Search Results (2,753)

The sustainable restoration of tropical dry forest (TDF)—one of the planet’s most threatened ecosystems—is constrained by low community integration and limited cultural relevance, particularly in Afro-descendant territories, where the erosion of oral tradition and forest degradation reinforce one another. This study proposes a quantitative framework to systematize the biocultural memory embedded in oral tradition as an input for socially grounded, sustainable TDF restoration. A corpus of 401 works from the Afro-descendant community of the Patía Valley (Cauca, Colombia) was coded in a multidimensional database of 10 categories, and three indices were computed: the Biocultural Density Index (IDBC), the Biocultural Vulnerability Index (IVB), and an adaptation of Winter’s flora framework (IVBw), in a total version and a version restricted to wild/native species. The framework identifies the works of greatest biocultural density (IDBC max. = 84) and separates cultivated/introduced species of high cultural value (Limón, Yuca, Caña de azúcar), ones relevant to food sovereignty, and from wild/native species (e.g., Caña brava, Cañafístula, Guayacán, Ceiba) that constitute the restorable core. Cultural practices are the hubs of the system, traditional medicine is the most at risk of loss, and food security concentrates 43% of the flora records. These replicable tools link Afro-descendant knowledge to measurable, monitorable restoration priorities, advancing sustainable land management, biodiversity conservation and cultural sustainability. Full article

Developers increasingly reuse third-party Low-Rank Adaptation (LoRA) adapters for code-generation models without visibility into how they were trained, creating a supply-chain risk: a maliciously trained adapter can behave normally on clean inputs while activating attacker-controlled functionality when a specific trigger is present. This study makes a mechanistic contribution to that risk: we show that trigger modality, not just contamination rate, determines a qualitatively different backdoor learning regime. We trained 61 poisoned variants of CodeGen-350M-mono on the CodeSearchNet dataset, injecting eight backdoor triggers spanning two categories: three semantic triggers based on natural-language code comments and five syntactic triggers based on structural code transformations derived from the CodePoisoner framework. Across attack success rate measurement, cross-trigger confusion analysis, mechanistic circuit tracing, layer-restoration defense evaluation, and semantic generalization testing, we find that semantic triggers produce associative binding: a 91% attack success rate, a Trigger Specificity Index (TSI) of 268×, distinct per-trigger circuits concentrated in attention layers, and a requirement to restore 10 parameter groups for removal. Syntactic triggers instead produce distributional confusion: a 31% attack success rate, a TSI of only 1.25× (1.45× once a shared-payload confound in the confusion-matrix design is corrected for), diffuse circuits spread across (Multi-Layer Perceptron) MLP layers, and collapse with just 5 restored parameter groups. Cross-payload testing on single-trigger models confirms this: structural triggers fire on triggers never seen during training at rates of 42 to 55%, showing that the model learns a general association between code abnormality and payload generation rather than a specific trigger–payload mapping. Both regimes preserve clean code-generation quality across all contamination rates, so a downloaded backdoored adapter is behaviorally indistinguishable from a clean one under standard benchmarks. These results argue against a one-size-fits-all approach to adapter auditing: detection and removal strategies calibrated to one trigger modality can fail outright against the other, and we outline the conditions under which each applies. Full article

Urban streets are critical public spaces that support residents’ daily psychological recovery, and their landscape quality is directly related to pedestrians’ physical and mental well-being. In the rapid urbanization process, numerous urban streets have exhibited problems such as excessive building density, cluttered visual interfaces, a lack of natural elements, and an absence of regional characteristics, leading to a continuous decline in the psychological restorative capacity of street spaces and failure to meet residents’ demands for a healthy urban environment. Existing research mostly employs qualitative assessment methods to evaluate walking experiences and psychological restoration levels of street environments, lacking high-precision, pixel-level quantification of street landscape elements and rarely incorporating regional cultural elements into the analytical framework of restorative environments. This study takes Foshan, a famous historical and cultural city in China, as the research object, and selects five typical streets in the main urban area, including comprehensive streets, living streets, landscape streets, commercial streets, and historical–cultural streets, to construct a technical route of “data collection–element quantification–model construction–effect analysis.” Leveraging the pre-trained Mask2Former semantic segmentation model and pedestrian-perspective street-view images (SVIs), combined with field research, the study quantifies 22 street landscape elements across five dimensions: environment, transportation, social interaction, facilities, and culture. Through PCA principal component analysis and K-means clustering, 20 typical photos were objectively sampled, and public psychological evaluations were conducted using the Perceived Restorativeness Scale (PRS). A stepwise multiple linear regression model was then employed to construct an exploratory explanatory model for street psychological restoration, identifying key influencing factors and their effect intensities. The results indicate the following: (1) Environmental and cultural elements are the core characteristics associated with pedestrians’ psychological restoration, whereas transportation, social, and facility elements are correlated only with certain restoration dimensions and show no significant association with the overall psychological restoration level. (2) Among the 22 element indicators, the Green View Index showed the strongest positive association with psychological restoration (β = 0.681, p < 0.001); historical memory markers and the Blue View Index also exhibited significant positive associations. (3) By integrating the elements associated with pedestrians’ psychological restoration and their association strengths, an exploratory explanatory model of the psychological restoration benefits of urban street landscapes was constructed, with an adjusted coefficient of determination of 69.8%, accounting for 69.8% of the variation in street psychological restoration levels. The findings establish an exploratory analytical framework and furnish empirical evidence for healthy city planning and street renewal in similar historical and cultural cities. Full article

The loss of sand foundation often induces differential settlement in immersed tunnel segments, potentially causing structural damage and reducing structural resilience. Accurately assessing the damage characteristics and their effects on resilience during sand foundation loss is essential for ensuring tunnel safety. This study adopts a typical immersed tunnel project as a case study. Long-term structural deformation data acquired by distributed optical fiber sensing technology and sand foundation detection data are adopted to analyze the response characteristics and damage state of the tunnel. A refined three-dimensional tunnel–stratum interaction model is established and validated against monitoring data to investigate mechanical response characteristics, including deformation and bending moment distributions. A redundancy factor is proposed as a quantitative index for tunnel resilience under foundation loss, and a multi-level resilience grading framework is established accordingly. Furthermore, the evolution of tunnel resilience under various displacement recovery ratios, which represent the extent of differential settlement remediation, is investigated using the refined numerical model. Field detection results show that over 50% of the foundation area is affected by loosening or voids. These defects are highly consistent with regions of abnormal structural deformation, leading to a bending–torsional deformation mode, with a maximum joint differential settlement of 106.7 mm. Stress concentration occurs in the tunnel floor above denser sand zones, with a maximum crack width of 0.43 mm. The tunnel is classified as severely damaged (low resilience) based on the proposed standard, with a redundancy factor of 1.59. Bending-torsional deformation and stress concentration are gradually mitigated as the displacement recovery ratio increases. The redundancy factor exhibits a parabolic relationship with the recovery ratio, indicating that tunnel resilience can be restored to a relatively high level when the displacement recovery ratio exceeds 70%. The proposed redundancy factor and grading framework provide quantitative guidance for designing and optimizing resilience improvement strategies following sand foundation loss. Full article

As urban renewal shifts from expansion-led development toward quality improvement of existing urban areas, the restorative quality of urban village public spaces has become an important concern in high-density human settlements. Existing restorative environment research has focused mainly on parks, green spaces, waterfronts, and other settings with strong natural attributes, while audio–visual studies have often examined environmental perception, restorative appraisal, or physiological response as separate analytical components. Limited evidence therefore exists on how scene-level visual and acoustic characteristics, subjective sensory appraisal, perceived restoration, and short-term physiological responses are related within the same analytical framework in high-density urban village public spaces. This study investigated six selected public spaces in Shenzhen urban villages using a VR-based repeated-measures experiment involving 33 participants and 198 participant–scene observations, together with computer vision indicators, psychoacoustic analysis, subjective evaluation, EDA and HRV monitoring, linear mixed-effects models, and multilevel models of statistical indirect associations. Green view index (B = 0.322, p < 0.001) and color complexity (B = 0.422, p < 0.001) were positively associated with perceived restoration, whereas building enclosure (B = −0.271, p < 0.001) and sound roughness (B = −0.328, p < 0.001) were negatively associated with perceived restoration. Green view index showed a positive indirect association with perceived restoration through visual perception (ab = 0.386, 95% CI [0.304, 0.484]), whereas roughness showed a negative indirect association through soundscape perception (ab = −0.277, 95% CI [−0.385, −0.176]). In the full six-scene models, negative objective interaction estimates were compatible with acoustic constraints on favorable visual associations, but several interaction coefficients were sensitive to scene omission and should be regarded as exploratory; at the subjective level, soundscape perception and visual perception showed a positive interaction (B = 0.095, p = 0.028). Leave-one-scene-out analysis showed that several scene-level main-effect and objective interaction estimates were sensitive to the omission of S5 or S2, whereas the directions of the green-view-index and roughness indirect associations were retained. Perceived restoration was positively correlated with the reversed EDA recovery score (r = 0.416, p < 0.001), whereas HRV showed a negative association with LAeq (B = −0.150, p = 0.008), suggesting different short-term response patterns across subjective and physiological measures. The findings suggest that restorative responses in the examined urban village scenes were associated with both subjective sensory appraisal and audio–visual interactions. The study contributes by integrating scene-level visual and acoustic characteristics, subjective sensory appraisal, perceived restoration, and short-term physiological responses within a high-density urban village context. For renewal practice, the results support the coordinated consideration of adverse sound sources, visible greenness, and visual order; however, the observed relationships should be interpreted as context-specific associations rather than as universal causal mechanisms or validated design thresholds. Full article

Sudden tailings dam breaches trigger large-scale heavy metal compound pollution in coupled surface water–groundwater systems, requiring systematic full-cycle ecological damage quantification tools applicable to diverse contamination types. This study constructs an integrated full-cycle ecological damage assessment framework for sudden water pollution accidents, integrating three core modules: multi-model pollutant migration prediction, multi-scale aquatic biological damage diagnosis, and three-dimensional ecological-economic loss accounting. The framework adopts a modular design that can potentially accommodate heavy metals (Cd, Cr, As, Pb) and organic pollutants such as polycyclic aromatic hydrocarbons (PAHs), with standardized molecular, individual, and population-level biological endpoints and corresponding pollutant dose–response templates reserved as reference calculation modules. However, applicability beyond this case has not been validated and requires case-specific calibration. To verify the operability and accuracy of the proposed integrated system, a typical tailings dam leakage incident dominated by hexavalent chromium (Cr(VI)) and arsenic (As) pollution was selected as the practical validation case; all field monitoring, pollutant simulation, and final economic loss quantification in this case exclusively rely on on-site measured Cr(VI) and As data, while Cd and PAH-related biological response curves and remediation cost formulas retained in the manuscript only serve as illustrative universal template components of the framework rather than case-measured results. For the Cr(VI)/As pollution case, the advection–diffusion model simulation revealed that the Cr(VI) contamination plume horizontally spread 250 m within 48 h and extended to 560 m after seven days, and anaerobic groundwater environments drove the transformation of toxic mobile trivalent arsenic (As(III)) from primary pentavalent arsenic. The calibrated SWAT model achieved Nash–Sutcliffe efficiency (NSE) coefficients of 0.75 for dissolved Cr(VI) and 0.68 for particulate As. The graph theory-based rapid prediction model cut computation duration down to minutes; when validated against independent field monitoring data, it yielded an average relative error of 14.2%, and its consistency with the SWAT model reached 10.5% relative deviation, satisfying the accuracy requirement for emergency early warning. Field biological monitoring demonstrated substantial ecological impairment: metallothionein (MT) expression in fish tissues was markedly elevated (the reported 6.2-fold induction value derives from standard Cd exposure template tests within the framework, with analogous MT upregulation also observed for field Cr(VI)/As co-stress), and benthic community Shannon diversity declined by over 50% in polluted river reaches. The standardized Ecological Damage Index (EDI) of the case was calculated as 480.2, indicating severe aquatic ecosystem damage, with total comprehensive ecological and economic losses reaching 17.25 million CNY. This study innovatively couples high-precision physical transport models with fast emergency prediction algorithms and establishes a complete multi-tier biological indicator chain linking molecular biomarkers to community integrity metrics; the three-dimensional loss accounting system integrating ecosystem service impairment, restoration expenditure, and post-pollution recovery loss realizes closed-loop full-cycle damage evaluation. The proposed framework, demonstrated for Cr(VI) and As pollution, has a modular design that may potentially be extended to other pollutants such as Cd and PAHs by adjusting model parameters, providing a quantitative reference for emergency disposal, pollution remediation, and ecological compensation of water contamination accidents, although further validation across different pollutants and hydrological settings is required. Full article

Maintaining water ecological health is essential for the sustainable development of plain water-network regions, where natural hydrological conditions and intensive human activities jointly shape ecological processes. To address the limited understanding of driving pathways, system-level contributions, and key internal factors affecting water ecological health, this study focused on the Lixiahe Plain, a typical plain water-network region in China. A water ecological health assessment system was developed based on the DPSIRM framework, and the Water Ecological Health Index (WEHI) was calculated at a 1 km grid scale from 2000 to 2020. PLS-SEM, RDA-VPA, and XGBoost-SHAP were further integrated to identify subsystem pathways, independent and interactive explanatory contributions, and key driving factors. The results showed that WEHI exhibited an overall fluctuating upward trend, increasing from 0.541 in 2000 to 0.594 in 2020, with the lowest value of 0.511 observed in 2010. Spatially, high-WEHI areas were mainly distributed in the southern and central–eastern regions and gradually expanded. PLS-SEM revealed stage-dependent differences in the direction and magnitude of subsystem effects, with the explained variance increasing from 21.7% in 2010 to 70.9% in 2020. The state and impact subsystems were the main carriers of WEHI variation. XGBoost-SHAP further identified vegetation coverage, RSEI, and soil moisture as key explanatory factors. The integrated results reveal the multilevel mechanisms underlying water ecological health in the Lixiahe Plain and provide a scientific basis for spatially differentiated ecological restoration and sustainable water and ecosystem management in plain water-network regions. Full article

Long-term exploitation of ion-adsorption rare earth deposits has played a vital role in ensuring the supply of strategic mineral resources. However, intensive mining activities have also resulted in severe ecological degradation, including vegetation loss, land degradation, and soil erosion. Although the Remote Sensing Ecological Index has been widely used for ecological environment assessment, it inadequately characterizes land degradation in ion-adsorption rare earth mining areas, while its fixed-weight framework is unable to effectively capture the influence of localized ecological limiting factors. To address these limitations, this study selected a typical ion-adsorption rare earth mining area in southern Jiangxi, China, as the study area. A Desertification Difference Index was incorporated into the conventional RSEI framework to establish a five-dimensional evaluation system consisting of greenness, wetness, dryness, heat, and desertification. Furthermore, a Dynamic Variable-Weight Remote Sensing Ecological Index (DV-RSEI) was developed by integrating variable-weight theory, enabling adaptive adjustment of indicator weights according to local ecological conditions. Using Landsat imagery from 2000, 2005, 2010, 2016, 2020, and 2023, the spatiotemporal evolution and spatial heterogeneity of ecological environmental quality were systematically investigated. The results indicate that: (1) ecological environmental quality exhibited a characteristic evolution process of mining disturbance–ecological degradation–comprehensive restoration–ecological recovery during 2000–2023, with an overall trend dominated by stability and improvement; (2) ecological environmental quality showed significant spatial clustering, with High–High clusters mainly distributed in areas with favorable ecological conditions, whereas Low–Low clusters were concentrated in regions strongly affected by mining activities; and (3) compared with the conventional RSEI, the DV-RSEI better characterized mining-related ecological degradation patterns and the spatial heterogeneity of ecological environmental quality. The proposed approach provides a scientific basis for dynamic ecological monitoring, evaluation of ecological restoration effectiveness, and the construction of green mines in ion-adsorption rare earth mining areas. Full article

Land consolidation (LC) is increasingly expected to support food security, ecological restoration, rural development, and climate mitigation, yet evidence on its carbon effects remains fragmented across engineering, ecological, spatial, and governance research. This review combines bibliometric mapping with structured evidence synthesis of 355 records from WoS and CNKI to examine knowledge evolution, analytical paradigms, and their integration. The field has expanded from component-based assessments of construction emissions, soil carbon, and land-cover change toward life-cycle accounting, carbon fractions, ecosystem-service interactions, spatial optimization, and policy evaluation. WoS-indexed and Chinese-language literature show distinct but increasingly convergent orientations shaped by differences in intervention contexts, disciplinary traditions, analytical scales, and available evidence. Four complementary paradigms are identified: carbon accounting, biogeochemical processes, spatial land systems, and decision support and governance. Together, these paradigms reveal interconnected carbon pathways but remain constrained by inconsistent accounting boundaries, weak process–scale–time linkages, and limited integration with land-governance decisions. Future research should therefore advance standardized life-cycle and multi-scale accounting, mechanism-based assessment of long-term carbon and ecosystem-service dynamics, and digitally and institutionally enabled low-carbon governance. Full article

Microwave-assisted recovery provides a potential approach for restoring fracture damage in asphalt mixtures, but previous studies have mainly focused on heating and curing conditions, while the role of the pre-heating fracture state remains less understood. This study investigated microwave-assisted fracture energy recovery from a damage-state regulation perspective by comparing a control asphalt mixture (CAM) with a basalt fiber-reinforced asphalt mixture (BFAM). Semi-circular bending (SCB) tests were combined with an L9 orthogonal design to evaluate three pre-heating conditions, target surface temperatures of 45–85 °C, and curing times of 6–24 h. Rather than directly enhancing binder recovery, basalt fiber reinforcement increased the initial fracture resistance and altered the relative fracture condition reached under a given external load. The recovery index RI ranged from 20.7% to 55.9% for CAM and from 35.2% to 82.3% for BFAM. Main-effects ANOVA showed that the pre-heating damage condition had the largest main-effect contribution within the adopted L9 framework, reaching 82.3% for CAM and 95.2% for BFAM, substantially exceeding those of target surface temperature and curing time. Under a comparable external load of approximately 2.5 kN, CAM reached the 70% P_max condition, whereas BFAM remained at the 40% P_max condition, with corresponding mean RI values of 42.8% and 76.9%. These results support a proposed conceptual damage-state regulation framework within the investigated material and experimental conditions, in which basalt fiber reinforcement preserves a more favorable pre-heating state and thereby greater recovery potential. The findings highlight the importance of improving fracture resistance and applying microwave-assisted treatment before extensive fracture development occurs, while broader validation is required before generalizing the proposed framework to other materials or field conditions. Full article

Climate change is increasing the frequency and intensity of extreme rainfall events, profoundly affecting vegetation–atmosphere–soil interactions and ecosystem stability. Northeast China (NEC), a major ecological region, is highly sensitive to precipitation variability. However, the annual mechanisms underlying vegetation responses to rainfall extremes, the mediating roles of soil moisture (SM) and vapor pressure deficit (VPD), and the ecosystem-specific differences remain insufficiently understood. This study investigates these processes during 2000–2022 by integrating precipitation extremes, normalized difference vegetation index (NDVI), SM, VPD, and land cover data. Ten rainfall extreme indices were evaluated using the Mann–Kendall (MK) test and Sen’s slope estimator, while NDVI responses were examined through correlation analysis, mixed-effects models, and structural equation modeling (SEM). Results show strong spatial heterogeneity in precipitation extremes, with intensified heavy rainfall in southern NEC and prolonged drought conditions in northern areas. Vegetation exhibited significant greening trends (NDVI slope = 0.0026 yr⁻¹, R² = 0.718, p < 0.001), accompanied by increasing SM (slope = 0.0478 yr⁻¹, p = 0.003) and mild warming (slope = 0.0005 yr⁻¹, p = 0.045). NDVI showed a strong correlation with SM (ρ = 0.65, p < 0.01) but a weak relationship with temperature (ρ = 0.04, p > 0.05), highlighting SM as the dominant driver of regional greening. Grasslands and cultivated lands were more sensitive to rainfall fluctuations, whereas forests showed greater resilience. SEM results indicate that extreme rainfall affects NDVI mainly through indirect pathways mediated by SM and VPD, with mediation effects exceeding 97%. These findings improve understanding of nonlinear vegetation–atmosphere–land interactions and provide scientific insights for climate adaptation, ecosystem management, and ecological restoration under future climate change. Full article

Urban and industrial activities have lasting effects on Earth ecosystems, impairing their functionality. Technosols offer a sustainable solution for restoring degraded urban and industrial ecosystems. In a 30-day microcosm experiment, a mixture of six pioneer plant species was sown in three substrates: a sewage sludge-based Technosol (GF), a zeolite-enriched Technosol (GZ), and a commercial potting mix control (GC). The plant population dynamics, final performance, and substrate biochemical properties were monitored. A strong environmental filter allowed only two species (Bromus inermis Leyss. and Lolium perenne L.) to establish. Technosols exerted a demographic bottleneck, delaying emergence and reducing the total biomass relative to the control. Zeolites in the GZ Technosol mitigated this delay, accelerating early establishment due to their microporous structure and high cation exchange capacity. However, GZ caused the greatest reduction in individual biomass and functional plant performance index, corresponding to a microbial shift toward oxidative activity at the expense of hydrolytic nutrient mineralization. These results show that sewage sludge Technosols can initiate functional ecological succession. While zeolites positively affect germination, their microbial interaction suggests a temporary decoupling between the establishment speed and final productivity. Integrated monitoring of demographic and biochemical dynamics is therefore essential to optimize Technosol-based environmental restoration. Full article

Against the backdrop of global climate change, drought–flood abrupt alternation (DFAA) has become a major compound climate extreme threatening ecosystem stability and sustainable watershed management. This study investigated the spatiotemporal characteristics and ecological responses of DFAA in the Jialing River Basin, China, using meteorological and hydrological observations from 1971 to 2020. DFAA events were identified using the Standardized Weighted Average Precipitation Index (SWAP) and run theory, and their spatiotemporal heterogeneity was characterized using spatial autocorrelation analysis. The Long-duration DFAA Index (LDFAI) was derived using the WEP-L distributed hydrological model. Ecological responses during 2000–2020 were evaluated by integrating the Remote Sensing Ecological Index (RSEI), grey relational analysis, and a Copula-based conditional probability model. The results showed that drought-to-flood events exhibited stronger spatial clustering than flood-to-drought events. Ecosystem responses showed significant lag effects, averaging 6.9 months for spring–summer events and 5 months for summer–autumn events, with greater sensitivity during the summer–autumn period. Under DTF events, the probability of maintaining relatively high ecological quality was significantly higher than under FTD events, whereas FTD events were associated with a higher probability of ecological degradation. Under compound scenarios, consecutive same-type events were more conducive to ecosystem stability, while alternating sequences of different event types significantly amplified negative ecological stress and represented high-risk scenarios for ecological degradation. These findings provide scientific support for adaptive watershed management, ecological restoration, and climate change adaptation in drought–flood-prone regions. Full article

Background/Objectives: Obstructive sleep apnea (OSA) is traditionally classified according to the apnea–hypopnea index (AHI), although AHI alone does not fully capture the heterogeneity of disease severity. This study introduces a novel polysomnography-derived biomarker, the Sleep Disturbance Ratio (SDR), and evaluates its contribution to OSA severity classification using explainable machine learning approaches. Methods: A retrospective study was conducted using polysomnographic data from 767 adults who underwent overnight sleep studies. SDR was calculated as the logarithmic ratio between light sleep (N1 + N2) and restorative sleep (N3 + REM). Predictive models were trained and evaluated using four established machine learning algorithms: Decision Tree, Random Forest, Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN). Model performance was assessed using accuracy, Cohen’s kappa, F1-score, multiclass AUC, ROC analysis, feature importance ranking, partial dependence plots, and clinical risk mapping. Results: Although SDR did not differ significantly across conventional OSA severity groups in univariate analysis (p = 0.77), explainable machine learning analyses consistently demonstrated that increasing SDR was associated with a higher probability of severe OSA, particularly in combination with lower mean oxygen saturation. SDR also showed strong physiological relevance by correlating positively with N2 sleep (r = 0.85) and negatively with N3 sleep (r = −0.83), supporting its role as a biomarker of sleep fragmentation. Among the predictive models, Random Forest achieved the highest classification accuracy (75.0%), whereas XGBoost demonstrated the best multiclass discrimination (AUC = 0.895) and the highest ROC performance for severe OSA (AUC = 0.962). ESS remained the most influential predictor across all models. Conclusions: This study introduces SDR as a novel polysomnography-derived biomarker that captures sleep architecture disruption beyond conventional AHI-based assessment. Although SDR is not an independent diagnostic marker, explainable machine learning analyses demonstrated that it provides complementary physiological information for OSA severity classification, particularly when integrated with oxygenation parameters. Full article

Background: Antimicrobial resistance (AMR) is among the most pressing challenges in modern infectious medicine, driving progressive failure of standard antibacterial therapy and rising mortality from infections caused by multidrug-resistant (MDR) pathogens. Antibiotic potentiation through adjuvant compounds capable of restoring the activity of existing drugs represents a promising strategy to overcome resistance without developing fundamentally new antibacterial molecules. This study aimed to evaluate the antibiotic-potentiating activity of a dextrin/polyvinyl alcohol/iodine complex (D/PVA/I-1), developed at the Scientific Center for Anti-Infectious Drugs JSC (Almaty, Kazakhstan), against clinically relevant MDR reference strains. Methods: Nine reference strains, Staphylococcus aureus (ATCC 33591, BAA-39), Escherichia coli (ATCC BAA-196, BAA-2523), Klebsiella pneumoniae (ATCC BAA-2524, 700603), Acinetobacter baumannii (ATCC BAA-1790), Streptococcus pneumoniae (ATCC BAA-660), and Haemophilus influenzae (ATCC 33930), and two clinical isolates, P. aeruginosa SCAID PHRX1-2019 and E. coli SCAID WND1-2021, were tested. Antibiotic-potentiating activity was assessed by checkerboard assay with calculation of the fractional inhibitory concentration index (FICI); bactericidal kinetics were evaluated by time-kill analysis. The effect of D/PVA/I-1 on cytoplasmic membrane permeability was investigated using a crystal violet uptake assay. Results: Of 45 D/PVA/I-1–antibiotic combinations tested across nine antibiotics, synergy (FICI ≤ 0.5) was demonstrated in 44.4% of cases, partial synergy in 48.9%, and additive effects in 6.7%; no antagonistic interactions were detected. The most pronounced potentiating effect occurred against Gram-positive pathogens, particularly MRSA strains (66.7% synergistic combinations). Time-kill analysis confirmed suppression of the regrowth phenotype characteristic of MDR strains under monotherapy and restoration of bactericidal activity against antibiotics to which strains exhibited intrinsic resistance. D/PVA/I-1 induced a dose- and time-dependent increase in membrane permeability in both Gram-positive and Gram-negative organisms. Conclusions: D/PVA/I-1 is an effective broad-spectrum antibiotic potentiator and represents a promising basis for combination therapy regimens against MDR infections. Full article