The base64 format is ubiquitous: it is used in emails and Web documents to encode binary data as text. By convention, base64 text often includes ignorable characters such as spaces. We introduce the first base64-decoding algorithms with support for ignorable characters designed for single-instruction-multiple-data (SIMD) instructions. Our novel functions multiply the base64 decoding speed compared to traditional methods. Our algorithms have been implemented for different hardware architectures (ARM and x64). Our approach has been widely adopted in JavaScript runtimes and Web browsers.

This paper proposes a novel framework for large-scale scene reconstruction based on 3D Gaussian splatting (3DGS) and aims to address the rendering deficiency and scalability challenges faced by existing embodied AI tasks. For tackling the scalability issue, we split the large scene into multiple cells, and the candidate point-cloud and camera views of each cell are correlated through a visibility-based camera selection and a progressive point-cloud extension. To reinforce the rendering quality, three highlighted improvements are made in comparison with vanilla 3DGS, which are a strategy of the ray-Gaussian intersection and the novel Gaussians density control for learning efficiency, an appearance decoupling module based on ConvKAN network to solve uneven lighting conditions in large-scale scenes, and a refined final loss with the color loss, the depth distortion loss, and the normal consistency loss. Finally, the seamless stitching procedure is executed to merge the individual Gaussian radiance field for novel view synthesis across different cells. Evaluation of Mill19, Urban3D, and MatrixCity datasets shows that our method consistently generates more high-fidelity rendering results than state-of-the-art methods of large-scale scene reconstruction. We further validate the generalizability of the proposed approach by rendering on self-collected video clips recorded by a commercial drone.

This paper addresses the multi-objective optimization problem in flexible job-shop scheduling, es-tablishing a model that optimizes completion time, total machine load, and energy consumption. An improved version of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is proposed. This algorithm is designed with multi-objective optimization in mind, featuring a population initializa-tion method for multiple objective functions that enhance the quality and diversity of the population. It also introduces an adaptive crossover and mutation operator, incorporating evaluation during the crossover and mutation process to raise the quality of the offspring. An experience-based im-proved elite preservation strategy has been designed to prevent the reduction of population diver-sity in the later stages of evolution while protecting high-quality individuals from degradation during the genetic process. The results demonstrate that the advantages of this algorithm can more effectively solve the multi-objective flexible job-shop scheduling problem.

A document by Dina Mistarihi. Click on the document to view its contents.

Directional data analysis plays a critical role in paleomagnetism, where observations are distributed in a spherical surface. Existing methods for analyzing directional data often fail to incorporate prior physical information about plate geodynamics, significantly constraining their potential. To address this limitation, we developed a hybrid, physics-informed machine learning model that uses a neural network to learn the underlying rotations responsible for generating directional data. We found that our method efficiently fits both synthetic and real paleomagnetic datasets. Additionally, by leveraging differentiable programming, we can incorporate physical constraints in the form of regularizations. These results could enhance future estimations of apparent polar wander paths, advancing the reconstruction of past tectonic plate motions.

Grasslands play a critical role in global food security, biodiversity, and ecosystem services, particularly in tropical regions where they serve as the primary base for livestock production. However, grasslands are increasingly vulnerable to degradation due to poor management practices such as the Cerrado biome in Brazil. This study focuses on grasslands in Goiás, a state located in central-western Brazil, which relies heavily on pastures for its agricultural economy. We present a comprehensive field-based assessment of grassland degradation in Goiás, aiming to estimate pasture degradation area according to three categories: non-degraded, in process of degradation, and severely degraded. A three-stage cluster sampling design was employed to evaluate 460 field points, incorporating visual assessments of key variables using a novel Pasture Assessment Score (PAS) system developed specifically for this study. The scores were analyzed using Multiple Correspondence Analysis (MCA) to reduce dimensionality and identify clusters corresponding to degradation levels. Monte Carlo simulations were used to estimate confidence intervals for each degradation class, with standard errors ranging from 3.1% to 4.5%. The results revealed that 39.35% of the pasture area in Goiás was classified as non-degraded, 31.98% as being in process of degradation, and 28.67% as severely degraded. This study highlights the need for targeted management interventions and improved monitoring frameworks to mitigate grassland degradation. Future works should focus on integrating field data with satellite-derived indices to improve mapping accuracy, and enable large-scale, high-frequency monitoring and detection of early-stage degradation for more effective land management strategies.

Identifying which lines deserve attention within large software log files can be a challenging task. Log files have consistently increased, reflecting the growth of software development platforms that are becoming larger and integrated. However, software engineers rarely have the time to thoroughly analyze these files to identify important information. To address this issue, data mining methods have been proposed with the intent of summarizing log lines within large log datasets. In this work, we propose a supervised log summarization method based on clustering, which groups log data by using integrated information from (i) log line embeddings, (ii) identified variables extracted from parsed log lines, and (iii) the proximity between log lines. From the obtained clusters, we apply methods for topic modeling and word analysis to summarize and indicate which lines deserve more attention in a log file. Our quantitative analysis on various log datasets demonstrates that our approach outperforms state-of-the-art text summarization methods, thereby showing that the clustering method and the combination of (i)-(iii) are crucial in achieving high accuracy scores for diverse log structures. Finally, we outline the implementation of our method with our corporate partner, highlighting the feedback received and the adjustments made to enhance its practical use.

In response to the demands of digital transformation, organizations increasingly rely on IT outsourcing to integrate advanced technologies and optimize operations. This study examines the challenges that IT vendors confront in outsourced IT development projects, with a specific focus on project initiation, execution, and implementation. Using a case study of a Danish IT consultancy firm, the research identifies relational and organizational factors, comprising client digital maturity, communication, relationship management, and contract clarity as essential for project success beyond the typical project constraints of time, resources, and deliverables. The initiation phase emerges as particularly critical, which underscores the need to assess client maturity and align expectations early. Moreover, strong client-vendor relationships foster trust, effective communication, and improved project continuity. The findings suggest a framework that combines both operational and relational factors, emphasizing that a holistic approach helps IT consultancies align more effectively with client expectations, achieve better project outcomes, and build lasting partnerships. This study contributes to the literature on IT outsourcing by highlighting the multifaceted nature of outsourced IT projects and by providing practical insights for IT vendors when navigating the complexities of client engagements.

Global soil salinity is a problem that jeopardizes ecosystem health and agricultural productivity. Applying traditional soil salinity analysis techniques over large areas can be challenging, time-consuming, and costly because they rely on laboratory-based measurements. Thus, machine learning techniques and remote sensing are being utilized more and more to determine soil salinity quickly and accurately. By using satellites and aerial sensors to record the spectral characteristics of the soil surface, remote sensing technologies can identify indirect markers of soil salt buildup. Specifically, the salinity of soil is often mapped using spectral data from the visible, near-infrared (VNIR), and thermal bands. In order to forecast soil salinity, machine learning algorithms analyze these spectral data to model intricate and non-linear relationships. Methods such as, Support Vector Machines (SVM), Artificial Neural Networks (ANN), Random Forest, and Deep Learning provide high accuracy rates in predicting soil salinity. These methods have a lot of promise for tracking changes in soil salinity over time and space, improving farming methods, and creating potent anti-salinity plans. Additionally, by assisting with decisions about soil management, particularly in large-scale farming areas, these techniques support the growth of sustainable agricultural practices. Consequently, it is recognized that a promising approach to controlling and tracking soil salinity is the combination of machine learning and remote sensing.

Objective:The study was proposed to identify echocardiographic variables related to cardiotoxicity and/or death, analyzed by machine learning. Methods: The study was initially composed of a cohort of 101 patients with breast cancer, from February 2010 to June 2011, undergoing chemotherapy treatment with anthracyclines, with clinical evaluation, electrocardiogram and complete echocardiogram, including tissue Doppler and myocardial deformation indices. The exams were performed before the first session, 3, 6, 12 and 24 months, and 58 patients completed the study. Results: The mean age of the patients was 52.49 ± 12.97 years. Thus, of the 9 patients with a 10% point drop and a LVEF lower than 50% at the last moment, there were 8 with a reduction in GLS greater than 15% between the first and subsequent moments of the study, totaling 17 patients now defined as having cardiotoxicity. Associated with the outcomes of death (12 patients) and/or cardiotoxicity at the final moment (17 patients), totaling 29 patients. The random forest classifier presented the best result of the study with 77.78% accuracy, 87.89% area under the ROC curve and 80% recall, the KNN method showed 72.22% accuracy, 62.31% area under the ROC curve and 40% recall, while the XGBClassifier presented 81% accuracy, 83% area under the ROC curve and 66.6% recall. After optimizing the echocardiographic variables demonstrated by the Shap values, through the library to explain the predictions of machine learning models, it was observed that, in addition to the reduction in the LVEF, indices such as the TAPSE, e’ wave velocity of the lateral region of the mitral valve, the myocardial performance index and MAPSE, were related to cardiotoxicity and/or death. Conclusion: This study demonstrated that echocardiographic parameters obtained by transthoracic echocardiography are predictors of cardiotoxicity and death. New studies with a larger sample of patients may confirm these findings

A document by Sami Ibrahim. Click on the document to view its contents.

A document by Xavier Pardell Peña. Click on the document to view its contents.

Chilin fish (Varicorhinus macrolepis) is classified as a second-class protected animal in China. The wild-type body color is predominantly black. however, recent observations have documented individuals with a gold color mutation. To identify the genetic mutations responsible for this change, we investigated the mechanisms underlying body color mutations in Chilin fish. Transcriptome sequencing was conducted on skin samples from wild-type and mutant fish. A total of 72,663 gene sequences were screened using the DESeq2 criterion (P-value < 0.05 and |log2 fold-change| > 1), identifying 605 up-regulated and 602 down-regulated genes in the mutant samples. A heatmap illustrated the expression differences between genes. Seven genes with significant differential expression were identified: LOC107600971, MLLT1, tmem41ab, LN590753, Zbed4, RASSF2, and LN590710. KEGG pathway enrichment analysis revealed their involvement in the p38 MAP kinase and nuclear factor NF-kappa-β p105 subunit signaling pathways. Transcriptomic data from wild-type and mutant skin tissues were provided, identifying seven DEGs associated with body color variation. Experimental analysis confirmed their strong correlation with body color mutation. We hypothesized that these two signaling pathways influence the body color mutation in Chilin fish. This finding enhances our understanding of the complex molecular mechanisms regulating body color mutations. Furthermore, it provides novel insights for investigating body color mutations in other Cyprinidae species.

Conservation and management of wild bees is hindered by the variety of ways wild bee occurrence data are recorded, managed, and shared. Here, we present solutions to address this issue and introduce The Wild Bee Data Standard, a standardized means of recording and reporting data associated with wild bee occurrences, including physical specimens and photo observations. This standard aligns with contemporary data management practices widely adopted

In nailfold video recordings, the micro-shaking of the hand is amplified and interferes with physician observations and parameter measurement. We developed a fast and accurate registration method for large-field-of-view nailfold video images. Nailfold videos are first represented in the YCrCb color space, with the Cb spatial component replacing the original grayscale image to reduce sensitivity to illumination. The projection variance of each row/column is employed to improve registration accuracy and processing speed. The method was compared with Origin GrayDrop, feature point matching, DUT, and Adobe Premiere Pro in terms of the peak signal-to-noise ratio, structural similarity index, and mean squared error. The peak signal-to-noise ratio and structural similarity index are enhanced, and the mean squared error is reduced compared to the original projection method. The proposed method is faster than the comparison methods and provides the best combination of registration accuracy and fast processing for nailfold video image registration.

In an era where data privacy regulations are evolving rapidly, organizations face the dual challenge of ensuring compliance while fostering marketing innovation. The article "Thriving Amid Regulation: Strategies for Balancing Privacy Compliance and Marketing Innovation" explores the dynamic intersection of these imperatives. It examines how businesses can navigate stringent legal landscapes, such as GDPR, CCPA, and other global privacy frameworks, without stifling their marketing efforts. The paper outlines key strategies, including adopting privacy-bydesign principles, leveraging anonymized and aggregated data, and integrating artificial intelligence to develop privacy-respecting personalization techniques. It also delves into fostering consumer trust through transparency and empowering customers with greater control over their data. Real-world case studies highlight companies successfully innovating while adhering to regulatory requirements, demonstrating the viability of a balanced approach. By advocating for ethical marketing practices and strategic investment in privacy-centric technologies, this research underscores the potential for businesses to not only comply with regulations but also gain a competitive edge. It emphasizes the role of cross-functional collaboration between legal, marketing, and technology teams to achieve sustainable growth in a privacy-conscious world.

In an era of heightened awareness around data privacy, digital marketing strategies are being redefined by stringent privacy laws such as GDPR, CCPA, and global equivalents. These regulations challenge businesses to innovate within a framework of compliance, reshaping how marketers approach data collection, personalization, and consumer engagement. This paper delves into the intersection of privacy and innovation, exploring the constraints imposed by privacy legislation and the opportunities it creates for ethical and transparent marketing practices. By analyzing emerging trends and adaptive strategies, this study provides actionable insights for businesses to thrive in a privacy-conscious world while fostering trust and innovation.

Biotic complexity, encompassing both competitive interactions within trophic levels and consumptive interactions among trophic levels, plays a fundamental role in maintaining ecosystem stability. While theory and experiments have established that plant diversity enhances ecosystem stability, the role of consumers in the diversity−stability relationships remains elusive. In a decade-long grassland biodiversity experiment, we investigated how heterotrophic consumers (e.g., insects and fungi) interact with plant diversity to affect the temporal stability of plant community biomass. Plant diversity loss reduces community stability due to increased synchronization among species but enhances the population-level stability of the remaining plant species. Reducing trophic complexity via pesticide treatments does not directly affect either community- or population-level stability but further amplifies plant species synchronization. Our findings demonstrate that loss of arthropod or fungal consumers can destabilize plant communities by exacerbating synchronization, underscoring the crucial role of trophic complexity in maintaining ecological stability.

not-yet-known not-yet-known not-yet-known unknown Our awareness and understanding of neck conditions in the horse is increasing, along with options for treatment. The ability to perform a thorough and considered orthopaedic and neurologic clinical examination is a vital clinical tool that is accessible to all. Clinical presentations of cervical dysfunction may vary widely in severity and can include one or more components of the three functional diagnoses of; neck pain; neurologic signs of spinal cord compression; and/or signs of radiculopathy. Radiography and ultrasonography are valuable imaging modalities and can be performed in the field, however achieving quality diagnostic radiographs can be challenging in this setting and both have their limitations. Computed tomography (CT) has emerged as the current gold standard imaging modality which offers excellent multiplanar anatomical detail of bony structures and reasonable soft tissue detail, with the risks of anaesthesia being low. Myelography is a key adjunct in the ataxic patient but objective measures for interpretation are not clear. Development of new diagnostic techniques are an active area of research and clinically useful functional testing methods are eagerly awaited to help interpret the clinical relevance of imaging findings.

not-yet-known not-yet-known not-yet-known unknown Efficient information retrieval in distributed and dynamic networks remains a critical challenge, as data is often scattered across multiple nodes with varying topologies and conditions. Mobile Intelligent Agents (MIAs) require adaptive and robust routing mechanisms to optimize performance metrics such as latency, energy consumption, and throughput. This study introduces the Information Retrieval Mobile Agent (IRMA) framework, which leverages Proximal Policy Optimization (PPO), a deep reinforcement learning (RL) algorithm, for adaptive routing of Mobile Intelligent Agents (MIAs) to dynamically navigate networks and retrieve data. PPO’s stable and efficient policy updates make it ideal for continuous state-action spaces in complex environments. We address critical challenges in distributed information retrieval, demonstrating significant improvements over traditional methods. We address critical challenges in distributed information retrieval, demonstrating significant improvements over traditional methods. Our novel application of PPO to MIA routing offers superior adaptability and performance compared to conventional techniques. Experimental results show that IRMA achieves lower latency, reduced energy consumption, and higher success rates in various network scenarios significantly outperforms competing methods in various metrics, making it robust solution for information retrieval in modern distributed networks.

Many organizations have a long-standing history of using Enterprise Resource Planning (ERP) systems, but there is a growing trend of adopting and integrating with cloud computing capabilities and digital technologies to drive operational processes and business model transformations. While the existing literature has contributed to our understanding of ERP, there is a need for more comprehensive insights into the evolving nature of ERP within the cloud context to support exploitational and/or explorational activities. In this study, we conducted a systematic literature review (SLR) involving 33 articles sourced from seven major academic databases and mapped them within the exploration-exploitation framework of March to uncover (1) the purpose of researching ambidexterity within the context of cloud ERP, and (2) the way in which an exploration-exploitation framework manifests in a cloud ERP context. Our findings reveal a substantial evolution in the ambidextrous context of ERP research and uncovers aspects and dimensions unique to the cloud ERP domain. Building upon these results, we propose a context specific version of the exploration-exploitation framework that addresses the prevailing topics identified in this review. The proposed framework provides practical assistance to academic researchers, industry practitioners, and organizations in navigating digital transformation, achieving ambidexterity, and maximizing the benefits of cloud ERP.

Resource quantity in the environment often changes over time and influences the nutritional status of hosts that may encounter parasites. If resource availability significantly alters both infection success and within-host growth of a parasite, fluctuations in resources may underlie the seasonal disease outbreaks that have been observed for some parasites. Moreover, resource quantity may affect how a parasite impacts host survivorship and traits, including feeding rate and assimilation of nutrients. Some parasites, such as intracellular microsporidia with highly reduced genomes, may be particularly sensitive to variation in host nutritional status and more likely to have resource-dependent impacts. To determine how resource quantity affects infection success and parasite impacts on hosts, we conducted laboratory experiments using the microsporidian parasite Ordospora pajunii and its zooplankton host, the dominant grazer Daphnia dentifera. We found that infection probability and spore burden were higher when resources were more abundant, suggesting O. pajunii benefits when host quality is higher. However, parasite virulence, which was measured in terms of host mortality, was greater when resources were more limited. Parasite exposure depressed host feeding rate, but the timing of this reduction differed across the resource gradient. Further investigation of host carbon assimilation efficiency and dissolved organic carbon release during infections when resources were more limited revealed no significant impact of infection. Overall, resource-dependent impacts of O. pajunii on hosts, including reduced host feeding rate and increased host mortality, may contribute to seasonal disease outbreaks and drive trophic cascades in lakes.

We explain and demonstrate how a classic LC filter circuit can be repurposed as an unpowered, high-data-rate Boolean NOT gate, and we experimentally validate its operation at up to 5 Gbps. We describe its operation and note that it potentially completes existing unpowered logic families.

not-yet-known not-yet-known not-yet-known unknown Reliable population estimates are essential for the management of harvested species. Line transect distance sampling using pointing dogs has been proposed for monitoring of rock ptarmigan. In this study we test assumptions of line transect distance sampling: (i) That birds on the transect are detected with certainty and (ii) birds are detected at their initial location. We also investigated factors potentially influencing the detection probability. We conducted experiments in 2022 and 2023, by walking towards GPS-tagged rock ptarmigans, accompanied by a pointing dog. Our result shows that all rock ptarmigans within 0-30 m from the transect were detected with certainty. It was more likely that birds became passive after being active than the opposite as the dog and handler approached it. The birds were found close to their initial position, also in situations where they became active as the dog and handler approached it. The best models to explain detection probability included the bird’s distance to the transect and distance moved. The distance to the transect had a strong negative effect, which is expected in line transect distance sampling designs. The distance the bird moved from its initial location had a weak positive effect on detection. This might indicate that birds are more likely to be detected if they are on the move. Models with other external factors had less support. Our results indicate that line transect distance sampling using pointing dogs does not violate the key assumptions when applied on rock ptarmigan. Based on these results, we suggest that the existing line transect survey design for willow ptarmigan is extended in altitude to also cover rock ptarmigan habitat, and that further investigation is needed to assess the usefulness of covariates to improve estimates.