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PurposeWhile only a few postmenopausal women exhibit biochemical signs of hypovitaminosis D, it has been shown that vitamin D insufficiency has detrimental effects on the overall skeleton mineralization, bone turnover rate, and may be a significant risk factor for osteoporosis and fracture. We evaluated the concurrent effect of Vitamin D levels and biochemical markers of bone turnover on bone health of postmenopausal women.MethodsThe present study was conducted within the framework of the Bushehr Elderly Health (BEH) program, a population-based prospective cohort study being conducted in Bushehr, a southern province of Iran. In summary, 400 persons from participants of the second stage were selected; serum bone turnover markers (bone-specific alkaline phosphatase (bALP), amino-terminal procollagen propeptides of type I collagen (P1NP), osteocalcin (OC), tartrate-resistant acid phosphatase isoenzyme 5b (TRAP)) and vitamin D were measured. Vitamin D deficiency was defined as serum 25(OH) D below 20 ng/ml, and Bone health was considered in terms of Osteoporosis and degraded bone microarchitecture.ResultsThe number of partially or significantly degraded microstructure based on TBS was higher than the number of osteoporotic women based on BMD. Women with higher CTX levels were more prone to being degraded microarchitecture. Although we couldn't find any association between vitamin D deficiency and bone health, when we considered the vitamin D levels in univariable modelling, the effect of it was significant.ConclusionsIn this study, only CTX was found to be significantly correlated with TBS while BMD was associated with CTX, OC, and TRAP., Funding Agencies|INFS; Tehran University of Medical Sciences (TUMS) [:92030410]; Iran National Science Foundation (INSF)

Resting state functional magnetic resonance imaging (rsfMRI) provides researchers and clinicians with a powerful tool to examine functional connectivity across large-scale brain networks, with ever-increasing applications to the study of neurological disorders, such as traumatic brain injury (TBI). While rsfMRI holds unparalleled promise in systems neurosciences, its acquisition and analytical methodology across research groups is variable, resulting in a literature that is challenging to integrate and interpret. The focus of this narrative review is to address the primary methodological issues including investigator decision points in the application of rsfMRI to study the consequences of TBI. As part of the ENIGMA Brain Injury working group, we have collaborated to identify a minimum set of recommendations that are designed to produce results that are reliable, harmonizable, and reproducible for the TBI imaging research community. Part one of this review provides the results of a literature search of current rsfMRI studies of TBI, highlighting key design considerations and data processing pipelines. Part two outlines seven data acquisition, processing, and analysis recommendations with the goal of maximizing study reliability and between-site comparability, while preserving investigator autonomy. Part three summarizes new directions and opportunities for future rsfMRI studies in TBI patients. The goal is to galvanize the TBI community to gain consensus for a set of rigorous and reproducible methods, and to increase analytical transparency and data sharing to address the reproducibility crisis in the field.

BACKGROUND: While the exploration of serotonergic psychedelics as psychiatric medicines deepens, so does the pressure to better understand how these compounds act on the brain. METHODS: We used a double-blind, placebo-controlled, crossover design and administered lysergic acid diethylamide (LSD), 3,4-methylenedioxymethamphetamine (MDMA), and d-amphetamine in 25 healthy participants. By using spectral dynamic causal modeling, we mapped substance-induced changes in effective connectivity between the thalamus and different cortex types (unimodal vs. transmodal) derived from a previous study with resting-state functional magnetic resonance imaging data. Due to the distinct pharmacological modes of action of the 3 substances, we were able to investigate specific effects mainly driven by different neurotransmitter systems on thalamocortical and corticothalamic interactions. RESULTS: Compared with placebo, all 3 substances increased the effective connectivity from the thalamus to specific unimodal cortices, whereas the influence of these cortices on the thalamus was reduced. These results indicate increased bottom-up and decreased top-down information flow between the thalamus and some unimodal cortices. However, for the amphetamines, we found the opposite effects when examining the effective connectivity with transmodal cortices, including parts of the salience network. Intriguingly, LSD increased the effective connectivity from the thalamus to both unimodal and transmodal cortices, indicating a breach in the hierarchical organization of ongoing brain activity. CONCLUSIONS: The results advance our knowledge about the action of psychedelics on the brain and refine current models aiming to explain the underlying neurobiological processes.

Village development and regional governance are central to Indonesia's national development agenda. However, in practice, monitoring demographic data, potential resources, and potential threats often transpires without the utilization of technology, yielding less accurate information. Furthermore, numerous villages, including Jatimulyo Tourism Village in Kulon Progo, have yet to fully embrace technology to harness the full potential of their local resources. Within this context, a pressing need exists for technological innovations to leverage the power of data in facilitating informed decision-making. The primary objective of this community engagement endeavor is to establish a data-driven village monitoring system to enhance the efficacy and efficiency of village monitoring processes while simultaneously promoting transparency, accountability, and community participation. The data-driven approach facilitates the collection of precise, automated data. Technology is anticipated to play a pivotal role in rectifying inaccuracies in information, offering crucial support to local government authorities and tourism managers in making well-informed decisions. This community service initiative's tangible outcome is creating a village monitoring system dashboard designed to facilitate decision-making processes and foster greater community involvement. Moreover, it is envisioned that this undertaking will maximize rural development and regional governance, instigate data-driven decision-making practices, foster the development of a robust village ecosystem, and ultimately enhance the overall well-being of the residents of Jatimulyo Tourism Village.

Mitochondrial dysfunction and low nicotinamide adenine dinucleotide (NAD+) levels are hallmarks of skeletal muscle ageing and sarcopenia1–3, but it is unclear whether these defects result from local changes or can be mediated by systemic or dietary cues. Here we report a functional link between circulating levels of the natural alkaloid trigonelline, which is structurally related to nicotinic acid4, NAD+ levels and muscle health in multiple species. In humans, serum trigonelline levels are reduced with sarcopenia and correlate positively with muscle strength and mitochondrial oxidative phosphorylation in skeletal muscle. Using naturally occurring and isotopically labelled trigonelline, we demonstrate that trigonelline incorporates into the NAD+ pool and increases NAD+ levels in Caenorhabditis elegans, mice and primary myotubes from healthy individuals and individuals with sarcopenia. Mechanistically, trigonelline does not activate GPR109A but is metabolized via the nicotinate phosphoribosyltransferase/Preiss–Handler pathway5,6 across models. In C. elegans, trigonelline improves mitochondrial respiration and biogenesis, reduces age-related muscle wasting and increases lifespan and mobility through an NAD+-dependent mechanism requiring sirtuin. Dietary trigonelline supplementation in male mice enhances muscle strength and prevents fatigue during ageing. Collectively, we identify nutritional supplementation of trigonelline as an NAD+-boosting strategy with therapeutic potential for age-associated muscle decline.

Which systems/organisms are conscious? New tests for consciousness (‘C-tests’) are urgently needed. There is persisting uncertainty about when consciousness arises in human development, when it is lost due to neurological disorders and brain injury, and how it is distributed in nonhuman species. This need is amplified by recent and rapid developments in artificial intelligence (AI), neural organoids, and xenobot technology. Although a number of C-tests have been proposed in recent years, most are of limited use, and currently we have no C-tests for many of the populations for which they are most critical. Here, we identify challenges facing any attempt to develop C-tests, propose a multidimensional classification of such tests, and identify strategies that might be used to validate them., SCOPUS: re.j, info:eu-repo/semantics/published

Many AI platforms, including traffic monitoring systems, use Federated Learning (FL) for decentralized sensor data processing for learning-based applications while preserving privacy and ensuring secured information transfer. On the other hand, applying supervised learning to large data samples, like high-resolution images requires intensive human labor to label different parts of a data sample. Multiple Instance Learning (MIL) alleviates this challenge by operating over labels assigned to the 'bag' of instances. In this paper, we introduce Federated Multiple-Instance Learning (FedMIL). This framework applies federated learning to boost the training performance in video-based MIL tasks such as vehicle accident detection using distributed CCTV networks. However, data sources in decentralized settings are not typically Independently and Identically Distributed (IID), making client selection imperative to collectively represent the entire dataset with minimal clients. To address this challenge, we propose DPPQ, a framework based on the Determinantal Point Process (DPP) with a quality-based kernel to select clients with the most diverse datasets that achieve better performance compared to both random selection and current DPP-based client selection methods even with less data utilization in the majority of non-IID cases. This offers a significant advantage for deployment on edge devices with limited computational resources, providing a reliable solution for training AI models in massive smart sensor networks.

Given the rapid advancement of artificial intelligence, understanding the foundations of intelligent behaviour is increasingly important. Active inference, regarded as a general theory of behaviour, offers a principled approach to probing the basis of sophistication in planning and decision-making. In this paper, we examine two decision-making schemes in active inference based on 'planning' and 'learning from experience'. Furthermore, we also introduce a mixed model that navigates the data-complexity trade-off between these strategies, leveraging the strengths of both to facilitate balanced decision-making. We evaluate our proposed model in a challenging grid-world scenario that requires adaptability from the agent. Additionally, our model provides the opportunity to analyze the evolution of various parameters, offering valuable insights and contributing to an explainable framework for intelligent decision-making., Comment: 13 pages, 8 figures

Purpose: This study aims to analyze the vulnerability and resilience of the national economy in Aceh Province during the Special Autonomy period. Factors affecting Aceh's economic vulnerability will be carefully discussed in the context of the implementation of Special Autonomy. Theoretical Reference: This study investigates corruption practices in Aceh Province using the Fraud Triangle Theory perspective, which identifies three key factors that influence corrupt behavior: pressure, rationalization, and opportunity. The research reveals that Aceh, as an autonomous region, has complex dynamics of corruption. Method: Both internal and external pressures, including economic and political factors, drive corrupt actors to seek opportunities for personal gain. Furthermore, the rationalization of corrupt actors, such as economic dissatisfaction or loose ethical justifications, plays a crucial role in legitimizing their corrupt actions. External factors, primarily the opportunity, play a pivotal role in facilitating corruption practices in Aceh. The existence of the Aceh Special Autonomy Fund (DOKA) provides opportunities for corrupt practices, especially when strong oversight mechanisms are lacking. Results and Conclusion: This study highlights that the available opportunities influence individuals' choices in dealing with internal pressures and rationalizations, in line with the Fraud Triangle Theory. Therefore, corruption prevention and intervention efforts need to focus on reducing opportunities for individuals to engage in corrupt activities. Implication of Research: The research findings provide valuable insights into the factors influencing corruption in Aceh and serve as a basis for further efforts to mitigate corrupt practices in the region. During the Special Autonomy period, Aceh faced various economic challenges and threats, including vulnerability to changes in global economic conditions, dependence on certain economic sectors, and potential economic in

This paper explores the potential of Large Language Models(LLMs) in zero-shot anomaly detection for safe visual navigation. With the assistance of the state-of-the-art real-time open-world object detection model Yolo-World and specialized prompts, the proposed framework can identify anomalies within camera-captured frames that include any possible obstacles, then generate concise, audio-delivered descriptions emphasizing abnormalities, assist in safe visual navigation in complex circumstances. Moreover, our proposed framework leverages the advantages of LLMs and the open-vocabulary object detection model to achieve the dynamic scenario switch, which allows users to transition smoothly from scene to scene, which addresses the limitation of traditional visual navigation. Furthermore, this paper explored the performance contribution of different prompt components, provided the vision for future improvement in visual accessibility, and paved the way for LLMs in video anomaly detection and vision-language understanding.

Untrained Physics-based Deep Learning (DL) methods for digital holography have gained significant attention due to their benefits, such as not requiring an annotated training dataset, and providing interpretability since utilizing the governing laws of hologram formation. However, they are sensitive to the hard-to-obtain precise object distance from the imaging plane, posing the $\textit{Autofocusing}$ challenge. Conventional solutions involve reconstructing image stacks for different potential distances and applying focus metrics to select the best results, which apparently is computationally inefficient. In contrast, recently developed DL-based methods treat it as a supervised task, which again needs annotated data and lacks generalizability. To address this issue, we propose $\textit{reverse-attention loss}$, a weighted sum of losses for all possible candidates with learnable weights. This is a pioneering approach to addressing the Autofocusing challenge in untrained deep-learning methods. Both theoretical analysis and experiments demonstrate its superiority in efficiency and accuracy. Interestingly, our method presents a significant reconstruction performance over rival methods (i.e. alternating descent-like optimization, non-weighted loss integration, and random distance assignment) and even is almost equal to that achieved with a precisely known object distance. For example, the difference is less than 1dB in PSNR and 0.002 in SSIM for the target sample in our experiment.

The rise of machine learning in recent years has brought benefits to various research fields such as wide fire detection. Nevertheless, small object detection and rare object detection remain a challenge. To address this problem, we present a dataset automata that can generate ground truth paired datasets using diffusion models. Specifically, we introduce a mask-guided diffusion framework that can fusion the wildfire into the existing images while the flame position and size can be precisely controlled. In advance, to fill the gap that the dataset of wildfire images in specific scenarios is missing, we vary the background of synthesized images by controlling both the text prompt and input image. Furthermore, to solve the color tint problem or the well-known domain shift issue, we apply the CLIP model to filter the generated massive dataset to preserve quality. Thus, our proposed framework can generate a massive dataset of that images are high-quality and ground truth-paired, which well addresses the needs of the annotated datasets in specific tasks.

We delve into the dynamics of opinions within a multiplex network using coordination games, where agents communicate either in a one-way or two-way interactions, and where a designated leader may be present. By employing graph theory and Markov chains, we illustrate that despite non-positive diagonal elements in transition probability matrices or decomposable layers, opinions generally converge under specific conditions, leading to a consensus. We further scrutinize the convergence rates of opinion dynamics in networks with one-way versus two-way interactions. We find that in networks with a designated leader, opinions converge towards the initial opinion of the leader, whereas in networks without a designated leader, opinions converge to a convex combination of the opinions of agents. Moreover, we emphasize the crucial role of designated leaders in steering opinion convergence within the network. Our experimental findings corroborate that the presence of leaders expedites convergence, with mono-directional interactions exhibiting notably faster convergence rates compared to bidirectional interactions.

This paper presents a comprehensive literature review of the current state of in-vehicle human-computer interaction (HCI) in the context of self-driving vehicles, with a specific focus on inclusion and accessibility. This study's aim is to examine the user-centered design principles for inclusive HCI in self-driving vehicles, evaluate existing HCI systems, and identify emerging technologies that have the potential to enhance the passenger experience. The paper begins by providing an overview of the current state of self-driving vehicle technology, followed by an examination of the importance of HCI in this context. Next, the paper reviews the existing literature on inclusive HCI design principles and evaluates the effectiveness of current HCI systems in self-driving vehicles. The paper also identifies emerging technologies that have the potential to enhance the passenger experience, such as voice-activated interfaces, haptic feedback systems, and augmented reality displays. Finally, the paper proposes an end-to-end design framework for the development of an inclusive in-vehicle experience, which takes into consideration the needs of all passengers, including those with disabilities, or other accessibility requirements. This literature review highlights the importance of user-centered design principles in the development of HCI systems for self-driving vehicles and emphasizes the need for inclusive design to ensure that all passengers can safely and comfortably use these vehicles. The proposed end-to-end design framework provides a practical approach to achieving this goal and can serve as a valuable resource for designers, researchers, and policymakers in this field.

Wildfires have emerged as one of the most destructive natural disasters worldwide, causing catastrophic losses in both human lives and forest wildlife. Recently, the use of Artificial Intelligence (AI) in wildfires, propelled by the integration of Unmanned Aerial Vehicles (UAVs) and deep learning models, has created an unprecedented momentum to implement and develop more effective wildfire management. Although some of the existing survey papers have explored various learning-based approaches, a comprehensive review emphasizing the application of AI-enabled UAV systems and their subsequent impact on multi-stage wildfire management is notably lacking. This survey aims to bridge these gaps by offering a systematic review of the recent state-of-the-art technologies, highlighting the advancements of UAV systems and AI models from pre-fire, through the active-fire stage, to post-fire management. To this aim, we provide an extensive analysis of the existing remote sensing systems with a particular focus on the UAV advancements, device specifications, and sensor technologies relevant to wildfire management. We also examine the pre-fire and post-fire management approaches, including fuel monitoring, prevention strategies, as well as evacuation planning, damage assessment, and operation strategies. Additionally, we review and summarize a wide range of computer vision techniques in active-fire management, with an emphasis on Machine Learning (ML), Reinforcement Learning (RL), and Deep Learning (DL) algorithms for wildfire classification, segmentation, detection, and monitoring tasks. Ultimately, we underscore the substantial advancement in wildfire modeling through the integration of cutting-edge AI techniques and UAV-based data, providing novel insights and enhanced predictive capabilities to understand dynamic wildfire behavior.

Tindak pidana pidana revenge porn diatur dalam Undang-Undang Republik Indonesia Nomor 19 Tahun 2016 tentang Informasi dan Transaksi Elektronik Pasal 27 Ayat 1 dan Undang-Undang Republik Indonesia Nomor 12 Tahun 2022 tentang Tindak Pidana Kekerasan Seksual. Tindak pidana revenge porn termasuk tindak pidana khusus sehingga dalam pemberantasannya menjadi kewenangan dimana salah satu kasus yang pernah ditangani oleh Ditreskrimsus Kepolisian Daerah Sumatera Barat adalah MPA (24) Mahasiswa UA karena mengancam menyebarkan video asusila dirinya KA ke media sosial. Rumusan masalah: 1) Bagaimanakan peranan Kepolisian Polda Sumbar dalam memberikan perlindungan hukum terhadap korban revenge porn?: 2) Apakah kendala yang ditemui Kepolisian Polda Sumbar dalam memberikan perlindungan hukum terhadap korban revenge porn? Jenis penelitian berupa penelitian hukum empiris; sumber data berasal dari data primer dan data sekunder; teknik pengumpulan data dengan studi dokumen dan wawancara; data dianalisis secara kualitatif. Hasil penelitian menunjukkan: 1) Peran Ditreskrimsus Polda Sumbar dalam memberikan bentuk perlindungan hukum bagi korban revenge porn: 2) Kendala yang dihadapi oleh penyidik Ditreskrimsus Polda Sumbar dalam pelaksanaan proses penyidikan tindak pidana revenge porn yaitu kendala internal (hilangnya barang bukti, keterbatasan alat dan perangkat dan kualifikasi penegak hukun) dan kendala eksternal (enkripsi data, kurangnya keterbukaan korban kepada penyidik, dan penjaminan terhadap hak-hak korban).

How do biological systems and machine learning algorithms compare in the number of samples required to show significant improvements in completing a task? We compared the learning efficiency of in vitro biological neural networks to the state-of-the-art deep reinforcement learning (RL) algorithms in a simplified simulation of the game `Pong'. Using DishBrain, a system that embodies in vitro neural networks with in silico computation using a high-density multi-electrode array, we contrasted the learning rate and the performance of these biological systems against time-matched learning from three state-of-the-art deep RL algorithms (i.e., DQN, A2C, and PPO) in the same game environment. This allowed a meaningful comparison between biological neural systems and deep RL. We find that when samples are limited to a real-world time course, even these very simple biological cultures outperformed deep RL algorithms across various game performance characteristics, implying a higher sample efficiency. Ultimately, even when tested across multiple types of information input to assess the impact of higher dimensional data input, biological neurons showcased faster learning than all deep reinforcement learning agents., Comment: 13 Pages, 6 Figures - 38 Supplementary Pages, 6 Supplementary Figures, 4 Supplementary Tables

Sharing and joint processing of camera feeds and sensor measurements, known as Cooperative Perception (CP), has emerged as a new technique to achieve higher perception qualities. CP can enhance the safety of Autonomous Vehicles (AVs) where their individual visual perception quality is compromised by adverse weather conditions (haze as foggy weather), low illumination, winding roads, and crowded traffic. To cover the limitations of former methods, in this paper, we propose a novel approach to realize an optimized CP under constrained communications. At the core of our approach is recruiting the best helper from the available list of front vehicles to augment the visual range and enhance the Object Detection (OD) accuracy of the ego vehicle. In this two-step process, we first select the helper vehicles that contribute the most to CP based on their visual range and lowest motion blur. Next, we implement a radio block optimization among the candidate vehicles to further improve communication efficiency. We specifically focus on pedestrian detection as an exemplary scenario. To validate our approach, we used the CARLA simulator to create a dataset of annotated videos for different driving scenarios where pedestrian detection is challenging for an AV with compromised vision. Our results demonstrate the efficacy of our two-step optimization process in improving the overall performance of cooperative perception in challenging scenarios, substantially improving driving safety under adverse conditions. Finally, we note that the networking assumptions are adopted from LTE Release 14 Mode 4 side-link communication, commonly used for Vehicle-to-Vehicle (V2V) communication. Nonetheless, our method is flexible and applicable to arbitrary V2V communications.

Large Language Models (LLMs) have emerged as powerful tools in various research domains. This article examines their potential through a literature review and firsthand experimentation. While LLMs offer benefits like cost-effectiveness and efficiency, challenges such as prompt tuning, biases, and subjectivity must be addressed. The study presents insights from experiments utilizing LLMs for qualitative analysis, highlighting successes and limitations. Additionally, it discusses strategies for mitigating challenges, such as prompt optimization techniques and leveraging human expertise. This study aligns with the 'LLMs as Research Tools' workshop's focus on integrating LLMs into HCI data work critically and ethically. By addressing both opportunities and challenges, our work contributes to the ongoing dialogue on their responsible application in research.

Deep neural networks, including transformers and convolutional neural networks, have significantly improved multivariate time series classification (MTSC). However, these methods often rely on supervised learning, which does not fully account for the sparsity and locality of patterns in time series data (e.g., diseases-related anomalous points in ECG). To address this challenge, we formally reformulate MTSC as a weakly supervised problem, introducing a novel multiple-instance learning (MIL) framework for better localization of patterns of interest and modeling time dependencies within time series. Our novel approach, TimeMIL, formulates the temporal correlation and ordering within a time-aware MIL pooling, leveraging a tokenized transformer with a specialized learnable wavelet positional token. The proposed method surpassed 26 recent state-of-the-art methods, underscoring the effectiveness of the weakly supervised TimeMIL in MTSC. The code will be available at https://github.com/xiwenc1/TimeMIL., Comment: Accepted by ICML2024

Inverse imaging problems (IIPs) arise in various applications, with the main objective of reconstructing an image from its compressed measurements. This problem is often ill-posed for being under-determined with multiple interchangeably consistent solutions. The best solution inherently depends on prior knowledge or assumptions, such as the sparsity of the image. Furthermore, the reconstruction process for most IIPs relies significantly on the imaging (i.e. forward model) parameters, which might not be fully known, or the measurement device may undergo calibration drifts. These uncertainties in the forward model create substantial challenges, where inaccurate reconstructions usually happen when the postulated parameters of the forward model do not fully match the actual ones. In this work, we devoted to tackling accurate reconstruction under the context of a set of possible forward model parameters that exist. Here, we propose a novel Moment-Aggregation (MA) framework that is compatible with the popular IIP solution by using a neural network prior. Specifically, our method can reconstruct the signal by considering all candidate parameters of the forward model simultaneously during the update of the neural network. We theoretically demonstrate the convergence of the MA framework, which has a similar complexity with reconstruction under the known forward model parameters. Proof-of-concept experiments demonstrate that the proposed MA achieves performance comparable to the forward model with the known precise parameter in reconstruction across both compressive sensing and phase retrieval applications, with a PSNR gap of 0.17 to 1.94 over various datasets, including MNIST, X-ray, Glas, and MoNuseg. This highlights our method's significant potential in reconstruction under an uncertain forward model., Comment: Accepted by PBDL-CVPR 2024

Generative Artificial Intelligence (AI) is integrated into everyday technology, including news, education, and social media. AI has further pervaded private conversations as conversational partners, auto-completion, and response suggestions. As social media becomes young people's main method of peer support exchange, we need to understand when and how AI can facilitate and assist in such exchanges in a beneficial, safe, and socially appropriate way. We asked 622 young people to complete an online survey and evaluate blinded human- and AI-generated responses to help-seeking messages. We found that participants preferred the AI-generated response to situations about relationships, self-expression, and physical health. However, when addressing a sensitive topic, like suicidal thoughts, young people preferred the human response. We also discuss the role of training in online peer support exchange and its implications for supporting young people's well-being. Disclaimer: This paper includes sensitive topics, including suicide ideation. Reader discretion is advised.

In the rapidly evolving landscape of computing disciplines, substantial efforts are being dedicated to unraveling the sociotechnical implications of generative AI (Gen AI). While existing research has manifested in various forms, there remains a notable gap concerning the direct engagement of knowledge workers in academia with Gen AI. We interviewed 18 knowledge workers, including faculty and students, to investigate the social and technical dimensions of Gen AI from their perspective. Our participants raised concerns about the opacity of the data used to train Gen AI. This lack of transparency makes it difficult to identify and address inaccurate, biased, and potentially harmful, information generated by these models. Knowledge workers also expressed worries about Gen AI undermining trust in the relationship between instructor and student and discussed potential solutions, such as pedagogy readiness, to mitigate them. Additionally, participants recognized Gen AI's potential to democratize knowledge by accelerating the learning process and act as an accessible research assistant. However, there were also concerns about potential social and power imbalances stemming from unequal access to such technologies. Our study offers insights into the concerns and hopes of knowledge workers about the ethical use of Gen AI in educational settings and beyond, with implications for navigating this new landscape.

Motion analysis plays a critical role in various applications, from virtual reality and augmented reality to assistive visual navigation. Traditional self-driving technologies, while advanced, typically do not translate directly to pedestrian applications due to their reliance on extensive sensor arrays and non-feasible computational frameworks. This highlights a significant gap in applying these solutions to human users since human navigation introduces unique challenges, including the unpredictable nature of human movement, limited processing capabilities of portable devices, and the need for directional responsiveness due to the limited perception range of humans. In this project, we introduce an image-only method that applies motion analysis using optical flow with ego-motion compensation to predict Motor Focus-where and how humans or machines focus their movement intentions. Meanwhile, this paper addresses the camera shaking issue in handheld and body-mounted devices which can severely degrade performance and accuracy, by applying a Gaussian aggregation to stabilize the predicted motor focus area and enhance the prediction accuracy of movement direction. This also provides a robust, real-time solution that adapts to the user's immediate environment. Furthermore, in the experiments part, we show the qualitative analysis of motor focus estimation between the conventional dense optical flow-based method and the proposed method. In quantitative tests, we show the performance of the proposed method on a collected small dataset that is specialized for motor focus estimation tasks.

A vehicle exposed to flooding may lose its stability and wash away resulting in potential injuries and fatalities. Traffic disruption, infrastructure damage, and economic losses are also additional effects of the washed vehicles. Therefore, understanding the responses of passenger vehicles during flood events is of the utmost importance to reduce flood risks and develop accurate safety guidelines. Previously, flooded vehicle stability was investigated experimentally, theoretically, and numerically. However, numerical investigations are insufficient, of which only a few studies have been published since 1967. Furthermore, coupled motion simulations have not been employed to investigate the hydrodynamic forces on flooded vehicles. In this paper, a numerical framework was proposed to assess the response of a full-scale medium-size passenger vehicle exposed to floodwaters through three-dimensional computational fluid dynamic modelling. The vehicle was simulated under subcritical and supercritical flows with the Froude number ranging between 0.09 and 2.46. The results showed that the vehicle experienced the floating instability mode once the flow depth reached 0.38 m, while the sliding instability mode was observed once the depth × velocity threshold function exceeded 0.36 m2/s. In terms of hydrodynamic forces, it was noticed that the drag force decreased with the increment of the Froude number and flow velocity. On the other hand, the fraction and buoyancy forces are mainly governed by the flow depth at the vehicle vicinity. The drag coefficient was noticed to be less than 1 for supercritical flows and more than 1 for subcritical flows. The numerical results obtained through the framework introduced in this study demonstrate favorable agreement with three different previously published experimental outcomes.

Chimeric antigen receptor (CAR) T cell therapy is introduced as an effective, rapidly evolving therapeutic to treat cancer, especially cancers derived from hematological cells, such as B cells. CAR T cell gene constructs combine a tumor-targeting device coupled to the T cell receptor (TCR) zeta chain domain with different signaling domains such as domains derived from CD28 or 4-1BB (CD137). The incorporation of each specific co-stimulatory domain targets the immunometabolic pathways of CAR T cells as well as other signaling pathways. Defining the immunometabolic and signaling pathways by which CAR T cells become and remain active, survive, and eliminate their targets may represent a huge step forward in this relatively young research field as the CAR gene can be tailored to gain optimal function also for solid tumors with elaborate immunosuppression and protective stroma. There is a close relationship between different signaling domains applied in CAR T cells, and difficult to evaluate the benefit from different tested CAR gene constructs. In this review, we attempt to collect the latest findings regarding the CAR T cell signaling pathways that affect immunometabolic pathways.

Time-Triggered (TT) architectures are widely used in safety-critical computer systems for their deterministic and predictable behavior. However, existing solutions lack observability and debugging capabilities. To address this, we propose GALI, a novel simulation technique. GALI, which stands for "Globally Accurate, Locally Inaccurate," speeds up the simulation of time-triggered systems while enhancing observability and debugging. It utilizes an instruction-accurate simulation with a predetermined time-triggered system configuration and applies discrete timed execution to produce fast and accurate results. The supporting tooling ecosystem generates platform and application components, including board-support packages, binary files, communication channels, and required infrastructure. Our evaluation demonstrates that GALI achieves precise behavior similar to cycle-accurate simulations while incurring minimal overhead, comparable to untimed simulations.

Maze is a place that has many paths with tortuous paths that are misleading and full of dead ends and can be viewed as a grid graph. A non-perfect maze is a maze that has a cycle. This research produces an algorithm that can form a non-perfect maze with a size of m×n which has two types of bias. The first bias is the composition of the percentage of horizontal and vertical partitions. The second bias is the percentage of the number of cycles. The algorithm created in this study was generated by modifying Prim’s algorithm and the use of Fisher-Yates algorithm which is used in random selection in Prim’s algorithm. The non-perfect maze algorithm begins with the calculation of the parameter values of the two types of bias and continues with forming a perfect maze and ends with forming a non-perfect maze. The algorithm that has been designed can form a non-perfect maze with a complexity of O(|E|^2), where E is the set of edges of an m×n grid graph. Flash-based application development is also carried out in order to implement algorithms to obtain a non-perfect maze. The non-perfect maze is produced in a two-dimensional visual form in the form of an image along with its corresponding grid graph. The application is capable of displaying up to the first 20 solutions of the biased maze.

Agricultural expansion has had a detrimental effect on tropical forests and the animal communities that depend on them. Agroforestry systems, however, with their more complex tree and plant communities, have been shown to be important habitats for a range of globally threatened species, including nocturnal animals. Here, we present novel data on the encounter rates of seven species of nocturnal mammals in relation to agroforestry systems within four national parks and associated plantations in Bangladesh to examine if encounter rates were influenced by the human population density, presence of plantations, and human access as represented by a Human Influence Index of anthropogenic disturbance. We walked 70.3 km of transects with only semi-natural forest, 26.9 km of transects with semi-natural forest and gardens, and 21.7 km of transects with semi-natural forest and monocultures over 55 nights from 2017–2019. Of the seven species of nocturnal mammals we detected, all were present in Satachari National Park, whereas six occurred in Lawachara National Park, Rajkandi Forest Range, and Rema-Kalenga Wildlife Sanctuary. Within these national parks, three species (Bengal slow loris, large Indian civet, particolored flying squirrel) were more frequently recorded in areas with human disturbance, especially agroforestry plantations. With declining forest cover in Bangladesh, we highlight here the potential of agroforestry systems as emerging important habitats for these species. We encourage long-term studies of these lesser-studied taxa to understand fully the capacity of agroforestry systems in order to support their long-term conservation.

This paper proposes a novel stochastic agent-based framework to predict the day-ahead charging demand of electric vehicles (EVs) considering key factors including the initial and final state of charge (SOC), the type of the day, traffic conditions, and weather conditions. The accurate forecast of EVs charging demand enables the proposed model to optimally determine the location of common prime urban parking lots (PLs) including residential, offices, food centers, shopping malls, and public parks. By incorporating both macro-level and micro-level parameters, the agents used in this framework provide significant benefits to all stakeholders, including EV owners, PL operators, PL aggregators, and distribution network operators. Further, the path tracing algorithm is employed to find the nearest PL for the EVs and the probabilistic method is applied to evaluate the uncertainties of driving patterns of EV drivers and the weather conditions. The simulation has been carried out in an agent-based modeling software called NETLOGO with the traffic and weather data of the city of Newcastle Upon Tyne, while the IEEE 33 bus system is mapped on the traffic map of the city. The findings reveal that the total charging demand of EVs is significantly higher on a sunny weekday than on a rainy weekday during peak hours, with an increase of over 150kW. Furthermore, on weekdays higher load demand could be seen during the night time as opposed to weekends where the load demand usually increases during the day time.

This study aimed to investigate the diabetes mellitus (DM) and prediabetes epidemiology, care cascade, and compliance with global coverage targets. We recruited the results of the nationally representative Iran STEPS Survey 2021. Diabetes and prediabetes were two main outcomes. Diabetes awareness, treatment coverage, and glycemic control were calculated for all population with diabetes to investigate the care cascade. Four global coverage targets for diabetes developed by the World Health Organization were adopted to assess the DM diagnosis and control status. Among 18,119 participants, the national prevalence of DM and prediabetes were 14.2% (95% confidence interval 13.4–14.9) and 24.8% (23.9–25.7), respectively. The prevalence of DM treatment coverage was 65.0% (62.4–67.7), while the prevalence of good (HbA1C < 7%) glycemic control was 28.0% (25.0–31.0) among all individuals with diabetes. DM diagnosis and statin use statics were close to global targets (73.3% vs 80%, and 50.1% vs 60%); however, good glycemic control and strict blood pressure control statistics, were much way behind the goals (36.7% vs 80%, and 28.5% vs 80%). A major proportion of the Iranian population are affected by DM and prediabetes, and glycemic control is poorly achieved, indicating a sub-optimal care for diabetes and comorbidities like hypertension.

In this paper, a new cyclic structure of a max pressure travel time-based traffic signal control is developed to seek an optimal coordination in large-scale urban networks. The focus of the current paper is on dynamic manipulation of cycle lengths within cyclic structure. Following the application of a decentralized approach, which requires only local information in order to offer proper phase durations, the control strategy aims at maximizing the overall network throughput. Previous works of cyclic max-pressure control have presented a cyclic notion to actuate the controller in a cyclic manner. However, no input has been provided on the optimal cycle length for each intersection to be chosen in a network, and along with the dynamic and stochastic nature of the trips, it is not clear what are the main phases of the intersections and how to coordinate them. The developed cyclic max pressure control schemes are compared with an exiting cyclic scheme in the literature. Simulation results show that the newly proposed cyclic structure of the time-based approach offers better decision-making.

In recent years, mangrove forests have rapidly become an endangered ecosystem in the world. On the coastal shorelines, the saltwater intrusion (SI) phenomenon is a main concern that can affect the quality of water. The saltwater intrusion might occur due to sea level rise, and extreme natural disasters along with changes in mangrove ecosystem distribution. The purpose of this research is to evaluate the effectiveness of mangroves for strengthening coastal bunds in Tanjong Laboh, Batu Pahat, Johor. By using the non-destructive geophysical method, the presence of saltwater intrusion and subsurface profile of the survey line in the coastal area with and without mangroves was determined. The coastal bund conditions with and without mangroves were also compared in order to evaluate how the mangrove forest acts as coastal protection. The result shows that the resistivity values of 0.1 to 1.0 ohm.m are more dominant in the mangrove bund area than in the engineered coastal bund and rock outcrop areas. The saltwater intrusion integrity map was produced from the 2D subsurface image profiles for both survey lines. The comparison of the coastal bund conditions for two different scenarios, which with and without mangroves, has shown that the coastal bund without the mangroves experienced cracking and erosion compared to the coastal bund with the presence of mangroves. This indicates the importance of mangroves in protecting the coastal regions.

Drug driving is recognised as a major road safety problem in many countries. In Australia, the primary response to this problem involved the adoption of roadside drug testing (RDT), which was modelled on the policies and practices used to conduct random breath testing (RBT) for alcohol. However, there remain important differences in the way that RDT and RBT are conducted, which might produce differential effects on drug and drink driving behaviour. In addition, various psychosocial factors are known to influence the two behaviours. Thus, there is a need to investigate the relative influence of legal and psychosocial factors on drug driving and explore how they may be similar or different to drink driving. Accordingly, this study utilised Deterrence Theory and Akers' Social Learning Theory, augmented by measures of dependence and a range of psychosocial factors, to examine the factors associated with self-reported drink and drug driving. An online survey was completed by 1394 licensed drivers from the three most populous states in Australia: Queensland, New South Wales and Victoria. Self-reported drink and drug driving became more likely as level of dependence increased. In relation to legal factors, direct experience of avoiding detection was the strongest predictor of self-reported drink and drug driving. Among the psychosocial factors, a significant positive relationship was found between holding favourable attitudes toward both drink driving and drug driving and self-reported behaviours. The findings suggest that applying legal sanctions in isolation without addressing the psychosocial rewards and punishments for the behaviours is not sufficient to reduce drug or drink driving. Future research should identify countermeasures that integrate deterrence and psychosocial principles in order to reduce these risky driving behaviours., Safety and Security Science

A vehicle exposed to flooding may lose its stability and wash away resulting in potential injuries and fatalities. Traffic disruption, infrastructure damage, and economic losses are also additional effects of the washed vehicles. Therefore, understanding the responses of passenger vehicles during flood events is of the utmost importance to reduce flood risks and develop accurate safety guidelines. Previously, flooded vehicle stability was investigated experimentally, theoretically, and numerically. However, numerical investigations are insufficient, of which only a few studies have been published since 1967. Furthermore, coupled motion simulations have not been employed to investigate the hydrodynamic forces on flooded vehicles. In this paper, a numerical framework was proposed to assess the response of a full-scale medium-size passenger vehicle exposed to floodwaters through three-dimensional computational fluid dynamic modelling. The vehicle was simulated under subcritical and supercritical flows with the Froude number ranging between 0.09 and 2.46. The results showed that the vehicle experienced the floating instability mode once the flow depth reached 0.38 m, while the sliding instability mode was observed once the depth×velocity threshold function exceeded 0.36 m2/s. In terms of hydrodynamic forces, it was noticed that the drag force decreased with the increment of the Froude number and flow velocity. On the other hand, the fraction and buoyancy forces are mainly governed by the flow depth at the vehicle vicinity. The drag coefficient was noticed to be less than 1 for supercritical flows and more than 1 for subcritical flows. The numerical results obtained through the framework introduced in this study demonstrate favorable agreement with three different previously published experimental outcomes., Peer Reviewed, Postprint (published version)

Vehicles are one of the most common objects that are usually washed away by the water flow during flood events resulting in several damages and casualties. Regardless of the vehicle type and flow condition, vehicle orientations in relation to the incoming flow play a significant role on the vehicles stability limits. Therefore, studying the hydrodynamic forces on a vehicle placed at different angles with respect to the incoming flow can help to understand the most unstable orientation. As a result, parking lots design may improve by rearranging the vehicles in a way that the critical vehicle orientation will not face the expected incoming flow. Herein, 3-dimensional numerical modelling was conducted to assess the critical orientation of a parked full-scale passenger vehicle exposed to water flow at three different angles, namely 0o, 45o, and 90o. The highest drag force and displacement were observed for the vehicle at 90o orientation making it the critical one. While the highest torque on the vehicle centre of mass was reported for the vehicle at 45o orientation, making it more rotatable when compared with other orientations. Obtained results were compared with previously published experimental studies and good agreements were observed., Peer Reviewed, Postprint (published version)

Urban stormwater management is a critical challenge facing cities globally, with naturalbased solutions (NBS) emerging as a promising approach for mitigating the impacts of urban stormwater runoff. This bibliometric review examined the research trends and hot topics related to NBS for urban stormwater management. The study utilized a combination of qualitative and quantitative methods to analyze 176 articles from the Web of Science database, covering the period from 2016 to 2022. Results showed that NBS is a widely researched topic with a growing trend in publications in recent years, led by the United States, China, and several European countries. The majority of NBS articles were research papers (82%) with a focus on environmental performance rather than social and economic dimensions. Quantitative methods were more frequently used in research articles, particularly statistical analysis/modeling. Interviews and discussions were the most common qualitative method used. The review identified the most relevant countries, affiliations, authors, and journals in the field. Furthermore, hot topics in NBS research were highlighted, including ecosystem services, climate change, and sustainability. The study also emphasized that future research perspective should focus on interdisciplinary and collaborative research, scaling up and mainstreaming NBS, and exploring new ways of integrating different disciplines and stakeholders in the research process. The findings of this review provided insights into the current state of NBS research and offer valuable information for researchers, policymakers, and practitioners in the field of urban stormwater management.

The trade in primates as pets is a global enterprise and as access to the Internet has increased, so too has the trade of live primates online. While quantifying primate trade in physical markets is relatively straightforward, limited insights have been made into trade via the Internet. Here we followed a three-pronged approach to estimate the prevalence and ease of purchasing primates online in countries with different socioeconomic characteristics. We first conducted a literature review, in which we found that Malaysia, Thailand, the USA, Ukraine, South Africa, and Russia stood out in terms of the number of primate individuals being offered for sale as pets in the online trade. Then, we assessed the perceived ease of purchasing pet primates online in 77 countries, for which we found a positive relationship with the Internet Penetration Rate, total human population and Human Development Index, but not to Gross Domestic Product per capita or corruption levels of the countries. Using these results, we then predicted the levels of online primate trade in countries for which we did not have first-hand data. From this we created a global map of potential prevalence of primate trade online. Finally, we analysed price data of the two primate taxa most consistently offered for sale, marmosets and capuchins. We found that prices increased with the ease of purchasing primates online and the Gross Domestic Product per capita. This overview provides insight into the nature and intricacies of the online primate pet trade and advocates for increased trade regulation and monitoring in both primate range and non-range countries where trade has been substantially reported., The trade in primates as pets is a global enterprise and as access to the Internet has increased, so too has the trade of live primates online. While quantifying primate trade in physical markets is relatively straightforward, limited insights have been made into trade via the Internet. Here we followed a three-pronged approach to estimate the prevalence and ease of purchasing primates online in countries with different socioeconomic characteristics. We first conducted a literature review, in which we found that Malaysia, Thailand, the USA, Ukraine, South Africa, and Russia stood out in terms of the number of primate individuals being offered for sale as pets in the online trade. Then, we assessed the perceived ease of purchasing pet primates online in 77 countries, for which we found a positive relationship with the Internet Penetration Rate, total human population and Human Development Index, but not to Gross Domestic Product per capita or corruption levels of the countries. Using these results, we then predicted the levels of online primate trade in countries for which we did not have first-hand data. From this we created a global map of potential prevalence of primate trade online. Finally, we analysed price data of the two primate taxa most consistently offered for sale, marmosets and capuchins. We found that prices increased with the ease of purchasing primates online and the Gross Domestic Product per capita. This overview provides insight into the nature and intricacies of the online primate pet trade and advocates for increased trade regulation and monitoring in both primate range and non-range countries where trade has been substantially reported.

Secara geografiis, daerah penelitian terletak pada koordinat 0°21’12." - 0° 22’ 50." Lintang Utara dan 00°10’53” - 100°33’44.4620" Bujur Timur. Penelitian terletak di daerah koto lamo dan sekitarnya, Kecamatan Kapur sembilan, Kabupaten Sumatera Barat. Tujuan penelitian ini adalah untuk mengetahui tingkat kestabilan lereng pada daerah penelitian dengan menggunakan metorde pemetaan geologi, analisis UCS (Uniaxial Compressive Strength) kekuatan batuan utuh, RMR (Rock Mass Rating) dan Stereografis, dari data lapangan. Terdapat 10 scanline utama yang menjadi objek penelitian. Hasil pemetaan geologi menunjukkan lereng tersebut terdapat batuan batupasir, dari batuan tersebut analisis UCS kekuatan batuan utuh memiliki nilai rata – rata dari uji Schmidt Hammer bernilai 13.08, sedangkan nilai densitas sampel batuan nya 25,86

Introduction. Caesarean method widely used and become popular among pregnant women. In general, this method mostly used regional anesthesia (spinal, epidural and CSE). However, many reports incidence of complication related to spinal anesthesia after the procedure. Maternal hypotension is the most common complication after spinal anesthesia procedure. Therefore, anesthesiologist need to prevent hypotension during spinal anesthesia in caesarean section. Method. The researcher examines several scientific journals from PubMed, BJA, Elsevier, Cochrane Library and BMC research until 10th August, 2023 for abstracts, conference proceedings, and full-text papers that evaluated prevention of hypotension in non-preeclampsia patients undergoing caesarean delivery under spinal anesthesia. In addition, the researchers examine the research conducted during 2016 to 2023. Result & Analysis. The researchers examine 28 scientific article (11.153 parturient) with 6 different methods to prevent hypotension. It was found that vasopressor is widely used in most research with incidence of hypotension varied from 4%-87,4%. IVCU studies reported the incidence of hypotension in 15,3%-43%. Leg Elevation a non-invasive technique studies reported the incidence of hypotension in 33,3-41,4%. Injection speed adjustment methods resulted incidence of hypotension in Fast injection 50 %-75% and Slow Injection in 36,7%-61%. In addition, granisetron methods diluted in normal saline reported the incidence of hypotension in 17,5%-42%. Low dose spinal anesthesia studies reported incidence of hypotension in 0% - 62,9%. Discussion. From all the research that has been reviewed, vasopressor (Phenylephrine), Leg Elevation and low dose spinal anesthesia could become primary options to prevent hypotension during spinal anesthesia in caesarean section since its effective to prevent hypotension, cost effective, and easy to applied in every medical facility.

Highlights: Calciferol has cardioprotective properties, and a severe deficiency of calciferol is associated with septic shock, a longer duration of mechanical ventilation and treatment in the intensive care unit, and mortality. The role of calciferol in critical conditions has been described in several research reports, but relatively little is known about its function in pediatric cardiac surgery. Abstract Calciferol is a micronutrient and a secosteroid hormone that plays a role in maintaining the health of bones and soft tissues in the body, such as the myocardium, as well as the immune system. Hypovitaminosis D has been reported to be associated with poor clinical outcomes and child mortality in pediatric intensive care units. Children with congenital heart disease (CHD) are vulnerable to critical conditions and require early intervention due to heart failure. This study aimed to investigate the influence of calciferol status on the clinical outcomes of pediatric CHD patients who underwent heart surgery. A systematic literature review was conducted using the electronic databases from PubMed, Elsevier, and Cochrane. This study included observational and randomized control studies that assessed the calciferol status of pediatric CHD patients undergoing cardiac surgery. From a total of 168 studies, 8 studies were selected for review. The preoperative and postoperative calciferol status as well as clinical outcomes following pediatric cardiac surgery were reviewed. According to the findings, most pediatric CHD patients suffered from calciferol deficiency prior to corrective heart surgery, which further decreased postoperatively and was associated with clinical outcomes in the intensive care unit (ICU). Preoperative calciferol supplementation has been reported to enhance serum calciferol levels and is associated with good clinical outcomes in pediatric patients undergoing cardiac surgery.