The Xavier Institute of Engineering (XIE), a sister institution of St. Xavier College, Mumbai, Fort, is an engineering college in central Mumbai, established in the year 2005 to givea quality technical education to all, with a preferential option for economically backward and Christians. The institute is managed by the Society of Jesus, Jesuits, and is affiliated to Mumbai University,approved by AICTE New Delhi, and recognized by Directorate of Technical Education, Government of Maharashtra. XIE is a member of AJIT, the Association of JesuitInstitutes of Technologyand IAJES (International Association of Jesuit Engineering Schools) . It is accredited by National Assessment and Accreditation Council of India. Since XIE is affiliated to Mumbai University, it follows its syllabus and examination system.It began in the early 1930s as a technical institute on the premises of St. Xavier College, Mumbai providing basic professional courses. In 2005 it became a polytechnic founded in Mahim, near what was called "Fishermen's Colony", afifteen-minute walk from the Mahim Junction Railroad Station. At the same time, the Xavier Institute of Engineering (XIE) was startedon the same premises.XIE offers an undergraduate degree course in Engineering (B.E.) in Electronics and Telecommunication Engineering (60 seats), Computer Engineering (60 seats), and Information Technology (60 seats) . There is one-year diploma course in Sound Engineering(20 seats). In collaboration with Marquette University, XIE has set up the "Gait Lab", where a person's walk is captured in 3-Dimaging and to which Government hospitals are sending children to seek help as it is the only lab in Mumbai City.
The efficient handling of large volumes of low-concentration coal mine methane is crucial for safety and the environment protection. Currently, the crushing and forming processes of traditional bulk supports would reduce the distribution and exposure degree of active components, resulting in lower catalytic activity. Looking for materials with a self-supporting function and hierarchical pore structure as a methane catalyst support is an effective strategy. In this study, a series of PdO-PtO/Al2O3 monolithic catalysts with hierarchical pore structures were obtained by sol-gel method combined with post-impregnation method. Pd3PtOx/Al2O3 monolith with both economy and high activity was optimized. At a space velocity of 12000 mL center dot g-( 1)center dot h-( 1), this catalyst exhibited the highest performance, with methane conversion temperatures of 298 degrees C (T-50) and 347 degrees C (T-90). This high catalytic activity is attributed to the catalyst's self-supporting structure with hierarchical pores. The macropores enhance the contact opportunity between methane and the active sites of the catalyst; while the mesopores provide more active sites, thereby improving the catalytic activity. The self-supporting structure of the support itself improves the stability of the catalyst. This study provides a scientific basis for catalytic oxidation to reduce coal mine methane emissions.
Reactive dividing wall column (RDWC) integrates the advantages of high conversion efficiency, low energy consumption, and reduced investment. However, this further intensification of reaction and separation increases system coupling and significantly complicates process optimization. To address this challenge, this work proposed a machine learning-aided multi-objective optimization (ML-MOO) framework for determining the optimal RDWC design. Taking the dichlorosilane anti-disproportionation RDWC process as a case study, random forest (RF), backpropagation neural network (BPNN), and support vector machine (SVM) were integrated with multi-objective particle swarm optimization (MOPSO) algorithm to optimize the steady-state operating parameters of RDWC. The hyperparameters of three ML models were tuned using Bayesian optimization algorithm (BOA) with 5-fold cross-validation. The results showed that, compared with the rigorous Aspen simulation-based optimization, the SVM surrogate model reduces total annual cost, flow rate of silicon tetrachloride, and environmental impact potential of energy by 5.3 %, 23.5 %, and 7.6 %, respectively, while reducing computation time by 19.3 %. Additionally, due to the existence of internal reactions, the dynamic behavior of RDWC is constrained by both product quality and safety redundancy. To address this, a distributed MPC (DMPC) strategy was proposed, using two sub-MPC controllers to separately control inventory and quality loops, thereby enhancing system fault tolerance. Dynamic response results indicated that benefiting from communication between sub-controllers, the integral absolute error (IAE) value of linear DMPC structure based on linear time-invariant state space (LTI-SS) model differs from that of centralized MPC (CMPC) structure by only 3 % to 10 %, demonstrating similar dynamic response performance while achieving enhanced safety.
Conventional MoS2 catalysts face challenges, including weak piezoelectric response, reliance on energy-intensive ultrasound, and limited active sites, reducing their efficiency in degrading organic pollutants. In this work, 1T/ 2H heterostructured MoS2 nanoflowers were synthesized via an ionic liquid-assisted hydrothermal method, enabling efficient piezocatalytic activation of peroxymonosulfate (PMS) under mild mechanical stirring. The 1T/ 2H MoS2/PMS system rapidly degraded 95.9 % of Rhodamine B in 15 min, with a kinetic rate 4.43 times higher than conventional MoS2, and showed excellent cycling stability. This superior performance was due to enhanced piezoelectric polarization at heterointerfaces, which improved charge carrier separation, and the use of low-energy mechanical stirring instead of ultrasound for activation. Optimized interfacial charge transfer pathways also boosted the generation of superoxide anions (center dot O2-) and singlet oxygen (1O2). Moreover, immobilizing 1T/2H MoS2 on conductive carbon cloth enhanced recyclability and operational feasibility, offering a sustainable solution for water treatment technologies.
Zinc sulfide (ZnS) is a critical white pigment, yet the quantitative relationship between ion doping, phase evolution, and its pigment performance remains unclear. To fill this gap, we systematically investigate the modulation effects of Co2+ doping on the chroma, crystal phase, and morphology of ZnS via a combined experimental and density functional theory (DFT) approach. Experimental characterizations (XRD, SEM/TEM, XPS) reveal that Co2+ substitutes Zn2+ sites, inducing lattice contraction, promoting sphalerite-to-wurtzite transition (wurtzite content: 74.4 %-> 80.5 %), and suppressing particle growth to form uniform nanoparticles (80-130 nm vs. 80-300 nm in pure ZnS). Optical analyses show that <= 0.3 % Co doping widens the bandgap (3.53-3.77 eV) and reduces yellowness (b & lowast;: 1.18 -> 0.79) to enhance whiteness, while excessive doping (>0.3 %) introduces a green hue (a & lowast;: -0.10 -> -4.72). DFT calculations corroborate these findings by confirming reduced phase transition energy barriers and strengthened Co-S bonds. This work establishes a clear structure-property relationship, providing a basis for optimizing ZnS pigment performance via Co2+ doping.
A tremendous growth of Internet of Things (IoT) devices and the corresponding requirement of low-latency services have resulted in cloud-based standalone architectures to be insufficient to fulfil the demand for realtime commerce applications. This study investigates the computing efficiency of Cloud-only, Edge-only as well as Hybrid Edge-Cloud system using a digital commerce notice delivery system developed on a Raspberry Pi. Two prototype models are developed and evaluated empirically. Model 1 deploys Google Drive as the cloud backend with passive edge caching over Web Sockets. On the other hand, Model 2 employs Supabase as a structured cloud database combined with RFID-based edge processing activity over MQTT. Latency measurements across multiple trials resulted in a Hybrid Edge Cloud approach with lower latency in displaying as well as uploading. The results indicated that active edge intelligence, combined with lightweight messaging protocols and structured cloud storage, substantially enhances computational efficiency in digital commerce deployments.
