Many reasons have caused a worldwide water stress problem. Thus, the recycling of wastewater streams has been extensively studied. In this work, eco-friendly mixed matrix membranes (MMMs) were fabricated, characterized, and tested for the removal of two separate dyes from simulated waste streams. The environmentally friendly nano activated carbon (NAC) was extracted from water hyacinth to be impregnated as a membrane nano-filler to enhance the neat membrane performance. The extracted NAC was further studied and characterized. Cellulose acetate (CA)-based membranes were obtained by phase inversion and electrospinning mechanisms. All four synthesized blank and MMMs were characterized via scanning electron microscope (SEM) and contact angle to study their structure and hydrophilic nature, respectively. However, the membrane with optimum performance was further characterized using Fourier transfer infrared (FTIR) and X-ray diffraction (XRD). The four prepared cast and electro-spun, blank, and mixed matrix CA-based membranes showed an acceptable performance in the removal and selectivity of methylene blue (MB) dye over Congo red (CR) dye with a removal percentage ranging from 31 to 70% depending on the membrane used. It was found that the CA/NAC hybrid nanofiber membrane possessed the highest removal efficiency for MB, where the dye concentration declined from 10 to 2.92 mg/L. In contrast, the cast blank CA membrane showed the least removal percentage among the synthesized membranes with only 30% removal. As a result, this paper suggests the use of the CA/NAC hybrid membrane as an alternative and cost-effective solution for MB dye removal.

Human–Robot Collaboration (HRC) has emerged as a fundamental element of new industrial and service systems, wherein humans and robots function within common physical and cognitive environments to achieve shared goals. In addition to traditional issues of safety and productivity, trust has become a critical element influencing cooperation efficiency, human dependence, and sustained system acceptability. This review offers a thorough and reliability-focused summary of HRC research, highlighting the significance of explainable intelligence and adaptive control in promoting trustworthy collaboration. The study initially identifies trust as a fundamental design target in HRC, delineating its dynamic, multifaceted characteristics and its impact on human decision-making and interaction behavior. A systematic review methodology is employed to analyze cutting-edge approaches across essential dimensions, including trust modeling and estimation, multimodal human state and intention recognition, explainable artificial intelligence (XAI) techniques, and adaptive and learning-based control architectures. The analysis emphasizes the role of transparency, interpretability, and context-aware adaptation in establishing trust calibration within safety-critical and dynamic collaborative environments. A cross-sectional synthesis of the literature reveals several critical gaps, including the lack of standardized trust evaluation metrics, limited integration of explainability and control adaptation, inadequate consideration of long-term trust dynamics, and insufficient validation in real-world, unstructured environments. The analysis closes by delineating potential research avenues for cohesive, human-centered HRC frameworks that effortlessly incorporate trust modeling, explainable decision-making, and adaptive control. The ideas offered seek to inform the creation of advanced collaborative robots that are safe, efficient, transparent, adaptive, and trustworthy.

The use of biodiesel as an alternative fuel is becoming increasingly popular nowadays due to global energy shortage. The interest in using Jatropha as a non-edible oil feedstock is rapidly growing. Biodiesel produced from crude Jatropha oil with NaOH as a catalyst is investigated. Transesterification by methanol is carried out in a closed vessel as a batch system. Factors affecting the process which included the reaction temperature and pressure, reaction time, the molar ratio of methanol to oil and catalyst amount are investigated. The maximum conversion ratio of methyl ester yield of 97.7% was recorded under the conditions of 65 °C, 1% (by mass) NaOH of the oil mass and 6:1 methanol to oil ratio.

Incineration is a thermal treatment process which mainly used to treat the waste material and convert it to ashes, flus gases and thermal energy. This technique has a positive impact on the environment as it successfully addresses the dangerous waste materials. Incorporating a heat exchanger with the incinerator was proved to have a high positive impact on the overall performance. Numerical and/or experimental investigation of incorporating heat exchanger with incinerator could be used to study the system performance.The commercial software, Ansys 2020, was used in the current workto analyze the various parameter of the heat exchanger such as heat transfer, pressure drop, quantity of heat,velocity, and temperature. The present paper mainly focusses on the temperature distribution and the efficiency of the heat exchanger and improving the exhaust gases’ conditions in the incineration. The current work shows the validity of the outlet cooling water to be used as a source of heat. The proposed design shows an excellent cooling for the exhaust gases compared to that of current design.The results of the current work show that the outlet temperature of the cooling water for the proposed heat exchanger is dramatically higher than that of the current heat exchanger. For instance, at the flow rate of 0.4 kg/s, the outlet cooling water temperatures were found to be 70 ºC and 30 ºC, for the proposed and the old heat exchanger, respectively. تهدف هذه الدراسة الى تحسين البيئة من خلال التخلص من النفايات الخطرة بواسطة عملية الاحراق حيث يتم معالجة النفايات وتحويلها الى رماد وغازات متدفقة وطاقة حرارية من خلال عملية الحرق وهذه التقنية لها تاثير ايجابى على البيئة. وذلک بعد ثبات التاثير الايجابي لدمج المبادل الحرارى مع المحرقة على الاداء العام . ويمکن دراسة اداء نماذج الدمج للمبادل الحرارى مع المحرقة باستخدام التحقيق العددي و / أو التجريبي ، البرنامج التجاري Ansys 2020 للاستخدام فى تحليل المتغيرات المختلفة للمبادل الحراري مثل انتقال الحرارة وانخفاض الضغط وکمية الحرارة والسرعة ودرجة الحرارة . وفى هذا البحث يتم الترکيز بشکل اساسى على توزيع درجة الحرارة وکفاءة المبادل الحرارى وتحسين ظروف غازات العادم فى الحرق. ويوضح العمل الحالى صلاحية مخرج مياه التبريد لاستخدامه کمصدر للحرارة ويظهر التصميم المقترح...

Early gesture and intention recognition from biosignals may provide a useful perception capability for future human–robot collaboration systems. This study proposes and evaluates an explainable reliability-gated EEG–EMG fusion framework for offline classification of seven upper-limb hand gestures and associated instructed intentions. The framework uses modality-specific EEG and EMG encoders followed by an adaptive reliability gate that learns how much each biosignal modality should contribute to the final prediction. The model was evaluated on synchronized EEG–EMG upper-limb gesture data using leakage-free trial-level splitting, multi-seed evaluation, classical and deep learning baselines, ablation variants, early-prefix testing, uncertainty-aware selective prediction, Integrated Gradients explainability, leave-one-subject-out validation, and few-shot adaptation. The results show that EEG–EMG fusion improved trial-level Macro F1 from 0.3566 ± 0.0217 for EEG-only and 0.7117 ± 0.0870 for EMG-only to 0.7985 ± 0.0178. Under the primary development/validation trial-level split, the final reliability-gated model achieved 0.8204 ± 0.0234 trial-level Macro F1 and 0.7238 ± 0.0151 window-level Macro F1. Because this validation subset was also used for training control and checkpoint selection, these values represent internal validation estimates rather than independent test-set performance. Under leave-one-subject-out evaluation, trial-level Macro F1 decreased to 0.4411 ± 0.3654, demonstrating weak and highly variable calibration-free generalization to unseen users. Early-prefix evaluation showed useful prediction after 2 s and near-complete performance after 5 s. Uncertainty-aware selective prediction increased accuracy to 0.9012 ± 0.0107 at 70% coverage. Integrated Gradients attributed 69.15% of the model-level decision evidence to EMG and 30.85% to EEG, indicating that the trained model relied more strongly on EMG features under the evaluated dataset while retaining a complementary contribution from EEG. These findings demonstrate the potential of the proposed reliability-gated framework for explainable offline EEG–EMG intention recognition on the evaluated benchmark dataset while highlighting the need for validation on larger and more diverse subject populations before broader generalization to practical HRC applications.