CECOS University

The anticancer drug, doxorubicin induces multiorgan toxicity, and the liver and kidneys are no exceptions. Since the herb, Ferula asafetida, is antioxidant and anti-inflammatory, the objective was to assess its possible ameliorative activity as a hydroethanolic extract (FaE) against doxorubicin hepatotoxicity or nephrotoxicity in Wistar rats. In vitro, FaE phytochemical screening, evaluated the total phenolic content and antioxidant potential using the DPPH assay. In vivo testing, entailed administration of doxorubicin (10 mg/kg, bolus dose) alone or combined with FaE (100-300 mg/kg orally, daily for 28-days). Animal weights, liver biomarkers (total bilirubin, ALT, AST, and ALP) and kidney biomarkers (serum creatinine, urea, and BUN) were measured and liver and kidney tissues were scrutinized histopathologically. Phytochemical analysis of FaE divulged alkaloids, carbohydrates, flavonoids, terpenoids, phenols, tannins, saponins, coumarins, and phlobatannins. The total phenolic content of the dry plant extract was 105.4 ± 3.7 mg/g equivalent of gallic acid. FaE (1000 µg/ml) expressed antioxidant activity (81.4%) that was <9.0% lower than ascorbic acid. In vivo, doxorubicin induced a weight loss that was counteracted by FaE. The plant extract also dose-dependently reversed doxorubicin boosted serum concentrations of hepatorenal biomarkers plus the histopathological injury. This study endorses a protective activity of FaE against doxorubicin hepatorenal toxicity.

Modern factories still struggle with unexpected machine failures because traditional maintenance systems depend on fixed rules and threshold-based alerts. These older approaches often overlook subtle or complex patterns in multimodal sensor data, causing them to miss early signs of wear and leading to late or incorrect maintenance decisions. As a result, production can slow down, costs increase and equipment reliability suffers. To address this challenge, this study introduces a smart and interpretable fault diagnosis and predictive maintenance framework designed to detect wear, degradation and potential failures before they disrupt operations. The proposed framework integrates multiscale feature extraction, multimodal sensor fusion and cross-sensor correlation analysis with advanced temporal modeling using a Temporal Convolutional Network (TCN). By jointly performing tool-health classification and Remaining Useful Life (RUL) estimation, the framework provides a comprehensive assessment of machine condition. When evaluated on the NASA Ames milling dataset, the model achieved an overall accuracy of 86%, correctly classifying healthy and failed tools in more than 88% of cases and worn tools in over 75%, demonstrating consistent performance across different stages of tool wear. Explainable artificial intelligence (XAI) techniques, including attention-based visualizations and SHAP-based feature attribution, reveal that electrical and vibration signals are the most influential early indicators of tool degradation. The proposed framework exhibits low computational latency and minimal memory requirements, making it suitable for real-time fault diagnosis and deployment on industrial edge devices. Overall, the framework balances predictive accuracy, interpretability and practical applicability, enabling proactive and reliable maintenance decisions that enhance machine uptime and support efficient smart manufacturing operations.

Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness. This study improves on prior methods by developing a small yet robust system that can predict when turbofan engines will fail. It uses the NASA CMAPSS dataset, which has over 200,000 engine cycles from 260 engines. The process begins with systematic preprocessing, which includes imputation, outlier removal, scaling, and labelling of the remaining useful life. Dimensionality is reduced using a hybrid selection method that combines variance filtering, recursive elimination, and gradient-boosted importance scores, yielding a stable set of 10 informative sensors. To mitigate class imbalance, minority cases are oversampled, and class-weighted losses are applied during training. Benchmarking is carried out with logistic regression, gradient boosting, and a recurrent design that integrates gated recurrent units with long short-term memory networks. The Long Short-Term Memory-Gated Recurrent Unit (LSTM-GRU) hybrid achieved the strongest performance with an F1 score of 0.92, precision of 0.93, recall of 0.91, Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) of 0.97, and minority recall of 0.75. Interpretability testing using permutation importance and Shapley values indicates that sensors 13, 15, and 11 are the most important indicators of engine wear. The proposed system combines imbalance handling, feature reduction, and Interpretability into a practical design suitable for real industrial settings.

Purpose This study investigates the impact of green intellectual capital (GIC) on green innovation (GI) and firm performance (FP) in Pakistani manufacturing small and medium-sized enterprises (SMEs). The study aims to analyze the mediating role of organizational learning capability (OLC) between GIC and GI and evaluate the direct and indirect effects of GIC and GI on FP. Design/methodology/approach A quantitative research approach was employed, using partial least squares structural equation modeling to test the proposed relationships. The data were collected from 452 executives across Pakistani manufacturing SMEs in sectors including textile and apparel, food processing, and chemical manufacturing. A structured, self-administered survey was conducted over a three-month period (February to April 2025). Findings The results demonstrate that GIC significantly influences GI and FP, with OLC mediating the relationship between GIC and GI. GI also positively impacts FP, both directly and indirectly through GIC and OLC. Practical implications This study contributes to the resource-based and dynamic capabilities literature by highlighting the role of intangible green resources in driving sustainable innovation and enhancing firm performance. For SMEs, investing in intangible green assets and integrating organizational learning capabilities are crucial for fostering innovation and achieving sustainability. Policymakers and business leaders can leverage these insights to support green innovation and promote long-term sustainable growth. Originality/value This study expands on the RBT and dynamic capabilities theory by examining the role of GIC and OLC in driving GI and sustainable firm performance within Pakistani SMEs, offering a unique contribution to the literature on sustainability in emerging economies.

Plant leaf spectrophotometry has been used successfully as a means to detect stress, and it has been complemented by fluorescence analysis. This identification can be achieved in the ultraviolet (UV), visible (red, green, blue; RGB), near-infrared (NIR), and infrared (IR) spectral regions. Hyperspectral (measuring continuous wavelength bands) and multispectral (measuring discrete wavelength bands) imaging modalities can provide detailed information concerning the physiological well-being of plants, often diagnosing them at an earlier stage than visual or other more traditional biochemical assays. Because hyperspectral methods are highly sensitive and accurate, they cost a lot and produce vast quantities of data, which demand sophisticated computing software, and compared to multimedia, multispectral, and RGB cameras, they are less expensive and easier to carry but have reduced spectral resolution. Such methods are justified by thermal and fluorescence images revealing variations in the temperature and efficiency of photosynthesis of the leaves in response to stress. New digital imaging, thermal imaging, and optical filter technologies, and advancements in smartphone cameras have rendered low-cost, field-deployable platforms to monitor plant stress in real time feasible. Machine learning also supports these techniques by automating feature extraction, classification, and prediction to reduce the use of expensive instrumentation and human skill. But also problems like sensor calibration in a changing field, low model generalization across species and environments, and large, annotated datasets are needed. Beyond highlighting the relative strengths of the conventional and contemporary sensing approaches, the paper also examines the possibility of applying machine learning to multimodal images, as well as the growing impact of smartphone- based solutions in supplying inexpensive agricultural diagnostics. It concludes by overviewing the current limitations and limits to future research into scalable, cost-effective, and generalizable plant stress models.