Search Results (632)

As Chinese open-pit mines progressively transition to deeper operations, challenges such as rising stripping ratios, declining slope stability, and environmental degradation have become increasingly pronounced. The sustainability of traditional open-pit mining models faces substantial challenges. Underground mining, offering higher resource recovery rates and minimal environmental disruption, is emerging as a pivotal technological pathway for the green transformation of mining. Consequently, the transition from open-pit to underground mining has emerged as a central research focus within mining engineering. This paper provides a comprehensive review of key technological advancements in this transition, emphasizing core issues such as mine development system selection, mining method choices, slope stability control, and crown pillar design. A typical case study of the Anhui Xinqiao Iron Mine is presented to analyze its engineering approaches and practical experiences in joint development, backfilling mining, and ecological restoration. The findings indicate that the mine has achieved multi-objective optimization of resource utilization, environmental coordination, and operational capacity while ensuring safety and recovery efficiency. This offers a replicable and scalable technological demonstration for the green transformation of similar mines around the world. Full article

This study presents the first systematic empirical comparison of neural architectures for Universal Dependencies (UD) parsing in Old English, thus addressing central questions in computational historical linguistics and low-resource language processing. We evaluate three approaches—a baseline spaCy pipeline, a pipeline with a pretrained tok2vec component, and a MobileBERT transformer-based model—across datasets ranging from 1000 to 20,000 words. Our results demonstrate that the pretrained tok2vec model consistently outperforms alternatives, because it achieves 83.24% UAS and 74.23% LAS with the largest dataset, whereas the transformer-based approach substantially underperforms despite higher computational costs. Performance analysis reveals that basic tagging tasks reach 85–90% accuracy, while dependency parsing achieves approximately 75% accuracy. We identify critical scaling thresholds, with substantial improvements occurring between 1000 and 5000 words and diminishing returns beyond 10,000 words, which provides insights into scaling laws for historical languages. Technical analysis reveals that the poor performance of the transformer stems from parameter-to-data ratio mismatches (1250:1) and the unique orthographic and morphological characteristics of Old English. These findings defy assumptions about transformer superiority in low-resource scenarios and establish evidence-based guidelines for researchers working with historical languages. The broader significance of this study extends to enabling an automated analysis of three million words of extant Old English texts and providing a framework for optimal architecture selection in data-constrained environments. Our results suggest that medium-complexity architectures with monolingual pretraining offer superior cost–benefit trade-offs compared to complex transformer models for historical language processing. Full article

This paper addresses text simplification task for Kazakh, a morphologically rich, low-resource language, by introducing KazSim, an instruction-tuned model built on multilingual large language models (LLMs). First, we develop a heuristic pipeline to identify complex Kazakh sentences, manually validating its performance on 400 examples and comparing it against a purely LLM-based selection method; we then use this pipeline to assemble a parallel corpus of 8709 complex–simple pairs via LLM augmentation. For the simplification task, we benchmark KazSim against standard Seq2Seq systems, domain-adapted Kazakh LLMs, and zero-shot instruction-following models. On an automatically constructed test set, KazSim (Llama-3.3-70B) achieves BLEU 33.50, SARI 56.38, and F1 87.56 with a length ratio of 0.98, outperforming all baselines. We also explore prompt language (English vs. Kazakh) and conduct human evaluation with three native speakers: KazSim scores 4.08 for fluency, 4.09 for meaning preservation, and 4.42 for simplicity—significantly above GPT-4o-mini. Error analysis shows that remaining failures cluster into tone change, tense change, and semantic drift, reflecting Kazakh’s agglutinative morphology and flexible syntax. Full article

Cross-border energy trading activity via interconnection has received much attention in Southern Asia to help the South Asian Association for Regional Cooperation (SAARC) region’s energy deficit states. This research article proposed a smart metering system to reduce energy losses and increase distribution sector efficiency. The implementation of smart metering systems in utility management plays a pivotal role in advancing several Sustainable Development Goals (SDGs), i.e.; SDG (Affordable and Clean Energy), and SDG Climate Action. By enabling real-time monitoring, accurate measurement, and data-driven management of energy resources, smart meters promote efficient consumption, reduce losses, and encourage sustainable behaviors among consumers. The adoption of a smart metering system along with Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis, socio-economic analysis, current challenges, and future prospects was also investigated. Besides the economics of the electrical distribution system, one feeder with non-technical losses of about 16% was selected, and the cost–benefit analysis and cost–benefit ratio was estimated for the SAARC region. The import/export ratio is disturbing in various SAARC grids, and a solution in terms of community microgrids is presented from Pakistan’s perspective as a case study. The proposed work gives a guidelines for SAARC countries to reduce their losses and improve their system functionality. It gives a composite solution across multi-faceted evaluation for the betterment of a large region. Full article

Weakness, spasticity, and muscle shortening are common in children with cerebral palsy (CP), leading to deficits in gross motor, gait, and selective motor functions. While traditional assessments, such as the Gross Motor Function Measure (GMFM-66), instrumented gait analysis, and the Selective Control Assessment of the Lower Extremity (SCALE), are widely used, they are often limited by the resource-intensive nature of hospital-based evaluations. We employed cyclogram-based analysis, utilizing simple hip and knee joint kinematics to assess clinical measures, including GMFM-66, normalized gait speed, the gait deviation index (GDI), and the gait profile score (GPS). Principal component analysis was used to quantify the cyclogram shape characteristics. A total of 144 children with ambulatory spastic CP were included in the study. All the cyclogram parameters were significantly correlated with GMFM-66, gait speed, the GDI, and the sagittal plane subscore of the GPS for the hip and knee, with the swing phase area showing the strongest correlation. Regression models based on the swing phase area were used to estimate the GMFM-66 (R² = 0.301) and gait speed (R² = 0.484). The PC1/PC2 ratio showed a moderate correlation with selective motor control, as measured by the SCALE (R² = 0.320). These findings highlight the potential of hip–knee cyclogram parameters to be used as accessible digital biomarkers for evaluating motor control and gait function in children with bilateral spastic CP. Further prospective studies using wearable sensors, such as inertial measurement units, are warranted to validate and build upon these results. Full article

Understanding the interplay between plant leaf functional traits and plant and soil factors under different soil thicknesses is significant for quantifying the interaction between plant growth and the environment. However, in the context of ecological restoration of vegetation in mining areas, there has been a lot of research on trees, shrubs, and grasses, but the characteristics and correlations of leaf functional traits of vines have not been fully studied to a large extent. Here, we report the differences in leaf functional traits of six vine plants (Parthenocissus quinquefolia, Pueraria lobata, Hedera nepalensis, Campsis grandiflora, Mucuna sempervirens, and Parthenocissus tricuspidata) with distinct growth forms in different soil cover thicknesses (20 cm, 40 cm, and 60 cm). In addition, soil factor indicators under different soil cover thicknesses were measured to elucidate the linkages between leaf functional traits of vine plants and soil factors. We found that P. lobata showed a resource acquisition strategy, while H. nepalensis demonstrated a resource conservation strategy. C. grandiflora and P. tricuspidata shifted toward more conservative resource allocation strategies as the soil cover thickness increased, whereas M. sempervirens showed the opposite trend. In the plant trait–trait relationships, there were synergistic associations between specific leaf area (SLA) and leaf nitrogen content (LNC); leaf moisture content (LMC) and leaf nitrogen-to-phosphorus ratio (LN/P); and leaf specific dry weight (LSW), leaf succulence degree (LSD), and leaf dry matter content (LDMC). Trade-offs were observed between SLA and LSW, LSD, and LDMC; between leaf phosphorus content (LPC) and LN/P; and between LMC, LSW, and LDMC. In the plant trait–environment relationships, soil nutrients (pH, soil total phosphorus content (STP), and soil ammonium nitrogen content (SAN)) and soil enzyme activities (cellulase (CB), leucine aminopeptidase (LAP), enzyme C/N activity ratio, and enzyme N/P activity ratio) were identified as the primary drivers of variation in leaf functional traits. Interestingly, nitrogen deficiency constrained the growth of vine plants in the mining area. Our study revealed that the responses of leaf functional traits of different vines under different soil thicknesses have significant species specificity, and each vine shows different resource acquisition and conservation strategies. Furthermore, soil cover thickness primarily influences plant functional traits by directly affecting soil enzyme activities and nutrients. However, the pathways through which soil thickness impacts these traits differ among various functional traits. Our findings provide a theoretical basis and practical reference for selecting vine plants and optimizing soil cover techniques for ecological restoration in mining areas. Full article

Phosphorous slag is an industrial by-product generated in the process of producing yellow phosphorus by electric furnace, which occupies a substantial number of land resources and causes serious environmental pollution. The comprehensive utilization of phosphorous slag is a major topic relevant to the sustainability of the yellow phosphorus industry. In this paper, we attempted to utilize phosphorous slag as a supplementary cementing material to prepare silica-aluminum based cementitious material (SAC-PHS). To determine how phosphorus influences the early-age hydration reaction process of silica-aluminum based cementitious material, three groups of samples, PHS₂₀, PHS₂₅, and PHS₃₀, with better mechanical properties were selected to deeply investigate their one-week hydration characteristics. Characterization results showed that the main hydration products of SAC-PHS were C-A-S-H gels and ettringite. PHS₂₅ specimen produced more C-A-S-H gels and ettringite than the other two samples after one-week hydration. Interestingly, the P/Si atomic ratio indicated that chemical bonds were formed between Si and P during the formation of C-A-S-H gels, which improved the strength of SAC-PHS. Our findings offer valuable insights for the application of phosphorous slag in construction and building materials and promote the efficient resource utilization of phosphorous residue. Full article

Background: Early identification of sepsis is critical for improving outcomes, particularly in low-resource emergency settings. In Thai community hospitals, where physicians may not always be available, triage is often nurse-led. Selecting accurate and practical sepsis screening tools is essential not only for timely clinical decision-making but also for timely diagnosis and promoting appropriate antibiotic use. Methods: This cross-sectional study analyzed 475 adult patients with suspected sepsis who presented to the emergency department of a Thai community hospital, using retrospective data from January 2021 to December 2022. Six screening tools were evaluated: Systemic Inflammatory Response Syndrome (SIRS), Quick Sequential Organ Failure Assessment (qSOFA), Modified Early Warning Score (MEWS), National Early Warning Score (NEWS), National Early Warning Score version 2 (NEWS2), and Search Out Severity (SOS). Diagnostic accuracy was assessed using International Classification of Diseases, Tenth Revision (ICD-10) codes as the reference standard. Performance metrics included sensitivity, specificity, predictive values, likelihood ratios, and the area under the receiver operating characteristic (AUROC) curve, all reported with 95% confidence intervals. Results: SIRS had the highest sensitivity (84%), while qSOFA demonstrated the highest specificity (91%). NEWS2, NEWS, and MEWS showed moderate and balanced diagnostic accuracy. SOS also demonstrated moderate accuracy. Conclusions: A two-step screening approach—using SIRS for initial triage followed by NEWS2 for confirmation—is recommended. This strategy enhances nurse-led screening and optimizes limited resources in emergency care. Early sepsis detection through accurate screening tools constitutes a feasible public health intervention to support appropriate antibiotic use and mitigate antimicrobial resistance, especially in resource-limited community hospital settings. Full article

With the rapid expansion of Internet of Things (IoT) and edge computing applications, efficient video transmission under constrained bandwidth and limited computational resources has become increasingly critical. In such environments, perception-based video coding plays a vital role in maintaining acceptable visual quality while minimizing bit rate and processing overhead. Although newer video coding standards have emerged, H.264/AVC remains the dominant compression format in many deployed systems, particularly in commercial CCTV surveillance, due to its compatibility, stability, and widespread hardware support. Motivated by these practical demands, this paper proposes a perception-based video coding algorithm specifically tailored for low-bit-rate H.264/AVC applications. By targeting regions most relevant to the human visual system, the proposed method enhances perceptual quality while optimizing resource usage, making it particularly suitable for embedded systems and bandwidth-limited communication channels. In general, regions containing human faces and those exhibiting significant motion are of primary importance for human perception and should receive higher bit allocation to preserve visual quality. To this end, macroblocks (MBs) containing human faces are detected using the Viola–Jones algorithm, which leverages AdaBoost for feature selection and a cascade of classifiers for fast and accurate detection. This approach is favored over deep learning-based models due to its low computational complexity and real-time capability, making it ideal for latency- and resource-constrained IoT and edge environments. Motion-intensive macroblocks were identified by comparing their motion intensity against the average motion level of preceding reference frames. Based on these criteria, a dynamic quantization parameter (QP) adjustment strategy was applied to assign finer quantization to perceptually important regions of interest (ROIs) in low-bit-rate scenarios. The experimental results show that the proposed method achieves superior subjective visual quality and objective Peak Signal-to-Noise Ratio (PSNR) compared to the standard JM software and other state-of-the-art algorithms under the same bit rate constraints. Moreover, the approach introduces only a marginal increase in computational complexity, highlighting its efficiency. Overall, the proposed algorithm offers an effective balance between visual quality and computational performance, making it well suited for video transmission in bandwidth-constrained, resource-limited IoT and edge computing environments. Full article

The promising field of low-temperature plasma treatment, known for its non-invasive and environmentally sustainable nature, is being actively investigated for its ability to enhance germination, emergence, yield, and overall plant development in a broad spectrum of crops. For gene bank requirements, low-temperature plasma technologies can also improve germination parameters and promote the development seeds suitable for long-term storage. Seeds from four selected cultivars of wheat, oats, flax, and rapeseed stored in the gene bank for 1, 10, and 20 years were subjected to plasma treatments for 20, 25, and 30 min. The study evaluated the mean root and shoot length, root–shoot ratio, and seedling vigour index. Additionally, the malondialdehyde level, total polyphenol content, total flavonoid content, and total antioxidant capacity were analysed. Plasma treatment displayed varying effects on the morphological characteristics and antioxidant activity of the tested cultivars, which were influenced by treatment duration and cultivar. A positive effect of plasma treatment on seedling length, seedling vigour index, and root–shoot ratio was observed in flax cultivar ‘N-9/62/K3/B’ in all periods and in variants T2 and T3. Conversely, the wheat cultivar ‘Granny’ showed variable results, and the oat cultivar ‘Risto’ showed variable negative results in regards to mean root length and mean shoot length after plasma treatment. The indicators of oxidative stress and antioxidant capacity were affected in all the cultivars studied. A positive effect of plasma treatment on these indicators was observed in the wheat cultivar ‘Granny’, while flax cultivar ‘N-9/62/K3/B’ exhibited inconsistent results. While in cereals, a decrease in malondialdehyde content after plasma treatment was associated with an increase in polyphenol and flavonoid content as the treatment duration increased, small-seeded species responded somewhat differently. The rapeseed cultivar ‘Skrivenskij’ and flax cultivar ‘N-9/62/K3/B’ showed an increase in polyphenol and flavonoid content following a decrease in malondialdehyde levels. This study highlights the potential of low-temperature plasma treatment for long-term-stored seeds and its applicability to plant genetic resources. The findings emphasize the need for the further optimization of low-temperature plasma treatment conditions for different plant species and cultivars. Full article

The nicotine metabolite ratio (NMR) has been informative in selecting treatment choices for nicotine dependence and increasing treatment efficacy in Western settings; however, the clinical utility of the NMR among smokers in low-resource settings remains unclear. Prospective analysis was conducted using data from a randomized controlled trial of smoking cessation among adults living with HIV, to examine the association between the NMR and response to smoking cessation treatment. NMR was assessed using bio-banked urine samples collected at baseline. Self-reported smoking at 6 months was verified using a urine cotinine test and exhaled breath carbon monoxide (CO). We found no associations between the NMR and smoking abstinence (adjusted risk ratio (aRR) = 0.82; 95% CI: 0.45, 1.49; p = 0.53). No evidence of effect modification by treatment conditions was observed on the multiplicative scale (aRR = 1.17; 95% CI: 0.32, 4.30; p = 0.81) or additive scale (adjusted relative excess risk due to interaction (aRERI) = 0.10; 95% CI: −1.16, 1.36; p = 0.44). Our results suggest that the NMR may not be a viable approach for selecting smoking cessation treatment in this setting, given the minimal variability in our sample and racial/ethnic makeup of this population. Full article

The efficient use of energy is a sign of conscious environmental responsibility. Sustainable management also refers to water resources, where emphasis is placed on the possibility of retaining rainwater at the point of the precipitation occurrence. This article focused on the reuse of runoff from a rest area (RA) along the expressway, wherever drinking water quality is not required. The runoff from RAs can be significantly contaminated due to the traffic-related issues. The objective of this article was to evaluate the energy efficiency of preliminary treatment of raw meltwater from a selected rest area using electrodialysis for Cl⁻ and Na⁺ removal. The treatment was carried out under various conditions, including different solution temperatures (20 °C and 30 °C) and electric voltages (10 V, 20 V, 30 V). The energy efficiency assessment was preceded by a characterization of runoff quality and the analysis of pollutant removal efficiency in the electrodialysis process. The most energy-efficient variant was characterized with the 0.097 Wh/(mg/L) energy expenditure ratio and 93% efficiency removal for Cl⁻ and 0.147 Wh/(mg/L) and 90% for Na⁺. In this variant, the permissible Cl⁻ and Na⁺ concentrations limits were achieved after 27 min with an energy consumption of 57 Wh. In general, the observed highest energy efficiency occurred at the beginning of the electrodialysis process and decreased over time. Full article

Coastal cities and low-lying areas are increasingly vulnerable, and accurate data is needed to identify where interventions are most required. We compared 53 cities affected by a 1 m increase in land levels and a 2 m rise in sea levels. The geographical scope of this study covered selected coastal cities in Europe and northern Africa. Data were sourced from the European Environment Agency (EEA) in the form of prepared datasets, which were further processed for analysis. Statistical methods were applied to compare the extent of urban flooding under two sea level rise scenarios—1 m and 2 m—by calculating the percentage of affected urban areas. To assess social vulnerability, the analysis included several variables: MAPF65 (Mean Area Potentially Flooded for people aged 65 and older, indicating elderly exposure), Age (the percentage of the population aged 65+ in each city), MAPF (Mean Area Potentially Flooded, representing the average share of urban area at risk of flooding), and Unemployment Ratio (the percentage of unemployed individuals living in the areas potentially affected by sea level rise). We utilized t-tests to analyze the means of two datasets, yielding a mean difference of 2.9536. Both parametric and bootstrap confidence intervals included zero, and the p-values from the t-tests (0.289 and 0.289) indicated no statistically significant difference between the means. The Bayes factor (0.178) provided substantial evidence supporting equal means, while Cohen’s D (0.099) indicated a very small effect size. Ceuta’s flooding value (502.8) was identified as a significant outlier (p < 0.05), indicating high flood risk. A Grubbs’ test confirmed Ceuta as a significant outlier. A Wilcoxon test highlighted significant deviations between the medians, with a p << 0.001, demonstrating systematic discrepancies tied to flood frequency and sea level anomalies. These findings illuminated critical disparities in flooding trends across specific locations, offering essential insights for urban planning and mitigation strategies in cities vulnerable to rising sea levels and extreme weather patterns. Information on coastal flooding provides awareness of how rising sea levels affect at-risk areas. Examining factors such as MAPF and population data enables the detection of the most threatened zones and supports targeted action. These perceptions are essential for strengthening climate resilience, improving emergency planning, and directing resources where they are needed most. Full article

Background: Community-acquired bacterial pneumonia (CABP) is a common and costly cause of hospitalization. Although severe CABP (sCABP) occurs in 10–25% of all pneumonia hospitalizations, little generalizable data examine its characteristics and outcomes or hospital resource utilization. Methods: We conducted a retrospective single-group cohort study of adults within the IQVIA hospital Charge Data Master, 2018–2022. We identified CABP via an ICD-10 code algorithm and sCABP was defined as an episode requiring ICU care. We examined baseline characteristics and outcomes, including mortality, costs, and readmission rates. We developed models to identify risk factors associated with readmissions. Results: Among 24,149 patients with sCABP, 14,266 (58.4%) were ≥65 years old and 55.2% were male. The majority were hospitalized in large (300+ beds, 50.9%), urban (91.9%) teaching (62.7%) institutions in the US Southern region (52.3%). The mean (SD) Charlson Comorbidity Index was 1.35 (2.33). The most common comorbidities were hypertension (16.7%), diabetes mellitus (15.7%), and chronic obstructive pulmonary disease (COPD) (12.9%). Hospital mortality was 15.9%. The mean (SD) hospital length of stay (LOS) and costs were 13.6 (12.1) and USD 91,965 (USD 133,734), respectively. An amount of 20% required a readmission within 30 days. Readmission was most strongly associated with older age and the presence of select comorbidities (diabetes mellitus, congestive heart failure, and COPD), each with an odds ratio > 1.4 and 95% confidence intervals excluding 1.0. Conclusions: Patients with sCABP comprise a large population with high mortality and 30-day readmissions. The intrinsic factors related to the latter lend themselves to early recognition and aggressive efforts at reducing complications. Full article

Lignocellulose is an important renewable biomass resource. However, at present, there is a lack of efficient and environmentally friendly catalytic systems that can selectively convert lignocellulose components into high-value sugars, and the value realization of agricultural waste (such as straw) remains challenging. Carbon-based solid acids are used in the valorization of biomass due to their simple preparation and excellent catalytic performance. In this study, the magnetic carbon microspheres catalyst was prepared using concentrated sulfuric acid and hydroxyethyl sulfonic acid as sulfonating agents. Two sulfonation catalysts were applied to the hydrolysis of typical agricultural waste (rice straw). The performance of catalyst conversion to reducing sugar was compared, and the glucose yield was lower than 30%. The sulfonation catalyst of hydroxyethyl sulfonic acid obtained a higher yield of pentose (76.67%) than that of concentrated sulfuric acid (74.25%) in 110 min. The optimal reaction conditions were found: substrate was 0.04 g straw, catalyst was 0.04 g, H₂O/γ-valerolactone ratio was 8:2 in the solvent, and the reaction time was 110 min at 140 °C. Under these conditions, the sulfonation properties of hydroxyethyl sulfonic acid as a green sulfonating agent are similar to those of concentrated sulfuric acid. Its excellent catalytic performance is attributed to the medium B/L acid density ratio on the catalyst surface. In addition, the prepared catalyst can be effectively separated from the reaction residue in the catalytic system. This work provides a green catalytic system for the high-value utilization of agricultural waste from renewable carbon sources. Full article