Digital Commons Network^™

Greening The Workplace: Can Sustainable Practices Reduce Anxiety And Enhance Meaningful Work Engagement?, Cyril Tom T. Sunny, Peter Muttungal, Benny G. Davidson

Northeast Journal of Complex Systems (NEJCS)

This academic research examines the relationship between job engagement, green work climate, job-related anxiety, meaningfulness at work within the organization. It draws attention to identify the significant relations among all these factors and highlights the role of a green work climate in promoting meaningful work and alleviating job-related anxiety. The research emphasizes a diverse sample of employees from various organisations using structural modelling to find the mediating roles of job engagement and work meaningfulness in the correlation between organizational practices, environmental sustainability, and employee satisfaction. The study finds that a green work climate significantly enhances meaningful work experiences and reduces …

Impact Of Node Failures On Productivity In Multilayer Supply Chain Networks: An Influence Network Analysis In The Indian Electronics Sector, Surendra Orupalli, Hiroki Sayama

Northeast Journal of Complex Systems (NEJCS)

Supply chain networks are essential for the delivery of goods and information, but disruptions such as natural disasters or trade embargoes can severely impact them. Resilience of entire networks under different types of disruptions when nodes or edges fail has been extensively studied. However, the extent to which the failure of a particular company affects another company of interest within a network has not been widely explored. To address this, we created a multilayer physical supply chain network of companies in an electronics supply chain concentrated in India. Through systematic node removal simulations, we examined how the productivity of one …

A Novel Preprocessing Model For Multi Modal Brain Mri Image Classification For Stroke Prognosis, Alwin Joseph, Chandra J

Northeast Journal of Complex Systems (NEJCS)

Magnetic Resonance Imaging (MRI) is an imaging technique used for the diagnosis and observing the progression in various neurological disorders. Stroke is one of the prominent neurological disorders that creates significant impacts in the patients. It occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissues from getting oxygen and nutrients. Multimodal data from various modalities help clinicians in proper prognosis of stroke. Ischemic Stroke Lesion Segmentation Challenge (ISLES22) provides data of stroke data for various stroke patients, the dataset consists of three modalities of data – Fluid Attenuated Inversion Recovery (FLAIR), Apparent …

Limitations In Speech Recognition For Young Adults With Down Syndrome, Franceli L. Cibrian, Yingying 'Yuki' Chen, Kayla Anderson, Cecilia Marie Abrahamsson, Vivian Genaro Motti

Engineering Faculty Articles and Research

Speech recognition has the potential to make technology more accessible to users. However, the accuracy of speech recognition remains limited for users with disabilities, including those with Down Syndrome, and the types and frequencies of recognition errors are poorly understood. This paper characterizes these problems, focusing on errors occurring when recognizing Down Syndrome speech. We analyze the transcripts from six speech recognition algorithms (Google, IBM, Otter.ai, Microsoft, AssemblyAI, OpenAI) using the audio content of 15 individuals with Down Syndrome (331 dialogues; 3428 words). Our analysis shows: (1) significant difference in speech recognition accuracy for people with Down Syndrome compared to …

Leveraging Network Science For Customer Segmentation And Product Recommendation, Ali Nasirzonouzi

Northeast Journal of Complex Systems (NEJCS)

The rapid growth in e-commerce has forced the development and implementation of enhanced customer segmentation and recommendation systems, improving business results and improving customer experience. Traditional approaches, such as RFM analysis and clustering algorithms like K-means, are very helpful in many situations but usually fail to catch complex interdependencies among customers and products. This paper proposes a new approach using network science methodologies, a bipartite graph model, toward the advancement of customer segmentation and product recommendation. It implements a bipartite graph of customers and products using the "Online Retail II" dataset and proceeds with community detection, segmenting customers into unique …

Integrating Neural Networks For Predictive Torque Control And Obstacle Avoidance In Autonomous Robot, Viswanath Kodali, Harsha Vardhan Borra, Kiran P

Northeast Journal of Complex Systems (NEJCS)

In the field of robotics, precise motion control and accurate computation of joint forces are critical for ensuring optimal performance. Traditional methods, such as using the Jacobian matrix for joint angle determination and Euler-Lagrange equations for torque computation, are reliable but computationally intensive, making them less suitable for real-time applications. This paper presents an advanced approach to improving the productivity and efficiency of a 3-Degree of Freedom (DOF) robotic arm by utilizing Artificial Neural Network (ANN). The proposed system dynamically predicts joint angles and torque, enabling faster and more efficient motion control.

To address the challenge of obstacle avoidance in …

Multimodal Search On A Line, Jared Coleman, Dmitry Ivanov, Evangelos Kranakis, Danny Krizanc, Oscar Morales Ponce

Computer Science Faculty Works

Inspired by the diverse set of technologies used in underground object detection and imaging, we introduce a novel multimodal linear search problem whereby a single searcher starts at the origin and must find a target that can only be detected when the searcher moves through its location using the correct of p possible search modes. The target’s location, its distance d from the origin, and the correct search mode are all initially unknown to the searcher. We prove tight upper and lower bounds on the competitive ratio for this problem. Specifically, we show that when p is odd, the optimal …

Fault Diagnosis And Fault Tolerant Structure For Multilevel Inverters Using Machine Learning Techniques, Sudha V

Theses and Dissertations

A paradigm shift towards electric drives in domestic and industrial sectors has significantly increased the use of multilevel inverters (MLI). MLIs are constructed using more semiconductor devices, which hinders safety and reliability. Literature states 31.2% of failures in MLIs are due to semiconductor devices. Hence, there is a need for fault detection and tolerant mechanisms to ensure the safety and reliability of MLIs.

MLIs like Cascaded H-bridge(CHB) and Packed U cell(PUC) are mostly preferred due to low harmonic distortion, which is considered in this work. The complexity associated with fault diagnosis with more components in MLIs is addressed by machine …

The Hidden Cost Of Using Time Series Aggregation For Modeling Low-Carbon Industrial Energy Systems: An Investors’ Perspective, Markus Fleschutz

Publications

Time series aggregation (TSA) is commonly used in energy system optimization to reduce model complexity and computational expenses by selecting periods to represent the entire time series. TSA’s accuracy has traditionally been assessed by comparing the objective values between the original and TSA models (assumed error). However, evaluating TSA from an investor’s standpoint involves analyzing the performance of TSA-based energy system designs using the original time series. Therefore, we introduce the hidden error and total error, novel error metrics, to evaluate the financial implications of TSA through backtesting the TSA-based system designs using the original time series. Our analysis extends …

Design And Implementation Of Uvm-Based Verification Framework For Deep Learning Accelerators, Randa Ahmed Hussein Aboudeif

Theses and Dissertations

Recent advancements in deep learning (DL) have made hardware accelerators, known as deep learning accelerators (DLAs), a preferred solution for numerous high-performance computing (HPC) applications, including speech recognition, computer vision, and image classification. DLAs are composed of hundreds of parallel processing engines to speed up computations and can gain access to pre-trained networks from the cloud or through on-chip memory to implement the DNN inference process. DLA verification is becoming an important and challenging phase. The verification process is required to handle the complex DLA design. Moreover, the reliability of DLAs is critical for assessment as they are involved in …

New Insights Into Controlling The Functional Properties Of Tin Oxide-Based Materials, Alexandra Kuriganova, Nina Smirnova

Journal of Electrochemistry

Development of methodologies for fabrications of nanostructured materials that provide control over their microstructural features and compositions represents a fundamental step in the advancement of technologies for productions of materials with well-defined functional properties. Pulse electrolysis, a top-down electrochemical approach, has been demonstrated to be a viable method for producing nanostructured materials with a particular efficacy in the synthesis of tin oxides. This method allows for significant control over the composition and shape of the resulting tin oxides particles by modifying the anionic composition of the aqueous electrolyte, obviating the need for additional capping agents in the synthesis process and …

A Cnt Intercalated Co Porphyrin-Based Metal Organic Framework Catalyst For Oxygen Reduction Reaction, Pei-Pei He, Jin-Hua Shi, Xiao-Yu Li, Ming-Jie Liu, Zhou Fang, Jing He, Zhong-Jian Li, Xin-Sheng Peng, Qing-Gang He

Journal of Electrochemistry

The poor electronic conductivity of metal-organic framework (MOF) materials hinders their direct application in the field of electrocatalysis in fuel cells. Herein, we proposed a strategy of embedding carbon nanotubes (CNTs) during the growth process of MOF crystals, synthesizing a metalloporphyrin-based MOF catalyst TCPPCo-MOF-CNT with a unique CNT-intercalated MOF structure. Physical characterization revealed that the CNTs enhance the overall conductivity while retaining the original characteristics of the MOF and metalloporphyrin. Simultaneously, the insertion of CNTs generated adequate mesopores and created a hierarchical porous structure that enhances mass transfer efficiency. X-ray photoelectron spectroscopic analysis confirmed that the C atom in CNT …

Nanostructured Graphitic Carbon Nitride For Photocatalytic And Electrochemical Applications, Muhammad Abdul Qadeer, Iqra Fareed, Asif Hussaine, Muhammad Asim Farid, Sadia Nazir, Faheem K. Butt, Ji-Jun Zou, Muhammad Tahir, Shang-Feng Du

Journal of Electrochemistry

Graphitic carbon nitride (g-C₃N₄) exhibits great mechanical as well as thermal characteristics, making it a valuable material for use in photoelectric conversion devices, an accelerator for synthesis of organic compounds, an electrolyte for fuel cell applications or power sources, and a hydrogen storage substance and a fluorescence detector. It is fabricated using different methods, and there is a variety of morphologies and nanostructures such as zero to three dimensions that have been designed for different purposes. There are many reports about g-C₃N₄ in recent years, but a comprehensive review which covers nanostructure dimensions …

Sensing Rotational Direction Using Paramagnetic Nanoparticles For Gyroscopic Applications, Jacob C. Pung

Masters Theses

Gyroscopes have long been used for measuring an object’s angular orientation. Methods range from mechanical spinning disks to oscillating spring-mass systems. However, previous gyroscopes can suffer from high maintenance or fragility. A recently invented novel gyroscope aims to curb these restrictions through the use of ferrofluid.

This thesis proposes a new method of modeling and measuring the signals from the novel gyroscopic device. Rather than simply measuring the device’s average current consumption via slow RMS measurements, an attempt was made to model the voltage amplitude at any time. This allows a faster examination of both the changing magnitude and phase …

Feasibility And Acceptability Of The Mazi Umntanakho Digital Tool In South African Settings: A Qualitative Evaluation, Catherine E. Draper, Caylee J. Cook, Elizabeth A. Ankrah, Jesus A. Beltran, Franceli L. Cibrian, Kimberley D. Lakes, Hanna Mofid, Lucretia Williams, Gillian R. Hayes

Engineering Faculty Articles and Research

To address the need for interventions targeting social emotional development and mental health of young children in South Africa, the Mazi Umntanakho (‘know your child’) digital tool was co-designed, and piloted with caregivers and 3–5-year-old children involved in home visiting programmes promoting early childhood development. The aim of this study was to qualitatively evaluate the feasibility and acceptability of this tool in four urban and four rural low-income communities, from the perspective of home visitors and caregivers. Focus groups were conducted with home visitors (n = 117) and caregivers (n = 72). Issues relating to the feasibility of …

Novel Approach For The Micro Cracks Detection Of Solar Wafers And Cells, Mohd Israil, Arvind Kumar Sharma, Ekta Gupta

Al-Bahir

This paper deals with the review of various existing technique for the microcracks detection in silicon solar cell and wafer. In addition to this, we proposed a novel approach for the machine learning technique for the inspection of the cracks those are existed in the solar cell and wafer and not able to detect by the naked eyes. There are many techniques have been developed by the various researchers around the world to inspect solar cells for defect. All the techniques discussed in this article having some features and some weakness too. This paper present here gives the two-fold solution …

Dunbar’S Number In Motion: Agent-Based Simulations Of Friendship Formation, Christopher R. Cooke, Cameron D. Lutz

Northeast Journal of Complex Systems (NEJCS)

By contrasting Lévy flight and random walk strategies in simulated agents, we discern the effect of movement behavior on the total duration of social interactions. Our agent-based simulation results approximate empirically observed Dunbar social circle formation using simple behavioral rules of interaction and compatibility to mimic exogenous attribute-based friendship formation. We simulate the complexities of social interactions among agents with unique attributes and a time budget for social engagement over a one-year period. Two distinct simulations were conducted to evaluate the behavioral contributions of Lévy flight and random walk movement patterns on cumulative interaction duration and the formation of Dunbar …

A Novel Mu-Metal Based Weak Magnetic Energy Harvester For Self-Powered Monitoring Of Power Grid Assets, Arsalan Habib Khawaja, Hassan Pervaiz, Dongsheng Cai, Jian Li, Qi Huang

Turkish Journal of Electrical Engineering and Computer Sciences

This paper presents a novel magnetic field driven contactless energy harvester with improved flux concentration capabilities for potential utilization in Power system monitoring where stray magnetic field is abundant and readily available. The designed harvester employs multilayered Mu-Metal based cone shaped core to maximize magnetic flux density. To achieve the final design, this work investigates magnetic flux concentration ability of various geometries and material properties in magnetic flux conditions typical to overhead 11 kV power distribution circuits. Impact of layers in core-coil region of harvesting coil on magnetic flux concentration is evaluated by means of Finite Element analysis. Resultantly, the …

Advanced Prediction Of Events And Temporal Expressions In Medical Text Using The Jena Api: Integrating Ontologies And Deep Learning, Hafida Tiaiba, Lyazid Sabri, Okba Kazar

Turkish Journal of Electrical Engineering and Computer Sciences

The automatic recognition of medical concepts and temporal expressions in narrative clinical text enhances the utility of electronic health records (EHRs) and supports clinical decision-making and research. However, challenges arise due to the complexity of medical language, ambiguity of terms, and variability in expression. To address these issues, the use of medical ontologies significantly improves data management in healthcare. A novel approach integrates various medical ontologies covering drugs, symptoms, diseases, anatomy, disease drivers, and food, and with convolutional neural networks (CNNs) -including Standard, Transposed, and Separable convolution models (CONSEPTR)- to extract both medical events (e.g., clinical departments, treatments, problems) and …

Machine Learning Models Approach For The Quantitative Classification Of Ferricyanide Compound Using Electrochemical Detection With Cpe-Fe3o4nps, Süleyman Aşir, Nemah Abu Shama, Najya Maroof Saleem, Devri̇m Kayali, Kami̇l Di̇mi̇li̇ler

Turkish Journal of Electrical Engineering and Computer Sciences

Most common electrochemical analysis techniques used to evaluate enzymes, proteins, and heavy metals over a wide potential range include electrochemical impedance EIS, differential pulse voltammetry DPV, and square wave voltammetry SQWV. Machine leaning algorithms MLA are employed to classify the Potassium ferricyaniyde K3Fe(CN)6 concentrations using a modified carbon paste electrode CPE embedded with iron (II, III) oxide (Fe3O4) NPs. The CV, DPV, and SQWV voltametric data collected from all K3Fe(CN)6 concentrations were used as input data to the machine learning algorithms. Signaling current of K3Fe(CN)6 concentrations improved at Fe3O4 modified with nanoparticles NPs CPE in a comparison with the unmodified …

Balancing Anarchy And Efficiency: Partial Team Formations And Learning In Potential Games, Muhammed Sayin

Turkish Journal of Electrical Engineering and Computer Sciences

Non-cooperative multi-agent learning, focusing on individual rationality (anarchy), often falls short in achieving system-wide efficiency in potential games, a class of games with applications in decentralized control and optimization. On the other hand, cooperative approaches prioritize system efficiency but often via global coordination, which could be impractical, e.g., for large-scale and less controlled environments. To address this dilemma, we propose a novel framework that introduces partial team formations, allowing team members with shared objectives to coordinate their actions while maintaining team-wise rationality for improved system-wide efficiency without the burden of global coordination. We model such interactions as a multi-team game …

A Content-Based Recommender System For The Uav Caching In The Field Of Entertainment In Fog Computing, Elham Darbanian, Mohsen Nickray

Turkish Journal of Electrical Engineering and Computer Sciences

The Unmanned Aerial Vehicle (UAV) can be used as good flying base stations to cache popular content and follow a user mobility pattern, to help them in a suitable services. Conventional edge caching algorithms often prioritize cache contents with higher popularity. Nevertheless, the cache capacity of mobile devices is restricted, and diverse clients may have expansive varieties in content inclination designs. In this manner, the performance and effectiveness of the cache will be so constrained without great strategies. The composition of recommender system and edge caching is considered as a new research topic, which is used to reduce cost and …

Simultaneous Optimal Sizing And Placement Of Electrical Vehicles Charging Stations And Distributed Generation Units In Electrical Distribution Networks Using Abc Technique, Abdelazim M. Salem Dr., Nora M. El-Ayaat, Ismail M. Hafez Prof., Adel Abou El Ela Prof.

Journal of Engineering Research

The continuous increment in the utilization of electric vehicles (EV’s) that represent new loads on the electrical distribution networks. The distributed generators (DG’s) have been used in the distribution networks as renewable energy sources (RES) with the main electrical distribution network for supplying significant amount of the total connected loads. The using of DG’s lead to reduce the electrical active power losses and improving the voltage profile on the distribution network. A well-planned and efficient EV’s charging infrastructure is required for effective electric mobility. This paper presents a planning study for supplying up to four EVCS’s, each one increases the …

Design And Implementation Of A Low-Cost Educa-Tional Robot For Engineering Curriculums, Diana Refaat Henry Jacob, Ashraf Mohamed Hafez, Mohamed Tarek Elawa

Journal of Engineering Research

The role of educational robots is becoming increasingly important. Many commercial educational robots differ in their abilities and price. In general, the cost of these robots presents the main cause of their limited use in the teaching process, especially in countries with limited financial resources. This work attempts to solve this problem by designing and implementing an educational robot with a limited budget. The robot was initially intended to serve higher education students' needs, especially in engineering curriculums but can be extended to other application areas. This work presents robot hardware functional blocks, mechanical struc-ture design, and 3D printing implementation. …

Performance Evaluation Of Free Space Optical Communication In Dar Es Salaam: Impact Of Scintillation And Modulation Schemes, Mustafa H. Mohsini

Tanzania Journal of Engineering and Technology (TJET)

Free space optical communication (FSO) holds significant relevance in the modern communication system as it offers high and unlimited data rates, enhanced security, rapid deployment, and low cost for installation. However, the performance of FSO transmission is greatly affected by harsh atmospheric conditions such as wind, temperature, and humidity, which induce scintillation. With the rapid growth of internet users and Dar es Salaam being a business city in Tanzania, higher and unlimited bandwidth for communication is highly demanded. This study primarily aims to evaluate the performance of FSO transmission in Dar es Salaam, Tanzania, by investigating the impact of atmospheric …

Improved Minimum Variance Channel Estimation Techniques For Ofdm Systems, Kwame S. Ibwe

Tanzania Journal of Engineering and Technology (TJET)

Orthogonal frequency division multiplexing (OFDM) systems face challenges in channel estimation due to noise, variability, and the doubly dispersive nature of wireless channels, which degrade performance. To address these challenges, a multichannel minimum variance double dispersive channel estimator is proposed. The method employs a hybrid approach that combines subspace and minimum variance techniques, optimizing the filter bank output power under a signal-to-noise ratio (SNR) constraint. This design preserves the desired signal while effectively suppressing disturbances, achieving robust performance with reduced computational complexity compared to existing methods. Simulation results demonstrate that the proposed estimator outperforms subspace and asymptotic methods in terms …

Design And Implementation Of Secured Hybrid Gateway Node For Securing Iot - Enabled Distribution Automation, Ally Bitebo

Tanzania Journal of Engineering and Technology (TJET)

The integration of smart grid and Internet of Things (IoT) technologies plays a crucial role in enhancing the quality of services provided by traditional electrical grids. This combination has enabled the introduction of new services, such as demand response, automatic meter reading, and IoT-enabled Distribution Automation (IoT-DA), which incorporates sensors, actuators, intelligent electrical devices (IEDs), and information and communication technologies to monitor and control the grid. However, this integration also introduces network security risks, including Denial of Service (DoS) attacks, false data injection, and masquerading attacks, such as system node impersonation that can transmit incorrect readings, trigger false alarms, and …

A Bibliometric Analysis Of Pat-Seig Evolution As An Alternative Energy Generation Method, Emanuel J. Nyirenda

Tanzania Journal of Engineering and Technology (TJET)

Pumps as Turbines (PATs) coupled with Self-Excited Induction Generators (SEIGs) as an alternative energy generation method have been the subject of significant research in recent years. This paper presents a bibliometric analysis of the evolution of PAT-SEIG technology as an alternative energy generation source. An analysis of a comprehensive dataset of peer-reviewed journal articles, conference proceedings, and patents related to PAT-SEIG systems, using advanced bibliometric techniques and PRISMA to identify key research themes, influential authors, the growth of the field and patterns of collaboration, and changes in research focus over time. Over a publication period of 32 years, a total …

Effects Of Technical Debt On Software Interoperability, Leonard Peter Binamungu

Tanzania Journal of Engineering and Technology (TJET)

Technical debt (TD) refers to sub-optimal development decisions that make the software costly to maintain and evolve. Examples of TD include structural complexity, violation of coding styles, and code complexity. Existing research has investigated the nature, causes and indicators of TD, as well as tools and strategies for managing TD. However, although TD could hinder the ability of a software system to be interoperable with others, existing literature has limited evidence on how TD affects systems interoperability. This limits the ability of software engineering teams to manage TD in ways that do not hinder systems interoperability. To fill this void, …

An Ultra-Low-Power 0.8 V, 60 Nw Temperature Sensor For Battery-Less Wireless Sensor Networks, Fnu Naveed, Jeff Dix

Electrical Engineering and Computer Science Faculty Publications and Presentations

This work presents a nano-watt digital output temperature sensor featuring a supply-insensitive, self-biased current source. Second-order temperature dependencies of the MOS diode are canceled to produce a stable reference and a linear temperature-sensitive voltage. The sensor integrates a sensing unit, voltage-controlled differential ring oscillators, and a low-power frequency-to-digital converter, utilizing a resistor-less design to minimize power and area. The delay element in the ring oscillator reduces stage count, improving noise performance and compactness. Fabricated in 65 nm CMOS, the sensor occupies 0.02 mm2 and consumes 60 nW at 25 °C and 0.8 V. Measurements show an inaccuracy of +1.5/−1.6 °C …