Abstract Background The western Himalayan forest ecosystem faces escalating pressures from climate change and anthropogenic activities, demanding improved conservation strategies. Effective management requires understanding the seasonal fluctuations in vegetation, soil properties and microbial communities, but they remain poorly characterized across high altitude forests. We assessed these variables in 10 forest sites during the winter of 2023 and summer of 2024, analysing vegetation diversity, soil parameters, and microbial metagenomics. Results We found pronounced seasonal shifts in plant and microbial diversities, and in soil properties. Plant species richness, and Shannon and Simpson diversity indices were higher (p < 0.001) in summer than in winter while the community maturity index was higher (p < 0.02) in winter than in summer. Soil properties exhibited clear seasonal patterns: pH, available phosphorus (AP), microbial biomass carbon (MBC) and cation exchange capacity (CEC) were higher (p < 0.05) in summer, whereas soil moisture (SM) and soil organic carbon (SOC) were higher (p < 0.05) in winter. Microbial alpha diversity indices (Shannon, Chao, and Sobs) were elevated (p < 0.05) in summer, while the Simpson index was elevated in winter, indicating a shift in community dominance. Beta diversity analyses revealed a significant seasonal shift in overall metabolic potential (KEGG orthologs; ANOSIM R = 0.222, p = 0.016), but not in general protein functions (COG), carbohydrate-active enzymes (CAZy), or taxonomic composition (RefSeq). Therefore, despite taxonomic turnover, core metabolic functions were maintained, indicating strong functional redundancy. Structural equation models (SEM) confirmed distinct seasonal dynamics, revealing stronger plant-soil-microbe interactions and a greater proportion of variance explained by the model in summer (R2=0.64–0.72 for key paths) than in winter (R2=0.52–0.63). Conclusions The findings demonstrate that the western Himalayan ecosystem undergoes a fundamental seasonal reorganization. Summer is characterized by increased biodiversity, distinct soil conditions, and more dynamic microbial-ecosystem interactions, while winter exhibits greater community maturity and functional stability. The resilience of core ecosystem processes is underpinned by microbial functional redundancy, which ensures metabolic continuity despite taxonomic shifts. We recommend that forest management strategies account for these seasonal dynamics and focus on preserving the conditions that support this critical functional redundancy.
Asadullah Shaikh, Wahidur Rahman, Kaniz Roksana
et al.
Bangladesh has plentiful water, which is essential to its freshwater fish traditions. Environmental concerns and other causes have reduced the country's water resources, threatening many native freshwater fish species. Thus, the younger generation deficiencies recognition of local freshwater fish and struggles to recognize them. Traditional methods are very insufficient to overcome these issues. To address these research gaps, the research proposes an automatic system for categorizing Bangladesh's freshwater fish. The proposed methodology involves several key steps, including building a comprehensive dataset, extracting features from fish images using pre-trained Convolutional Neural Network (CNN) models, and employing typical ML approaches. Initially comprising eight classes, the dataset undergoes feature extraction using CNN algorithms, followed by the utilization of various feature selection methods such as Support Vector Classifier, Principal Component Analysis, Linear Discriminant Analysis, and optimization models like Particle Swarm Optimization, Bacterial Foraging Optimization, and Cat Swarm Optimization. In the final phase, seven conventional ML techniques are applied to classify the images of local fishes. Empirical measurements are gathered and analyzed to assess the proposed framework's performance. Particularly, the present approach achieves the highest accuracy of 98.71% through the utilization of the ML mechanism Logistic Regression with Resnet50, SVC, and CSO models.
Control engineering systems. Automatic machinery (General), Automation
BackgroundImmersive virtual reality (VR) and artificial intelligence have been used to determine whether a simulated clinical exam setting can reduce anxiety in first-year occupational therapy students preparing for objective structured clinical examinations (OSCEs). Test anxiety is common among postsecondary students, leading to negative outcomes such as increased dropout risk, lower grades, and limited employment opportunities. Students unfamiliar with specific testing environments are particularly prone to anxiety. VR simulations of OSCEs may allow students to become familiar with the exam setting and reduce anxiety.
ObjectiveThis study aimed to assess the efficacy of a VR simulation depicting clinical settings to reduce student anxiety about a clinical exam while gathering perspectives on their first-year coursework experiences to better understand their learning environment.
MethodsAn experimental, nonrandomized controlled trial compared state anxiety, trait test anxiety, and OSCE grades in 2 groups of first-year occupational therapy students analyzed using independent t tests (2-tailed). Group 1 (NoVR) was not exposed to the VR simulation and acted as a control group for group 2 (YesVR), who were exposed to the VR simulation. The VR used artificial intelligence in the form of a generative pretrained transformer to generate responses from virtual patients as students interacted with them in natural language. Self-reported psychometric scales measured anxiety levels 3 days before the OSCE. YesVR students completed perceived preparation surveys at 2 time points—3 weeks and 3 days before the OSCE—analyzed using dependent t tests. Semistructured interviews and focus groups were conducted within 1 week after the OSCE. Student perspectives on their classes and VR experiences were summarized using interpretative thematic analysis.
ResultsIn total, 60 students—32 (53%) in the NoVR group and 28 (47%) in the YesVR group—participated in the study, and the YesVR group showed a significant reduction in state anxiety (t58=3.96; P<.001; Cohen d=1.02). The mean difference was 11.96 units (95% CI 5.92-18.01). Trait test anxiety and OSCE scores remained static between groups. There was an increase in all perceived preparedness variables in the YesVR group. In total, 42% (25/60) of the participants took part in interviews and focus groups, providing major themes regarding factors that affect OSCE performance, including student experience and background, feedback and support, fear of unknown, self-consciousness, and knowledge of the exam environment.
ConclusionsIntolerance of uncertainty may lead students to interpret ambiguous exam situations as overly precarious. Findings suggest that VR simulation was associated with reduced state anxiety, although results from this small, nonrandomized sample should be interpreted cautiously. Qualitative data indicated that VR helped students gain familiarity with clinical exam settings, potentially decreasing uncertainty-based anxiety. Future research with larger or randomized samples is needed to confirm these findings and explore advanced VR tools offering feedback to enhance learning.
Information technology, Public aspects of medicine
Abstract The quest to develop energy-efficient and fast optoelectronic control of memory devices is essential. In this respect, ferroelectric materials are gaining tremendous importance in information and communication technology. Here, we demonstrate light-controlled polarisation switching on a subsecond timescale ( <500 ms) in a freestanding BaTiO3 membrane, which is nearly 1200 times faster than the previously reported response using a BaTiO3 thin film. We reveal the potential of optically controlled computing by demonstrating the associated resistance change in the membrane as a result of the polarisation reversal induced by illumination. By combining theoretical and experimental studies, we demonstrate that the imprint effect coupled with the reduced energy barrier of domain wall motion influences the optically controlled domain switching response in the membrane. It is established that the fast domain switching response in the freestanding film compared to the clamped film is attributed to the removal of substrate-induced strain and the subsequent increase in domain wall velocity. Additionally, ferroelectric fatigue behaviour is not observed in our system even after 75 electrical and optical cycles, demonstrating the robustness of the observed phenomenon. Our work provides a step forward towards wireless sensing and dual optical and electronic control for computing.
Abstract Global Navigation Satellite Systems (GNSS) is able to achieve centimeter-level accuracy in open-sky areas. However, their performance declines in urban canyons and outdoor shadow areas. Conversely, commercial Fifth Generation Mobile Communications Technology (5G) New Radio (NR) signals, with their wider bandwidth and shorter wavelengths, offer better range accuracy. To enhance positioning accuracy in challenging environments, we developed a deeply integrated method to combine commercial 5G NR signals with the GNSS. This method involves three key steps: Firstly, we use the Secondary Synchronization Signal to aid the Demodulation Reference Signal (SA-DMRS) in the 5G NR synchronization channel, which aims to improve the tracking loop robustness. Secondly, a Phase-Stabilized Kalman Filter (PSKF) is integrated into the Phase-Locked Loop to boost performance under low Carrier-to-Noise Density Ratio conditions. Lastly, the Extended Kalman Filter (EKF) is applied to fuse 5G and GNSS signals for positioning, and the results are fed back to correct the 5G NR tracking loop. Field tests revealed that SA-DMRS boosted range accuracy by 42.3%, PSKF contributed a further 17% improvement, and GNSS-aided improved the range accuracy by about 33.3%. Compared to the GPS (Global Positioning System)-EKF method, our fusion approach enhances horizontal positioning accuracy by approximately 49.8%, and the vertical positioning accuracy is improved by about 53.3%. Additionally, compared to the GPS-only method, the proposed method can still provide positioning services when there are three usable satellites. Compared with the GNSS-only method, the deep coupled method improved the accuracy in the horizontal and vertical by about 51.2% and 24.0%, respectively. These confirm the method’s effectiveness for accurate and reliable positioning in challenging environments.
Russia’s invasion of Ukraine has profoundly altered the global geopolitical landscape. Owing to its geographical proximity, the conflict has had a considerable impact on Europe. Marked by the professionalisation and democratisation of technology, it has underscored the growing significance of hybrid warfare, in which disinformation and propaganda serve as additional instruments of war. Within this context, the aim of this article is to examine the characteristics of false information related to the war between Russia and Ukraine in four European countries between 2022 and 2023. To this end, a content analysis of 297 hoaxes was conducted across eight fact-checking platforms, complemented by ten in-depth interviews with specialised professionals. The findings indicate that disinformation is characterised by viral audiovisual hoaxes, particularly on Facebook and X (formerly Twitter), with a notable surge in disinformation flows at the onset of the invasion. In the early months, misleading content predominantly consisted of decontextualised images of the conflict, whereas a year later, the focus shifted to narratives concerning international support and alliances. The primary objective of this disinformation is to polarise public opinion against a perceived common enemy. The conclusions provide a broader and more nuanced understanding of wartime disinformation within the European context.
Journalism. The periodical press, etc., Communication. Mass media
This paper presents a hopeful perspective on the potentially dramatic impacts of Large Language Models on how we children learn and how they will expect to interact with technology. We review the effects of LLMs on education so far, and make the case that these effects are minor compared to the upcoming changes that are occurring. We present a small scenario and self-ethnographic study demonstrating the effects of these changes, and define five significant considerations that interactive systems designers will have to accommodate in the future.
Simon Lupart, Daniël van Dijk, Eric Langezaal
et al.
Personalized Conversational Information Retrieval (CIR) has seen rapid progress in recent years, driven by the development of Large Language Models (LLMs). Personalized CIR aims to enhance document retrieval by leveraging user-specific information, such as preferences, knowledge, or constraints, to tailor responses to individual needs. A key resource for this task is the TREC iKAT 2023 dataset, designed to evaluate personalization in CIR pipelines. Building on this resource, Mo et al. explored several strategies for incorporating Personal Textual Knowledge Bases (PTKB) into LLM-based query reformulation. Their findings suggested that personalization from PTKBs could be detrimental and that human annotations were often noisy. However, these conclusions were based on single-run experiments using the GPT-3.5 Turbo model, raising concerns about output variability and repeatability. In this reproducibility study, we rigorously reproduce and extend their work, focusing on LLM output variability and model generalization. We apply the original methods to the new TREC iKAT 2024 dataset and evaluate a diverse range of models, including Llama (1B-70B), Qwen-7B, GPT-4o-mini. Our results show that human-selected PTKBs consistently enhance retrieval performance, while LLM-based selection methods do not reliably outperform manual choices. We further compare variance across datasets and observe higher variability on iKAT than on CAsT, highlighting the challenges of evaluating personalized CIR. Notably, recall-oriented metrics exhibit lower variance than precision-oriented ones, a critical insight for first-stage retrievers. Finally, we underscore the need for multi-run evaluations and variance reporting when assessing LLM-based CIR systems. By broadening evaluation across models, datasets, and metrics, our study contributes to more robust and generalizable practices for personalized CIR.
With the continuous penetration of Internet applications in our lives, the ever-increasing data on clicking behavior has made online services a critical component of the economic sectors of internet companies over the past decade. This development trend has brought a large amount of information that reflects user needs but is relatively chaotic. Extracting user interests and needs from complex click behaviors is crucial for advancing online business development and precisely targeting product information The interactive attention-based capsules (IACaps) network is proposed in this paper to collate and analyze complex and changing click information for user behavior representation. Specifically, an interactive attention dynamic routing mechanism is proposed to mine the potential association information among different browsing behaviors, which facilitates the extraction and understanding of seemingly irrelevant information hidden in massive click data. To ensure the practicability of the proposed method, three different types of datasets were selected from Amazon Dataset for experiments, and the results of which shows the superior performance of the proposed method when compared with other models. Specifically, the reasonableness and effectiveness of the reported model are further proved by improvements of metrics obtained in the main experiments and ablation studies. Optimization of Hyper-parameters is also analyzed from the number of iterations, the number of capsules, and the dimension of capsules for better understanding of operating principles.
We optimized and fabricated an ultra-bend-resistant 4-core simplex cable (SXC) employing 4-core multicore fiber (MCF) suitable for short-reach dense spatial division multiplexing (DSDM) optical transmission in the O-band. The characteristics of transmission loss, macro-bending and cross-talk (XT) between adjacent cores after cabling were firstly clarified. By introducing the trapezoid index and optimizing the cabling process, the maximum values of added XT of 1.17 dB/km due to 10 loops with a bending radius of 6 mm imposed over the 4-core SXC and a macro-bending loss of 0.37 dB/10 turns were, respectively, achieved.P Then, the optical transmission with low bit error rate (BER) was presented using a 100GBASE-LR4 transceiver over the 1.2 km long 4-core SXC. The excellent bending resistance of the 4-core SXC may pave the way for a reduction in space pressure and increase in access density on short-reach optical interconnect (OI) based on DSDM.
Electromagnetic information theory (EIT) is an interdisciplinary subject that serves to integrate deterministic electromagnetic theory with stochastic Shannon's information theory. Existing EIT analysis operates in the continuous space domain, which is not aligned with the practical algorithms working in the discrete space domain. This mismatch leads to a significant difficulty in application of EIT methodologies to practical discrete space systems, which is called as the discrete-continuous gap in this paper. To bridge this gap, we establish the discrete-continuous correspondence with a prolate spheroidal wave function (PSWF)-based ergodic capacity analysis framework. Specifically, we state and prove some discrete-continuous correspondence lemmas to establish a firm theoretical connection between discrete information-theoretic quantities to their continuous counterparts. With these lemmas, we apply the PSWF ergodic capacity bound to advanced MIMO architectures such as continuous-aperture MIMO (CAP-MIMO) and extremely large-scale MIMO (XL-MIMO). From this PSWF capacity bound, we discover the capacity saturation phenomenon both theoretically and empirically. Although the growth of MIMO performance is fundamentally limited in this EIT-based analysis framework, we reveal new opportunities in MIMO channel estimation by exploiting the EIT knowledge about the channel. Inspired by the PSWF capacity bound, we utilize continuous PSWFs to improve the pilot design of discrete MIMO channel estimators, which is called as the PSWF channel estimator (PSWF-CE). Simulation results demonstrate improved performances of the proposed PSWF-CE, compared to traditional minimum mean squared error (MMSE) and compressed sensing-based estimators.
This paper studies an integrated sensing and communication (ISAC) system where a multi-antenna base station (BS) aims to communicate with a single-antenna user in the downlink and sense the unknown and random angle parameter of a target via exploiting its prior distribution information. We consider a general transmit beamforming structure where the BS sends one communication beam and potentially one or multiple dedicated sensing beam(s). Firstly, motivated by the periodic feature of the angle parameter, we derive the periodic posterior Cramér-Rao bound (PCRB) for quantifying a lower bound of the mean-cyclic error (MCE), which is more accurate than the conventional PCRB for bounding the mean-squared error (MSE). Then, note that more sensing beams enable higher flexibility in enhancing the sensing performance, while also generating extra interference to the communication user. To resolve this trade-off, we formulate the transmit beamforming optimization problem to minimize the periodic PCRB subject to a communication rate requirement for the user. Despite the non-convexity of this problem, we derive the optimal solution by leveraging the semi-definite relaxation (SDR) technique and Lagrange duality theory. Moreover, we analytically prove that at most one dedicated sensing beam is needed. Numerical results validate our analysis and the advantage of having a dedicated sensing beam.
User simulation is a promising approach for automatically training and evaluating conversational information access agents, enabling the generation of synthetic dialogues and facilitating reproducible experiments at scale. However, the objectives of user simulation for the different uses remain loosely defined, hindering the development of effective simulators. In this work, we formally characterize the distinct objectives for user simulators: training aims to maximize behavioral similarity to real users, while evaluation focuses on the accurate prediction of real-world conversational agent performance. Through an empirical study, we demonstrate that optimizing for one objective does not necessarily lead to improved performance on the other. This finding underscores the need for tailored design considerations depending on the intended use of the simulator. By establishing clear objectives and proposing concrete measures to evaluate user simulators against those objectives, we pave the way for the development of simulators that are specifically tailored to their intended use, ultimately leading to more effective conversational agents.
The GRAPES (Global/Regional Assimilation and Prediction System) global medium-range forecast system (GRAPES_GFS) is a new generation numerical weather forecast model developed by the China Meteorological Administration (CMA). However, the forecasts of surface latent heat fluxes and surface air temperature have systematic biases, which affect the forecasts of atmospheric dynamics by modifying the lower boundary conditions and degrading the application of GRAPES_GFS since the 2 m air temperature is one of the key components of weather forecast products. Here, we add a soil resistance term to reduce soil evaporation, which ultimately reduces the positive forecast bias of the land surface latent heat flux. We also reduce the positive forecast bias of the ocean surface latent heat flux by considering the effect of salinity in the calculation of the ocean surface vapor pressure and by adjusting the parameterizations of roughness length for the exchanges in momentum, heat, and moisture between the ocean surface and atmosphere. Moreover, we modify the parameterization of the roughness length for the exchanges in heat and moisture between the land surface and atmosphere to reduce the cold bias of the nighttime 2 m air temperature forecast over areas with lower vegetation height. We also consider the supercooled soil water to reduce the warm forecast bias of the 2 m air temperature over northern China during winter. These modified parameterizations are incorporated into the GRAPES_GFS and show good performance based on a set of evaluation experiments. This paper highlights the importance of the representations of the land/ocean surface and boundary layer processes in the forecasting of surface heat fluxes and 2 m air temperature.
Monitoring the morphology of blood leukocytes, plays an important role in medical research, especially in the treatment of diseases such as immunodeficiency. Traditional manual detection methods are susceptible to numerous interference factors. Therefore, blood cells are often segmented using deep-learning algorithms. This study proposes a U-Net model based on a combination of an attention mechanism and dilated convolutions. First, the traditional convolution in a double convolutional module in a network is replaced by dilated convolution, and multi-scale features are obtained by expanding the receptive field. Second, after each convolution layer in the upsampling layer, an attention mechanism module is combined to refine the adaptive features and improve the segmentation performance of the model. Finally, the RAdam optimizer was used to enhance the robustness of the learning rate variations. Through the ablation experiment of the three improvement directions, it was concluded that all three improvement directions had a positive effect on the segmentation result, and the improvement was the most effective when the three improvements were combined. The experimental results show that compared with the original U-Net model, the segmentation indicators of blood leukocytes, intersection over union (IOU), recall and accuracy were increased by 5.1%, 5.7% and 1.2%, respectively, which more accurately segmented blood leukocytes, which may be used for a greater degree of auxiliary leukocyte detection in the application of immunodeficiency and other diseases.
Despite its importance, relatively little attention has been devoted to studying the effects of exposing individuals to digital choice interfaces. In two pre-registered lottery-choice experiments, we administer three information-search technologies that are based on well-known heuristics: in the ABS (alternative-based search) treatment, subjects explore outcomes and corresponding probabilities within lotteries; in the CBS (characteristic-based search) treatment, subjects explore outcomes and corresponding probabilities across lotteries; in the Baseline treatment, subjects view outcomes and corresponding probabilities all at once. We find that (i) when lottery outcomes comprise gains and losses (experiment 1), exposing subjects to the CBS technology systematically makes them choose safer lotteries, compared to the subjects that are exposed to the other technologies, and (ii) when lottery outcomes comprise gains only (experiment 2), the above results are reversed: exposing subjects to the CBS technology systematically makes them choose riskier lotteries. By combining the information-search and choice analysis, we offer an interpretation of our results that is based on prospect theory, whereby the information-search technology subjects are exposed to contributes to determine the level of attention that the lottery attributes receive, which in turn has an effect on the reference point.
Jan C. Brammer, Gerd Blanke, Claudia Kellner
et al.
Abstract TUCAN is a canonical serialization format that is independent of domain-specific concepts of structure and bonding. The atomic number is the only chemical feature that is used to derive the TUCAN format. Other than that, the format is solely based on the molecular topology. Validation is reported on a manually curated test set of molecules as well as a library of non-chemical graphs. The serialization procedure generates a canonical “tuple-style” output which is bidirectional, allowing the TUCAN string to serve as both identifier and descriptor. Use of the Python NetworkX graph library facilitated a compact and easily extensible implementation. Graphical Abstract