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by Liliana Rodríguez-Flores, César Camacho-López, Claudia Romo-Gómez, Otilio A. Acevedo-Sandoval, Fernando Salas-Martínez, José B. Leyva-Morales and César. A González-Ramírez

Environments 2026, 13(4), 210; https://doi.org/10.3390/environments13040210 - 10 Apr 2026

Abstract

Acetaminophen, more commonly known as paracetamol (APAP), is one of the most widely used analgesics and antipyretic drugs worldwide. Its presence in the environment poses a risk to the organisms it comes into contact with, which is why it has been classified as an emerging contaminant. Given its adverse effects and continuous discharge into water bodies, it is necessary to study efficient, environmentally sustainable processes for its complete removal. Denitrification is a biological process that has been studied for the biodegradation of recalcitrant compounds and certain pharmaceuticals such as 17β-estradiol and ampicillin, transforming them into harmless products such as N₂ and HCO₃⁻. In the present study, the biodegradation of 6 mg L⁻¹ of APAP-C was evaluated through a denitrifying process. Batch experiments were conducted, achieving acetaminophen (APAP) removal efficiencies (E_APAP-C) of 83.3 ± 0.86% and nitrate removal efficiencies (E_N-NO3⁻) of 100%. The substrates were predominantly converted into HCO₃⁻ and N₂, with yields greater than 0.9, while intermediates such as NO₂⁻ were observed only transiently during the reaction. At the end of the experimental period, no secondary metabolites were detected, indicating that intermediates did not accumulate to quantifiable levels. Full article

(This article belongs to the Special Issue Advanced Research on the Removal of Emerging Pollutants)

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31 pages, 3673 KB

Open AccessArticle

Unveiling Systemic Risks in Sustainable Safety Management: Integrating BERTopic, LLM, and SNA for Accident Text Mining

by Lanjing Wang, Rui Huang, Yige Chen, Yunxiang Yang, Jing Zhan and Haiyuan Gong

Sustainability 2026, 18(8), 3787; https://doi.org/10.3390/su18083787 - 10 Apr 2026

Abstract

To unveil the underlying risk structures in complex industrial systems, this paper proposes a hybrid analytical framework that integrates BERTopic modeling, a large language model (LLM), and social network analysis (SNA). This framework aims to extract systemic safety intelligence from unstructured accident reports. It first employs BERTopic to identify latent causal topics based on 745 Chinese accident investigation reports and utilizes DeepSeek-V3.1 (LLM) for semantic refinement and causal mapping of these topics. Subsequently, a semantic network of causal keywords based on positive pointwise mutual information (PPMI) is constructed, and its topological structure is analyzed using SNA methods. The study identifies and analyzes five major risk communities: confined spaces, fire, mining, construction, and road traffic. It reveals that accident causation exhibits the small-world characteristics of multi-factor coupling and non-linearity, with core risk nodes concentrated in systemic inducements such as organizational management and compliance deficiencies. The results demonstrate that this framework effectively identifies the latent systemic risk patterns embedded within the texts, providing methodological support for developing sustainable safety management mechanisms based on design for safety. Full article

(This article belongs to the Special Issue Achieving Sustainability in Safety Management and Design for Safety)

21 pages, 3920 KB

Open AccessArticle

Agricultural Use of Lands Affected by Deep-Seated Landslides in the Transylvanian Basin and Its Consequences on Soil Physicochemical Properties

by Gheorghe Roșian, Mihai Buta and Csaba Horvath

Appl. Sci. 2026, 16(8), 3744; https://doi.org/10.3390/app16083744 - 10 Apr 2026

Abstract

Land leveling deep-seated landslides for agricultural use alters soil profile integrity and soil functionality. In the mid-20th century, such interventions in the Transylvanian Basin (Romania) involved grading and converting landslide bodies into arable land. This study evaluates the consequences of interventions on soil physicochemical properties and erosion susceptibility in the case of two deep-seated landslides. Soil samples collected from leveled landslide bodies were analyzed for pH, total nitrogen, available phosphorus (P-AL), available potassium (K-AL), calcium carbonates, humus content, and texture. The results, in the case of the two studied deep-seated landslides, indicate contrasts between areas where the Ah horizon is preserved and where leveling exposed the C horizon or parental material at the surface. Exposed zones exhibit reduced nitrogen and humus content, altered textures, and higher carbonate influence, indicating lower fertility potential despite 65 years of pedogenesis. Spatial assessment using Sentinel-2-derived NDMI and USLE-based erosion modelling confirms increased moisture stress and higher erosion susceptibility in areas with exposed substratum. These findings demonstrate that the leveling of the two studied deep-seated landslide bodies, although effective in expanding arable surfaces, leads to persistent soil degradation patterns and reduced agro-ecological resilience. Sustainable cultivation of such terrains requires targeted soil conservation measures, including erosion control and adapted land management practices. The results provide important implications for land-use planning in landslide-prone agricultural landscapes. Full article

23 pages, 7932 KB

Open AccessArticle

Walls That Grow: Fungal Species-Driven Performance of Mycelium-Based Composites Grown on Rice-Husk Waste

by Zahra Parhizi, Ewa Jadwiszczak, John Dearnaley, Deirdre Mikkelsen and Paulomi (Polly) Burey

Polymers 2026, 18(8), 932; https://doi.org/10.3390/polym18080932 - 10 Apr 2026

Abstract

Mycelium-based composites (MBCs) are gaining attention as sustainable alternatives to conventional materials because they are grown biologically rather than produced through resource-intensive extraction and processing. This study evaluates MBCs for non-load-bearing wall panels and environmentally responsible substitutes for traditional building materials. A reproducible manufacturing process is presented, and heat-pressed panels are characterised for physical, mechanical, and chemical performance. Novelty lies in species-driven evaluation using rice-husk waste as the sole lignocellulosic substrate and a Queensland-native Amauroderma species. Five fungal species, Trametes hirsuta, Ganoderma sp., Amauroderma sp., Pycnoporus coccineus and Trametes versicolor, were cultivated on rice husks and compared under identical processing conditions. Statistical analysis showed species selection significantly influenced tensile strength, whereas flexural and compressive performance showed no significant interspecies differences. Panels achieved tensile, compressive, and flexural strengths up to approximately 0.47, 0.35, and 1.35 MPa, respectively, with Amauroderma exhibiting the highest stiffness and compressive performance. Composites from four of the five species showed low moisture sensitivity and favourable thermal behaviour relative to previously reported mycelium materials. These results demonstrate that fungal species selection is a key design lever and supports rice-husk-derived MBCs as sustainable insulation and non-load-bearing construction materials. Full article

(This article belongs to the Special Issue Advances in Biopolymer-Based Composites: Manufacturing, Characterization, and Applications)

20 pages, 788 KB

Open AccessArticle

Sustainable Practices and Climate Change Adaptation in Olive Farming: Insights from Producers in Aetolia–Acarnania, Greece

by Vassiliki Psilou, Eleni Zafeiriou, Chrysovalantou Antonopoulou, Christos Chatzissavvidis and Garyfallos Arabatzis

Agriculture 2026, 16(8), 845; https://doi.org/10.3390/agriculture16080845 - 10 Apr 2026

Abstract

Olive cultivation represents a key pillar of rural economies and cultural heritage in Mediterranean regions, including western Greece. Despite its socio-economic importance, the sector faces increasing pressures from climate change, market volatility, and technological transformation, while progress toward environmentally sustainable production remains uneven. This study investigates how olive farmers’ perceptions of carbon footprint and climate risks are influenced by their demographic characteristics. Primary data were collected through 402 structured questionnaires distributed to olive producers in the Aetolia–Acarnania region. The sample was designed to represent farmers directly engaged in olive production, ensuring the relevance and reliability of the collected data. The findings, based on descriptive statistics, reveal significant heterogeneity in producers’ perceptions of climate risks and their capacity to respond through sustainable practices. Demographic characteristics appear to play an important role in shaping awareness of carbon footprint and the potential adoption of environmentally responsible farming strategies. These results suggest that sustainability transitions in perennial cropping systems depend not only on technological availability but also on social, informational, and institutional capacities. Strengthening agricultural advisory services, farmer training, and climate adaptation strategies may therefore support the adoption of climate-smart practices in olive cultivation. Furthermore, cooperation and value-chain integration are identified as potentially important mechanisms for facilitating knowledge transfer and supporting the adoption of sustainable practices (e.g., efficient irrigation and optimized input use). However, their contribution to environmental performance and greenhouse gas mitigation cannot be directly inferred from the present perception-based analysis and should be examined in future research using appropriate quantitative or environmental assessment frameworks. Full article

(This article belongs to the Special Issue Advances in Olive and Olive Oil Quality: From Orchard Management to Consumer Products)

40 pages, 8661 KB

Open AccessArticle

Explainable Ensemble Machine Learning for the Prediction and Optimization of Pozzolanic Concrete Compressive Strength

by Sebghatullah Jueyendah and Elif Ağcakoca

Polymers 2026, 18(8), 933; https://doi.org/10.3390/polym18080933 - 10 Apr 2026

Abstract

Pozzolanic concrete demonstrates intricate, highly nonlinear material interactions that pose significant challenges for the accurate prediction of compressive strength (CS). This study introduces a novel, interpretable ensemble machine learning (ML) framework for predicting CS based on 759 mixture records encompassing cement, aggregates, supplementary cementitious materials (pozzolans), water/binder (W/B), superplasticizer, water, and curing age. Descriptive analysis and ANOVA were used to identify key predictors, followed by an 80/20 train–test split with 10-fold cross-validation to ensure robust and generalizable modeling. To further enhance model reliability, 5% of outliers were removed using an isolation forest algorithm, after which data were normalized and ensemble hyperparameters optimized. Among the evaluated models, the extra trees algorithm with standard scaling demonstrated the most stable generalization, achieving a coefficient of determination (R²) of 0.978 and a root mean square error (RMSE) of 4.197 MPa on the test set, and R² = 0.966 (RMSE = 5.053 MPa) under 10-fold cross-validation. Feature importance, SHAP, and partial dependence analyses consistently demonstrated that W/B, curing age, and cement are the principal determinants of CS. Finally, multi-objective optimization generated high-strength, low-impact mixtures, confirming the framework’s effectiveness as a transparent decision-support tool for performance- and sustainability-oriented pozzolanic concrete design. This study is novel in combining interpretable ensemble ML with multi-objective optimization to simultaneously achieve precise CS prediction and the formulation of sustainable, performance-optimized pozzolanic concrete mixtures. Full article

(This article belongs to the Section Artificial Intelligence in Polymer Science)

22 pages, 3220 KB

Open AccessArticle

Influence of Printing Orientation on the Mechanical Performance and Environmental Footprint of PLA/TPU Heterogeneous Composites

by Wenxuan Wang, Zhiheng Hu, Ruoyi Zhou, Yitao Huang, Yilun Wang, Bo Mu and Mingzhang Chen

Sustainability 2026, 18(8), 3786; https://doi.org/10.3390/su18083786 - 10 Apr 2026

Abstract

This study examines how 3D-printing orientation affects the mechanical behavior and environmental impact of polymer materials and heterogeneous PLA/TPU composites. Tensile properties of PLA, TPU, and PLA/TPU heterogeneous samples were systematically compared in horizontal and vertical printing orientations. Results show that printing orientation governs mechanical performance: vertical printing generally reduces ductility and exhibits unstable post-peak behavior, with heterogeneous samples performing worse than their single-material counterparts. In contrast, horizontal printing enhances strength, ductility, and energy absorption due to continuous load transfer along the filament path, improved interlayer adhesion, and larger effective contact areas. Specifically, TPU demonstrates higher ductility and energy absorption in the horizontal orientation, while PLA achieves higher strength but lower ductility; both materials degrade substantially in the vertical orientation. For heterogeneous composites, vertical printing yields the poorest outcomes due to load transfer across multiple perpendicular interfaces and thermal shrinkage mismatch, which promote crack initiation and propagation. Horizontal printing delivers an optimal balance of strength and toughness via stronger interface bonding. Life cycle assessment (LCA) indicates that horizontal printing reduces environmental burdens by lowering energy consumption and waste, whereas vertical printing amplifies these impacts, particularly for TPU and composite systems. Based on these findings, we recommend employing horizontally printed PLA/TPU heterogeneous composites to achieve favorable load paths and interface integrity while prioritizing bio-based PLA to enhance sustainability. Full article

(This article belongs to the Special Issue 3D Printing for Multifunctional Applications and Sustainability)

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29 pages, 4477 KB

Open AccessArticle

The Effectiveness of an Augmented Reality-Based Early Intervention Program Using Interactive Games to Enhance Eye Contact as a Nonverbal Communication Skill in Children with Autism: A Single-Case Experimental Design

by Shoeb Saleh and Rommel AlAli

J. Intell. 2026, 14(4), 64; https://doi.org/10.3390/jintelligence14040064 - 10 Apr 2026

Abstract

Children with Autism Spectrum Disorder (ASD) frequently exhibit marked impairments in nonverbal communication, particularly in eye contact, which serves as a foundational element for social interaction and relational development. This study evaluated the effectiveness of an early intervention program utilizing interactive games supported by Augmented Reality (AR) technology to enhance eye contact behaviors, specifically initiation and maintenance, in children with autism. Using a multiple baseline across participants single-case experimental design, four boys (aged 5–7 years) diagnosed with ASD participated in an 8-week intervention at a specialized center in Saudi Arabia. The intervention featured tablet-based, gamified AR tasks incorporating real-time visual feedback, graduated difficulty levels, and reinforcement mechanisms designed to elicit social gaze and sustained eye contact. Eye contact duration and frequency were measured during structured social interactions via systematic direct observation. The results demonstrated significant improvements across all participants, with the mean duration of eye contact increasing from a baseline of 2.0 s to 5.8 s post-intervention. Visual analysis revealed robust treatment effects, further supported by substantial Tau-U effect sizes (range = 0.89–0.96; M = 0.93). Follow-up data collected three weeks post-intervention confirmed the maintenance of gains for three of the four participants. These findings suggest that AR-based interventions provide an effective and culturally responsive approach for enhancing specific nonverbal communication behaviors among children with autism in Middle Eastern contexts. Implications for clinical practice and directions for future research are discussed. Full article

(This article belongs to the Special Issue Advancements in Understanding and Supporting Intelligence and Diversity in Autism Spectrum Disorder)

12 pages, 669 KB

Open AccessArticle

Axillary Reverse Mapping Improves Quality of Life by Significantly Reducing Clinically Relevant Lymphedema After Axillary Lymph Node Dissection in Older Women with Breast Cancer

by Merve Tokocin, Turan Pehlivan and Atilla Celik

Curr. Oncol. 2026, 33(4), 212; https://doi.org/10.3390/curroncol33040212 - 10 Apr 2026

Abstract

Background: Breast cancer-related lymphedema (BCRL) is one of the most debilitating long-term morbidities after axillary lymph node dissection (ALND), severely impairing quality of life through reduced mobility, independence, and chronic burden, especially in older women. Axillary reverse mapping (ARM) aims to preserve upper extremity lymphatics while maintaining oncologic safety. Evidence in older adult populations with long-term follow-up remains limited. Methods: This retrospective cohort study included 138 female patients (median age 72.5 years) undergoing ALND for invasive breast cancer between January 2018 and January 2024. Patients were divided into ARM (n = 72) and non-ARM (n = 66) groups. BCRL was graded 0–3 according to adapted International Society of Lymphology (ISL) criteria (2013 consensus document). Assessments were performed preoperatively and at 3, 6, 12, 24, 36, 48, and 60 months using blinded circumference measurements and bioimpedance spectroscopy. Results: Baseline characteristics were comparable. Mean follow-up was 46.5 ± 8.8 months. Clinically relevant BCRL (Grades 2–3) was dramatically lower in the ARM group (18.1% vs. 60.6%, p < 0.0001), while subclinical changes (Grade 1) were similar (31.9% vs. 27.3%, p = 0.55). Kaplan–Meier analysis showed significantly better clinically relevant lymphedema-free survival with ARM (log-rank p = 0.00019), with curve separation after 30–40 months—indicating a sustained long-term benefit for quality of life in this frail population. Recurrence rates were comparable (8.3% vs. 10.6%, p = 0.776). Multivariable Cox regression confirmed ARM as an independent protective factor (adjusted HR 0.22, 95% CI 0.11–0.44, p < 0.0001). Conclusions: In older women with breast cancer, ARM significantly reduces clinically relevant lymphedema—a major determinant of long-term quality of life—without compromising oncologic safety. These findings support the routine consideration of ARM during ALND to preserve upper-extremity function, mobility, and independence in this vulnerable population, thereby balancing aggressive oncologic treatment with enhanced long-term quality of life and reduced treatment-related morbidity. Full article

(This article belongs to the Special Issue Quality of Life in Surgical Oncology Patients)

20 pages, 2251 KB

Open AccessArticle

Applied Biodiversity Metrics; Concepts to Choose Them Well

by Marie-Ève Roy, Sylvain Delagrange and Yann Surget-Groba

Diversity 2026, 18(4), 222; https://doi.org/10.3390/d18040222 - 10 Apr 2026

Abstract

The evaluation of biodiversity is an essential tool for conservation, management of natural resources, and assessment of ecosystem functioning. Choosing an appropriate and understandable diversity metric is critical to ultimately make better decisions and apply more sustainable resource management. However, biodiversity metrics are numerous, and care must be taken when using them. So, should one consider all these metrics to obtain the right information? If not, how should one choose? This paper aims to demonstrate the importance of understanding and selecting the appropriate diversity metrics to reach accurate conclusions. We simulated theoretical plant communities for which calculations of different biodiversity metrics were carried out to understand why and how to use them. We explored Richness, Evenness and Disparity components of biodiversity using both scales of diversity partitioning (i.e., alpha and beta diversity). In doing so, a decision tree is proposed to select diversity metrics according to user objectives. We also suggest an add-in term if alpha metrics are calculated with subsamples to better reflect biodiversity. Finally, we recommend that when dealing with ecosystem functioning or conservation concerns, species-dependent metrics should be used, as they reflect Disparity. However, there is a critical need to increase knowledge and data availability on species traits or phylogeny to be able to better analyze Disparity. Full article

(This article belongs to the Special Issue Plant Diversity Discovery and Resource Utilization)

39 pages, 5852 KB

Open AccessArticle

SAPIENT: A Multi-Agent Framework for Corporate Reputation Intelligence Through Sentinel Monitoring and LLM-Based Synthetic Population Simulation

by Alper Ozpinar and Saha Baygul Ozpinar

Systems 2026, 14(4), 425; https://doi.org/10.3390/systems14040425 - 10 Apr 2026

Abstract

Corporate reputation teams rely on media monitoring and qualitative research, both limited in speed and coverage when digital narratives form rapidly. This paper proposes SAPIENT (Sentinel-Augmented Population Intelligence for Emerging Narrative Tracking), a multi-agent system that links a sentinel layer over public text streams with a simulation layer that runs moderated, repeatable in silico focus-group sessions. The sentinel layer ingests social media, news, and forum text to produce a compact signal state (topics, sentiment, anomaly scores, risk labels), which conditions the simulation layer through an orchestrator. Persona agents and a moderator follow an Agentic Focus Group (AFG) protocol with repeated runs, variance reporting, and human review gates. We describe four sustainability communication scenarios: greenwashing backlash prediction, greenhushing risk assessment, campaign pre-testing, and crisis communication simulation. Nine experiments span 280 AFG runs across 20 conditions, three LLM backends (Claude Sonnet 4, GPT-4o, and Gemini 2.5 Flash), and a preregistered pilot human validation study with 54 participants. Signal conditioning improved simulation specificity (

p = 0.012

). Cross-lingual sessions revealed a sentiment asymmetry between English and Turkish (

p = 0.001

) with preserved persona rank ordering (

r = 0.81

p = 0.015

). Cross-model comparison showed consistent persona differentiation across all three backends (Pearson

r > 0.92

p < 0.002

for all pairs). Sentiment was robust to prompt paraphrasing (

p = 0.061

, n.s.), though credibility was sensitive to prompt wording (

p < 0.001

). All significant results from Experiments 1–8 survived Benjamini–Hochberg correction. A preregistered pilot with 54 human participants on Prolific replicated the predicted credibility ranking across framing variants (

p = 0.004

) but not the sentiment ranking, identifying a specific calibration target for future work. Full article

(This article belongs to the Section Artificial Intelligence and Digital Systems Engineering)

19 pages, 264 KB

Open AccessArticle

Short-Stay Sedentarism: The Local Battle over Migrant Workers’ Housing in The Netherlands

by Tesseltje de Lange and Masja van Meeteren

Soc. Sci. 2026, 15(4), 245; https://doi.org/10.3390/socsci15040245 - 10 Apr 2026

Abstract

This article investigates the housing precarity of EU migrant workers in the Dutch–German border region, focusing on the Venlo Greenport area. Drawing on documentary analysis, 28 interviews, field observations, and stakeholder engagement, it explores how local governance, market dynamics, and framing practices shape housing outcomes. While EU law guarantees free movement, housing remains excluded from the EU rights frameworks, leaving workers dependent on employer-linked or agency-controlled short-stay facilities. These arrangements—often overcrowded, surveilled, and formally temporary—become long-term solutions, producing what we term short-stay sedentarism: prolonged residence in housing designed to deny permanence. The study conceptualises the local “battleground” where municipalities, employers, housing providers, NGOs, and residents negotiate competing interests. Seven interpretive frames—nuisance/disorder, cowboys, human rights, NIMBY, shadow power, integration, and unwanted accumulation—structure these debates, legitimising certain strategies while obscuring structural deficiencies. Findings reveal that certification and enforcement, while intended to improve standards, often entrench precariousness by sustaining the short-stay model. Emerging integration-oriented policies signal a shift but remain fragile amid economic imperatives and spatial constraints. The paper argues that addressing housing precarity requires structural reforms: expanding access to regular housing, reducing employer dependency, and recognising migrant workers as long-term residents rather than temporary labour inputs. Full article

(This article belongs to the Special Issue Migration and Housing)

22 pages, 5260 KB

Open AccessArticle

Effect of Particle Size Distribution and Dosage of Clam Shell-Derived Filler on the Mechanical Performance of Cementitious Mortars

by Benjamín Antonio García Montecinos, Meylí Valin Fernández, Luis Enrique Merino Quilodrán, Iván Ignacio Muñoz Soto and José Luis Valin Rivera

Appl. Sci. 2026, 16(8), 3736; https://doi.org/10.3390/app16083736 - 10 Apr 2026

Abstract

From an environmental perspective, the use of clam shells contributes positively to marine waste management and promotes more sustainable construction practices. This study aims to analyze the influence of clam shell-derived filler on the mechanical properties of cementitious mortars, evaluating its effect as a function of dosage and particle fineness, in order to determine its potential as a sustainable additive in construction applications. The shells were ground for 0.5, 1.0, and 1.5 h and incorporated at percentages ranging from 0.5% to 5.0% by mass of cement. Slump (reduced Abram’s cone) was performed in the fresh state for each specimen mixture, while flexural strength, and compressive strength tests were performed at 7, 14, and 28 days of curing. Microstructural characterization was also performed using scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDX) analysis. In addition, particle size distribution parameters were determined to quantify the effect of grinding time on particle refinement and its relationship with mechanical performance. A multifactor ANOVA was conducted to evaluate the statistical significance of grinding time and filler dosage on compressive strength. The results showed that the combination of 0.5 h of grinding and 1.0% filler provided the best mechanical performance for both flexural and compressive strength, with values of 7.27 MPa and 26.16 MPa, respectively. Dosages higher than 2.0% tended to decrease strength, which is associated with saturation of non-cementing particles. EDX analysis showed adequate calcium distribution without generating chemical segregation. The results showed that the combination of 0.5 h of grinding and 1.0% filler provided the best mechanical performance for both flexural and compressive strength, with values of 7.27 MPa and 26.16 MPa, respectively. Dosages higher than 2.0% tended to decrease strength, which is associated with saturation effects and increased specific surface area. The statistical analysis confirmed that both grinding time and filler dosage significantly influence compressive strength, highlighting the importance of optimizing particle size distribution and filler content to achieve improved mechanical performance. Full article

15 pages, 2314 KB

Open AccessArticle

Effects of Reduced N Application on Soil Ammonia Volatilization in Maize–Soybean Intercropping and Monocropping Systems

by Shenqiang Lv, Yueming Chen, Xilin Guan, Yixuan Feng, Pengchuang Jia, Shenzhong Tian and Xinhao Gao

Sustainability 2026, 18(8), 3784; https://doi.org/10.3390/su18083784 - 10 Apr 2026

Abstract

A systematic elucidation of soil ammonia (NH₃) volatilization (SAV) and the underlying drivers is imperative for evaluating NH₃ pollution mitigation strategies and advancing sustainable agricultural practices. Currently, no scientific consensus has been established on the effects of maize–soybean intercropping on SAV across varying nitrogen (N) application rates. A consecutive field experiment was conducted over a 2-year period from 2024 to 2025 with a split-plot design. The experiment comprised three cropping systems (maize monocropping (MM), soybean monocropping (MS), and maize–soybean intercropping (IMS)) and three N application rates (no N application (NN), 20% reduced N application (20%RN), and conventional N application (ConN)). The results demonstrated that N application markedly increased SAV. Accumulative SAV was 4.94–6.01 kg ha⁻¹ under NN treatment, whereas it was 8.21–27.89 kg ha⁻¹ under ConN treatment, 7.25–21.52 kg ha⁻¹ under 20%RN treatment. Under ConN treatment, the accumulative SAV in IMS was 21.34 kg·ha⁻¹ and 27.89 kg·ha⁻¹ in 2024 and 2025, respectively, which were significantly higher than those in MM by 16.80% and 13.33%. Under 20% RN treatment, the accumulative SAV in IMS was 15.46 kg·ha⁻¹ and 19.24 kg·ha⁻¹ in 2024 and 2025, respectively, which were lower than those in MM by 3.07% and 10.59%. SAV was positively correlated with soil ammonium N concentration. Moreover, within an appropriate range, SAV increased in response to rising soil water content and temperature. Collectively, maize–soybean intercropping integrated with a 20% nitrogen reduction mitigated environmental risks associated with reactive nitrogen losses. This system constitutes a stable yield, resource-efficient, and ecologically sustainable cropping practice. Full article

(This article belongs to the Special Issue Diversified Cropping for Sustainable Soil Fertility and Efficient Crop Production)

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27 pages, 3213 KB

Open AccessSystematic Review

Pedagogical Use of Responsible Generative AI in Higher Education; Opportunities and Challenges: A Systematic Literature Review

by Md Zainal Abedin, Ahmad Hayajneh and Bijan Raahemi

AI Educ. 2026, 2(2), 11; https://doi.org/10.3390/aieduc2020011 - 10 Apr 2026

Abstract

Generative Artificial Intelligence (GenAI) is transforming higher education in terms of pedagogy, student involvement, and academic management. This systematic literature review examines 30 peer-reviewed articles published from 2019 to 2025, adhering to PRISMA 2020 and Kitchenham’s methodologies. Descriptive and thematic analyses highlight five opportunities: (a) tailored and adaptive education; (b) deliberate fostering of critical thinking; (c) enhanced accessibility for varied learners; (d) teaching innovation via multimodal content development and feedback; and (e) collaborative methods that regard AI as a co-teacher. Four ongoing challenge categories also surface: (a) risks to academic integrity; (b) excessive dependence on GenAI that may hinder learner independence; (c) inconsistent faculty preparedness and change-management abilities; and (d) differences in infrastructure and policy both regionally and globally. Intersecting ethical issues, such as data privacy, algorithmic bias, transparency, and accountability, highlight the necessity for governance that aligns with institutional risk and reflects societal values. Analyzing the recent literature, this systematic review offers four contributions: (a) a recommendation model for responsible GenAI implementation in higher education institutions; (b) a framework for sustainable integration of GenAI; (c) a highlight of the future research recommendations; and (d) an integrated policy and pedagogical recommendations roadmap. These models emphasize the integration of AI literacy, ethical considerations, and critical thinking goals into educational programs. The review advocates for a strategic, stakeholder-focused approach to implementation that enhances rather than replaces human instruction, thus connecting GenAI’s educational potential with ethical, context-aware avenues for institutional transformation. Full article

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