Search Results (5,646)

What does it mean for a complex system to “compute” or perform “computations”? Intuitively, we can understand complex “computation” as occurring when a system’s state is a function of multiple inputs (potentially including its own past state). Here, we discuss how computational processes in complex systems can be generally studied using the concept of statistical synergy, which is information about an output that can onlybe learned when the joint state of all inputs is known. Building on prior work, we show that this approach naturally leads to a link between multivariate information theory and topics in causal inference, specifically, the phenomenon of causal colliders. We begin by showing how Berkson’s paradox implies a higher-order, synergistic interaction between multidimensional inputs and outputs. We then discuss how causal structure learning can refine and orient analyses of synergies in empirical data, and when empirical synergies meaningfully reflect computation versus when they may be spurious. We end by proposing that this conceptual link between synergy, causal colliders, and computation can serve as a foundation on which to build a mathematically rich general theory of computation in complex systems. Full article

The paper presents research related to coastal observation using a camera and LiDAR (Light Detection and Ranging) mounted on an unmanned surface vehicle (USV). Fusion of data from these two sensors can provide wider and more accurate information about shore features, utilizing the synergy effect and combining the advantages of both systems. Fusion is used in autonomous cars and robots, despite many challenges related to spatiotemporal alignment or sensor calibration. Measurements from various sensors with different timestamps have to be aligned, and the measurement systems need to be calibrated to avoid errors related to offsets. When using data from unstable, moving platforms, such as surface vehicles, it is more difficult to match sensors in time and space, and thus, data acquired from different devices will be subject to some misalignment. In this article, we try to overcome these problems by proposing the use of a point matching algorithm for coordinate transformation for data from both systems. The essence of the paper is to verify algorithms based on selected basic neural networks, namely the multilayer perceptron (MLP), the radial basis function network (RBF), and the general regression neural network (GRNN) for the alignment process. They are tested with real recorded data from the USV and verified against numerical methods commonly used for coordinate transformation. The results show that the proposed approach can be an effective solution as an alternative to numerical calculations, due to process improvement. The image data can provide information for identifying characteristic objects, and the obtained accuracies for platform dynamics in the water environment are satisfactory (root mean square error—RMSE—smaller than 1 m in many cases). The networks provided outstanding results for the training set; however, they did not perform as well as expected, in terms of the generalization capability of the model. This leads to the conclusion that processing algorithms cannot overcome the limitations of matching point accuracy. Further research will extend the approach to include information on the position and direction of the vessel. Full article

Electrochemotherapy (ECT) is an emerging therapeutic approach gaining growing interest for its potential immunomodulatory effects in cancer treatment. This narrative review systematically examines the current state of knowledge regarding the interplay between ECT and the immune system. Through an analysis of preclinical and clinical studies, the review highlights ECT capacity to induce immunogenic cell death, activate dendritic cells, release tumor antigens, trigger inflammatory responses, and occasionally manifest systemic effects—the abscopal phenomenon. These mechanisms collectively suggest the ECT potential to influence both local tumor control and immune responses. While implications for clinical practice appear promising, warranting the consideration of ECT as a complementary treatment to immunotherapy, the evidence remains preliminary. Consequently, further research is needed to elucidate the underlying mechanisms, optimize treatment protocols, explore potential synergies, and decipher the parameters influencing the abscopal effect. As the field advances, the integration of ECT's potential immunomodulatory aspects into clinical practice will need careful evaluation and collaboration among clinical practitioners, researchers, and policymakers. Full article

The purpose of this study was to analyze the lower-limb muscle synergies of young and older adults during stationary cycling across various mechanical conditions to reveal adaptive strategies employed by the elderly to address various common pedaling tasks and function degradation. By comparing lower-limb muscle synergies during stationary cycling between young and old people, this study examined changes in muscle synergy patterns during exercise in older individuals. This is crucial for understanding neuromuscular degeneration and changes in movement patterns in older individuals. Sixteen young and sixteen older experienced cyclists were recruited to perform stationary cycling tasks at two levels of power (60 and 100 W) and three cadences (40, 60, and 90 rpm) in random order. The lower-limb muscle synergies and their inter- and intra-individual variability were analyzed. Three synergies were extracted in this study under all riding conditions in both groups while satisfying overall variance accounted for (VAF) > 85% and muscle VAF > 75%. The older adults exhibited lower variability in synergy vector two and a higher trend in the variability of activation coefficient three, as determined by calculating the variance ratio. Further analyses of muscle synergy structures revealed increased weighting in major contribution muscles, the forward-shifting peak activation in synergy one, and lower peak magnitude in synergy three among older adults. To produce the same cycling power and cadence as younger individuals, older adults make adaptive adjustments in muscle control—increased weighting in major contribution muscles, greater consistency in the use of primary force-producing synergies, and earlier peak activation of subsequent synergy. Full article

Background/Objectives: Carbapenem resistance poses a significant threat to public health by undermining the efficacy of one of the last lines of antibiotic defense. Addressing this challenge requires innovative approaches that can enhance our understanding and ability to combat resistant pathogens. This review aims to explore the integration of machine learning (ML) and epidemiological approaches to understand, predict, and combat carbapenem-resistant pathogens. It examines how leveraging large datasets and advanced computational techniques can identify patterns, predict outbreaks, and inform targeted intervention strategies. Methods: The review synthesizes current knowledge on the mechanisms of carbapenem resistance, highlights the strengths and limitations of traditional epidemiological methods, and evaluates the transformative potential of ML. Real-world applications and case studies are used to demonstrate the practical benefits of combining ML and epidemiology. Technical and ethical challenges, such as data quality, model interpretability, and biases, are also addressed, with recommendations provided for overcoming these obstacles. Results: By integrating ML with epidemiological analysis, significant improvements can be made in predictive accuracy, identifying novel patterns in disease transmission, and designing effective public health interventions. Case studies illustrate the benefits of interdisciplinary collaboration in tackling carbapenem resistance, though challenges such as model interpretability and data biases must be managed. Conclusions: The combination of ML and epidemiology holds great promise for enhancing our capacity to predict and prevent carbapenem-resistant infections. Future research should focus on overcoming technical and ethical challenges to fully realize the potential of these approaches. Interdisciplinary collaboration is key to developing sustainable strategies to combat antimicrobial resistance (AMR), ultimately improving patient outcomes and safeguarding public health. Full article

The monitoring of coastal evolution (coastline and associated geomorphological features) caused by episodic and persistent processes associated with climatic and anthropic activities is required for coastal management decisions. The availability of open access, remotely sensed data with increasing spatial, temporal, and spectral resolutions, is promising in this context. The coastline of Northern Tunisia is currently showing geomorphic process, such as increasing erosion associated with lateral sedimentation. This study aims to investigate the potential of time-series optical data, namely Landsat (from 1985–2019) and Google Earth^® satellite imagery (from 2007 to 2023), to analyze shoreline changes and morphosedimentary and geomorphological processes between Cape Serrat and Kef Abbed, Northern Tunisia. The Digital Shoreline Analysis System (DSAS) was used to quantify the multitemporal rates of shoreline using two metrics: the net shoreline movement (NSM) and the end-point rate (EPR). Erosion was observed around the tombolo and near river mouths, exacerbated by the presence of surrounding dams, where the NSM is up to −8.31 m/year. Despite a total NSM of −15 m, seasonal dynamics revealed a maximum erosion in winter (71% negative NSM) and accretion in spring (57% positive NSM). The effects of currents, winds, and dams on dune dynamics were studied using historical images of Google Earth^®. In the period from 1994 to 2023, the area is marked by dune face retreat and removal in more than 40% of the site, showing the increasing erosion. At finer spatial resolution and according to the synergy of field observations and photointerpretation, four key geomorphic processes shaping the coastline were identified: wave/tide action, wind transport, pedogenesis, and deposition. Given the frequent changes in coastal areas, this method facilitates the maintenance and updating of coastline databases, which are essential for analyzing the impacts of the sea level rise in the southern Mediterranean region. Furthermore, the developed approach could be implemented with a range of forecast scenarios to simulate the impacts of a higher future sea-level enhanced climate change. Full article

With the vigorous promotion in China of efforts to reduce pollution and carbon emissions, examining their synergies becomes increasingly crucial. This study used the multi-regional input–output (MRIO) table to build the consumption-based industrial emissions inventories of CO₂ and three major air pollutants (PM_2.5, NO_x, and SO₂) and constructed synergistic emission indices of the intensity and magnitude to identify and evaluate the synergy between carbon emissions and air pollutants in inter-industrial trade among 30 provinces in mainland China. The results show that more than 85% and 40% of inter-provincial and inter-industrial trades have synergistic emissions between CO₂ and air pollutants, respectively. We identified 77 inter-provincial trades and 84 inter-industrial trades among provinces with strong synergistic emissions. They are mainly reflected in the demand of the construction industry in Zhejiang and Guangdong for the nonmetal mineral products manufacturing industry in Henan, and the metal smelting and processing industry in Hebei, along with the demand of the service industry in Beijing for the electric power, steam, and hot water production and supply industry in Inner Mongolia. Our study provides new insights into the synergistic reduction of CO₂ and air pollutants within the supply chain, thereby enriching the discourse on regional and industrial synergies in achieving sustainable development goals. Full article

We introduce a novel methodological framework for robust trend analysis (RTA) using remote sensing data to enhance the accuracy and reliability of detecting significant environmental trends. Our approach sequentially integrates the Theil–Sen (TS) slope estimator, the Contextual Mann–Kendall (CMK) test, and the false discovery rate (FDR) control. This comprehensive method addresses common challenges in trend analysis, such as handling small, noisy datasets with outliers and issues related to spatial autocorrelation, cross-correlation, and multiple testing. We applied this RTA workflow to study tree cover trends in Los Alcornocales Natural Park (Southern Spain), Europe’s largest cork oak forest, analysing interannual changes in tree cover from 2000 to 2022 using Terra MODIS MOD44B data. Our results reveal that the TS estimator provides a robust measure of trend direction and magnitude, but its effectiveness is dramatically enhanced when combined with the CMK test. This combination highlights significant trends and effectively corrects for spatial autocorrelation and cross-correlation, ensuring that genuine environmental signals are distinguished from statistical noise. Unlike previous workflows, our approach incorporates the FDR control, which successfully filtered out 29.6% of false discoveries in the case study, resulting in a more stringent assessment of true environmental trends captured by multi-temporal remotely sensed data. In the case study, we found that approximately one-third of the area exhibits significant and statistically robust declines in tree cover, with these declines being geographically clustered. Importantly, these trends correspond with relevant changes in tree cover, emphasising the ability of RTA to detect relevant environmental changes. Overall, our findings underscore the crucial importance of combining these methods, as their synergy is essential for accurately identifying and confirming robust environmental trends. The proposed RTA framework has significant implications for environmental monitoring, modelling, and management. Full article

The purpose of the present study was to investigate the mechanism of action of our newly developed green sanitizer formulation comprising a natural phenolic compound, gallic acid (GA), strengthened by the Generally Recognized as Safe (GRAS) materials hydrogen peroxide (H₂O₂) and DL-lactic acid (LA). Combining 8 mM GA with 1 mM H₂O₂ resulted in an abundant generation of reactive oxygen species (ROS) and a bactericidal effect towards Gram-negative (Escherichia coli, Pseudomonas syringae, and Pectobacterium brasiliense) and Gram-positive (Bacillus subtilis) bacteria (4 to 8 log CFU mL⁻¹ reduction). However, the exposure to this dual formulation (DF) caused only a modest 0.7 log CFU mL⁻¹ reduction in the Gram-positive L. innocua population. Amending the DF with 20 mM LA to yield a triple formulation (TF) resulted in the efficient synergistic control of L. innocua proliferation without increasing ROS production. Despite the inability to grow on plates (>7 log CFU mL⁻¹ population reduction), the TF-exposed L. innocua maintained high intracellular ATP pools and stable membrane integrity. The response of L. innocua to TF could be qualified as a “viable but nonculturable” (VBNC) phenomenon, while with the other species tested this formulation caused cell death. This research system may offer a platform for exploring the VBNC phenomenon, a critical food safety topic. Full article

Rare-earth-metal-based materials have emerged as frontrunners in the quest for high-performance hydrogen storage solutions, offering a paradigm shift in clean energy technologies. This comprehensive review delves into the cutting-edge advancements, challenges, and future prospects of these materials, providing a roadmap for their development and implementation. By elucidating the fundamental principles, synthesis methods, characterization techniques, and performance enhancement strategies, we unveil the immense potential of rare-earth metals in revolutionizing hydrogen storage. The unique electronic structure and hydrogen affinity of these elements enable diverse storage mechanisms, including chemisorption, physisorption, and hydride formation. Through rational design, nanostructuring, surface modification, and catalytic doping, the hydrogen storage capacity, kinetics, and thermodynamics of rare-earth-metal-based materials can be significantly enhanced. However, challenges such as cost, scalability, and long-term stability need to be addressed for their widespread adoption. This review not only presents a critical analysis of the state-of-the-art but also highlights the opportunities for multidisciplinary research and innovation. By harnessing the synergies between materials science, nanotechnology, and computational modeling, rare-earth-metal-based hydrogen storage materials are poised to accelerate the transition towards a sustainable hydrogen economy, ushering in a new era of clean energy solutions. Full article

Grasping how scale influences the interactions among ecosystem services (ESs) is vital for the sustainable management of multiple ESs at the regional level. However, it is currently unclear whether the actual ES interactions and their driving mechanisms are consistent across different spatial and temporal scales. Therefore, using the Lishui River Basin of China as a case study, we analyzed the spatial and temporal distribution of five key ESs across three scales (grid, sub-watershed, and county) from 2010 to 2020. We also innovatively used Pearson correlation analysis, Self-organizing Mapping (SOM), and random forest analysis to assess the dynamic trends of trade-offs/synergies among ESs, ecosystem service bundles (ESBs), and their main socio-ecological drivers across different spatiotemporal scales. The findings showed that (1) the spatial distribution of ESs varied with land use types, with high-value areas mainly in the western and northern mountainous regions and lower values in the eastern part. Temporally, significant improvements were observed in soil conservation (SC, 3028.23–5023.75 t/hm²) and water yield (WY, 558.79–969.56 mm), while carbon sequestration (CS) and habitat quality (HQ) declined from 2010 to 2020. (2) The trade-offs and synergies among ESs exhibited enhanced at larger scales, with synergies being the predominant relationship. These relationships remained relatively stable over time, with trade-offs mainly observed in ES pairs related to nitrogen export (NE). (3) ESBs and their socio-ecological drivers varied with scales. At the grid scale, frequent ESB flows and transformations were observed, with land use/land cover (LULC) being the main drivers. At other scales, climate (especially temperature) and topography were dominant. Ecosystem management focused on city bundles or downstream city bundles in the east of the basin, aligning with urban expansion trends. These insights will offer valuable guidance for decision-making regarding hierarchical management strategies and resource allocation for regional ESs. Full article

Background: Chronic Ankle Instability (CAI) is a common condition characterized by repeated episodes of ankle “giving way” and impaired balance, leading to functional limitations. Various rehabilitation techniques, including balance training, proprioceptive exercises, whole-body vibration (WBV), and novel approaches like stroboscopic vision, are used to address these deficits. This review evaluates the effectiveness of different rehabilitation interventions for CAI management. Methods: A review was conducted by analyzing 11 randomized controlled trials that investigated the impact of balance and proprioceptive training programs on CAI. The primary outcomes assessed were the Star Excursion Balance Test (SEBT), Cumberland Ankle Instability Tool (CAIT), and Foot and Ankle Ability Measure (FAAM). Methodological quality was assessed using the PEDro scale, and the risk of bias was evaluated with the ROB 2 tool. Results: All rehabilitation interventions demonstrated significant improvements in SEBT, CAIT, and FAAM scores. However, no single intervention was found to be consistently superior. Traditional balance training, strength exercises, BAPS, and WBV all provided meaningful functional gains. Stroboscopic vision training showed similar effectiveness compared to conventional approaches. The evidence supports a combination of balance and strength training for optimal recovery. Conclusions: Balance and proprioceptive exercises are effective in managing CAI, with improvements in both dynamic stability and subjective outcomes. No intervention stands out as the best, but personalized programs incorporating various methods are recommended. Future research should explore the long-term effects and potential synergies of combined interventions. Full article

Wildfires are a critical driver of landscape transformation on Earth, representing a dynamic and ephemeral process that poses challenges for accurate early detection. To address this challenge, researchers have increasingly turned to deep learning techniques, which have demonstrated remarkable potential in enhancing the performance of wildfire detection systems. This paper provides a comprehensive review of fire detection using deep learning, spanning from 1990 to 2023. This study employed a comprehensive approach, combining bibliometric analysis, qualitative and quantitative methods, and systematic review techniques to examine the advancements in fire detection using deep learning in remote sensing. It unveils key trends in publication patterns, author collaborations, and thematic focuses, emphasizing the remarkable growth in fire detection using deep learning in remote sensing (FDDL) research, especially from the 2010s onward, fueled by advancements in computational power and remote sensing technologies. The review identifies “Remote Sensing” as the primary platform for FDDL research dissemination and highlights the field’s collaborative nature, with an average of 5.02 authors per paper. The co-occurrence network analysis reveals diverse research themes, spanning technical approaches and practical applications, with significant contributions from China, the United States, South Korea, Brazil, and Australia. Highly cited papers are explored, revealing their substantial influence on the field’s research focus. The analysis underscores the practical implications of integrating high-quality input data and advanced deep-learning techniques with remote sensing for effective fire detection. It provides actionable recommendations for future research, emphasizing interdisciplinary and international collaboration to propel FDDL technologies and applications. The study’s conclusions highlight the growing significance of FDDL technologies and the necessity for ongoing advancements in computational and remote sensing methodologies. The practical takeaway is clear: future research should prioritize enhancing the synergy between deep learning techniques and remote sensing technologies to develop more efficient and accurate fire detection systems, ultimately fostering groundbreaking innovations. Full article

Accurate prediction of electricity consumption is crucial for energy management and allocation. This study introduces a novel approach, named Attention-based Multivariate Temporal Convolutional Network (AMTCN), for electricity consumption forecasting by integrating attention mechanisms with multivariate temporal convolutional networks. The method involves feature extraction from diverse time series of different feature variables using dilated convolutional networks. Subsequently, attention mechanisms are employed to capture the correlation and contextually important information among various features, thereby enhancing the model’s predictive accuracy. The AMTCN method exhibits universality, making it applicable to various prediction tasks in different scenarios. Experimental evaluations are conducted on four distinct datasets, encompassing electricity consumption and weather temperature aspects. Comparative experiments with LSTM, ConvLSTM, GRU, and TCN—widely-used deep learning methods—demonstrate that our AMTCN model achieves significant improvements of 57% in MSE, 37% in MAE, 35% in RRSE, and 12% in CORR metrics, respectively. This research contributes a promising approach to accurate electricity consumption prediction, leveraging the synergy of attention mechanisms and multivariate temporal convolutional networks, with broad applicability in diverse forecasting scenarios. Full article

In smart ranch management, cattle behavior recognition and tracking play a crucial role in evaluating animal welfare. To address the issues of missed and false detections caused by inter-cow occlusions and infrastructure obstructions in the barn environment, this paper proposes a multi-object tracking method called YOLO-BoT. Built upon YOLOv8, the method first integrates dynamic convolution (DyConv) to enable adaptive weight adjustments, enhancing detection accuracy in complex environments. The C2f-iRMB structure is then employed to improve feature extraction efficiency, ensuring the capture of essential features even under occlusions or lighting variations. Additionally, the Adown downsampling module is incorporated to strengthen multi-scale information fusion, and a dynamic head (DyHead) is used to improve the robustness of detection boxes, ensuring precise identification of rapidly changing target positions. To further enhance tracking performance, DIoU distance calculation, confidence-based bounding box reclassification, and a virtual trajectory update mechanism are introduced, ensuring accurate matching under occlusion and minimizing identity switches. Experimental results demonstrate that YOLO-BoT achieves a mean average precision (mAP) of 91.7% in cattle detection, with precision and recall increased by 4.4% and 1%, respectively. Moreover, the proposed method improves higher order tracking accuracy (HOTA), multi-object tracking accuracy (MOTA), multi-object tracking precision (MOTP), and IDF1 by 4.4%, 7%, 1.7%, and 4.3%, respectively, while reducing the identity switch rate (IDS) by 30.9%. The tracker operates in real-time at an average speed of 31.2 fps, significantly enhancing multi-object tracking performance in complex scenarios and providing strong support for long-term behavior analysis and contactless automated monitoring. Full article