Search Results (361)

Driven by global urbanization and increasing emphasis on sustainable building practices, indoor volatile organic compounds (VOCs) have emerged as a major environmental and health challenge. This review specifically focuses on room-temperature air-only catalytic oxidation of representative indoor VOCs under a recently matured and highly application-relevant research direction. Recent advances are systematically summarized, highlighting catalyst design strategies, air-phase reaction mechanisms, and performance of noble metal catalysts (NMCs), transition metal oxides (TMOs), bimetallic synergistic catalysts (BSCs), and single-atom catalysts (SACs). Emphasis is placed on thermodynamic feasibility, reaction kinetics, oxidation behavior of non-formaldehyde VOCs, and mechanistic insights associated with SACs interfacial synergy, which enable efficient O₂ activation, high selectivity, and operational stability without external oxidants even under high VOC concentrations. This review provides theoretical foundations and technical guidance for VOCs mitigation and supports the advancement of green, low-carbon, and safe indoor air purification strategies worldwide. Full article

Background/Objectives: Several chemotherapeutic regimens and targeted therapies are currently established as standard second-line treatments for patients with advanced biliary tract cancer (BTC). However, evidence regarding the benefits of treatment after first-line therapy failure remains limited, particularly among Thai populations. This study aimed to explore the efficacy of second-line chemotherapy in patients with advanced BTC. Methods: We conducted a single-institution, retrospective study including patients with locally advanced or metastatic BTC who experienced disease progression following first-line treatment between January 2017 and December 2019. Overall survival (OS) was defined as the primary endpoint. The secondary endpoint was the restricted mean survival time (RMST). To minimize confounding, propensity scores were estimated and applied using inverse probability of treatment weighting (IPTW). Results: A total of 110 patients were included, of whom 69 (62%) received second-line chemotherapy in combination with best supportive care (2LCMT + BSC), while 41 (38%) received best supportive care (BSC) alone. The majority of cases were intrahepatic cholangiocarcinoma (73.9% and 70.7% in each group, respectively). The median OS was 5.3 months (95% CI 3.5–7.0) in the 2LCMT + BSC group and 1.0 months (95% CI 0.5–1.9) in the BSC-only group (unadjusted HR 0.40, 95% CI 0.26–0.59; p < 0.001). In IPTW-adjusted flexible parametric regression analysis, second-line chemotherapy was associated with a 53% reduction in the risk of death compared with BSC alone (p = 0.009). The restricted mean survival time (RMST) differences between groups at 3, 6, and 12 months were 1.3 months (95% CI 0.9–1.6; p < 0.001), 2.6 months (95% CI 1.9–3.3; p < 0.001), and 3.9 months (95% CI 2.7–5.1; p < 0.001), sequentially. Conclusions: These findings demonstrate that second-line chemotherapy provides a significant overall survival benefit compared with best supportive care alone in patients with advanced BTC. Full article

Standard interpolation methods degrade pixel art through blurring or geometric distortion. We propose a lossless scaling algorithm that detects the intrinsic block size to normalize the image grid, thereby expanding the set of valid scaling factors beyond standard integer multiples. This approach enables precise, distortion-free resizing closer to user-specified scales. To validate this approach, we introduce a novel evaluation framework consisting of Color Loss (CL), Block Size Consistency (BSC), and reversibility (REV) tests. Experimental results demonstrate that the proposed method maintains the original palette and grid structure without introducing interpolation artifacts. Furthermore, the reversibility tests confirm that the scaling process remains mathematically lossless, ensuring the genre’s structural and chromatic integrity. Full article

Biological soil crusts (BSCs) are critical ecological components in arid lands. Their formation and stability hinge on the assembly and interactive networks of cyanobacteria-led bacterial communities. Yet, how different functional cyanobacteria shape the underlying microbial structure and assembly rules is poorly understood. Here, we cultivated artificial algal crusts using two representative cyanobacteria: the nitrogen-fixing Leptolyngbya sp. and the non-nitrogen-fixing Microcoleus vaginatus (M. vaginatus CM01). A total of six treatments were established based on the presence or absence of spraying with in situ BSCs leachate: a control group without inoculation of algae or bacteria (soil, S); a treatment group sprayed only with bacterial suspension (soil + bacteria, SB); a treatment group sprayed only with M. vaginatus CM01 (soil + M. vaginatus CM01, SM); a treatment group co-inoculated with both BSCs leachate and M. vaginatus CM01 (soil + M. vaginatus CM01 + bacteria, SMB); a treatment group inoculated only with Leptolyngbya sp. CT01 (soil + Leptolyngbya sp. CT01, SL); and a treatment group co-inoculated with Leptolyngbya sp. CT01 and biocrust leachate (soil + Leptolyngbya sp. CT01 + bacteria, SLB). By integrating 16S rRNA gene sequencing, neutral community modeling (NCM), and structural equation modeling (SEM), we dissected differences in Cyano-BSCs development, bacterial community composition, co-occurrence networks, and assembly mechanisms. Inoculation with M. vaginatus CM01 (SM, SMB) superiorly promoted Cyano-BSCs development: the SM group achieved the highest coverage (23.33%), while the SMB group showed marked increases in organic matter (OM, 4.10 g·kg⁻¹) and chlorophyll a (Chla, 13.40 μg·g⁻¹), alongside a >5-fold rise in bacterial, cyanobacterial, and nitrogen-fixation gene abundances versus controls. The mechanism centers on extracellular polymeric substances (EPS) secreted by M. vaginatus, which homogenized the microenvironment, suppressed stochastic bacterial dispersal (NCM, SM: R² = 0.698), and enhanced deterministic selection. This process forged a highly cooperative network (89.74% positive links, average degree 34.71) that directionally enriched Cyanobacteria (relative abundance 40.40%). The Shannon index of Cyano-BSCs from the group (SMB) reached 7.72 ± 0.09, reflecting high microbial community diversity. SEM confirmed M. vaginatus directly regulated bacterial assembly (path coefficient = 0.59, p < 0.05) and indirectly improved the soil environment (path coefficient = 0.64, p < 0.05), establishing a “cyanobacteria-community-environment” feedback loop. Conversely, the Leptolyngbya sp. groups (SL, SLB), despite enriching nitrogen-fixing bacteria and fungi, exhibited low carbon fixation efficiency (notably 1.26 g·kg⁻¹ OM in SL) and lack of EPS; communities remained stochastic (NCM, SL: R² = 0.751) with no effective regulatory pathway—a pattern mirrored in S and SB groups. Our findings demonstrate that M. vaginatus acts as a core engineer of biological soil Cyano-BSCs formation via an “EPS-mediated habitat filtering—functional group enrichment—cooperative network assembly” cascade, enforcing deterministic community construction. Leptolyngbya sp., with limited niche-constructing ability, fails to exert comparable control. This work provides a targeted framework for the artificial restoration of Cyano-BSCs in arid zones. Full article

The growing number of Internet of Things (IoT) devices has driven the need for energy-efficient communication in long-range, low-power networks like LoRa. LoRa offers wide coverage with minimal transmission power. However, radio communication remains the main energy consumer in end devices. Data compression can mitigate this issue by reducing packet size and transmission frequency. This work presents a comprehensive evaluation of classical and cutting-edge lossless compression algorithms applied to LoRa networks. Evaluated algorithms include Huffman, LZW, BSC, CMIX, PAQ8PX, GMIX, and LSTM-compress. Experiments were conducted using a Raspberry Pi 5 integrated with an RFM95W LoRa module and INA219 sensors to measure real-time power consumption, CPU load, and memory usage. Results show that classical methods, particularly LZW, achieve the best energy efficiency and reduce LoRa transmission energy by up to 7.41%. In contrast, cutting-edge machine learning (ML)-based algorithms, such as CMIX and PAQ8PX, achieve higher compression ratios but exhibit excessive computational and memory overhead, resulting in negative energy gains. Metadata overheads, including dynamic Huffman tables (28–128 bytes), also affect payload efficiency for small packets. These findings indicate that LZW is the most practical choice for energy-constrained LoRa nodes. At the same time, modern compressors, including ML-based ones, are better suited for gateways or edge servers with higher computational capacity. An open-source implementation of the experimental framework and scripts used in this study is available in the project’s public GitHub repository. Full article

This study explores how integrating digital technologies into STEM-based physics instruction can transform learning outcomes for adult learners in Greek Second Chance Schools, which provide educational opportunities for adults over 18 who have not completed compulsory education. In a comparative design, participants were divided into two groups: the experimental group experienced an innovative STEM approach, combining educational robotics, mobile sensing, and 3D printing within the Biological Sciences Curriculum Study (BSCS) 5E Instructional Model; the control group received enriched lecture-based instruction. Learning gains were measured using a rigorously developed, psychometrically validated multiple-choice physics test administered before and after the intervention. Results reveal that adults exposed to technology-enhanced STEM lessons achieved statistically significant improvements, outperforming their peers in the lecture-based group, who showed no measurable progress. Notably, these gains were consistent across gender and age. The findings highlight the transformative potential of digital technologies and learner-centered STEM pedagogies in alternative education settings, offering new directions for adult education and lifelong learning. Full article

Multiphoton endomicroscopy (MPEM) has recently become a key development in optical biomedical diagnostics, providing histologically relevant in vivo images that are eliminating both the need for tissue damage during biopsy sampling and the need for dye injections. Due to its ability to visualize structures at the epithelial, extracellular matrix, and subcellular levels, MPEM offers a promising diagnostic method for precancerous conditions and early forms of gastrointestinal (GI) cancer. The high specificity of multiphoton signals—the two-photon fluorescence response of endogenous fluorophores (NADH, FAD), the second-harmonic generation signal from collagen, and others—makes this method a promising alternative to both traditional histology and confocal endoscopy, enabling real-time assessment of metabolic status, intestinal epithelial cell status, and stromal remodeling. Despite the promising prospects of multiphoton microscopy, its practical implementation is progressing extremely slowly. The main factors here include the difficulty of delivering ultrashort pulses with high peak power, which is necessary for multiphoton excitation (MPE), and obtaining these pulses at the required wavelengths to activate the autofluorescence mechanism. One of the most promising solutions is the use of specialized multimode optical fibers that can both induce beam self-cleaning (BSC), which allows for the formation of a stable beam profile close to the fundamental mode, and significantly broaden the optical spectrum, which can ultimately cover the entire region of interest. This review presents the biophysical foundations of multiphoton microscopy of GI tissue, existing endoscopic architectures for MPE, and an analysis of the potential for using novel nonlinear effects in multimode optical fibers, such as the BSC effect and supercontinuum generation. It is concluded that the use of optical fibers in which the listed effects are realized in the tracts of multiphoton endomicroscopes can become a key step in the creation of a new generation of high-resolution instruments for the early detection of malignant neoplasms of the GI tract. Full article

The informal street food sector serves as a vital component of urban economies in South Africa, providing affordable nutrition and employment. However, this industry struggles to comply with required health and safety practices and standards. This study protocol outlines a mixed-methods investigation into hygiene practices, regulatory compliance, and the intersection with business sustainability among informal food vendors in Johannesburg’s inner city. This study aims to investigate how vendors’ perceptions of health risks and benefits influence compliance behaviours and, in turn, how these behaviours impact operational efficiency, financial stability, and customer trust. Grounded in the Health Belief Model (HBM) and the Balanced Scorecard (BSC) framework, the research seeks to explore both behavioural drivers and performance outcomes associated with hygiene adherence. The study will employ structured stall observations, semi-structured vendor interviews, and customer surveys across high-density vending zones. Quantitative data will be analysed using descriptive and inferential statistics, while qualitative data will be thematically analysed and triangulated with observed practices. The expected outcome is to identify key barriers and enablers of hygiene compliance and demonstrate how improved food safety practices contribute to business resilience, customer trust, and urban public health. The findings aim to inform inclusive policy and innovative business support strategies that integrate informal vendors into safer and more sustainable food systems. Full article

This study proposes a hybrid machine learning framework that integrates structured financial indicators and unstructured textual strategy disclosures to improve firm-level management performance prediction. Using corporate business reports from South Korean listed firms, strategic text was extracted and categorized under the Balanced Scorecard (BSC) framework into financial, customer, internal process, and learning and growth dimensions. Various machine learning and deep learning models—including k-nearest neighbors (KNNs), support vector machine (SVM), light gradient boosting machine (LightGBM), convolutional neural network (CNN), long short-term memory (LSTM), autoencoder, and transformer—were evaluated, with results showing that the inclusion of strategic textual data significantly enhanced prediction accuracy, precision, recall, area under the curve (AUC), and F1-score. Among individual models, the transformer architecture demonstrated superior performance in extracting context-rich semantic features. A soft-voting ensemble model combining autoencoder, LSTM, and transformer achieved the best overall performance, leading in accuracy and AUC, while the best single deep learning model (transformer) obtained a marginally higher F1 score, confirming the value of hybrid learning. Furthermore, analysis revealed that customer-oriented strategy disclosures were the most predictive among BSC dimensions. These findings highlight the value of integrating financial and narrative data using advanced NLP and artificial intelligence (AI) techniques to develop interpretable and robust corporate performance forecasting models. In addition, we operationalize information security narratives using a reproducible cybersecurity lexicon and derive security disclosure intensity and weight share features that are jointly evaluated with BSC-based strategic vectors. Full article

The monkeypox virus (MPXV) is an emerging zoonotic pathogen responsible for mpox, a disease characterized by some smallpox-like symptoms, typically mild but occasionally fatal. The largest mpox recorded global outbreak began in May 2022, with over 162,000 cases across 140 countries. Herein, we have analyzed the intra-clonal diversity of MPXV obtained from a single skin lesion sample from a male patient (June 2022). Three viral clones were obtained following phenotypic evaluation of MPXV lysis plaque characteristics over a three-course infection in BSC-40 cells. Unlike the vaccinia virus Western Reserve (VACV-WR) strain, MPXV clones did not produce comet-like structures, suggesting reduced extracellular enveloped virus (EEV) morphotype release, which is associated with viral dissemination. Whole-genome sequencing and assembly identified subtle differences among clones. Comparative genomic analyses, including synteny and single nucleotide variation (SNV) calling, revealed intra-clonal differences and divergence from clade I and II references, although the variety of mutations found did not reveal possible variations at the protein level. Altogether, these findings suggest that although similar, it is possible that distinct MPXV variants may circulate together and can be found in a single exanthematous lesion. Full article

In recent years, extreme climate events such as high temperatures and droughts have become increasingly frequent and intense, posing significant threats to the carbon sink stability of C₃, C₄, and CAM plants. As a result, identifying photosynthetic strategies that balance adaptability with resilience has emerged as a critical focus in carbon sink research. C₂ photosynthesis offers a promising solution by recycling photorespiratory CO₂ through the glycine shuttle between mesophyll cells (MCs) and bundle sheath cells (BSCs), thereby optimizing carbon concentration and recovery without additional ATP expenditure, thus minimizing carbon loss. This review provides a comprehensive analysis of the diversity, distribution, evolutionary status, and regulatory mechanisms of C₂ photosynthesis, emphasizing its physiological and ecological resilience in carbon sequestration. In comparison to C₃ and C₄ pathways, C₂ photosynthesis demonstrates distinct carbon sink resilience, positioning it as a vital strategy for addressing both current and future global climate challenges. The review also highlights existing gaps in C₂ research, particularly in species identification, molecular mechanisms, and ecological studies, and recommends prioritizing these areas to fully harness its potential for enhancing climate resilience. Full article

Background: Practical, quantitative ultrasound-based tools for measuring hepatic steatosis are needed in everyday MASLD care. We evaluated a new multiparametric quantitative ultrasound (QUS) platform that integrates ultrasound-guided fat fraction (UGFF), attenuation coefficient (AC), backscatter coefficient (BSC), and signal-to-noise ratio (SNR), using Controlled Attenuation Parameter (CAP) as the reference and examining the effect of breathing. Methods: In a prospective single-center study, adult patients underwent same-day liver QUS and FibroScan. QUS measurements were performed during breath-hold and during normal breathing. Regions of interest were placed in right-lobe parenchyma 2 cm below the capsule, avoiding vessels. Primary outcomes were correlation with CAP and ROC performance at CAP cutoffs for S1 (≥230 dB/m), S2 (≥275 dB/m), and S3 (≥300 dB/m). Results: QUS was feasible in almost all examinations. UGFF, BSC, and SNR were consistent across breathing conditions, while AC was slightly higher during normal breathing. UGFF showed strong correlation with CAP and high accuracy for detecting steatosis. Across grades, AUCs were around 0.89–0.91, with cutoffs (UGFF ≈ 4% for ≥S1 and ≈11% for ≥S3). Conclusions: Multiparametric QUS provides reliable liver fat quantification that aligns closely with CAP and remains robust in practice whether patients hold their breath or breathe normally. These findings support UGFF as a practical, reliable point-of-care alternative for liver fat quantification that can be embedded in routine ultrasound in real time. Validation against MRI-PDFF or histology and multicenter studies will further define cutoffs and generalizability. Full article

Background: Whilst maternal body mass index (BMI) is linked to suboptimal breastfeeding outcomes, maternal body composition has not been assessed with respect to milk production (MP). Methods: Lactating mothers 1–6 months postpartum (n = 281) completed a demographic questionnaire and a 24 h MP measurement using the test-weigh method, enabling the calculation of 24 h MP parameters, breast storage capacity (BSC) and the percentage of available milk removed (PAMR). Body composition was measured with bioimpedance spectroscopy. Linear regression models were used to determine maternal and infant factors associated with MP parameters; structural equation modelling was used to assess the mediating role of BSC. Results: Higher maternal adiposity was associated with lower BSC (p ≤ 0.028), MP (p ≤ 0.003), infant breast milk intake (p ≤ 0.003) and total milk intake (p ≤ 0.026). Higher BSC was associated with higher MP (p < 0.001), with BSC confirmed as a mediator of the relationship between adiposity and MP (67.5%). Mean PAMR was negatively associated with BSC and milk removal frequency (both p < 0.001), and was lower in occasionally pumping compared to breastfeeding only (p = 0.037) and exclusively pumping mothers (p = 0.012). Conclusions: Our findings confirm maternal adiposity as a major contributor to low MP and reveal BSC, which is a measure of glandular tissue volume or breast development, as a mediator between adiposity and MP. This provides a rationale for antenatal lactation assessment of mothers and timely intervention in high-risk mothers to ensure they reach their full lactation potential. Full article

Reducing energy consumption and improving energy efficiency are essential objectives in the productive sector to ensure economic growth and reduce emissions. However, some energy management models do not include tools such as the balanced scorecard (BSC) and energy-based key performance indicators (KPIs). These tools help organisations make decisions and support continuous improvement actions. To address this gap, this study developed a methodology to facilitate the implementation of an Energy Management System. Specifically, this system evaluates the energy performance of processes within the abrasives industry, using KPIs based on energy efficiency. The proposed model, based on the Deming Cycle (PDCA, Plan-Do-Check-Act), consists of three stages: first, profiling and planning; second, implementation and maintenance; and third, surveillance. To support these stages, the main KPIs of energy typology were determined using AHP. Following this, the KPIs were prioritised based on energy efficiency. The results indicate that the company’s highest priority is meeting international goals, followed by reducing production costs and avoiding energy-related penalties. The energy baseline developed through regression analysis yielded a coefficient of 0.7794 and a specific consumption of 0.0345 kWh per manufactured piece for electricity alone, which increases by 107.25% when all energy sources used in the process are included. Within this context, the key indicators for monitoring energy efficiency strategies were established, demonstrating that model-assisted energy management not only supports the identification of improvement opportunities and internal control of production parameters but also provides a robust framework for evaluating, measuring, reporting, and improving energy efficiency targets. Full article

Dried banana slices can be nutritious snacks that meet consumers’ needs. However, preserving their color, texture, and antioxidant properties is challenging during convective drying. The new approach aimed to produce high-quality dried banana slices with higher antioxidant activity and lower browning. In this paper, the simultaneous application of ultrasound (at three levels: 0 W, 500 W, and 1000 W) and carboxymethyl cellulose (CMC) coating (the ratio of banana slice mass to the coating solution mass (BS:CS) at three levels: 1:2, 1:3, and 1:4) pretreatments, and their combined effects on various characteristics of the finally obtained dried banana slices were examined. The convective drying of banana slices was carried out at 80 °C and 3 m/s air velocity to achieve a consistent moisture content of roughly 10% (kg water/kg dry matter). As the power of ultrasound was increased from 0 W to 1000 W and with changing the BS:CS ratio from 1:2 to 1:4, the results demonstrated that the effective water diffusion coefficient (D_eff), water absorption capacity (WAC), and antioxidant activity (AA) of the dried banana slices were enhanced; however, their browning index (BI) decreased. Consequently, prior to convective drying, CMC coating using an ultrasonic system can be used as a practical strategy to produce fruit chips with desirable qualitative and nutritional properties. Full article