Manufacturing lines operating under high demand and constrained resources require systematic load planning to achieve timely delivery and efficient utilization of available capacity. In multi-stage production systems, imbalance between upstream and downstream operations often leads to bottlenecks, excessive Work-in-Process (WIP) inventory, and dependence on overtime. This study presents a computational load planning and capacity analysis of a Two-Wheeler Rocker Arm (TWRA) manufacturing line using actual shop-floor data. Python-based computational programs were developed to evaluate daily and weekly production capacity, identify bottleneck stations, quantify demand gaps, and estimate buffer inventory under different operating scenarios, including normal working hours, demand-gap-based selective overtime, and extended overtime configurations. The results demonstrate that line throughput is governed primarily by downstream bottlenecks rather than total installed capacity and that targeted overtime at critical stations effectively bridges demand gaps. The proposed computational framework provides a transparent and repeatable decision-support tool for capacity planning and continuous improvement in multi-stage manufacturing systems.

Poor seed germination and weak early vigor limit growth and yield of okra (Abelmoschus esculentus L.). Seed priming is a promising strategy for improving crop establishment, and nanoparticle-based priming may enhance nutrient delivery compared to ionic sources. In this study, copper oxide nanoparticles (CuO NPs) were green-synthesized using radish (Raphanus sativus L.) leaf extract and characterized by FTIR, FESEM–EDS, and zeta potential analysis. The CuO NPs exhibited near-spherical morphology (150–250 nm), confirmed Cu–O bonding, and moderate colloidal stability (−23.21 mV). Seed priming did not significantly affect germination percentage; however, seedling growth responses were strongly treatment dependent. CuO NP priming at 25–50 ppm significantly enhanced root length and fresh biomass, while CuSO4 treatments caused growth inhibition. Pot experiments showed that CuO NP priming significantly increased plant height, pod number, pod length, and total yield, with maximum yield recorded at 50 ppm. Strong positive correlations were observed among vegetative growth, pod traits, and yield. In conclusion, green-synthesized CuO NPs applied through seed priming were more effective than ionic copper in enhancing okra growth and productivity, highlighting their potential as a nano-enabled strategy for sustainable crop improvement.

This study assesses the influence of the Djorf Torba Dam on the Turonian aquifer system in the Béchar region, southwestern Algeria, focusing on reservoir water infiltration into the underlying Turonian and Quaternary aquifers hosted in fractured limestone formations. Infiltration processes are controlled by aquifer hydrodynamic properties, reservoir water levels, and surface area.Monitoring data from external piezometers show that 72.2% of the instruments screened in the Turonian aquifer exhibit groundwater level variations closely correlated with reservoir water levels, with correlation coefficients ranging from 0.75 to 0.94. Piezometric mapping indicates groundwater levels between 693 m and 697.67 m, forming two recharge domes connected to the main branches of the reservoir. When reservoir levels exceed 695 m, infiltration significantly increases along major Turonian fault zones. Water balance calculations estimate an average infiltrated volume of 22.57 hm³/year, including 17.56 hm³/year recharging the Turonian aquifer and 5.02 hm³/year contributing to the Quaternary groundwater. Hydrochemical analyses reveal that groundwater chemistry reflects reservoir water composition, with higher solute concentrations resulting from leaching processes. These findings highlight the strong hydraulic and geochemical impact of the Djorf Torba Dam on the Turonian aquifer system.

This paper examines whether export diversification at the firm level causally enhances productivity in emerging markets. While export diversification is widely promoted as a development strategy, empirical evidence on its microeconomic foundations remains limited. Using harmonised firm-level customs and production data from multiple emerging economies, we analyse how changes in export variety—across products and destination markets—affect firm-level total factor productivity. To address endogeneity arising from self-selection and reverse causality, we employ a combination of firm fixed effects, country-by-year fixed effects, instrumental variables based on exogenous global demand shocks, and event-study designs around major diversification episodes. We find robust evidence that increases in export variety lead to economically meaningful productivity gains. These effects materialise gradually over time, consistent with learning, capability accumulation, and organisational adjustment. Productivity gains are larger when diversification involves more complex products and are stronger in countries with better trade facilitation and institutional quality. Counterfactual analyses suggest that export diversification accounts for a non-trivial share of aggregate productivity growth in several emerging markets. The results highlight export diversification as an active channel of productivity growth rather than a mere outcome of firm performance, with important implications for development and trade policy.

Artificial Intelligence (AI) is reshaping higher education by redefining how learners’ access, process, and apply knowledge. While technological adoption is expanding rapidly, less attention has been paid to how learners and educators experience this transformation. This study explores the evolving relationship between AI tools and learning behavior in higher education using a qualitative interpretivist approach. Drawing on Transformative Learning Theory and Connectivism, this research examines how critical reflection, networked knowledge, and ethical awareness mediate learners’ engagement with AI-driven environments. Semi-structured interviews were conducted with learners and educators from diverse disciplines to capture the nuanced experiences of AI integration. Thematic analysis revealed four progressive stages—Exposure and Curiosity, Adoption and Adaptation, Reflection and Reorientation, and Transformation and Integration— that collectively illustrate the cyclical journey of learning transformation. This study proposes the AI-Supported Learning Transformation Model (AISLTM), which depicts how learners transition from initial experimentation to deep, ethical, and reflective use of AI. Comparative analysis revealed distinct yet convergent trajectories among online and offline learners, affirming that AI-driven transformation integrates both individual reflection and collaborative learning within the AISLTM framework. The findings highlight that trust, creativity, and institutional support are key mediators in enabling this transformation. This study contributes an empirically grounded framework for understanding how AI fosters critical reflection and self-directed learning, offering insights for educators, policymakers, and institutions aiming to embed AI ethically and effectively in higher education.

In this work, we introduce two new connectivity indices for a graph , referred to as the first and second degree status connectivity indices. These indices are defined as follows: where denotes the edge set of the graph and denotes degree of a vertex . These indices have been evaluated for some standard class of graphs. Additionally, certain bounds were investigated. We further investigated the applicability of these indices in modeling the physicochemical properties of benzenoid hydrocarbons. Specifically, This study examines the predictive potential of the indices and through cubic regression modeling for benzenoid hydrocarbons. The results indicate that serves as a more effective predictive index compared to among the two newly introduced indices. Furthermore, a comparison between the actual and predicted physicochemical properties has been presented for both indices to provide clearer insights.

A nonlinear energy harvesting system is proposed, consisting of a tip-loaded slender vertical cantilever beam with a strain gauge attached. The beam is oriented with its free end pointing upward and its fixed end at the bottom, and is subjected to vertical base excitations. Under such excitation, the slender beam experiences a compressive force resulting from both the base motion and the tip mass. The theory of buckling is explored, and it is shown that beyond a critical value of the crippling force, the beam undergoes buckling. To understand the system’s stability, the potential energy equation is analyzed, revealing conditions for monostability, biostability, and stochastic responses. Free vibration tests and corresponding simulations show good agreement in the natural frequencies of the proposed beam. Experimental results under frequency-swept vertical excitation demonstrate that the combined inertial forces from the tip mass and base motion can transform the system’s behavior from monostable to bistable. This resulted in non-linear large amplitude horizontal vibrations of the harvester. The strain gauge measurements confirm the feasibility of using this vertical cantilever beam for the efficient vibration energy harvesting.

Integrated Farming Systems (IFS) and crop diversification are increasingly recognized as pivotal strategies for achieving sustainable, climate-resilient agriculture. This paper critically examines the synergy between IFS and crop diversification in enhancing productivity, resource-use efficiency, and environmental sustainability. IFS promotes the integration of crops, livestock, aquaculture, agroforestry, and other enterprises on a single farm unit, optimizing nutrient cycling and reducing external inputs. Crop diversification, meanwhile, introduces temporal and spatial heterogeneity into farming systems, reducing risks associated with monoculture, improving soil health, and enhancing farm income stability. The integration of these approaches creates multifunctional landscapes capable of withstanding climate shocks and market fluctuations. Empirical evidence from both smallholder and commercial farming contexts suggests that combining IFS with diverse cropping patterns improves livelihood security, strengthens ecological balance, and supports long-term agricultural resilience. However, adoption barriers such as knowledge gaps, institutional support, and market access must be addressed through targeted policy and extension interventions. This paper argues for mainstreaming IFS and crop diversification in national and regional agricultural development frameworks to ensure food and livelihood security under increasing climatic and economic uncertainties.

The distance between two nodes x1, x2 contained in V(G) is the minimum size of x1 – x2 paths in G. An x1 – x2 path of the size dG(x1, x2) is known as geodesic. We indicate IG(x1, x2) as the set of nodes which are lies inside some x1 – x2 geodesics of G. A node is said to lie on x1 – x2 geodetic if c is an inner node of P. The bounded interval I(x1, x2) includes x1, x2 and all nodes lying on some x1 – x2 geodesic of G. Consider a non- empty set S V(G). For a set I(S) = . If G is connected graph, thus S is a geodetic set with the condition that I(S) = V (G) [2]. The minimum cardinality S of G is called geodetic number defined by g(G). A j – node coloring of G is an allotment of j colors to the nodes of G. The coloring is proper if no two joining nodes accept same color such that = j is said to be j – chromatic, where j k. a minimum cardinality of a chromatic number of G is called chromatic set [1, 3, 4]. Beulah Samli and Robinson Chellathurai [9] introduced the concepts of the Geo Chromatic number of graph. In this paper, we consider a graph to be connected, finite, simple where V(G) is node set and E(G) is link set [5,6]. In this paper, we have defined geo chromatic number for certain graph and have discussed few results.

In the era of social commerce, recommendation systems must transcend traditional paradigms by incorporating multimodal data to personalize content more effectively. This study proposes a sentiment-guided, multimodal recommendation framework that integrates text and image-based features to enhance product recommendation strategies, particularly in influencer marketing and fashion retail. The proposed architecture utilizes Gated Recurrent Units (GRUs) for analyzing user-generated textual comments to capture emotional tone and sentiment directionality. Concurrently, a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) pipeline processes product images, extracting semantic features and generating descriptive captions that encapsulate visual attributes such as color, category, and brand. By fusing textual sentiment with visual embeddings, the system produces contextually relevant and emotionally resonant recommendations. The GRU-based sentiment classifier achieves a classification accuracy of 89%, while the CNN-LSTM image captioning module attains a BLEU-1 score of 0.68 and 85% accuracy in visual attribute prediction. The real-time filtering mechanism dynamically modulates content delivery based on sentiment polarity—suppressing recommendations during negative user sentiment to reduce cognitive load and enhance user trust. Designed with scalability in mind, the system supports real-time interaction through streaming APIs and can be deployed on cloud platforms or edge devices. It enables automated visual tagging, reduces manual annotation overhead, and improves content discovery by aligning recommendations with both emotional and aesthetic user preferences. This architecture thus transforms passive browsing into an adaptive, emotionally intelligent interaction—offering a significant leap forward in personalized marketing and user engagement in visually rich environments.