Multiple Environments Research Articles

Water-resistant conformal hydrogels toward underwater human-machine interfaces based on synergistic immersion method and supramolecular interactions strategy

Flexible electronic skin based on hydrogel provides a non-invasive method for real-time acquisition of body movement signals and biosignals, which is essential for future smart health monitoring and healthcare. However, it is critical yet challenging to achieve motional and physiological signals monitoring in an aquatic environment because the interface electrodes may lose their conformability under wet environments. Here, inspired by the CB[8] enhanced hydrophobic polar–π interactions, hydrogel electrode based on the synergistic immersion method and supramolecular interactions is introduced, which exhibits excellent underwater conformability while maintaining tissue-mimetic mechanical properties. No need to add any organic solvents or hydrophobic monomers, the immersion behavior strengthens the non-covalent interactions in the network, and the conformal hydrogel sensor based on hydrophilic monomers has outstanding underwater adhesion capacity (adhesion strength of 139.62 kPa), excellent mechanical behavior (toughness of 5812.31 kJ m−3), and superior underwater stability (swelling ratio of 0.01) by rationally modulating the gel factors and immersion time. The ability to sensitively monitor movement signals and electrocardiogram (ECG) in multiple wet environments offers unique prospects for the development of next-generation wearable medical devices based on the facile preparation strategy.

Genome-Wide Association Study and Identification of Candidate Genes Associated with Seed Number per Pod in Soybean.

Soybean (Glycine max [L.] Merr.) is one of the primary sources of plant protein and oil for human foods, animal feed, and industrial processing. The seed number per pod generally varies from one to four and is an important component of seed number per unit area and seed yield. We used natural variation in 264 landraces and improved cultivars or lines to identify candidate genes involved in the regulation of seed number per pod in soybean. Genome-wide association tests revealed 65 loci that are associated with seed number per pod trait. Among them, 11 could be detected in multiple environments. Candidate genes were identified for seed number per pod phenotype from the most significantly associated loci, including a gene encoding protein argonaute 4, a gene encoding histone acetyltransferase of the MYST family 1, a gene encoding chromosome segregation protein SMC-1 and a gene encoding exocyst complex component EXO84A. In addition, plant hormones were found to be involved in ovule and seed development and the regulation of seed number per pod in soybean. This study facilitates the dissection of genetic networks underlying seed number per pod in soybean, which will be useful for the genetic improvement of seed yield in soybean.

Identification of environment-insensitive genes for oil content by combination of transcriptome and genome-wide association analysis in rapeseed.

The primary objective of rapeseed breeding is to enhance oil content, which is predominantly influenced by environmental factors. However, the molecular mechanisms underlying the impact of these environmental factors on oil accumulation remain inadequately elucidated. In this study, we used transcriptome data from two higher (HOC) and two lower oil content (LOC) inbred lines at 35days after pollination (DAP) to investigate genes exhibiting stable expression across three different environments. Meanwhile, a genome-wide association study (GWAS) was utilized to detect candidate genes exhibiting significant associations with seed oil content across three distinct environments. The study found a total of 405 stable differentially expressed genes (DEGs), including 25 involved in lipid/fatty acid metabolism and 14 classified as transcription factors. Among these genes, BnBZIP10-A09, BnMYB61-A06, BnAPA1-A08, BnPAS2-A10, BnLCAT3-C05 and BnKASIII-C09 were also found to exhibit significant associations with oil content across multiple different environments based on GWAS of 50 re-sequenced semi-winter rapeseed inbred lines and previously reported intervals. Otherwise, we revealed the presence of additive effects among BnBZIP10-A09, BnKASIII-C09, BnPAS2-A10 and BnAPA1-A08, resulting in a significant increase in seed oil content. Meanwhile, the majority of these stable DEGs are interconnected either directly or indirectly through co-expression network analysis, thereby giving rise to an elaborate molecular network implicated in the potential regulation of seed oil accumulation and stability. The combination of transcription and GWAS revealed that natural variation in six environment-insensitive gene regions exhibited significant correlations with seed oil content phenotypes. These results provide important molecular marker information for us to further improve oil content accumulation and stability in rapeseed.

Phenotypic and genomic characteristics of clinical IMP-producing Klebsiella spp. Isolates in China

BackgroundIMP-producing Klebsiella spp. (IMPKsp) strains have spread globally, including in China. Currently, the prevalence and genomic characterization of IMPKsp is largely unknown nationwide. Here we aimed to provide a general overview of the phenotypic and genomic characteristics of IMPKsp strains.Methods61 IMPKsp strains were obtained from 13 provinces in China during 2016-2021. All strains were tested for their susceptibility to antimicrobial agents by the microdilution broth method and sequenced with Illumina next-generation sequencing. We performed conjugation experiments on thirteen representative strains which were also sequenced by Oxford nanopore sequencing technology to characterize blaIMP-encoding plasmids.ResultsWe find that all IMPKsp strains display multidrug-resistant (MDR) phenotypes. All strains belong to 27 different STs. ST307 emerges as a principal IMP-producing sublineage. blaIMP-4 is found to be the major isoform, followed by blaIMP-38. Seven incompatibility types of blaIMP-encoding plasmids are identified, including IncHI5 (32/61, 52.5%), IncN-IncR (10/61, 16.4%), IncFIB(K)-HI1B (7/61, 11.5%), IncN (5/61, 8.2%), IncN-IncFII (2/61, 3.3%), IncFII (1/61, 1.6%) and IncP (1/61, 1.6%). The strains carrying IncHI5 and IncN plasmids belong to diverse ST types, indicating that these two plasmids may play an important role in the transmission of blaIMP genes among Klebsiella spp. strains.ConclusionsOur results highlight that multi-clonal transmission, multiple genetic environments and plasmid types play a major role in the dissemination process of blaIMP genes among Klebsiella spp. IncHI5 type plasmids have the potential to be the main vectors mediating the spread of the blaIMP genes in Klebsiella spp.

Robust state-of-charge estimation for LiFePO4 batteries under wide varying temperature environments

During the driving process of electric vehicles, the ambient temperature exhibits diverse variations with regional characteristics. To achieve robust state of charge (SOC) estimation for lithium-ion batteries under various varying temperature environments, this paper proposes an enhanced model-based closed-loop SOC estimation approach. First, beginning with a mechanistic analysis of batteries, the traditional second-order equivalent circuit model is enhanced by incorporating critical solid-phase diffusion effects during battery operation. Furthermore, utilizing data collected from multiple constant temperature environments, the complete enhanced battery model that accounts for the influence of current rates across a wide temperature range is constructed. Subsequently, under environments of different varying temperature settings, we design a series of complex operation experiments to verify the accuracy and generalizability of the established battery model. Meanwhile, a high-performance adaptive diagonalization of matrix cubature Kalman filter is introduced to address the challenge of fluctuating sampling noises in battery operation. Finally, the robustness and generalization of the proposed SOC estimation method are verified in multiple complex operating experiments under varying temperatures with non-Gaussian noise interferences and with non-full charging schemes. Remarkably, the proposed approach consistently delivers high-precision SOC estimation results across all scenarios, maintaining root mean square error and mean absolute error below 1.5%.

Approaches and progress in breeding drought-tolerant maize hybrids for tropical lowlands in west and central Africa.

Drought represents a significant production challenge to maize farmers in West and Central Africa, causing substantial economic losses. Breeders at the International Institute of Tropical Agriculture have therefore been developing drought-tolerant maize varieties to attain high grain yields in rainfed maize production zones. The present review provides a historical overview of the approaches used and progress made in developing drought-tolerant hybrids over the years. Breeders made a shift from a wide area testing approach, to the use of managed screening sites, to precisely control the intensity, and timing of drought stress for developing drought-tolerant maize varieties. These sites coupled with the use of molecular markers allowed choosing suitable donors with drought-adaptive alleles for integration into existing elite maize lines to generate new drought-tolerant inbred lines. These elite maize inbred lines have then been used to develop hybrids with enhanced tolerance to drought. Genetic gains estimates were made using performance data of drought-tolerant maize hybrids evaluated in regional trials for 11 years under managed drought stress, well-watered conditions, and across diverse rainfed environments. The results found significant linear annual yield gains of 32.72kg ha-1 under managed drought stress, 38.29kg ha-1 under well-watered conditions, and 66.57kg ha-1 across multiple rainfed field environments. Promising hybrids that deliver high grain yields were also identified for areas affected by drought and variable rainfed growing conditions. The significant genetic correlations found among the three growing conditions highlight the potential to exploit the available genetic resources and modern tools to further enhance tolerance to drought in hybrids.

11-Plex DiLeu Isobaric Labeling Enables Quantitative Assessment of Brain Region Protein Association Networks Impacted by the Gut Microbiome.

Gut microbiota can regulate host brain functions and influence various physiological and pathological processes through the brain-gut axis. To systematically elucidate the intervention of different gut environments on different brain regions, we implemented an integrated approach that combines 11-plex DiLeu isobaric tags with a "BRIDGE" normalization strategy to comparatively analyze the proteome of six brain regions in germ-free (GF)- and conventionally raised (ConvR)-mice. A total of 5945 proteins were identified and 5656 were quantifiable, while 1906 of them were significantly changed between GF- and ConvR-mice; 281 proteins were filtered with FC greater than 1.2 in at least one brain region, of which heatmap analysis showed clear protein profile disparities, both between brain regions and gut microbiome conditions. Gut microbiome impact is most overt in the hypothalamus and the least in the thalamus region. Collectively, this approach allows an in-depth investigation of the induced protein changes by multiple gut microbiome environments in a brain region-specific manner. This comprehensive proteomic work improves the understanding of the brain region protein association networks impacted by the gut microbiome and highlights the critical roles of the brain-gut axis.

Geography and ecology shape the phylogenetic composition of Amazonian tree communities

AbstractAimAmazonia hosts more tree species from numerous evolutionary lineages, both young and ancient, than any other biogeographic region. Previous studies have shown that tree lineages colonized multiple edaphic environments and dispersed widely across Amazonia, leading to a hypothesis, which we test, that lineages should not be strongly associated with either geographic regions or edaphic forest types.LocationAmazonia.TaxonAngiosperms (Magnoliids; Monocots; Eudicots).MethodsData for the abundance of 5082 tree species in 1989 plots were combined with a mega‐phylogeny. We applied evolutionary ordination to assess how phylogenetic composition varies across Amazonia. We used variation partitioning and Moran's eigenvector maps (MEM) to test and quantify the separate and joint contributions of spatial and environmental variables to explain the phylogenetic composition of plots. We tested the indicator value of lineages for geographic regions and edaphic forest types and mapped associations onto the phylogeny.ResultsIn the terra firme and várzea forest types, the phylogenetic composition varies by geographic region, but the igapó and white‐sand forest types retain a unique evolutionary signature regardless of region. Overall, we find that soil chemistry, climate and topography explain 24% of the variation in phylogenetic composition, with 79% of that variation being spatially structured (R2 = 19% overall for combined spatial/environmental effects). The phylogenetic composition also shows substantial spatial patterns not related to the environmental variables we quantified (R2 = 28%). A greater number of lineages were significant indicators of geographic regions than forest types.Main ConclusionNumerous tree lineages, including some ancient ones (>66 Ma), show strong associations with geographic regions and edaphic forest types of Amazonia. This shows that specialization in specific edaphic environments has played a long‐standing role in the evolutionary assembly of Amazonian forests. Furthermore, many lineages, even those that have dispersed across Amazonia, dominate within a specific region, likely because of phylogenetically conserved niches for environmental conditions that are prevalent within regions.

Superionic lithium transport via multiple coordination environments defined by two-anion packing.

Fast cation transport in solids underpins energy storage. Materials design has focused on structures that can define transport pathways with minimal cation coordination change, restricting attention to a small part of chemical space. Motivated by the greater structural diversity of binary intermetallics than that of the metallic elements, we used two anions to build a pathway for three-dimensional superionic lithium ion conductivity that exploits multiple cation coordination environments. Li7Si2S7I is a pure lithium ion conductor created by an ordering of sulphide and iodide that combines elements of hexagonal and cubic close-packing analogously to the structure of NiZr. The resulting diverse network of lithium positions with distinct geometries and anion coordination chemistries affords low barriers to transport, opening a large structural space for high cation conductivity.

A retrospective analysis of historical data of multi‐environment trials for dry bean (Phaseolus vulgaris L.) in Michigan

AbstractPlant breeding programs use multi‐environment trials (METs) to test multiple genotypes with the aim of selecting superior lines and increasing genetic gain. However, METs can be affected by the genotype × environment interactions (GEI) which hinders the ability to identify stable and high‐yielding dry bean varieties. This study investigates the impact of GEI on grain yield (GY) in three dry bean market classes across multiple environments in Michigan. The goal is to identify genotypes with superior performance under GEI effects and describe the genetic correlation between locations and other agronomic traits. Furthermore, we evaluate genetic yield trends on three major dry bean market classes in Michigan. Using linear mixed models, stability analysis, and a multi‐trait index, this study accounted for the effects of the environment on genotype performance to improve the prediction of GY. Our results highlight the importance of considering GEI in dry bean breeding programs to enhance the selection accuracy of GY. The genotype by environment interaction biplot analysis and the GEI model aided in identifying and classifying mega‐environments, leading to specific strategies for testing commercial bean classes across target production environments. Multiple‐trait performance selection showed positive results, although limited gains were observed for early maturing genotypes. Finally, the analysis of genetic trends reveals a positive trend in yield for black beans, while navy beans and small red beans show a slight decrease which can be attributed to unfavorable growing conditions and lack of genetic diversity in these market classes.

Pengaruh Lingkungan Kerja Fisik dan Motivasi Kerja terhadap Kinerja Guru di SMP Negeri 03 dan SMA Negeri 03 Kecamatan Talang Ubi Kabupaten Penukal Abab Lematang Ilir

This study aims to determine the effect of Physical Work Environment and Work Motivation on Teacher Performance in SMP Negeri 03 and SMA Negeri 03 Talang Ubi District, Penukal Abab Lematang Ilir Regency. The population of this study were all teachers at SMP Negeri 03 and SMA Negeri 03. The sample in this study were 36 respondents totaling 56 respondents.. This type of research is Descriptive research with a Quantitative approach. The data used is primary data. Data collection methods using questionnaire techniques. Data analysis techniques are Multiple Linear Regression, F test, Determination Coefficient T test, and Correlation Coefficient test. The results of Multiple Linear Regression Variable Physical Work Environment and Work Motivation have a positive influence on Teacher Performance in SMP Negeri 03 and SMA Negeri 03 Talang Ubi District Penukal Abab Lematang Ilir Regency. The results of the F test analysis show that simultaneously there is a significant effect of Physical Work Environment Variables and Work Motivation have a significant effect on Teacher Performance in State Junior High School 03 and State Senior High School 03 Talang Ubi District Penukal Abab Lematang Ilir Regency. The partial t test results show that the Physical Work Environment and Work Motivation Variables have a significant effect on Teacher Performance in SMP Negeri 03 and SMA Negeri 03, Talang Ubi District, Penukal Abab Lematang Ilir Regency. Analysis of the Coefficient of Determination shows that 67.1% of the variables of Physical Work Environment and Work Motivation affect Teacher Performance in State Junior High School 03 and State Senior High School 03, Talang Ubi District, Penukal Abab Lematang Ilir Regency, while the remaining 32.9% is influenced by other variables not included in this study.

Examination of Traditional Botnet Detection on IoT-Based Bots

A botnet is a collection of Internet-connected computers that have been suborned and are controlled externally for malicious purposes. Concomitant with the growth of the Internet of Things (IoT), botnets have been expanding to use IoT devices as their attack vectors. IoT devices utilise specific protocols and network topologies distinct from conventional computers that may render detection techniques ineffective on compromised IoT devices. This paper describes experiments involving the acquisition of several traditional botnet detection techniques, BotMiner, BotProbe, and BotHunter, to evaluate their capabilities when applied to IoT-based botnets. Multiple simulation environments, using internally developed network traffic generation software, were created to test these techniques on traditional and IoT-based networks, with multiple scenarios differentiated by the total number of hosts, the total number of infected hosts, the botnet command and control (CnC) type, and the presence of aberrant activity. Externally acquired datasets were also used to further test and validate the capabilities of each botnet detection technique. The results indicated, contrary to expectations, that BotMiner and BotProbe were able to detect IoT-based botnets—though they exhibited certain limitations specific to their operation. The results show that traditional botnet detection techniques are capable of detecting IoT-based botnets and that the different techniques may offer capabilities that complement one another.

Transfer Learning for UWB Error Correction and (N)LOS Classification in Multiple Environments

Transfer Learning for UWB Error Correction and (N)LOS Classification in Multiple Environments

Implementation and Evaluation of Wet Bulb Globe Temperature Within Non‐Urban Environments in the Community Land Model Version 5

AbstractGlobal heat stress is a phenomenon that impacts the livelihood of humans worldwide. Due to climate change, heatwaves are already increasing negatively impact outdoor laborers and activities. However, calculating heat stress on a global scale is disparaged due to the interplay and treatment of temperature, humidity, and radiation. To help resolve this issue, the Wet Bulb Globe Temperature (WBGT), a standardized heat stress metric, is implemented into the Community Land Model (CLM5), the land surface component of the Community Earth System Model (CESM2). This resolves a long lasting, complex issue within global heat stress: the treatment of solar and thermal radiation. A default configuration of CLM5 is executed and shows the advantages of simulating the WBGT within multiple environments. Additionally, two commonly used WBGT approximations are implemented for solar exposed (sWBGT) and shaded (FiWBGT) conditions. The 1995 Chicago Heatwave is examined as a case study, focusing on the rural regions impacted by the heatwave. Derivative functions of labor capacity show that assumptions about calculating a non‐linear algorithm generate non‐negligible biases that can grossly over or underestimate the impact of heat stress on future climate change projections. For example, a difference of 0.5°C from WBGT can result in >10% change in labor capacity. Using a conservative difference of ±0.3°C, 100% of land surface extreme sWBGT values and >77% extreme shaded conditions (FiWBGT) differ from WBGT. Therefore, to accurately assess the direct exposure, risk, and damage from climate change on people, it is critical to implement diagnostics directly into Earth system models.

Genome-Wide Association Study and Prediction of Tassel Weight of Tropical Maize Germplasm in Multi-Parent Population.

Tassel weight (TW) is a crucial agronomic trait that significantly affects pollen supply and grain yield development in maize breeding. To improve maize yield and develop new varieties, a comprehensive understanding of the genetic mechanisms underlying tassel weight is essential. In this study, tropical maize inbred lines, namely CML312, CML373, CML444, and YML46, were selected as female parents and crossed with the elite maize inbred line Ye107, which served as the common male parent, to develop a multi-parent population comprising four F8 recombinant inbred line (RIL) subpopulations. Using 6616 high-quality single nucleotide polymorphism (SNP) markers, we conducted genome-wide association analysis (GWAS) and genomic selection (GS) on 642 F8 RILs in four subpopulations across three different environments. Through GWAS, we identified 16 SNPs that were significantly associated with TW, encompassing two stable loci expressed across multiple environments. Furthermore, within the candidate regions of these SNPs, we discovered four novel candidate genes related to TW, namely Zm00001d044362, Zm00001d011048, Zm00001d011049, and Zm00001d031173 distributed on chromosomes 1, 3, and 8, which have not been previously reported. These genes are involved in processes such as signal transduction, growth and development, protein splicing, and pollen development, all of which play crucial roles in inflorescence meristem development, directly affecting TW. The co-localized SNP, S8_137379725, on chromosome 8 was situated within a 16.569 kb long terminal repeat retrotransposon (LTR-RT), located 22.819 kb upstream and 26.428 kb downstream of the candidate genes (Zm00001d011048 and Zm00001d011049). When comparing three distinct GS models, the BayesB model demonstrated the highest accuracy in predicting TW. This study establishes the theoretical foundation for future research into the genetic mechanisms underlying maize TW and the efficient breeding of high-yielding varieties with desired tassel weight through GS.

Out-of-Band Information Aided mmWave/THz Beam Search: A Spatial Channel Similarity Perspective

The transition to higher frequency bands, e.g., millimeter-wave (mmWave) and terahertz (THz), will be capitalized on the long term for future wireless communications. One of challenges relates to rapid establishment of mmWave/THz links with low beam training overhead due to highly directional transmission. A promising solution is to take advantage of the coexistence of sub-6 GHz, mmWave, and THz wireless networks and to use out-of-band spatial information for enabling fast beam search. The success depends on the spatial similarity of radio channels across different frequency bands. In this article we promote a feasibility study of low-frequency spatial channel information assisted high-frequency beam search from a radio channel point of view. We develop multi-band channel similarity measure of desired beam directions extracted from radio channels, which are obtained via filtering propagation paths by different beampatterns at different frequencies. Measurementand ray-tracing-based evaluations across multiple frequencies and environments are performed, which prove the usability of out-of-band information aided beam search strategy in line-ofsight (LOS) dominated scenario and even in non-LOS scenario. Finally, we discuss the challenges associated with exploiting spatial channel similarity.

Sequence Analysis of Six Candidate Genes in Miniature Schnauzers with Primary Hypertriglyceridemia.

Miniature Schnauzers are predisposed to primary hypertriglyceridemia (HTG). In this study, we performed whole genome sequencing (WGS) of eight Miniature Schnauzers with primary HTG and screened for risk variants in six HTG candidate genes: LPL, APOC2, APOA5, GPIHBP1, LMF1, and APOE. Variants were filtered to identify those present in ≥2 Miniature Schnauzers with primary HTG and uncommon (<10% allele frequency) in a WGS variant database including 613 dogs from 61 other breeds. Three variants passed filtering: an APOE TATA box deletion, an LMF1 intronic SNP, and a GPIHBP1 missense variant. The APOE and GPIHBP1 variants were genotyped in a cohort of 108 Miniature Schnauzers, including 68 with primary HTG and 40 controls. A multivariable regression model, including age and sex, did not identify an effect of APOE (estimate = 0.18, std. error = 0.14; p = 0.20) or GPIHBP1 genotypes (estimate = -0.26, std. error = 0.42; p = 0.54) on triglyceride concentration. In conclusion, we did not identify a monogenic cause for primary HTG in Miniature Schnauzers in the six genes evaluated. However, if HTG in Miniature Schnauzers is a complex disease resulting from the cumulative effects of multiple variants and environment, the identified variants cannot be ruled out as contributing factors.

A Case Study of Learner Engagement of English Learners with Different Language Proficiency in a Multiple Feedback Environment

Based on Ellis’s three-dimensional learner engagement framework, this study examined the learner engagement in multiple feedback of nine second language learners with different language proficiency through writing texts, retrospective interviews, and semi-structured interviews. It was found that (1) learners generally have a positive attitude towards multiple feedback; they experience positive emotions while negative emotions such as disappointment, doubtful, angry, guilty are also generated in the process of revision; learners tend to absorb more teacher feedback and more meaning-level feedback and they are relatively poor at self-regulated learning. (2) Language proficiency has an effect on learner engagement, with a more significant effect for lowerlevel learners. Learners with lower language proficiency tend to be opposed to multiple feedback and show lower behavioral and cognitive engagement. (3) There is a correlation between the three dimensions of learner feedback engagement: Affective engagement influences behavioral and cognitive engagement, and the behavioral engagement is inspired by learners’ cognitive engagement. This study provides insights into the implementation of multiple feedback and second language writing, which is beneficial to the cultivation of second language talents in the new era.

Dual polymer electrochromic adaptive camouflage devices for multiple environments based on quaterthiophene polymer and optional ion storage layers

The potential for electrochromic materials/devices to be utilized as adaptive camouflage for multiple environments remains to be investigated. In this work, the solution processible electrochromic polymers 4TBC and 4TCSIB were constructed using quaterthiophene as the basic electrochromic unit and tetraphenylsilane/meta benzene units as the conjugation break spacers. Taking advantage of the well-defined redox behavior of quaterthiophene, both polymers can switch from yellow to green series. In particular, 4TBC has better electrochromic performance, including high optical contrast and available cycle stability. Furthermore, the color-complementary polymer pEKE and pEBE were selected as the optional ion storage layer to design and construct dual polymer electrochromic adaptive camouflage devices. The electrochromic device-1 (ECD-1 (4TBC-pEKE)) and electrochromic device-2 (ECD-2 (4TBC-pEBE)) can switch from diverse yellow to different green at an operating voltage of −0.3 V ∼ 1.2 V. Most importantly, we introduce the ECD-12, which integrates ECD-1 and ECD-2, can realize multiple modes (up to eighteen modes) conversion, and is expected to be applied to savanna, mountain and multiple environments. This work proposes an existing generic strategy of optional ion storage layers for constructing dual polymer electrochromic devices toward multiple environments adaptive camouflage.

The connectivity threshold of the wetlands based on water environment response, and a case study of China’s Baiyangdian wetland

The quality of the water environment of wetlands is closely related to the changes in hydrologic connectivity, so wetland responses to changing connectivity exhibit a range of optimal connectivity values. Identifying these ranges of values is of great significance for wetland ecological restoration. Current studies mostly determine the hydrologic connectivity ranges based on a single target, even though connectivity simultaneously affects multiple water environment targets. Hence, the optimal hydrologic connectivity should be determined by accounting for multiple targets. With the Baiyangdian Wetland as the study area, we developed a method to model how the water environment responded to changes in hydrologic connectivity from three perspectives: hydrodynamics, water quality, and aquatic habitat. To do so, we used the BP neural network model and a non-dominated sorting genetic algorithm to calculate the hydrologic connectivity values in a context with multiple targets. We found that under the optimal hydrodynamic target, the integral index of connectivity (IIC) threshold was [0.23, 0.61] and the probability connectivity (PC) threshold was [0.32, 0.68]; under the optimal water quality target, the IIC threshold was [0.42, 0.70] and the PC threshold was [0.48, 0.82]; under the optimal aquatic habitat target, the IIC threshold was [0.38, 0.69] and the PC threshold was [0.35, 0.71]; and under the comprehensive optimization target, the IIC threshold was [0.48, 0.64] and the PC threshold was [0.51, 0.73]. These ranges of values will help wetland managers determine the optimal ecological water releases to meet the ecosystem quality targets.