Search Results (1,303)

The ubiquitous presence of modified mycotoxins such as zearalenone-14-glucoside (ZEN-14G) in agricultural resources presents a critical challenge to livestock safety, specifically regarding their capacity to disrupt lipid metabolism and fermentation in the rumen. This study aimed to evaluate the dose- and time-dependent direct effects of ZEN-14G on rumen fermentation characteristics, nutrient disappearance, and mycotoxin biotransformation under controlled in vitro conditions, and to explore the associated microbial and metabolic responses. An in vitro batch fermentation system was established with three ZEN-14G doses, namely control (CON), low dosage (GL), and high dosage (GH), across three time points (6, 12, and 24 h). Basic fermentation parameters, fat disappearance rates, and ZEN-14G metabolites were quantified, while 16S rRNA gene sequencing and untargeted metabolomics were conducted specifically on 24 h endpoint samples from the CON and GH groups to investigate downstream mechanistic disruptions. The results showed that while baseline pH homeostasis remained unaffected across groups, ZEN-14G dose-dependently inhibited fat disappearance, with the GH group exhibiting a significant reduction throughout fermentation. Targeted quantification confirmed that ZEN-14G was predominantly deglucosylated to free ZEN and reduced to α/β-ZEL. Endpoint multi-omics revealed that although overall α/β-diversity was maintained at 24 h, ZEN-14G induced fine-scale strain-level replacement (12% shared ASVs), marked by the depletion of the key lipolytic bacterium Prevotella sp. R79. Untargeted metabolomics showed marked disruptions in sphingolipid metabolism and linoleic acid oxidation, where the accumulation of cytotoxic oxidized fatty acids (12,13-EpOME) and membrane turnover markers (sphingosine) positively correlated with enriched Bacillota. In summary, under macroscopic acid–base homeostasis, masked ZEN-14G directly impairs ruminal lipid metabolism, alters microbial community structure at the strain level, and perturbs cell membrane lipid turnover strictly within an in vitro system. These findings establish a key mechanistic baseline for masked mycotoxin biotransformation in the rumen, highlighting the necessity for future in vivo feeding trials to fully evaluate their systemic risk profile in ruminants. Full article

The configuration of AUTOSAR Basic Software (BSW) modules relies on verbose AUTOSAR XML (ARXML) files that are complex, error-prone, and costly to produce manually—particularly for safety-critical modules governed by ISO 26262. This paper presents a two-stage approach to automating BSW configuration generation that generalizes to any ECU Configuration (ECUC)-based module: a fine-tuned large language model (LLM) generates a compact JSON intermediate representation capturing only semantically meaningful parameters, which a deterministic expansion function reconstructs into schema-conformant ARXML. We fine-tune three open-weight models (Qwen3-8B, Ministral-3-8B-Instruct, Llama 3.1 8B) with Quantized Low-Rank Adaptation (QLoRA) on 6050 compositionally generated Watchdog Manager (WdgM) samples spanning five complexity tiers with 30+ prompt templates, and introduce a hierarchical evaluation pipeline combining schema validation with referential integrity, structural completeness, parameter accuracy, and semantic constraint satisfaction. The compact representation reduces output tokens by approximately 8–10× compared to full ARXML. All three models achieve closely comparable performance (0.815–0.836 overall score), with Llama 3.1 8B scoring highest (0.836) and every model reaching ≥93% schema validity and ≥72% parameter accuracy—an 8.4× improvement over zero-shot baselines. Decomposing generation into LLM-driven semantic capture and deterministic expansion is an effective strategy for verbose, schema-governed configuration formats, extensible to other AUTOSAR modules beyond WdgM. Full article

Limonitic nickel laterite is difficult to sinter because of its high loss on ignition, high combined-water content, and complex refractory mineral composition, which often result in poor sinter strength and high solid fuel consumption. In this study, a pellet-sintering process was adopted to improve the sintering performance of limonitic nickel laterite. Pot sintering tests were carried out to investigate the effects of key process parameters—including moisture content, solid fuel dosage, return fines dosage, and drying–holding regime—on yield, tumble index, productivity index, and solid fuel consumption. The selected conditions were determined as follows: moisture content of 21%, solid fuel dosage of 5.8%, return fines dosage of 25%, drying at 450 °C for 5 min, and holding at 1000 °C for 10 min, under a fixed basicity of 1.5 and a bed height of 850 mm. Under these conditions, the yield, tumble index, productivity index, and solid fuel consumption reached 70.19%, 57.87%, 1.37 t·m⁻²·h⁻¹, and 95.86 kg·t⁻¹, respectively. Compared with conventional sintering, pellet sintering increased the yield, tumble index, and productivity index by 24.05%, 35.56%, and 31.73%, respectively, while reducing solid fuel consumption by 22.87%. XRD, OM, quantitative image analysis, and SEM–EDS analyses showed that pellet sintering reduced the average two-dimensional pore area fraction from 33.29% to 18.05% and the large-pore area fraction from 22.25% to 11.61%, while promoting a more continuous bonding structure characterized by a spinel-rich mineral framework, spinel–olivine eutectic-type composite bonding phases, and SFCA-type bonding phases. These results demonstrate that pellet sintering is a feasible route for improving the sintering performance and consolidation behavior of limonitic nickel laterite. Full article

To address the difficulty in accurately characterizing the fault current response of renewable energy grid-connected devices during high-/low-voltage ride-through, this paper proposes a fault current response modeling and parameter identification method based on adaptive nonlinear compensation. First, with the fault voltage and pre-fault operating point as input variables, a basic quadratic equivalent model is established to describe the main variation characteristics of active and reactive currents during high-/low-voltage ride-through. Second, nonlinear compensation terms are introduced into the basic model to correct the response deviation caused by the simplification of fast electromagnetic control links in the electromechanical transient equivalent process, thereby improving the representation capability of the model for complex fault current characteristics. Furthermore, considering that the structural parameters of the nonlinear compensation terms are difficult to directly identify using the traditional least squares method, a differential evolution–ridge regression (DE–Ridge) hierarchical identification method is proposed. In this method, the differential evolution algorithm is used in the outer layer to adaptively optimize the nonlinear structural parameters, while ridge regression is used in the inner layer to solve the corresponding linear coefficients. Case study results show that, compared with the traditional quadratic equivalent model and the fixed nonlinear compensation model, the proposed method further reduces the fault current identification error on the validation set and improves the identification accuracy and generalization capability of fault current responses during high-/low-voltage ride-through. Full article

TiAl alloy exhibits excellent strength, oxidation resistance and creep resistance, making it a preferred candidate material to replace high-temperature alloys. Currently, TiAl alloy has been widely applied in aerospace, the marine industry and other fields involving high-stress contact or highly corrosive environments. Selective laser melting (SLM) technology provides a brand-new approach for the fabrication of TiAl alloys, which enables direct forming of workpieces with complex structures and significantly reduces manufacturing cycles. However, the quality and performance of SLM fabricated TiAl alloys are highly dependent on laser energy input. Therefore, this study fabricated TiAl4822 alloy under different SLM process parameters, and systematically conducted investigations on its tribological properties and electrochemical corrosion behavior. The experimental results show that the SLM process did not alter the basic phase composition of TiAl4822 alloy, with Ti_0.6Al_0.4 as the dominant phase. TiAl4822 alloys fabricated under the parameter combinations of 1000 mm/s + 140 W exhibited outstanding wear resistance, and the wear mechanism transformed from severe adhesion and abrasive wear to mild oxidative wear. When the laser power was 100 W and the scanning speed was 1200 mm/s, the alloy achieved the highest corrosion resistance, with the corrosion potential reaching the maximum value of −390.065 mV and the corrosion current density decreasing to the minimum value of 8.73 × 10⁻⁶ A/cm². Thus, different parameter combinations can realize the optimization of tribological properties and electrochemical corrosion performance respectively. This study lays a theoretical foundation for promoting the high-performance engineering application of this alloy in harsh wear-resistant and corrosion-resistant environments. Full article

This tutorial takes the reader on a historical and technical journey from the simple Perceptron (1958) to modern Convolutional Neural Networks (CNNs) that dominate spatial data processing (images and video). We start with the Perceptron’s linear classifier, then expose its inability to learn non-linear patterns (e.g., XOR), which motivates the Multi-Layer Perceptron (MLP) and the backpropagation algorithm. Next, we discuss the limitations of MLP when faced with structured data like images—parameter explosion, loss of spatial information, and lack of translation invariance—and use these limitations as a natural springboard to the core ideas of CNNs: local connectivity, weight sharing, and hierarchical feature learning. Throughout, we provide intuitive explanations, mathematical formulations, and step-by-step numerical examples (e.g., a complete forward and backward pass for a small network, and a manual 2D convolution). Clear graphical representations and examples help readers understand each concept. The tutorial concludes with a detailed walkthrough of influential CNN architectures (LeNet-5, AlexNet, VGG, GoogLeNet, ResNet, DenseNet, and EfficientNet) and also discusses more recent attention-based models (e.g., Vision Transformers and ConvNeXt), explaining why each was necessary and how it advanced the field. Aimed at students and practitioners with a basic knowledge of calculus and linear algebra, this tutorial connects foundational ideas to state-of-the-art deep learning, focusing on spatial data. It is designed for readers who want to understand why each architectural choice was made, not just what the final model looks like. Full article

Advertisement calls convey individual information and play a vital role in amphibian reproduction. Body size is one of the key factors determining the diversity of frog advertisement calls. P. terentievi is distributed in mountainous areas across Tajikistan and Xinjiang, China. However, bioacoustic research on this species remains an unexplored field. In this study, the advertisement calls of 20 male frogs were recorded in the breeding season and six distinct call types were described based on spectrum morphology and auditory characteristics. The coefficient of variation analysis indicates some of their call types may serve as effective cues for individual recognition. Their advertisement calls exhibit a typical sentence structure and often conclude with a distinctive ending word. We also observed a distinct diel rhythm in their calls: advertisement calls begin before sunset, peak after sunset, and continue until noon. Furthermore, their acoustic parameters are significantly correlated with their head size, leg size and body weight. Males with larger heads and longer legs often make calls at higher frequencies, possibly to stand out in the louder chorus to enhance the efficiency of attracting females. This basic study can provide support for the behavioral and evolutionary biology research of Pelophylax species. Full article

Clayey sand is a typical transitional coastal alluvial soil controlled by both coarse-grain friction and fine-grain cementation. Current studies focus mostly on remolded samples, lacking systematic understanding of water-induced structural degradation and quantitative physico-mechanical correlations for natural undisturbed clayey sand. In this work, 74 intact undisturbed specimens (0.5–23.0 m depth) were tested via basic physical tests, one-dimensional consolidation and consolidated-undrained triaxial shear tests. Pearson correlation analysis was performed to establish prediction relationships between routine physical indices and mechanical parameters. Results show the soil is classified as SC clayey sand with 39.70% fines and an average natural water content of 23.17%. Natural water content dominates soil engineering performance, presenting strong linear correlations with dry density and void ratio (|r| = 0.90). Higher water content and void ratio increase compressibility and reduce shear strength. The compression coefficient and compression modulus exhibited a consistent nonlinear relationship, reflecting the inherent linkage between these two compression parameters. Burial depth has little influence on soil properties, and plasticity index only serves for soil classification. Mechanistically, increasing moisture may thicken adsorbed water films, weaken interparticle contact and matric suction, and the fine particle-filled skeleton may further enhance the water sensitivity of the soil. The established prediction models support fast evaluation of soil mechanical behaviors, offering theoretical and practical support for geotechnical design of similar coastal clayey sand strata. Full article

To address three major industry pain points—the lack of quantified profit-sharing standards in the two-stage pricing under the separation model of military research and production, the absence of stable Nash equilibrium in single-layer synchronous optimization, and insufficient incentives for full-cycle process optimization in design units—this paper constructs a two-level Stackelberg leader–follower game model with the general contracting unit as the leader and design and general contracting units as followers. This aligns with current prototype incentives and phased pricing policies for production rewards and penalties. At the theoretical level, it improves the complete proof system for the two-stage concave profit two-level Stackelberg Nash equilibrium, distinguishes the mathematical differences in equilibrium existence between sequential decision-making and synchronous optimization, and extracts general rules for phased differentiated profit sharing: high-innovation segments should be allocated more profit weight; simply maximizing total alliance profit may cause imbalanced interests, while introducing a minimum net profit-weighted objective can achieve Pareto improvements without profit loss. This conclusion can be applied to multi-stage general contracting scenarios across industries, such as EPC and military–civil collaborative innovation, enriching the basic theory of profit sharing and hierarchical games. Theoretically, the existence of the lower-level Nash equilibrium is proven using Brouwer’s fixed-point theorem, and combining it with the strictly monotonically decreasing feature of the best response function, uniqueness of the equilibrium is derived. Multiple sets of differentiated initial values are simulated to rule out multi-equilibrium bifurcation risk. The model incorporates the military’s reward and penalty policies as rigid exogenous constraints, sets dual individual rationality constraints of ‘cooperative profit greater than baseline profit with no allocation, and both parties’ net profit non-negative,’ and introduces differentiated cost-reduction efficiency and quadratic increasing effort costs to characterize the heterogeneous input of the two types of development entities. For models with piecewise nonlinearity and multi-constraint nonconvex structures, this paper modifies the standard PSO into a Bi-PSO solving framework through hierarchical temporal adaptation. It does not innovate the underlying particle update mechanism and is only used to match the sequential decision order of the leader–follower game. By comparing five algorithms—IPM, GA, SA, DE, and adaptive PSO—through 20 repeated simulations: gradient-based interior point methods easily get stuck in locally invalid solutions that violate cooperation thresholds; differential evolution has the best numerical global search performance, but all general evolutionary algorithms optimize allocation and effort variables simultaneously, disrupting the Stackelberg hierarchical timing. Only Bi-PSO maintains consistent game logic. Using a pricing case for a certain type of equipment and jointly calibrating all parameters with policy documents, three simulation scenarios were set up: no allocation, equal 50/50 split, and single-layer profit maximization. Under the no-allocation mode, R&D investment from the design unit drops to zero and alliance benefits plummet; a blanket equal split ignores differences in technical contributions across two stages, leading to clear efficiency losses; single-layer optimization only pursues total profit maximization, causing a severe imbalance in profit distribution. The two-layer basic framework can achieve the upper limit of alliance benefits, and by adding a weighted optimization goal that considers both total profit and cooperation fairness, it can achieve equal net profits for both parties without reducing overall profit. Through single-parameter sweeps and two-factor heatmap simulations, the study further revealed the coupled effects of main party efficiency and mass production rewards and penalties on equilibrium input and optimal sharing ranges. A robust check was performed by replacing the logarithmic concave output function, producing a standardized allocation range resilient to parameter perturbations: optimal split for the prototype stage is 0.4–0.6 for the design unit, and for mass production stage 0.7–0.9. The findings suggest that high-contribution stages in multi-phase collaboration contracts should receive more benefits, and a weighted fairness objective can achieve Pareto improvements. These conclusions can extend to multi-stage collaboration scenarios such as EPC and military–civilian cooperation. Theoretically, this research further completes the equilibrium proof system for two-party concave payoff two-layer games, providing a new reference for the theory of phased differentiated benefit-sharing contracts in the military sector. Methodologically, it proposes a two-layer intelligent solving tool adapted to leader–follower sequential decisions, effectively mitigating issues where single-layer model equilibria fail or analytical algorithms struggle with multi-constraint nonconvex games. The results can provide quantitative support for the military, general contracting unit, and design unit in drafting equipment incentive pricing contracts and managing full-cycle cost collaboration. Full article

This study presents a controllable alkaline degradation strategy for preparing biomimetic archaeological Chinese fir through potassium hydroxide (KOH) impregnation, addressing the limitations of scarce and heterogeneous authentic archaeological wood for conservation research. Four KOH concentrations (5%, 10%, 20%, and 30%) combined with different treatment cycles (2–6 cycles) were applied to induce controlled degradation states under laboratory conditions. The results demonstrated that KOH concentration and treatment cycles effectively controlled the structure–property evolution of Chinese fir. Mass loss increased to 42.14%, while maximum water content reached 427.35%, accompanied by reductions in oven-dry and basic densities. X-ray diffraction analysis revealed a decrease in cellulose crystallinity from 34.10% to approximately 24%–26%, indicating partial disruption of cellulose crystalline domains, while Fourier transform infrared spectroscopy confirmed preferential degradation of hemicellulose through alkaline hydrolysis and relative preservation of lignin structures. Scanning electron microscopy further demonstrated tracheid deformation, lumen collapse, and enhanced pore connectivity after severe treatment. Based on the combined evaluation of physical, chemical, mechanical, and microstructural parameters, a three-level biomimetic archaeological wood grading system was established to correlate KOH treatment conditions with different degradation states. This study provides a reproducible approach for fabricating standardized biomimetic archaeological wood models and offers a reliable platform for conservation material evaluation and degradation mechanism studies. Full article

Artificial Intelligence (AI) is being integrated into the architectural profession via processes such as generating images based on design parameters; providing support to help create written descriptions of designs; assisting in the visualisation of buildings before they are built; allowing for greater use of parameters in construction; providing documentation to help complete projects in record time; and assisting in making design decisions. The success of integrating AI into practice depends not only on the use of tools but also on firms’ maturity in integrating AI into their workflows, employees’ capabilities, project teams’ operational efficiency, the impact of investment decisions, and the overall governance of the profession. This research evaluates the maturity of AI integration in architectural practice in the Kurdistan Region of Iraq. To do so, it develops and operationalises the AI Integration Maturity Index (AIMI), an eight-component formative composite index scored 0–37 and organised into five maturity bands (Non-adopter, Exploratory, Occasional, Integrated, and Advanced Strategic). The index comprises adoption, usage, diversity of tools, breadth of workflows, project penetration, staff involvement, training/capacity building, and governance/strategic focus. The AIMI is treated as a literature-derived formative diagnostic tool rather than a universal weighting standard, and was developed through a structured literature synthesis, expert pilot review, and internal-structure validation. Accordingly, its component logic and internal statistics are reported as a transparency and coherence check rather than as reflective reliability claims: Cronbach’s alpha (0.922) is presented descriptively to show component co-movement given the formative specification, while inter-coder reliability (kappa = 0.96) supports the qualitative benchmark coding. The study employs a mixed-methods descriptive comparative methodology consisting of a structured survey instrument administered to 100 architectural firms operating in the local market and structured asynchronous text-based interviews with 10 international architectural firms, comparing the resulting profiles with selected, generally accepted benchmarks and with a sample of leading firms engaged in architectural practice worldwide. On average, total AIMI scores in the local sample were 18.14 out of 37, indicating that local firms are broadly adopting and using AI (82% currently use AI on a regular or occasional basis). By contrast, the international sample yielded a mean AIMI score of 28.60, indicating that local firms exhibit a significantly lower level of maturity than the international benchmark, with the largest gaps in staff involvement, project penetration, and overall engagement with AI use. The paper concludes that the primary challenge facing architectural firms in the Kurdistan Region is no longer basic awareness or technological infrastructure, but rather the transition from broad and superficial AI adoption to a systematic, structured, project-based, and well-governed integration of AI within the architectural profession. Full article

This article presents an Artificial Intelligence and Internet of Things (AI–IoT/AIoT) decision-making framework for smart water management and sustainable aquaponic systems. The framework connects sensors, IoT telemetry, machine learning algorithms, and real-time monitoring of key water quality parameters, with the aim of early detection of deviations, operational decision support, and risk reduction in system management. A special contribution of the paper is that water is viewed simultaneously as a limiting resource, a biological factor and an operational cost. The proposed framework defines the structure of a decision support system, including monitoring of temperature, pH value, dissolved oxygen, ammonia/ammonium, EC/TDS value, water flow, feeding regime, and basic biological indicators. In the methodological sense, the paper presents a conceptual-methodological framework based on publicly available data, scenario estimates, and a clearly defined protocol for future pilot validation of high-frequency operational data. In addition to the technical architecture, the framework includes elements of responsible application of AIoT systems: data quality control, sensor deviation and drift detection, model explainability through XAI/SHAP, data transfer security, and the possibility of human confirmation before risky interventions. The economic part of the paper shows ROI/NPV as a scenario estimate, based on explicit assumptions about costs, resource consumption and possible operational savings, and not as a confirmed financial result. The framework is aligned with the principles of the circular bioeconomy, as it links the monitoring of water quality, the reduction in nutrient losses, the reuse of resources, and better planning of interventions in aquaculture and aquaponics. The results indicate the potential of AIoT approaches to improve monitoring, transparency and operational decision-making, while the actual effects on productivity, water consumption, food consumption, energy, and economic sustainability must be confirmed in a pilot phase. Full article

School gymnasiums in Taiwan often serve as emergency shelters, demanding vital seismic evaluation. Conventional collapse ground acceleration (Ac) assessment via finite element software requires exhaustive computational time and detailed reinforcement data unavailable during early-stage planning. To bridge this operational gap, this study develops a data-driven framework for preliminary seismic evaluation. This framework offers structural designers or architects a rapid planning tool for prospective facilities and enables regional screening to identify vulnerable existing gymnasiums, complementing high-fidelity modeling. An evolutionary support vector machine inference system (ESIS) is proposed to efficiently predict Ac utilizing basic geometric and layout parameters. Concurrently, gene expression programming derives explicit equations to enhance engineering interpretability, while multivariate statistical analyses, including principal component analysis and cluster analysis, together with correlation analysis, are incorporated to investigate seismic characteristics statistically and identify dominant seismic factors. Based on an empirical inventory of 479 standalone gymnasiums, results reveal that dominant seismic factors vary distinctly among structural clusters. Compared with several baseline AI models, the ESIS achieves superior generalization performance, yielding RMSE values of 0.067–0.086 and R² values of 0.794–0.918 on testing datasets. Ultimately, this integrated framework provides an efficient and highly accurate decision-support tool for macro-scale screening and preliminary seismic performance assessment. Full article

Despite the growing demand for sustainable agricultural systems, the long-term effects of tillage practices remain controversial. No-tillage (NT) systems offer several potential benefits, including improved soil structure, enhanced soil biological activity, and reduced environmental stress, but their application can also be associated with challenges such as difficulty in weed control or variable crop yield. Although previous studies have extensively investigated the effects of NT systems on soil and crop, limited knowledge is available about the cellular adaptation mechanisms of plants, especially gene expression and biochemical responses. The aim of this study was to compare the effects of conventional tillage (CT) and NT systems in sunflower plants using an integrated transcriptomic and biochemical approach. We performed genome-wide transcriptomic analysis based on next-generation sequencing on leaf samples from three different field sites, supplemented by measurements of biochemical parameters related to selected metabolic processes. Exploratory transcriptomic analysis indicated that several gene expression changes related to primary metabolic processes occurred in plants grown in the NT system compared to the CT system. These included processes related to photosynthesis, cellular respiration, carbohydrate metabolism and the biosynthesis of some amino acids. In parallel, we observed transcriptional patterns indicating increased activity of several secondary metabolic pathways, which may be related to adaptation mechanisms to environmental stress. Determination of total soluble sugar, crude protein, total phenolics and total flavonoids provided independent biochemical support for the changes indicated by the transcriptomic results. Our results suggest that the tillage system affects the cellular regulatory processes of sunflower. During adaptation to a no-tillage environment, plants can simultaneously maintain basic metabolic processes and activate defense mechanisms that may contribute to adaptation to changed growing conditions. Our study contributes to a better understanding of the molecular and physiological consequences of tillage systems in plants. Full article

One of the most promising absorber materials for solar applications is copper zinc tin sulfide/selenide (CZTS), which has good optical properties and basic elements that are readily available, affordable, and environmentally acceptable. This study examines how pulse timing and current density affect the electrodeposition of Cu₂ZnSnS₄/Cu₂ZnSn(S,Se)₄ (CZTS/CZTSSe) thin films from a diluted electrolyte, including deposition quality, film configuration, elemental composition, crystallinity, and photovoltaic performance. It evaluates the impact of these factors on device performance, film properties, layer’s compactness, surface homogeneity, microcrack-free morphology, compositional homogeneity, crystallinity, and suitability for solar device manufacturing. Using a pulsed-current technique, CZTS precursor layers were electrodeposited in a low-concentration solution with periodic changes in current density of roughly 5.3–5.9 mA/cm² and pulse-on/off durations of 50/50, 100/100, and 250/250 ms. The deposited precursors were then added to fully built CZTS-based solar cell topologies after sulphurization or selenization. Structural characteristics were analyzed using X-ray diffraction (XRD), and composition and elemental distribution were assessed using energy-dispersive X-ray spectroscopy (EDS). Measurements of transmittance and reflectance were used to evaluate optical properties relevant to photovoltaic performance. In contrast to films deposited at higher current densities and longer off-times, moderate current densities combined with short off-times yield dense, microcrack-free films with improved crystallinity and near-stoichiometric Cu/(Zn + Sn), Zn/Sn, and chalcogen/metal ratios. Additionally, absorber layers with appropriate optical band gaps and improved device performance are produced by these optimized pulse parameters. Overall, the study shows that controlling pulse parameters in diluted electrolytes is a useful tactic for improving the quality of CZTS films and developing low-cost, solution-based fabrication techniques for high-performance CZTS solar cells. Full article