This study presents a nanosensor network system for autonomous microclimate optimization in precision horticulture, leveraging a field-programmable gate array (FPGA)-based control architecture that is integrated with an edge-level machine learning inference. Unlike the conventional greenhouse automation systems, which exhibit thermal and hygroscopic hysteresis often exceeding 32 °C and 78% relative humidity, the proposed framework embeds a random forest regression (RFR) model directly within the Altera DE2-115 FPGA fabric to enable predictive environmental regulation. The model achieved an R² of 0.985 and root mean square error (RMSE) of 0.28 °C, allowing proactive compensation for the thermodynamic disturbances from the high-intensity light-emitting diode (LED) lighting with a 120 s predictive horizon. The real-time monitoring and remote supervision were supported via a NodeMCU-based IoT gateway, achieving a 140 ms mean communication latency and a 99.8% packet delivery reliability. The preliminary validation using lettuce (Lactuca sativa) optimized the environmental parameters, while the subsequent experiments with pepper (Capsicum annuum), a commercially important and environmentally sensitive crop, demonstrated system performance under real-world conditions. The control system maintained a temperature and humidity within ±0.3 °C and ±1.2% of the setpoints, respectively, and outperformed the baseline rule-based control with a 28% increase in fresh biomass, a 22% improvement in dry matter accumulation, a 25% reduction in actuator duty-cycle switching, and an 18% decrease in overall energy consumption. These results highlight the efficacy of FPGA-integrated edge intelligence combined with low-latency IoT telemetry as a scalable, energy-efficient, and high-fidelity solution for sub-degree environmental control in next-generation, controlled-environment, and vertical farming systems.

Purpose: The increasing use of the terms “fake data” and “fake information” in digital systems has led to conceptual ambiguity and inconsistent usage across the literature. This study aims to clarify the definitional boundaries and relationships between these terms by examining how they are used and interpreted in existing research. Design/methodology/approach: A structured literature review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A total of 334 studies published between 2009 and 2026 were selected after screening and eligibility assessment. Their abstracts were analyzed using thematic analysis, where recurring patterns were identified and encoded based on dimensions such as intent of creation, transformation processes and usage context. Findings: The results reveal that neither “fake data” nor “fake information” is explicitly defined in the literature, with meanings typically inferred from context. The analysis also shows a marked increase in the use of these terms since 2020, with fake data more prevalent in technical domains such as cybersecurity and data science, and fake information more common in media and social contexts. Research limitations/implications: The review is restricted to the computer science domain and its related fields (e.g. IT, data science and artificial intelligence), which may limit the generalizability of the findings to other disciplines. Despite these limitations, the study highlights the need for consistent terminology and provides a foundation for future research to refine and validate the proposed conceptual distinctions across broader contexts. Practical implications: The proposed framework supports improved classification, risk assessment and intervention strategies by distinguishing between data-level manipulation and information-level distortion in digital systems. Originality/value: This study introduces a data–information–knowledge-grounded, two-level framework that clarifies ambiguity in the use of fake data and fake information. The first level distinguishes between legitimate and deceptive intent, while the second differentiates based on what is being produced (data vs. information). This structure enables a clearer conceptual separation between fake data, fake information and synthetic data.

Causality extraction is an important task in natural language processing, yet it remains underexplored in informal Arabic social media text, particularly in dialectal contexts. This study investigates causal-reason extraction from Saudi Arabic tweets related to sick-leave requests. A gold-standard dataset was annotated for multiple causality-related tasks, including cause-presence detection, cause-span extraction, cause-category classification, causal-marker detection, and causal marker text identification. The study compares two modeling paradigms: fine-tuned BERT-based models, represented by SaudiBERT and AraBERT, and prompting-based large language models (LLMs), represented by GPT-4.1-mini and Gemini-2.5-flash. The descriptive analysis showed strong class imbalance, substantial implicit causality, and uneven cause-category distributions. Results showed that SaudiBERT generally outperformed AraBERT when macro-level and minority-class performance were considered. Among LLMs, Gemini-2.5-flash achieved the strongest overall performance, particularly under natural 10-shot single-tweet prompting, while balanced few-shot prompting improved macro-F1 for cause-category classification. However, step-wise prompting did not consistently improve performance and may have introduced error propagation. Overall, the findings show that causality extraction in informal Saudi Arabic remains challenging, especially for implicit causal expression. The study highlights the complementary strengths of dialect-specific transformers and LLM-based prompting for Arabic causality extraction.