Continuous Adaptation Research Articles

Exploring Genetic Silencing: RNAi and CRISPR-Cas Potential against Drug Resistance in Malaria

Malaria has been one of the most lethal infectious diseases throughout history, claiming a high number of human lives. The genomic plasticity of <i>Plasmodium falciparum</i>, the causative agent of the most severe and deadly form of malaria, gives the parasite a constant resistance to drugs developed for its control. Despite efforts to control and even eradicate the disease, these have largely been unsuccessful due to the parasite's continuous adaptations. This study aims to examine the key genes involved in parasite resistance and propose a shift in the combat strategy. Gene silencing techniques offer promise in combating malaria, yet further research is needed to harness their potential for disease control fully. Although there is still a long way to go for the implementation of gene silencing-based therapeutic strategies, this review addresses examples of the use of such techniques in various human diseases and how they could be extrapolated for malaria treatment.

Development of a supportive cancer care model for patients with CAncer and pre-existing SEvere MEntal Disorders

BackgroundCancer patients with pre-existing severe mental disorders (SMD), including moderate to severe depression, bipolar disorder and schizophrenia, have reduced life expectancy and are less likely to get optimal cancer treatment. The aim of this study is to develop and pilot test a supportive care model, to enhance cancer care. MethodsThe model was developed through three phases. In phase I, a barrier analysis and prototype of the model were conducted form reviewing the literature and through 162 informal interviews with healthcare professionals, patients and patient representatives. In phase II, the prototype was refined through four workshops with a total of five cancer nurses, four clinical oncologists, three psychiatrists, two general practitioners, one psychologist, and 16 patient representatives. Thereafter, a pilot test with 13 patients was carried out, where continuous adaptations to the prototype from phase II, were made. The quantitative and qualitative data were analysed focusing on components which were prominent and able to fit into the clinical setting. ResultsThe final CASEMED supportive cancer care model included: Early identification of psychiatric comorbidity, engagement of significant caregivers, education of the oncological HCPs, securing continuity among staff and enhanced collaboration between sectors. The latter was achieved through an online psychiatric multidisciplinary team conference where the patient’s general practitioner, a psychiatrist and the patient’s oncologist participated. ConclusionThis study indicates that the model can be implemented in practice and has the potential to optimize cancer care for patients with cancer and pre-existing SMD. A larger feasibility study is currently being conducted.

Fortifying industrial cybersecurity: a novel industrial internet of things architecture enhanced by honeypot integration

The industrial internet of things (IIoT) has significantly transformed the industrial sectors by connecting devices, machines, and systems to enhance automation, efficiency, and decision-making. However, the increased interconnectivity also poses significant security challenges because IIoT devices control critical infrastructures and processes. Our work presents an implementation of a robust industrial cybersecurity strategy with a segmented network architecture, collaborative efforts between information technology (IT) and operational technology (OT) teams for enhanced resilience and effectiveness, and vertical honeypots across all Industry 4.0 levels integrated with Wazuh for log transmission and proactive threat response, alongside Snort intrusion detection system (IDS) monitoring network traffic. Additionally, we reinforce our architecture by Wazuh with Elasticsearch and Kibana as a security information and event management solution, facilitating data analysis and compliance enforcement through custom rulesets and cybersecurity threat intelligence (CTI) integration, with automatic updates for continuous adaptation against emerging threats.

Ethical frameworks for AI in journalism: Balancing technological innovation and journalistic integrity

This study examined the moral dilemmas and ramifications of incorporating artificial intelligence (AI) into journalism, a discipline that is rapidly changing due to technology. Natural language processing and machine learning are two examples of AI technologies that have greatly improved journalistic practices by automating repetitive activities, enhancing data analysis, and opening up new narrative styles. But there are serious ethical challenges with this integration, such as algorithmic bias, editorial control loss, and transparency problems. The study used a qualitative research approach and evaluated case studies of AI applications in journalism using content analysis and ethical principles from top companies. The results demonstrated AI's dual effects on journalism. Although AI technologies streamline operations and increase accessibility, they also run the danger of strengthening social prejudices, undermining journalistic standards, and decreasing human supervision in editorial procedures. The New York Times and the BBC are two well-known examples of how to balance AI innovation with journalistic responsibility through openness and human monitoring. Disparities in acceptance around the globe and the absence of legally binding, situation-specific ethical frameworks, however, continue to be major obstacles. According to the study, integrating AI into journalism should be guided by a thorough ethical framework that prioritizes accountability, openness, diversity, and justice. Newsrooms and developers must work together to ensure ethical compliance, according to implementation rules, and continuous adaptation supported by dynamic assessment tools like audience feedback and ethical audits. In addition to tackling ethical challenges, this framework aims to uphold fundamental journalistic principles while utilizing AI's revolutionary potential, providing useful advice for encouraging creativity and trust in the changing media environment.

Landing sustainable innovation values in business management

Introduction: Values define the organizational culture, identity, and other essential attributes of the company. Hence the importance of their definition, which must be assumed by all parties involved, and if possible be aligned with them. This is especially relevant in companies seeking sustainability-oriented innovation. Methodology: In this work, we analysed 234 studies focused on sustainability-oriented innovation, in order to learn about the values that govern these types of organizations and how they are put into practice. In the research conducted, we found four types of values that influence the implementation of sustainability-oriented innovation in the company: institutional values, organization’s values, individual values and consumer values. Results: The implementation of these values in the company is problematic due to a lack of knowledge about them and show the need for symbiotic learning. We present a conceptual framework of sustainability-oriented innovation values that is not static but feeds back. Likewise, we collect innovative practices of sustainability-oriented companies, organized according to the typology of values studied. Conclusions: Sustainable innovation in companies is deeply influenced by a complex interplay of institutional, organizational, individual, and consumer values. Continuous learning and adaptability, driven by strong leadership and stakeholder engagement, are crucial for embedding sustainability into corporate practices and achieving long-term sustainable development goals.

Evolution of Libraries in the Digital Era: Redefining Access, Education, and Cultural Preservation

The digital revolution has significantly reshaped libraries, transforming them from traditional repositories into dynamic, technology-driven centres of information, education, and cultural preservation. This study investigates the comprehensive digital transformation of libraries, with a focus on the integration of electronic resources, virtual reality (VR), augmented reality (AR), digital catalogues, multimedia labs, and maker spaces. Utilizing a multi-method approach, the research analyzes statistical data on user engagement, revealing a consistent 20% annual increase in users accessing e-books and journals globally since 2010. Additionally, surveys conducted among students and researchers in Azerbaijan indicate a 25% rise in electronic resource usage since 2015, with modern search tools enhancing information retrieval speed by 30%. The incorporation of VR and AR technologies is explored for their potential to enrich educational experiences and preserve cultural heritage. At the same time, multimedia labs and maker spaces are examined for their roles in fostering innovation and skill development. Despite these advancements, libraries face significant challenges such as the digital divide, information overload, privacy concerns, data security, and ensuring accessibility for all users. Libraries have started to address these challenges through continuous adaptation, investing in cutting-edge technologies, and developing digital literacy programs to bridge the digital divide, manage information effectively, and protect user privacy. The findings highlight the critical role of libraries in the digital age, underscoring their evolution into inclusive, technologically advanced institutions that support education, research, and cultural enrichment. The study underscores the importance of sustained investment in digital infrastructures to maintain libraries' pivotal role in modern society.

P1021 Treatment Patterns for Crohn’s Disease: a Multinational Analysis from 2011 to 2022 in the United States, Japan, and China

Abstract Background Crohn’s Disease (CD) is one of the two major forms of inflammatory bowel disease (IBD) and leads to high disease burden. Early and effective pharmacological treatments is critical to manage CD, however, sparse data on treatment patterns resulted in a gap in our understanding of the alignment between management strategies in practice and existing knowledge and evidence. This multi-country study aimed to describe treatment patterns for CD, providing insights into regional practices and their evolution over time. Methods Employing a retrospective cohort design, three large-scale healthcare electronic databases between 2011 and 2022 from the U.S., Japan and China were used to identify patients diagnosed with CD and those who initiated any treatment within the period. Medications use at treatment initiation and during subsequent treatments, including 5-aminosalicyclic acid (5-ASA), corticosteroids (CS), immunomodulators (IMM) and biologics (BIO) were analysed, and the temporal trend of medication use at treatment initiation was assessed by the annual percent change (APC) to identify shifts in treatment preferences over the period. Results We identified 93,573 CD patients in the U.S. cohort, 9,617 in the Japan cohort, and 928 in the China cohort, respectively. CS was the most used medication at treatment initiation (64.2%) for the U.S. cohort, while 5-ASA was the predominant medication for Japan and China cohorts (63.9% and 63.5%, respectively). BIO was utilized at treatment initiation by 12.7%, 28.5% and 10.0% of patients in the cohorts from the U.S., Japan and China, respectively. During subsequent treatment, CS or 5-ASA remained more common than BIO in the U.S. and China cohorts, while BIO use was higher in the Japan cohort. Regarding the trends from 2011 to 2022, percentage of patients using 5-ASA or IMM at treatment initiation significantly declined for the U.S. and Japan cohorts. Conversely, percentage of patients using CS at treatment initiation rose significantly for the U.S. cohort, along with a marked increase in utilization in the China cohort from 2018 to 2022. Percentage for BIO also increased significantly for the U.S. and China cohorts while it declined for the Japan cohort. Conclusion This study documented treatment patterns for CD by leveraging healthcare databases from 3 countries, providing insights into real-world clinical practices. These findings indicate significant variability in medication used at treatment initiation and subsequent treatments across the countries studied. These insights underscore the need for continuous evaluation and adaptation of treatment strategies to align with evolving evidence-based guidelines and improve patient outcomes. References Lichtenstein GR, Loftus EV, Isaacs KL, Regueiro MD, Gerson LB, Sands BE. ACG Clinical Guideline: Management of Crohn’s Disease in Adults. The American Journal of Gastroenterology. 2018;113(4):481-517. Gordon H, Minozzi S, Kopylov U, Verstockt B, Chaparro M, Buskens C, et al. ECCO Guidelines on Therapeutics in Crohn’s Disease: Medical Treatment. Journal of Crohn’s and Colitis. 2024. Nakase H, Uchino M, Shinzaki S, Matsuura M, Matsuoka K, Kobayashi T, et al. Evidence-based Clinical Practice Guidelines for Inflammatory Bowel Disease 2020. Journal of Gastroenterology. 2021;56(6):489-526. Chen M, Wu K, Ji Y. 2023 Chinese National Clinical Practice Guideline on Diagnosis and Management of Crohn’s disease. Chinese Medical Journal. 2024;137(14):1647-50.

Self-trainable and adaptive sensor intelligence for selective data generation

With the increasing integration of machine learning into IoT devices, managing energy consumption and data transmission has become a critical challenge. Many IoT applications depend on complex computations performed on server-side infrastructure, necessitating efficient methods to reduce unnecessary data transmission. One promising solution involves deploying compact machine learning models near sensors, enabling intelligent identification and transmission of only relevant data frames. However, existing near-sensor models lack adaptability, as they require extensive pre-training and are often rigidly configured prior to deployment. This paper proposes a novel framework that fuses online learning, active learning, and knowledge distillation to enable adaptive, resource-efficient near-sensor intelligence. Our approach allows near-sensor models to dynamically fine-tune their parameters post-deployment using online learning, eliminating the need for extensive pre-labeling and training. Through a sequential training and execution process, the framework achieves continuous adaptability without prior knowledge of the deployment environment. To enhance performance while preserving model efficiency, we integrate knowledge distillation, enabling the transfer of critical insights from a larger teacher model to a compact student model. Additionally, active learning reduces the required training data while maintaining competitive performance. We validated our framework on both benchmark data from the MS COCO dataset and in a simulated IoT environment. The results demonstrate significant improvements in energy efficiency and data transmission optimization, highlighting the practical applicability of our method in real-world IoT scenarios.

Generative Artificial Intelligence Use in Healthcare: Opportunities for Clinical Excellence and Administrative Efficiency

Generative Artificial Intelligence (Gen AI) has transformative potential in healthcare to enhance patient care, personalize treatment options, train healthcare professionals, and advance medical research. This paper examines various clinical and non-clinical applications of Gen AI. In clinical settings, Gen AI supports the creation of customized treatment plans, generation of synthetic data, analysis of medical images, nursing workflow management, risk prediction, pandemic preparedness, and population health management. By automating administrative tasks such as medical documentations, Gen AI has the potential to reduce clinician burnout, freeing more time for direct patient care. Furthermore, application of Gen AI may enhance surgical outcomes by providing real-time feedback and automation of certain tasks in operating rooms. The generation of synthetic data opens new avenues for model training for diseases and simulation, enhancing research capabilities and improving predictive accuracy. In non-clinical contexts, Gen AI improves medical education, public relations, revenue cycle management, healthcare marketing etc. Its capacity for continuous learning and adaptation enables it to drive ongoing improvements in clinical and operational efficiencies, making healthcare delivery more proactive, predictive, and precise.

Cross-patient seizure prediction via continuous domain adaptation and similar sample replay.

Seizure prediction based on electroencephalogram (EEG) for people with epilepsy, a common brain disorder worldwide, has great potential for life quality improvement. To alleviate the high degree of heterogeneity among patients, several works have attempted to learn common seizure feature distributions based on the idea of domain adaptation to enhance the generalization ability of the model. However, existing methods ignore the inherent inter-patient discrepancy within the source patients, resulting in disjointed distributions that impede effective domain alignment. To eliminate this effect, we introduce the concept of multi-source domain adaptation (MSDA), considering each source patient as a separate domain. To avoid additional model complexity from MSDA, we propose a continuous domain adaptation approach for seizure prediction based on the convolutional neural network (CNN), which performs sequential training on multiple source domains. To relieve the model catastrophic forgetting during sequential training, we replay similar samples from each source domain, while learning common feature representations based on subdomain alignment. Evaluated on a publicly available epilepsy dataset, our proposed method attains a sensitivity of 85.0% and a false alarm rate (FPR) of 0.224/h. Compared to the prevailing domain adaptation paradigm and existing domain adaptation works in the field, the proposed method can efficiently capture the knowledge of different patients, extract better common seizure representations, and achieve state-of-the-art performance.

ASSESSMENT OF UNIVERSAL NEONATAL HEARING SCREENING: OUTCOMES AND PREVALENCE AT A TERTIARY CARE CENTER

Objective: Early detection of hearing impairment through Universal Neonatal Hearing Screening (UNHS) is crucial for timely intervention. This study assesses the outcomes and prevalence of hearing loss in neonates screened at a tertiary care center. Methods: This prospective observational study involved 1175 neonates born at SDM Medical College and Hospital between December 2019 and November 2020. Neonates underwent initial and, if necessary, repeat hearing screenings using the Otoacoustic Emission (OAE) technique. Data on gender and birth weight distribution were also analyzed to explore demographic correlations with screening outcomes. Results: Of the neonates screened, 99.40% passed the initial hearing test, while 0.60% were referred for further evaluation. Among those referred, 71.43% passed on repeat screening, with 28.57% continuing to show potential hearing issues. The majority of neonates weighed over 2.5 kg, indicating a lower presence of very low birth weight infants in the screened population. Conclusion: The high pass rate in initial screenings demonstrates the effectiveness of the OAE method in a tertiary care setting, though the importance of follow-up testing for those referred remains critical. The study highlights the successful implementation of UNHS and underscores the need for continuous monitoring and adaptation of screening protocols to address all potential risks in neonatal hearing loss.

El contrato de larga duración y su compleja configuración técnica

The long-term contract, characterized by its complexity and extended duration, requires continuous adaptations due to changes in circumstances that affect its execution. These contracts, common in business relationships and strategic agreements, are more flexible than traditional ones and aim to promote collaboration between the parties. Relational theory, developed by Macneil, emphasizes the importance of cooperation and good faith in these agreements. The Argentine Civil and Commercial Code (Art. 1011) acknowledges this category, highlighting the essential nature of time and the duty to renegotiate in the event of unilateral termination. On an international level, the Unidroit Principles have updated their approach to reflect the dynamic nature of these contracts. Article 1011, while useful, has been the subject of debate due to its lack of specificity in certain aspects, such as termination, and the need for flexible interpretation according to the circumstances.

The systemic evolutionary theory of the origin of cancer (SETOC): an update

The Systemic Evolutionary Theory of the Origin of Cancer (SETOC) is a recently proposed theory founded on two primary principles: the cooperative and endosymbiotic process of cell evolution as described by Lynn Margulis, and the integration of complex systems operating in eukaryotic cells, which is a core concept in systems biology. The SETOC proposes that malignant transformation occurs when cells undergo a continuous adaptation process in response to long-term injuries, leading to tissue remodeling, chronic inflammation, fibrosis, and ultimately cancer. This process involves a maladaptive response, wherein the 'endosymbiotic contract’ between the nuclear-cytoplasmic system (derived from the primordial archaeal cell) and the mitochondrial system (derived from the primordial α-proteobacterium) gradually breaks down. This ultimately leads to uncoordinated behaviors and functions in transformed cells. The decoupling of the two cellular subsystems causes transformed cells to acquire phenotypic characteristics analogous to those of unicellular organisms, as well as certain biological features of embryonic development that are normally suppressed. These adaptive changes enable cancer cells to survive in the harsh tumor microenvironment characterized by low oxygen concentrations, inadequate nutrients, increased catabolic waste, and increased acidity. De-endosymbiosis reprograms the sequential metabolic functions of glycolysis, the TCA cycle, and oxidative phosphorylation (OxPhos). This leads to increased lactate fermentation (Warburg effect), respiratory chain dysfunction, and TCA cycle reversal. Here, we present an updated version of the SETOC that incorporates the fundamental principles outlined by this theory and integrates the epistemological approach used to develop it.

Best Practices in Cybersecurity Training: Enhancing Employee Awareness to Mitigate Risks

This technical article explores the critical role of cybersecurity training programs in addressing the growing challenges of cyber threats in modern organizations. The article examines how human factors contribute to security vulnerabilities while also presenting opportunities for strengthening organizational defense through comprehensive training initiatives. Through this detailed article of current research and industry reports, this article investigates the implementation of structured training approaches, measurement frameworks, and cultural transformation strategies. The article encompasses key aspects including baseline assessments, multi-tiered training architectures, performance metrics, security-conscious culture development, technical integration through learning management systems, compliance documentation, and future-proofing methodologies. The article demonstrates that organizations implementing comprehensive security awareness programs experience significant improvements in threat detection, incident response, and overall security posture while highlighting the importance of leadership engagement and continuous adaptation in maintaining effective cybersecurity training programs.

Real-Time On-Device Continual Learning Based on a Combined Nearest Class Mean and Replay Method for Smartphone Gesture Recognition.

Sensor-based gesture recognition on mobile devices is critical to human-computer interaction, enabling intuitive user input for various applications. However, current approaches often rely on server-based retraining whenever new gestures are introduced, incurring substantial energy consumption and latency due to frequent data transmission. To address these limitations, we present the first on-device continual learning framework for gesture recognition. Leveraging the Nearest Class Mean (NCM) classifier coupled with a replay-based update strategy, our method enables continuous adaptation to new gestures under limited computing and memory resources. By employing replay buffer management, we efficiently store and revisit previously learned instances, mitigating catastrophic forgetting and ensuring stable performance as new gestures are added. Experimental results on a Samsung Galaxy S10 device demonstrate that our method achieves over 99% accuracy while operating entirely on-device, offering a compelling synergy between computational efficiency, robust continual learning, and high recognition accuracy. This work demonstrates the potential of on-device continual learning frameworks that integrate NCM classifiers with replay-based techniques, thereby advancing the field of resource-constrained, adaptive gesture recognition.

Continual Test-Time Adaptation with Weighted Contrastive Learning and Pseudo-Label Correction

Continual Test-Time Adaptation with Weighted Contrastive Learning and Pseudo-Label Correction

Developing Students Communicative Competence using Authentic Materials with Information Technologies

In the globalized landscape of higher education, developing communicative competence in foreign language learners is essential for academic, social, and professional success. This article explores innovative, student-centered strategies beyond traditional grammar-based instruction to enhance linguistic proficiency, cultural understanding, and pragmatic awareness. This study highlights their impact on fostering real-world communication skills by emphasizing interactive methods such as role-playing, task-based learning, debates, and integrating authentic materials. Furthermore, the findings demonstrate that students engaging in these approaches show marked improvements in fluency, adaptability, and cultural sensitivity. Role-playing scenarios and debates enhance spoken communication, critical thinking, and argumentation skills, while task-based activities encourage collaboration and purposeful language use. Exposure to authentic resources bridges the gap between classroom learning and practical application, deepening students' comprehension of idiomatic expressions, cultural norms, and socio-linguistic nuances. Despite challenges like resource limitations and technological barriers, the study underscores the transformative potential of these methods. By addressing implementation hurdles through digital innovation and tailored pedagogy, educators can create a robust framework that prepares students for the demands of global communication. This article advocates for continued research and adaptation to refine these strategies, ensuring their effectiveness across diverse educational contexts.

Mobile application: learning zapotec diidxazá

"Learning Zapotec Diidxazá" is a mobile application that combines various innovative technologies and artificial intelligence. Its goal is to facilitate and promote the learning of Zapotec Diidxazá. An Incremental methodology was used, which allows the application to be built progressively, integrating new functionalities in each phase of communication, planning, modeling, construction, and deployment. This approach facilitates continuous feedback and adaptability to possible changes (Mills, 1993). The results include various functional modules such as: a trilingual dictionary with audio recordings by native speakers, a multilingual bidirectional translator, an object detector, a real-time image translator, learning sessions, assessments, and educational games based on the pedagogical model of Critical Theory by projects. The application has not only succeeded in offering an interactive and accessible educational resource but also has the potential to become a driving force for cultural revitalization in the future, by strengthening the appreciation and preservation of this ancestral language.

Structural Developments in The Pedagogy Of The Art of Acting: From Student To Actor in The New Dynamics of Performance Art

In this article, I will prove the urgent need to structure courses and adopt a new pedagogical system aimed at catching the attention as well as the interest of students. The use of technology has resulted in students having to face issues related to attention, motor skills, and even interest. Scientific research shows that, currently, even the mere presence of a phone near the brain affects individuals and reduces cognitive activity. Consequently, in pedagogical environments, the structure of acting courses and paradigms must undergo multiple stages of revitalization. This implies continuous adaptation to new social and technological realities, in order to ensure a better understanding and effective communication between educators and students. Additionally, it is necessary to encourage more flexible approaches that take into account the diversity of learning styles and the challenges contemporary students face.

TRAINING A MODERN MATHEMATICS TEACHER: THE EXPERIENCE OF THE YURY FEDKOVYCH CHERNIVTSY NATIONAL UNIVERSITY

The article analyzes research on the issues of readiness of a modern mathematics teacher; the advantages of using innovative technologies over traditional ones by a teacher are presented. The purpose of the article is to substantiate the feasibility of training future mathematics teachers by introducing innovative technologies into the educational process of a higher education institution. Research methods include theoretical – analysis, synthesis, comparison, study of best practices and empirical – pedagogical experiment, expert assessment, testing, questionnaires. It is proven that the main function of innovative technologies is the direct inclusion of students in life itself and the implementation of the doctrine of “lifelong learning”. It is determined that the use of innovations and the application of pedagogical innovative technologies in work is one of the components of the innovative competence of a mathematics teacher. The features of the formation of the readiness of future mathematics teachers for innovative pedagogical activity and the classification of pedagogical technologies that are implemented in their work are presented. The academic disciplines of the educational program of the specialty 014.04 «Secondary education (mathematics)» for the field of knowledge 01 «Education/Pedagogy», which is implemented at the Faculty of Mathematics and Informatics of Yuriy Fedkovych Chernivtsi National University, are considered. As a result of studying professionally oriented disciplines, a complex of innovative teacher competencies is formed. The integration of innovative technologies in mathematics teacher training at Yuriy Fedkovych Chernivtsi National University sets a benchmark for modern education. By fostering competencies such as digital literacy, critical thinking, and adaptability, the university prepares future teachers to navigate the challenges of contemporary classrooms effectively. These advancements contribute to the broader goal of creating a resilient, innovative, and capable educational workforce. Future research and continuous adaptation will further enhance the training process, ensuring alignment with global trends and local needs.