Search Results (3,409)

Peatlands cover approximately 10% (640,000 ha) of Latvia’s territory, of which about 51,000 ha is officially classified as degraded due to peat extraction and related activities. This study assesses the current status of peat extraction site recultivation in Latvia and evaluates future after-use requirements under contrasting policy pathways using a review of scientific literature, project reports, national statistics, and updated peat extraction licence records. A simple allocation model was applied to estimate recultivation trajectories for the nationally defined degraded peatland area under two scenarios: (i) a licence-expiry baseline scenario and (ii) an accelerated immediate-stop-peat-mining scenario. The results show that full recultivation would require average annual efforts of approximately 1500 ha yr⁻¹ under the baseline scenario and around 2000 ha yr⁻¹ under the accelerated scenario. Although European Union-funded projects and corporate initiatives have demonstrated the potential of rewetting, paludiculture, and renewable energy integration, only a limited number of sites have been officially recognised as fully recultivated or restored. Because ecological recovery of peatland functions may take decades, administrative closure alone does not guarantee climate or biodiversity benefits. A phased recultivation strategy linked to licence expiry and prioritising degraded and self-regenerating sites emerges as the most pragmatic pathway for Latvia, balancing European Union climate objectives, institutional capacity, and socio-economic constraints. Full article

Balancing ecological conservation with agricultural production in protected areas remains a critical challenge, particularly regarding the nexus of food, water, land, and ecosystems (FWLE). Yet, the spatiotemporal trade-offs, synergies, and underlying drivers within the FWLE remain poorly understood. Focusing on the Henan Funiu Mountain National Nature Reserve (HFMNNR), we quantified water yield (WY), habitat quality (HQ), and food production (FP) using the InVEST model and statistical yearbook data. The XGBoost-SHAP framework was applied to dissect the key drivers and mechanisms governing the FWLE system. Results indicate a significant increasing trend in FP (2000–2020), contrasting with the unimodal (increase-then-decline) trajectories of HQ and WY. Pronounced trade-offs were identified between HQ and WY, and between HQ and FP. Topographic and vegetative factors predominated in shaping the spatial patterns of HQ and FP, whereas climatic factors dictated WY distribution. Specifically, HQ declined when NDVI fell below 0.87, population density surpassed 0.01, or slope was gentler than 7°. WY was constrained when precipitation dropped below 947 mm, actual evapotranspiration exceeded 752 mm, or temperature ranged between 12.5–16.2 °C. FP was suppressed under conditions of slopes > 7°, NDVI within 0–0.61 or 0.61–0.86, or DEM > 373 m. These findings underscore the necessity of spatially explicit management strategies grounded in spatial heterogeneity. We advocate for a multi-objective governance framework centered on HQ to harmonize production and ecological functions. Our findings provide critical insights for formulating policies aimed at sustainably managing protected areas facing similar ecological-production conflicts. Full article

Enriching bakery products with highly nutritious ingredients, such as microalgae and insect powder, is a promising strategy for developing functional foods. This study aimed to evaluate the effects of spirulina, chlorella, and cricket powder on the quality of sponge cakes. The assessed parameters included color, nutritional value, mineral composition, antioxidant activity, predicted glycemic index (pGI), and sensory properties. The addition of microalgae significantly reduced the L* value and altered the color shade of the sponge cakes, while the insect powder caused milder color changes. The enriched samples contained higher levels of protein (by up to 14%) and minerals, including calcium, magnesium, iron, and zinc. Antioxidant activity was enhanced across all variations, particularly in sponge cakes with insect powder, which showed the highest TPC (47.96 mg GAE), DPPH· (0.107 mM TE), and ABTS·⁺ (0.208 mM TE) levels. Cakes containing spirulina exhibited the highest total flavonoid content (63.95 mg EPI). Additionally, the enriched samples demonstrated a statistically significant reduction in the pGI. Among all the supplemented samples, the sponge cake with cricket powder received the highest consumer acceptance. Overall, enriching sponge cakes with microalgae and cricket powder improved their nutritional value and antioxidant properties, with insect powder offering the best balance between sensory quality and functionality. Full article

Background: This paper presents a systematic review and meta-analysis to identify the effects of Rhythmic Auditory Stimulation (RAS) delivered via wearable devices on the gait and balance in patients with Parkinson’s disease. Method: The PICO criteria were established according to the PRISMA 2020 guidelines, and literature searches were performed across five databases covering studies published between 2015 and 2025: PubMed, Embase, Cochrane, Scopus, and Web of Science. After applying the inclusion criteria, eleven randomized controlled trials (RCTs) were selected. The quality of the studies was evaluated using the PEDro Scale and ROB-2. Statistical analyses were performed using Review Manager 5.4 based on the number of samples, means, and standard deviations to calculate the effect sizes. Result: The analysis results showed that wearable RAS significantly improved the gait speed (SMD = 0.49, p < 0.05) and balance ability (SMD = 0.40, p < 0.05), while no significant differences in the gait pattern, FOG-Q, or UPDRS-III were observed. The heterogeneity among studies was low, and the funnel plots were distributed symmetrically, indicating minimal publication bias. The average PEDro score was 7.33, suggesting moderate-to-high methodological quality. Conclusion: wearable RAS was identified as an evidence-based intervention effective in improving the gait speed and balance in patients with Parkinson’s disease. Full article

Multi-objective evolutionary optimization faces significant challenges due to guidance mismatch under complex Pareto-front geometries. This paper proposes a dynamic self-organizing map-assisted evolutionary algorithm (D-SOMA), a manifold-aware framework that harmonizes knowledge-informed priors with unsupervised objective-space characterization. Specifically, a knowledge-informed guided resampling strategy is formulated to bridge stochastic initialization and targeted exploitation. By distilling spatial distribution priors from the decision-variable boundaries of early-stage elite solutions, it establishes a high-quality starting population biased towards promising regions. To capture the intrinsic geometry of the evolving population, a self-organizing map (SOM)-based adaptive subregion characterization strategy leverages the topological preservation of self-organizing maps to extract latent modeling parameters. This strategy adaptively determines subregion centers and influence radii, enabling a data-driven partitioning that respects the underlying manifold structure. Furthermore, a density-driven phase-responsive scale adjustment strategy is introduced. By synthesizing spatial density feedback and temporal evolutionary trajectories, it dynamically modulates the characterization granularity K, thereby maintaining a rigorous balance between geometric modeling fidelity and computational overhead. Extensive experiments on 50 benchmark problems from the DTLZ, WFG, MaF and RWMOP suites demonstrate that D-SOMA is statistically superior to seven state-of-the-art algorithms, exhibiting robust convergence and superior diversity across diverse problem landscapes. Full article

Under high renewable energy penetration, wind power forecasting faces pronounced challenges due to strong randomness and uncertainty, making conventional point-forecast-centric paradigms insufficient for risk-aware and reliable power system scheduling. An uncertainty-aware temporal transformer framework for wind power forecasting is presented, integrating probabilistic modeling with deep temporal representation learning to jointly optimize prediction accuracy and uncertainty characterization. Crucially, rather than treating uncertainty quantification merely as a post-processing step, the central conceptual contribution lies in modularizing uncertainty directly within the attention mechanism. A probability-driven temporal attention mechanism is incorporated at the encoding stage to emphasize high-variability and high-risk time slices during feature aggregation, while a multi-quantile output and interval modeling strategy is adopted at the prediction stage to directly learn the conditional distribution of wind power, enabling simultaneous point and interval forecasts with statistical confidence. Extensive experiments on multiple public wind power datasets demonstrate that the proposed method consistently outperforms traditional statistical models, deep temporal models, and deterministic transformers, as validated by formal statistical significance testing. Specifically, the method achieves an MAE of $0.089$, an RMSE of $0.132$, and a MAPE of $10.84\%$ on the test set, corresponding to reductions of approximately $8\%$–$10\%$ relative to the deterministic transformer. In uncertainty evaluation, a PICP of $0.91$ is attained while compressing the MPIW to $0.221$ and reducing the CWC to $0.241$, indicating a favorable balance between coverage reliability and interval compactness. Compared with mainstream probabilistic forecasting methods, the model further reduces RMSE while maintaining coverage levels close to the 90% target, effectively mitigating excessive interval conservatism. Moreover, by adaptively generating heteroscedastic intervals that widen during high-volatility events and narrow under stable conditions, the model achieves a highly focused and effective capture of critical uncertainty information. Full article

Digital transformation reshapes firms into more digital, data-driven, and customer-centric organizations. Because it often supports innovation, firms are widely expected to benefit from higher performance and productivity. However, it remains unclear whether higher national levels of digital integration translate into higher aggregate enterprise productivity. This study adopts a socio-technical and ecosystem perspective to examine the relationship between digital technology integration and enterprise labor productivity across the 27 EU member states, while also considering the role of key ecosystem enablers. A balanced country-year panel of data (N = 162) was constructed from Eurostat Structural Business Statistics on the apparent labor productivity of total enterprises, together with Digital Economy and Society Index (DESI) indicators on the integration of digital technology, human capital, connectivity, and Gross Domestic Product (GDP) per capita, covering the period from 2017 to 2022. To this end, fixed-effects regression models were estimated using robust standard errors clustered by country and combined with correlated random effects (CRE/Mundlak) decomposition. This methodological approach was adopted to distinguish short-run within-country dynamics from persistent between-country differences. The study contributes to ecosystem-level DESI research by using this distinction to assess how country-level digital integration is associated with enterprise productivity. The fixed-effects results provide no evidence that year-to-year changes in digital technology integration, on their own, are associated with higher enterprise productivity. Additionally, no statistically significant interaction effect was observed with either human capital or digital connectivity. By contrast, GDP per capita was found to be a robust positive predictor of enterprise productivity. The CRE/Mundlak results indicate that the majority of between-country productivity differences are attributable to differences in economic development. Furthermore, there is evidence of a positive association between the average level of digital technology integration and human capital. Taken together, these findings suggest that national digital technology integration reflects business environment conditions at the ecosystem level. While it may create opportunities for enterprise business model innovation, its productivity implications are more likely to emerge gradually through stronger absorptive capacity and complementary capabilities. Consequently, the study suggests that enterprise digital transformation policies should be aligned with investments in digital skills and broadband infrastructure. These policies should also support process redesign, greater interoperability, and the implementation of AI-enabled technologies. Full article

Human endogenous retroviruses (HERVs), remnants of ancient germline infections, constitute ~8% of the human genome. Although mostly silenced, these elements can be expressed and play physiological or pathological roles. We investigated HERV expression dynamics, proviral load, and systemic inflammatory status in young and older adults, as well as the impact of regular physical exercise. PBMC and serum samples were collected from 30 young controls (YC), 30 inactive older adults (INAC) and 30 regularly exercising older adults (REG). Expression of HERV-W, -K, -H, Syncytin-1 and -2 was assessed by qPCR using the −2ΔΔCt method, and proviral load (HERV-W, -K, -H) was estimated by relative copy number. Serum cytokines (IL-1β, IL-6, IL-17, TNF-α, IFN-γ, IL-10) were quantified by ELISA. Statistical significance was set at p < 0.05. INAC participants showed higher proviral load of HERV-K, -W and -H compared to YC (p = 0.025), but overall lower HERV expression, except for HERV-K. REG presented increased expression of HERV-W (~1.5-fold, p < 0.0001), HERV-H (~1.8-fold, p < 0.0001; higher than YC p = 0.01), HERV-K (vs. YC p = 0.02) and Syncytin-1 (~1.4-fold vs. INAC and YC, p < 0.01). HERV-K was the most upregulated element in INAC. HERV-W and HERV-H expression were strongly correlated in all groups. INAC showed a pro-inflammatory profile, with elevated IL-6/IL-10, IL-1β/IL-10, and IFN-γ/IL-10 ratios. Older adults exhibit higher HERV proviral load, suggesting possible age-related insertions. Regular physical exercise modulates HERV expression, whereas inactivity is associated with reduced expression and increased inflammation. HERV-W and HERV-H maintain coordinated expression across ages, indicating interplay between inflammatory balance, aging, and retroviral activity. Full article

Kazakhstan is one of the world’s major wheat producers and exporters, playing an important role in regional and global food security. However, increasing quality requirements in domestic and export markets have exposed limitations in the country’s capacity to consistently supply high-quality spring bread wheat (Triticum aestivum L.). This review aims to assess the current status of spring wheat grain quality in Northern Kazakhstan, identify the main factors driving its variation, and outline pathways for quality improvement. The analysis is based on published literature, official statistics, national quality standards, and recent data on wheat production, grading, breeding systems, agronomic practices, and trade patterns. The review reveals that wheat production is dominated by medium-quality grain (primarily class 3), while high-quality classes suitable for premium and improver markets represent a small share. Compared with major exporters such as Canada, the United States, and Australia, Kazakh wheat is generally inferior across key quality parameters. Structural constraints include the limited integration of quality assessments within breeding programs, insufficient laboratory infrastructure, weak agroecological zoning by quality classes, and suboptimal agronomic management, particularly regarding nitrogen use. Environmental heterogeneity and climate change further influence the yield–quality balance. Overall, the findings suggest that improving wheat grain quality in Kazakhstan will require coordinated advances in breeding, agronomy, institutional capacity, and market alignment, enabling a gradual shift toward a more competitive, quality-oriented wheat production system. Full article

The energy transition and electrification are increasing the use of power electronics in low-voltage networks, increasing losses and reducing service availability when harmonic currents are concentrated in the neutral. This study statistically evaluates power quality in a campus-type microgrid with a high proportion of nonlinear loads. The novelty of the work lies in combining field measurements, percentile-based neutral-current severity analysis, and standards-based comparative mitigation assessment in a low-voltage 3P4W campus microgrid. A campaign was carried out using a Fluke 1775 analyzer, recording trends, frequency, and events. Approximately 1900 events were recorded, mainly waveform deviations, interruptions, and rapid voltage changes. Voltage distortion was moderate, with a 95th percentile between 3.6% and 3.8%, while the neutral conductor concentrated the highest severity: neutral-current THD exceeded 220% in the 95th percentile and reached maximums above 700%, with 16.78 A in the 95th percentile at the measurement point. Based on IEC 61000-2-2 and IEEE 519, four mitigation measures were evaluated in DIgSILENT PowerFactory 2024 to estimate and reduce losses and heating: load balancing, detuned compensation, passive filtering, and active filtering. Active mitigation reduced the neutral harmonic component by 80% and the combined strategy decreased the neutral current at the measuring point by 78% (16.78 A to 3.69 A), with an estimated reduction in resistive losses of close to 95%. These results suggest sustainability benefits by reducing energy wasted as heat, extending the useful life of the infrastructure and improving operational resilience. Full article

The histopathological classification of ovarian carcinoma is fundamental for patient management. While microscopic evaluation by pathologists is the current diagnostic standard, it is known to be subject to interobserver variability, which can affect consistency in treatment decisions. This study addresses this clinical need by developing and validating a deep learning-based diagnostic support tool designed to enhance the objectivity and reproducibility of this classification. In this work, we address a key challenge in computational pathology—the tendency of attention mechanisms to overfit by concentrating on limited features—by systematically evaluating a direct regularization method within multiple instance learning (MIL) models. The models were trained and validated using 10-fold cross-validation on a public training set of 538 whole-slide images and further tested on an independent public dataset for the more challenging task of molecular subtype classification. We utilized features from a foundational model pre-trained on histopathology data to represent tissue morphology. Our findings demonstrate that directly regularizing the attention mechanism with a stochastic approach provides a statistically significant improvement in accuracy and generalization, highlighting its power as a robust technique to mitigate overfitting for this clinical task. In direct contrast to the reported variability in manual assessment, our final model achieved high consistency and accuracy, with a balanced accuracy of 0.854 and a Cohen’s Kappa of 0.791. The model also demonstrated strong generalization on the molecular classification task. Its attention mechanism provides visual heatmaps for pathologist review, fostering interpretability and trust. We have developed a highly accurate and generalizable artificial intelligence tool that directly addresses the challenge of interobserver variability in ovarian cancer classification. Its performance highlights the potential for artificial intelligence to serve as a decision support system, standardizing histopathological assessment. Full article

Chronic exposure to even low levels of pesticides is a serious public health issue, mainly due to the role of oxidative stress in damaging the liver and promoting cancer. This has driven interest in finding natural, plant-based antioxidants that can counteract this kind of chemical injury. In this study, we tested whether a methanol extract from the leaves of Pteropyrum scoparium (PSE) could protect the liver against oxidative harm caused by four common pesticides: acetochlor, deltamethrin, thiamethoxam, and rotenone. Chemical analysis showed that the extract contains high levels of phenolics (345.1 ± 7.6 mg GAE/g) and flavonoids (17.3 ± 1.3 mg CAE/g). GC–MS profiling revealed a diverse set of compounds, including fat-soluble antioxidants like squalene, α-tocopherol, and γ-sitosterol, and water-soluble phenolics like pyrogallol and catechol, suggesting PSE is equipped with a multi-layered antioxidant defence. In the animal experiment, rats were given each pesticide for 30 days, with or without PSE. All four pesticides caused clear oxidative stress in the liver: glutathione (GSH), total antioxidant capacity (TAC), antioxidant enzymes activities dropped, while markers of lipid damage (MDA) and free radical activity (DPPH) rose. Co-administration of PSE significantly restored GSH, TAC and antioxidant enzymes levels and reduced MDA and residual DPPH values compared to pesticide-only groups; these parameters were statistically comparable to the controls (p > 0.05), indicating a substantial recovery of hepatic redox balance. Histopathological examination of liver tissues confirmed these findings, as pesticide treatment caused visible liver injury; deltamethrin and thiamethoxam led to congestion in central veins, while rotenone and acetochlor triggered clusters of inflammatory Kupffer cells. In animals that also received PSE, liver structure remained largely normal, with much less congestion and inflammation. These results show that the combination of antioxidant constituents in PSE might contribute to hepatoprotection through redox modulation and preservation of endogenous antioxidant balance, as suggested by the observed biochemical and histological improvements. Full article

This research investigates the capacity of Artificial Intelligence (AI) to serve as a generative and interpretative framework for revitalizing Al-Qatt Al-Asiri art. By developing sustainable design solutions for temporary exhibitions, the study seeks to reinforce Saudi Arabia’s cultural and touristic identity through a synthesis of heritage and technology. The study adopts a descriptive–analytical and applied methodology to examine the potential of AI to support creative design processes that integrate authenticity and innovation while preserving local heritage and meeting environmental sustainability requirements. Utilizing this descriptive–analytical and applied methodology. the study evaluates the efficacy of AI in augmenting creative design processes. The primary objective is to reconcile cultural authenticity with modern innovation, ensuring the preservation of local heritage while adhering to rigorous environmental sustainability standards. A controlled design experiment was executed for a temporary heritage exhibition, employing AI applications to simulate the complex decorative motifs of Al-Qatt Al-Asiri art. These technologies were used to generate sustainable exhibition units constructed from reusable local materials, bridging the gap between the digital generation and physical sustainability. This study presents a theoretical framework, a review of previous studies, the research methodology, quantitative and qualitative evaluation results, and an expert panel assessment. It involved three expert reviewers who evaluated the proposed design models based on eight sustainability criteria. This study also utilized a structured evaluation tool and AI applications, including ChatGPT-5.2, OpenAI and Gemini 3 Pro—Nano Banana. The results of the exploratory study indicate that the use of AI contributes to achieving a balance between preserving traditional aesthetic identity and promoting sustainable design practices derived from the characteristics of Al-Qatt Al-Asiri art. It also enhances cultural and tourism engagement by integrating AI applications into artistic design processes. The findings also revealed no statistically significant differences among the experts’ evaluations regarding the sustainability criteria of the implemented models. This study recommends integrating AI technologies into art and design education programs at Saudi universities and developing ethical and technical guidelines that ensure the preservation of heritage and cultural identity when applying AI in design practices. Full article

Residential photovoltaic/thermal (PV/T) systems can reduce electricity consumption by supplying both electricity and heat; however, their performance depends on household composition and lifestyle-driven demand profiles. This study simulates a PV/T system for a detached house in Tokyo while accounting for occupant-behavior variability using Japanese time-use statistics from 2015 and 2020, which capture the pandemic-related increase in time spent at home in 2020. Both a PV/T system and a conventional PV system were evaluated for four representative household scenarios, reflecting changes in domestic hot water (DHW), space conditioning, and appliance electricity demand. In the 2020 dataset, the large-household case (Case C) showed the largest improvement in net electricity balance relative to the PV system, with an improvement of 1.8 GJ, while the elderly-couple case (Case D) achieved the highest overall thermal efficiency, with a DHW COP of 6.26 and a space-heating COP of 5.75. In the young-couple case (Case A), the CO₂ reduction increased from 169 kg in the 2015 dataset to 239 kg in the 2020 dataset, showing that lifestyle changes affected the energy-saving benefit. These findings indicate that lifestyle-dependent behavioral changes should be considered in PV/T performance assessment and system sizing. Full article

High-spatiotemporal-resolution land surface temperature (LST) data are crucial for analyzing surface energy balance, modeling temperature-related processes, and monitoring thermal environments. However, despite advancements in multi-source fusion and reconstruction techniques, high-frequency LST data remain susceptible to anomalies such as abrupt changes and outliers due to retrieval uncertainties and varying observation conditions. Conventional statistical outlier detection methods risk misidentifying physically plausible rapid weather changes as data errors, introducing systematic biases. To address this, we propose a two-stage anomaly detection framework that follows a “temporal physical pre-screening first, spatial statistical verification later” logic. First, a piecewise empirical model, based on typical diurnal LST variation characteristics, is constructed to identify points violating physical patterns. Subsequently, a spatial consistency test using median absolute deviation (MAD) is introduced to distinguish real weather-driven fluctuations from genuine data anomalies from a spatial synergy perspective. This sequential design effectively reduces the risk of mis-correcting physically reasonable temperature variations. Validated using hourly seamless LST data (2016–2021) and ground observations in the Heihe River Basin, our method outperformed Seasonal-Trend decomposition using Loess (STL), double standardization methods, and robust Holt–Winters. For over 87% of the detected anomalies, the proposed method demonstrated positive improvement rates in RMSE, MAE, R, and R². The overall average improvement rates reached 23.61%, 18.79%, 16.46%, and 61.33%, respectively, indicating robust performance. The results underscore that explicitly incorporating physical constraints enhances the reliability and interpretability of quality control for high-temporal-resolution remote sensing LST data. Full article