artificial intelligence

DOACT.Vasc Study

The project was conceived and developed based on an automated computational flowchart structured through a decision tree using the CART (Classification and Regression Tree) method, and it was approved by the Ethics Committee of Santa Casa de São Paulo. The resulting algorithm, named DOACT, was fully documented and made available in a private GitHub repository, ensuring scientific transparency and reproducibility. The code repository associated with this study can be accessed at: https://github.com/italoeugenioabreu/doact . The flowchart was structured according to the most recent recommendations from leading international clinical guidelines, including those from the American College of Chest Physicians (CHEST) and the European Society for Vascular Surgery (ESVS). Study Design This was an observational, comparative, and exploratory single-blind study conducted across three analytical groups: Vascular surgery specialists Non-vascular medical specialists Artificial intelligence (AI) models based on Large Language Models (LLMs) - (ChatGPT-4o, Gemini 2.5, Grok 3.0, and Claude Sonnet 4.0) The total sample consisted of 53 participants, and the main objective was to evaluate the correct responses and diagnostic performance of the DOACT tool across 15 clinical case vignettes involving superficial venous thrombosis, deep vein thrombosis, and pulmonary thromboembolism.Case

When Words Become Obligations: Terminology, Contracts and Compliance in Artificial Intelligence

European artificial intelligence governance is administered largely through words. Obligations attach to defined roles, evidence is assembled in documents, and claims travel through contracts and procurement files long after the conversation that produced them has ended. This research note examines a practical problem: terms that appear synonymous in ordinary professional usage — user and deployer, provider and vendor, training and AI literacy, certificate and certification, compliance and conformity — carry different legal, contractual and evidentiary weight. The method is documentary: each definition and provision is traced to a primary source, and every material claim is classified as law, official guidance, standard, accreditation practice, author analysis or author's proposed model. Two symmetrical findings organise the argument. A single substantive concept can be relabelled during the legislative process, as the 2021 Commission proposal's "user" became the adopted Regulation's "deployer". Conversely, an obligation can be materially rewritten while keeping its name and its place in the text, as Article 4 of the AI Act was by Regulation (EU) 2026/1744. A third finding runs through both: the evidentiary value of a document depends on the regime that issued it, not on the word printed on its face. The note proposes a TERM–CLAIM–EVIDENCE model — an author's operational instrument, not a legal requirement — for organisations whose vocabulary has to survive a contract, a tender file or an audit.

PENGARUH PEMANFAATAN AI DAN LINGKUNGAN BELAJAR TERHADAP PRESTASI AKADEMIK

This study is motivated by the increasing use of Artificial Intelligence (AI) in learning and the critical role of the learning environment in shaping students’ academic performance. However, a gap persists between the potential of AI utilization and the quality of learning environments in achieving optimal academic outcomes. This study aims to examine the empirical effects of AI utilization and learning environment on students’ academic performance. A quantitative approach with a survey design was employed, involving 117 active student respondents from the Primary School Teacher Education for Madrasah (PGMI) Study Program at IAIN Curup. Data were collected using a structured questionnaire based on a Likert scale and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The analysis procedures included outer model evaluation to assess validity and reliability, and inner model evaluation to test the relationships between variables and the proposed hypotheses. The findings indicate that AI utilization has a positive and significant effect on academic performance ( ; ), while the learning environment also has a positive and more substantial effect ( ; ). The R-square value of 0.735 demonstrates that both variables explain 73.5% of the variance in academic performance. This study concludes that the integration of effective AI utilization and a supportive learning environment is a key determinant in enhancing students’ academic achievement. ABSTRAK Penelitian ini dilatarbelakangi oleh meningkatnya pemanfaatan Artificial Intelligence (AI) dalam pembelajaran serta pentingnya learning environment (lingkungan belajar) dalam menentukan keberhasilan akademik mahasiswa. Namun, masih terdapat kesenjangan antara potensi penggunaan AI dan kualitas lingkungan belajar terhadap pencapaian prestasi akademik. Penelitian ini bertujuan untuk menganalisis pengaruh pemanfaatan AI dan lingkungan belajar terhadap prestasi akademik mahasiswa secara empiris. Penelitian menggunakan pendekatan kuantitatif dengan desain survei terhadap 117 responden mahasiswa aktif Program Studi Pendidikan Guru Madrasah Ibtidaiyah (PGMI) IAIN Curup. Data dikumpulkan melalui kuesioner berbasis skala Likert dan dianalisis menggunakan Structural Equation Modeling berbasis Partial Least Squares (PLS-SEM). Tahapan analisis meliputi evaluasi outer model untuk menguji validitas dan reliabilitas, serta evaluasi inner model untuk menguji hubungan antarvariabel dan hipotesis penelitian. Hasil penelitian menunjukkan bahwa pemanfaatan AI berpengaruh positif dan signifikan terhadap prestasi akademik ( ; ), dan lingkungan belajar juga berpengaruh positif dan signifikan dengan pengaruh yang lebih kuat ( ; ). Nilai R-square sebesar menunjukkan bahwa kedua variabel mampu menjelaskan 73,5% variasi prestasi akademik. Penelitian ini menyimpulkan bahwa integrasi pemanfaatan teknologi AI dan lingkungan belajar yang kondusif menjadi faktor kunci dalam meningkatkan prestasi akademik mahasiswa.

THE SIGNIFICANCE OF ENGLISH LANGUAGE PROFICIENCY IN THE AI ERA

The integration of artificial intelligence technologies has sparked scholarly debate regarding the necessity of human linguistic competence across globalized labor sectors. This study aims to examine employers’ institutional perceptions of the importance of English language proficiency in enhancing employment opportunities amid the rapid expansion of artificial intelligence. Employing a qualitative case study design, empirical data were gathered through semi-structured interviews with organizational leaders across four sectors: education, healthcare, public service, and banking. The thematic findings demonstrate that English remains an indispensable core competency across all examined industries, functioning alongside digital infrastructures. The education and banking sectors position English as a fundamental recruitment criterion, whereas healthcare and public service view it as a contextually valuable skill. Rather than rendering linguistic expertise obsolete, artificial intelligence tools serve as assistive mechanisms that demand active human proficiency for optimal operational prompt engineering. This study establishes that English language mastery remains a strategic socio-economic asset, reinforcing workforce competitiveness and expanding institutional career mobility in an increasingly automated global knowledge economy. ABSTRAK Integrasi teknologi kecerdasan buatan telah memicu perdebatan akademis mengenai pentingnya kompetensi linguistik manusia di berbagai sektor ketenagakerjaan global. Penelitian ini bertujuan untuk mengkaji persepsi institusional pemberi kerja mengenai pentingnya kemahiran berbahasa Inggris dalam meningkatkan peluang kerja di tengah pesatnya perkembangan kecerdasan buatan. Dengan menggunakan desain studi kasus kualitatif, data empiris dikumpulkan melalui wawancara semi-terstruktur dengan para pemimpin organisasi di empat sektor: pendidikan, layanan kesehatan, layanan publik, dan perbankan. Temuan tematis menunjukkan bahwa bahasa Inggris tetap menjadi kompetensi inti yang tak tergantikan di seluruh industri yang diteliti, serta berperan berdampingan dengan infrastruktur digital. Sektor pendidikan dan perbankan menempatkan bahasa Inggris sebagai kriteria perekrutan yang mendasar, sementara sektor layanan kesehatan dan layanan publik memandangnya sebagai keterampilan yang bernilai secara kontekstual. Alih-alih membuat keahlian linguistik menjadi usang, perangkat kecerdasan buatan justru berfungsi sebagai mekanisme pendukung yang menuntut kemahiran aktif manusia demi optimalisasi prompt engineering dalam operasional. Penelitian ini menegaskan bahwa penguasaan bahasa Inggris tetap menjadi aset sosial-ekonomi yang strategis, yang memperkuat daya saing tenaga kerja serta memperluas mobilitas karier institusional dalam ekonomi pengetahuan global yang semakin terotomatisasi.

Continuous Mission Intelligence: An Architectural Requirement for Coherent Command Across Heterogeneous Autonomous Systems

Defense artificial intelligence has advanced quickly across four layers: operational data integration, command and control, fleet-scale coordination, and platform autonomy. This paper argues that the success of those layers has produced a distinct problem that none of them addresses. As reasoning distributes into sensors, platforms, software agents and command applications, a force can become locally intelligent without becoming collectively coherent. Continuous mission intelligence is defined as the ability of a distributed human-machine system to maintain a current and reconstructable mission state across time, systems and operating conditions, and to use observed consequences to improve the next operating cycle. Six leading systems and programmes are assessed against that definition: Palantir AIP and Gotham, Anduril Lattice, Shield AI Hivemind, the US Army NGC2 programme, NATO digital transformation, and DARPA DICE. Each addresses a major layer of the stack; none addresses continuity end to end. The paper then identifies seven mechanisms by which mission coherence fractures at machine speed, derives eight architectural properties a continuity layer must exhibit, argues that coherence requires federation rather than centralisation, and proposes a two-cycle evaluation protocol that tests whether a second operating cycle begins better informed than the first. This is a position and architecture paper. It presents no empirical validation, no deployment data and no performance claims. The author declares a competing interest as co-founder and Chief Executive Officer of Rebootix AI, Inc.

Single-phase fault diagnosis in distribution networks via cyber-secure hybrid models of artificial intelligence

DIGITAL TRANSFORMATION MARKETING STRATEGY FOR SECURITY SERVICES: A NICE MARKETING MIX APPROACH FOR AI AND IOT ADOPTION IN EMERGING MARKETS

Digital transformation has become a critical strategic agenda across industrial security services to resolve recurring operational inefficiencies and visibility limitations. However, technology-based security solutions face persistent customer acceptance barriers within business-to-business emerging markets. This quantitative study aims to formulate an integrated marketing strategy framework for digital security services and identify priority initiatives supporting technology adoption. Data were gathered through structured questionnaires administered to 19 enterprise security decision-makers across industrial sectors in the Greater Jakarta region. Quantitative assessments utilized multi-criteria strategic evaluation models, including internal-external factor matrices and strategic priority matrices. The empirical findings reveal that data-based performance measurement difficulties and reporting delays represent the most critical customer pain points. Furthermore, quantitative priority testing established drone patrol system development as the paramount strategic initiative. The investigation concludes that implementing the networking, interaction, common interest, and experience framework with advanced artificial intelligence and internet-of-things ecosystems optimizes market penetration, strengthens corporate partnerships, and establishes sustainable competitive advantages in emerging markets. ABSTRAK Transformasi digital telah menjadi agenda strategis yang krusial di seluruh layanan keamanan industri untuk mengatasi inefisiensi operasional dan keterbatasan visibilitas yang berulang. Namun, solusi keamanan berbasis teknologi menghadapi hambatan penerimaan pelanggan yang persisten di pasar berkembang business-to-business (B2B). Studi kuantitatif ini bertujuan merumuskan kerangka strategi pemasaran terintegrasi untuk layanan keamanan digital serta mengidentifikasi inisiatif prioritas yang mendukung adopsi teknologi. Data dikumpulkan melalui kuesioner terstruktur yang dibagikan kepada 19 pengambil keputusan keamanan perusahaan di berbagai sektor industri di wilayah Jabodetabek (Greater Jakarta). Penilaian kuantitatif menggunakan model evaluasi strategis multikriteria, termasuk matriks faktor internal-eksternal (internal-external factor matrices) dan matriks prioritas strategis (strategic priority matrices). Temuan empiris mengungkapkan bahwa kesulitan pengukuran kinerja berbasis data dan keterlambatan pelaporan merupakan titik masalah pelanggan (pain points) yang paling kritis. Selain itu, pengujian prioritas kuantitatif menetapkan pengembangan sistem patroli drone (drone patrol system) sebagai inisiatif strategis utama. Investigasi ini menyimpulkan bahwa penerapan kerangka kerja networking, interaction, common interest, and experience (NICE) dengan ekosistem kecerdasan buatan (artificial intelligence) dan internet of things tingkat lanjut mampu mengoptimalkan penetrasi pasar, memperkuat kemitraan korporasi, serta membangun keunggulan kompetitif yang berkelanjutan di pasar berkembang (emerging markets).

The Great Amnesia: AI Agent Memory as the Binding Constraint on Autonomous Systems in Enterprise and Defense

Frontier model capability has improved rapidly for four years while the rate at which organisations convert artificial intelligence pilots into production has not. This paper argues that the two facts are connected, and that the binding constraint is no longer reasoning quality but continuity: what an autonomous system carries from one operating cycle to the next. Four independent lines of published evidence are assembled. A frontier laboratory's own usage data shows computer and mathematical tasks at roughly 35 percent of consumer conversations and close to 44 percent of first-party API traffic, with software error correction the single most frequent task. Enterprise research attributes an approximately 95 percent pilot failure rate not to model quality but to a learning gap. Time-horizon measurement shows long 50 percent horizons alongside much shorter 80 percent horizons. And long-horizon agent performance degrades sharply when a task is embedded in a longer interaction history even while the required information remains inside the context window, which locates the failure in architecture rather than in information availability. The paper then examines the same pattern in defense programmes, argues that stored facts, larger context windows and similarity-based retrieval are each insufficient for continuity, specifies eight properties a continuity layer must exhibit, proposes a falsifiable two-cycle evaluation protocol, and states four checkable predictions. This is a position and architecture paper. It presents no original empirical work and no validation of the author's own systems. The author declares a competing interest as co-founder and Chief Executive Officer of Rebootix AI, Inc. Companion preprint: 10.5281/zenodo.22557263.

Continuous Mission Intelligence: An Architectural Requirement for Coherent Command Across Heterogeneous Autonomous Systems

Defense artificial intelligence has advanced quickly across four layers: operational data integration, command and control, fleet-scale coordination, and platform autonomy. This paper argues that the success of those layers has produced a distinct problem that none of them addresses. As reasoning distributes into sensors, platforms, software agents and command applications, a force can become locally intelligent without becoming collectively coherent. Continuous mission intelligence is defined as the ability of a distributed human-machine system to maintain a current and reconstructable mission state across time, systems and operating conditions, and to use observed consequences to improve the next operating cycle. Six leading systems and programmes are assessed against that definition: Palantir AIP and Gotham, Anduril Lattice, Shield AI Hivemind, the US Army NGC2 programme, NATO digital transformation, and DARPA DICE. Each addresses a major layer of the stack; none addresses continuity end to end. The paper then identifies seven mechanisms by which mission coherence fractures at machine speed, derives eight architectural properties a continuity layer must exhibit, argues that coherence requires federation rather than centralisation, and proposes a two-cycle evaluation protocol that tests whether a second operating cycle begins better informed than the first. This is a position and architecture paper. It presents no empirical validation, no deployment data and no performance claims. The author declares a competing interest as co-founder and Chief Executive Officer of Rebootix AI, Inc.

Applied Informatics: Librarian Scepticism of Artificial Intelligence in Information Retrieval — Does AI Promote Library Usage or Displace It? Evidence from the Nigerian University Library Landscape

Librarian scepticism towards artificial intelligence (AI) tools in information retrieval is routinely reframed in African higher education discourse as a capacity gap. This paper challenges that reframing. Using a convergent mixed-methods design — a survey of 214 librarians across 27 Nigerian universities, 32 semi-structured interviews, and documentary analysis of 81 institutional documents spanning 2015–2025 — the study pursues three objectives: (1) to examine the nature and structural determinants of librarian scepticism towards AI-assisted retrieval; (2) to determine whether AI adoption promotes or displaces substantive library usage in resource-constrained institutions; and (3) to investigate gendered and geographic dimensions of differential AI scepticism. Findings confirm that scepticism is epistemically rational and structurally grounded. In institutions where AI chatbots generate citation lists that the local collection cannot fulfil, the information outcome is zero regardless of interface sophistication — what the study theorises as an information supply chain failure (ISCF), structurally analogous to a health system that achieves a correct diagnosis but cannot dispense the medication. Only 27.1% of librarians reported that AI-generated references were typically retrievable locally (17.8% in rural institutions). Female librarians in northern Nigeria reported the highest scepticism and the lowest institutional support. No sampled institution held an operational AI-resource integration policy. The study concludes that AI adoption without collection integrity undermines library utility, and recommends a policy-first, resource-second integration framework.

The Great Amnesia: AI Agent Memory as the Binding Constraint on Autonomous Systems in Enterprise and Defense

Frontier model capability has improved rapidly for four years while the rate at which organisations convert artificial intelligence pilots into production has not. This paper argues that the two facts are connected, and that the binding constraint is no longer reasoning quality but continuity: what an autonomous system carries from one operating cycle to the next. Four independent lines of published evidence are assembled. A frontier laboratory's own usage data shows computer and mathematical tasks at roughly 35 percent of consumer conversations and close to 44 percent of first-party API traffic, with software error correction the single most frequent task. Enterprise research attributes an approximately 95 percent pilot failure rate not to model quality but to a learning gap. Time-horizon measurement shows long 50 percent horizons alongside much shorter 80 percent horizons. And long-horizon agent performance degrades sharply when a task is embedded in a longer interaction history even while the required information remains inside the context window, which locates the failure in architecture rather than in information availability. The paper then examines the same pattern in defense programmes, argues that stored facts, larger context windows and similarity-based retrieval are each insufficient for continuity, specifies eight properties a continuity layer must exhibit, proposes a falsifiable two-cycle evaluation protocol, and states four checkable predictions. This is a position and architecture paper. It presents no original empirical work and no validation of the author's own systems. The author declares a competing interest as co-founder and Chief Executive Officer of Rebootix AI, Inc. Companion preprint: 10.5281/zenodo.22557263.

Audience Interpretation of AI-Driven Product Visualization and Virtual Influencers in Culinary Marketing: S–O–R and Parasocial Approaches

Background: Artificial intelligence has transformed how culinary products are visualized and communicated in digital marketing. Objective: This study develops a model of audience interpretation of AI-driven product visualization and virtual influencers in culinary digital marketing by integrating the Stimulus–Organism–Response (S–O–R) framework and Parasocial Interaction Theory. Methods: An interpretive qualitative approach with a phenomenological design was applied. Data were obtained from 12 informants: 10 audience members aged 18–35 who had encountered AI-driven culinary content on Instagram or TikTok, one digital marketing practitioner, and one AI expert. In-depth interviews, digital observations, and documentation were analyzed using NVivo 15 through open, axial, and selective coding, thematic categorization, and triangulation. Results: The findings show that AI-driven product visualization and virtual influencers function as stimuli that attract initial attention through aesthetic, modern, and realistic visuals. Audiences process these stimuli through cognitive, affective, and conative evaluations, including assessments of visual realism, message credibility, AI transparency, emotional appeal, uncertainty, and content authenticity. Audience responses appear in the form of engagement, information seeking, intention to try, and purchase intention, but depend on the consistency between promotional visuals and the actual products. Conclusion: This study recommends a human–AI hybrid strategy combining AI-generated visual appeal, transparency, authentic evidence, and human involvement.