Artificial Intelligence (AI) is becoming a critical component across industries, transforming traditional workflows through automation and smart technologies. This research investigates the application of Mathematics and AI in automating resume screening, particularly through its integration with the global job platform, Expertini.com. The study evaluates how AI-driven algorithms streamline recruitment processes, improving candidate identification and reducing manual screening times. These technologies can help global recruitment teams to quickly identify candidates with the right skill sets, reducing hiring time and increasing the diversity of applicant pools. This study also demonstrates how mathematical formulations combined with AI can significantly accelerate recruitment workflows. In conclusion, the research highlights how these innovations enhance talent acquisition by analyzing resumes and identifying candidates with the appropriate skills for specific job postings.

Despite the frenetic pace of e-commerce growth, over 80% of grocery sales remain carried out in physical retail environments around the globe. This traditional retail structure causes a number of serious challenges: frenzied product searches, crowded aisles, and long lines at the tills-all of which severely affect customer satisfaction. On account of such deficiency mentioned above, here we are presenting TechCart: an intelligent shopping framework to help improve the in-store shopping experience of a customer who utilizes AI, deep learning, and wireless communication technologies. Integration of independent navigation, immediate item recognition, personalized recommendations, and budget management takes place via a high-level mobile application. The architectures of this system are: the intelligent shopping cart, the mobile application, and the central server that make easy intercommunication and information exchange between these parts happen. From the results of evaluation, TechCart could identify 5,351 objects with a Mean Average Precision of 84.59%. This, therefore, means that this application is also full of producing results with strong accuracy in object detection. The grocery items commonly detected have a realized accuracy of 95%. The average time saved in shopping was 30% as perceived by the users.

This paper presents a method to determine the impact of low-voltage direct current circuit breakers on the availability of the dc bus. Low-voltage direct current is gaining traction in industry, and protection is an important aspect. New circuit breaker technologies are extensively researched, but the impact of this speed of interruption on direct current bus availability has not yet been investigated. This paper demonstrates how to determine the fault clearance probability, which is used to set up the state transition diagrams for the feeder states. These state transition diagrams are solved using the semi-Markov process. The solutions of the semi-Markov process are used in a UGO method to determine the availability of the direct current bus. Furthermore, it is shown that the results depend on the length of the feeder and the capacitance of the direct current bus. For feeders with a fault clearance probability lower than 100percent, the number of feeders also influences the availability of the direct current bus.

Introduction: Natural Language Processing (NLP) has the potential to bring about a quantum shift to the field of mental health. NLP techniques engender the birth of strategies to analyze text which may bode significant mental health outcomes, individually as well as socially. It can help us anticipate and intercept fallouts associated with any kind of deterioration in mental health statistics, most commonly depression and suicide, leading to better social upshots. It also establishes pathbreaking approaches to preventing, detecting, diagnosing, and treating various mental health conditions. This book article provides an extensive overview of the applications of NLP approaches in mental health. It will focus on both well-established methods and emerging trends within this area.Scope and Objectives: The article aims to explore the wide range of NLP applications in mental health, inter alia, sentiment analysis, emotion recognition, and predictive modeling. It discusses the utility of these applications in mental health screening, diagnosis, treatment, and monitoring. The article examines the role of NLP in identifying specific mental health conditions such as depression, anxiety, post-traumatic stress disorder (PTSD), and schizophrenia, as well as its integration into therapy settings and self-help tools like accessible online chatbots that assist with behavioral and mental struggles.Key Applications of NLP in Mental Health: There is a plethora of applications of NLP that can be thought of in the field of mental health. A summary of some of these applications is hereby provided. Potential applications of sentiment analysis, a practice in NLP, include detection of mood fluctuations and mental states by analyzing user-generated content. Emotion recognition in text identifies specific feelings to assist in affect assessments. Depression and suicide risk detection, assessment, and prevention, including detection of suicidal ideation in text data, are also prospective applications. The text data fundamentally includes social media posts and online communication. Cognitive Behavioral Therapy (CBT) chatbots automate therapeutic interventions by acting as digital or virtual therapists. Automated analysis of therapy transcripts helps us discern corrective insights. NLP is also employed in mental health screening through surveys when text-based responses to online posts and publications are linguistically analyzed. The identification of PTSD symptomatology involves detecting signs of PTSD in text, especially text data generated by veterans, because they form a category most susceptible to PTSD. Predictive modeling for mental health crises uses NLP to predict potential emergencies, most commonly suicide, crimes like manslaughter, incidents involving bodily harm to oneself or to another person, and vengeful destruction of valuable property through arson and other means. Detection of substance abuse identifies discussions related to sale, purchase, use and addiction of drugs in online forums, telephonic conversations, and conversations incidentally overheard and recorded in public spaces. Monitoring relapse risks entails tracking recovery and relapse risks in patients. Among other applications of NLP in the domain mental healthcare, early detection of psychological conditions like bipolar disorder (BPD), which are enhanced by NLP techniques which brandish the facility to identify the occurrence of such ailments using text and speech.NLP methodologies: The article aims to throw light on a variety of NLP methodologies including text classification, sentiment analysis, emotion detection algorithms, statistical machine learning models, and deep learning architectures. Tools such as speech-to-text analytics, topic modeling, dependency parsing, and vectors and word embeddings are highlighted to illustrate their use in mental health applications. The article will further explore the integration of machine learning and deep learning methods, such as Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs) and transformers, in mental health domains like diagnostics.NLP includes several methods for analyzing applications in mental health. Sentiment analysis detects emotional tones in text. This helps in various aspects connected to mental health, notably identifying signs of anxiety. Topic modeling demystifies common themes in mental health discussions. Text classification categorizes textual data and helps discern mental health insights from it. The textual data includes social media posts, hospital-obtained data like patient notes and therapy transcripts, suicide letters and voice notes of people just before they attempted suicide and so on. Named entity recognition supplements and enhances the amount and relevance of information obtained from clinical records. Chatbots based on advanced language models, especially large language models (LLMs) like Mistral, simulate therapeutic conversations. They are also used in automating medical diagnostics, and to provide self-help advice to caregivers of patients undergoing mental health treatments. Speech and audio analysis detect emotional states through changes in tone and pitch. Longitudinal analysis tracks language use over time to monitor mental health progression and intervention effectiveness, while cross-sectional analysis investigates the similarities and differences in mental health outcomes over various regions, populations, languages, cultures, ethnicities, and other determinants of social identity.Challenges: The application of NLP within mental health encompasses several significant challenges. Data privacy and security are paramount challenges. Most data analyzed involves personal and confidential information of potential mental health patients and sometimes also of medical staff and others. Stringent measures must be put in place to protect sensitive information and comply with regulations such as Health Insurance Portability and Accountability Act (HIPAA) in the United States and General Data Protection Regulation (GDPR) in Europe. Issues associated with data quality and completeness arise from gathering such data which is noisy, incomplete, and inconsistent. These issues necessitate robust data cleaning and preprocessing within the NLP pipeline. Fairness concerns are critical, as NLP models can possibly inherit biases from training data (and, in case of transfer learning, from parent models). Unresolved biases in training data may cause class imbalances. Inherent class imbalances are handled using tools like Synthetic Minority Oversampling Technique (SMOTE). Biases negatively influence the accuracy and fairness of predictions, whereby the models beg regular auditing and updating. The context and nuance of mental health expressions can be challenging for NLP models to interpret accurately. It highlights the requirement of generating advanced contextual embeddings and vectors. Ambiguity and polysemy in language, where terms can have multiple meanings, pose difficulties in text analysis. This needs sophisticated semantic analysis to resolve. Integration with clinical practice involves complexities in adapting to novel technologies and affirming their utility. It necessitates user-friendly interfaces and seamless Emergency Help Response (EHR) system integration. Ethical concerns, such as potential misuse of data, emphasize the demand for establishing ethical guidelines. Generalization and adaptation of models to miscellaneous populations is another challenge. This involves continuous adaptation and retraining of the model using various datasets. Real-time processing stipulates efficiency and scalability to confront computational challenges. The interpretation of results from NLP models can be arduous, particularly with complex language patterns. This necessitates the use of explainable AI (XAI) methods like Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). Cultural and linguistic variability not only affects the prevalence of mental health issues but also the effectiveness of NLP tools across different demographics. It calls for customization and validation of architectures and algorithms across varied populations. Reliability of NLP tools through rigorous testing must be ensured to validate their clinical relevance.Impact and Future Directions: The article emphasizes how NLP tools provide actionable inferences in mental health. They assist in enhancing early detection of mental health conditions, in automating therapy support, in providing new opportunities for large-scale mental health monitoring, and so on. Current cutting-edge advancements and future applications of NLP, including the integration of multimodal data (e.g., combining text, audio, video, music, images etc.), the use of more sophisticated machine learning models (e.g., Transformers, Generative Adversarial Networks or GANs, Graph Neural Networks or GNNs), and ethical considerations surrounding data privacy and mental health, are discussed. The article lays weight to the fact that Artificial Intelligence (AI) must be used responsibly in the domains of mental health, especially in interventions to prevent incidence of depression and suicide. Conclusion: This article is an endeavor to provide a comprehensive exploration of the growing intersection of NLP and mental health. It attempts to provide a general assessment of the potential applications, benefits, challenges, and future directions of NLP in mental health care. Key areas of application are elaborated. This article contributes to the existing body of knowledge as well as to the ongoing research and development that goes on in this rapidly evolving field.

We investigate a simultaneous transmitting and reflecting (STAR)-RIS-assisted near-field wideband terahertz communication system featuring multiple users and eavesdroppers on both sides of the STAR-RIS. Our objective is to maximize the minimum secure energy efficiency (EE) of the system while adhering to constraints related to transmit power, unit-modulus analog beamforming, STAR-RIS energy conservation, and the time-delay of true time delay units. This problem is challenging due to several factors, including the non-convex nature of the fractional EE objective function, the difference of the two rate expressions in the secrecy rate that appear in the numerator of the EE, the presence of multiple eavesdroppers, and the coupling of optimization variables. To address it, we employ a penalty dual decomposition method, where the inner loop optimizes a penalized objective function using a block coordinate descent approach, while the outer loop updates dual variables and the penalty factor. Our simulation results demonstrate that the proposed algorithm outperforms various benchmarks, including fully-digital beamforming design, conventional hybrid beamforming approach, simple RIS-assisted design, and the state-ofthe-art solution that does not account for eavesdroppers. This underscores the significance of a novel secure EE optimization design for the STAR-RIS-assisted near-field wideband terahertz communication system in the presence of eavesdroppers.

A novel, compact multiple-input-multiple-output (MIMO) antenna design, occupying a mere 25 × 39 mm², is proposed for ultra-wideband (UWB) applications. This innovative antenna comprises dual square-shaped radiating elements and a modified T-shaped stub, incorporating a vertical slot to mitigate mutual coupling. Key performance metrics, including impedance matching, inter-port mutual coupling, voltage standing wave ratio (VSWR) and correlation coefficient, are scrutinized. Simulation outcomes reveal an impressive impedance bandwidth spanning 2.5 to 11.8 GHz (S₁₁ ≤-10 dB), accompanied by an exceptionally low envelope correlation coefficient (<0.002) across the entire frequency spectrum. Notably, inter-element isolation exceeds-23 dB throughout the operational band. The proposed UWB MIMO antenna efficiently covers prominent frequency bands, encompassing WCDMA, WLAN, WiMax and UWB, making it an attractive solution for multi-standard wireless communication systems.

6G communications networks will be highly decentralized and distributed in nature. Existing security policies, procedures, and technologies, however, are mostly based on centralized control. Such centralized security will have serious consequences from two perspectives in 6G. First, the security systems will not be able to meet the strict requirements, such as latency, of emerging services, for example industrial and vehicular communications. Second, centralized security will lead to high consumption of resources, such as spectrum, in securing distributed systems in 6G. Therefore, in this paper we discuss intriguing challenges arising from security centralization that lead to another pressing challenge of the time, i.e., sustainability, as a consequence. Furthermore, we evaluate centralized and decentralized artificial intelligence (AI)-based security to demonstrate the resource and efficiency trade-off. Ultimately, this paper presents important research insights and visions that can pave the way toward distributed security in 6G.

Amorphous magnetic alloys are now widely used in electronics, electrical engineering, robotics, sensors, etc. They are mainly used in the form of wires, tapes and foils. The technology of manufacturing products from amorphous magnetic alloys is constantly improving and new methods are being implemented. In our work, microspirals (microsprings) made from amorphous microwires are studied. Measurements of hysteresis loops for different types of microspirals have been carried out and the relationship between the hysteresis anisotropy and the parameters of the spirals is discussed. Special attention is given to the study of the magnetic impedance. It is found that the field dependence of the impedance of microspirals is asymmetric, and the sign of the asymmetry is related to the chirality of the spirals. The possibility of using amorphous magnetic microspirals for the development of sensor and microrobotic elements is discussed.

Determining the electric field distribution using electromagnetic solvers requires significant computational effort, a high time consumption, and operator training. In this paper, a deep learning (DL)-based method is specifically developed to reconstruct the electric field distribution generated by antennas, based on the dielectric properties of the breast. ResNet and UNet were employed to predict the electric field within complex breast structures. Nine distinct breast dielectric models were utilized to create the initial dataset. A novel data augmentation technique was applied and the size was increased by three to address the challenge of limited dataset size. The electric field distribution data were generated using commercially available finite element method-based electromagnetic simulations. DL architectures were customized to fit the study and trained over 4374 breast slices. ResNet demonstrated an average Signal-to-Noise Ratio (SNR) of 23.7±6.7 dB and an average Normalized Mean Squared Error (NMSE) of 0.0029±0.0039 across 504 test breast slices, while UNet averaged 24.55±6.64 dB and 0.0024±0.0031 respectively. Additionally, a masked loss calculation was incorporated to enhance the learning accuracy of the implemented models. This approach yielded an increase of 0.9 dB in average SNR for ResNet and a 0.8 dB SNR increase for the UNet architecture on average. These results indicate that the proposed DL models effectively reconstruct electric field distributions with substantial accuracy, highlighting its potential for practical applications in real-time microwave medical imaging and treatment planning. Further refinement and validation with a more diverse set of breast models could enhance the model's applicability and performance.

Hypoplastic Left Heart Syndrome (HLHS) is a severe and rare congenital heart disease characterized by the underdevelopment of the left side of the heart, which is responsible for pumping oxygenated blood throughout the body. This underdevelopment results in an insufficient supply of oxygenated blood to vital organs and tissues. Current treatments for HLHS face significant challenges and typically involve complex surgical interventions that can only extend patient survival to a limited degree. Heart transplantation is another potential option, but it is constrained by the scarcity of donors and the risk of immune rejection. Induced pluripotent stem cells (iPSCs), derived from somatic cells, possess the remarkable ability to differentiate into any cell type within the three primary germ layers, making them a promising tool for tissue regeneration, disease modeling, and repair. However, despite their potential, the clinical application of iPSCs remains limited, primarily due to the risk of tumorigenesis. This review explores the role of iPSCs in advancing the understanding and treatment of HLHS, examining their cellular and molecular mechanisms, historical development, and their application in cardiovascular research. Additionally, we consider the major challenges and limitations associated with the use of iPSCs in the context of HLHS, underscoring the critical need for further research to enhance their therapeutic efficacy and safety.

Sidelink communication has evolved as a distributed device-to-device (D2D) communication system within the third-generation partnership project (3GPP) for the advancements in next-generation distributed autonomous systems. However, the current sidelink communication system basically being considered for operation at microwave band is not suitable for distributed wireless networking with a Gbit/s-level data rate. In this context, we have conceptualized a millimeter wave (mmWave) sidelink-based wireless personal area network (WPAN) to enable high-speed distributed wireless networking among indoor proximate devices. Particularly, inspired by the notion of "common-mode signaling (CMS)" in the IEEE 802.15.3c/11ad mmWave WPAN standards, we proposed an innovative physical sidelink broadcast channel (PSBCH) coined CMS-PSBCH to reach a robust inter-node control signaling to maintain a connection under harsh mmWave channel characteristics. As a sequential study, this paper proposes a robust signal detection and timing synchronization scheme tailored for CMS-PSBCH to lead a successful detection and demodulation, thereby providing a complete set of robust synchronization signal block (SSB) for the sidelink-based mmWave WPAN. Our linklevel evaluation shows that the proposed scheme effectively detects synchronization signals and symbol timing even under the signalto-noise ratio (SNR) of-18 dB. Moreover, the proposed scheme leads to successful demodulation of CMS-PSBCH payload for various multipath channels for a wideband indoor short-range communication scenario at the 60 GHz band.

This article addresses the growing problem of deep nudes, images generated by artificial intelligence techniques that create manipulated and non-consensual representations of nudity. These images pose a significant challenge to privacy and the ethical use of technology. The study proposes a machine learning-based solution integrated into web applications to automatically detect and censor these images. Our methodology combines machine learning algorithms with a web framework to provide an automated real-time detection system. We discussed the challenges, presented the model’s accuracy results, and outlined the next steps to improve the system. The proposed solution can significantly mitigate the spread of deep nudes, helping to combat the misuse of AI-generated content.

Critical illness is a condition that requires special attention, as it often involves life-threatening situations with a higher mortality rate. Attention is further focused on diabetic patients, who have recently become common comorbid cases among critically ill patients. Patients with these conditions are often prone to hyperglycemia and hypoglycemia due to the nature of critical illness and diabetes itself. Intensive Insulin Therapy (IIT) is known to be beneficial in maintaining blood glucose levels at the optimal target in such patients. However, the application of IIT is not straightforward. To date, there is no standardized IIT protocol, and its use is highly dependent on the expertise of each clinician. Moreover, each protocol does not always guarantee the maintenance of blood glucose levels because it depends on the patient's response to insulin, while the protocol is designed to be general. With the rapid advancement of Artificial Intelligence (AI), particularly Machine Learning (ML), a personalized IIT can be developed to address this issue. With the power of data, AI algorithms can analyze and assist in creating personalized IITs according to the patient's condition, with the goal of maintaining 2 their blood glucose levels. In this review, we present a future directive on the potential role of AI in creating personalized IITs for critically ill diabetic patients, as well as the challenges to be overcome in the development process.

The rapid growth in the scale and complexity of language models has brought about significant challenges in terms of computational efficiency, memory usage, and the occurrence of hallucinations during text generation. Addressing these issues requires a novel approach to streamline token processing without compromising accuracy or coherence in generated outputs. Adaptive Neural Token Streaming (ANTS) introduces a dynamic token filtering mechanism, which adjusts the token input based on contextual importance, thereby optimizing the allocation of computational resources. Through this mechanism, substantial improvements were observed in inference speed, model accuracy, and a marked reduction in hallucinations, while simultaneously reducing memory and energy consumption. Experiments conducted on a recent open-source language model demonstrated that ANTS not only enhances performance but also ensures that models can operate efficiently even under resource constraints. This innovation in token-level optimization has the potential to reshape how language models handle large input sequences, leading to more sustainable, scalable solutions for a wide range of tasks. The research outcomes indicate that token filtering techniques such as ANTS can contribute to the development of more robust and resource-conscious models, thereby pushing the boundaries of what language models can achieve in real-world applications.

Recent advancements in large language models, including GPT-4 and its variants, and Generative AI-assisted coding tools like GitHub Copilot 1 , ChatGPT 2 , and Tabnine 3 , have significantly transformed software development. This paper analyzes how these innovations impact productivity and software test development metrics. These tools enable developers to generate complete software programs with minimal human intervention before deployment. However, thorough review and testing by developers are still crucial. Utilizing the Test Pyramid concept 4 , which categorizes tests into unit, integration, and end-to-end tests, we evaluate three popular AI coding assistants by generating and comparing unit tests for opensource modules. Our findings show that AI-generated tests are of equivalent quality to original tests, highlighting differences in usage and results among the tools. This research enhances the understanding and capabilities of AI-assistant tools in automated testing.

Spiking Neural Networks (SNNs) offer a promising avenue for energy-efficient computations on neuromorphic hardware, leveraging the unique advantages of spike-based signaling. Despite their potential, SNNs often lag behind Artificial Neural Networks (ANNs) in performance, mainly due to the complexity of effectively translating ANN activation values into the time domain spikes. This challenge introduces quantization and unevenness errors, which arise from mapping continuous activations to discrete spikes and irregular spike timing. This paper introduces NeuBridge, an innovative ANN-SNN conversion method that utilize temporal coding to significantly reduce the number of required timesteps without compromising accuracy. NeuBridge addresses these errors by employing a decode-encode neuron and adaptive temporal coding, effectively bridging the performance gap between ANNs and SNNs. By establishing an equivalence between quantized ANNs and SNNs and optimizing the temporal coding process, we improve SNN performance with as few as 2 timesteps. Empirical evaluations on CIFAR-10 and ImageNet datasets demonstrate that NeuBridge consistently performs better than existing conversion methods in both accuracy and efficiency, achieving high performance within just 3 timesteps.

Lower limb exoskeletons play an increasingly important role in rehabilitation and mobility assistance, often requiring real-time human activity recognition (HAR) to deliver seamless assistance and natural interaction with the user. This study evaluates the effectiveness of various sensor types in HAR, employing three deep neural network models (MLP, LSTM, and CNN-LSTM) on an open-source dataset featuring 21 subjects performing six different locomotion activities. The dataset includes data from inertial measurement units (IMUs), goniometers, and marker-based motion capture derived joint angles using inverse kinematics (IK). To ensure real-time applicability, we trained these networks with a very short sliding window (50 ms) of timeseries data from different sensor combinations. Our results show that using bilateral IK joint angle data from all three lower limb joints can achieve an accuracy of 98.98%. Accuracy decreases as fewer joints are used, with 97.77% for the hip+knee combination and 91.79% for the hip joint alone. The addition of a thigh IMU to the IK data consistently improves HAR accuracy across all configurations. Moreover, incorporating derived joint angular velocity notably enhances HAR accuracy, improving accuracy of goniometer up to 10% for a single joint (knee) and 2% for two joints. For bilateral IK data, this improvement goes up to 15% for a single joint (knee).These findings highlight the substantial benefits of utilizing joint angle, velocity, and acceleration data obtainable from wearable exoskeletons and IMUs for real-time HAR and adaptive assistance in lower limb exoskeletons. The insights gained are pivotal for developing minimalistic HAR approaches and optimizing the seamless integration of exoskeletons into everyday activities.

This paper explores the need to implement the use of AI in Educational Institutions through a personal journey as a student in the School of Advanced Technology at Algonquin College during the appearance of ChatGPT in November 2022. The work done in the following months as President of the Student's Association to remove some of the fears and unsupported prejudices that developed into restrictive practices for the use of AI within College. This paper argues for the necessity of policy-driven approaches over mere guidelines to harness AI's potential while mitigating risks. Emphasizing the importance of ethical considerations, the paper advocates for a balanced view of AI as a complementary tool rather than a threat, urging educational institutions to shift from traditional control methods to more adaptive and inclusive strategies. Looking for a human-centered approach to AI, this paper intends to describe the latest technological trend from a social sciences perspective.