Abstract:This study develops a practical framework for evaluating AI-generated brand visuals in a marketing communication context. Using a qualitative case study of an Indonesian sustainable startup (I-NewBee), the research employs…
ys photo-elicitation interviews to compare consumer evaluations of brand assets generated by three text-to-image tools (Midjourney, Neural Love AI, and Leonardo AI). Twelve target-market participants assessed anonymized visual sets (A/B/C) produced under a standardized prompt structure, using a semi-structured protocol informed by attention-stage cues from the AISAS model. Expert input from a visual communication design practitioner was used to triangulate judgments on visual quality and brand fit. Data were analyzed through iterative coding and thematic synthesis to identify recurring evaluation dimensions and decision cues. The findings suggest that perceived brand fit is shaped by visual realism, compositional clarity, brand-consistent signals, and message interpretability, while prompt ambiguity and inconsistent visual cues reduce credibility. The paper contributes an actionable evaluation framework expressed as evaluation dimensions and prompt-design considerations for startups seeking to deploy generative AI responsibly in brand communication
Abstract:This study aims to analyze the effect of Human Resource Accounting (HRAC) disclosure on firm value, with financial performance as a mediating variable. This quantitative study uses secondary data in the form of annual reports…
ports and sustainability reports from 14 companies during the 2020–2024 period, with a total of 70 observations. HRAC disclosure is measured using the Human Resource Disclosure Index through a content analysis approach. Firm value is proxied by Net Asset Value (NAV) transformed into the natural logarithm, while financial performance is measured using ROA. Data analysis was performed using path analysis with SPSS software, and the Sobel test to examine the role of financial performance as a mediating variable. The results show that HRAC disclosure affects firm financial performance, but does not directly affect firm value. Financial performance is proven to affect firm value in a negative direction. The results of the Sobel test indicate that financial performance plays a significant role as a mediating variable in the relationship between HRAC disclosure and firm value. These findings indicate that HRAC disclosure affects firm value indirectly through financial performance. ASDM disclosure functions as supporting information and additional signals for investors, but is not yet able to become the main determinant in the direct formation of company value.
Abstract:The increased integration of operational technology (OT), Internet of Things (IoT), and business IT systems has allowed sophisticated attackers to circumvent isolated security features and launch cross-platform assaults.…
Current fragmented techniques, with discrete detectors monitoring Modbus, Kubernetes, MQTT, or other domain-specific protocols, cannot handle cross-system risks. These methodologies overlook 68% of multi-vector marketing that uses both physical and digital channels. This study introduces a transfer learning architecture to integrate detection capabilities by correlating threats across protocols, devices, and settings. The architecture generates a unified feature space that extracts behavioral semantics from industrial control system logs, cloud telemetry, network traffic, and device-level signals to produce protocol-agnostic threat representations. Adversarial domain adaptation and semantic graph embeddings enable cross-domain knowledge transfer with minimum retraining. Security teams may now discover kill chains like infected cloud containers preceding illegal PLC command execution every 23 minutes. Validated against real-world attack datasets from water treatment facilities (OT) and cloud infrastructure (IT), the system achieved 93.4% cross-platform attack recall, a 41.3 percentage point improvement over prior methodologies. It reduced OT data labeling by 89% and false positives by 93.5%. This paradigm shift transforms threat correlation from a reactive, domain-specific process to adaptive intelligence, boosting resilience for critical infrastructure, industrial ecosystems, and smart environments facing cyber-physical hazards. The framework's practical validation in energy, industry, and vital infrastructure shows its importance in protecting an increasingly linked world.
Abstract:This study investigates the role of acquisition premiums in mergers and acquisitions (M&A) and their impact on shareholder wealth, focusing on five major Indian deals in pharmaceuticals, retail, banking, steel, and renewable…
able energy sectors. Potential synergies often justify acquisition premiums ranging from approximately 15% to 40% above target companies’ market values. However, market responses suggest that such premiums may not consistently result in value creation for acquiring firms' shareholders. Empirical findings reveal mixed outcomes: Sun Pharma’s acquisition of Ranbaxy led to a 9.8% share price increase within five days, while Tata Steel’s high-premium acquisition of Bhushan Steel saw only a 1.7% gain. In contrast, deals like Reliance–Future Retail and Tata Power–Welspun Power showed minimal or negative returns, despite sizable premiums. These patterns indicate that premium size alone is not a reliable predictor of post-deal shareholder wealth creation. The study concludes that M&A success depends more on strategic fit, market timing, and sectoral dynamics than on the premium offered. This analysis contributes to the broader M&A discourse by offering evidence-based insights into how premium valuations can either maximise or dilute shareholder value, aiding investors, corporate strategists, and policy analysts in deal assessment
Abstract:Signal interference or noise is one of the main problems in data transmission that can reduce information quality in both analog and digital systems. This study aims to visually analyze the effects of noise using audio software.…
oftware. The method used is simulation-based, where a pure signal is used as an initial reference before being subjected to various levels of interference. The analysis results show that in analog signals, noise causes permanent waveform distortion that is difficult to recover. In contrast, digital signals tend to maintain data integrity as long as the interference does not exceed a certain threshold. These differences in signal characteristics can be visually observed through waveform displays in the software. The results indicate that digital systems have advantages in maintaining signal quality in noisy environments and have the potential to be used as educational media for basic telecommunication and signal processing studies.
Abstract:Abstract: Anomalous sound detection is essential for industrial predictive maintenance, as machine failures often originate from subtle acoustic changes during operation. However, high background noise and limitations of…
conventional Convolutional Neural Networks (CNN) reduce detection reliability. This study proposes a 1D-CNN-based anomaly detection framework with multi-view feature fusion and temporal segmentation to enhance detection performance. The approach combines MFCC, Log-Mel Spectrogram, and Chroma STFT features, while temporal segmentation divides audio signals into 5-second segments to better capture transient anomalies. Experiments on the MIMII dataset under varying Signal-to-Noise Ratio (SNR) conditions show that MFCC and Log-Mel fusion achieves the best performance, with 97.90% accuracy and ROC-AUC of 0.9789. The model maintains accuracy above 90% at −6 dB, demonstrating strong robustness in noisy industrial environments.
Keywords: industrial anomaly detection; 1D-CNN; multi-view feature fusion; temporal segmentation; MIMII dataset.
Abstrak: Deteksi anomali suara merupakan komponen penting dalam sistem pemeliharaan prediktif industri, karena kegagalan mesin sering diawali oleh perubahan akustik yang bersifat halus selama proses operasi. Namun, tingkat kebisingan yang tinggi serta keterbatasan arsitektur Convolutional Neural Network (CNN) konvensional dapat menurunkan keandalan deteksi. Penelitian ini bertujuan mengusulkan kerangka deteksi anomali berbasis 1D-CNN yang mengintegrasikan strategi fusi fitur multi-view dan segmentasi temporal untuk meningkatkan kinerja deteksi. Pendekatan yang digunakan menggabungkan fitur MFCC, Log-Mel Spectrogram dan Chroma STFT, sementara teknik temporal splitting membagi sinyal audio menjadi segmen berdurasi 5 detik untuk menangkap anomali yang bersifat sementara. Eksperimen menggunakan dataset MIMII pada berbagai kondisi Signal-to-Noise Ratio (SNR) menunjukkan bahwa kombinasi MFCC dan Log-Mel Spectrogram menghasilkan kinerja terbaik dengan akurasi 97,90% dan ROC-AUC sebesar 0,9789. Model juga mempertahankan akurasi di atas 90% pada kondisi kebisingan ekstrem (−6 dB) yang menunjukkan ketahanan yang baik dalam lingkungan industri yang bising.
Kata kunci: deteksi anomali industri; 1D-CNN; fusi fitur multi-view; segmentasi temporal; dataset MIMII
Abstract:This study investigates the rhetorical and discourse functions of repetition in the English translation of Surah Al-Mursalat by M.A.S. Abdel Haleem, focusing on the refrain “Woe on that Day to those who denied the truth.”…
h.” Using a qualitative descriptive method grounded in stylistics and discourse analysis, the study explores how this fixed phrase, repeated ten times across the chapter, operates at multiple linguistic levels. The analysis reveals that the refrain serves three interrelated functions: stylistically, it foregrounds divine warning and creates rhythmic cohesion; structurally, it segments the surah into thematic units and signals discourse boundaries; rhetorically, it intensifies condemnation and engages the reader emotionally through cumulative repetition. Abdel Haleem’s consistent rendering of the refrain retains these functions effectively, unlike other translations that introduce lexical variation. The findings demonstrate that repetition in Qur’anic translation is not merely ornamental but performs essential linguistic and communicative roles. This study contributes to Qur’anic stylistics, translation studies, and discourse analysis by showing that sacred texts, when translated with rhetorical sensitivity, can preserve the stylistic integrity of the original.