Abstract: The study was developed in the context of the search for bioactive compounds of interest present in medicinal plants, among them phenolic compounds, recognized for their func-tional relevance. To this end, the extraction process was optimized using soursop leaves as a model, combining different particle sizes (< 2 mm, 2–6.3 mm, and 6.3–9.5 mm), mac-eration times (12, 24, and 48 h), and ethanol:water ratios (25:75, 50:50, and 75:25), which generated 27 extracts. The optimal process corresponded to an ethanol:water ratio of 50:50, fine particle size (< 2 mm), and 24 h of maceration, reaching 584.64 mg·L⁻¹ of gallic acid equivalents (GAE). Additionally, other plant species were evaluated: horsetail, kiswara, matico, muña, and thyme. Antioxidant capacity was determined using the DPPH method through IC₅₀ val-ues, where soursop, kiswara, and muña recorded the lowest values (0.52, 0.52, and 0.61 mg·L⁻¹), even lower than ascorbic acid (19.10 mg·L⁻¹). Thyme and horsetail showed inter-mediate activity, while matico presented the lowest response. The results indicate that these species have high potential as sources of functional bioac-tive compounds, highlighting the importance of medicinal plants for the development of natural products with antioxidant properties.

Abstract: Kidney transplantation for patients affected by end stage renal failure is considered the best therapeutic option, but this possibility is limited by deceased donor shortage. Living donor kidney transplantation (LDKD) is a valuable option, frequently limited by immunological incompatibility between donor and recipient. This review will consider the to date possibility of performing living donor kidney donation in the case of AB0 blood group incompatibility and the progresses that have been made in this field. Kidney paired donation is one possibility. Such technique offers the best possibility if the number of pairs available is wide. This is possible by performing national and also international registries. The technique more diffuse is the desensitization of the recipients. Desensitization may be obtained by several ways that are extensively treated in this review. In the recent period some published studies document the possibility to enzymatically convert A or B group from the cells of the donor to 0 group. This possibility is only on the beginning, but may represent the future eventually associated to a mild desensitization.

Abstract: This study addresses the role of cane density as a key agronomic factor in floricane raspberry production and explores its relationship with canopy structure assessed using UAV-based multispectral imaging. Although cane density is widely recognized as critical for yield formation, its interaction with remotely sensed vegetation indices remains insufficiently understood. The experiment was conducted in an open-field plantation in eastern Poland using four floricane raspberry cultivars and four cane-density treatments (two to five canes per plant). Yield components were measured throughout the harvest period, and multispectral data were acquired using an unmanned aerial vehicle to derive vegetation indices and assess spatial and temporal canopy variability. Yield per plant and fruit number increased consistently with cane density, while yield per cane decreased, indicating a trade-off between individual cane productivity and total yield. Fruit size remained relatively stable and was primarily influenced by harvest timing. Vegetation indices followed a common seasonal pattern, with moderate and variable responses to cane density. The integration of yield measurements with multispectral data revealed that cane density influences not only productivity but also canopy structure and its spatial uniformity. These findings highlight the potential of combining agronomic practices with remote sensing approaches to support data-driven optimization of raspberry production systems.

Abstract: Population aging is reshaping oral health systems in ways that are difficult to ignore. Such challenges become particularly complex in contexts suffering from strong social inequities, limited access to dental services and/or a growing dependency on informal healthcare. In spite of being largely preventable, oral and dental disease remain highly prevalent among older people. This results particularly worrisome since it is known that oral disease in the elderly has enormous consequences, well beyond the oral cavity and may affect nutrition, frailty and quality of life. It is in this context that mobile health technologies (mHealth) have emerged as potentially valuable tools to support, not only the promotion of oral health, but also its monitoring, education and the training of caregivers. However, it is not yet characterized, to what extent do existing mobile applications properly respond to the specific needs of the older population and their caregivers. This narrative review aims to critically examine the current landscape of mobile health applications designed to improve oral health in older people; with a strong focus on tools oriented to self-care, clinical monitoring, support to caregivers and training of primary attention personnel. We decided to follow the SANRA methodological framework, by summarizing the evidence published in the literature between the years 2000 and 2024 and further analyzing the application’s functionalities, their target users, their usability and the strength of the evidence supporting their development and implementation. Our findings reveal a substantial heterogeneity regarding scope, design and validation, as well as a persisting lack of culturally adapted solutions, focus on geriatrics and oriented towards caregivers. In spite of the potential, most applications show a limited clinical validation, a weak integration into health systems and an insufficient consideration of the cognitive, functional and social determinants of aging. Interpreted within global and regional policy frameworks, including the WHO Healthy Aging 2021–30 and the Global strategy and action plan on oral health 2023–30, these results highlight critical gaps and future directions for the development of equitable, evidence-based mHealth interventions in geriatric oral care.

Abstract: The social dimension of sustainability is increasingly recognized as essential to the aviation sector, yet systematic assessment of social impacts across aircraft systems and their associated design and production processes remains limited. This study applies Social Life Cycle Assessment (SLCA) principles, guided by the UNEP/SETAC guidelines and the ISO 14075:2024 standard, to perform a country-based screening that identifies, quantifies, and analyzes hotspot impacts associated with materials production and manufacturing in the aviation sector. A tailored SLCA framework is developed to reflect the specific characteristics of the aviation sector and to identify relevant stakeholder groups, including workers, local communities, consumers, value chain actors, and society. Aviation-specific social indicators are defined in line with industry needs and regulatory expectations, enabling socially informed decision-making during early design stages. The methodology is demonstrated through a comparative assessment of two major commercial aircraft, examining social impacts across global supply chains, identifying social hotspots and country-specific risk drivers, and evaluating targeted improvement measures. In addition, alternative component production locations are assessed to explore supply-chain configurations with lower social risks. The results provide actionable insights for policymakers and industry stakeholders and support holistic sustainability assessments by explicitly integrating the social dimension into sustainable aircraft design.

Abstract: Background: Vaccination coverage has been declining in many countries during the COVID-19 pandemic. Bosnia and Herzegovina is no exception. We evaluated a tele-phone-based outreach intervention implemented in primary healthcare facilities (PHCs) in the Federation of Bosnia and Herzegovina. The intervention aimed to target missed routine vaccinations among children aged 0-7 years. Method: By using a programmatic, non-randomized pre-post design, healthcare teams reviewed registries to identify un-der-vaccinated children. Parents were contacted by phone to facilitate catch-up visits. Re-sults: Among age-eligible children, vaccination coverage increased from 66.5% to 74.2% for measles-mumps-rubella (MMR) dose 1, from 43.4% to 51.7% for MMR dose 2, and from 50.4% to 55.9% for the fourth dose of diphtheria–tetanus–acellular pertussis–inactivated poliovirus–Haemophilus influenzae type b vaccine (DTaP IPV Hib). Mixed-effects models adjusting for age, sex, and clustering by facility and canton showed higher odds of vac-cination post-intervention for MMR dose 1 (adjusted odds ratio [aOR] 1.65), MMR dose 2 (aOR 1.61), and DtaP-IPV-Hib dose 4 (aOR 1.39; all p< 0.001). Conclusions: These results show that registry-based, proactive outreach can yield significant improvements in rou-tine childhood vaccination coverage in real-world settings and may be a scalable ap-proach for decentralized health systems recovering from pandemic disruptions.

Abstract: This study integrates Vedic philology, ritual history, and philosophical hermeneutics in a multi-layered analysis of Agnihotra. Particularly in the Yajurveda, where its exterior performance is linked to varṇa and āśrama, it elucidates Agnihotra's technical structure and śākhā-specific methods by drawing on Śruti sources. The conceptual extension of ritual eligibility when dharma declines is explained by an analysis of Purāṇic and Smṛti depictions of Yuga decline, however retaining its normative foundation in Śruti remains authoritative.Then, passages from the Upaniṣadic and Bhagavadgītā are considered to demonstrate how Agnihotra is internalized as niṣkāma-karma and jñāna-yajña, creating a continuum between philosophical insight and ritual practice leading to mokṣa. Lastly, the Mādhyandina and Kāṇva recensions of the Śukla Yajurveda are compared to see whether they are appropriate for Agnihotra during the Kali Yuga. The latter maintains earlier, more intricate ritual levels, while the former provides systematic clarity. This study concludes that the Kāṇva recension offers greater scope for robust analysis śākhā for Agnihotra practice in Kali Yuga.

Abstract: Bell tests and Bell's theorem used to interpret the test results opened the door to quantum information processing, such as quantum computation and quantum communication. Based on the erroneous interpretation of the test results, quantum information processing contradicts a well-established mathematical fact in point-set topology. In this study, the feasibility of quantum computation and quantum communication is investigated. The findings are as follows. (a) Experimentally confirmed statistical predictions of quantum mechanics are not evidence of experimentally realized quantum information processing systems. (b) Physical carriers of quantum information coded by quantum bits (qubits) do not exist in the real world. (c) Einstein's ensemble interpretation of wave-function not only will eliminate inexplicable weirdness in quantum physics but also can help us see clearly none of quantum objects in the real world carries quantum information. The findings lead to an inevitable conclusion: Without carriers representing quantum information, physical implementations of quantum information processing systems are merely an unrealizable myth. This conclusion contradicts relevant results in the literature. While all relevant results published nowadays support quantum information processing, the present work shows that quantum information processing systems rely on the assumption that qubits exist physically, but the assumption does not hold. Examples are given for illustrating the results reported here. For readers who are unfamiliar with point-set topology, the examples may alleviate difficulty in understanding the results.

Abstract: The global market for homeland security satellite imagery is undergoing a paradigm shift, transitioning from raw data acquisition to the delivery of actionable intelligence. While the volume of Earth Observation (EO) data is projected to grow exponentially through 2034—with the data services segment alone anticipating a compound annual growth rate (CAGR) of up to 18.4%—the "data-to-decision" gap remains a primary operational hurdle. This explosion of available information has triggered a "data deluge" that frequently overwhelms traditional analytical frameworks. This article argues that the future of sovereign Geospatial Intelligence (GEOINT) depends on the maturation of specialised, multi-layered Satellite Data Pipelines, combined with deep learning and multi-sensor fusion techniques. By fusing orbital streams with SATCOM-enabled Unmanned Aerial Vehicles (UAVs), and by embedding safety-aware AI governance into the decision layer, nations can achieve decision superiority in an increasingly contested geopolitical landscape.

Abstract: Improving regional environmental performance is crucial for strengthening environmental governance. Polycentric governance theory, which emphasizes the interaction of multiple actors—including market, government, and society—within rule-based constraints, provides an essential framework for understanding the mechanisms that shape environmental performance in complex institutional contexts. This paper, set within the context of China’s environmental governance, applies polycentric governance theory to develop a three-dimensional analytical framework of "economic pressure–government attention–social supervision." Using panel data from 30 Chinese provinces between 2015 and 2023, the study employs dynamic Qualitative Comparative Analysis (dynamic QCA) to examine the asymmetric impacts of various governance factors on regional environmental performance and their dynamic evolutionary mechanisms. The study finds that: (1) no single factor is necessary for either high or low environmental performance; (2) six configurational paths for achieving high environmental performance are identified, categorized into three types: "industry structure-driven," "multi-dimensional collaboration-driven," and "economic output–social media opinion dual-driven"; (3) these paths remain relatively stable over time but exhibit periodic fluctuations, with a clear evolution point around 2021; (4) these pathways show significant spatial differentiation. This study uncovers the multi-dimensional driving mechanisms behind improvements in regional environmental performance, offering both theoretical insights and practical guidance for local governments to develop differentiated environmental governance strategies tailored to regional contexts.

Abstract: This study investigates the impact of ESG performance on the tax avoidance behavior of 118 non-financial listed firms in Vietnam from 2020 to 2024. Employing a Random Effects Model (REM), empirical results reveal that sustainability reporting quality-measured by individual E, S, and G pillars and a composite ESG index-is negatively associated with corporate tax avoidance, proxied by the Effective Tax Rate (ETR). Among these, the social (S) pillar exerts the most pronounced effect; however, individual component impacts remain less substantial than the comprehensive ESG index. Furthermore, findings indicate that the mitigating effect of ESG on tax avoidance significantly weakens when firms face financial constraints or operate under state ownership. Notably, applying machine learning techniques demonstrates that a CatBoost algorithm integrating the ESG variable achieves 52.92% predictive accuracy for tax avoidance, outperforming an XGBoost model lacking ESG inclusion (38.14%). Additionally, feature importance analysis of financial and non-financial variables highlights ROA as the dominant financial predictor (35.5%), while ESG contributes a notable 10.35% to the model's explanatory power. Ultimately, these findings provide vital insights for policymakers and investors regarding the interplay between sustainability commitments, ownership structures, and corporate tax strategies.

Abstract: Accurate per-wagon occupancy accounting at freight stations — knowing which wagon entered or exited which track and when — is a prerequisite for automated shunting management, yet existing technologies — axle counters, RFID, computer vision, and LPWAN IoT — each provide only a subset of the required information and depend on dedicated infrastructure or favourable conditions. This paper investigates whether two fixed BLE gateways, combined with Eddystone-TLM beacon nodes proposed for mounting on freight wagon bodies, can classify passage direction from RSSI signals without training data, site-specific calibration, or track modification. The enabling mechanism is wagon-body attenuation: as a wagon passes between the receivers, its metallic body creates a temporal asymmetry in the RSSI envelopes that encodes travel direction. We present a five-stage online pipeline at O (1) memory per packet: a two-sided CUSUM detector with adaptive per-event baseline estimation segments the RSSI stream; a three-stage validation filter rejects partial passes, lateral paths, and near-gateway reversals; and direction is classified by the normalized Temporal Centroid shift — a speed-invariant feature requiring no training data — with a cascade fallback for ambiguous short windows. Combined with the beacon MAC address as a wagon identifier, the system generates structured occupancy events directly consumable by station management systems. Validated on 151 labelled events across eight scenario categories at Urtaul freight station and the TSTU test polygon, the pipeline achieves 96.7% accuracy (95% Wilson CI: [92.5%, 98.6%]) zero wrong-direction predictions across all 84 directional events (exact Clopper-Pearson 95% CI for the wrong-direction rate: [0%, 3.5%])", a Random Forest baseline on the same features confirms supervised learning adds no measurable benefit over the training-free approach within this feature space.

Abstract: Background/Objectives: The prognostic and predictive role of BMI in patients with HR+/HER2– MBC remains controversial, particularly in the era of CDK4/6 inhibitors. This study aimed to evaluate the association between baseline BMI and clinical outcomes in patients treated with palbociclib in a real-world setting. Methods: We conducted a multicenter retrospective observational cohort study including 326 patients with HR+/HER2− MBC treated with palbociclib in combination with endocrine therapy across six oncology centers in Romania. Only patients who received palbociclib for at least three months were included. Patients were stratified according to BMI into <25 kg/m² and ≥25 kg/m² groups. PFS and OS were the primary endpoints, while ORR and CBR were secondary endpoints. Results: Among the 326 patients, 66.6% were classified as overweight or obese (BMI ≥25 kg/m²). Median PFS was 23.66 months in the BMI <25 group and 26.78 months in the BMI ≥25 group, with no statistically significant difference (HR 0.86, 95% CI 0.62–1.20; p = 0.373). Median OS was not reached in the BMI <25 group and was 43.73 months in the BMI ≥25 group, also without significant difference (HR 0.82, 95% CI 0.52–1.30; p = 0.397). ORR (29.07% vs. 28.89%) and CBR (90.7% vs. 88.3%) were comparable between BMI groups. In multivariate analysis, liver metastases and brain metastases were independently associated with worse outcomes, whereas BMI was not an independent prognostic factor. Conclusions: In this selected real-world cohort of patients with HR+/HER2− MBC who tolerated at least three months of palbociclib, baseline BMI was not associated with treatment response, PFS, or OS. While clinically informative, these results should not be interpreted as definitive evidence that body weight has no influence on palbociclib efficacy, given the methodological constraints of the analysis. BMI alone may be insufficient to capture the complex interplay between body composition and treatment outcomes, highlighting the need for more refined biomarkers of body composition in this setting.

Abstract: As cities worldwide face challenges of rapid urbanization and declining public transit ridership, traditional fixed-route systems often fail to meet evolving mobility needs. Urban planning issues, such as suburban sprawl and fragmented land use, exacerbate these limitations, leading to underutilized services, higher operational costs, and accessibility gaps, particularly for underserved communities. Demand-Responsive Transit (DRT) systems have emerged as an effective solution, offering flexible, on-demand services that dynamically adjust routes based on user demand. This review synthesizes insights from 65 studies, including 20 real-world implementations, examining DRT's potential to enhance accessibility, cost efficiency, and environmental sustainability. Key findings demonstrate that DRT systems reduce operational costs by 25-35% while increasing ridership up to 300%. Integration of AI-driven routing algorithms improves service reliability by 90-98% and reduces travel times by 35-50%. Multiple booking interfaces increase adoption by 40-60%, while multimodal integration expands service coverage by 100-150%. However, significant barriers persist, with 58% of DRT system models requiring subsidies and 51% facing equity challenges. The study proposes hybrid funding models, integrated multimodal platforms, and inclusive design approaches to address these challenges. By aligning with urban design principles and leveraging advanced technologies, DRT systems can enhance urban resilience while promoting sustainable development.

Abstract: The educational pathway for expertise in Intraoperative Neurophysiological Monitoring (IONM) is complex and lengthy, requiring a solid foundation in neuroscience, neurophysiology, and neuroanatomy. It also demands direct familiarity with a broad range of neurosurgical scenarios, including supratentorial, infratentorial, and spinal procedures, gained through exposure to at least ten distinct surgical approaches. Intraoperative neurophysiology must be tailored to each patient's preoperative assessments. It relies on a variety of methods to collect, analyse, and report neurophysiological signals that are relevant to the surgical procedure. Despite its importance, there remains a substantial shortage of training tools designed to support realistic practice and skill development. To address this gap, we developed a comprehensive framework (ION-Sim) that integrates all laboratory testing modalities and adapts them to the operating room environment. ION_sim supports the simulation and analysis of spontaneous EEG and EMG activity, a wide range of evoked potentials, and intraoperative stimulus–response testing protocols. The framework provides a unified environment for practising, testing, and validating the core neurophysiological procedures employed during neurosurgical interventions. In addition, it incorporates a robust data-management architecture, maintaining a database with system setups, user profiles, educational performance metrics, and automatically generating reports. This structure enables the longitudinal tracking of objective skill acquisition and facilitates standardised assessments of trainee progress. ION_Sim is distributed both as a ready-to-use application, suitable for direct integration into teaching and training programmes, and as a modular scientific library. Through its dedicated APIs, users can design customised configurations, create novel simulation scenarios, and extend the platform to support additional research or educational objectives. It is available upon request for educational purposes, open-source and released under the GNU General Public License, ensuring transparency, reproducibility, and long-term accessibility for the scientific and clinical communities.

Abstract: One of the most frequent reasons why pipeline structures fail is corrosion. Corrosion may occur on the inside, exterior, or even both surfaces of the pipeline, and it is particularly challenging for insulated pipelines. While insulation helps prevent corrosion damage, there is still a potential for corrosion under insulation (CUI). The current inspection methods require removing the insulation layer, which is time-consuming and expensive. In this research, the Pulsed Eddy Current (PEC) method was applied to detect CUI. Several factors affect PEC signals, including sample material thickness, insulation material, insulation material thickness, and coil parameters. Understanding these characteristics is crucial for designing a suitable PEC system. Comparative analysis using eddy current reveals that thicker insulation materials generally result in higher initial signal strength, with the highest values observed for 5mm insulation across all materials. However, as the carbon steel thickness increases, the signal strength consistently decreases for all insulation types. Wood insulation maintains the highest signal strength across all thicknesses, followed by acrylic, which shows higher signal strength than rubber at comparable thicknesses. Overall, increasing the thickness of the carbon steel substrate consistently reduces the signal strength. Based on error analyses, the thickness of samples and insulation should be considered carefully as they impact accuracy.

Abstract: Background: The expanding interest of chatbots within the medical domain underscores the imperative for a comprehensive understanding of their capabilities and limitations, particularly in the context of anatomical education. Chatbots possess the potential to comprehend intricate anatomical concepts, deliver both advanced and contextually relevant information, and could serve as a valuable resource for medical students and educators. This study aimed to evaluate the proficiency and constraints of chatbots in the domain of neuroanatomy. Methods: We developed 30 questions and administered them to ChatGPT-4, Google Gemini, Microsoft Copilot, and Perplexity.ai, in their open versions. Questions were collaboratively constructed by the research team, selected through a semi-randomized process within the domain of neuroanatomy. Chatbots' responses were evaluated in a blinded manner for validity and appropriateness, utilizing a 5-point Likert scale. Results: The optimal performance was exhibited by ChatGPT-4 and Perplexity.ai, which achieved scores of 4.6 ± 0.5 and 4.5 ± 0.5, respectively. Microsoft Copilot (4.4 ± 0.5) and Google Gemini (4.1 ± 1.0) followed. The least successful performance was observed in the task of generating a neuroanatomical structure: only Microsoft Copilot attempted to fulfill the request, albeit with a dramatically flawed outcome. Conversely, Google Gemini and Perplexity.ai provided web links to anatomical illustrations. Conclusions: Despite technological advancements, AI models have not yet reached a level of sophistication sufficient to entirely supplant the role of educators or facilitators in a neuroanatomy course; however, they can serve as valuable adjunct tools for medical educators and students when utilized with careful consideration.

Abstract: Building energy conservation and emission reduction have become global priorities. Conventional sports facilities, owing to their substantial spatial dimensions, predominantly depend on mechanical HVAC systems, leading to elevated energy consumption and operational expenses. Consequently, the judicious application of natural ventilation is crucial for attaining a sustainable transformation of these structures. This study focuses on the National Fitness Center in Shenyang, a representative city located in a chilly climate. Utilizing the Ladybug Tools platform alongside computational fluid dynamics (CFD) numerical simulation techniques, multi-scenario simulations are performed for omnidirectional wind conditions and two varieties of window openings. An analysis is conducted on the indoor airflow distribution and wind speed characteristics across several functional regions of the large-space gymnasium under different wind directions. The study developed methodologies for identifying optimal ventilation durations and target wind velocities annually, quantified the influence of incident wind angles on ventilation efficacy, confirmed that appropriate building orientation can enhance ventilation efficiency by roughly 45%, and clarified the mechanisms and selection criteria for window types affecting indoor airflow patterns. The research findings offer a solid theoretical foundation and practical technical assistance for the ventilation design of national fitness centers to accommodate climatic conditions.

Abstract: This study reports on the design and characterisation of thermo-reversible gelatine hydrogels incorporating beeswax-structured oil-in-water emulsions as novel 3D-printable food inks. Beeswax oil-in-gel emulsions (BOGEs) were prepared by varying the sunflower oil to beeswax (SFO:BW) mass fraction (1:0, 3:1, 1:1, 1:3 and 0:1) at a fixed lipid loading (10% wt.) within a 4% wt. gelatine matrix. The BOGEs were evaluated in terms of microstructure, thermophysical properties, small and large amplitude oscillatory shear rheological behaviour, instrumental hardness, and 3D printability. CLSM images revealed a progressive transition from an emulsion-filled to a bigel-like microstructure with increasing beeswax content, driven by partial crystallisation and percolation of lipid droplets. Differential scanning calorimetry confirmed that beeswax incorporation progressively suppressed the gelatine hydrogel fusion enthalpy, indicating that wax crystal lattices govern the supramolecular organisation of the gelatine network. SAOS tests showed that BW enhanced the elastic modulus, with a critical solid fat content threshold (Φc = 0.294) above which lipid droplet percolation provided an additional structural reinforcement. LAOS characterisation revealed a type III nonlinear viscoelastic response, with delayed yielding and enhanced structural integrity at higher BW fractions. Instrumental hardness measurements confirmed the active filler role of BW at mass fractions ≥0.5. 3D printing assessment demonstrated that intermediate SFO:BW ratios (3:1 and 1:1) afforded the highest printing fidelity, combining favourable extrusion flow with adequate post-deposition shape retention. Overall, the results demonstrate that beeswax-structured emulsions can effectively tailor the structure–function properties of gelatine hydrogels, enabling the development of clean-label, multiphase food inks suitable for extrusion-based 3D printing applications.

Abstract: The crop system of soybean (summer)—maize or other cereals (fall/winter) succession has been adopted widely in the Neotropics. It inadvertently provides food in sequence to stink bugs (Hemiptera: Heteroptera: Pentatomidae), forming green bridges, which favor their outbreaks. Attempts to control these outbreaks, usually consists of chemical control on isolated crop scenarios. Analyzing the literature available, it is possible to conclude that stink bugs must be managed having a broader and more holistic perspective, taking the whole landscape into consideration, rather than the usual individualized perspective. Multidisciplinary recommendations should include insect pests plus weed and disease controls, crop harvest, sowed cultivars or varieties, and neighboring vegetation (cultivated or native) for effective stink bug management. In conclusion, during the first crop season, stink bugs should be controlled only in the reproductive stage of soybean (from R3 to R6 plant development stage), when population is equal or higher than ET (2 stink bugs.m⁻¹). Biologicals should be used instead of chemicals whenever possible. When ET is surpassed at R7 or R8, more tolerant maize varieties (fast growing) should be sowed in the second crop season with the adoption of seed treatment. Always, grain losses during harvest and the presence of weeds must be avoided at the end of soybean season. Additionally, chemical insecticides sprayings on maize might still be necessary if Diceraeuss spp. outbreaks equal or surpass three insects.m⁻¹ during maize early stages. This more precise and less impactful management of the agroecosystem will promote more sustainable and resilient management of these polyphagous pests.