science

Abstract

: With the increasing use of online meetings, there is a growing need for efficient tools that can automatically generate meeting minutes from recorded sessions. Current solutions often rely on proprietary systems, limiting adaptability and flexibility. This paper investigates whether various open-source models and methods such as audio-to-text conversion, summarization, keyword extraction, and optical character recognition (OCR) can be integrated to create a meeting minutes generation tool for recorded video presentations. For this purpose, a series of evaluations are conducted to identify suitable models. Then, the models are integrated into a system that is modular yet accurate. The utilization of an open-source approach ensures that the tool remains accessible and adaptable to the latest innovations, thereby ensuring continuous improvement over time. Furthermore, this approach also benefits organizations and individuals by providing a cost-effective and flexible alternative. This work contributes to creating a modular and easily extensible open-source framework that integrates several advanced technologies and future new models into a cohesive system. The system was evaluated on ten videos created under controlled conditions, which may not fully represent typical online presentation recordings. It showed strong performance in audio-to-text conversion with a low word-error rate. Summarization and keyword extraction were functional but showed room for improvement in terms of precision and relevance, as gathered from the users’ feedback. These results confirm the system’s effectiveness and efficiency in generating usable meeting minutes from recorded presentation videos, with room for improvement in future works.

Abstract

: This study examines the role of humanitarian diplomacy during the Tigray humanitarian crisis in Ethiopia, a humanitarian disaster marked by severe shortages in food, healthcare, and essential services that deeply affect civilians. A qualitative approach using both primary and secondary data grounds the study by exploring key actors in humanitarian diplomacy, their successes, and barriers to aid delivery. Humanitarian actors, such as UN agencies, international NGOs, donor countries, the EU, the US, and the African Union, have engaged with the Ethiopian government, the TPLF, and the Tigray Transitional Government to alleviate the crisis. Notable achievements in humanitarian diplomacy include negotiations, information gathering, communication, civilian needs assessment, resource mobilization, advocacy for international law, and distressed civilians. Humanitarian diplomacy has facilitated international aid operations, saving lives during critical periods, despite practical difficulties. Diplomatic efforts have faced significant interruptions due to access restrictions imposed by the Ethiopian government, security threats from ongoing fighting leading to attacks on aid convoys and casualties among aid workers, and bureaucratic obstacles imposed by the Ethiopian government. This study highlights the necessity for effective humanitarian diplomacy in accounting for complex political landscapes in conflict-affected regions, developing flexible strategies that enhance access to aid, and improving humanitarian interventions.

Abstract

Approximately one billion women require family planning services worldwide, 842 million women are currently using modern contraceptives, and 80 million are using traditional contraceptive methods for family planning (FP). Health literacy (HL) can significantly make women aware of contraceptives for family planning. This study aims to assess the effectiveness of health education in determining perceptions and intended contraceptive use among university students in Pakistan. A pre-and post-approach with cross-sectional study design was conducted in the public sector University of Islamabad from July to December 2022. A self-administered and structured questionnaire comprising variables on knowledge level and intended contraceptive use was conducted among randomly selected students (n = 396). Survey respondents were given Health Education Intervention (HEI), which included a lecture regarding contraception use for family planning, and a posttest assessment was conducted on the same group of students. Statistical Package for Social Science (SPSS) version 24 was used for statistical data analysis. An Institutional Review Board (IRB) of the Health Services Academy (HSA) approved this study. A majority, 217 (55%), were female respondents. There was a significant difference in the overall mean score between pretest and posttest assessments after the intervention. Hence, the findings proved that this intervention successfully improved the knowledge and intention of the participants to use FP. Moreover, after the HEI, 99% of respondents had improved knowledge about family planning methods and the benefits of contraceptive use. Knowledge, perceived benefits, and intended use of contraceptives for FP were significantly improved after the HEI (P < 0.05). In the posttest, the most learned methods were intrauterine devices, injections, implants, and withdrawal. Most of the participants were already well aware of condoms and oral pills. The study concludes that health education is an effective intervention to improve the knowledge, perception, and intended contraceptive use among undergraduate university students regarding family planning.

"Exposure to short duration gravity load changes including microgravity, as sustained in a parabolic flight statistically significantly decreases the sperm motility and vitality of human fresh sperm samples," the team found, adding that this may have huge importance for any prolonged human settlement missions in space. 

"In the future, should humans remain in space for long periods of time with exposure to different microgravity and hypergravity peaks, which could range from months to a number of years, reproduction may pose a problem to be tackled."

The mechanism by which sperm motility was decreased remains unknown, with further study needed.

Abstract

: With global leadership as the new norm, discussion about followers’ preferred leader behaviors across cultures is growing in significance. This study proposes a comprehensive predictive model to explore significant preferred leadership factors, drawn from the Leader Behavior Description Questionnaire (LBDQXII), across cultures using automated machine learning (AML). We offer a robust empirical measurement of culturally contingent leader behavior and entrepreneurship behaviors and provide a tool for assessing the cultural predictors of preferred leader behavior to minimize predictive errors, explore patterns in the data and make predictions in an empirically robust way. Hence, our approach fills a gap in the literature relating to applications of AML in leadership studies and contributes a novel empirical method to better predict leadership preferences. Cultural indicators from Global Leadership and Organizational Behavior (GLOBE) predict the likelihood of the preferred leader behaviors of “Role Assumption”, “Production Emphasis” and “Initiation of Structure”. Hofstede’s Long-Term/Short-Term Orientation is the most critical predictor of preferences for “Tolerance of Uncertainty” and “Initiation of Structure”, whereas the value of restraint impacts the likelihood of preferring leaders with skills in “Integration” and “Consideration”. Significant entrepreneurial values indicators have a significant impact on preferences for leaders focused on “Initiation of Structure”, “Production Emphasis” and “Predictive Accuracy”. Findings also support earlier studies that reveal age and gender significantly impact our preferences for specific leader behaviors. We discuss and offer conclusions to support our findings that foster development of global business managers and practitioners.

Abstract

: The field of digital forensics relies on expertise from multiple domains, including computer science, criminology, and law. It also relies on different toolsets and an analyst’s expertise to parse enormous amounts of user-generated data to find clues that help crack a case. This process of investigative analysis is often done manually. Artificial Intelligence (AI) can provide practical solutions to efficiently mine enormous amounts of data to find useful patterns that can be leveraged to investigate crimes. Natural Language Processing (NLP) is a subdomain of research under AI that deals with problems involving unstructured data, specifically language. The domain of NLP includes several tools to parse text, including topic modeling, pairwise correlation, word vector cosine distance measurement, and sentiment analysis. In this research, we propose a digital forensic investigative technique that uses an ensemble of NLP tools to identify a person of interest list based on a corpus of text. Our proposed method serves as a type of human feature reduction, where a total pool of suspects is filtered down to a short list of candidates who possess a higher correlation with the crime being investigated.

Abstract

X-irradiation of blood products is an alternative for gamma-ray to prevent post-transfusion GvHD. However, commercial X-irradiators are not widely available while little is known about their safety and efficacy for platelet products. This study introduces an efficient, accessible and cost-effective “X irradiation system” for platelet concentrates (PCs). By constructing a suitable radiation box (phantom) for a clinically available linear accelerator, an “X irradiation system” was designed specifically for PCs. PCs were divided into three equal bags either exposed to X- and gamma-irradiation or kept unirradiated (control). Irradiation-induced inhibition of T cells proliferation was examined by MTT and cell cycle assays on mononuclear cells (MNCs) obtained from PCs. The inhibitory effect of irradiation on allorecognition ability of MNCs was assessed by mixed lymphocyte reaction where MTT evaluated lymphocyte proliferation responses and flowcytometry examined CD8+T lymphocytes activity. Platelet activation was also examined with P-selectin expression and PAC-1 binding by flowcytometry. X- and gamma-irradiation reduced T cell proliferation while disturbing the cell-cycle with reduced entry of T-cells into the S phase and their G2 arrest. Both types of irradiations also effectively reduced “lymphocyte allorecognition responses” while inactivating CD8+T lymphocytes in platelet products but with no significant effect on platelet activity. This is the first study that showed “X irradiation system” effectively suppresses T cell proliferation and CD8+T lymphocyte activity in platelet products, with no effect to platelet quality and activation markers. This may suggest the LINAC-based “X irradiation system” with a dose of 30Gy as efficient and safe as gamma-irradiation for platelet products.

Abstract

: Leak detection in oil and gas pipeline networks is a climacteric and frequent issue in the oil and gas field. Many establishments have long depended on stationary hardware or traditional assessments to monitor and detect abnormalities. Rapid technological progress; innovation in engineering; and advanced technologies providing cost-effective, rapidly executed, and easy to implement solutions lead to building an efficient oil pipeline leak detection and real-time monitoring system. In this area, wireless sensor networks (WSNs) are increasingly required to enhance the reliability of checkups and improve the accuracy of real-time oil pipeline monitoring systems with limited hardware resources. The real-time transient model (RTTM) is a leak detection method integrated with LoRaWAN technology, which is proposed in this study to implement a wireless oil pipeline network for long distances. This study will focus on enhancing the LoRa network parameters, e.g., node power consumption, average packet loss, and delay, by applying several machine learning techniques in order to optimize the durability of individual nodes’ lifetimes and enhance total system performance. The proposed system is implemented in an OMNeT++ network simulator with several frameworks, such as Flora and Inet, to cover the LoRa network, which is used as the system’s network infrastructure. In order to implement artificial intelligence over the FLoRa network, the LoRa network was integrated with several programming tools and libraries, such as Python script and the TensorFlow libraries. Several machine learning algorithms have been applied, such as the random forest (RF) algorithm and the deep extreme learning machine (DELM) technique, to develop the proposed model and improve the LoRa network’s performance. They improved the LoRa network’s output performance, e.g., its power consumption, packet loss, and packet delay, with different enhancement ratios. Finally, a hybrid deep extreme learning machine model was built and selected as the proposed model due to its ability to improve the LoRa network’s performance, with perfect prediction accuracy, a mean square error of 0.75, and an exceptional enhancement ratio of 39% for LoRa node power consumption.

Abstract

Previous studies have reported that a potential association between exposure to outdoor light at night (LAN) and diseases. However, there is no evidence regarding the impacts of outdoor LAN exposure on lipid metabolism in humans. This study aims to evaluate the associations between outdoor LAN exposure and the prevalence of dyslipidemia, using a nationwide sample of 10,894 adults aged ≥ 45 years from 150 investigated sites across China in 2011–2012. Outdoor LAN exposure was assessed by using satellite imaging data. The prevalence ratio (PR) was calculated. The present study found that a per-quintile LAN exposure was positively associated with the prevalence of high LDL-cholesterol (PR = 1.10, 95% CI = 1.04–1.16), high triglyceride (PR = 1.07, 95% CI = 1.03–1.12), low HDL-cholesterol (PR = 1.11, 95% CI = 1.08–1.16), and dyslipidemia (PR = 1.06, 95% CI = 1.03–1.09). The fifth quintile of LAN exposure was associated with a significantly increased prevalence of dyslipidemia (PR = 1.20, 95% CI = 1.07–1.35) compared with the first quintile of exposure. Long-term exposure to outdoor LAN was positively associated with dyslipidemia prevalence. Public policies aimed at reducing light pollution at night, guided by the spatial distribution patterns, could lessen the adverse effects.

Abstract

Social media use is endemic among emerging adults, raising concerns that this trend may harm users. We tested whether reducing the quantity of social media use, relative to improving the way users engage with social media, benefits psychological well-being. Participants were 393 social media users (ages 17–29) in Canada, with elevated psychopathology symptoms, who perceived social media to negatively impact their life somewhat. They were randomized to either (a) assistance to engage with social media in a way to enhance connectedness (tutorial), (b) encouragement to abstain from social media (abstinence), or (c) no instructions to change behavior (control). Participants’ social media behaviors were self-reported and tracked using phone screen time apps while well-being was self-reported, over four timepoints (6 weeks in total). Results suggested that the tutorial and abstinence groups, relative to control, reduced their quantity of social media use and the amount of social comparisons they made on social media, with abstinence being the most effective. Tutorial was the only condition to reduce participants’ fear of missing out and loneliness, and abstinence was the only condition to reduce internalizing symptoms, relative to control. No condition differences emerged in eating pathology or the tendency to make social comparisons in an upward direction. Changes in social media behaviors mediated the effects of abstinence (but not of tutorial) on well-being outcomes. Participant engagement and perceptions of helpfulness were acceptable, but the abstinence group possibly perceived the content as less helpful. In conclusion, using social media differently and abstaining from social media may each benefit well-being.

Abstract

Ketogenic diet (KD) is an excess fat, enough protein, and minimal carbohydrate diet. The high fat content in KD lowers the oesophageal sphincter tone, slows gastric emptying, and decreases intestinal transit time. The primary aim of the current clinical trial was to study the effect of L-carnitine supplementation on gastric emptying in children with drug resistant epilepsy (DRE) on KD. Assessment of the protective effect of L-carnitine on bowel function and habits in those patients was a secondary aim. The current study recruited 30 patients aged 12 months to 18 years newly diagnosed with DRE assigned to start KD who were following up at the Pediatric Clinical Nutrition and Neurology Outpatient Clinics or were admitted due to DRE at the Pediatric Neurology Inpatient Department, Children’s Hospital, Ain Shams University (Egypt). Participants were assigned randomly into 2 arms; arm I: received KD with L-carnitine supplementation, arm II: received KD only. Patients were assessed at baseline and after 3 months of starting KD, the assessments of children included: 24-hour dietary recall, Chalfont Seizures Severity Scale, gastrointestinal symptoms score and Bristol stool chart, frequency of defecation per week, anthropometric measurements assessment, fasting serum lipid profile and measurement of the antral length by ultrasound. There was significant increase in antral length in the patients who received KD with L-carnitine supplementation compared to the non-supplemented group. The antral length showed a significant negative correlation with GI symptoms score in all cases and the L-carnitine supplemented group. It also showed a significant positive correlation with Bristol stool score in all patients and a significant positive correlation with stool frequency in the L-carnitine supplemented group only. L-carnitine supplementation to children with DRE on KD has a significant role in improving gastric motility and it increases the frequency of defecation. Further studies are recommended to explore additional benefits, meanwhile it is prudent to advise L-carnitine supplementation for such patients.

Abstract

Developing instant detection systems with disease diagnostic capabilities holds immense importance for remote or resource-limited areas. However, the task of creating these systems—which are simultaneously easy to operate, rapid in detection, and cost-effective—remains a challenge. In this study, we present a compact highly sensitive photothermal reverse transcriptase–loop-mediated isothermal amplification (RT-LAMP) chip (SPRC) designed for the detection of multiple diseases. The nucleic acid (NA) amplification on the chip is achieved through LAMP driven by either LED illumination or simple sunlight focusing. SPRC performs sample addition and amplification within a limited volume and autonomous enrichment of NA during the sample addition process, achieving a limit of detection (LOD) as low as 0.2 copies per microliter. Through 120 clinical samples, we achieved an accuracy of 95%, with a specificity exceeding 97.5%. Overall, SPRC has achieved promising progress in the application of point-of-care testing (POCT) by using light energy to simultaneously detect multiple diseases.

Abstract

: Lifetime’s “Married at First Sight” (MAFS) aired its seventeenth season in 2024, averaging 2.58 million viewers per weekly episode. In this paper, we ask, how does MAFS reflect and intervene in contemporary marriage politics, particularly regarding race, gender, class, and sexuality in the U.S.? To answer this question, we draw on scholarship about marriage as a political institution, and on reality TV as a window into contemporary socio-economic issues. Using interpretive, feminist methods of analysis, we find that MAFS reflects and intervenes in contemporary marriage politics by offering viewers a very traditional and exclusionary version of the institution at a time when it and everything else (reproductive rights and same-sex marriages, to name just two examples) is in flux. However, even as it attempts to offer a “balm” to all of this upheaval, in practice, the show’s “experimental results” offer something more complex, which both reflects the contemporary realities of marriage and attracts viewers.

Abstract

A growing concern is that as local newspapers disappear, communities lose trusted gatekeepers and develop information voids, creating openings for misinformation to thrive. Previous work has not evaluated whether residents of news deserts have developed different information acquisition habits. We fill this gap by directly comparing information consumption and referral patterns inside and outside of news deserts in a novel dataset of engagement with online media by millions of users on the Edge browser. We find little evidence that those in news deserts consume more low-quality sites or are more likely to be referred to low-quality sites from search engines or social media. We find some evidence that those in news deserts do consume more national news than locations with local media outlets. These results contribute to our understanding of how the loss of local newspapers has impacted online information acquisition.

Abstract

Humans perceive a range of basic emotional connotations from music, such as joy, sadness, and fear, which can be decoded from structural characteristics of music, such as rhythm, harmony, and timbre. However, despite theory and evidence that music has multiple social functions, little research has examined whether music conveys emotions specifically associated with social status and social connection. This investigation aimed to determine whether the social emotions of dominance and affiliation are perceived in music and whether structural features of music predict social emotions, just as they predict basic emotions. Participants (N = 1513) listened to subsets of 750 music excerpts and provided ratings of energy arousal, tension arousal, valence, dominance, and affiliation. Ratings were modelled based on ten structural features of music. Dominance and affiliation were readily perceived in music and predicted by structural features including rhythm, harmony, dynamics, and timbre. In turn, energy arousal, tension arousal and valence were also predicted by musical structure. We discuss the results in view of current models of music and emotion and propose research to illuminate the significance of social emotions in music.

Abstract

To investigate air pollution’s effect in the form of PM~2.5~ (particulate matter measuring less than 2.5 microns) on head and neck aerodigestive cancer incidence, an epidemiological cohort analysis was performed using data from the Surveillance Epidemiology and End Results national cancer database from the years 2002–2012. The relationship between US county mean PM~2.5~ levels and head and neck cancer (HNC) incidence rates were examined using a linear mixed model. Lagged effect of the pollutant’s effect on HNC incidence was analyzed. Our results showed a significant association between the incidence of HNC and certain subtypes with PM~2.5~ exposure after controlling for demographic characteristics, smoking and alcohol use. We observed the highest association at a 5-year lag period (β = 0.24, p value < 0.001). We observed significant associations at no lag (β = 0.16, p value = 0.02) and up to a 20-year lag period (β = 0.15, p value < 0.001). PM~2.5~ exposure is associated with an increased incidence of HNC, with the strongest association at a 5-year lag period. To better understand the relationships between exposure and cancer pathogenesis, further subgroup analysis is needed.

INDEPENDENCE, Mo. (KCTV) - An Independence woman, who doctors told would be partially blind for the rest of her life, is regaining her vision due to a relatively new implant approved by the Food and Drug Administration (FDA).

AlphaFold3 is open at last. Six months after Google DeepMind controversially withheld code from a paper describing the protein-structure prediction model, scientists can now download the software code and use the artificial intelligence (AI) tool for non-commercial applications, the London-based company announced on 11 November.

When AlphaFold3 was first published the code wasn't publicly available (which is pretty bad for computational research), so this is good news that they finally released the code repository.

The GitHub repository: https://github.com/google-deepmind/alphafold3

Note that to request access one needs to sign a form & has to represent a non-commercial entity. If you receive access then allegedly you can easily run AlphaFold3 via docker