Search Results (58)

Directed grey-box fuzzers focus on testing specific target code. They have been utilized in various security applications, such as reproducing known crashes and identifying vulnerabilities resulting from incomplete patches. Distance-guided directed fuzzers calculate the distance to the target node for each node in a CFG or CG, which has always been the mainstream in this field. However, the distance can only reflect the relationship between the current node and the target node, and it does not consider the impact of the reaching sequence before the target node. To mitigate this problem, we analyzed the properties of the instrumented function’s call graph after selective instrumentation, and the complexity of reaching the target function sequence was estimated. Assisted by the sequence complexity, we proposed a two-stage function call sequence-based seed-scheduling strategy. The first stage is to select seeds with a higher probability of generating test cases that reach the target function. The second stage is to select seeds that can generate test cases that meet the conditions for triggering the vulnerability as much as possible. We implemented our approach in SEZZ based on SelectFuzz and compare it with related works. We found that SEZZ outperformed AFLGo, Beacon, WindRanger, and SelectFuzz by achieving an average improvement of 13.7×, 1.50×, 9.78×, and 2.04× faster on vulnerability exposure, respectively. Moreover, SEZZ triggered three more vulnerabilities than the other compared tools. Full article

Under the scenario of global climate warming, meteorological risks affecting sunflower cultivation in Xinjiang’s 10th Division were investigated by developing a meteorological-growth coupling model. Field experiments were conducted at three representative stations (A1–A3) during 2023–2024 to assess temperature and precipitation impacts on yield and quality traits among sunflower cultivars with varying maturation periods. The main findings were: (1) Early-maturing cultivar B1 (RH3146) exhibited superior adaptation at low-temperature station A1, achieving 12% higher plant height and an 18% yield increase compared to regional averages. (2) At thermally variable station A2 (daily average temperature fluctuation ± 8 °C, precipitation CV = 25%), the late-maturing cultivar B3 showed enhanced stress resilience, achieving 35.6% grain crude fat content (15% greater than mid-maturing B2) along with 8–10% increases in seed setting rate and 100-grain weight. These improvements were potentially due to optimized photoassimilated allocation and activation of stress-responsive genes. (3) At station A3, characterized by high thermal-humidity variability (CV > 15%) during grain filling, B3 experienced a 15-day delay in maturation and a 3% reduction in ripeness. Two principal mitigation strategies are recommended: preferential selection of early-to-mid maturing cultivars in regions with thermal-humidity CV > 10%, improving yield stability by 23%, and optimization of sowing schedules based on accumulated temperature-precipitation modeling, reducing meteorological losses by 15%. These evidence-based recommendations provide critical insights for climate-resilient cultivar selection and precision agricultural management in meteorologically vulnerable agroecosystems. Full article

Modeling complex, non-stationary dynamics remains challenging for deterministic neural networks. We present the Chaos-Integrated Synaptic-Memory Network (CISMN), which embeds controlled chaos across four modules—Chaotic Memory Cells, Chaotic Plasticity Layers, Chaotic Synapse Layers, and a Chaotic Attention Mechanism—supplemented by a logistic-map learning-rate schedule. Rigorous stability analyses (Lyapunov exponents, boundedness proofs) and gradient-preservation guarantees underpin our design. In experiments, CISMN-1 on a synthetic acoustical regression dataset (541 samples, 22 features) achieved R² = 0.791 and RMSE = 0.059, outpacing physics-informed and attention-augmented baselines. CISMN-4 on the PMLB sonar benchmark (208 samples, 60 bands) attained R² = 0.424 and RMSE = 0.380, surpassing LSTM, memristive, and reservoir models. Across seven standard regression tasks with 5-fold cross-validation, CISMN led on diabetes (R² = 0.483 ± 0.073) and excelled in high-dimensional, low-sample regimes. Ablations reveal a scalability–efficiency trade-off: lightweight variants train in <10 s with >95% peak accuracy, while deeper configurations yield marginal gains. CISMN sustains gradient norms (~2300) versus LSTM collapse (<3), and fixed-seed protocols ensure <1.2% MAE variation. Interpretability remains challenging (feature-attribution entropy ≈ 2.58 bits), motivating future hybrid explanation methods. CISMN recasts chaos as a computational asset for robust, generalizable modeling across scientific, financial, and engineering domains. Full article

Intensive and large-scale drip irrigation plays a crucial role in ensuring cotton production in Northwest China. However, significant differences in cotton production have occurred at times within large-scale irrigation systems, and quantitative information on the importance and interactions of factors related to cotton growth and constraints is scarce. In 2018–2019, we monitored six possible constraints (irrigation depth, soil texture, soil salt, soil moisture, soil inorganic nitrogen and soil organic matter) associated with drip irrigation management and seed cotton yields in a collective drip irrigation system (CDIS, composed of several drip irrigation subsystems (DISs)) in southern Xinjiang to assess the importance of different factors and identify the main constraints. In 2023, other more refined field trials were conducted to further evaluate the influencing mechanism of the main constraints on crop growth in one typical DIS within the selected CDIS. The results revealed large yield differences within the CDIS; although the average seed cotton yield was good (2018: 8051 kg ha⁻¹, 2019: 6617 kg ha⁻¹). Excessive irrigation depths (>500 mm) and coarse soil texture (sand content > 70%) were identified as the main constraints, affecting more than 45% of the plant area in the CDIS based on boundary line analysis (a typical analysis method to study the responses between variables) The results from the DISs revealed that the two constraints directly affected the soil moisture and soil inorganic nitrogen in the root zone, which reduced the effectiveness of irrigation and fertilization under drip irrigation. The Structural Equation Model (used to evaluate the causal relationships among multiple variables) revealed that both irrigation depth and soil texture indirectly affect yield by affecting soil inorganic nitrogen and plant N uptake and that soil nitrogen management is critical in resisting yield decline caused by constraints. An optimized irrigation schedule, improved uniformity of the drip irrigation network and adjusted drip fertilization strategies could be used for site-specific management to address the yield decline due to the main constraints and improve water and fertilizer use efficiency under drip irrigation management. Full article

Background: The increasing detection of non-palpable breast lesions necessitates accurate preoperative localization to ensure complete excision while preserving healthy tissue and optimizing cosmetic outcomes. Traditional wire-guided localization (WL) has been the gold standard; however, it has several drawbacks, including patient discomfort and scheduling challenges. This study evaluates the accuracy and feasibility of radiofrequency identification (RFID) tag localization using the Hologic LOCalizer™ system as an alternative technique. Methods: This retrospective study included 258 consecutive patients who underwent image-guided RFID tag localization from March 2021 to February 2023 from a single-center London breast unit. The primary outcome measured was the accuracy of RFID tag placement, defined as within 10 mm of the target lesion on post-clip mammograms. Secondary outcomes included type and size of lesions, re-excision rates, review of post-operative specimen radiographs, and patient demographics. Results: A total of 297 RFID tags were placed, with 95.6% accurately positioned within the target range. The median target size was 29 mm, with the most common abnormalities being mass lesions (64%). Among the 13 inaccurately placed RFID tags (4.4%), all were identified preoperatively, with two requiring additional wire placements. RFID tags were successfully identified in 92% of specimen radiographs, and 8% of patients required re-excision due to positive or close margins. Notably, patients with multiple RFID tags showed a higher incidence of re-excision. Conclusions: The LOCalizer™ RFID system demonstrated a high accuracy rate for preoperative localization of breast lesions, presenting a viable alternative to WL. This technique improves surgical scheduling flexibility and enhances patient comfort. Comparative studies with other wire-free localization technologies, such as magnetic seeds and radar reflectors, are needed to determine the optimal approach for clinical practice. Full article

In network protocol fuzzing, effective seed scheduling plays a critical role in improving testing efficiency. Traditional state-driven seed scheduling methods in network protocol fuzzing are often limited by imbalanced seed selection, monolithic scheduling strategies, and ineffective power allocation. To overcome these limitations, we propose SCFuzz, specifically by employing a multi-armed bandit model to dynamically balance exploration and exploitation across multiple fuzzing phases. The fuzzing process is divided into initial, middle, and final phases with seed selection strategies adapted at each phase to optimize the discovery of new states, paths, and code coverage. Additionally, SCFuzz employs a power allocation method based on state weights, focusing power on high-potential messages to improve the overall fuzzing efficiency. Experimental evaluations on open-source protocol implementations show that SCFuzz significantly improves state and code coverage, achieving up to 17.10% more states, 22.92% higher state transitions, and 7.92% greater code branch coverage compared to AFLNet. Moreover, SCFuzz improves seed selection effectiveness by 389.37% and increases power utilization by 45.61%, effectively boosting the overall efficiency of fuzzing. Full article

Malt barley is a crucial irrigated crop in the semi-arid Western United States, where the states of Idaho, Colorado, Wyoming, and Utah account for 92% of the irrigated production acreage and 30% of total U.S. production. In this region, spring malt barley’s seasonal evapotranspiration ranges from 400 to 650 mm, and competition for limited water supplies, coupled with drought, is straining regional water resources. This study aimed to investigate the use of canopy temperature for deficit irrigation scheduling of malt barley. Specifically, the objectives were to use data-driven models to estimate well-watered (T_LL) and non-transpiring (T_UL) canopy temperatures, correlate the crop water stress index (CWSI) with malt barley yield and quality measures, and assess the applicability of CWSI for malt barley irrigation scheduling in a semi-arid climate. A 3-year field study was conducted with five irrigation treatments relative to estimated crop evapotranspiration (full, 75%, 50%, 25%, and no irrigation) and four replicates each. Continuous canopy temperature measurements and meteorological data were collected, and a feedforward neural network model was used to predict T_LL, while a physical model was used to estimate T_UL. The neural network model accurately predicted T_LL, with a strong correlation (R² = 0.99), a root mean square error of 0.89 °C, and a mean absolute error of 0.70 °C. Significant differences in calculated season-average CWSI were observed between the irrigation treatments, and relative evapotranspiration, malt barley relative yield, test weight, and plump kernels were negatively correlated with the season-average CWSI, while seed protein was positively correlated. The relationship between daily CWSI and fraction of available soil water was well described by an exponential decay function (R² = 0.72). These results demonstrate the applicability of data-driven models for computing CWSI of irrigated spring malt barley in a semi-arid environment and their ability to assess plant water stress and predict crop yield and quality response from CWSI. Full article

Korean pine is one of the most important tree species in northeastern China, where Korean pine plantations produce timber and edible seeds. Often, seeds create more income than timber. Predicting the timber and cone yields of alternative management schedules of the plantations involves uncertainty because the future climatic conditions for tree growth and cone production are unknown. This study developed a simulation model that generates stochastic variation around the predictions of tree growth and cone yield models, allowing the forest manager to seek cutting schedules that maximize the expected amounts of timber or cones, or the expected economic profit, under uncertain future states of nature. Stochastic analysis also facilitates management optimizations for different risk attitudes. The differential evolution algorithm and the developed stochastic simulation model were used to optimize the management of planted Korean pine. Timber and cone yields of a management schedule were calculated under 100 different scenarios for tree growth and cone production. When the growth and cone yield scenarios were stationary (no temporal trends), the optimal management schedules were similar to those of deterministic optimization. The benefits of stochastic optimization increased when it was assumed that the tree growth scenarios may contain climate-change-induced trends. Non-stationary growth variation led to shorter optimal rotation lengths than stationary growth variation. Increasing risk tolerance shortened optimal rotations. Full article

Microcontroller Units (MCUs) play a vital role in embedded devices due to their energy efficiency and scalability. The firmware in MCUs contains vulnerabilities that can lead to digital and physical harm. However, testing MCU firmware faces challenges due to various tool limitations and unavailable firmware details. To address this problem, research is turning to fuzzing and rehosting. Due to the inherent imbalance in computational resources of the fuzzing algorithm and the lack of consideration for the computational resource requirements of rehosting methods, some hardware behavior-related paths are difficult to discover. In this work, we propose a novel Dynamically Co-directional Guidance Fuzzing (DCGFuzz) method to improve security analysis efficiency. Our method dynamically correlates computational resource allocation in both fuzzing and rehosting, computing a unified power schedule score. Using the power schedule score, we adjust test frequencies for various paths, boosting testing efficiency and aiding in the detection of hardware-related paths. We evaluated our approach on nine real-world pieces of firmware. Compared to the previous approach, we achieved a maximum increase of 47.9% in path coverage and an enhancement of 27.6% in effective model coverage during the fuzzing process within 24 h. Full article

Directed greybox fuzzing can mainly be used for vulnerability mining and vulnerability replication. However, there are still some issues with existing directional fuzzing tools. One is that after providing problematic changes or patches, it is not possible to quickly target and discover the problem. Secondly, it is difficult to break through the magic byte path, making it difficult to mine deep vulnerabilities. This article proposes a new vulnerability mining and repair framework: American Fuzz Lop Plus (AFL++). Firstly, we utilize alias analysis to enhance inter-procedural control flow graphs and redefine the distance calculation formula to obtain more accurate distances. Secondly, the Newton interpolation method is used for the energy initialization of each seed to prevent test cases from being filtered out due to low energy. A heuristic energy scheduling algorithm is proposed to judiciously schedule the energy of seeds. During the path exploration phase, by adjusting the seed energy, shorter-distance seeds quickly reach the target; with increasing time, seeds tend to explore deeper paths. We then represent the symbolic distance by the number of instructions passed to reach the target and investigate the shortest path search strategy to achieve path pruning, alleviating the problem of path explosion. Finally, based on the above methods, we implement the AFL++ prototype system, integrating directed greybox fuzzing with symbolic execution technology for vulnerability discovery. By interleaving directed symbolic execution and directed greybox fuzzing, the efficiency of vulnerability discovery and reproduction is effectively enhanced. Full article

Coverage-guided greybox fuzzing (CGF) has become the mainstream technology used in the field of vulnerability mining, which has been proven to be effective. Seed scheduling, the process of selecting seeds from the seeds pool for subsequent fuzzing iterations, is a critical component of CGF. While many seed scheduling strategies have been proposed in academia, they all focus on the explored regions within programs. In response to the inefficiencies of traditional seed scheduling strategies, which often allocate resources to ineffective seeds, we introduce a novel seed scheduling strategy guided by untouched edges. The strategy generates the optional seed set according to the information on the untouched edges. We also present a new instrumentation method to capture unexplored areas and guide the fuzzing process toward them. We implemented the prototype UntouchFuzz on top of American Fuzzy Lop (AFL) and conducted evaluation experiments against the most advanced seed scheduling strategies. Our results demonstrate that UntouchFuzz has improved in code coverage and unique vulnerabilities. Furthermore, the method proposed is transplanted into the fuzzer MOpt, which further proves the scalability of the method. In particular, 13 vulnerabilities were found in the open-source projects, with 7 of them having assigned CVEs. Full article

Ensemble fuzzing in parallel with heterogeneous fuzzers has been proposed to leverage the advantages of diverse fuzzers and improve testing efficiency. However, in current ensemble fuzzing methods, the collaboration among different fuzzers is achieved solely by synchronizing the seeds discovered by each fuzzer. This results in a high likelihood of different fuzzers choosing the same seeds and creating a large number of equivalent testcases, thus reducing overall fuzzing efficiency. Meanwhile, the existing task division method proposed by AFLTeam is highly coupled with the fuzzer specially designed for it, making it challenging to apply to ensemble fuzzing directly. So, in this paper, we proposed a callgraph-based task division method suitable for ensemble fuzzing. Firstly, we divided the target program’s callgraph into subgraphs (subtasks) balancing expected workloads. Then, we divided the global seed corpus into subcorpora, each corresponding to a subtask, making fuzzers easily accept the subtasks. Finally, we designed synchronization mechanisms for coverage bitmaps and seeds to realize the collaborative fuzzing among different fuzzers and a cyclic subtask scheduling strategy to fully leverage the benefits of ensemble fuzzing. We implemented a prototype called TAEF. The evaluation results show that in the best-case scenario, our method has up to 24% more branch coverage than previous work. Full article

Rhizosphere bacteria can provide multiple benefits to plants, including increased nutrient supply, pathogen/disease control, and abiotic stress tolerance, but results from pot trials do not always translate to field conditions. This study tested whether rhizosphere biocontrol bacteria can also provide plant growth promotion and how benefits can be provided at a commercial farm. Commercial lettuce seeds and plants were treated with rhizosphere biocontrol bacteria Bacillus velezensis UQ9000N, B. amyloliquefaciens 33YE, Brevibacillus laterosporus 4YE, and Pseudomonas azotoformans UQ4510An. 33YE increased the head diameter, plant height, and fresh weight of the Green Moon cultivar, while 33YE, UQ4510An, and UQ9000N increased the fresh and dry weight of Liston, a more heat-tolerant cultivar, via a single seed treatment or repeat root treatments under nursery and field conditions across different inoculation schedules and growth stages. Significant growth promotion was also demonstrated when inoculating field plants after transplanting (in particular for 33YE). Applications of these microbial biostimulants to lettuce seeds or plantlets potentially enable earlier transplanting and earlier harvests. Repeat inoculations using irrigation water and long-lasting formulations may further advance the benefits of these biostimulants as microbial biofertilisers for plant growth promotions in the field. Full article

Guayule (Parthenium argentatum, A. Gray) is a perennial desert shrub with ratoon-cropping potential for multiple harvests of its natural rubber, resin, and bagasse byproducts. However, yield expectations, water use requirements, and irrigation scheduling information for ratooned guayule are extremely limited. The objectives of this study were to evaluate dry biomass (DB), contents of rubber (R) and resin (Re) and yields of rubber (RY) and resin (ReY) responses to irrigation treatments, and develop irrigation management criteria for ratooned guayule. The water productivity (WP) of the yield components were also evaluated. Guayule plants that were direct-seeded in April 2018 were ratooned and regrown starting in April 2020, after an initial 2-year harvest at two locations in Arizona: Maricopa and Eloy on sandy loam and clay soils, respectively. Plots were irrigated with subsurface drip irrigation (SDI) at 50, 75, and 100% replacement of crop evapotranspiration (ET_c), respectively, and furrow irrigation at 100% ET_c replacement, as determined by soil water balance measurements. The Eloy location did not include the 100% irrigation treatment under SDI due to unsuccessful regrowth for this specific treatment. The irrigation treatments at the locations were replicated three times in a randomized complete block design. After 21–22 months of regrowth, the guayule plants were harvested in plots. The results showed that DB increased with the amount of total water applied (TWA, irrigation plus precipitation), while R and Re were reduced at the highest TWA received at both locations. Ultimately, the SDI treatments with 75% ET_c replacement resulted in the best irrigation management in terms of maximizing RY and ReY, and WP for both locations and soil types. Compared to the initial 2-year direct-seeded guayule crop, ratooned guayule required less TWA and attained higher DB, RY, and ReY, as well as higher WP, with average increases of 25% in dry biomass, 33% in rubber yield, and 32% in resin yield. A grower’s costs for planting the initial direct-seeded guayule crop would be offset by the additional yield revenue of the ratooned crop, which would have comparatively small startup costs. Full article

Directed fuzzing aims to focus on fuzzing specific locations within a target program to enhance the efficiency of vulnerability discovery. However, directed fuzzing may yield fewer vulnerabilities and obtain lower code coverage when the specified locations have little to no vulnerabilities. Additionally, the existing directed fuzzing approaches often overlook the differences in variable values when calculating distances between seeds and specific locations. In order to address these issues, this paper introduces RegFuzz, a method that improves seed scheduling in directed fuzzing. RegFuzz utilizes a linear regression model to predict the effectiveness of a seed and allocates more fuzzing opportunities to efficient seeds. Specifically, first, RegFuzz defines several labels with the corresponding trainable weights for each seed. These labels encompass seed coverage, crash efficiency, seed distance, and more. In the calculation of seed distance, RegFuzz takes into account not only the basic block distance but also the variable distance contained within those basic blocks. Second, the linear regression model continually optimizes the label weights during fuzzing, and these optimized weights are employed to predict the effectiveness of seeds. In comparison with AFLGo, AFL, and AFL++, RegFuzz demonstrates higher code coverage and a more efficient bug-finding capability across seven real-world open-source programs. Full article