Abstract: To address the challenge of separating fine-grained apatite from layered silicate gangue minerals (chlorite and biotite) in medium-low grade collophanite ores, this study systematically investigated the effect of carboxymethyl cellulose sodium (CMC-Na) as a selective depressant on flotation behavior of different particle size fractions and its underlying mechanism. Pure mineral and artificial mixed ore flotation experiments demonstrated that at pH 9 and collector dosage of 5 kg/t, CMC-Na enabled selective separation of apatite from gangue minerals, with optimal dosage showing significant particle size effects: for the -0.5+0.074 mm fraction, effective separation was achieved with collector alone; for the -0.074+0.023 mm fraction, the optimal CMC-Na dosage was 10~100 mg/L, yielding 87% apatite recovery for pure minerals and 41.8% recovery with 23.7% P₂O₅ grade for mixed ores; for the -0.023 mm fine fraction, the optimal dosage was 30~300 mg/L, achieving 24.8% recovery and 13.2% grade. Mechanism studies revealed that CMC-Na significantly enhanced the hydrophilicity of chlorite and biotite, enlarging their surface property differences with apatite. FTIR and XPS analyses indicated that CMC-Na adsorbed on biotite via ion exchange with interlayer K⁺ and coordination with octahedral Fe²⁺/Mg²⁺, and on chlorite through chemical coordination with octahedral Mg²⁺, whereas only weak physical adsorption occurred on apatite surface Ca²⁺. The adsorption strength followed the order: biotite > chlorite > apatite. This study provides an effective reagent scheme and theoretical basis for flotation separation of fine-grained phosphate ores.

Abstract: The preparation of high-performance radiation detector materials such as cadmium zinc telluride (CZT) relies on rigorous and efficient quality control to ensure the consistency of device performance. Traditional manual evaluation based on wafer-by-wafer inspection is time-consuming and makes it difficult to assess the downstream product yield at the ingot level in advance. This paper proposes a machine-learning-based prediction framework for CZT ingots, in which the product-level yield of test wafers from the same ingot is predicted using the double-sided electrical performance and spectral characterization data of a limited number of evaluation wafers. To address the limited number of ingot samples and the significant internal variability among wafers, statistical aggregate features, A/B-side difference features, threshold-ratio features, and intra-ingot Bootstrap resampling were combined, and multiple regression methods, including linear models, Random Forest, XGBoost, and neural networks, were systematically evaluated. The results show that the XGBoost model achieved the best overall performance, with the lowest mean squared error of 0.0352, a mean absolute error of 0.1448, and a Pearson correlation coefficient of 0.3187 on the test set. Furthermore, after combining model prediction with empirical rules, the true yield of test wafers for the top 22% candidate ingots increased from 61.50% to 63.59%. These results indicate that the proposed method can effectively support early ingot screening and processing-priority decisions. This study demonstrates the application potential of data-driven methods in early-stage quality evaluation of CZT crystals and provides a reference framework for yield prediction in similar multi-wafer crystalline materials.

Abstract: Agricultural waste accumulation offers potential for sustainable soil management in climate-resilient farming systems, but it also poses ongoing environmental challenges. This study examines the effects of vermicomposting, which turns agricultural waste into nutrient-rich organic fertilizer using Eisenia fetida, on crop productivity and soil fertility. Treatments were compared using a randomized experimental design that included many combinations of organic waste and a control. Crop growth and yield indices were examined in addition to soil physicochemical characteristics such as pH, organic carbon, total nitrogen, available phosphorus, and exchangeable potassium. Comparing vermicompost treatments to the control, the soil's nutritional content and structural quality significantly increased (p < 0.05). Mixed organic waste substrate trials outperformed single substrate trials, suggesting synergistic interactions that enhance microbial activity and nutrient cycling. Vermicompost application improved soil fertility indicators and increased crop growth and production. These findings show that vermicomposting is an effective waste valorization technique that supports the circular economy and sustainable agriculture. The study demonstrates how it can reduce environmental pollutants while enhancing soil health, agricultural yield, and fertilizer use efficiency. All factors considered, vermicomposting is a scalable and environmentally friendly way to increase the climate resilience of agricultural systems. More research should be done on long-term field performance, economic viability, and substrate combination optimization under different agroecological conditions.

Abstract: Background: Globally, over 2.1 billion people lack safe drinking water, leading to significant impacts, especially from diarrhea. This study evaluates the health and economic impacts of point-of-use water filter distribution in an urban setting with partial water infrastructure. Methods: In 2024 and 2025, households (N=7,973) in Ciudad Victoria, Mexico, re-ceived a hollow fiber membrane point-of-use water filter and basic WASH training. A pseudo-randomized study design was used to assign household to receive one of three different filter implementation systems (tap, bucket, or squeeze). Baseline and follow-up surveys with each household were conducted over an 8–14 week time frame. Results: Filter utilization was high across all three delivery types, with only minor differences in outcomes observed. Self-reported two-week diarrhea prevalence de-clined from 24.3% at baseline to 3.1% at 8–14 weeks, with similar reductions in oth-er health symptoms. Household water expenditures decreased by 56%, and work-days missed due to diarrhea declined by 94%. Impacts were similar in covariate ad-justed statistical models. Conclusions: This study suggests that point-of-use filtration combined with WASH training can substantially reduce illness and economic burden in urban contexts, with effectiveness comparable across different filter implementation approaches. Limitations include reliance on self-reported data and a short follow-up period, which should be explored in future studies.

Abstract: Polarimetric color cameras are a forefront technology that simultaneously capture polarimetric and color information by analyzing polarization states across different color channels, commonly red, green, and blue. In general, each of these color channels can carry different polarization information. Therefore, measuring the polarization Stokes vector at several discrete wavelengths simultaneously and with the highest possible resolution is of interest in multiple research areas. Nonetheless, this situation has not yet been investigated in specialized literature, where it is still commonly assumed that all color channels transport the same polarization information. In practice, polarimetric color cameras often come with the difficulty of color filter overlapping. For instance, the green filter partially transmits red and blue wavelengths, causing polarization-color crosstalk. In this work, we present a method to solve this problem. In addition, Fourier domain demosaicing techniques are applied to interpolate the data and reconstruct the images. The present study demonstrates how the proposed method leads to a successful recovery of chromatic and polarimetric information on both synthetic and real-world datasets. To test our approach, narrowband light beams at three wavelengths (470, 554, 630 nm), with different spatial polarization and degree of linear polarization distributions have been simulated and validated with experimental data. The results demonstrate the feasibility of the method for accurate three polarization channels measurements.

Abstract: University students face dietary transitions shaped by time constraints, campus food environments, and intensive exposure to food-related content on social media, yet the mechanisms linking digital exposure to observable food choices and overall diet quali-ty remain insufficiently modeled in Latin American contexts. This study examined whether social-media-driven food norms (NI) and in-restaurant food choices (CD) se-quentially mediate the effect of Instagram (IG) and TikTok (TK) exposure on overall diet quality (Y), while incorporating physical activity (PA) as an independent predic-tor. A cross-sectional survey was administered to 615 university students consuming in campus restaurants in La Libertad, Northern Peru. Data were analyzed through PLS-SEM (SmartPLS 4) with 5,000 bootstrap resamples and BCa 95% confidence in-tervals; Y was operationalized through a culturally adapted KIDMED index. All five structural hypotheses were supported: TK → NI (β = 0.479) exceeded IG → NI (β = 0.349); NI → CD (β = 0.473) and PA → CD (β = 0.216) operated as independent path-ways; and CD → Y (β = 0.255) confirmed the distal link. NI fully mediated both digital pathways toward food choices. Diet quality in university restaurants is reconfigured primarily through normative, not informational, digital mechanisms, suggesting norm-based interventions over nutrition-information campaigns.

Abstract: Both data utility and data privacy must be carefully considered when datasets containing users’ sensitive information are released for use beyond the scope of the data‑collecting organization. To achieve an appropriate balance between data utility and data privacy in such released datasets, a variety of privacy preservation models have been proposed, including k‑Anonymity, l‑Diversity, Anatomy, t‑Closeness, and Differential privacy. Unfortunately, these privacy preservation models can sufficiently address concerns of privacy violation issues in simple datasets. Moreover, to the best of our knowledge about them, they still have various vulnerabilities that must be improved such as data utility, complexity, and the new privacy violation techniques that are discovered after they are proposed. Furthermore, we found that they are not well-suited to address concern of privacy violation issues in datasets containing content-sensitive values (or sometimes they are called content-based datasets). To rid these vulnerabilities, a new privacy preservation model is proposed in this work, it is called (d, c, l)-Privacy. It is proposed to address concerns of privacy violation issues in content-sensitive datasets. It is based on expert and mechanism term document measurements. That is, the released datasets can reduce about the concern of privacy violation issues after they are satisfied by the d, c, and l parameters. To achieve (d, c, l)-Privacy constraints, there are three algorithms that are proposed in this work, i.e., SCFS, greedy, and optimal (d, c, l)-Privacy algorithms. The aim of SCFS (d, c, l)-Privacy is that aside from the released dataset is satisfied by the d, c, and l parameters, the execution time is maintained as much as possible. While the greedy (d, c, l)-Privacy will be mindful of both the execution time and data utility. Another proposed algorithm, the optimal (d, c, l)-Privacy algorithm, aims to maintain the meaning or usefulness of the data as much as possible.The experimental results show that the proposed algorithms are effective in mitigating privacy breaches in released datasets under the (d, c, l)-Privacy constraints. Among the evaluated algorithms, FCFS achieves the highest time efficiency, whereas the greedy algorithm offers a better balance by preserving data semantics within a reasonable computational cost. The optimal algorithm consistently maintains the highest level of data utility, albeit at increased computational expense. These findings indicate that the proposed algorithms are not only effective in preserving privacy data in released datasets but are also suitable for practical deployment in real‑world data‑releasing scenarios.

Abstract: Traumatic tracheal injuries and congenital defects can be life threatening. Regenerating the trachea through tissue engineered scaffolds has emerged as an innovative alternative to traditional therapies. At present time, challenges in tracheal regeneration preclude clinical adoption, such as revascularization and promotion of favorable paracrine and immune signaling responses. This review summarizes current advances in tracheal regeneration and highlights key biological and engineering barriers to address to achieve functional tracheal regeneration.

Abstract: The aim of this study was to evaluate whether edible coatings and plant derived ex-tracts can help maintain the quality and microbiological safety of fresh-cut sweet peppers during short-term refrigerated storage. Two bell pepper cultivars, Sunny F1 (yellow) and Yecla F1 (red), were sliced and subjected to five treatments: water wash-ing (control), washing with BioActiW 2000 Food sanitizer (BAW), BAW followed by carboxymethylcellulose (CMC) coating (BAW+CMC), CMC coating enriched with 3.5% alcoholic chokeberry pomace extract (CMC+AE), and soaking in 3.5% aqueous chokeberry pomace extract (AAE). Samples were stored at 5 °C for 7 days and evalu-ated for physicochemical analysis, microbiological contamination, postharvest quality, and sensory properties. The treatments influenced quality attributes in a cultivar de-pendent manner. All coating based treatments reduced polyphenol and L-ascorbic acid content relative to the control, although formulations containing chokeberry extract tended to limit these losses compared with BAW+CMC. Total sugar and carotenoid contents were not significantly affected. In both cultivars, BAW and BAW+CMC were the most effective treatments for reducing mesophilic bacteria and yeast counts, limit-ing softening, reducing weight loss, and maintaining marketable quality. By contrast, AAE applied without prior sanitization increased microbial counts in Sunny F1, indi-cating that the extract alone was not sufficient to control native microflora. Sensory analysis showed clear cultivar specific responses: Sunny F1 generally tolerated CMC+AE and BAW+CMC better, whereas Yecla F1 was more sensitive to off-flavors associated with the chokeberry extract. PCA analysis indicated that smell and taste at-tributes were the main drivers of perceived quality. These results suggest that CMC based coatings can support fresh-cut pepper quality, but their practical value depends strongly on prior sanitization. The addition of chokeberry pomace extract may be beneficial for some quality traits, yet its overall ef-fect depends on cultivar and treatment conditions, including extract concentration and pH.

Abstract: The diverse clinical manifestations of allergic skin diseases often overlap with other pathologies, posing significant diagnostic challenges. Allergen-specific IgE (sIgE) testing is essential for identifying triggers and personalizing treatments in allergic skin diseases. A retrospective study was conducted on 4,277 medical records of patients with allergic skin diseases who underwent sIgE testing at the HCMC Hospital of Dermato-Venereology, a tertiary referral dermatology center serving Southern Vietnam, from January to December 2024. The results revealed that 64.09% of the patients exhibited sIgE sensitization. House dust mites (D. farinae: 30.91%, D. pteronyssinus: 25.67%) and fire ants (24.06%) were the predominant allergens. Food sensitization was predominantly mild, with almond being the most common (10.26%). Significantly higher sensitization severity was observed in males, young adults (12–35 years), and non-urban residents (p &lt; 0.001). Correlation analysis demonstrated robust co-sensitization and cross-reactivity among aeroallergens, predominantly driven by house dust mites. In conclusion, allergen sensitization profiles are strongly influenced by demographic and geographic factors, with a characteristic pattern observed in Southern Vietnam. Multiplex sIgE testing provides substantial value in disease stratification, prognostic assessment, and the development of personalized treatment strategies in this tropical setting.

Abstract: Accurate evaluation of indoor daylighting performance is essential for improving visual comfort and reducing lighting energy use in office buildings. However, simulation-based daylighting analysis is often too time-consuming to support rapid comparison of multiple design options in early-stage design. To address this issue, this study proposes MTL-Light, an explainable chained multi-task learning framework for fast daylighting performance prediction in typical office units. A parametric simulation dataset was constructed, and multiple representative daylighting indicators were extracted from the spatial distribution of daylight factors on the work plane. MTL-Light was then developed to jointly predict these indicators by modeling their interdependencies within a lightweight multi-task learning architecture. In addition, SHAP was employed to interpret the prediction results by quantifying the marginal contributions of geometric design variables. The results show that, compared with single-task models, MTL-Light achieves higher accuracy and more stable performance across multiple indicators, particularly for metrics sensitive to spatial distribution. Moreover, it reduces daylighting evaluation from minute-level simulation to millisecond-level inference. The interpretability analysis further indicates that room depth and window geometry dominate daylighting performance, while different indicators exhibit different sensitivities to geometric variables.

Abstract: This paper investigates the oscillation properties of solutions to a second-order nonlinear difference equation in noncanonical form with bounded and unbounded neutral terms. By employing the monotonicity of the neutral term together with a linearization technique, we establish new conditions that guarantee all solutions of the equation oscillate. Our results are applicable to various nonlinear forms of the equation, and, notably, the oscillation of all solutions is ensured through a single condition. Consequently, the proposed oscillation criteria are straightforward to apply and distinct from existing results on nonlinear difference equations. Four examples are presented to demonstrate the novelty and significance of the main findings.

Abstract: Background: Hair loss is a multifactorial condition influenced by aging, oxidative stress, hormonal regulation, and nutritional status. Nutraceutical supplementation has emerged as a potential strategy to support hair follicle function. This study evaluated the clinical efficacy of a nutraceutical ingredient (Kyoh®) at two concentrations versus placebo in individuals with hair loss. Methods: A randomized, parallel-group study was conducted in 150 volunteers aged 30–60 years. Participants received a high-dose (Kyoh BB-01), low-dose (Kyoh BB-02), or placebo (Kyoh BB-03) for 84 days. Hair parameters were assessed by digital trichoscopy at baseline, day 56, and day 84. Endpoints included hair density, follicular unit density, hair diameter, and hairs per follicular unit. Hair shedding was evaluated by comb test, and subjective perception by questionnaires. Results: The high-dose group showed significant increases in hair density and follicular unit density at days 56 and 84, as well as higher hairs per follicular unit at day 84. The low-dose group also improved these parameters to a lesser extent. No significant changes were observed in the placebo group. Hair diameter and shedding remained unchanged. Subjective results supported instrumental findings. Conclusion: The nutraceutical improved key hair growth parameters, with greater efficacy at higher dose.

Abstract: This study presents a comprehensive geospatial framework for landslide risk assessment across the 4,523 km road network of the Region of Epirus in Greece. Utilizing a field-verified inventory of 295 active landslides, the research evaluates five key predisposing factors—lithology, slope inclination, elevation, land use, and cumulative annual precipitation—using the bivariate Frequency Ratio (FR) statistical model. Among six tested scenarios, the most robust model integrated all factors, achieving high predictive accuracy by classifying nearly 80% of the study area within Moderate to Very High susceptibility zones. The resulting Landslide Susceptibility Index (LSI) was converted into a Landslide Hazard Index (LHI) and integrated with a weighted Road Vulnerability Map, which categorized road sectors based on functional importance and traffic volume. The final Landslide Risk Map indicates that the most critical risk zones are clustered along major transportation corridors that traverse geologically weak formations, moderate to high precipitation areas and steep mountainous sectors. This quantitative approach provides a vital decision-support tool for regional authorities, enabling the prioritization of geotechnical monitoring and the strategic allocation of resources for infrastructure stabilization. The methodology offers a replicable workflow for enhancing the resilience of transportation networks in landslide-prone Mediterranean regions facing evolving climatic threats.

Abstract: Background: Gender equity is an increasingly relevant dimension in psychosocial risk assessment within Organisational seĴings. This pilot study examines the integration of a Perceived Gender Disparity subscale into the Organisational and Psychosocial Risk Assessment (OPRA) inventory in an Italian manufacturing sample (N = 65). Methods: The five-item subscale, grounded in the literature on workplace gender discrimination and harassment, underwent reliability analysis (Cronbach’s α = 0.706), bivariate correlations (Pearson’s r), Exploratory Factor Analysis (EFA), and non-parametric sensitivity analysis (Spearman’s ρ). Results: results indicate statistically significant associations with seven of nine OPRA subscales: a large correlation with Role (r = 0.508, p < 0.001), medium-sized associations with Organisational Culture, Workload, Quality of Relationships, and Career Development (|r| = 0.331–0.427, p ≤ 0.007), and small-to-medium associations with Environment and Safety and Autonomy (|r| = 0.268–0.275, p < 0.05). No significant associations emerged for Working Hours or Work–Life Balance. Spearman’s correlations confirmed and strengthened all significant associations. EFA revealed a unidimensional structure with adequate loadings (λ = 0.475–0.785), accounting for 41.90% of common variance. Conclusions: Despite the limitations of a single-site, male-dominated sample, findings provide preliminary support for including perceived gender disparity as a source of Organisational risk within the OPRA framework.

Abstract: This study examined parent–child physiological synchrony within the context of interactions and attachment-related differences. Specifically, this study investigated physiological synchrony, as indexed by the association between parent and child root mean square of successive differences (RMSSD) during free-play interactions, and differences in children’s mean heart rates according to attachment classification. The participants were 25 parent–child dyads (mean child age = 36.48 months). Physiological responses were assessed during free-play interactions using heart rate (HR) and heart rate variability (HRV). Children’s attachment was classified as secure or resistant based on their behaviors observed during the separation–reunion procedure. The results showed a significant positive association between the parent and child RMSSD (ρ =.48, p <.05). Parental anxiety was positively associated with both parents’ and children’s physiological arousal. Attachment-related group differences were observed only in the mean heart rate, with children with resistant attachment showing a significantly higher HR than those with secure attachment (t = 2.69, p <.05). No significant group differences were observed in the RMSSD or HR/RMSSD ratios. Overall, these findings suggest that the parent–child RMSSD association, as a component of physiological synchrony, may reflect a normative feature of parent–child interaction that emerges across attachment classifications. In addition, attachment-related differences were primarily observed in physiological arousal.

Abstract:

Cocaine use disorder (CUD) remains a significant global health issue, with no FDA-approved pharmacological treatments. Cannabidiol (CBD), a non-psychoactive phytocannabinoid primarily derived from Cannabis sativa L., has demonstrated promising results in preclinical research to disrupt the consolidation and retrieval of drug-associated memories, thereby reducing relapse behaviors linked to substance use disorders such as cocaine dependence. This study evaluates the effects of a non-psychoactive cannabis extract (NPCE) on the reinstatement and reconsolidation of cocaine-induced conditioned place preference (CPP) in CD1 male mice—processes that, to our knowledge, have not been previously examined. The results showed that NPCE significantly inhibited both priming -induced and stress-induced reinstatement of cocaine-induced CPP, suggesting its potential to disrupt drug-associated memories. Additionally, NPCE effectively impaired the reconsolidation of cocaine-induced CPP, suggesting an effect on memory reconfiguration lasting at least two weeks. Additionally, NPCE alone did not produce any effect on CPP acquisition. These findings underscore the potential of NPCE, in targeting memory-related mechanisms underlying cocaine addiction, specifically in the reconsolidation and reinstatement. These results indicated that NPCE may reduce relapse risk by modulating drug-reward memories, potentially through interactions with CB1 receptors and other molecular signaling pathways like serotonergic receptors. This research contributes to the growing body of evidence, which suggests that cannabinoids, particularly non-psychoactive extracts, could offer novel therapeutic options for treating CUD. Further studies are needed to explore the individual effects of other cannabinoids on cocaine dependence and to assess clinical applicability of these findings.

Abstract: Language agents that act through tools, files, browsers, APIs, and persistent sessions are shaped by more than the base model or a single prompt. Their reliability depends on a harness layer that determines which instructions remain authoritative, what actions are available, how state is carried forward, and how failures are handled over time. This paper argues that this layer warrants explicit treatment in NLP. We propose and operationalize a working decomposition of the harness layer as control, agency, and runtime (CAR); situate harness engineering in the arc from software engineering through prompt and context engineering; and audit 63 harness-relevant works, suggesting a meaningful visibility gap between academic papers and public engineering notes. We further argue that many reported agent gains may be partly harness-sensitive rather than purely model-driven, and propose HarnessCard as a lightweight reporting artifact. Grounded in papers, benchmarks, protocols, and engineering notes collected through April 21, 2026, we argue that progress in language agents should report not only the model, but also the harness layer that turns capability into governed action.

Abstract: Maternal undernutrition during gestation can impair fetal muscle development, inducing lasting consequences for offspring growth and carcass quality. This study evaluated the effects of early- to mid-gestation nutrient restriction on postnatal skeletal muscle development in Boer goat offspring. Pregnant does (n = 12 per treatment) were assigned to either a control diet (CON; 100% of NRC recommendations) or a nutrient-restricted diet (NR; 60% of CON) from days 45 to 100 of gestation, then all does were realimented to the CON diet until parturition; male offspring (n = 6 per treatment) were maintained on a CON diet until 5 months of age. Longissimus dorsi muscle samples were collected for histological evaluation of fiber number, diameter, and collagen content, and for gene expression analysis of insulin receptor (IR), insulin receptor substrate-1 (IRS1), glucose transporter-4 (GLUT4), myogenic regulatory factors (MYF5, MYF6, MYOD, MYOG), and collagen genes (COL1A1 and COL3A1) using RT-PCR. Plasma glucose and cortisol were also measured. Muscle fiber number tended to be reduced (p = 0.06) in NR offspring, accompanied by decreased MYOG expression (p < 0.05) and trends for reduced MYF5 and MYF6 expression (p < 0.10), as well as reduced IR expression (p < 0.05). Collagen content did not differ, although COL3A1 expression was increased in NR offspring. Plasma glucose was lower (p < 0.05) at 3 months, and cortisol tended to be higher (p < 0.10) at 5 months. These results indicate that maternal undernutrition during early- to mid-gestation alters postnatal skeletal muscle development in Boer goats by reducing muscle fiber number and affecting myogenic and metabolic signaling pathways. Such changes may negatively affect the efficiency of muscle growth and meat quality.

Abstract: Caspases orchestrate metazoan apoptosis, regulating processes such as embryogenesis, the death of old and infected cells and immune tolerance. Structural orthologs of caspases have been identified in bacteria, plants, protists and fungi and regulated cell death has been demonstrated in these organisms. This led some researchers to conclude that fungal metacaspases might perform a similar function to caspases. This review discusses regulated cell death, beginning with an account of RCD and the central role of caspases in mammalian RCD. It goes on to give examples of RCD in fungi, compares the structure and activity of caspase orthologs and outlines examples of metacaspase-dependent and metacaspase-independent cell death in fungi, focusing on S. cerevisiae. Finally, it addresses the question “are metacaspases caspases?”, identifies alternative cell death proteases and recommends future research objectives.