Volume 8 Issue 2 2026

Atmospheric Water Harvesting (AWH) has emerged as a sustainable solution to address global water scarcity by extracting potable water directly from humid ambient air. This paper presents the design and experimental evaluation of a smart atmospheric water harvesting system powered by renewable energy sources such as solar or wind energy. The proposed system employs thermoelectric (Peltier) cooling and vapor condensation principles to convert atmospheric moisture into liquid water, followed by multi-stage purification including sediment filtration, activated carbon filtering, and ultraviolet sterilization. To enhance automation and operational efficiency, image processing techniques are integrated for realtime monitoring of condensation behavior, water generation rate, and storage tank levels. The closedloop intelligent control reduces energy consumption, improves reliability, and minimizes manual supervision. Experimental results demonstrate effective water production under varying humidity and temperature conditions, confirming the feasibility of decentralized potable water generation for rural, remote, and disaster-affected regions. The system offers a compact, eco-friendly, and cost-effective approach for sustainable water supply infrastructure.

Negative affect is a contentious issue of affective computing and computer-assisted mental health, especially in a dialogical context, where emotions change over time. More context-sensitive interpretations of human behavior are possible with recent developments of Large Language Models (LLMs), but most of the current systems use a single utterance to interpret an emotion and do not provide longitudinal models which are based on psychologically grounded assumptions. In this article, the study will advance a behavior-sensitive neural framework of negative affect prediction with the help of LLMs based on multi-turn and longitudinal conversational analysis instead of single-turn and single-classified emotion. The suggested framework combines the representation of contextual language, psycholinguistic signs, and the tracking of behavioral trajectory to make the inferences about the trends in emotions and to clearly place the system in the frame of non-diagnostic and supporting. The framework also includes strategies of safety constrained responses, human ethics, and human-inthe-loop considerations in the deployment of the framework in emotionally sensitive applications. Representative results of the past benchmark studies on the conversational emotion datasets are discussed as the motivation to the design choices and as an explanation of the benefits of the contextual transformer-based architectures compared with the baseline models. Instead of a fully-implemented system, this work provides the design advice and conceptual insights based on the recent (1000, 2023,2025) literature on affective computing, empathetic AI, and digital mental health. The proposed framework will be used in future research to inform on behavior-aware, ethically responsible conversational agents, which can endorse emotional awareness and resilience without the involvement of clinical diagnosis and treatment.

Solar photovoltaic energy represents a promising alternative for Republic of Guinea, where access to electricity is limited. This study analyzes the impact of climatic conditions on an 885.5 W PV installation, modeled in MATLAB/Simulink with photovoltaic modules, a booster converter, and a Perturbation & Observation type MPPT to maximize power. The objective is to identify the regions of Republic of Guinea that are most favorable for these installations, regardless of climate. RETScreen and PVsyst software were used to obtain temperature and solar radiation data. In April, the KINDIA region has the highest average daily solar radiation (6.89 kWh/m²/day), while FARANAH has the highest annual solar radiation (5.6 kWh/m²/day). The careful analysis of the collected data made it possible to identify the optimal sites for photovoltaic energy production. The simulations achieved average monthly energy yields of 42.6%, 42.3%, 45.6%, 44.7%, 43.9%, 44.5%, 43.9%, and 41.0% for BOKE, CONAKRY, FARANAH, KANKAN, KINDIA, LABE, MAMOU, and N'ZEREKORE, respectively. These values show that Guinea's solar photovoltaic potential offers the Guinean government the opportunity to significantly reduce its dependence on fossils energy. Its exploitation will contribute to the reduction of greenhouse gases.

Many metaheuristic algorithms are created by taking inspiration from nature, society, art, or human behavior. However, sometimes these ideas look new only because of the story behind them. If the mapping between the story and the mathematical search process is not clear, measurable, and repeatable, the method may be misleading. In this paper, based on basic principles of stochastic search, population behavior, and standard benchmark testing, we do two things. First, we propose a general guideline that shows how a metaheuristic algorithm can be systematically built from a poem stanza or from the known traits of a famous person. Second, we introduce a new algorithm called Shakespearean Soliloquy Optimization (SSO). This method is inspired by dramatic elements in Shakespeare’s works such as soliloquy (selfreflection), rhythmic movement, chorus interaction, and the conflict–resolution story arc. We test SSO on a simple quadratic function 𝑧 = (𝑥 − 5)2 + (𝑦 − 4)2and compare its performance with PSO, ABC, and a continuous version of MBO. Then we evaluate it on five well-known benchmark functions: Sphere, Rosenbrock, Rastrigin, Ackley, and Griewank.

In this work, we consider rational groups containing 𝑍 and their automorphism groups. We have established, for two rational groups 𝐴 and 𝐵 both containing 𝑍, conditions for the isomorphism 𝐻𝑜𝑚(𝐴, 𝐴𝑢𝑡(𝐴)) ≅ 𝐻𝑜𝑚(𝐵, 𝐴𝑢𝑡(𝐵)) to hold. We have then shown that 𝐻𝑜𝑚(𝐴𝑢𝑡(𝐴), 𝐴) ≅ 𝐻𝑜𝑚(𝐴𝑢𝑡(𝐵), 𝐵) if and only if 𝐴 ≅ 𝐵.

This study examines the influence of nano marketing orientation on customer engagement and purchasing behavior in the online fashion retail industry, with consumer characteristics serving as control variables. Drawing upon relationship marketing theory (Morgan & Hunt, 1994), source credibility theory (Hovland et al., 1953), and customer engagement theory (Brodie et al., 2011), the research develops and tests a structural model integrating perceived personalization, trust in influencers, nano marketing orientation, customer engagement, and purchasing behavior. A quantitative research design was employed using a structured questionnaire distributed to consumers who have experience purchasing fashion products online and engaging with nano-influencers on social media platforms. The data were analyzed using descriptive statistics, Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM) to examine both direct and indirect relationships among the variables. Reliability and validity were assessed through Cronbach’s alpha, composite reliability, and average variance extracted. The findings indicate that perceived personalization and trust in influencers significantly influence nano marketing orientation. Nano marketing orientation positively affects customer engagement, which in turn has a strong positive impact on purchasing behavior. The results also demonstrate that nano marketing orientation and customer engagement serve as mediating mechanisms linking personalization and trust to purchasing outcomes. Consumer characteristics show partial effects on engagement and marketing orientation, confirming their role as control variables. This study contributes to the literature by integrating personalization, influencer trust, and nano marketing orientation into a comprehensive structural framework. The findings provide practical implications for online fashion retailers, emphasizing the importance of personalized communication, authentic influencer partnerships, and engagementdriven strategies to enhance purchasing behavior in digital environments.