Abstract:

This study addresses the persistent inefficiencies in incident management within regional public institutions, where dispersed offices and limited digital infrastructure constrain timely technical support. The research aims to evaluate whether a hybrid web architecture integrating AI-assisted interaction and mobile notifications can significantly improve efficiency in this context. The system was designed using a Laravel 10 MVC backend, a responsive Bootstrap 5 interface, and a relational MariaDB/MySQL model optimized with migrations and composite indexes, and incorporated two low-cost integrations: a stateless AI chatbot through the OpenRouter API and asynchronous mobile notifications using the Telegram Bot API managed via Laravel Queues and webhooks. Developed through four Scrum sprints and deployed on an institutional XAMPP environment, the solution was evaluated from January to April 2025 with 100 participants using operational metrics and the QWU usability instrument. Results show a reduction in incident resolution time from 120 to 31 minutes (74.17%), an 85.48% chatbot interaction success rate, a 94.12% notification open rate, and a 99.34% incident resolution rate, alongside an 88% usability score. These findings indicate that a modular, low-cost, and scalable architecture can effectively strengthen digital transformation efforts in the public sector, especially in regions with resource and connectivity constraints.

Abstract: A new philosophy of hypothesis testing - the constrained Bayesian method (CBM) and its application for testing different types of statistical hypotheses such as: simple, composite, asymmetric, multiple hypotheses, are considered in the work. The advantage of the CBM over existing classical methods is theoretically proven in the form of theorems and practi-cally demonstrated by the results of numerous example computations. Examples of the use of CBM to solve some practically important problems are presented, which confirm the flexibility of the method and its great ability to deal with difficult problems.

Abstract: The increasing adoption of Microservices Architecture (MSA) and Cloud Computing offers significant agility but introduces substantial challenges for traditional Business Process Compliance (BPC). This paper addresses these challenges using a Design Science Research Methodology (DSRM). We identify persistent research gaps, including a “Broken Feedback Loop” between design-time and runtime systems. To address these gaps, we present BPC4MSA, a cloud-native framework for holistic BPC management spanning the full lifecycle—from design-time verification to resilient, asynchronous runtime auditing—derived from systematic requirements analysis. This work’s primary contribution is the BPC4MSA framework, a novel conceptual artifact whose design theoretically addresses these gaps. We evaluate this design via a dual-method approach: first, a Systematic Literature Review confirms the framework’s conceptual novelty against the state-of-the-art. Second, a preliminary empirical study of a runtime prototype component is conducted not as a performance benchmark, but as a feasibility check. This check confirms the expected architectural trade-offs of its event-driven pattern, validating its suitability for asynchronous auditing use cases and highlighting critical implementation challenges, such as consumer scaling.

Abstract: In digital forensics, one of the complicated task is analyzing web browser data due to different types of devices, browsers and no updated approaches. Browsers store a large amount of information about user activity because users most often access the internet through them. However, existing approaches to analyzing this browser data still have gaps. One of the main problem developed platforms based on the old methods can not show complete information about the user's activity and have issues with precision. The article discusses the internal architecture of the browser, which is stored in the memory drives inside devices, for instance, computers or mobile devices. The research paper offers solution with developed module based on new method which integrates machine learning algorithms, such as K-NN algorithm and Naive Bayes. The main purpose of the paper it is shows new method which can automatically analyzes browser's data, detects suspicious login activity, and generates user behavior profile. The results show that the proposed new method , on which the developed platform is based, demonstrates user's profile by interests, emotional state and financial state. Also it possible to see list of top visited domain and main user's favorite website categories. It has been found that our methods shows with high accuracy 99.9\% . Also the result of new method , on which the developed platform is based shows the suspicious web-sites and user's logins. Compared to Oxygen Forensics and Nirsoft which less capabilities., the proposed method provides increased accuracy , automated user profiling and detection of suspicious user's activity.

Abstract: This work presents the integration of Mixture of Experts (MoE) architectures into the LEMUR neural network dataset to enhance model diversity and scalability. The MoE framework employs multiple expert networks and a gating mechanism for dynamic routing, enabling efficient computation and improved specialization across tasks. Eight MoE variants were implemented and benchmarked on CIFAR-10, achieving up to 93% accuracy with optimized routing, regularization, and training strategies. This integration provides a foundation for benchmarking expert-based models within LEMUR and supports future research in adaptive model composition and automated machine learning. The project work and its plugins are accessible as open source projects under the MIT license at https://github.com/ABrain-One/nn-dataset.

Abstract: Subtle vibrations induced in everyday objects by ambient sound, especially speech, carry rich acoustic cues that can potentially be transformed into meaningful text. This paper presents a Vision-based Subtitle Generator (VSG). This is the first attempt to recover text directly from high-speed videos of sound-induced object vibrations using a generative approach. To this end, VSG introduces a phase-based motion estimation (PME) technique that treats each pixel as an “independent microphone”, and extracts thousands of pseudo-acoustic signals from a single video. Meanwhile, the pretrained Hidden-unit Bidirectional Encoder Representations from Transformers (HuBERT) serves as the encoder of the proposed VSG-Transformer architecture, effectively transferring large-scale acoustic representation knowledge to the vibration-to-text task. These strategies significantly reduce reliance on large volumes of video data. Experimentally, text was generated from vibrations induced in a bag of chips by AISHELL-1 audio samples. Two VSG-Transformer variants with different parameter scales (Base and Large) achieved character error rates of 13.7 and 12.5%, respectively, demonstrating the remarkable effectiveness of the proposed generative approach. Furthermore, experiments using signal upsampling techniques showed that VSG-Transformer performance was promising even when low-frame-rate videos were used, highlighting the potential of the proposed VSG approach in scenarios featuring conventional cameras.

Abstract: Reaction–diffusion equations provide a fundamental framework for modelling spatial population dynamics and invasion processes in mathematical biology. Among these, the Fisher equation combines diffusion with logistic growth to describe the spread of an advantageous gene and the formation of travelling population fronts. In this work, we investigate the one-dimensional Fisher equation using Lie symmetry analysis to obtain a deeper analytical understanding of its wave propagation behaviour. The Lie point symmetries of the governing partial differential equation are derived and used to construct similarity variables that reduce the Fisher equation to ordinary differential equations. These reduced equations are then solved by a combination of direct integration and the tanh method, yielding explicit invariant and travelling–wave solutions. Symbolic computations in MAPLE are employed throughout to compute the symmetries, verify the reductions, and generate illustrative plots of the resulting wave profiles. The obtained solutions capture sigmoidal fronts connecting stable and unstable steady states, providing clear information about wave speed and shape. Overall, the study demonstrates that Lie group methods, combined with hyperbolic-function techniques, offer a powerful and systematic approach for analysing Fisher-type reaction–diffusion models and interpreting their biologically relevant invasion dynamics.

Abstract: The 5G network adopts a cloud-native, service-based architecture (SBA) that enables support for diverse services via virtualized Network Functions (NFs). A key advantage of this architecture is its decoupling of the control plane and user plane, which enhances network flexibility and scalability. However, the reliance on virtualized implementations and cloud processing also expands the network’s attack surface. For example, the centralized Network Repository Function (NRF) inherently faces the risk of single points of failure. Additionally, the processes for authorizing and accessing services across network functions (NFs) remain susceptible to a variety of security threats. Addressing these gaps requires a resilient security architecture that builds on the existing 5G security framework; yet, current research on security and privacy management for network function services remains relatively limited. To fill this research gap, this paper proposes 5G-DAuth: a decentralized security management scheme for NF services in 5G networks. 5G-DAuth is built on a consortium blockchain and integrates a trusted off-chain Trusted Execution Environment (TEE) pool. The consortium blockchain forms the foundation of a decentralized cross-domain security management platform for NF services, enabling automated registration, authentication, authorization, and access control for NFs. This design directly resolves the single-point failure risk associated with the centralized NRF. To ensure the confidentiality and integrity of service data, the off-chain TEE pool is specifically designed to support smart contract execution and secure service data storage. Additionally, we enhance access tokens using digital signature to achieve fine-grained access control for service authorization while protecting against man-in-the-middle (MITM) attacks and replay attacks during service access. We validate the security of 5G-DAuth through two complementary approaches: informal security analysis and formal verification via a dedicated verification tool. Experimental results further demonstrate that 5G-DAuth delivers high performance across different service management operations, with strong performance in terms of latency and throughput.

Abstract: The first aim of this study is to point out new aspects of approximation theory applied to a few classes of holomorphic functions, via Vitali’s theorem. The approximation is made with the aid of the complex moments of the involved functions, that are defined similarly to the moments of a real valued continuous function. Applying uniform approximation of continuous functions on compact intervals via Korovkin’s theorem, the hard part concerning uniform approximation on compact subsets of the complex plane follows according to Vitali’s theorem. The theorem on the set of zeros of a holomorphic function is also applied. In the end, existence and uniqueness of solution for a mul-tidimensional moment problem is characterized in terms of limits of sums of quadratic expressions. This is an application appearing at the end of the title. Consequences resulting from the first part of the paper are pointed out with the aid of functional calculus for self-adjoint operators.

Abstract: Finding all possibly efficient solutions of an interval multiple objective linear programming (IMOLP) problem with interval coefficients in the objective functions, the constraint matrix and the right-hand side vector is dealt with. Up to now, there are no known methods that can find all possibly efficient solutions of an IMOLP problem with interval coefficients in the objective functions and the right-hand side vector. In this paper, we propose a method to find all possibly efficient solutions of an IMOLP problem with interval coefficients in the objective functions and the right-hand side vector. Some sufficient conditions to obtain all possibly efficient solutions of an IMOLP problem in a general case are also given.

Abstract: We establish that the set S = {−1, 0, +1} is the unique finite algebra satisfying the conditions of identity, reproduction, and cancellation. Beginning from three primordial states; Creation, Destruction, and Potential. We demonstrate that demanding closure, totality, and stability forces exactly one algebraic structure. This structure generates all subsequent number systems through iteration and extension, terminating uniquely at the field of complex numbers \( \mathbb{C} \) . We prove that Euler’s Identity, \( e^{i\pi} + 1 = 0 \) serves as the formal termination certificate of this extension sequence, resolving entirely to the elements of the primordial alphabet. The central result: S = {−1, 0, +1} is the unique algebra that is complete before extension and generative after it.

Abstract: Since the beginning of modern computer history, the Turing machine has been a dominant architecture for most computational devices, which consists of three essential components: an infinite tape for input, a read/write head, and finite control. In this structure, what the head can read (i.e., bits) is the same as what it has written/outputted. This is actually different from the ways in which humans think or do thought/tool experiments. More precisely, what humans imagine/write on paper are images or texts, and they are not the abstract concepts that they represent in the human brain. This difference is neglected by the Turing machine, but it actually plays an important role in abstraction, analogy, and generalization, which are crucial in artificial intelligence. Compared with this architecture, the proposed architecture uses two different types of heads and tapes, one for traditional abstract bit inputs/outputs and the other for specific visual ones (more like a screen or a workspace with a camera observing it). The mapping rules among the abstract bits and the specific images/texts can be realized by neural networks like Convolutional Neural Networks, YOLO, Large Language Models, etc., with a high accuracy rate. Logical reasoning is thus performed through the transfer of mapping rules. As an example, this paper presents how the new computer architecture (what we call "Ren machine" for simplicity here) autonomously learns a distributive property/rule of multiplication in the specific domain and further uses the rule to generate a general method (mixed in both the abstract domain and the specific domain) to compute the multiplication of any positive integers based on images/texts. The machine's strong reasoning ability is also corroborated in proving a theorem in Plane Geometry. Moreover, a robotic architecture based on Ren machine is proposed to address the challenges faced by the Vision-Language-Action (VLA) models in unsound reasoning ability and high computational cost.

Abstract: The primary objective of the paper is to make a case that the evaluation of the expected returns in the Two-Envelope Paradox (TEP) is problematic due to the ill-defined framing of X and Y as random variables representing two identical envelopes, where one contains twice as much money as the other. In the traditional literature, when X is selected, Y is defined in terms of the amount of money x in X using the values y= x and y=2x with equal probability .5, and vice versa when Y is selected. The problem is that the event X=x stands for two distinct but unknown values representing the money in the two envelopes, say $θ and $2θ. This renders X and Y ill-defined random variables whose spurious probabilities are used to evaluate the traditional expected returns. The TEP is resolved by applying formal probability-theoretic reasoning to frame the two random variables in terms of the two unknown values {θ, 2θ}, giving rise to sound probability distributions, whose expected returns leave a player indifferent between keeping and switching the chosen envelope.

Abstract: The Finite Ring Continuum (FRC) models physical structure as emerging from a sequence of finite arithmetic shells of order \(q = 4t+1\). While Euclidean shells \(\mathbb{F}_{p}\) support reversible Schr\"odinger dynamics, causal structure arises only in the quadratic extension \(\mathbb{F}_{p^2}\), where the finite-field Dirac equation is defined. This paper resolves the conceptual tension between the quadratic expansion \(\mathbb{F}_{p} \to \mathbb{F}_{p^2}\) and the linear progression of symmetry shells by introducing an algebraic innovation-consolidation cycle. Innovation corresponds to the temporary access to Lorentzian structure in the quadratic extension; consolidation extracts a finite invariant family and encodes it into the arithmetic of the next shell via a uniform G\"odel recoding procedure. We prove that any finite invariant set admits such a recoding, and we demonstrate the full mechanism through an explicit worked example for \(p = 13\). The results provide a coherent algebraic explanation for how finite representational systems---biological, computational, and physical---can acquire, assimilate and preserve structure.

Abstract: LAMBDASTORE is a new serverless platform with an integrated storage engine tailored for stateful serverless workloads. Its compute-storage co-design colocates serverless functions with their associated data, yielding significant performance gains. It also leverages the transaction interface of its storage engine to provide serializable workflows and exactly-once semantics. This paper presents the design of LAMBDASTORE and introduces three key contributions. First, it adopts an object-oriented model in which functions are bundled with their associated data, enabling function execution to be scheduled directly at the data’s location. Second, the storage layer provides efficient transaction processing by dynamically adjusting lock granularity and employing a customized optimistic concurrency control protocol. Third, to enable colocation without sacrificing elasticity, the system supports data migration and lightweight replication at the granularity of individual objects. Experiments show that LAMBDASTOREoutperformsconventional serverless platforms, especially in read-heavy workloads. In such settings, LAMBDASTORE achieves throughput orders of magnitude higher than existing systems, while maintaining average end-to-end latencies below 20 ms.

Abstract: This study proposes an integrated agility assessment system that combines Millimeter-Wave (MMW) radar, Ultra-Wideband (UWB) ranging, and Mixed Reality (MR) technologies to quantitatively evaluate athlete performance with high accuracy. The system utilizes the fine motion-tracking capability of MMW radar and the immersive real-time visualization provided by MR to ensure reliable operation under low-light conditions and multi-object occlusion, thereby enabling precise measurement of mobility, reaction time, and movement distance.To address the challenge of player identification during doubles testing, a one-to-one UWB configuration was adopted, in which each base station was paired with a wearable tag to distinguish individual athletes. UWB identification was not required during single-player tests. The experimental protocol included three specialized agility assessments—Table Tennis Agility Test I (TTAT I), Table Tennis Doubles Agility Test II (TTAT II), and the Agility T-Test (ATT)—conducted with more than 80 table tennis players of different technical levels (80% male and 20% female). Each athlete completed two sets of two trials to ensure measurement consistency and data stability.Experimental results demonstrated that the proposed system effectively captured displacement trajectories, movement speed, and reaction time. The MMW radar achieved an average measurement error of less than 10%, and the overall classification model attained an accuracy of 91%, confirming the reliability and robustness of the integrated sensing pipeline. Beyond local storage and MR-based live visualization, the system also supports cloud-based data uploading for graphical analysis and enables MR content to be mirrored on connected computer displays. This feature allows coaches to monitor performance in real time and provide immediate feedback.By integrating the environmental adaptability of MMW radar, the real-time visualization capability of MR, UWB-assisted athlete identification, and cloud-based data management, the proposed system demonstrates strong potential for professional sports training, technical diagnostics, and tactical optimization. It delivers timely and accurate performance metrics and contributes to the advancement of data-driven sports science applications.

Abstract: Current management strategies for sorghum aphids heavily rely on indiscriminate chemical application, leading to severe environmental consequences and impacting food safety. While precision spraying offers a viable remediation for pesticide overuse, its effectiveness depends on accurate pest location and classification. To address the critical challenge of segmenting small, swarming aphids in complex field environments, we propose FESW-UNet, a dual-domain attention network that integrates Fourier-enhanced attention, spatial attention, and wavelet-based downsampling into a UNet backbone. We introduce an Efficient Multi-scale Aggregation (EMA) module between the encoder and decoder to improve global context perception, allowing the model to better capture relationships between global and local features in the field. In the feature extraction stage, we embed a Similarity-Aware Activation module (SimAM) to target key infestation regions while suppressing background noise, thereby enhancing pixel-level discrimination. Furthermore, we replace conventional downsampling with Haar Wavelet Decomposition (HWD) to reduce resolution while preserving structural edge details. Finally, a Fourier-enhanced attention module (FEAM) is added to the skip-connection layers. By using complex-valued weights to regulate frequency-domain features, FEAM fuses global low-frequency structures with local high-frequency details, improving feature representation diversity. Experiments on the Aphid Cluster Segmentation dataset show that FESW-UNet outperforms other models, achieving an mIoU of 68.76% and mPA of 78.19%. The model also demonstrated strong adaptability on the AphidSeg-Sorghum dataset, reaching an mIoU of 81.22% and mPA of 87.97%. The proposed method provides an efficient and feasible technical solution for monitoring and controlling sorghum aphids via image segmentation and demonstrates broad application potential.

Abstract: In this paper, we propose an alternative kernel estimator for the regression operator of a scalar response variable S given a functional random variable T that takes values in a semi-metric space. The new estimator is constructed through the minimization of the least absolute relative error (LARE). The latter is characterized by its ability to provide a more balanced and scale-invariant measure of prediction accuracy compared to traditional standard absolute or squared error criterion. The LARE is an appropriate tool for reducing the influence of extremely large or small response values, enhancing robustness against heteroscedasticity or/and outliers. This feature makes LARE suitable for functional or high-dimensional data, where variations in scale are common. The high feasibility and strong performance of the proposed estimator is theoretically supported by establishing its stochastic consistency. The latter is derived with precision of the converge rate under mild regularity conditions. The ease implementation and the stability of the estimator are justified by simulation studies and an empirical application to near-infrared (NIR) spectrometry data. Of course the to explore the functional architecture of this data, we employ random matrix theory (RMT) which is a principal analytical tool of econophysics.

Abstract:

Accurate segmentation of thyroid nodules on ultrasound images remains a challenging task in computer-aided diagnosis (CAD) mainly because of low contrast, speckle noise, and large inter-patient variability of nodule appearance. Here a new deep learning-based segmentation method has been developed on the SwinUNet architecture supported by spatial attention mechanisms to enhance feature discrimination and localization accuracy. The model takes advantage of the hierarchical feature extraction ability of the Swin Transformer to learn both global context and local fine-grained details, whereas attention modules during the decoder process selectively highlight informative areas and suppresses irrelevant background features. We checked out the system's design using the TN3K thyroid ultrasound info that's out there. It got better as it trained, peaking around the 800th run with some good numbers: a Dice Similarity Coefficient (F1 Score) of 85.51%, Precision of 87.05%, Recall of 89.13%, IoU of 78.00%, Accuracy of 97.02%, and an AUC of 99.02%. These numbers are way better than when we started (like a 15.38% jump in IoU and a 12.05% rise in F1 Score), which proves the system can learn tricky shapes and edges well. The longer it trains, the better it gets at spotting even hard-to-see thyroid lumps. This SwinUnet_withAttention thing seems to work great and could be used in clinics to help doctors figure out thyroid problems.

Abstract: Background: In Japan, the number of older adults living alone has been increasing, raising the risk of unnoticed health decline or solitary death. Continuous monitoring using sensors can help detect behavioral changes indicating health issues and has the potential to support both older adults and their families. Methods: We obtained behavior and temperature data, continuously recorded over a long period at 15-min intervals from sensors installed in the homes of nine older adults living alone. After data cleaning, behavioral signals were analyzed using Fourier spectral analysis and multiple regression to extract 13-dimensional behavioral feature vectors. We attempted to detect temporal changes and behavioral characteristics by whitening these data and performing correspondence analysis. Results: Spectral analysis revealed 24-hour periodicity in all users’ behavior. Based on changes in the maximum component value and adjusted R2, individuals were classified into a stable group (SG) and a fluctuating group (FG). Boundary variance and false error analyses confirmed that behavioral temporal changes and individual characteristics could be detected objectively. Conclusions: The findings showed that temporal changes in daily behavior among older adults living alone can be detected using simple continuous sensor data, suggesting potential for early detection of health-related changes and preventive support in home.