Digital Commons Network^™

Black Widow Optimization Of Hybrid Renewable-Hydrogen Microgrids For Cost-Effective Water Pumping In Off-Grid Systems, Ayodeji Akinsoji Okubanjo, Ignatius Kema Okakwu, Samson Oladayo Ayanlade, Abayomi Temitope Layeni, Etinosa Noma-Osaghae, Christian Chukwuemaka Ike, Olufemi Peter Alao, Ayodeji Joseph Akinremi, Ayo Isaac Oyedeji

Trends in advanced sciences and technology

The intermittent nature of renewable energy resources, together with limited infrastructure in off-grid communities, presents major obstacles to sustainable water supply. To overcome these challenges, this study develops a Black Widow Optimization (BWO)-based framework for the optimal sizing of a hybrid renewable–hydrogen microgrid integrating photovoltaic (PV) systems, wind turbines, battery energy storage, and hydrogen storage for reliable and cost-effective water pumping applications. The optimization goals are to reduce the Levelized Cost of Water (LCOW), Levelized Cost of Hydrogen (LCOH), and Net Present Cost (NPC), while system reliability and energy efficiency are quantified using the probability of unmet water demand (PUWD) …

Automatic Teeth Segmentation In Panoramic Radiographs Using Attention U-Net, Shehabeldin Mohamed Saber Prof.Dr.

Dentistry

Aim: The aim of this study is to investigate the effectiveness of the Attention U-Net architecture for teeth segmentation in panoramic dental images. By exploring the potential of Attention U-Net, the study aims to contribute to the development of more precise and automated dental imaging analysis, which could enhance diagnosis, treatment planning, and computer-aided dental systems. Subjects and methods: The TUFTS University dataset consisting of 1000 de-identified panoramic radiographs was used for training and testing an Attention U-Net architecture with 10-fold cross-validation. These scores surpass those of all other networks implemented on the same dataset. using a newly available benchmark …

Dual Regulation Of Osteogenesis And Inflammation By Pomegranate (Punica Granatum L.) Extract In Periodontal Ligament-Derived Stem Cells: Implications For Regenerative Medicine, Mai Hafez

Dentistry

Objectives

This article aims to compare the osteogenic along with anti-inflammatory activity of different concentrations of pomegranate extract (PG) on human periodontal ligament-derived stem cells (hPDLSCs) in vitro.

Materials and Methods

hPDLSCs were acquired from the root surface of removed molars, cultured, and then distinguished by flow cytometry analysis and several lineage differentiation potentials. To identify the effect of PG on hPDLSCs' viability, an MTT assay was performed. hPDLSCs were maintained in osteogenic induction medium with varying concentrations of PG. At the end of the induction period, osteogenesis was assessed using Alizarin Red staining, ALP assay, and qRT-PCR to determine …

A Narrative Review Of The Central Neural Control Of Swallowing And Its Implications For Dysphagia Rehabilitation, Kuo-Chang Wei, Tyng-Guey Wang, Ming-Yen Hsiao

Rehabilitation Practice and Science

Swallowing is controlled by a distributed neural network spanning the brainstem central pattern generator, subcortical modulatory circuits, and cortical regions responsible for volitional planning and sensorimotor integration. Evidence from lesion studies, neuroimaging, and neurophysiology demonstrates that dysphagia arises from disruptions at different nodes of this network, producing lesion-specific impairment profiles rather than a uniform swallowing deficit. This network-based perspective has shifted rehabilitation from predominantly compensatory management toward mechanism-driven interventions aimed at restoring physiological function. Behavioral skill-training and resistance-based exercises harness motor learning and cortical plasticity, whereas neuromodulatory approaches—including repetitive transcranial magnetic stimulation (rTMS), transcranial direct current stimulation (tDCS), pharyngeal electrical …

An Anatomy-Aware Efficientnetb0 And Cbam Framework For Suppressing Hard Boundary Bias In Mri Brain Tumor Classification, Ali Abdulzahraa Jalil, Ahmad Fatlawi, Hussein Ali Hussein Al Naffakh, Umar Farooq Khattak

Polytechnic Journal

The accurate classification of brain tumors from Magnetic Resonance Imaging (MRI) is important for prognosis and treatment planning in neuro-oncology. Although Convolutional Neural Networks (CNNs) dominate medical image analysis and achieve high scores, many rely on a clinically dangerous shortcut that we call Hard Boundary Bias (HBB). When an MRI slice is saved with zero padding, the boundary between the bright skull and the dark background forms the strongest gradient in the image, and the network fixes on this artificial edge instead of the tumor. To address this, we propose a lightweight framework that combines an EfficientNetB0 backbone with a …

Infusing Authenticity Into A Service-Learning Program: Examining Long-Term Impacts On Professional Development, Mohan Yang, Jonathan Harbor, Belle Li, Jacqueline Nikiema

Educational Leadership & Workforce Development Faculty Publications

Purpose

The employability of postgraduate students remains a critical concern in higher education, as postgraduate programs, particularly at the doctoral level, traditionally focus on disciplinary and research competencies. This leads to a lack of adequate preparation for employment, especially for non-academic roles. This study examines the long-term impacts of an authentic service-learning program on trainees' professional lives, with a particular emphasis on academia. This addresses the need for insight into the long-term impacts of initiatives that enhance postgraduate programs with opportunities for students to acquire transferable competencies prior to entering employment.

Design/methodology/approach

We adopted a narrative inquiry approach to investigate …

Workflow-Induced Evaluation Targets In Automation-Assisted Screening: Implications For Cumulative Evidence In Research Synthesis, John Alan Nunnery

Educational Leadership & Workforce Development Faculty Publications

Automation-assisted title-abstract screening is routinely evaluated using metrics such as recall, precision, and workload reduction, and results are increasingly summarized across studies. Synthesis presupposes that evaluation results refer to comparable target quantities. This article examines whether screening evaluation results produced under diverse automation-assisted workflows can meaningfully support cumulative inference, and under what conditions cross-study comparison is warranted. This conceptual article treats screening evaluation targets as workflow-induced rather than task-inherent. Evaluation design is decomposed into four workflow dimensions (representation, inference, governance, and evaluation) plus the reference-decision set produced or specified. Recurring choice patterns across these elements …