Shift Operator Research Articles

Symbolic dynamics of planar piecewise smooth vector fields

It is well known that many results obtained for piecewise smooth vector fields do not have an analogous for smooth vector fields and vice-versa. These differences are generated by the non-uniqueness of trajectory passing through a point. Inspired by the classical fact that one-dimensional discrete dynamic systems can produce chaotic behavior, we construct a conjugation between shift maps and piecewise smooth vector fields presenting homoclinic loops which are associated to symbols in such a way that the flow restricted to a homoclinic loop is codified with a symbol. The construction of the topological conjugation between the quoted piecewise smooth vector fields and the respective shift spaces needs several technicality which were solved considering a specific family of piecewise smooth vector fields (Theorem A) and then generalizing the result for an entire class of piecewise smooth vector fields (Theorem B). By means of the results obtained and the techniques employed, a new perspective on the study of piecewise smooth vector fields is brought to light and, through already established results for discrete dynamic systems, we will be able to obtain results regarding piecewise smooth vector fields.

The Potential of Quality Target Product Profile in the Optimization of Nanoemulsions

The application of Quality Target Product Profile (QTPP) in optimizing nanoemulsion (NEM) shows immense potential in advancing pharmaceutical formulation design for effective drug delivery. By aligning QTPP with nanoemulsion attributes, this approach offers a pathway to tailored formulations that meet specific therapeutic objectives and responses. Incorporating QTPP facilitates informed choices in formulating design, covering pivotal factors like stability, drug release kinetics, bioavailability, and precise targeting. Moreover, this review extensively explores the real-world application of QTPP-guided tactics in refining nanoemulsion optimization. It highlights their pivotal role in anticipating and regulating <i>in vivo</i> responses, encompassing vital aspects like pharmacokinetics and pharmacodynamics. By conducting thorough examinations of case studies and research outcomes, this article clarifies the effectiveness of aligning QTPP criteria with NEM characteristics. This approach fosters the creation of customized formulations precisely suited to achieve distinct therapeutic objectives. This review amalgamates contemporary insights into harnessing QTPP for nanoemulsion optimization, illuminating its capacity to streamline formulation design, amplify treatment effectiveness by desiring drug release, and catalyze transformative shifts in pharmaceutical research.

Advanced cloud-based real-time credit scoring models: Leveraging big data and AI

Cloud-based credit scoring systems represent a transformative shift in financial technology, leveraging artificial intelligence and machine learning to revolutionize creditworthiness assessment. These platforms integrate traditional and alternative data sources, enabling real-time processing and comprehensive risk evaluation. The evolution from conventional methods has enhanced accessibility for underserved populations while maintaining robust security measures and regulatory compliance. Through advanced technical architecture incorporating data collection, processing, and analysis layers, these systems deliver faster, more accurate credit decisions. The implementation of sophisticated fraud detection mechanisms, coupled with machine learning algorithms, has significantly reduced false positives and improved risk assessment capabilities. Industry leaders like TransUnion demonstrate the practical success of these innovations through improved operational efficiency and expanded market reach. Despite challenges in data quality management and regulatory compliance, the future of credit scoring continues to advance with blockchain integration, enhanced real-time processing, and innovative fraud detection capabilities.

Cyclicity of the shift operator through Bezout identities

Cyclicity of the shift operator through Bezout identities

Hankel Operators Between Köthe Spaces

This paper is about the operators defined between Köthe spaces whose associated matrix is a Hankel matrix. After demonstrating how these operators are defined, the conditions for continuity and compactness of these operators are constructed. It is shown that the backward and forward shift operators are mean ergodic and Ces´aro bounded by establishing a relationship between the backward and forward shift operators and Hankel and Toeplitz operators on power series spaces.

A quantum solution to blind millionaire problem with only single-particle states

Blind millionaire (BM) problem is an extended version of the initial millionaire problem required to compare the sum of the participants’ secrets between different groups. As a new topic of quantum secure multiparty computing, existing protocols with some special entangled states may not be easily achieved in practice. This study proposes a non-entangled method of solving the quantum blind millionaire (QBM) problem with special d-level single-particle states for the first time. To protect the confidentiality of transmission secrets, this protocol exploits the property of randomly generated d-level single-particle states. Furthermore, simple shift operations are used to encode the respective secrets. Detailed security analysis demonstrates that this protocol is impervious to internal and external threats. The presented methods can not only be used to solve the blind millionaire problem but also be used as a basic module to solve other secure multiparty computing problems.

A Technique for Image Encryption Using the Modular Multiplicative Inverse Property of Mersenne Primes

Mersenne prime numbers, expressed in the form (2n − 1), have long captivated researchers due to their unique properties. The presented work aims to develop a symmetric cryptographic algorithm using a novel technique based on the logical properties of Mersenne primes. Existing encryption algorithms exhibit certain challenges, such as scalability and design complexity. The proposed novel modular multiplicative inverse property over Mersenne primes simplifies the encryption/decryption process. The simplification is achieved by computing the multiplicative inverse using cyclic bit shift operation. The proposed image encryption/decryption scheme involves a series of exor, complement, bit shift, and modular multiplicative inversion operations. The image is segmented into blocks of 521 bits. Each of these blocks is encrypted using a 521-bit key, ensuring high entropy and low predictability. The inclusion of cyclic bit shifting and XOR operations in the encryption/decryption process enhances the diffusion properties and resistance against attacks. This approach was experimentally proven to secure the image data while preserving the image structure. The experimental results demonstrate significant improvements in security metrics, including key sensitivity and correlation coefficients, confirming the technique’s effectiveness against cryptographic attacks. Overall, this method offers a scalable and secure solution for encrypting high-resolution digital images without compromising computational efficiency.

Proximity-based solutions for optimizing autism spectrum disorder treatment: integrating clinical and process data for personalized care

Autism Spectrum Disorder (ASD) affects millions of individuals worldwide, presenting challenges in social communication, repetitive behaviors, and sensory processing. Despite its prevalence, diagnosis can be lengthy, and access to appropriate treatment varies greatly. This project utilizes the power of Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), to improve Autism Spectrum Disorder diagnosis and treatment. A central data hub, the Master Data Plan (MDP), will aggregate and analyze information from diverse sources, feeding AI algorithms that can identify risk factors for ASD, personalize treatment plans based on individual needs, and even predict potential relapses. Furthermore, the project incorporates a patient-facing chatbot to provide information and support. By integrating patient data, empowering individuals with ASD, and supporting healthcare professionals, this platform aims to transform care accessibility, personalize treatment approaches, and optimize the entire care journey. Rigorous data governance measures will ensure ethical and secure data management. This project will improve access to care, personalize treatments for better outcomes, shorten wait times, boost patient involvement, and raise ASD awareness, leading to better resource allocation. This project marks a transformative shift toward data-driven, patient-centred ASD care in Italy. This platform enhances treatment outcomes for individuals with ASD and provides a scalable model for integrating AI into mental health, establishing a new benchmark for personalized patient care. Through AI integration and collaborative efforts, it aims to redefine mental healthcare standards, enhancing the well-being for individuals with ASD.

Design and Evaluation of Noise Simulation Algorithm Using MATLAB Ray Tracing Engine for Noise Assessment and Prediction

The Malaysian Department of Occupational Safety and Health (DOSH) reported that noise-induced hearing loss (NIHL) accounted for 92% of occupational diseases in 2019. To address this, accurate risk assessment is crucial. The current noise evaluation methods are complex and time-consuming, relying on manual calculations and field measurements. An easy-to-use, open-source noise simulator that directly compares the output with national standards would help mitigate this issue. This research aims to develop an advanced noise evaluation tool to assess and predict unregulated workplace noise, providing tailored safety recommendations. Using a representative plant layout, the Sound Pressure Level (SPL) is calculated using MATLAB’s ray tracing propagation model. The model simulates all possible transmission paths from the source to the receiver to derive the resultant SPL. A noise simulation application featuring a graphical user interface (GUI) built with MATLAB’s App Designer (version: R2024a) automates these computations. The simulation results are validated against the DOSH’s safety standards in Malaysia. Additional safety metrics, such as the recommended maximum exposure time and the required Noise Reduction Rating (NRR) for hearing protection, are calculated based on the SPLs for hazardous locations. The simulation algorithm’s functionality is validated against manual calculations, with an average deviation of just 3.06 dB, demonstrating the model’s precision. This tool can assess and predict indoor noise levels, provide information on optimal exposure limits, and recommend necessary protective measures, ultimately reducing the risk of NIHL in factory environments. It can potentially optimise plant floor operations for existing and new facilities, ensuring safer shift operations and reducing worker noise hazard exposure.

Streamlining Mortgage Workflows with Automation: A Technical Implementation Guide

Mortgage industry automation represents a transformative shift in lending operations, offering solutions to longstanding challenges in processing efficiency and operational costs. This article presents a comprehensive framework for implementing automation technologies, including robotic process automation (RPA), artificial intelligence, and machine learning, within existing mortgage workflows. Through detailed analysis of integration methodologies, technical architecture requirements, and performance optimization strategies, this article provides practical guidance for mortgage professionals seeking to modernize their operations. The discussion encompasses critical aspects of successful automation implementation, from initial assessment and strategic planning to risk mitigation and staff training. Special attention is given to maintaining compliance standards and ensuring human oversight in crucial decision-making processes. By addressing both technical and operational considerations, this article serves as a roadmap for lending institutions aiming to achieve operational excellence through automation while preserving the essential human elements of mortgage processing.

Mixed Attention and Channel Shift Transformer for Efficient Action Recognition

Mixed Attention and Channel Shift Transformer for Efficient Action Recognition

Generation Z in Plastic Surgery: Challenges, Solutions and New Horizons - A European and transatlantic overview.

The entrance of Generation Z (born 1997-2012) into professional life, including the practice of medicine, marks a transformational shift in priorities and values. This generation, shaped by digital immersion and a strong focus on work-life balance, is redefining the landscape of Plastic Surgery. Their preferences for inclusivity, innovation, and structured working hours challenge traditional models of mentorship, patient care, and surgical training. The aim of this survey was to explore the impact of Generation Z on the field of Plastic Surgery through insights offered by leaders of major Plastic Surgery societies in Europe and the United States, who together provide a comprehensive perspective on generational change and its implications for clinical practice, education, and healthcare systems in Plastic Surgery. In October 2024 eight leaders of national Plastic Surgery societies and associations were invited to complete a structured questionnaire with five open-ended questions. They provided detailed responses on challenges, opportunities, and structural changes needed to address Generation Z's influence. Key findings highlight a generational shift in the approach to professional life, with an emphasis on digital innovation, work-life balance, and the interaction with social media. Respondents noted an increased interest in aesthetic over reconstructive surgery and a preference for private practice among younger surgeons, raising concerns about workforce shortages in public healthcare. Advances in digital learning and simulation-based training were identified as opportunities to facilitate medical education. However, the ethical challenges of misinformation on social media and a decline in hierarchical mentorship were emphasized as critical concerns. Despite generational tensions, the integration of Generation Z's strengths in technology and advocacy for systemic reform holds promise for addressing burnout and enhancing healthcare delivery. The arrival of Generation Z in Plastic Surgery represents a possible pivotal moment to reimagine traditional models of surgical education, patient care, and professional priorities. Collaboration between generations and proactive adaptation to these changes is an imperative to ensure a dynamic, inclusive, and sustainable future for the specialty.

Hermanas in dialogue: amplifying female South American voices in Australian Health and Physical Education Teacher Education

ABSTRACT In the landscape of a multicultural society, this paper explores the absence of diversity in Australian Health and Physical Education Teacher Education (HPETE): a prevailing White profession. This study delves into the complex and non-linear narratives of silenced voices, shedding light on the urgent need to investigate the experiences of non-White (female) HPETE scholars with English as an additional language. The paper aims to explore the intersections of ethnicity, gender, age, sexuality and language of three South American women who live in Australia and work in HPETE (us). Inspired by decolonial and Southern theories, we want to introduce knowledge that was created in the Global South and, therefore, we use the work of Freire [(1987). Pedagogia Do Oprimido [Pedagogy of the oppressed] (17th ed). Paz e Terra.] as a theoretical framework mainly with his concepts of praxis and conscientização [critical consciousness], together with critical intersectional feminism. We also utilised a Freirean-inspired method for data generation (‘pedagogical letters’), and feminist-originated collective biographies. Findings reveal our struggles to survive colour-blindness Australian HPETE, particularly to being labelled as ‘exotic’, how our South American physical education knowledge is not often valued in our workplaces, and the struggles with the neoliberal university system. In our final letter addressed to other non-White female HPETE scholars, or las hermanas, we underscore the importance of amplifying our voices, fostering dialogue and collectively challenging the existing structures that perpetuate exclusionary practices within HPETE. Our hope with this paper is to inspire a transformative shift towards inclusivity and recognition of diverse voices within HPETE, fostering a more equitable and reflective HPETE space.

Artificial intelligence and machine learning in veterinary medicine: a regulatory perspective on current initiatives and future prospects.

The US FDA's Center for Veterinary Medicine (CVM) is advancing its leadership in veterinary science by integrating AI and machine learning (ML) into its regulatory framework and scientific initiatives. This paper explores the CVM's strategic approach to harnessing these technologies to enhance human and animal health by supporting innovative products and methods. Key areas of focus include regulatory adaptation, genomic research, and information technology modernization. The Animal and Veterinary Innovation Agenda outlines the Center's commitment to fostering innovation in veterinary medicine while addressing emerging challenges. This includes developing AI/ML-driven tools for antimicrobial resistance research, genome editing safety, and postmarketing safety surveillance. The paper discusses the CVM's participation in the FDA's role in shaping guidance documents for AI in regulatory decision making. In genomic research, the CVM is utilizing AI/ML to study antimicrobial resistance and improve genomic editing techniques. These technologies enhance the understanding of resistance mechanisms and facilitate the precise identification of genetic alterations. Artificial intelligence is also pivotal in information technology modernization efforts, aimed at streamlining data management and enhancing operational efficiency. The paper highlights the efforts to integrate AI/ML in safety surveillance, including signal detection and case processing. It emphasizes the importance of human-led governance, data quality, and model validation in ensuring the ethical deployment of AI technologies. The CVM's initiatives represent a transformative shift toward more efficient and innovative regulatory approaches. The paper concludes with a call for continued collaboration among researchers, industry, and regulatory bodies to advance AI integration and achieve mutual goals in animal health.

An Autoethnography of an Islamic Teacher Education Programme

This article explores Islamic Teacher Education through an autoethnographic account of the author’s experience with the Graduate Certificate of Education (Islamic Pedagogy) at the University of South Australia. It addresses the lack of research on how Islamic Pedagogy is taught, contributing to the growing scholarship on faith-based teacher education. Autoethnography is a qualitative research method that combines autobiography and ethnography, emphasising personal experiences to explore cultural communities. It is especially useful in studying emerging concepts like Islamic Pedagogy and Faithful Praxis. This approach challenges Western positivism, promoting epistemic reflexivity, and offering critical insights into marginalised perspectives and educational practices. This paper employs autoethnography to present the author’s Faithful Praxis journey as a transformative pedagogical shift, shaped by their experiences with Western and Islamic epistemologies, aiming to empower Muslim voices in education and challenge marginalization, with the Graduate Certificate fostering epistemic reflexivity and providing a platform to reconcile Islamic and Western knowledge in the classroom. This paper also clarifies the distinction between Islamic Pedagogy and Islamic Integration through autoethnography by highlighting their complementary nature as opposed to the author’s initial assumptions around their interchangeability. Whilst this article contributes to the growing Islamic Teacher Education scholarship through an autoethnographic perspective, further research to assess broader program efficacy is still needed.

The Integration of Artificial Intelligence in Communication Design. Case Studies from the Polytechnic of Milan: from Digital Culture to Sociology of Media

L’INTEGRAZIONE DELL’INTELLIGENZA ARTIFICIALE NEL DESIGN DELLA COMUNICAZIONE. CASI DI STUDIO DEL POLITECNICO DI MILANO: DALLA CULTURA DIGITALE ALLA SOCIOLOGIA DEI MEDIA Abstract The advent of Artificial Intelligence (AI), particularly Generative AI (GenAI), has catalyzed a transformative shift in educational methodologies, bridging traditional pedagogy with advanced digital technologies. This article presents a comparative analysis of two case studies conducted within the Communication Design Program at Polytechnic of Milan, exploring the integration of GenAI as a collaborative tool in Digital Culture and Sociology of Media courses. By examining the implications, challenges, and outcomes of embedding GenAI within these educational frameworks, we aim to highlight the role of AI as a «co-pilot» in learning processes, offering innovative approaches to knowledge creation and problem-solving. The case studies underscore common themes, such as the democratization of creative tools through AI, the necessity for critical AI literacy, and the exploration of AI as a facilitator of innovative content generation. Simultaneously, the article delves into the differences between the two courses, including participant numbers, course structures, and project aspects, providing a comprehensive understanding of GenAI’s impact on educational experiences and students’ problem-solving skills.

Fair and Ethical Resume Screening: Enhancing ATS with JustScreen the ResumeScreeningApp

Abstract— In today's fast-paced job market, the efficiency and fairness of the resume screening process are paramount. "JustScreen" emerges as a cutting-edge solution leveraging advanced Natural Language Processing (NLP) to automate resume evaluation, thus eliminating biases and promoting merit-based candidate selection. This thesis explores JustScreen's innovative approach to integrating NLP and machine learning algorithms to enhance the recruitment workflow, ensuring a more streamlined, unbiased, and efficient candidate assessment process. The methodology involves several key components: data preprocessing, NLP information extraction, fairness metrics calculation, bias mitigation, and interpretability techniques. By utilizing frameworks such as spaCy for NLP tasks, JustScreen aims to overcome the challenges of traditional manual screening processes, improving both accuracy and fairness. This thesis explores the transition from developing a full Application Tracking System (ATS) to creating a powerful enhancement for existing ATS systems. The ResumeScreeningApp/ JustScreen integrates generative AI to provide comprehensive resume analysis, adding significant value to traditional ATS functionalities. Initial evaluations indicate a significant advancement in talent acquisition practices, promoting equal opportunities and reducing the impact of potentially discriminatory factors. This research signifies a transformative shift in recruitment, setting new standards for ethical and efficient hiring practices using Generative AI.

Implementation of Core Values "BerAKHLAK" In Improving Professionalism and Performance of State Civil Apparatus

The State Civil Apparatus (ASN) in Indonesia is undergoing a transformative shift with the implementation of the BerAKHLAK core values—Berkualitas (Quality), Akuntabel (Accountability), Harmonis (Harmonious), Loyal, Adaptif (Adaptive), and Kolaboratif (Collaborative). Launched in 2021, BerAKHLAK aims to enhance professionalism, accountability, and service quality in public administration. However, research on its localized implementation in complex administrative settings like East Java remains limited. This study evaluates the implementation and impact of BerAKHLAK on the professionalism and performance of ASN in East Java, with a focus on internalization processes, challenges, and outcomes. The research employed a qualitative case study approach, gathering data from 30 diverse participants, including ASN personnel, supervisors, and service recipients. Data collection methods included semi-structured interviews, focus group discussions, and document reviews. Thematic analysis was conducted within the framework of organizational culture theory and performance management models. The study identified structured implementation strategies such as monthly workshops, mentoring programs, and performance dashboards. Leadership played a critical role in fostering a values-driven culture. Key challenges included resistance from older employees, inconsistent leadership commitment, and limited digital infrastructure. Despite these barriers, BerAKHLAK significantly improved service quality, interdepartmental collaboration, and public satisfaction. The findings highlight the transformative potential of BerAKHLAK in fostering ethical governance and professionalism. However, strategic leadership, adaptive training, and technological investment are necessary to address challenges and sustain its impact.

Fusion of Heterocyclic Aromatic Hydrocarbons With Multiple Resonance: Balancing Spectral Redshifts and Broadening

AbstractEfforts to fabricate ideal narrowband, long‐wavelength (>520 nm) multiple resonance (MR) emitters are often hampered by the mutual obstacles of effective spectral redshift and maintaining a small full width at half maximum (FWHM). This review focuses on a sophisticated molecular design methodology called “fusing heterocyclic aromatic hydrocarbons (HAHs) with MR” to overcome the aforementioned challenges. Here, HAH serves as a rigid structural fragment with a modest reorganization energy, which mainly plays the role of reducing ground‐excited state structural displacement and spectral redshift, while the MR fragment mainly plays the role of restricting low‐frequency vibrational coupling and narrowband emission. The synergy of these two advantages theoretically allows the significant redshift of the emission peak while further narrowing the spectrum. Utilizing the ingeniously designed emitters, the corresponding MR devices can achieve superior external quantum efficiencies of over 20% while maintaining remarkably small FWHMs of <25 nm in the green to red regions. These discoveries will spur the development of narrowband, full‐color, high‐efficiency MR emitters and usher in a transformative shift in the organic light‐emitting diode industry.

Pipeline ShiftAddNet: An FPGA‐Based CNN Implementation With Low Hardware Consumption Targeting Constrained Devices

ABSTRACTShiftAddNet is a recently proposed multiplier‐less CNN that replaces conventional multiplication with cheaper shift and add operations, which makes it suitable for hardware implementation. In this paper, we present the first implementation of ShiftAddNet FPGA inference core, which achieves low area consumption and fast computation. ShiftAddNet combined the convolutional layer of the DeepShift‐PS (denoted as Shift‐Accumulate, sac) and AdderNet (denoted as Add‐Accumulate, aac) into a single computational stage. Due to this reason, there are data dependencies between the sac and aac, which prohibits them from being executed in parallel, resulting in more operations compared to other multiplier‐less CNNs like DeepShift‐PS and AdderNet. To overcome this performance bottleneck, we proposed a novel technique to allow pipeline processing between sac and aac, effectively reducing the latency. The proposed ShiftAddNet‐18 was evaluated on a small ResNet‐18, achieving 11.37 ms of latency per image, which is 69.21% faster than the original version that takes 19.24 ms. On a denser network, the proposed pipeline ShiftAddNet‐101 requires only 61.92 ms as compared to the original version of 98.85 ms, showing a latency reduction of 37.1%. Compared to the state‐of‐the‐art multiplier‐less CNN core (e.g., AdderNet), our work is 20% slower in latency but provides higher accuracy and consumes less DSP.