PDM University is a co-educational private university from the state of Haryana, India. The university has its campus in Bahadurgarh, Delhi NCR. The University has been established by the Haryana State Legislature under the Haryana Private Universities Act No. 32 of 2006, as amended by the Haryana Private Universities (Amendment), Act, 2015 (Haryana Act No.1 of 2016) and notified in the Haryana Govt. Gazette (Extra) Notification No. Leg.2/2016, dated 14 January 2016. The University has also been recognised by UGC under section 2(f) of the UGC Act 1956. The University is established and managed by Prabhu Dayal Memorial Religious & Educational Association (PDMREA).
Psychosomatic oral manifestations in children are often overlooked, despite their strong association with underlying psychological stress and anxiety. This cross-sectional study evaluated 100 pediatric patients aged 6-16 years to determine the prevalence and pattern of stress-related oral conditions. Findings showed that 68% exhibited at least one psychosomatic oral manifestation, with significantly higher rates among children with moderate and high anxiety scores. Common findings included bruxism, aphthous ulcers, parafunctional habits, temporomandibular disorders and psychogenic pain, all strongly linked to elevated stress levels. This study advances existing knowledge by highlighting the need for integrated psychological screening in pediatric dentistry to improve early detection and management of psychosomatic oral conditions.
In this paper, we study rings satisfying the identities a[a,b]=0 and a(b,c,a)=0. We prove that semiprime rings satisfying the identities a[a,b]=0 and a(b,c,a)=0 with characteristic not equal 3 satisfy the cyclic law a(bc)=b(ca). As a consequence, we get that semiprime rings satisfying the identities a[a,b]=0 and a(b,c,a)=0 with characteristic not equal 3 are associative and commutative.
The widespread release of synthetic dyes from the textile industry poses a serious environmental threat, but current wastewater treatment methods frequently lack sustainability, efficiency, and selectivity. Considering their high surface area, surface functionalization potential, and superior adsorption capabilities, nanomaterials have been extensively studied for dye removal; yet, the existing research is still dispersed, primarily concentrating on isolated modeling techniques or experimental adsorption performance. For the advancement of predictive optimization and mechanistic assessment, there is a prominent research gap in the systematic integration of modern computational, machine learning, and molecular modeling techniques with nanomaterial-based dye removal. In addition to machine learning techniques like artificial neural networks, support vector machines, decision trees, gradient boosting, adaptive neuro-fuzzy inference systems, and hybrid optimization frameworks, response surface methodology is discussed. By thoroughly summarizing current developments at the interface of nanotechnology, data-driven modeling, and molecular-level simulations for textile dye remediation, this review addresses this problem. Although findings of high predicted accuracies of R2 > 0.99 are frequently reported, this review also draws attention to issues with model interpretability, data quality, overfitting, and emphasizing the significance of suitable validation techniques such as k-fold cross-validation and external datasets. Adsorption energetics, binding affinities, and surface interactions at the atomic scale are investigated by molecular docking and molecular dynamics simulations. Environmental effect, process scalability, and adsorbent regeneration are also taken into consideration. This review offers an insightful framework for rational nanomaterial design, data-assisted decision-making, and the creation of effective and sustainable methods for eliminating dyes by integrating process optimization with molecular-level insights.
