Search Results (7,175)

Stationary energy storage (SES) is increasingly needed to integrate variable renewable generation and improve consumer self-consumption, but technology choices involve associated trade-offs between cost, efficiency, and life-cycle impacts. This study evaluates the role of second-life lithium-ion (Li-ion) batteries repurposed from electric vehicles for stationary applications, compared to lead-acid (Pb-acid) batteries and power-to-hydrogen-to-power (PtH₂P) systems. We develop an optimization-based sizing and dispatch framework using measured PV–load profiles and hourly market electricity prices, and evaluate performance per 1 MWh delivered to the load over a 10-year life cycle. Economic performance is quantified through discounted cash flows equal to levelized cost of storage (LCOS), while environmental performance is assessed through life-cycle metrics with explicit representation of recycling and second-life credits. In addition to global warming potential (GWP), the analysis considers additional resource and impact metrics, as well as key operational efficiency metrics, including bidirectional consumption efficiency, autonomy, and share of self-consumption/export of photovoltaic systems. Scenario and sensitivity analyses examine the impact of policy and financial parameters, in particular feed-in tariff remuneration and discount rate, on the comparative ranking of technologies. The results highlight how circular economy pathways, especially second-life distribution for Li-ion batteries and high end-of-life recovery for lead-acid batteries, have a significant impact on the life-cycle burden for delivered energy, while market-driven conditions for dispatching and export activities shape economic outcomes. Overall, the proposed workflow provides a transparent, circularity-aware basis for selecting stationary storage technologies associated with photovoltaic systems, under realistic operational constraints. Full article

The hydrogen fuel cell electric vehicles (FCEVs) are becoming a worldwide recognized eco-friendly choice which produces no tailpipe emissions while providing better energy efficiency than traditional internal combustion engine vehicles. The review delivers an in-depth evaluation of FCEVs through their assessment which focuses on their transportation and power generation functions. The research investigates hydrogen production methods together with storage and distribution systems and vehicle integration practices and performance enhancement techniques. The paper highlights major technical challenges such as high production costs, limited refueling infrastructure, storage inefficiencies, and fuel cell durability. The research uses battery electric and hybrid vehicle comparisons to assess FCEV market competitiveness. The life-cycle environmental impact assessment proves that using clean hydrogen sources and sustainable end-of-life strategies is essential for achieving FCEV operational capabilities. The review examines new electrochemistry materials science and hybridization solutions which have become essential methods for creating better efficiency and durability while decreasing costs. The study shows how policy regulations and collaborative programs fast-track hydrogen adoption through their impact on future hydrogen grid integration and renewable hydrogen production and circular economy methods. The review shows how experts from different fields reached their achievements while still facing challenges to improve FCEVs as fundamental components of environmentally friendly transportation systems and clean energy networks. Full article

This paper studies the effects of leasing on credit risk and access to credit. The repossession of a leased asset is generally easier than the enforcement of collateral associated with securing a standard loan agreement. We argue that this greater efficiency in enforcement mitigates, ceteris paribus, the counterparty’s moral hazard. To test this hypothesis, we developed a credit rationing model in which income is privately observed and non-verifiable, and financial intermediaries share credit risk information about borrowers. Financial contracts that are more rapidly enforced, such as in leasing, enable the screening of relatively safer projects or credit rationing reduction. We provide empirical evidence consistent with this prediction for the Italian credit market and considerations for the effects of monetary policy variables on the model’s equilibrium. Full article

In contemporary debates on the United Nations Sustainable Development Goals, there is growing recognition that artificial intelligence (AI) may contribute meaningfully to SDG 2 (Zero Hunger), particularly by enhancing the efficiency of food aid distribution and resource allocation. However, such optimism must be critically situated within the broader institutional and ethical contexts in which AI operates. This study argues that the effectiveness of AI in conflict-affected settings is contingent not only on technical capacity but also on governance structures, ethical safeguards, and institutional trust, dimensions closely aligned with SDG 16 (Peace, Justice, and Strong Institutions). Using the Gaza Strip as a case study, this article demonstrates that AI-driven food assistance mechanisms may inadvertently reinforce structural vulnerabilities. Specifically, algorithmic targeting of aid risks deepening dependency, exacerbating digital exclusion, and weakening already fragile governance systems. The absence of robust data accountability frameworks further complicates these dynamics, raising concerns regarding transparency, fairness, and long-term sustainability. The findings caution against privileging technical efficiency at the expense of socio-political stability. Rather, they highlight that the sustainability of AI interventions in humanitarian contexts fundamentally depends on the credibility and legitimacy of institutions. Accordingly, this study proposes a conceptual model for AI in hunger relief and digital humanitarianism that integrates technical innovation with institutional accountability and social trust. This study presents a narrative review informed by structural searching that examines the influence of AI on food security interventions in fragile contexts. This analysis applies a combined ethical governance and sustainability lens to assess current applications and risks. This research advances a broader analytical framework that moves beyond purely technical interpretations of AI, emphasizing its role as a socio-political tool, through identifying five key pillars for sustainable AI governance: data sovereignty, algorithmic accountability, inclusive system design, community-led governance, and market integrity. Full article

Using daily data from Investing.com South Africa, this study investigates the forecasting performance of four Rand currency rate markets (EUR/ZAR, CHF/ZAR, BRL/ZAR, and CNY/ZAR) from 13 February 2018 until 24 February 2025. The predictive fitness of three competing models, Support Vector Regression (SVR), Principal Component Regression (PCR), and eXtreme Gradient Boosting (XGBoost), is explored between 80%/20% and 95%/5% training-testing splits. Forecasting accuracy is evaluated based on evaluation errors, i.e., Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The Diebold–Mariano test is employed to check for statistical significance. Empirical results show that the linear SVR model outperforms PCR across all markets, while XGBoost achieves competitive predictive accuracy on average; the trade-offs between SVR and XGBoost are often very small. The data indicate that linear kernel methods provide a robust prediction pipeline, especially when macroeconomic factors (gold, oil, platinum prices, and the USD/ZAR exchange rate) and calendar-based factors are taken into account, and offer a strong framework for predicting daily exchange rate fluctuations. The results of this research provide practitioners (traders, risk managers, and policymakers) with insights into the relative efficiency of the kernel vs. ensemble learning approaches for forecasting the value of emerging-market currencies in the presence of structural volatility. Full article

The rapid growth of e-commerce has highlighted the critical need for efficient customer review sentiment analysis, yet natural language complexities like sarcasm and mixed sentiments remain challenging. To address these ambiguities, this study proposes a novel sentiment analysis architecture. The methodology integrates a bidirectional Long Short-Term Memory (Bi-LSTM) network with a Luong Attention mechanism. The Bi-LSTM component models the sequential and bidirectional context of the text, while the Luong Attention mechanism isolates and emphasizes the most significant parts of the reviews for precise sentiment detection. The proposed hybrid model demonstrates exceptional performance compared to traditional methods, achieving an accuracy of 96.67%, a precision of 96.83%, and a recall of 96.67%, alongside relatively low overfitting. Ultimately, the findings confirm that this architecture effectively manages ambiguous language and is highly capable of large-scale, real-time sentiment analysis, offering robust analytical tools for shaping e-commerce marketing strategies. Full article

This study addresses the imperative need to extend the shelf life of the cape gooseberry (Physalis peruviana L.), a highly perishable yet nutritionally valuable fruit, through the development and optimization of active edible coatings (ECs). The synergy between cassava starch (Manihot esculenta Crantz) and lemon verbena essential oil (Aloysia citrodora), both bioactive components, was investigated for the formulation of protective coatings. A 2² factorial design explored the impact of cassava starch concentrations (8% and 10% w/v) and lemon verbena essential oil (LVEO) (1% and 3% v/v) on the sensory acceptability of coated cape gooseberries. Through binomial logistic regression analysis, it was determined that the formulation with 10% cassava starch and 3% LVEO (T4) exhibited significantly superior sensory acceptability, optimizing the perception of color, odor, flavor, texture, and overall appearance. This optimized formulation (T4) demonstrated a significant improvement in extending the shelf life of cape gooseberries up to 27 days at 10 °C, which is comparable to or exceeds values reported in previous studies on starch–based coatings in similar fruits (e.g., 15–21 days depending on formulation and storage conditions). This performance also exceeded the storage periods observed at 6 °C (6 days) and 8 °C (20 days). Physicochemical analyses revealed remarkable stability of pH and titratable acidity, as well as effective control of moisture loss and the maturity index, even at higher temperatures. Crucially, T4 exhibited superior antimicrobial activity, with a significant reduction in molds, yeasts, and total aerobes, particularly at 10 °C, suggesting an optimal synergistic interaction between the coating and the LVEO under slightly warmer storage conditions. These findings contribute to the advancement of sustainable preservation strategies of cape gooseberries, offering a sustainable solution that reconciles efficient shelf-life extension with consumer acceptability and optimizes storage conditions, with significant implications for reducing food waste and enhancing the global marketability of this fruit. Full article

Persistent misconceptions about the alleged presence of bisphenol A (BPA) in major commodity plastics continue to distort public perception and, in some cases, regulatory discourse. This occurs despite scientific evidence showing that these polymers are synthesized without BPA. This review examines five widely used plastics—PET, PE, PP, PS, and PVC—focusing on their synthesis, structure–property relationships, and technological changes affecting the sector. We highlight recent innovations in green catalysis, bio-based feedstocks, polymer redesign, and advanced recycling. These advances are speeding the shift to efficient, sustainable processes and a circular polymer economy. We discuss market trends and regulatory frameworks to explain their global and Brazilian relevance, showing how communication gaps can lead to misinformation. By uniting chemical, technological, and regulatory views, this review supports public understanding, evidence-based policy, and the development of safer, high-performance, sustainable polymers. Full article

This research article explores whether the climate transition risk (CTR) and climate physical risk (CPR) transmit greater shocks towards the sustainable, gold-backed, energy-related and Sharia-compliant cryptocurrencies during bullish market conditions as compared with the normal and bearish market conditions. We employ the novel quantile vector auto-regression (QVAR)-based connectivity framework. Overall findings suggested that CPR and CTR transmitted greater shocks towards cryptocurrency classes during extremely high and lower quantiles as compared with the median quantile. This U-shaped and non-linear climate risks shock transmission indicates that Sharia-compliant, energy-related and gold-backed cryptocurrencies become more vulnerable during extreme market conditions (higher and lower quantiles) and may not consistently serve as reliable hedging or diversification instruments, particularly during periods of heightened climate uncertainty. Overall findings suggested that both the CPR and CTR transmitted greater shocks towards energy-related, gold-backed, and Sharia-compliant cryptocurrencies as compared with the sustainable cryptocurrencies, across all the quantiles. Therefore, sustainable cryptocurrencies, particularly those with energy-efficient consensus mechanisms such as Stellar, Cardano and Ripple, exhibited resilience to climate risks and can therefore function as stabilizing core holdings in diversified portfolios. Fund managers should incorporate a rebalancing strategy that increases allocation to these climate-resilient, sustainable digital assets during periods of elevated climate risk. Fund managers should integrate CPR and CTR into the quantile-domain forecasting frameworks for predicting digital asset market returns to enhance financial stability. Portfolio managers should undertake dynamic and quantile-contingent climate risk hedging strategies that account for tail-risk exposure rather than relying on average market behavior. Full article

Financial time series forecasting remains a persistent challenge due to the non-stationary nature, inherent noise, and multi-scale temporal dependencies present in market data. This paper presents MSTFNet, a multi-scale temporal fusion network that combines dilated causal convolutions with a frequency-enhanced sparse attention mechanism for improved financial prediction. The proposed architecture consists of three core components: a multi-scale dilated causal convolution module that extracts temporal patterns across different time horizons through parallel convolutional branches with varying dilation rates, a frequency-enhanced sparse attention mechanism that leverages Fast Fourier Transform to identify dominant periodic components and modulate attention weights accordingly, and an adaptive scale fusion gate that learns to dynamically combine representations from multiple temporal scales. Extensive experiments conducted on three public financial datasets (S&P 500, CSI 300, and NASDAQ Composite) spanning the period from January 2015 to December 2024 show two key results. First, consistent with near-efficient markets, the random-walk benchmark (${\widehat{y}}_{t+1}=y_t$) outperforms all the data-driven models on level-error metrics (MAE, RMSE, MAPE, and $R^2$), establishing the martingale as the binding lower bound on point-prediction error. Second, MSTFNet achieves the highest directional accuracy (DA) across all three indices—56.3% on the S&P 500 versus 50.0% for the martingale—representing a 6.3 percentage-point improvement that generates positive pre-cost returns in a trading strategy backtest. Among the eight data-driven baselines (LSTM, GRU, TCN, Transformer, Autoformer, FEDformer, PatchTST, and iTransformer), MSTFNet also achieves the lowest MAE, reducing it by 13.6% relative to the strongest data-driven baseline (iTransformer) on the S&P 500. These results confirm that integrating multi-scale temporal modeling with frequency-domain guidance extracts a real, if modest, directional signal from financial time series. Full article

This paper proposes a multi-strategy Secretary Bird Optimization Algorithm (MS-SBOA) for solving global optimization problems and 3D wireless sensor network deployment. While preserving the original two-phase search framework of SBOA, the proposed algorithm achieves a dynamic balance between global exploration and local exploitation through the synergistic integration of multiple enhancement strategies, including a hybrid initialization scheme combining Latin hypercube sampling and quasi-opposition-based learning, a success-history-based adaptive parameter learning mechanism, a finance-inspired market-state trading operator, and an elite-guided population regulation strategy. Experimental results on the IEEE CEC2020 and CEC2022 benchmark test suites demonstrate that MS-SBOA significantly outperforms nine comparative algorithms, including VPPSO, IAGWO, and QHSBOA, under both 10-dimensional and 20-dimensional settings. The proposed algorithm exhibits superior optimization accuracy, faster convergence speed, and stronger robustness. Statistical analyses using the Wilcoxon rank-sum test and the Friedman mean rank test further confirm that the observed performance improvements are statistically significant. Moreover, MS-SBOA is applied to three-dimensional wireless sensor network (3D WSN) deployment optimization problems, where the average coverage rates reach 76.22% and 82.32% for 30-node and 50-node deployment scenarios, respectively. The resulting node distributions are more uniform, and the computational efficiency is improved compared with competing algorithms. Full article

Health and spa tourism is a rapidly growing sector that merges traditional healing with modern innovations to meet increasingly diverse client needs. Understanding professionals’ perspectives is crucial for developing sustainable strategies that enhance service quality, organizational performance, and long-term business viability. Drawing on qualitative narrative analysis and thematic network analysis, this study explores the key factors that spa tourism professionals in Băile Felix—the largest spa resort in Romania—associate with business success, competitive differentiation, and sustainable development. Data were collected through semi-structured interviews with 41 entrepreneurs and managers who provided detailed narratives on their strategic goals and market positioning. Rather than measuring customer psychological constructs directly, this study captures professionals’ expert attributions regarding service quality, staff professionalism, infrastructure investment, and economic objectives, and interprets these as managerial perceptions grounded in operational experience. Five research propositions guided the interpretive analysis: (P1) professionals narratively associate service quality and treatment diversity with perceived business performance and guest retention signals; (P2) staff professionalism and attitude are attributed as the primary drivers of competitive differentiation; (P3) infrastructure investment and innovation are framed as prerequisites for sustaining market positioning; (P4) the identified themes form a structurally interconnected network with key bridging nodes; and (P5) professional narratives reveal tensions between short-term economic objectives and longer-term commitments to service quality and sustainability. Thematic network analysis identified four central constructs—service quality and treatment diversity, staff professionalism and attitude, innovation and infrastructure investment, and economic and development objectives—and mapped 16 interconnected sub-themes, with modularity analysis (Q = 0.42) confirming a moderately cohesive structure. Sustainable innovation was operationalized across environmental efficiency, social value, and economic resilience dimensions, and found to be embedded systemically across multiple thematic clusters rather than treated as an isolated practice. The originality of this study lies in integrating narrative and thematic network analysis to reveal how these constructs co-evolve within a sustainability-oriented system, offering a novel methodological lens for spa tourism research in post-transitional Central and Eastern European contexts. Findings provide actionable insights for spa managers, policymakers, and investors seeking to balance modernization with tradition in resource-constrained destinations. Full article

As the cornerstone of contemporary electronics, the quality of electronic substrates—including Printed Circuit Boards (PCBs) and Ceramic Package Substrates (CPSs)—is intrinsic to product reliability. However, automated inspection is currently impeded by two persistent obstacles: the drastic multi-scale variation in defects and the acute class imbalance within defect datasets. Conventional deep learning approaches often fail to reconcile these challenges simultaneously, leading to suboptimal recognition of rare defect categories. To bridge this gap, we propose Multi-scale Partial Vision Transformer—Simple, Parameter-free Attention Module (MulPViT-SimAM), a robust framework designed for class-imbalanced electronic substrate defect detection. Our method features a novel multi-scale backbone (MulPViT) that synergizes partial convolutions with hierarchical attention mechanisms, facilitating the efficient extraction of both fine-grained local textures and global contextual dependencies. Additionally, we embed the Simple, Parameter-free Attention Module (SimAM) into the feature fusion stage to adaptively highlight defect-specific features while dampening background noise. To further mitigate data imbalance, we utilize the Equalized Focal Loss (EFL) function, which employs a category-specific modulating factor to dynamically equilibrate the learning focus across different classes. Comprehensive benchmarking reveals state-of-the-art performance, achieving mAP@0.5 scores of 95.7% on the standard PKU-MARKET-PCB dataset and 54.2% on the highly challenging CPS2D-AD dataset. Significantly, our approach effectively mitigates class imbalance, narrowing the performance deviation of rare categories to just 4.3% on the PKU-Market-PCB dataset and 1.4% on the CPS2D-AD dataset, compared to 11.8% and 7.5% in baseline models. These findings position MulPViT-SimAM as a viable and efficient solution for industrial quality control. Full article

This research aimed to determine the influence of human–computer interaction usability on the behavioral intention and self-reported continued usage intentions of e-commerce applications. Moreover, it investigated the moderating role of trust in the relationship between behavioral intention and self-reported continued usage intentions of e-commerce applications. The data were gathered from 398 Bahraini individuals using a convenience sampling approach and analyzed using SmartPLS 4. The results highlighted that human–computer interaction usability sub-characteristics, including appropriateness, recognizability, user interface esthetics, learnability, and operability, are significantly associated with behavioral intention toward e-commerce applications within this sample. Furthermore, the results reported that trust strengthens the influence of behavioral intention on self-reported continued usage intentions toward e-commerce applications. The research provides context-specific exploratory insights from a segment of the Bahraini e-commerce sector. Due to the study’s non-probabilistic convenience sampling design, the cross-sectional nature of the data, and a sample predominantly composed of young, male, English-proficient respondents, the findings should be interpreted as exploratory rather than representative of the entire Bahraini population. In addition, the research findings helped e-commerce application developers and marketing experts within e-commerce companies develop efficient, operable, attractive, and learnable applications. Full article

Governance is often treated as a slow-moving background condition in energy transition research, even though institutional reform and implementation capacity shape outcomes over long horizons. This study adopts a time–frequency perspective to examine how institutional quality aligns with energy-system and carbon-efficiency transition dynamics using multivariate wavelet coherence. Unlike mean-based regression approaches, the multivariate design allows assessment of whether governance aligns with carbon efficiency through three distinct systems—external integration, energy transition with resource rents, and governance coherence—using carbon intensity of GDP (CIGDP) as a common anchor. Using annual data for a comparative sample of GCC economies and non-GCC emerging economies over the period 1996–2022, we examine the evolution of coherence among governance indicators, energy use, renewable energy consumption, external economic exposure, and carbon efficiency, with emissions-related measures explicitly incorporated into the wavelet systems. Environmental implications are therefore interpreted only for systems that directly include carbon-efficiency indicators. The results indicate that institutional quality is most strongly associated with transition dynamics at low frequencies, pointing to persistent long-run alignment rather than short-run adjustment. Across GCC economies, low-frequency coherence is stronger and more continuous, while medium-term weakening appears as time-specific episodes that do not disrupt the underlying long-run structure. In non-GCC emerging economies, long-run coherence remains evident but is less continuous, and medium-horizon fragmentation is more frequent and more prolonged. At high frequencies, coherence is generally weak across countries, suggesting that short-run variation appears more closely associated with external shocks and market conditions than with structural or institutional alignment. Overall, the findings position institutional quality as a stabilising and conditioning factor in energy and carbon-efficiency transitions, operating primarily through long-run coherence and resilience. Systematic differences across governance regimes reflect variation in the continuity and stability of alignment across time horizons, rather than differences in the relevance of governance itself. Full article