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Woodland characteristics associated with the presence of Willow Tits Poecile montanus in a fragmented population
Full text linkExtremely Luminous Optical Afterglow of an Energetic Gamma-Ray Burst GRB 230204B
Full text linkRobotic telescope networks play an important role in capturing early and bright optical afterglows, providing critical insights into the energetics and emission mechanisms of GRBs. In this study, we analyze GRB 230204B, an exceptionally energetic and multipulsed long GRB, detected by the Fermi Gamma-ray Burst Monitor and MAXI detectors, with an isotropic equivalent gamma-ray energy exceeding 1054 erg. Time-resolved spectral analysis reveals a transition in the prompt emission from hard (sub-photospheric-dominated) spectra during early pulses to softer (synchrotron-radiation-dominated) spectra in later pulses, indicative of a hybrid jet composition. We report the discovery and characterization of the optical afterglow using the Mobile Astronomical System of Telescope-Robots (MASTER) and Burst Observer and Optical Transient Exploring System (BOOTES) robotic telescope networks, which enabled rapid follow-up observations starting at ∼1.3 ks post-burst. The optical luminosity at this time was exceptionally high, surpassing that of many other optically bright GRBs, such as GRB 990123 and GRB 080319B. This places the burst among the most luminous optical GRBs observed to date. Long-term radio observations extending to 335 days post-burst were conducted with the Australia Telescope Compact Array. Multiwavelength modeling, incorporating data from MASTER, BOOTES, Devasthal Optical Telescope, Swift/XRT, and radio observations, was conducted using an external interstellar medium (ISM) forward-shock top-hat jet model with afterglowpy. The results reveal a narrow and highly collimated jet with a circumburst density of n0 ∼ 28.12 cm−3, kinetic energy EK ∼ 4.18 × 1055 erg, and a relatively low value of ϵB = 2.14 × 10−6, indicating shock-compression of the magnetic field in the surrounding ISM. We constrained a low radiative efficiency of ∼4.3%. This study highlights the indispensable contribution of robotic networks to early afterglow observations and advances our understanding of GRB 230204B unique characteristics and underlying jet physics
The Role of Prehabilitation in Improving Brain Health and Cognition After Chemotherapy in Patients with Colorectal Cancer : Study Protocol of the Chemo Brain Prehab Project
No full textRetrofitting vernacular screens in contemporary facades in hot and dry regions : A climate control study using energy and CFD simulations
Full text linkThis research examines the perforated geometric screens (jaali, also called mashrabiya) extensively used in Islamic and Indo-Islamic architecture. Historically, these elements have been used not only for aesthetic purposes but also as passive design strategies to regulate indoor temperature through natural ventilation and shading. This study hypothesizes that the principles of traditional jaali can be reinterpreted and integrated into contemporary facade design to improve thermal comfort, particularly in hot and dry climates. To test this hypothesis, the research used a conceptual case study of the Kilis Resource and Community Center (KRCC) in Türkiye. The study assessed internal airflow patterns and thermal conditions using energy modeling and Computational Fluid Dynamics (CFD) simulations via the IESVE software. The analysis was done during the cooling period, with two representative summer days: May 3rd and July 15th. Results showed that all investigated ventilation scenarios with jaali-integrated facades had lower indoor temperatures throughout the day. However, the presence of open windows was crucial to maintain indoor temperatures below outdoor levels, allowing air movement. The findings suggest that using jaalis in hot climates should be encouraged, as it lowered temperatures by up to 2℃ during the cooling season with the help of natural ventilation
The impact of digitally-enabled interventions on frailty and other age-related outcomes – Systematic review and meta-analysis
Full text linkIntroduction: Frailty is defined as a clinically recognised state of increased vulnerability, reflecting a decline in an individual's psychological and physical reserves. Digitally-enabled interventions are increasingly utilised to monitor and support the health of older adults. Evidence on the effectiveness of digitally-enabled interventions in reducing frailty is limited. This systematic review aimed to investigate the types of digitally-enabled interventions tested, with what goals with respect to frailty, and the resulting outcomes. Method: Medline, CINAHL, Scopus, PsychInfo and Embase were searched from time of origin until July 2024. Peer-reviewed RCTs assessing the impact of digitally-enabled interventions on older adults were included. Outcome measures explored were frailty, cognitive status, mental health, quality of life, adherence and usability. Data was extracted independently by two people using Covidence platform. Narrative synthesis was performed for all studies and meta-analysis was performed for outcomes reported in four or more studies. Results: From 4476 titles and abstracts screened, 17 studies were included following full text review. Overall, 12 studies included exercises as a component or the sole form of intervention. The mean duration of intervention was 4.04(SD2.56) months. Mean adherence to the intervention was 59% which was lower in exercise-based intervention. The most and least reported frailty-specific outcome was walking speed (n = 8) and self-reported exhaustion level (n = 2). Meta-analysis showed non-exercise-based interventions showed significant improvements in SPPB. There was no statistically significant change in Timed-up and Go and handgrip strength. Narrative synthesis indicates there was insufficient evidence to evaluate the impact of digital interventions on frailty, frailty-specific outcomes, mental health, activities of daily living, health-related quality of life, sleep and cognition. Conclusion: The findings suggest low technological readiness and adherence among digitally-enabled interventions for older adults. Narrative synthesis of overall frailty and outcome measures showed mixed results and limited evidence on the impact of digital interventions on frailty and outcomes
A digital platform with activity tracking for energy management support in long COVID : A randomised controlled trial
No full textIn a 6-month pragmatic randomised controlled trial (RCT; ISRCTN16033549), we compared a just-in-time intervention to support energy management in adults with long COVID (LC) to standard care. Participants received either the ‘Pace Me’ app and a wearable activity tracker (intervention) or an app only with data entry screens (control). The intervention group received just-in-time messages on energy management when they reached 50%, 75%, and 100% of their daily ‘activity allowance’. The primary outcome was post-exertional malaise (PEM) measured by the DePaul Symptom Questionnaire-PEM (DSQ-PEM). Of 369 participants assessed for eligibility, 250 participants were randomised 1:1, and 77 controls and 84 intervention participants were included in the final per-protocol analysis. There was no time by group interaction for the DSQ-PEM. The intervention group value was 48 (95% CI 44-53) at baseline and 46 (95% CI 41-51) post-intervention (arbitrary units). The control group value was 47 (95% CI 42-52) at baseline and 44 (95% CI 39-49) at follow-up (interaction effect p=0.614, η²p=0.002; trivial). No individual question exhibited an interaction effect (p>0.05). Although the intervention had minimal effect compared to control, the substantial recovery rates previously reported in LC, coupled with our wide inclusion criteria may have masked intervention effects. Therefore, future studies should consider this energy management framework in conditions without such recovery rates, such as CFS
More-than-Human Design Pedagogy : Reimagining Design Education to Engender Sustainable Futures
No full textTeaching sustainable design praxis to next generation designers during their higher education is considered crucial to aiding societal transitions to more environmentally responsible futures. However, much scholarship detailing sustainable design methodology focusses upon its application in external settings e.g., commercial and organisational contexts, and speaks less to facilitating design tutors to embed sustainable design concerns, strategies and tools into their teaching and learning delivery. Responding to this lacuna, this paper utilizes a UK university design studio module as a substrate to explore how novel systemic and technological futures methods More-than-Human-Centred Design and Speculative Design can be leveraged, alongside Constructive Alignment techniques and ‘glocal’ education policies, to reimagine sustainable pedagogic practice. The paper argues that through this novel approach, educators can develop curricula and learning outcomes which better equip students with the critical and creative mindset and skills, they will need to tackle growing sustainability challenges once post-study and designing for the real world
The Well-being-Attrition Link : Quantifying the Human Cost of Integration Stress; Evidence from Pakistan
No full textThe research examines the vital role of employee well-being as a predictor of voluntary turnover in the post-merger and acquisition (M&A) context in the constantly changing business environment of Pakistan. The research is based on the tenets of the conservation of resources theory. A prospective research design was used, and data was gathered from 380 employees in three organizations in Pakistan operating in the banking and telecom sectors. The logistic regression analysis showed that pre-merger and post-merger well-being were significant negative predictors of turnover, controlling for job level, type, and financial incentives. It is important to mention that the proximal predictor had a stronger impact on post-merger well-being, and therefore, pre-merger well-being was made insignificant in the regression analysis. The findings of the mediation analysis supported that post-merger well-being completely mediated the relationship between pre-merger well-being and turnover, with well-being during the integration phase as a vital mechanism explaining post-merger turnover
A Method for Identification of Humans from Dorsal Hand Sub-images using Siamese Network Models
No full textThis study proposes a method for person identification utilizing Siamese networks, knuckle crease patterns, and fingernail sub-images. The framework incorporates automatic localization of multiple regions of interest (ROI) within hand images, component recognition and segmentation via bounding boxes, and similarity matching between segmented image sets. Feature extraction is a central element of the framework, implemented within Siamese networks. Various deep learning neural networks (DLNNs), including DenseNet201, ResNet152, and a finetuned DenseNet201, are employed to extract discriminative high-level features. Euclidean distance is used for similarity matching. The approach is validated on established benchmarks, specifically the 11k Hands dataset and the Hong Kong Polytechnic University Contactless Hand Dorsal Images (PolyU). Results indicate that knuckle and fingernail patterns are critical for person identification. Performance on left-hand images in the 11K Hands dataset surpasses that of right-hand images, potentially due to less frequent use of the left hand. Furthermore, finetuning the DenseNet201 model demonstrates high effectiveness across multiple finger regions in the 11K Hands dataset. The right hand achieves notable identification accuracy in the fingernail area, with 95.69% for the thumb and 96.19% for the ring finger. The model demonstrated even higher accuracy in the minor knuckle region, achieving 96.53% for the index finger and 97.68% for the ring finger on the right hand. On the left hand, the model attained 99.18% accuracy for the ring finger, 99.06% for the index finger, and 97.45% for the little finger. In the major knuckle region, the model achieved 97.39% accuracy for the middle finger on the right hand. Collectively, these findings underscore the robustness and discriminative power of the finetuned DenseNet201 model, particularly for major knuckle features, which consistently produced superior results across both hands and multiple fingers