The deployment of IoT platforms for SmartCity applications is demanding solutions to assure security and integrity levels. In this context, physical unclonable functions (PUF) may overcome the security drawbacks of storing the security key in a non-volatile memory. The existence of SRAM in embedded systems has driven the implementation of PUF solutions using memory circuit as entropy sources. The use of a non-specific memory design requires the need of identify the adequate cells useful for PUF applications. This work proposes a new methodology to distinguish the adequate cells based on mismatch factor calculation. The mismatch analysis using physical and performance parameters shows the viability of the selection methodology and computes the robustness of the responses in terms of probability of error in the start-up value.

Polyspectral models are a class of nonlinear behavioral models that were applied to power amplifiers over 20 years ago but have not seen subsequent development. Polyspectral models have a multiple-branch topology that is a special case of the Volterra series. They provide closed-form, optimum solutions to their constituting filters. Here we bring the model construction measurements up to date using modern test equipment. The two-branch version presented here is simple to implement and provides good accuracy and computational efficiency for system simulations. We present results for a GaN amplifier subject to orthogonal frequency-division multiplexing for signals of bandwidth up to 200 MHz.

The pharmaceutical industry faces significant cybersecurity challenges, increasing threats to sensitive data, intellectual property, and patient information. Traditional cybersecurity models, including signaturebased and threshold-based intrusion detection systems (IDS), often fail to address cyber threats' dynamic and evolving nature. This paper introduces a novel AI-driven cryptographic system designed to enhance cybersecurity in the pharmaceutical sector. The proposed model integrates advanced AI techniques, including reinforcement learning, game theory, and C*-algebra, to optimize resource allocation, improve threat detection, and enhance cryptographic protocols. Through a comprehensive evaluation, the AI model demonstrates superior performance over traditional methods, achieving a detection rate of 97.4%, significantly reducing false positive rates (2.6%) and response time (50ms), and achieving an F1-score of 0.95. Additionally, the AI model's ability to continuously adapt to emerging cyber threats makes it a future-proof solution for the pharmaceutical industry. The model's scalability ensures its applicability across various organizational sizes. Future research directions include the integration of blockchain, cloud computing, and post-quantum cryptography to strengthen the system's resilience further. This paper presents a scalable, adaptive, and efficient solution to address the growing cybersecurity challenges in the pharmaceutical industry, ensuring critical data protection in an increasingly complex threat landscape.

This study presents a systematic scoping review of delegated voting in decentralized autonomous organizations (DAOs), focusing on its governance implications, implementation forms, and challenges. While delegated voting is often adopted to address low participation and mitigate the cognitive burden of direct involvement, the existing literature highlights its potential to exacerbate centralization, particularly when whales or influential networks are disproportionate. Various implementation models, including offchain platforms (e.g., Snapshot), hybrid governance architectures, and token-based delegation systems, exhibit distinct trade-offs in transparency, cost, and adaptability. Although innovations such as quadratic voting, weighted delegation constraints, and reputation-based governance show promise for improving fairness and accountability, they also face vulnerabilities, such as gaming, collusion, and high implementation complexity. Synthesizing insights from 13 publications, this review offers recommendations ranging from multi-tier governance structures to AI-driven support tools that aim to balance efficiency, inclusivity, and trust in DAO voting processes, thereby informing robust context-aware governance.

Block-term tensor decomposition (BTD) was recently reconsidered in the context of array signal processing, and shown to be the natural choice in sensor arrays of increased dimensionality and channel multipath. Semi-blind joint channel estimation/data detection (JCD) was addressed in the special but important case of the uniform rectangular array (URA), via efficient algorithms that wed existing JCD schemes with BTD approximation. That work is revisited in this paper from a Bayesian standpoint to address the discrete nature of the input symbol constellation. This offers a new interpretation of the outstanding BTD JCD scheme among those previously developed. Moreover, two probabilistic methodologies are followed, which, when endowed with the BTD structure, result in additional JCD schemes that provide alternatives to our previous work in detection performance and computational complexity. The receivers are evaluated via simulation results at various noise levels, and different array and constellation sizes and favorably compared with the training-only-based solution.

Public service motivation (PSM) has been defined as a somebody's desire to advance community principles over committing facilities to the governmental sector. Whereas prosocial motivation (PM) is the desire of the total community to support individuals, PSM is a definite kind of prosocial motivation connected to public provision. High public service motivation is significantly linked with high levels of prosocial behavior between staffs in the governmental sector. PSM and PM have been proposed to affect work results. Although there is noteworthy study on PSM and PM, there is a lack of significant study on these motivations through detailed orientation to non-Western countries. This study examines the impact of PSM and PM on engagement, commitment, and organizational citizenship behavior (OCB) in the governmental sector in the Kurdistan Region of Iraq via using machine learning techniques. Data were gathered from 418 public workers from different cities in the Kurdistan Region. Machine learning procedures, with support vector machines (SVM), k-nearest neighbors (KNN), linear regression, and logistic regression, were used to analyze the data. The outcomes discovered that PSM assumes employee engagement, organizational commitment, and OCB between staffs. Moreover, the connection between PM and these variables is also positive, mentioning that both PSM and PM have impact on the whole performances and attitudes of staffs in the public sector setting. This study has inferences for worker motivation in the public sector, particularly for human resource departments and employees. Furthermore, it informs current discussions on the usage of motivational systems or practices in the governmental sector and their association to administrative efficiency.

This paper describes the properties of pin Ge1-ySny diodes (y=4.4-10% Sn) grown directly on Si(100) wafers as a way to investigate the impact of eliminating the Ge buffer layers used conventionally for the integration of GeSn devices on Si. The technology offers a simplified and potentially lower-cost alternative for SWIR-LWIR applications. Two device designs are discussed. The first design adopts a layer sequence n-Ge1-ySny/i-Ge1-ySny/p-Ge1-ySny/Si, featuring a single defected bottom interface between the p layer and the Si wafer. This was followed by an even simpler n-Ge1-ySny/i-Ge1-ySny /p-Si heterostructure design. In both cases, the top i/n interface is pseudomorphic and potentially defect-free. The Ge1-ySny layers are produced by CVD reactions of Ge3H8 and SnH4 at temperatures ranging from 300 o C to 390 o C. The n-type electrodes in the samples were doped with As using As(SiH3)3, and the p-type GeSn layers were doped using diborane as the source of B-atoms. All samples were characterized by XRD, RBS, IR-ellipsometry, AFM and TEM. The layers were found to be monocrystalline single-phase alloys exhibiting mostly relaxed strain states and top surfaces devoid of the cross-hatch surface patterns that are typical of Ge1-ySny films grown on Ge buffers. Current voltage I-V curves of fabricated devices over the 4.4-10% Sn range of interest showed that rectifying behavior is readily attained. It appears that the effect of eliminating the Ge-buffer is an increase of only one order magnitude in the density of defects responsible for the dark current, together with an increase in residual doping in the nominally intrinsic layer. The results suggest that these deleterious effects may be further reduced with improved sample designs, particularly at high Sn-concentrations, opening up new alternatives for the effective integration of GeSnand Si technologies.

The relative supply chain environment is rapidly changing due to technological advancement, increasing complexity, and pressure to improve sustainability and supply chain risk. Sourcing and procurement years were recognized as cost-focused processes. However, digital transformation is revolutionizing them. AI, blockchain, IoT, RPA, and cloud options are the technologies that can help organizations move procurement into the realm of a strategic value-creating process. Thus, concerning the focus of this article, it is possible to identify several major digital transformation trends for procurement: Increasing operational efficiency, Enhancing supplier relationships, Managing risks, and Achieving sustainable procurement. Using examples of case studies and a comparative analysis of traditional and digital procurement approaches, including observations, this paper illustrates the change inhered by digital tools. It also covers issues like organizational resistance, integration issues, and cybersecurity, which are further explained to reduce such problems. At last, this article underscores the management decision-makers focus and the value of digital procurement and sustainable competitive advantage in modern procurement and supply chain management.

The early identification of Autism Spectrum Disorder (ASD) is crucial for facilitating prompt therapies that can markedly enhance quality of life. This work presents an innovative EEG-based Brain-Computer Interface (BCI) system aimed at improving the precision and dependability of ASD classification. Utilizing the BCIAUT P300 dataset, comprising EEG recordings from 15 participants across 105 sessions, we established a novel multimodal framework. This system incorporates Vision Transformer (ViT) for spatial feature extraction and Long Short-Term Memory (LSTM) networks for temporal analysis, merging the advantages of both architectures. The ViT-LSTM model attained a validation accuracy of 94.75%, but the EfficientNet-LSTM model exhibited superior performance with a validation accuracy of 95.61%. Statistical investigations, including a P-value of 0.0288 and a T-value of 5.76 for the EfficientNet-based design, validated the reliability and robustness of these findings. ViT adeptly captures global spatial relationships using self-attention processes, whereas EfficientNet improves spatial representation through its pretrained feature extraction. To enhance usability in clinical environments, Explainable AI (XAI) methodologies, particularly Local Interpretable Model-Agnostic Explanations (LIME), were utilized. LIME offers transparent, feature-specific insights into model predictions, enabling physicians to comprehend and have confidence in the decision-making process. This system sets a new standard in AI-driven brain-computer interfaces by merging cutting-edge accuracy with interpretability, providing scalable and significant solutions for the diagnosis and treatment of ASD.

Color space is introduced to discuss 3-coloring of a graph G(V,E). It is a 3x|V| lattice that every column expresses a vertex and every row expresses a color. A color space can provide with all the information of a colored graph that include the topology, coloring and coloring transformation. So, it reduces calculation in searching 3-uncolorable vertices, And, we may try to find a deterministic algorithm to search some 3-uncolorable by using the color space.

The complexity of contemporary language tasks has driven substantial research toward enhancing model architectures capable of processing intricate linguistic patterns with heightened efficiency and contextual precision. Introducing the Dynamic Synaptic Filtering Mechanism (DSFM), a novel approach designed to enable neural pathway modulation, represents a breakthrough in adaptable pathway activation within language models, allowing for dynamic adjustments to input complexity and task-specific demands. Implemented within a transformer-based large language model, DSFM facilitates selective synaptic activation by filtering pathways based on probabilistic assessments, thus offering a level of contextual sensitivity and computational efficiency previously unachievable with traditional static architectures. Comprehensive experiments reveal that DSFM reduces processing latency, optimizes memory allocation, and improves model accuracy, particularly in tasks requiring complex contextual understanding and syntactic variation. Quantitative results indicate that DSFM's probabilistic filtering substantially balances pathway utilization across layers, promoting both adaptive efficiency and computational stability under variable task loads. The findings suggest that DSFM enhances the structural and operational flexibility of language models, achieving a significant reduction in resource demands while maintaining linguistic accuracy, which positions DSFM as a transformative advancement in the pursuit of adaptable, resource-efficient language model architectures.

The work reported in this article addresses the issue of reliability assessment from a systemic perspective. The general idea of the paper is based on the observation that failures occur at the level of elementary components but are induced by stresses emanating from the form of use of the system and the environment in which they are placed. Within this scope, a so-called U-Cycle approach is developed. It allows, in a matrix framework, to match the levels of decomposition of a system from a functional viewpoint and the resulting failures from a dysfunctional perspective. The modelling of this U-Cycle thus involves taking into account the repercussions of the systems' mission profiles in order to convert them into levels of stress on the basic components in a functional top-down analysis. Then, in a dysfunctional bottom-up study, it leads to characterise the impact of the failure of these components on the operation of the sub-systems to which they belong. The paper describes the principles of methodological instrumentation of this U-cycle based on the superposition of four descriptive views representing the structural, temporal, physical and logical forms of organisation. A case study based on the qualitative modelling of the reliability of an aircraft electrical flight control function is proposed to illustrate the developments.

Predicting signal phase and timing (SPaT) most likely switching times and confidence level is needed to enhance green light optimal speed advisory (GLOSA) systems. This study proposes an architecture based on transformer encoders to improve prediction performance. This architecture is combined with different deep learning methods, including Multilayer perceptrons (MLP), long-short-term memory neural networks (LSTM), and Convolutional long-short term memory neural networks (CNNLSTM) to form an ensemble of predictors. The ensemble is used to make data-driven predictions of SPaT information for six different intersections along the Gallows Road corridor in Virginia. The study outlines three primary tasks. Task one is predicting whether a phase would change within 20 seconds. Task two is predicting the exact change time within 20 seconds. Task three is assigning a confidence level to that prediction. The experiments show that the proposed transformer-based architecture outperforms all the previously used deep learning methods for the first two prediction tasks. For the first task, the transformer encoder model provides an average accuracy of 96%. For task two, the transformer encoder models provided an average mean absolute error (MAE) of 1.49 seconds, compared to 1.63 seconds for other models. Consensus between models is shown to be a good leading indicator of confidence in ensemble predictions. The ensemble predictions with the highest level of consensus are within one second of the true value for 90.2% of the time as opposed to those with the lowest confidence level, which are within one second for only 68.4% of the time.

An effective passive source localization technique is proposed as essential for industrial applications. Extensive research has been conducted on multi-station passive localization using two-dimensional angle of arrival (2D AOA), time difference of arrival (TDOA), and their combination. However, certain practical factors limit the observation to only a restricted set of one-dimensional angles of arrival (1D AOA). Additionally, there may be perturbations in the stations' locations. To address these issues, this paper proposes a localization model that hybrid a single 1D AOA with multiple TDOA measurements, taking into account the perturbations in station locations. For clarity, we refer to this combination as hybrid measurements. Two types of localization algorithms are proposed for the hybrid measurement model. The first type approximates the localization problem as a non-convex constrained weighted least squares optimization problem. We introduce two solution algorithms: the two-step weighted least squares algorithm (TWLS) and the semi-infinite relaxation method (SDR), both of which do not require an initial guess. The second type directly transforms the localization problem into a maximum likelihood estimation (MLE) minimum optimization problem. Several iterative algorithms are introduced, including iterative weighted least squares based on Taylor expansion (IRLS-TE), Gaussian Newton (GN), adaptive moment estimation (Adam), and simplification adaptive moment estimation (SAdam). Unlike the first type, these algorithms require an initial guess. Furthermore, we derive the Cramér-Rao Lower Bound (CRLB) for hybrid measurements and analyze the computational complexity and minimum station configuration needed for each solution algorithm. Simulations and the SWellEx96-S5 ocean acoustic experiments demonstrate the effectiveness of the proposed methods. The results indicate that the proposed algorithms can achieve CRLB performance at low noise levels, with the TWLS algorithm showing the lowest complexity, while the Adam algorithm achieves the highest accuracy. These proposed solution algorithms can be selected based on computational complexity, localization accuracy and source of interest location.

Adders are the most important element in the ALU. Speeding the addition improves the performance of the ALU in particular and the CPU in general. Pipelining is used to process tasks in parallel with minimal adjustment of the hardware needed to process the same tasks sequentially. In this paper, a pipelining procedure is presented to enable the half adder to perform the addition of two numbers in parallel. The resulted speed up factor is guaranteed to perform the same addition in half the clock cycle of full adder.

This research delves into the advancements in embedded systems, with a focus on three pivotal areas: IoT Implementation, Real-time computing, and Energy consumption. Embedded systems are now the foundation of most current technology, networked systems, smart gadgets, or even straightforward automation systems. In this paper, the author discusses ways IoT integration has increased the range of applications in embedded systems and enhanced their interaction. It also explores new developments in real-time processing; here, the focus is on the parts played by fast computation and quick response to them, such as medical, automobile, and industrial control. Also, the research raises the issue of the growing relevance of energy efficiency. It focuses on energy-efficient notions, innovative approaches, and hardware solutions aimed at reducing power consumption and sufficient performance. Security issues, system issues in general, and system scalability are also presented, providing a perspective of the complexity of the field. The research results should be helpful for young researchers, developers, and other industry members to understand the current trends that should be continued and the gaps that need to be filled in the future.

In this correspondence, we point out an error in the proof of Theorem 1 of [1] and provide the revised versions of definitions to correct this error. In addition, the statements of similar conditions are also corrected.

This paper presents an innovative wind energy harnessing mechanism, grounded in the fundamental principles of electromagnetic rotary generation. The proposed design introduces a significant advancement through the modification of the generator's shaft, which is engineered to be hollow and incorporates a precision-designed fin tunnel to optimize wind energy conversion efficiency. This approach addresses key limitations of conventional wind turbines, including elevated costs, substantial spatial requirements, and suboptimal performance under low wind speed conditions. Empirical case studies conducted in urban, offshore, and microgrid contexts demonstrate the practicality and effectiveness of this design. Results from simulations and theoretical analyses indicate enhanced energy conversion efficiency, lower manufacturing costs, and a compact structural profile adaptable to a variety of environmental applications. This study aims to contribute to the corpus of renewable energy research by offering a scalable, efficient, and cost-effective solution for wind energy generation, supported by rigorous theoretical underpinnings and empirical evidence. The proposed methodology capitalizes on advancements in composite material science, aerodynamic optimization, and magnetic flux dynamics. Comparative analyses with conventional wind energy systems underscore the superior performance and versatility of this novel design. By synthesizing insights from peer-reviewed literature and performing comprehensive evaluations, this research establishes a robust framework for advancing renewable energy technologies and provides a foundation for subsequent investigative and developmental endeavors.