Search Results (780)

In high-demand financial environments, employees’ capacity to regulate and sustain personal energy may constitute a critical yet underdeveloped organizational resource. Drawing on the Job Demands–Resources (JD-R) model and Conservation of Resources (COR) theory, this study conceptualizes energy management as a multidimensional personal resource that may support adaptive functioning and innovation under demanding work conditions. Despite increasing conceptual attention to energy-related constructs, systematic scale validation and cross-level performance evidence remain limited. This research adopts a two-study design to develop and validate a multidimensional Energy Management Scale within financial institutions. Study 1 (N = 299 employees from 11 financial institutions) examines the factorial structure, reliability, and nomological validity of the scale. Confirmatory factor analysis is used to examine the proposed four-dimensional configuration of physical, emotional, mental, and spiritual energy. The scale demonstrates acceptable internal consistency reliability and evidence of structural validity, including convergent and discriminant validity. Structural modeling results reveal that overall energy management is positively related to innovative behavior and organizational citizenship behavior. However, perceived workload was significantly associated only with physical energy, suggesting that demand-related mechanisms of energy may not operate uniformly across energy components. Additionally, exploratory institution-level aggregation analyses showed preliminary, counterintuitive negative associations between mean organizational energy levels and return on equity (ROE) in some years. Given the limited number of institutional clusters, these cross-level findings are preliminary and intended to provide initial external criterion evidence rather than confirmatory causal inference. Study 2 (N = 148 employees from two institutions) further examines alternative scale versions and external validity through stress coping capacity, job satisfaction, and life satisfaction. Results were discussed to examine the robustness and predictive validity of the scale across samples. Collectively, this study advances energy management research by providing a psychometrically supported measurement instrument and preliminary multilevel evidence of its organizational relevance. The findings position energy management as a measurable human-capital resource with implications for sustainable workforce innovation and performance in financial institutions. Full article

Zinnia (Zinnia elegans) is a widely cultivated ornamental plant whose growth and floral traits can be compromised by abiotic stresses, especially water deficit. Melatonin (MEL) has stood out as a plant growth regulator with antioxidant potential, capable of mitigating the adverse effects of water stress. This study aimed to evaluate the effects of foliar MEL application on the growth and floral characteristics of Z. elegans under different water regimes. The experiment was carried out in a greenhouse using a randomized block design in a 4 × 2 factorial scheme with five replications. The first factor consisted of four water conditions: 80% of field capacity (FC) (no stress), 20% of field capacity (severe stress), early water restriction (20% of FC followed by 80% of FC), and late water restriction (80% of FC followed by 20% of FC). The second factor corresponded to the foliar application of MEL at two concentrations (0.0 and 1.0 mM). Growth variables (plant height, stem diameter, number of leaves, leaf area, and dry mass of different organs) and floral characteristics (number of petals, area, perimeter, and diameter) were evaluated. Water deficit, especially under severe stress (20% FC), significantly reduced plant growth and floral traits, decreasing the total dry mass by 60.27% and total floral area by 47.57% compared to the control. However, the application of 1.0 mM MEL attenuated the deleterious effects of water deficit, increasing total dry mass by 50.26% and total floral area by 25.56% under severe stress (20% FC) compared to untreated plants, making it a promising strategy for zinnia production in environments with limited water availability. Full article

The geographical origin of aniseed (Pimpinella anisum L.) represents a key quality determinant, as it directly influences the chemical composition and commercial value of its essential oil. Agronomic traits of aniseed (plant height, umbel diameter, number of umbels per plant), productivity-related traits (number of seeds, thousand-seed weight, yield per plant, plant biomass, harvest index, yield per hectare, essential oil content and yield), and physiological traits (germination energy and total germination) exhibit variations depending on geographical origin. The study proposes an integrated framework for accurate classification by combining agronomic, productivity, and physiological data with GC-MS profiles and advanced machine learning (ML) techniques. A total of 144 samples were analyzed, based on a factorial design including three locations, six fertilizer treatments, two years, and four replications. trans-Anethole was the dominant compound in all samples (89.508–101.441%). Several classification models, including artificial neural networks, random forests, MARSplines, boosted trees, interactive trees, naïve Bayes, and support vector machines, were evaluated to discriminate samples by geographical origin using agro-meteorological and GC-MS data. The results indicate that AI and ML approaches effectively captured complex non-linear relationships. Overall, the multi-model framework highlights the strong potential of machine learning for agro-food authentication, supporting improved traceability, site-specific decision-making, and quality control. Full article

As plant factories with artificial lighting (PFALs) expand beyond leafy greens to include flowering crops, understanding the role of light quantity and quality in regulating plant development becomes increasingly important. This study investigated how far-red (FR) radiation and daily light integral (DLI) influences flowering time, plant morphology, and FLOWERING LOCUS T (FT) gene expression in petunia. Four facultative commercial cultivars and one obligate long-day model cultivar, ‘Mitchell Diploid’, were grown under two DLI conditions with a gradient of FR radiation. Increasing FR consistently accelerated flowering across both DLI conditions without reducing flower bud number, branch number, or shoot fresh weight at harvest. Higher DLI generally produced more compact plants by reducing plant height and canopy area, whereas increasing FR promoted stem elongation, particularly in ‘Mitchell Diploid’. Cultivar responses varied substantially, indicating that genotype is an important factor when applying FR-based lighting strategies. To explore potential molecular mechanisms associated with FR-induced flowering acceleration, the expression levels of five petunia homologs, PhFT1 to PhFT5, were analyzed across developmental stages under low and supplemental FR conditions. PhFT2-5 expression was generally associated with flower initiation, with PhFT2 showing the strongest positive relationship with flowering and responsiveness to supplemental FR, whereas PhFT1 showed a decreasing trend over time and was not positively associated with flowering. Overall, this study demonstrates that FR radiation can be used to accelerate petunia flowering in PFAL production without compromising key quality traits and suggests that specific FT homologs, particularly PhFT2, may contribute to FR-mediated flowering regulation. Full article

Central Composite Design (CCD) is often used in Response Surface Methodology (RSM) to fit second-order models. However, little is known about their behavior as the number of factors increases. The study develops a cross-dimensional evaluation methodology to investigate the behavior of three types of CCDs as the number of factors increases from $k=3$ to $k=10$. The CCD families considered in this study include Face-Centered CCD (FCCD), Rotatable CCD (RCCD), and Spherical CCD (SCCD). The designs are evaluated using the alphabetic optimality criteria of D-, A-, and G-optimality. A total of 1080 design configurations are generated by varying the replication levels of factorial points, axial points, and center points. The results show that classical CCDs encounter structural limitations in high-dimensional settings. When $k\ge 7$, the quadratic model matrix becomes rank deficient due to the fixed factorial core of 32 runs, which takes on a budget-constrained approach and uses regularization as a diagnostic tool to evaluate the stability of CCDs in the rank-deficient regime. In lower dimensions ($k\le 6$), RCCD consistently provides the highest efficiency, followed by SCCD and FCCD. In higher-dimensional settings, fixed regularization becomes less effective as the scale of the information matrix increases. To address this limitation, scaled regularization is introduced by adjusting the regularization parameter according to the average eigenvalue of the information matrix. The results indicate that rotatable designs provide greater efficiency and stability in high-dimensional settings compared with spherical and face-centered designs. Full article

Nitrogen (N) is a key nutrient for upland rice (Oryza sativa L.), and plant growth-promoting rhizobacteria (PGPR) have been investigated as a sustainable strategy to improve plant nutrition and crop performance. This study evaluated the effects of N topdressing and PGPR inoculation on leaf chlorophyll index (LCI), leaf nutrient concentrations, and yield components in upland rice. A field experiment was conducted in a randomized block design (4 × 6 factorial) with four N rates (0, 40, 80, and 120 kg ha⁻¹) and five PGPR strains (Azospirillum brasilense, Nitrospirillum amazonense, Bacillus subtilis, Priestia aryabhattai, and Methylobacterium symbioticum), plus a non-inoculated control. No significant interaction between N rates and PGPR inoculation was observed. Nitrogen increased leaf phosphorus (P), potassium (K), and magnesium (Mg) concentrations and panicle number; however, it also increased unfilled grains, reduced grain weight, and did not affect grain yield. Azospirillum brasilense increased LCI by 25.7%. Bacillus subtilis and A. brasilense increased leaf N, K, Mg, copper (Cu) and manganese (Mn) concentrations. Azospirillum brasilense, B. subtilis, N. amazonense, and P. aryabhattai reduced unfilled grains, increased grain weight and grain yield by up to 10.7%, whereas M. symbioticum did not differ from the control in grain yield. Under the conditions of this study, nitrogen was not limiting for grain yield, and all strains, except M. symbioticum, were associated with increases in grain yield and changes in plant nutritional status. Full article

Research on dishonest behavior within organizational contexts has expanded rapidly in recent years. However, the structural organization of dominant explanatory mechanisms within this literature remains insufficiently clarified. This study provides a theory-informed bibliometric analysis focusing on a deliberately selective segment of the workplace dishonesty literature. Rather than attempting an exhaustive census, the study maps a corpus centered on dominant socio-cognitive and organizational explanatory frameworks in order to examine how these mechanisms are positioned, interconnected, and evolving within this theory-filtered segment. To ensure a transparent and reproducible review process, the study was conducted in accordance with the PRISMA 2020 guidelines, which guided the identification, screening, and eligibility assessment of the literature. Drawing on a systematically constructed corpus retrieved from Web of Science and Scopus and covering the period 1989–2025, the bibliometric analysis was conducted using Biblioshiny 4.5.2 on a final dataset of 679 documents. The analysis integrates performance indicators with science-mapping techniques, including keyword co-occurrence networks, thematic mapping, multiple correspondence analysis, thematic evolution, and global citation analysis. The findings indicate that this theory-based subset of the literature has developed steadily over time alongside a clearer structuring of publication outlets. Conceptually, it remains largely organized around a small number of recurring mechanisms, most notably ethical climate and moral disengagement. Thematic analyses suggest a degree of theoretical stabilization alongside diversification within this selected corpus, while factorial mapping suggests recurring contrasts between cognitive, normative, and organizational explanatory logics. From a longitudinal dynamic perspective, the mapped patterns suggest a possible movement toward more context-sensitive and governance-oriented perspectives; however, this should be interpreted as an inferential reading of this selected corpus. Overall, the findings suggest that, within this corpus, unethical workplace behavior is increasingly conceptualized as a context-dependent socio-cognitive phenomenon shaped by justificatory mechanisms, organizational environments, and performance-related pressures. This review contributes to the fields of behavioral ethics and organizational behavior by providing a structured reading of this specific body of work, clarifying its conceptual organization, identifying its main developmental trajectories, and outlining a theoretically grounded future research agenda for this selected body of literature. Full article

The availability of water is a limiting factor for the growth and productivity of yellow passion fruit (Passiflora edulis Sims). The use of bioregulators has been investigated as a strategy to mitigate the effects of abiotic stress. Different concentrations of SNP were evaluated on growth, gas exchange, photosynthetic pigments, chlorophyll fluorescence, and enzymatic activity in Passiflora edulis seedlings under different water conditions. The experiment was conducted in a randomized block design, in a 2 × 4 factorial scheme, with two irrigation conditions (80 and 30% of field capacity), combined with three concentrations of SNP (50, 100 and 250 µM) and water (control), with five replications. Water deficit reduced morphological, physiological, and enzymatic parameters. The application of SNP increased root fresh mass (23.56 g at the 100 µM dose) and leaf dry mass (8.21 g at 250 µM SNP), with increases of 24.52% and 30.52% compared to the values obtained under the 50 µM dose, respectively. The highest number of leaves (14) and leaf area (1183.3 cm²) was observed at 250 µM SNP, corresponding to increases of 7.70% and 17.27%, respectively, compared to plants without SNP application. Water deficit reduced growth, gas exchange, chlorophyll fluorescence, and enzymatic activity. SNP promotes improvements in growth; however, it does not mitigate water deficit effects in Passiflora edulis seedlings. Full article

The incorporation of the Internet of Things (IoT) in indoor agricultural systems has become an essential tool for monitoring and analyzing environmental variables, contributing to more efficient decision-making. This article presents the design and implementation of an IoT-based digital agriculture system applied to a Plant Factory (PF) for hydroponic vegetable cultivation using the Nutrient Film Technique (NFT). The objective of this study was to develop a system capable of effectively monitoring and controlling the environmental variables that directly influence the microclimate of a closed agricultural environment. The proposed system integrates a four-layer IoT architecture based on a MODBUS RS-485 communication bus, which allows for continuous data acquisition and the operation of multiple sensors and controlled devices. Additionally, user-oriented tools such as a human–machine interface (HMI), a web application, a mobile application and an automatic alert module were incorporated, enhancing accessibility and remote supervision. Experimental results showed stable control performance of ambient temperature (TA), relative humidity (RH), photoperiod, and photosynthetic photon flux density (PPFD), along with continuous monitoring of CO₂ concentration. A 30-day validation experiment using Swiss chard (Beta vulgaris L. var. cicla) under controlled conditions was conducted. The results showed progressive plant development, with leaf area increasing from 15.17 cm² to 690.39 cm², plant height from 7 cm to 31 cm, fresh weight from 23 g to 171 g, and the number of leaves from 9 to 20. These results support the functional validity of the proposed system as a reliable platform for environmental monitoring and control in controlled-environment agriculture. Full article

With the continuous increase in the number of Retired Lithium-Ion Batteries (RLBs), accurately estimating their Maximum Remaining Capacity (MRC) has become a key challenge for rapid sorting and secondary utilization. Conventional detection methods often suffer from low efficiency and limited scalability for large-scale applications. To address these issues, this paper presents a rapid MRC estimation method using a hybrid Convolutional Neural Network (CNN), Conv Block Attention Module (CBAM), and Long Short-Term Memory (LSTM) architecture. The proposed approach extracts key voltage and capacity features from only the initial 30 min charging phase, integrating both factory and laboratory data. Specifically, the CNN captures local temporal patterns, the LSTM models long-term dependencies, and the CBAM adaptively emphasizes critical characteristics. Experimental results demonstrate that the proposed method significantly outperforms traditional approaches, achieving a testing R² of 98.05% and a Mean Absolute Percentage Error (MAPE) of 1.60%. These results highlight the superior performance of the proposed framework, exhibiting strong potential for high-throughput battery sorting and large-scale health monitoring systems. Full article

Climate change significantly impacts agricultural ecosystems through rising temperatures, changing precipitation patterns, increasing atmospheric CO₂ levels, and more frequent extreme weather events. These environmental changes have a pronounced effect on plant-parasitic nematodes (PPNs; phylum Nematoda), which cause serious crop losses on a global scale. This review aims to provide a comprehensive evaluation of current knowledge on how major climate change drivers influence the biology, population dynamics, host–plant interactions, and geographic distribution of PPNs in agricultural systems. Recent studies show that rising temperatures accelerate nematode development, increasing the number of generations within a production season and facilitating the spread of many economically important species toward higher latitudes and elevations. Changes in precipitation patterns and soil moisture directly affect nematode survival, mobility, and infection success, and these effects often vary depending on regional conditions and nematode species. Elevated atmospheric CO₂ levels modify plant–nematode interactions by increasing root biomass, altering rhizosphere processes, and regulating plant defense pathways (e.g., jasmonic acid and salicylic acid signaling), which may enhance host susceptibility and infection intensity. Furthermore, extreme climate events can disrupt the natural balance in soil ecosystems, weakening natural antagonist–nematode relationships. However, responses of PPNs to climate change are not uniform, and contrasting findings across studies indicate that these responses are strongly shaped by species-specific traits and environmental variability. In addition, future research should focus on long-term and multi-factorial field studies to better capture the combined effects of climate drivers. Overall, climate change is expected to increase PPN prevalence and drive shifts in their geographic distribution, highlighting the need for climate-sensitive and regionally adapted nematode management strategies. Full article

Quinoa is a highly nutritious Andean crop with considerable yield potential that remains underexploited in southern Ecuador. This study evaluated the effects of planting method (row seeding, hill seeding, and transplanting) and plant density (8–20 plants m⁻²) on quinoa growth and yield under Andean highland conditions. A factorial field experiment was conducted using a randomized complete block design with three replicates. Plant density significantly affected grain yield, increasing from 4.4 to 4.8 t ha⁻¹ at 8 plants m⁻² to a maximum of 6.97 t ha⁻¹ at 20 plants m⁻². This increase was mainly driven by a higher grain number per unit area, while thousand-grain weight remained stable across treatments. In contrast, the planting method and its interaction with plant density had no significant effect on yield or yield components. Grain yield showed a strong positive relationship with above-ground biomass, indicating that biomass accumulation was the main driver of yield variation. These results demonstrate that plant density is the primary agronomic factor controlling quinoa productivity under Andean conditions. Optimizing plant density to 15–20 plants m⁻² is recommended as a simple and cost-effective management strategy to maximize grain yield, regardless of planting method. Full article

Plasmid copy number (PCN) is a key factor limiting the expression level of heterologous proteins in yeast. Static strategies for enhancing PCN, such as reducing the transcriptional intensity of selection markers or increasing selection pressure, only maintain PCN at a single fixed level and struggle to achieve dynamic, precise, and reversible copy number regulation. This study established a dynamic plasmid copy number regulation strategy based on CRISPR interference (CRISPRi). Flexible control of PCN was achieved by designing specific guide RNAs (gRNAs) and integrating them into the inducible CRISPRi system. Optimization of the gRNA target site, inducer concentration, and induction timing resulted in a >2-fold increase in the fluorescence intensity of yeast-enhanced green fluorescent protein (yeGFP) compared with the group without induction. Using naringenin synthesis as proof-of-concept, this regulatory tool was applied to modulate the expression of chalcone synthase (CHS), the rate-limiting enzyme in naringenin biosynthesis. Finally, the yield of naringenin increased by 35.62% under the optimal induction conditions. Full article

The escalating demand for miniaturization in electronics necessitates advanced laser micromachining for precise micro-via fabrication in PTFE-based PCBs. This study addresses challenges in controlling CO₂ laser kerf depth in PTFE, a material known for properties that complicate material removal. Employing a two-level full factorial Design of Experiments, the effects of number of loops, aperture, and pulse duration were systematically investigated. This analysis revealed that while pulse duration statistically impacted ablation depth, the number of loops was operationally most critical due to its direct proportionality with kerf depth in PTFE, leveraging its low thermal conductivity. Aperture, defining the laser spot size, was often constrained by PCB geometric specifications. The predictive models developed demonstrated robust generalizability across different PTFE-based laminates. Validation of the production of PCBs achieved a 100% success rate in meeting geometric tolerances and surface integrity. This DoE-based framework establishes a process window, significantly reducing parameter identification time and scrap, thereby enhancing manufacturing yield. Full article

Baby maize (Zea mays L.) is a high-value horticultural crop widely cultivated due to its short growth cycle and strong market demand. However, intensive production systems often rely heavily on chemical fertilizers, leading to reduced nutrient use efficiency and potential soil degradation. The present study investigated the potential of the Priestia megaterium Thr45 to enhance soil fertility, improve crop performance, and optimize fertilizer management in baby maize cultivation. A field experiment was conducted using a three-factor factorial design consisting of bacterial inoculation, different urea application rates, and different KCl rates. Soil chemical properties, plant growth parameters, yield components, and nutrient composition of edible cobs were evaluated. The results showed that inoculation with P. megaterium Thr45 significantly increased available phosphorus and exchangeable potassium in soil compared with the non-inoculated control. Inoculated plants exhibited higher chlorophyll content, greater leaf development, and increased plant height during early growth stages. Bacterial inoculation also significantly improved yield components, including ear number, ear yield, edible cob yield, and plant biomass. Furthermore, the nutritional quality of baby corn was enhanced, as reflected by increased protein and mineral (N, P, and K) concentrations in edible cobs. Significant interactions between bacterial inoculation and fertilizer treatments indicated that the beneficial effects of P. megaterium Thr45 were closely associated with nutrient management practices. Notably, comparable yield and nutritional quality were achieved under reduced nitrogen and potassium fertilizer inputs when combined with bacterial inoculation. These findings highlight the novel potential of P. megaterium Thr45 as an effective biofertilizer for improving nutrient availability, maintaining high productivity, and supporting sustainable baby maize production with reduced chemical fertilizer inputs Full article