Search Results (1,192)

This paper presents new fast algorithms for the type VII discrete cosine transform (DCT-VII) applied to input data sequences of lengths ranging from 3 to 8. Fast algorithms for small-sized trigonometric transforms enable the processing of small data blocks in image and video coding with low computational complexity. To process the information in image and video coding standards, the fast DCT-VII algorithms can be used, taking into account the relationships between the DCT-VII and the type II discrete cosine transform (DCT-II). Additionally, such algorithms can be used in other digital signal processing tasks as components for constructing algorithms for large-sized transforms, leading to reduced system complexity. Existing fast odd DCT algorithms have been designed using relationships among discrete cosine transforms (DCTs), discrete sine transforms (DSTs), and the discrete Fourier transform (DFT); among different types of DCTs and DSTs; and between the coefficients of the transform matrix. However, these algorithms require a relatively large number of multiplications and additions. The process of obtaining such algorithms is difficult to understand and implement. To overcome these shortcomings, this paper applies a structural approach to develop new fast DCT-VII algorithms. The process begins by expressing the DCT-VII as a matrix-vector multiplication, then reshaping the block structure of the DCT-VII matrix to align with matrix patterns known from the basic papers in which the structural approach was introduced. If the matrix block structure does not match any known pattern, rows and columns are reordered, and sign changes are applied as needed. If this is insufficient, the matrix is decomposed into the sum of two or more matrices, each analyzed separately and transformed similarly if required. As a result, factorizations of DCT-VII matrices for different input sequence lengths are obtained. Based on these factorizations, fast DCT-VII algorithms with reduced arithmetic complexity are constructed and presented with pseudocode. To illustrate the computational flow of the resulting algorithms and their modular design, which is suitable for VLSI implementation, data-flow graphs are provided. The new DCT-VII algorithms reduce the number of multiplications by approximately 66% compared to direct matrix-vector multiplication, although the number of additions decreases by only about 6%. Full article

Background/Objectives: Hearing impairment in middle-aged adults is a significant, modifiable risk factor for cognitive decline and dementia, and therapy with hearing aids or cochlear implants has been suggested to reduce this risk. However, most research on auditory rehabilitation and cognition has focused on older adults, and evidence regarding cognitive outcomes in middle-aged adults remains scarce despite this group being identified as critical for dementia prevention. Thus, this study aimed to assess cognitive skills in middle-aged hearing-impaired individuals 1 year after receiving a cochlear implant (CI) as part of auditory rehabilitation. Methods: Thirty-two patients with a mean age of 52.4 were enrolled in a prospective pre-post study. Hearing was tested using the Freiburg Monosyllable Test (FS) and the Oldenburg Inventory (OI). Cognitive performance was assessed using the WAIS-IV, operationalized through the Working Memory Index (Digit Span, Arithmetic) and Processing Speed Index (Symbol Search, Coding). Quality of life was assessed with the NCIQ, tinnitus-related distress with the Tinnitus Questionnaire (TQ), and depressive symptoms with the ADS-L. Results: After one year, speech intelligibility (FS) improved from a median of 0 to 70.0 (Wilcoxon Z = −4.864, p < 0.001, r = −0.61), and subjective hearing from a median of 2.55 to 3.18 (Wilcoxon Z = −3.072, p = 0.002). The NCIQ score increased from 52.3 to 60.6 (Z = −3.899, p < 0.001), and tinnitus-related distress decreased from 25 to 21 (Wilcoxon Z = −2.209, p = 0.027). Depressive symptoms declined numerically, although this change did not reach statistical significance. Working memory improved from 82.0 to 89.0 (Wilcoxon Z = −4.090, p < 0.001), and processing speed from 89.5 to 95.5 (Wilcoxon Z = −2.533, p = 0.011). Before CI, WMI and PSI showed a strong correlation (ρ = 0.533, p = 0.002), and WMI correlated moderately with education level (ρ = 0.452, p = 0.012). One year after CI, correlations strengthened between PSI and NCIQ (ρ = 0.510, p = 0.006), PSI and OI (ρ = 0.400, p = 0.039), and WMI and TQ (ρ = –0.459, p = 0.021), indicating emerging associations between cognitive outcomes and auditory or psychosocial measures. Conclusions: One year of CI-based auditory rehabilitation improves auditory function, quality of life, tinnitus distress, and—critically—working memory and processing speed in middle-aged adults. These findings address a previously unfilled research gap and support the relevance of CIs for preserving cognitive health during midlife. Full article

This study examines how major global hotel groups construct sustainability through corporate communication, assessing both the thematic content and the internal coherence of their Environmental-Social-Governance (ESG) narratives. The research question is How do international hotel corporations construct sustainability through their corporate communication and ESG reporting? The research applies qualitative content analysis of sustainability reports from ten international hotel corporations and a four-layer discursive coherence model (performance, operational, narrative, strategic), the study analyses 888 coded quotations and 205 sustainability-theme occurrences in ATLAS.ti version 25, a qualitative data-analysis software. Results show that while measurable, performance-based disclosures dominate—such as digital food-waste monitoring, emissions-intensity reductions, and responsible sourcing—symbolic language remains strategically deployed to reinforce identity, purpose, and legitimacy. Across the sector, sustainability discourse converges around four recurring pillars: environmental performance leadership, community resilience, responsible business governance, and inclusive economic empowerment. The study advances theoretical work on sustainability communication by conceptualizing discursive coherence as an indicator of organizational authenticity and offers actionable insights for enhancing credibility and stakeholder trust in corporate ESG reporting. Full article

Microbial degradation of pectin is a fundamental process for the carbon cycle and a strategic approach for treating industrial residues. This study characterizes a novel marine bacterium, Paenarthrobacter sp. FR1, isolated from East China Sea intertidal sediment, which exhibits the ability to utilize pectin. Its draft genome (4.83 Mb, 62.92% GC content) is predicted to encode 4498 protein-coding genes. Genomic analysis revealed a rich repertoire of Carbohydrate-Active Enzymes (CAZymes) crucial for this process, including 108 glycoside hydrolases (GHs), 7 polysaccharide lyases (PLs), 35 carbohydrate esterases (CEs), and 11 auxiliary activities (AAs). Genomic analysis provides supportive evidence that FR1 may target both homogalacturonan (HG) and rhamnogalacturonan (RG) pectin domains, potentially through complementary hydrolytic and oxidative pathways. Phylogenomic analysis based on Average Nucleotide Identity (ANI, 83.56%) and digital DNA-DNA Hybridization (dDDH, 27.8%) confirmed its status as a potential novel species. Notably, FR1 is a rare Paenarthrobacter isolate with innate pectinolytic capability, a characteristic not previously documented in this genus. This strain’s unique enzymatic machinery highlights its importance in marine carbon cycling and provides a valuable biotechnological resource for degrading pectin-rich wastes. Full article

Background/Objectives: Safe discharge after neurosurgical traumatic brain injury (TBI) depends on documented counseling and appropriate referrals, yet real-world fidelity is uncertain in resource-constrained settings. We quantified discharge process quality and identified digitally actionable gaps. Methods: The sample for this study was a retrospective cohort of 559 consecutive neurosurgical TBI patients discharged from a Brazilian public tertiary center (2012–2022). Data were abstracted from electronic health records. The primary outcome was documentation of warning sign counseling at discharge. Proportions are reported with exact Clopper–Pearson 95% confidence intervals. Results: The median age was 66 years (IQR 47–79.5); 78.5% were male and most received care under the public health system. Subdural hematoma predominated; hematoma drainage was the most frequent procedure. Warning sign counseling was documented in 16.1% of cases (89/559; 95% CI 13.2–19.5), and no palliative care referrals were recorded. Conclusions: A low baseline for a safety-critical discharge element exposes an immediately actionable target. Embedding discharge order sets with mandatory counseling fields in the EHR, clinical decision support prompts for palliative care screening and follow-up, and QR-coded patient handouts represent a pragmatic path to improve discharge quality and end-of-life readiness in the digital era. Full article

Large Language Models (LLMs) have demonstrated significant potential in transforming software testing by automating tasks such as test case generation. In this work, we explore the integration of LLMs within a Model-Driven Engineering (MDE) approach to enhance the automation of test case generation for smart contracts. Our focus lies in the use of Role-Based Access Control (RBAC) models as formal specifications that guide the generation of test scenarios. By leveraging LLMs’ ability to interpret both natural language and model artifacts, we enable the derivation of model-based test cases that align with specified access control policies. These test cases are subsequently translated into executable code in Digital Asset Modeling Language (DAML) targeting blockchain-based smart contract platforms. Building on prior research that established a complete MDE pipeline for DAML smart contract development, we extend the framework with LLM-supported test automation capabilities and implement the necessary tooling to support this integration. Our evaluation demonstrates the feasibility of using LLMs in this context, highlighting their potential to improve testing coverage, reduce manual effort, and ensure conformance with access control specifications in smart contract systems. Full article

Transistor-level Integrated Circuit (IC) design is fundamental to modern electronics, yet it remains one of the most expertise-intensive and time-consuming stages of chip development. As circuit complexity continues to rise, the need to automate this low-level design process has become critical to sustaining innovation and productivity across the semiconductor industry. This study presents a fully automated methodology for transistor-level IC design using a novel framework that integrates Grammatical Evolution (GE) with Cadence SKILL code. Beyond automation, the framework explicitly examines how symmetry and asymmetry shape the evolutionary search space and resulting circuit structures. To address the time-consuming and expertise-intensive nature of conventional integrated circuit design, the framework automates the synthesis of both digital and analogue circuits without requiring prior domain knowledge. A specialised attribute grammar (AG) evolves circuit topology and sizing, with performance assessed by a multi-objective fitness function. Symmetry is analysed at three levels: (i) domain-level structural dualities (e.g., NAND/NOR mirror topologies and PMOS/NMOS exchanges), (ii) objective-level symmetries created by logic threshold settings, and (iii) representational symmetries managed through grammatical constraints that preserve valid connectivity while avoiding redundant isomorphs. Validation was carried out on universal logic gates (NAND and NOR) at multiple logic thresholds, as well as on a temperature sensor. Under stricter thresholds, the evolved logic gates display emergent duality, converging to mirror-image transistor configurations; relaxed thresholds increase symmetric plateaus and slow convergence. The evolved logic gates achieve superior performance over conventional Complementary Metal–Oxide–Semiconductor (CMOS), Transmission Gate Logic (TGL), and Gate Diffusion Input (GDI) implementations, demonstrating lower power consumption, a reduced Power–Delay Product (PDP), and fewer transistors. Similarly, the evolved temperature sensor exhibits improved sensitivity, reduced power, and Integral Nonlinearity (INL), and a smaller area compared to the conventional Proportional to Absolute Temperature (PTAT) or “gold” circuit, without requiring resistors. The analogue design further demonstrates beneficial asymmetry in device roles, breaking canonical structures to achieve higher performance. Across all case studies, the evolved designs matched or outperformed their manually designed counterparts, demonstrating that this GE-based approach provides a scalable and effective path toward fully automated, symmetry-aware integrated circuit synthesis. Full article

This study presents a sociotechnical framework for mitigating ethnic violent conflicts by integrating information and communication technologies (ICTs) with community-based social capital. Drawing on longitudinal case studies from three conflict-prone regions in Kenya, Mt. Elgon, Muhoroni, and the Turkana–West Pokot borderlands, the research examines how ICT-enabled peace networks, particularly the Early Warning and Early Response System (EWERS), mobilize bonding, bridging, and linking social capital to reduce violence. The study employs a multi-phase qualitative design, combining retrospective analysis, key informant interviews, focus group discussions, action participation, and thematic coding of EWERS data collected between 2009 and 2021. This approach enabled the reconstruction of system evolution, stakeholder dynamics, and community responses across diverse socio-political contexts. Findings demonstrate that embedding ICTs within trusted social structures fosters inter-ethnic collaboration, inclusive decision-making, and trust-building. EWERS facilitated confidential reporting, timely alerts, and coordinated interventions, leading to reductions in livestock theft, improved leadership accountability, emergence of inter-ethnic business networks, and enhanced visibility and response to gender-based violence. The system’s effectiveness was amplified by faith-based legitimacy, local governance integration, and adaptive training strategies. The study argues that ICTs can become effective enablers of peace when sensitively contextualized within local norms, relationships, and community trust. Operationalizing social capital through digital infrastructure strengthens community resilience and supports inclusive, sustainale peacebuilding. These insights offer a scalable model for ICT-integrated violence mitigation in low- and middle-income countries. This is among the first studies to operationalize bonding, bridging, and linking social capital within ICT-enabled peace networks in rural African contexts. By embedding digital infrastructure into trusted community relationships, the framework offers an analytical approach that can inform inclusive violence mitigation strategies across low- and middle-income settings. While the framework demonstrates potential for scalability, its outcomes depend on contextual adaptation and cannot be assumed to replicate uniformly across all environments. Full article

This study presents the development and evaluation of a Quick Response (QR) code-integrated, web-based, and GIS-supported interactive learning model designed to enhance field-based plant learning in landscape architecture education. Conducted on the Görükle Campus of Bursa Uludağ University (BUU), the research systematically inventoried 6869 individual woody plants belonging to 172 taxa, georeferenced them using GPS, and visualized the data on an interactive campus map. Unique QR codes were generated for each taxon, providing instant access to plant profiles via a web platform and the Landscape Plants mobile application. The pedagogical effectiveness of the system was evaluated through a survey administered to 158 students, yielding a high internal reliability (Cronbach’s Alpha = 0.969). The findings indicated a high level of student satisfaction and a strong positive correlation between web-based and QR code applications (r = 0.941, p ≤ 0.001). This research represents the most comprehensive campus-scale digital plant learning system in Turkey, in terms of both species diversity and individual count. It provides a scalable and sustainable smart campus model which is applicable to nature-based disciplines worldwide. Full article

Legal regulation and practical implementation of artificial intelligence (AI) in Kazakhstan’s criminal procedure are considered within the context of judicial digital transformation. Risks arise for fundamental procedural principles, including the presumption of innocence, adversarial process, and protection of individual rights and freedoms. Legislative mechanisms ensuring lawful and rights-based application of AI in criminal proceedings are required to maintain procedural balance. Comparative legal analysis, formal legal research, and a systemic approach reveal gaps in existing legislation: absence of clear definitions, insufficient regulation, and lack of accountability for AI use. Legal recognition of AI and the establishment of procedural safeguards are essential. The novelty of the study lies in the development of concrete approaches to the introduction of artificial intelligence technologies into criminal procedure, taking into account Kazakhstan’s practical experience with the digitalization of criminal case management. Unlike existing research, which examines AI in the legal profession primarily from a theoretical perspective, this work proposes detailed mechanisms for integrating models and algorithms into the processing of criminal cases. The implementation of AI in criminal justice enhances the efficiency, transparency, and accuracy of case handling by automating document preparation, data analysis, and monitoring compliance with procedural deadlines. At the same time, several constraints persist, including dependence on the quality of training datasets, the impossibility of fully replacing human legal judgment, and the need to uphold the principles of the presumption of innocence, the right to privacy, and algorithmic transparency. The findings of the study underscore the potential of AI, provided that procedural safeguards are strictly observed and competent authorities exercise appropriate oversight. Two potential approaches are outlined: selective amendments to the Criminal Procedure Code concerning rights protection, privacy, and judicial powers; or adoption of a separate provision on digital technologies and AI. Implementation of these measures would create a balanced legal framework that enables effective use of AI while preserving core procedural guarantees. Full article

Background: A key barrier to realizing the full potential and long-term collection of patient-reported outcomes (PROs) is the limited understanding of user experience (UX) factors that influence sustained patient engagement with digital PRO tools. Most existing research focuses on disease-specific or country-specific solutions, leaving a gap in identifying shared UX determinants that could inform scalable, cross-disease European digital health frameworks. This fragmentation hinders interoperability and increases development costs by requiring separate tools for each context. This case study aims to address this gap by identifying key UX features that optimize PRO collection across diverse chronic conditions in Europe within the Health Outcomes Observatory project, enhancing continuous (primary use) and large-scale (secondary use) data collection. Objective: This study aimed to identify and analyze key UX factors that support adoption and sustained use of PRO collection tools among patients with chronic diseases across multiple European countries. Methods: Patient focus groups were conducted in four chronic disease areas: cancer, inflammatory bowel disease (IBD), and diabetes (type I and II) across six European countries. Participants were recruited purposively through national patient advisory boards to ensure diversity in age, gender, and disease type. Sessions were moderated by trained qualitative researchers following a standardized guide, and discussions were transcribed verbatim and coded in researcher pairs to ensure intercoder reliability through iterative consensus. A modified thematic analysis, guided deductively by the UX Honeycomb model and inductively by emergent themes, was used to identify cross-disease UX determinants. Results: In total, 17 patients and patient representatives participated (76% female; 4 diabetes, 6 IBD and 7 cancer). We identified six core UX factors driving patient engagement for all disease groups: compatibility with other technologies, direct communication with the care team, personalization, ability to share data, the need for educational material and data protection were identified as key aspects of PRO technologies. However, the customizability of the app is crucial. Not all disease groups had the same needs, and participants specifically requested that the app provide information relevant to their own condition. Disease-specific needs, like T1D patients desiring glucose monitoring integration, were identified. IBD patients highlighted flare detection abilities and cancer patients especially sought side-effect comparisons. Conclusions: Our findings indicate that a unified yet customizable PRO platform can address shared UX needs across diseases, improving patient engagement and data quality. Incorporating features such as seamless data transfer, personalization, feedback, and strong privacy measures can foster trust and long-term adoption across European contexts. In addition to some disease-specific issues, most needs for the backbone of the app were shared among the disease areas. This shows that a shared app between diseases might be preferable and, in case of comorbidities, could ease self-management for patients. Last, to ensure full potential for every user and every disease, customization is crucial. Full article

This paper presents a PRISMA-guided systematic literature review (2015–2025) of 20 empirical studies on digital transformation in retail banking, examining how artificial intelligence (AI) strengthens cybersecurity, enables FinTech collaboration through interoperable APIs and open-banking infrastructures, and embeds data-driven decision-making across core functions. We searched major databases, applied predefined eligibility criteria, appraised study quality, and coded outcomes related to digital adoption, operational resilience, and customer experience. The synthesis indicates that AI-enabled controls and API-mediated partnerships are consistently associated with higher digital-maturity indicators, conditional on robust model-risk governance and prudent third-party/outsourcing management. Benefits span improved customer experience, efficiency, and inclusion; however, legacy systems, regulatory fragmentation, cyber threats, and organizational resistance remain binding constraints. We propose a unified framework linking technology choices, regulatory design, and organizational outcomes, and distill actionable guidance for policymakers (e.g., interoperable standards, proportional AI governance, sector-wide cyber resilience) and bank managers (sequencing AI use cases, risk controls, and partnership models). Future research should assess emerging technologies—including quantum-safe security and central bank digital currencies (CBDCs)—and their implications for digital-banking stability and trust. Full article

As a vital carrier of human intangible culture, dance plays an important role in cultural transmission through digital generation. However, existing dance generation methods rely heavily on high-precision motion capture and manually annotated datasets, and they fail to effectively model the culturally distinctive movements of Chinese ethnic folk dance, resulting in semantic distortion and cross-modal mismatch. Building on the Chinese traditional ethnic Helou Dance, this paper proposes a culture-aware Chinese ethnic folk dance generation framework, CAFE-Dance, which dispenses with manual annotation and automatically generates dance sequences that achieve high cultural fidelity, precise music synchronization, and natural, fluent motion. To address the high cost and poor scalability of cultural annotation, we introduce a Zero-Manual-Label Cultural Data Construction Module (ZDCM) that performs self-supervised cultural learning from raw dance videos, using cross-modal semantic alignment and a knowledge-base-guided automatic annotation mechanism to construct a high-quality dataset of Chinese ethnic folk dance covering 108 classes of curated cultural attributes without any frame-level manual labels. To address the difficulty of modeling cultural semantics and the weak interpretability, we propose a Culture-Aware Attention Mechanism (CAAM) that incorporates cultural gating and co-attention to adaptively enhance culturally key movements. To address the challenge of aligning the music–motion–culture tri-modalities, we propose a Tri-Modal Alignment Network (TMA-Net) that achieves dynamic coupling and temporal synchronization of tri-modal semantics under weak supervision. Experimental results show that our framework improves Beat Alignment and Cultural Accuracy by 4.0–5.0 percentage points and over 30 percentage points, respectively, compared with the strongest baseline (Music2Dance), and it reveals an intrinsic coupling between cultural embedding density and motion stability. The code and the curated Helouwu dataset are publicly available. Full article

This article presents a part of the industrial metaverse for electric drive system diagnostics. The advantages of using a low-code/no-code platform for electric drive systems diagnostics are demonstrated. Five diagnostic scenarios were developed, programmed, and implemented. The article demonstrates the implementation and use of the platform’s main functional blocks: a visualization block (which displays the state of electric machines in any user-friendly form—graphs, Park’s vector diagrams, or diagnostic curves); a digital twin block (which simulates various engine states); a digital twin block with an engine defect (which simulates faulty engine states); and an artificial intelligence block (which trains classification model to predict various engine states). Experiments on training the artificial intelligence block using a misalignment defect dataset are presented. The dataset was divided into six classes: engine operation with/without a defect under no load, engine operation with/without a defect under a 50% load, and engine operation with/without a defect under a 100% load. The workflow for training and using the model, the basic training approaches, and the distinguishability of the presented classes are demonstrated. The model training results are shown. The article presents a methodology for extensive testing of program functionality. The obtained results demonstrate the feasibility of implementing a low-code/no-code platform and the feasibility of solving the assigned tasks with its help, as well as the simplification and reduction in engineering solution development time. Full article

The actinobacterial strain Kitasatospora griseola JNUCC 62 was isolated from volcanic wetland soil at Mulyeongari Oreum, Jeju Island, and taxonomically identified through 16S rRNA gene and whole-genome analyses. The complete genome, assembled from PacBio Sequel I reads, spans 8.31 Mb with a GC content of 72.8% and contains 7265 coding sequences. Comparative genomic indices (Average nucleotide identity, ANI 97.46%; digital DNA–DNA hybridization, dDDH 84.4%) confirmed its conspecific relationship with K. griseola JCM 3339^T. Genome mining using antiSMASH 8.0 revealed 30 biosynthetic gene clusters (BGCs), including polyketide synthase (PKS), non-ribosomal peptide synthetase (NRPS), ribosomally synthesized and post-translationally modified peptide (RiPP), lanthipeptide, and terpene types, accounting for 18.6% of the genome. Several BGCs displayed homology to known formicamycin-, lankacidin-, and lanthipeptide-type clusters, while others were novel or cryptic, reflecting adaptation to the nutrient-poor volcanic environment. Ethyl acetate extraction of the culture broth, especially under tryptophan-supplemented conditions, yielded four metabolites—1-acetyl-β-carboline, perlolyrine, tryptopol, and 1H-pyrrole-2-carboxylic acid—identified by UV and NMR spectroscopy. These compounds correspond to NRPS–PKS hybrid and arylpolyene-type gene clusters predicted in the genome, suggesting precursor-directed biosynthesis of indole and pyrrole alkaloids. The ethyl acetate extract (JNUCC62 EA) exhibited strong antioxidant capacity in the ABTS assay, anti-inflammatory activity via inhibition of nitric oxide (31.09 ± 3.69% of control) and cytokines (IL-6, IL-1β, TNF-α) in lipopolysaccharide (LPS)-stimulated RAW 264.7 macrophages, and anti-melanogenic effects in α-melanocyte-stimulating hormone (MSH)-stimulated B16F10 melanoma cells, where melanin content and tyrosinase activity decreased to 61.49 ± 1.24% and 24.32 ± 0.31% of the control, respectively, without cytotoxicity. A human primary skin irritation test confirmed no irritation up to 50 µg/mL, establishing excellent dermal safety. Collectively, these findings highlight K. griseola JNUCC 62 from Mulyeongari Oreum as a volcanic wetland-derived actinomycete harboring rich biosynthetic potential for novel indole alkaloids with antioxidant, anti-inflammatory, and whitening properties, supporting its development as a safe and multifunctional cosmeceutical ingredient. Full article