Search

Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of Global Positioning System (GPS)-equipped mobile devices and other inexpensive location-Tracking technologies, mobility data is collected ubiquitously. In recent years, the use of mobility data has demonstrated a significant impact in various domains, including traffic management, urban planning, and health sciences. In this article, we present the domain of mobility data science. Towards a unified approach to mobility data science, we present a pipeline having the following components: mobility data collection, cleaning, analysis, management, and privacy. For each of these components, we explain how mobility data science differs from general data science, we survey the current state-of-The-Art, and describe open challenges for the research community in the coming years.

The objective of this research is to explore the impact of social media networks on the daily experiences of Arab-Bedouin adolescents. The study is based on semi-structured interviews with of 30 male youths from unrecognized villages in 2023. The outcomes highlight the significance of social media networks as an indispensable communication tool for males within a traditional societal structure, particularly for establishing connections with peers from diverse sectors and religions. The utilization of social media facilitates meaningful and comprehensive interaction between genders, as perceived by teenagers. It also fosters a profound and significant dialogue on an emotional plane, which remains inaccessible in the Bedouin boys' everyday lives.

The objective of this research is to explore the impact of social media networks on the daily experiences of Arab-Bedouin adolescents. The study is based on semi-structured interviews with of 30 male youths from unrecognized villages in 2023. The outcomes highlight the significance of social media networks as an indispensable communication tool for males within a traditional societal structure, particularly for establishing connections with peers from diverse sectors and religions. The utilization of social media facilitates meaningful and comprehensive interaction between genders, as perceived by teenagers. It also fosters a profound and significant dialogue on an emotional plane, which remains inaccessible in the Bedouin boys' everyday lives.

Ports are an essential pillar of economic development globally, as they contribute significantly to enhancing local and international trade exchange. Ports seek to improve their efficiency and provide high-quality services to meet customer needs. Consequently, many countries have developed their maritime capabilities using ICT, which has increased trade volume, reduced overall costs, and reduced port arrival time for ships. Since its emergence, container transportation has become vital in the field of logistics and global trade, as it contributes significantly to improving the efficiency of supply chains by simplifying shipping and tracking processes using modern technology, and effectively enhances local and international trade exchange. The study aims to understand the sources of weakness that affected the ability of the Aden Container Terminal to compete, and to provide future directions and suggestions for enhancing competitiveness through the application of technology and automation. The researcher used the descriptive analytical approach, where a questionnaire was created to measure the impact of automation of operational processes at the Aden Container Terminal, and this data was analyzed using the SPSS program. Received: 03 June 2024 Accepted: 14 July 2024 Published: 19 August 2024

We have designed, fabricated, and characterized a low polarization dependent multicast and select (MCS) switch based on a newly developed novel bulk SOA co-integrated with passive waveguides. The proposed new bulk SOA-based MCS switch requires the co-integration of active SOAs (gate and booster) with several passive InP elements. First, we have presented a theoretical analysis and the design and properties of the proposed bulk SOA and demonstrated its low polarization-dependent properties. Then, we evaluated and characterized the switch's performance, including gain, optical signal-to-noise ratio (OSNR), and data transmission features. The experimental results verify that the MCS switch exhibits polarization-dependent gain in the range of 0.1 to 2.2 dB for all ports and a broadband gain of approximately 70 nm across all ports. The measured OSNR, for all channels, is > 42 dB at 1580 nm. Moreover, experimental data transmission assessments show operation at high bitrates of 30 Gb/s with a low power penalty of less than 1 dB at 10-9. The dynamic power penalty range of the switch is also investigated. The MCS switch exhibits a wide range of > 20 dB input power dynamic range for a power penalty of less than 3 dB for a 10 Gb/s NRZ signal.

Low-complexity neural networks (NNs) have successfully been proposed for digital signal processing (DSP) in short-reach intensity-modulated directly detected optical links, where chromatic dispersion-induced impairments significantly limit the transmission distance. The NN-based equalizers are usually optimized independently from other DSP components, such as matched filtering. This approach may result in lower equalization performance. Alternatively, optimizing a NN equalizer to perform functionalities of multiple DSP blocks may increase transmission reach while keeping the complexity low with respect to the scenarios where DSP blocks that involve nonlinear equalizers are separated and optimized independently. In this work, we propose a low-complexity NN that performs samples-to-symbol equalization, meaning that the NN-based equalizer includes match filtering and downsampling. We compare it to a samples-to-sample equalization approach followed by match filtering and downsampling in terms of performance and computational complexity. Both approaches are evaluated using three different types of NNs combined with optical preprocessing. We numerically and experimentally show that the proposed samples-to-symbol equalization approach applied for 32 GBd on-off keying (OOK) signals outperforms the samples-domain alternative keeping the computational complexity low with respect to the sample-based approach. Additionally, the different types of NN-based equalizers are compared in terms of performance with respect to computational complexity.

We design and propose a novel low polarization-dependent InAlGaAs bulk SOA that can be integrated with passive waveguides. Low butt-coupled reflection < 2×10-5, low polarization-dependent gain < 3dB is achieved with > 15 dB gain.

We assess the performance of an O-band integrated optical 4x4 switch using the broadcast and select architecture with 100 Gbit/s PAM-4 signals. We measured a power penalty of <1 dB at the FEC-limit for multiple optical paths.

Ports are an essential pillar of economic development globally, as they contribute significantly to enhancing local and international trade exchange. Ports seek to improve their efficiency and provide high-quality services to meet customer needs. Consequently, many countries have developed their maritime capabilities using ICT, which has increased trade volume, reduced overall costs, and reduced port arrival time for ships. Since its emergence, container transportation has become vital in the field of logistics and global trade, as it contributes significantly to improving the efficiency of supply chains by simplifying shipping and tracking processes using modern technology, and effectively enhances local and international trade exchange. The study aims to understand the sources of weakness that affected the ability of the Aden Container Terminal to compete, and to provide future directions and suggestions for enhancing competitiveness through the application of technology and automation. The researcher used the descriptive analytical approach, where a questionnaire was created to measure the impact of automation of operational processes at the Aden Container Terminal, and this data was analyzed using the SPSS program. Received: 03 June 2024 Accepted: 14 July 2024 Published: 19 August 2024

In this paper, an intensive review was made to propose Fibers Pavement Concrete (FPC) for Salang-Road Afghanistan. Moreover, structural properties of FPC were also evaluated and compared with those of Asphalt road. There are various benefits in application of Fiber within rigid pavement. For instance, FPC has longer life and lower maintenance cost in compare with the flexible pavement. In rigid pavement design temperature and thickness are two effective parameters that could widely affect by inclusion of fiber to the total cost of the Salang Road-Project. Different types of fibers with volume fraction optimization which economical and safe are introduced. A concept was adopted to quantify the benefits of adding fiber in terms of extension of the pavement service life and also in terms of reduction in the concrete thickness for the same service life of both reinforced and unreinforced concrete pavement sections. that the use of fibers reduces the thickness of road pavement, enhance the durability and subsequently decreases the overall cost of road construction of Salang rigid pavement., En este artículo se realizó una revisión intensiva para proponer concreto de pavimento de fibras (FPC) para Salang-Road, afganistán. además, también se evalua-ron las propiedades estructurales del FPC y se com-pararon con las de la carretera asfáltica. Existen varios beneficios en la aplicación de fibra dentro de pavimen-to rígido. Por ejemplo, el FPC tiene una vida más larga y un menor costo de mantenimiento en comparación con el pavimento flexible. En el diseño de pavimento rígido, la temperatura y el espesor son dos parámetros efectivos que podrían afectar ampliamente la inclusión de fibra en el costo total del Proyecto de Carretera Salang. Se introducen diferentes tipos de fibras con optimización de la fracción volumétrica, económicas y seguras. Se adoptó un concepto para cuantificar los beneficios de agregar fibra en términos de extensión de la vida útil del pavimento y también en términos de reducción del espesor del concreto para la misma vida útil de secciones de pavimento de concreto reforzado y no reforzado, pues el uso de fibras reduce el espesor del pavimento de la carretera, mejora la durabilidad y posteriormente disminuye el costo total de la construc-ción de carreteras con pavimento rígido de Salang.

In this paper, an intensive review was made to propose Fibers Pavement Concrete (FPC) for Salang-Road Afghanistan. Moreover, structural properties of FPC were also evaluated and compared with those of Asphalt road. There are various benefits in application of Fiber within rigid pavement. For instance, FPC has longer life and lower maintenance cost in compare with the flexible pavement. In rigid pavement design temperature and thickness are two effective parameters that could widely affect by inclusion of fiber to the total cost of the Salang Road-Project. Different types of fibers with volume fraction optimization which economical and safe are introduced. A concept was adopted to quantify the benefits of adding fiber in terms of extension of the pavement service life and also in terms of reduction in the concrete thickness for the same service life of both reinforced and unreinforced concrete pavement sections. that the use of fibers reduces the thickness of road pavement, enhance the durability and subsequently decreases the overall cost of road construction of Salang rigid pavement., En este artículo se realizó una revisión intensiva para proponer concreto de pavimento de fibras (FPC) para Salang-Road, afganistán. además, también se evalua-ron las propiedades estructurales del FPC y se com-pararon con las de la carretera asfáltica. Existen varios beneficios en la aplicación de fibra dentro de pavimen-to rígido. Por ejemplo, el FPC tiene una vida más larga y un menor costo de mantenimiento en comparación con el pavimento flexible. En el diseño de pavimento rígido, la temperatura y el espesor son dos parámetros efectivos que podrían afectar ampliamente la inclusión de fibra en el costo total del Proyecto de Carretera Salang. Se introducen diferentes tipos de fibras con optimización de la fracción volumétrica, económicas y seguras. Se adoptó un concepto para cuantificar los beneficios de agregar fibra en términos de extensión de la vida útil del pavimento y también en términos de reducción del espesor del concreto para la misma vida útil de secciones de pavimento de concreto reforzado y no reforzado, pues el uso de fibras reduce el espesor del pavimento de la carretera, mejora la durabilidad y posteriormente disminuye el costo total de la construc-ción de carreteras con pavimento rígido de Salang.

This study examines the impact of school leadership practices on student performance in NWEA MAP assessments at an American curriculum school in Dubai. It aims at identifying effective strategies that enhance student achievement in standardized external testing by leveraging different leadership styles and teacher empowerment. The main research question is: How do school leaders and teachers empower students to enhance their NWEA MAP performance? The study investigates Transformational and Instructional leadership theories along with Self-Determination Theory to assess their influence on educational outcomes. A mixed-methods approach was employed, collecting qualitative data through semi-structured interviews and quantitative data via surveys to gain comprehensive and holistic insights into the phenomena. Ten school leadership participants took part in the qualitative interviews, and fifty-seven teachers participated in the quantitative surveys. The findings indicate that transformational leadership fosters high student expectations and participation, while distributed leadership promotes shared decision-making among teachers, significantly influencing and boosting MAP results. Additionally, increased access to technology platforms also played a significant role and improved performance. Nevertheless, the study's limitation to one school indicates that further research across diverse educational settings is necessary for generalizability. In conclusion, effective leadership, in addition to teacher and student empowerment, is vital for enhancing student performance on MAP assessments.

Clonal cytopenia of undetermined significance (CCUS) represents a distinct disease entity characterized by myeloid-related somatic mutations with a variant allele fraction of ≥2% in individuals with unexplained cytopenia(s) but without a myeloid neoplasm (MN). Notably, CCUS carries a risk of progressing to MN, particularly in cases featuring high-risk mutations. Understanding CCUS requires dedicated studies to elucidate its risk factors and natural history. Our analysis of 357 CCUS patients investigated the interplay between clonality, cytopenia, and prognosis. Multivariate analysis identified 3 key adverse prognostic factors: the presence of splicing mutation(s) (score = 2 points), platelet count <100×109/L (score = 2.5), and ≥2 mutations (score = 3). Variable scores were based on the coefficients from the Cox proportional hazards model. This led to the development of the Clonal Cytopenia Risk Score (CCRS), which stratified patients into low- (score <2.5 points), intermediate- (score 2.5-<5), and high-risk (score ≥5) groups. The CCRS effectively predicted 2-year cumulative incidence of MN for low- (6.4%), intermediate- (14.1%), and high- (37.2%) risk groups, respectively, by Gray's test (P <.0001). We further validated the CCRS by applying it to an independent CCUS cohort of 104 patients, demonstrating a c-index of 0.64 (P =.005) in stratifying the cumulative incidence of MN. Our study underscores the importance of integrating clinical and molecular data to assess the risk of CCUS progression, making the CCRS a valuable tool that is practical and easily calculable. These findings are clinically relevant, shaping the management strategies for CCUS and informing future clinical trial designs.

Take a thin cylindrical shell and twist it; it will buckle immediately. Such unavoidable torsional buckling can lead to systemic failure, for example by disrupting the blood flow through arteries. In this study, we prevent this torsional buckling instability using a combination of auxeticity and orthotropy in cylindrical metamaterial shells with a holey pattern. When the principal axes of the orthotropic meta-shell are relatively aligned with that of the compressive component of the applied stress during twisting, the meta-shell uniformly shrinks in the radial direction as a result of a local buckling instability. This shrinkage coincides with a softening-stiffening transition that leads to ordered stacking of unit cells along the compressive component of the applied stress. These transitions due to local instabilities circumvent the usual torsional instability even under a large twist angle. This study highlights the potential of tailoring anisotropy and programming instabilities in metamaterials, with potential applications in designing mechanical elements for soft robotics, biomechanics or fluidics. As an example of such applications, we demonstrate soft torsional compressor for generating pulsatile flows through a torsion release mechanism.

Cancer is among the leading causes of mortality worldwide. Although different diagnostic and therapeutic approaches have been implemented for cancer management and reducing the mortality rate, the limitations attributed to these strategies have hampered the improvements in prognosis and overall survival of cancer patients. Early detection and an appropriate therapeutic strategy are critical steps toward cancer management and successful therapy. Therefore, there is a crucial demand for advanced technologies to bring existing cancer diagnostic and therapeutic approaches to the next level. The emerging field of nanotechnology has involved nanoengineered materials and devices in upgrading the specificity and effectiveness of the current diagnostic and therapeutic strategies. The recent advances in developing nanocarriers and nano biosensors can enhance the sensitivity of detecting biomarkers and cancer cells. Moreover, engineered nanoparticles can increase therapeutic efficacy and diminish the side effects of conventional and novel therapeutic modalities. Theranostic agents are the next generation of multitasking nanoparticles that can detect and attack cancer cells simultaneously. This book chapter provides an insight into the advancement of nanotechnologies assisting early detection and cancer-targeted therapy and highlights the existing challenges in clinical trials and commercial products.

Cancer is among the leading causes of mortality worldwide. Although different diagnostic and therapeutic approaches have been implemented for cancer management and reducing the mortality rate, the limitations attributed to these strategies have hampered the improvements in prognosis and overall survival of cancer patients. Early detection and an appropriate therapeutic strategy are critical steps toward cancer management and successful therapy. Therefore, there is a crucial demand for advanced technologies to bring existing cancer diagnostic and therapeutic approaches to the next level. The emerging field of nanotechnology has involved nanoengineered materials and devices in upgrading the specificity and effectiveness of the current diagnostic and therapeutic strategies. The recent advances in developing nanocarriers and nano biosensors can enhance the sensitivity of detecting biomarkers and cancer cells. Moreover, engineered nanoparticles can increase therapeutic efficacy and diminish the side effects of conventional and novel therapeutic modalities. Theranostic agents are the next generation of multitasking nanoparticles that can detect and attack cancer cells simultaneously. This book chapter provides an insight into the advancement of nanotechnologies assisting early detection and cancer-targeted therapy and highlights the existing challenges in clinical trials and commercial products.

Cancer is among the leading causes of mortality worldwide. Although different diagnostic and therapeutic approaches have been implemented for cancer management and reducing the mortality rate, the limitations attributed to these strategies have hampered the improvements in prognosis and overall survival of cancer patients. Early detection and an appropriate therapeutic strategy are critical steps toward cancer management and successful therapy. Therefore, there is a crucial demand for advanced technologies to bring existing cancer diagnostic and therapeutic approaches to the next level. The emerging field of nanotechnology has involved nanoengineered materials and devices in upgrading the specificity and effectiveness of the current diagnostic and therapeutic strategies. The recent advances in developing nanocarriers and nano biosensors can enhance the sensitivity of detecting biomarkers and cancer cells. Moreover, engineered nanoparticles can increase therapeutic efficacy and diminish the side effects of conventional and novel therapeutic modalities. Theranostic agents are the next generation of multitasking nanoparticles that can detect and attack cancer cells simultaneously. This book chapter provides an insight into the advancement of nanotechnologies assisting early detection and cancer-targeted therapy and highlights the existing challenges in clinical trials and commercial products.

[Background] The issue of sex differences in stroke has gained concern in the past few years. However, multicenter studies are still required in this field. This study explores sex variation in a large number of patients and compares stroke characteristics among women in different age groups and across different countries., [Methods] This multicenter retrospective cross-sectional study aimed to compare sexes regarding risk factors, stroke severity, quality of services, and stroke outcome. Moreover, conventional risk factors in women according to age groups and among different countries were studied., [Results] Eighteen thousand six hundred fifty-nine patients from 9 countries spanning 4 continents were studied. The number of women was significantly lower than men, with older age, more prevalence of AF, hypertension, and dyslipidemia. Ischemic stroke was more severe in women, with worse outcomes among women (p: < 0.0001), although the time to treatment was shorter. Bridging that was more frequent in women (p:0.002). Analyzing only women: ischemic stroke was more frequent among the older, while hemorrhage and TIA prevailed in the younger and stroke of undetermined etiology. Comparison between countries showed differences in age, risk factors, type of stroke, and management., [Conclusion] We observed sex differences in risk factors, stroke severity, and outcome in our population. However, access to revascularization was in favor of women.

Origami cylinders have been studied extensively under compression, however, their normal force response under shear deformation is still ill-understood. We experimentally studied the shear-induced normal force of these systems and provided a simple model to predict this response in Yoshimura and Kresling origami cylinders in the range of small deformations. Besides that, the effect of disorder is little understood in those origami patterns. Therefore, we investigate the disorder effect and show it can lead to diverse normal responses perpendicular to shear deformation in crumpled Yoshimura, with slopes of the force perpendicular to torsion that can be positive, zero, or negative depending on the disordered pattern of folds. The disorder leads to an asymmetry concerning the shearing direction, which we quantified via an asymmetry index from the 2D Fast Fourier transform spectrum of the crease pattern images. This asymmetry index is found to be linearly related to the slope of the normal force-torsion relationship.

Gleason grading is an important prognostic indicator for prostate adenocarcinoma and is crucial for patient treatment decisions. However, intermediate-risk patients diagnosed in the Gleason grade group (GG) 2 and GG3 can harbour either aggressive or non-aggressive disease, resulting in under- or overtreatment of a significant number of patients. Here, we performed proteomic, differential expression, machine learning, and survival analyses for 1,348 matched tumour and benign sample runs from 278 patients. Three proteins (F5, TMEM126B, and EARS2) were identified as candidate biomarkers in patients with biochemical recurrence. Multivariate Cox regression yielded 18 proteins, from which a risk score was constructed to dichotomize prostate cancer patients into low- and high-risk groups. This 18-protein signature is prognostic for the risk of biochemical recurrence and completely independent of the intermediate GG. Our results suggest that markers generated by computational proteomic profiling have the potential for clinical applications including integration into prostate cancer management.

Community detection, which involves partitioning nodes within a network, has widespread applications across computational sciences. Modularity-based algorithms identify communities by attempting to maximize the modularity function across network node partitions. Our study assesses the performance of various modularity-based algorithms in obtaining optimal partitions. Our analysis utilizes 104 networks, including both real-world instances from diverse contexts and modular graphs from two families of synthetic benchmarks. We analyze ten inexact modularity-based algorithms against the exact integer programming baseline that globally optimizes modularity. Our comparative analysis includes eight heuristics, two variants of a graph neural network algorithm, and nine variations of the Bayan approximation algorithm. Our findings reveal that the average modularity-based heuristic yields optimal partitions in only 43.9% of the 104 networks analyzed. Graph neural networks and approximate Bayan, on average, achieve optimality on 68.7% and 82.3% of the networks respectively. Additionally, our analysis of three partition similarity metrics exposes substantial dissimilarities between high-modularity sub-optimal partitions and any optimal partition of the networks. We observe that near-optimal partitions are often disproportionately dissimilar to any optimal partition. Taken together, our analysis points to a crucial limitation of the commonly used modularity-based methods: they rarely produce an optimal partition or a partition resembling an optimal partition even on networks with modular structures. If modularity is to be used for detecting communities, we recommend approximate optimization algorithms for a methodologically sound usage of modularity within its applicability limits. This article is an extended version of an ICCS 2023 conference paper (Aref et al., 2023).

Internet of Things (IoT) devices are capable of allowing for far-reaching access to and evaluation of patient data to monitor health and diagnose from a distance. An electronic healthcare system that checks patient data, prepares medicines and provides financial assistance is necessary. Providing safe data transmission, monitoring, decentralization, preserving patient privacy, and maintaining confidentiality are essential to an electronic healthcare system. In this study, we introduce (SCALHEALTH) which is a blockchain-based scheme of the Hyperledger Fabric consortium. In this study, we use authentication to agree on a common key for data encryption to send data confidentially. Also, sending data through IPFS is decentralized. Non-fungible token (NFT) is used to send patient prescriptions to pharmacies and insurance companies to ensure the authenticity of patient prescriptions. As the system's main body, blockchain creates authorization and validation for all devices and institutions. Also, all metadata in the system is recorded on the blockchain to maintain integrity, transparency, and timely data monitoring. The proposed study uses two types of blockchain: a health blockchain and a financial blockchain. The financial blockchain is for financial transactions and is based on Ethereum. The health blockchain also introduces a mechanism that allows several blockchains to be active in parallel, instead of only one blockchain. The prototype of this mechanism is simulated in two scenarios. In comparison to the normal state, the proposed plan has superior results.

A recent research trend involves treating database index structures as Machine Learning (ML) models. In this domain, single or multiple ML models are trained to learn the mapping from keys to positions inside a data set. This class of indexes is known as "Learned Indexes." Learned indexes have demonstrated improved search performance and reduced space requirements for one-dimensional data. The concept of one-dimensional learned indexes has naturally been extended to multi-dimensional (e.g., spatial) data, leading to the development of "Learned Multi-dimensional Indexes". This survey focuses on learned multi-dimensional index structures. Specifically, it reviews the current state of this research area, explains the core concepts behind each proposed method, and classifies these methods based on several well-defined criteria. We present a taxonomy that classifies and categorizes each learned multi-dimensional index, and survey the existing literature on learned multi-dimensional indexes according to this taxonomy. Additionally, we present a timeline to illustrate the evolution of research on learned indexes. Finally, we highlight several open challenges and future research directions in this emerging and highly active field.

Skiplists have become prevalent in systems. The main advantages of skiplists are their simplicity and ease of implementation, and the ability to support operations in the same asymptotic complexities as their tree-based counterparts. In this survey, we explore skiplists and their many variants. We highlight many scenarios of how skiplists are useful and fit well in these usage scenarios. We study several extensions to skiplists to make them fit for more applications, e.g., their use in the multi-dimensional space, network overlaying algorithms, as well as serving as indexes in database systems. Besides, we also discuss systems that adopt the idea of skiplists and apply the probabilistic skip pattern into their designs.

While latent diffusion models (LDMs) excel at creating imaginative images, they often lack precision in semantic fidelity and spatial control over where objects are generated. To address these deficiencies, we introduce the Box-it-to-Bind-it (B2B) module - a novel, training-free approach for improving spatial control and semantic accuracy in text-to-image (T2I) diffusion models. B2B targets three key challenges in T2I: catastrophic neglect, attribute binding, and layout guidance. The process encompasses two main steps: i) Object generation, which adjusts the latent encoding to guarantee object generation and directs it within specified bounding boxes, and ii) attribute binding, guaranteeing that generated objects adhere to their specified attributes in the prompt. B2B is designed as a compatible plug-and-play module for existing T2I models, markedly enhancing model performance in addressing the key challenges. We evaluate our technique using the established CompBench and TIFA score benchmarks, demonstrating significant performance improvements compared to existing methods. The source code will be made publicly available at https://github.com/nextaistudio/BoxIt2BindIt.

In recent years, the healthcare sector's shift to online platforms has spotlighted challenges concerning data security, privacy, and scalability. Blockchain technology, known for its decentralized, secure, and immutable nature, emerges as a viable solution for these pressing issues. This article presents an innovative Electronic Health Records (EHR) sharing and drug supply chain management framework tailored to address scalability, security, data integrity, traceability, and secure data sharing. The framework introduces five layers and transactions, prioritizing patient-centric healthcare by granting patients comprehensive access control over their health information. This access facilitates smoother processes, such as insurance claims, while maintaining robust security measures. Notably, our implementation of parallelism significantly bolsters scalability and transaction throughput while minimizing network traffic. Performance evaluations conducted through the Caliper benchmark indicate a slight increase in processor consumption during specific transactions, mitigated effectively by parallelization. RAM requirements remain largely stable. Additionally, our approach notably reduces network traffic while tripling transaction throughput. The framework ensures patient privacy, data integrity, access control, and interoperability, aligning with traditional healthcare systems. Moreover, it provides transparency and real-time drug supply monitoring, empowering decision-makers with actionable insights. As healthcare evolves, our framework sets a crucial precedent for innovative, scalable, and secure systems. Future enhancements could focus on scalability, real-world deployment, standardized data formats, reinforced security protocols, privacy preservation, and IoT integration to comply with regulations and meet evolving industry needs.

The RoboCup competition was started in 1997, and is known as the oldest RoboCup league. The RoboCup 2D Soccer Simulation League is a stochastic, partially observable soccer environment in which 24 autonomous agents play on two opposing teams. In this paper, we detail the main strategies and functionalities of CYRUS, the RoboCup 2021 2D Soccer Simulation League champions. The new functionalities presented and discussed in this work are (i) Multi Action Dribble, (ii) Pass Prediction and (iii) Marking Decision. The Multi Action Dribbling strategy enabled CYRUS to succeed more often and to be safer when dribbling actions were performed during a game. The Pass Prediction enhanced our gameplay by predicting our teammate's passing behavior, anticipating and making our agents collaborate better towards scoring goals. Finally, the Marking Decision addressed the multi-agent matching problem to improve CYRUS defensive strategy by finding an optimal solution to mark opponents' players.

Soccer Simulation 2D (SS2D) is a simulation of a real soccer game in two dimensions. In soccer, passing behavior is an essential action for keeping the ball in possession of our team and creating goal opportunities. Similarly, for SS2D, predicting the passing behaviors of both opponents and our teammates helps manage resources and score more goals. Therefore, in this research, we have tried to address the modeling of passing behavior of soccer 2D players using Deep Neural Networks (DNN) and Random Forest (RF). We propose an embedded data extraction module that can record the decision-making of agents in an online format. Afterward, we apply four data sorting techniques for training data preparation. After, we evaluate the trained models' performance playing against 6 top teams of RoboCup 2019 that have distinctive playing strategies. Finally, we examine the importance of different feature groups on the prediction of a passing strategy. All results in each step of this work prove our suggested methodology's effectiveness and improve the performance of the pass prediction in Soccer Simulation 2D games ranging from 5\% (e.g., playing against the same team) to 10\% (e.g., playing against Robocup top teams).

Graph Neural Networks (GNNs) have advanced spatiotemporal forecasting by leveraging relational inductive biases among sensors (or any other measuring scheme) represented as nodes in a graph. However, current methods often rely on Recurrent Neural Networks (RNNs), leading to increased runtimes and memory use. Moreover, these methods typically operate within 1-hop neighborhoods, exacerbating the reduction of the receptive field. Causal Graph Processes (CGPs) offer an alternative, using graph filters instead of MLP layers to reduce parameters and minimize memory consumption. This paper introduces the Causal Graph Process Neural Network (CGProNet), a non-linear model combining CGPs and GNNs for spatiotemporal forecasting. CGProNet employs higher-order graph filters, optimizing the model with fewer parameters, reducing memory usage, and improving runtime efficiency. We present a comprehensive theoretical and experimental stability analysis, highlighting key aspects of CGProNet. Experiments on synthetic and real data demonstrate CGProNet's superior efficiency, minimizing memory and time requirements while maintaining competitive forecasting performance., Comment: 21 pages, 4 figures

Processing multidomain data defined on multiple graphs holds significant potential in various practical applications in computer science. However, current methods are mostly limited to discrete graph filtering operations. Tensorial partial differential equations on graphs (TPDEGs) provide a principled framework for modeling structured data across multiple interacting graphs, addressing the limitations of the existing discrete methodologies. In this paper, we introduce Continuous Product Graph Neural Networks (CITRUS) that emerge as a natural solution to the TPDEG. CITRUS leverages the separability of continuous heat kernels from Cartesian graph products to efficiently implement graph spectral decomposition. We conduct thorough theoretical analyses of the stability and over-smoothing properties of CITRUS in response to domain-specific graph perturbations and graph spectra effects on the performance. We evaluate CITRUS on well-known traffic and weather spatiotemporal forecasting datasets, demonstrating superior performance over existing approaches., Comment: 17 pages, 3 figures

Randomized smoothing has shown promising certified robustness against adversaries in classification tasks. Despite such success with only zeroth-order access to base models, randomized smoothing has not been extended to a general form of regression. By defining robustness in regression tasks flexibly through probabilities, we demonstrate how to establish upper bounds on input data point perturbation (using the $\ell_2$ norm) for a user-specified probability of observing valid outputs. Furthermore, we showcase the asymptotic property of a basic averaging function in scenarios where the regression model operates without any constraint. We then derive a certified upper bound of the input perturbations when dealing with a family of regression models where the outputs are bounded. Our simulations verify the validity of the theoretical results and reveal the advantages and limitations of simple smoothing functions, i.e., averaging, in regression tasks. The code is publicly available at \url{https://github.com/arekavandi/Certified_Robust_Regression}.

As generative AI progresses rapidly, new synthetic image generators continue to emerge at a swift pace. Traditional detection methods face two main challenges in adapting to these generators: the forensic traces of synthetic images from new techniques can vastly differ from those learned during training, and access to data for these new generators is often limited. To address these issues, we introduce the Ensemble of Expert Embedders (E3), a novel continual learning framework for updating synthetic image detectors. E3 enables the accurate detection of images from newly emerged generators using minimal training data. Our approach does this by first employing transfer learning to develop a suite of expert embedders, each specializing in the forensic traces of a specific generator. Then, all embeddings are jointly analyzed by an Expert Knowledge Fusion Network to produce accurate and reliable detection decisions. Our experiments demonstrate that E3 outperforms existing continual learning methods, including those developed specifically for synthetic image detection., Comment: 11 pages, 4 figures, To be published in CVPRWMF24

This paper introduces GTX a standalone main-memory write-optimized graph system that specializes in structural and graph property updates while maintaining concurrent reads and graph analytics with snapshot isolation-level transactional concurrency. Recent graph libraries target efficient concurrent read and write support while guaranteeing transactional consistency. However, their performance suffers for updates with strong temporal locality over the same vertexes and edges due to vertex-centric lock contentions. GTX introduces a new delta-chain-centric concurrency-control protocol that eliminates traditional mutually exclusive latches. GTX resolves the conflicts caused by vertex-level locking, and adapts to real-life workloads while maintaining sequential access to the graph's adjacency lists storage. This combination of features has been demonstrated to provide good performance in graph analytical queries. GTX's transactions support fast group commit, novel write-write conflict prevention, and lazy garbage collection. Based on extensive experimental and comparative studies, in addition to maintaining competitive concurrent read and analytical performance, GTX demonstrates high throughput over state-of-the-art techniques when handling concurrent transaction+analytics workloads. For write-heavy transactional workloads, GTX performs up to 11x better than the best-performing state-of-the-art systems in transaction throughput. At the same time, GTX does not sacrifice the performance of read-heavy analytical workloads, and has competitive performance similar to state-of-the-art systems., Comment: 12 pages, 13 figures, submitted to VLDB 2025

Processing, managing, and analyzing dynamic graphs are the cornerstone in multiple application domains including fraud detection, recommendation system, graph neural network training, etc. This demo presents GTX, a latch-free write-optimized transactional graph data system that supports high throughput read-write transactions while maintaining competitive graph analytics. GTX has a unique latch-free graph storage and a transaction and concurrency control protocol for dynamic power-law graphs. GTX leverages atomic operations to eliminate latches, proposes a delta-based multi-version storage, and designs a hybrid transaction commit protocol to reduce interference between concurrent operations. To further improve its throughput, we design a delta-chains index to support efficient edge lookups. GTX manages concurrency control at delta-chain level, and provides adaptive concurrency according to the workload. Real-world graph access and updates exhibit temporal localities and hotspots. Unlike other transactional graph systems that experience significant performance degradation, GTX is the only system that can adapt to temporal localities and hotspots in graph updates and maintain million-transactions-per-second throughput. GTX is prototyped as a graph library and is evaluated using a graph library evaluation tool using real and synthetic datasets., Comment: 4 pages 2 figures for VLDB 2024 DEMO

Recent advances in generative AI have led to the development of techniques to generate visually realistic synthetic video. While a number of techniques have been developed to detect AI-generated synthetic images, in this paper we show that synthetic image detectors are unable to detect synthetic videos. We demonstrate that this is because synthetic video generators introduce substantially different traces than those left by image generators. Despite this, we show that synthetic video traces can be learned, and used to perform reliable synthetic video detection or generator source attribution even after H.264 re-compression. Furthermore, we demonstrate that while detecting videos from new generators through zero-shot transferability is challenging, accurate detection of videos from a new generator can be achieved through few-shot learning., Comment: To be published in CVPRW24

One of the approaches to improve cancer treatment is by the application of magnetic nanotechnology. It is possible to control the spatial distribution of magnetically sensitive nanoparticles using a constant magnetic field. Based on this, it is appropriate to investigate the spatial distribution of magnetically sensitive nanoparticles using modifiers of the spatial distribution of a permanent magnetic field. Purpose:Study the distribution of magneto-sensitive nanoparticles under the influence of a constant magnetic field of neodymium magnets. Tasks:Develop stands and conduct experiments with their help in a Petri dish in air and in a simulator of human vessels in blood plasma;measure the magnetic induction for magnets with different coupling strengths using a Hall sensor;construct three-dimensional graphs of the distribution of the magnetic induction of neodymium magnets with different coupling strengths;calculate the heterogeneity of the permanent magnetic field of neodymium magnets;investigate the spatial distribution of conglomerates of nanoparticles in a vessel simulator.

This report documents the program and the outcomes of Dagstuhl Seminar 22021 "Mobility Data Science". This seminar was held January 9-14, 2022, including 47 participants from industry and academia. The goal of this Dagstuhl Seminar was to create a new research community of mobility data science in which the whole is greater than the sum of its parts by bringing together established leaders as well as promising young researchers from all fields related to mobility data science. Specifically, this report summarizes the main results of the seminar by (1) defining Mobility Data Science as a research domain, (2) by sketching its agenda in the coming years, and by (3) building a mobility data science community. (1) Mobility data science is defined as spatiotemporal data that additionally captures the behavior of moving entities (human, vehicle, animal, etc.). To understand, explain, and predict behavior, we note that a strong collaboration with research in behavioral and social sciences is needed. (2) Future research directions for mobility data science described in this report include a) mobility data acquisition and privacy, b) mobility data management and analysis, and c) applications of mobility data science. (3) We identify opportunities towards building a mobility data science community, towards collaborations between academic and industry, and towards a mobility data science curriculum.

Cast-mono silicon is a potential alternative to monocrystalline silicon solar cells as it produces mono-like silicon wafers using the cost effective process used for multicrystalline silicon wafers. This material has shown to have flaws in the ingot growth process which are mainly the propagation of dislocations during the casting of ingots. The crystallographic properties of these dislocations have been studied and their influence on solar cells produced from this material has been observed. In this thesis different regions of cast-mono silicon ingots (from mono-like to heavily dislocated areas) are studied using lifetime test structures and solar cells manufactured from these regions. The fundamental limitations are identified and presented using standard photovoltaic characterization methods. Hydrogenation has been introduced and implemented in this work as a method of overcoming the limiting defects caused by dislocations in cast-mono silicon. The efficacy of various hydrogenation methods is presented. Changes in the limitations identified in the previous section were observed and analyzed. The maximum benefit of hydrogenation in dislocated areas is shown in this work. Cells and lifetime test structures that were not hydrogenated and hydrogenated were light soaked at elevated temperatures to test their stability in regards to the improvement that was obtained in the dislocated regions. It was shown how stable cast-mono solar cells are before and after hydrogenation and if the benefits of hydrogenation remain after a long duration of light soaking. It was concluded that dislocations are a dominant and problematic defect in cast-mono solar cells with severe losses in the silicon bulk. Hydrogenation can be an effective method for mitigating these effects and improving the performance of cast-mono solar cells. Cast-mono solar cells degrade similar to their multicrystalline counterparts grown using the casting ingot growth method. Hydrogenation can be used to both mi

To flexibly accommodate diverse cloud workloads and AI applications, a novel disaggregated DCN architecture based on photonic integrated switches prototype with low latency and high bandwidth to interconnect computing and memory nodes is presented.

Identical symptoms of coronavirus disease-19 (COVID-19) with infectious and non-infectious disease have become challenging issue for health professions. In the present study, we describe a human case of amoebic dysentery mimicking severe acute respiratory syndrome coronavirus 2 (SARS−CoV-2) infection in Iran with an emphasis on morphological and molecular diagnostic aspects of Entamoeba histolytica parasite. A 32-year-old woman with a history of abdominal pain, diarrhea, and mild fever of 38°C was diagnosed as COVID-19 with no prescription of real-time RT-PCR. The patient was prescribed having azithromycin 500 mg/day for 5 days. Her later aggravated conditions such as fulminant dysentery has led to the diagnosis of E. histolytica with parasitological and molecular methods. SARS-CoV-2 infection was ruled out in the second visit using real-time RT-PCR and ELISA. Treatment was completed with antiparasitic drugs including oral metronidazole 500 mg, 3 times/day for ten days, and oral iodoquinol 650 mg 3 times/day for twenty days. In general, health professions must be vigilant in all aspects of diagnosis, treatment, and management of patients to prevent any medical errors and misdiagnosis.

Identical symptoms of coronavirus disease-19 (COVID-19) with infectious and non-infectious disease have become challenging issue for health professions. In the present study, we describe a human case of amoebic dysentery mimicking severe acute respiratory syndrome coronavirus 2 (SARS−CoV-2) infection in Iran with an emphasis on morphological and molecular diagnostic aspects of Entamoeba histolytica parasite. A 32-year-old woman with a history of abdominal pain, diarrhea, and mild fever of 38°C was diagnosed as COVID-19 with no prescription of real-time RT-PCR. The patient was prescribed having azithromycin 500 mg/day for 5 days. Her later aggravated conditions such as fulminant dysentery has led to the diagnosis of E. histolytica with parasitological and molecular methods. SARS-CoV-2 infection was ruled out in the second visit using real-time RT-PCR and ELISA. Treatment was completed with antiparasitic drugs including oral metronidazole 500 mg, 3 times/day for ten days, and oral iodoquinol 650 mg 3 times/day for twenty days. In general, health professions must be vigilant in all aspects of diagnosis, treatment, and management of patients to prevent any medical errors and misdiagnosis.