEG-hette fra antNeuro. Dataprotokollen viste en av fargene rød, grønn eller blå på en skjerm i 1,3 til 1,6 sekunder, med 140 epoker av hver farge samlet per forsøksperson. Datasett B består av data fra 23 forsøkspersoner, registrert med en 32-kanals EEG-hette fra Mentalab. Datasett B ble samlet inn av forfatterne av denne oppgaven i Trondheim ved Norges teknisk-naturvitenskapelige universitet (NTNU) i 2024, og dataprotokollen innebar å vise de tre fargene, rød, grønn og blå, på skjermen samtidig, med et kryss som indikerte hvilken farge forsøkspersonen skulle fokusere på, noe som økte datakompleksiteten betydelig. Totalt ble 252 epoker samlet for hver farge, hver med en varighet på 0,6 sekunder. For å undersøke effekten av datagrunnlaget, gikk en av forsøkspersonene med på å samle fem økter med den originale protokollen for Datasett B og fem økter med en forenklet protokoll. Dataen ble bandpassfiltrert mellom 0.1 Hz og 45 Hz, og et notch-filter på 50 Hz ble brukt for å redusere fellesmodusstøy. Den filtrerte dataen ble matet inn i CNN-modellen EEGNeX for å klassifisere de tre fargene. Generelle, individuelle og overførings-modeller ble undersøkt for begge datasettene. Resultatene indikerer at protokollen for Datasett B fører til mer komplekse hjernesignaler, som igjen reduserer gjennomsnittlig nøyaktighet fra 95.5% for Datasett A til 75.8% for Datasett B. Økning av datagrunnlaget gjennom å måle flere økter med data øker nøyaktigheten til forsøksperson 23 fra 70.6% til 91.3%, noe som indikerer at dataen er for kompleks for modellen til å trekke ut den relevante informasjonen som trengs for klassifisering med bare 756 fargestimuli tilgjengelig. Bruk av den forenklede protokollen fører til betydelig bedre nøyaktighet, og øker ytelsen for forsøksperson 23 til 98.8%, noe som indikerer at det nevrale nettet kan forutsi fargestimuli tilstrekkelig for reell bruk.This thesis investigates the feasibility of developing a real-time BCI communication system for patients with Locked-in Syndrome (LIS) using RGB evoked EEG signals. The work compares the classification accuracy and performance variability of various neural network models on two datasets, where Ensemble Average Voting (EAV) was used as a technique to increase performance. Dataset A consists of data from 21 subjects recorded in Helsinki, 2021, with a 58-channel EEG-cap from antNeuro. The data protocol showed one of the colors red, green, or blue on a screen for 1.3 to 1.6 seconds, with 140 epochs of each color collected per subject. Dataset B consists of data from 23 subjects, recorded with a 32-channel EEG-cap from Mentalab. Dataset B was collected by the authors of this thesis in Trondheim at the Norwegian University of Science and Technology (NTNU) in 2024, and the data protocol entailed showing the three colors, red, green, and blue, on the screen simultaneously, with a cross indicating which color the subject was to focus on, thus increasing the data complexity substantially. A total of 252 epochs were collected for each color, each lasting 0.6 seconds. To investigate the effect of data foundation, one of the subjects agreed to collect five sessions with the original protocol for Dataset B and five sessions with a simplified protocol. The data was bandpass-filtered between 0.1 Hz and 45 Hz, and a notch filter of 50 Hz was used to reduce the common mode noise. This filtered data was input to the Convolutional Neural Network (CNN) EEGNeX to classify the three colors. General, individual-based, and transfer-optimized models were investigated for both datasets. The results indicate that the protocol for Dataset B leads to more complex brain signals, which in turn decreases the mean accuracy from 95.5% for Dataset A to 75.8% for Dataset B. Increasing the data foundation through recording multiple sessions of data increases the accuracy of subject 23 from 70.6% to 91.3%, indicating that the data is too complex for the model to extract the relevant features needed for classification with only 756 color stimuli available. Using the simplified protocol leads to significantly better accuracy, increasing the performance for subject 23 to 98.8% after EAV, which indicates that the classifier can predict color stimuli sufficiently for real-life use

Towards Liberation: Enhancing BCI Communication in Locked-in Syndrome with RGB-Evoked EEG Signal Classification Using Ensemble Learning Techniques

Denne avhandlingen undersøker gjennomførbarheten av å utvikle et sanntids BCI-kommunikasjonssystem for pasienter med LIS ved bruk av RGB framkalte EEG-signaler. Arbeidet sammenligner klassifiseringsnøyaktighet av ulike nevrale nettverksmodeller på to datasett, der EAV ble brukt som en teknikk for å øke ytelsen. Datasett A består av data fra 21 forsøkspersoner samlet i Helsinki i 2021, med en 58-kanals EEG-hette fra antNeuro. Dataprotokollen viste en av fargene rød, grønn eller blå på en skjerm i 1,3 til 1,6 sekunder, med 140 epoker av hver farge samlet per forsøksperson. Datasett B består av data fra 23 forsøkspersoner, registrert med en 32-kanals EEG-hette fra Mentalab. Datasett B ble samlet inn av forfatterne av denne oppgaven i Trondheim ved Norges teknisk-naturvitenskapelige universitet (NTNU) i 2024, og dataprotokollen innebar å vise de tre fargene, rød, grønn og blå, på skjermen samtidig, med et kryss som indikerte hvilken farge forsøkspersonen skulle fokusere på, noe som økte datakompleksiteten betydelig. Totalt ble 252 epoker samlet for hver farge, hver med en varighet på 0,6 sekunder. For å undersøke effekten av datagrunnlaget, gikk en av forsøkspersonene med på å samle fem økter med den originale protokollen for Datasett B og fem økter med en forenklet protokoll. Dataen ble bandpassfiltrert mellom 0.1 Hz og 45 Hz, og et notch-filter på 50 Hz ble brukt for å redusere fellesmodusstøy. Den filtrerte dataen ble matet inn i CNN-modellen EEGNeX for å klassifisere de tre fargene. Generelle, individuelle og overførings-modeller ble undersøkt for begge datasettene. Resultatene indikerer at protokollen for Datasett B fører til mer komplekse hjernesignaler, som igjen reduserer gjennomsnittlig nøyaktighet fra 95.5% for Datasett A til 75.8% for Datasett B. Økning av datagrunnlaget gjennom å måle flere økter med data øker nøyaktigheten til forsøksperson 23 fra 70.6% til 91.3%, noe som indikerer at dataen er for kompleks for modellen til å trekke ut den relevante informasjonen som trengs for klassifisering med bare 756 fargestimuli tilgjengelig. Bruk av den forenklede protokollen fører til betydelig bedre nøyaktighet, og øker ytelsen for forsøksperson 23 til 98.8%, noe som indikerer at det nevrale nettet kan forutsi fargestimuli tilstrekkelig for reell bruk.This thesis investigates the feasibility of developing a real-time BCI communication system for patients with Locked-in Syndrome (LIS) using RGB evoked EEG signals. The work compares the classification accuracy and performance variability of various neural network models on two datasets, where Ensemble Average Voting (EAV) was used as a technique to increase performance. Dataset A consists of data from 21 subjects recorded in Helsinki, 2021, with a 58-channel EEG-cap from antNeuro. The data protocol showed one of the colors red, green, or blue on a screen for 1.3 to 1.6 seconds, with 140 epochs of each color collected per subject. Dataset B consists of data from 23 subjects, recorded with a 32-channel EEG-cap from Mentalab. Dataset B was collected by the authors of this thesis in Trondheim at the Norwegian University of Science and Technology (NTNU) in 2024, and the data protocol entailed showing the three colors, red, green, and blue, on the screen simultaneously, with a cross indicating which color the subject was to focus on, thus increasing the data complexity substantially. A total of 252 epochs were collected for each color, each lasting 0.6 seconds. To investigate the effect of data foundation, one of the subjects agreed to collect five sessions with the original protocol for Dataset B and five sessions with a simplified protocol. The data was bandpass-filtered between 0.1 Hz and 45 Hz, and a notch filter of 50 Hz was used to reduce the common mode noise. This filtered data was input to the Convolutional Neural Network (CNN) EEGNeX to classify the three colors. General, individual-based, and transfer-optimized models were investigated for both datasets. The results indicate that the protocol for Dataset B leads to more complex brain signals, which in turn decreases the mean accuracy from 95.5% for Dataset A to 75.8% for Dataset B. Increasing the data foundation through recording multiple sessions of data increases the accuracy of subject 23 from 70.6% to 91.3%, indicating that the data is too complex for the model to extract the relevant features needed for classification with only 756 color stimuli available. Using the simplified protocol leads to significantly better accuracy, increasing the performance for subject 23 to 98.8% after EAV, which indicates that the classifier can predict color stimuli sufficiently for real-life use

Penerapan Kepentingan Luar Negeri Indonesia Melalui Mekanisme ASEAN Outlook on The Indo-Pacific Dengan Filipina

Konflik kemaritiman antara Indonesia dan Filipina terkait praktik illegal fishing dan tantangan keamanan lainnya di kawasan perairan Asia Tenggara, khususnya setelah diperkenalkannya ASEAN Outlook on The Indo-Pacific (AOIP). Studi ini menyoroti bagaimana teori liberalisme dan konsep kepentingan nasional berperan dalam analisis strategi yang diambil kedua negara untuk menangani masalah ini melalui kerja sama multilateral. Pendekatan liberalisme diterapkan melalui kerangka kerja AOIP, yang mendorong dialog, diplomasi, serta penguatan keamanan maritim yang inklusif. Hasil penelitian menunjukkan bahwa kerja sama melalui AOIP dan peningkatan patroli bersama dapat memperkuat keamanan dan stabilitas kawasan. Implementasi AOIP juga menunjukkan komitmen kedua negara dalam memelihara hubungan damai dan stabilitas regional, meskipun tantangan tetap ada dalam upaya pengawasan yang efektif dan terbatasnya sumber daya. Melalui pendekatan ini, penelitian ini memberikan wawasan tentang pentingnya kerja sama multilateral dan diplomasi dalam menangani konflik kemaritiman di kawasan Indo-Pasifik.This thesis examines maritime conflicts between Indonesia and the Philippines concerning illegal fishing practices and other security challenges in the Southeast Asian waters, especially following the introduction of the ASEAN Outlook on the Indo Pacific (AOIP). This study highlights how liberalism theory and the concept of national interest contribute to analyzing the strategies employed by both countries to address these issues through multilateral cooperation. Liberalism is applied through the AOIP framework, which promotes dialogue, diplomacy, and inclusive maritime security enhancement. The findings show that cooperation under AOIP and joint patrol enhancements can strengthen regional security and stability. AOIP implementation also reflects both countries' commitment to maintaining peaceful relations and regional stability, although challenges persist in achieving effective surveillance and resource constraints. Through this approach, the study provides insights into the importance of multilateral cooperation and diplomacy in managing maritime conflicts in the Indo-Pacific region

EFSA Panel on Food Contact Materials, Enzymes, Flavourings and Processing Aids (CEF); Scientific Opinion on Flavouring Group Evaluation 20, Revision 3 (FGE.20Rev3): Benzyl alcohols, benzaldehydes, a related acetal, benzoic acids, and related esters from chemical groups 23 and 30

<p>The Panel on Food Contact Materials, Enzymes, Flavourings and Processing Aids of the European Food Safety Authority was requested to evaluate five flavouring substances in the Flavouring Group Evaluation 304, using the Procedure in Commission Regulation (EC) No 1565/2000. None of the substances were considered to have genotoxic potential. The substances were evaluated through a stepwise approach (the Procedure) that integrates information on structure-activity relationships, intake from current uses, toxicological threshold of concern, and available data on metabolism and toxicity. The Panel concluded that the three substances [FL-no: 16.117, 16.123 and 16.125] do not give rise to safety concerns at their levels of dietary intake, estimated on the basis of the MSDI approach. For the remaining two candidate substances [FL-no: 16.118 and 16.124], no appropriate NOAEL was available and additional data are required. Besides the safety assessment of these flavouring substances, the specifications for the materials of commerce have also been considered. Specifications including complete purity criteria and identity for the materials of commerce have been provided for all five candidate substances.</p&gt

The gut microbiota: a major player in the toxicity of environmental pollutants?

Exposure to environmental chemicals has been linked to various health disorders, including obesity, type 2 diabetes, cancer and dysregulation of the immune and reproductive systems, whereas the gastrointestinal microbiota critically contributes to a variety of host metabolic and immune functions. We aimed to evaluate the bidirectional relationship between gut bacteria and environmental pollutants and to assess the toxicological relevance of the bacteria–xenobiotic interplay for the host. We examined studies using isolated bacteria, faecal or caecal suspensions—germ-free or antibiotic-treated animals—as well as animals reassociated with a microbiota exposed to environmental chemicals. The literature indicates that gut microbes have an extensive capacity to metabolise environmental chemicals that can be classified in five core enzymatic families (azoreductases, nitroreductases, β-glucuronidases, sulfatases and β-lyases) unequivocally involved in the metabolism of >30 environmental contaminants. There is clear evidence that bacteria-dependent metabolism of pollutants modulates the toxicity for the host. Conversely, environmental contaminants from various chemical families have been shown to alter the composition and/or the metabolic activity of the gastrointestinal bacteria, which may be an important factor contributing to shape an individual’s microbiotype. The physiological consequences of these alterations have not been studied in details but pollutant-induced alterations of the gut bacteria are likely to contribute to their toxicity. In conclusion, there is a body of evidence suggesting that gut microbiota are a major, yet underestimated element that must be considered to fully evaluate the toxicity of environmental contaminants

Biotransformations of bisphenol A in a mammalian model: answers and new questions raised by low-dose metabolic fate studies in pregnant CD1 mice.

We investigated the metabolic fate of a low dose (25 micro g/kg) of bisphenol A [2,2-bis(4-hydroxy-phenyl)propane] (BPA) injected subcutaneously in CD1 pregnant mice using a tritium-labeled molecule. Analytic methods were developed to allow a radio-chromatographic profiling of BPA residues in excreta and tissues, as well as in mothers' reproductive tracts and fetuses, that contained more than 4% of the administered radioactivity. BPA was extensively metabolized by CD1 mice. Identified metabolite structures included the glucuronic acid conjugate of BPA, several double conjugates, and conjugated methoxylated compounds, demonstrating the formation of potentially reactive intermediates. Fetal radioactivity was associated with unchanged BPA, BPA glucuronide, and a disaccharide conjugate. The latter structure, as well as that of a dehydrated glucuronide conjugate of BPA (a major metabolite isolated from the digestive tract), showed that BPA metabolic routes were far more complex than previously thought. The estrogenicity of the metabolites that were identified but not tested for hormonal activity cannot be ruled out; however, in general, conjugated BPA metabolites have significantly lower potency than that of the parent compound. Thus, these data suggest the parental compound is responsible for the estrogenic effects observed in fetuses exposed to BPA during gestation in this mammalian model

Physiology and biochemistry of reduction of azo compounds by Shewanella strains relevant to electron transport chain

Azo dyes are toxic, highly persistent, and ubiquitously distributed in the environments. The large-scale production and application of azo dyes result in serious environmental pollution of water and sediments. Bacterial azo reduction is an important process for removing this group of contaminants. Recent advances in this area of research reveal that azo reduction by Shewanella strains is coupled to the oxidation of electron donors and linked to the electron transport and energy conservation in the cell membrane. Up to date, several key molecular components involved in this reaction have been identified and the primary electron transportation system has been proposed. These new discoveries on the respiration pathways and electron transfer for bacterial azo reduction has potential biotechnological implications in cleaning up contaminated sites