Cost Constraints Research Articles

Application of hybrid strategies of complex network attack and defense games

Critical infrastructure networks serve as the backbone of modern society’s operations, and the application of game theory to safeguard these networks against deliberate attacks under resource constraints is of paramount importance. Regrettably, the existing body of research on hybrid attack and defense strategies for nodes and edges is notably scarce, despite the ubiquity of such strategies in practical contexts. Current understanding regarding the establishment of a cost constraint model within the framework of hybrid attack and defense strategies, the strategic inclinations of both adversarial and defensive parties under varying cost constraint coefficients, and the influence of resource allocation ratios on equilibrium outcomes remains limited. Consequently, this study constructs a game-theoretic model for the engagement of critical infrastructure networks under non-uniform cost constraints, incorporating hybrid attack and defense strategies for nodes and edges, and conducts an equilibrium analysis across a range of cost constraint coefficients and resource allocation ratios. The findings reveal that when resource availability is limited, both attackers and defenders are inclined towards a strategy of concentrated resource allocation; in contrast, under most circumstances, defenders exhibit a preference for an equitable distribution of resources.

Multimodal Cross-Lingual Summarization for Videos: A Revisit in Knowledge Distillation Induced Triple-Stage Training Method.

Multimodal summarization (MS) for videos aims to generate summaries from multi-source information (e.g., video and text transcript), showing promising progress recently. However, existing works are limited to monolingual scenarios, neglecting non-native viewers' needs to understand videos in other languages. It stimulates us to introduce multimodal cross-lingual summarization for videos (MCLS), which aims to generate cross-lingual summaries from multimodal input of videos. Considering the challenge of high annotation cost and resource constraints in MCLS, we propose a knowledge distillation (KD) induced triple-stage training method to assist MCLS by transferring knowledge from abundant monolingual MS data to those data with insufficient volumes. In the triple-stage training method, a video-guided dual fusion network (VDF) is designed as the backbone network to integrate multimodal and cross-lingual information through diverse fusion strategies in the encoder and decoder; What's more, we propose two cross-lingual knowledge distillation strategies: adaptive pooling distillation and language-adaptive warping distillation (LAWD), designed for encoder-level and vocab-level distillation objects to facilitate effective knowledge transfer across cross-lingual sequences of varying lengths between MS and MCLS models. Specifically, to tackle lingual sequences of varying lengths between MS and MCLS models. Specifically, to tackle the challenge of unequal length of parallel cross-language sequences in KD, LAWD can directly conduct cross-language distillation while keeping the language feature shape unchanged to reduce potential information loss. We meticulously annotated the How2-MCLS dataset based on the How2 dataset to simulate MCLS scenarios. Experimental results show that the proposed method achieves competitive performance compared to strong baselines, and can bring substantial performance improvements to MCLS models by transferring knowledge from the MS model.

Facilitating phenotyping from clinical texts: the medkit library.

Phenotyping consists in applying algorithms to identify individuals associated with a specific, potentially complex, trait or condition, typically out of a collection of Electronic Health Records (EHRs). Because a lot of the clinical information of EHRs are lying in texts, phenotyping from text takes an important role in studies that rely on the secondary use of EHRs. However, the heterogeneity and highly specialized aspect of both the content and form of clinical texts makes this task particularly tedious, and is the source of time and cost constraints in observational studies. To facilitate the development, evaluation and reproducibility of phenotyping pipelines, we developed an open-source Python library named medkit. It enables composing data processing pipelines made of easy-to-reuse software bricks, named medkit operations. In addition to the core of the library, we share the operations and pipelines we already developed and invite the phenotyping community for their reuse and enrichment. medkit is available at https://github.com/medkit-lib/medkit. Documentation, examples and tutorials are available at https://medkit-lib.org/.

Reliability analysis of DDV and SPM in BOP-based Weibull model and expand fault dataset using deep learning

The hydraulic systems of deepwater Blowout Preventers (BOPs) are crucial for ensuring the reliability and safety of offshore oil and gas extraction operations. A functional failure can lead to uncontrolled blowouts, resulting in casualties and significant economic losses on the rig. The Direct Drive Valve (DDV) and the Subsea Plated Mounted (SPM) valve are key components that help maintain the proper functioning of the hydraulic system in deepwater BOPs. This study begins by utilizing the Weibull analysis method to assess the reliability of the DDV and SPM valves using limited fault data samples. To enhance the accuracy of predictions, Weibull parameters are estimated through various methods, including Maximum Likelihood Estimation (MLE), Least Squares Estimation (LSE), and a combination of Correlation Coefficient Optimization with Support Vector Regression (CCO + SVR).Given the challenges in gathering extensive fault data for DDV and SPM valves—due to complex subsea environments, cost constraints, time limitations, and other factors—this study proposes a method employing a Back Propagation Neural Network (BPNN) model to augment the limited fault data samples. To ensure the reliable operation of the DDV and SPM valves, preventive maintenance cycles are established at 2840 and 7550 operations, respectively. At the same reliability level, as the number of operational cycles increases, the remaining service life of the valves gradually decreases, leading to a higher probability of failure over a shorter timeframe. The Mean Remaining Life (MRL) of the DDV and SPM valves, corresponding to different operational times, is analyzed, providing essential reference points for their usage and maintenance. When the extended data sample is utilized for reliability evaluation, the reliability characteristics of the small fault data samples are effectively reflected, and the parameter prediction error remains low. This indicates that the extended data sample is more suitable for reliability evaluation.

Integrating Modern Design and Sustainability in Healthcare Architecture: A Comparative Case Study of Sri Lanka's National Nephrology Hospital and the UK's NHS New Hospital Programme

Hospital architecture has evolved significantly, influenced by advancements in medical science, technology, and societal needs. This paper examines the historical progression of hospital architecture, highlighting modern trends such as patient-centred design, modular construction, sustainability, and digital integration. It emphasizes the relevance of these trends to Low and Middle-Income Countries (LMICs), focusing on the China-Sri Lanka Friendship National Nephrology Hospital (NNSH) in Polonnaruwa. The study employs a qualitative approach, utilizing historical data, literature reviews, and case studies from developed and developing countries, primarily focusing on the NNSH and the UK's NHS New Hospital Programme (NHP).The NNSH exemplifies innovative hospital design, incorporating energy-saving technologies, climate-responsive features, and disaster resilience. It functions as a community hub, promoting health education and outreach. Insights from the NHP emphasize the integration of digital solutions, standardized designs, and sustainable practices, which are pivotal for enhancing healthcare infrastructure in LMICs.Strategic recommendations address challenges such as cost constraints, infrastructure gaps, and technical expertise in LMICs, including digital transformation, net-zero carbon strategies, green practices, and adherence to standardized guidelines. This comparative study underscores the importance of adaptable and sustainable hospital architecture in improving healthcare delivery and outcomes in Sri Lanka and other LMICs.

Revolutionizing Sports Education using AI & ML

In the realm of sports education and performance analysis, there exists a pressing need to overcome barriers such as geographical limitations, cost constraints, and time constraints inherent in traditional teaching methodologies. In response to these challenges, this paper presents a novel sports education system leveraging artificial intelligence (AI) technologies. This allinclusive system allows for easy access to sports academies and training facilities based on geographic proximity by introducing an innovative teaching platform driven by AI and GPS integration. Moreover, it offers an extensive library of sports laws, sophisticated strategies, and professional advice, encouraging ongoing skill development. Setting goals and closely observing performance are made easier by the system's integration of AI-driven forecasts, such as victory probabilities. The central aim of this initiative is to redefine the landscape of sports education by offering a convenient, cost-effective, and highly efficient solution accessible to individuals at all proficiency levels. In addition to its educational advantages, the system is poised to make significant contributions to sports analytics, supplying extensive data and insights for both researchers and practitioners. This proposal advocates a progressive approach that moves beyond traditional methods, heralding a new era in sports education and performance analytics

AutoQuo: An Adaptive plan optimizer with reinforcement learning for query plan selection

An efficient execution plan generation is crucial for optimizing database queries. In exploring large table spaces to identify the most optimal table join orders, traditional cost-based optimizers may encounter challenges when confronted with complicated queries. Thus, learning-based optimizers have recently been proposed to leverage past experience and generate high-quality execution plans. However, these optimizers demonstrate limited generalization capabilities for workloads with diverse distributions.In this study, an adaptive plan selector based on reinforcement learning is proposed to address these issues. However, three challenges remain: (1) How to generate optimal multi-table join orders? We adopt an exploration–exploitation strategy to traverse the vast search space composed of candidate tables, thereby evaluating the significance of each table. Long short-term memory (LSTM) networks are subsequently used to predict the performance of join orders and generate high-quality candidate plans. (2) How to automatically learn new features in novel datasets? We employ the Actor–Critic strategy, which involves jointly cross-training the policy and value networks. By adjusting the parameters based on real feedback obtained from the database, the new datasets are automatically learnt. (3) How to automatically select the best plan? We introduce a constraint-aware optimal plan selection model that captures the relationship between constraints and plans. This model guides the selection of the best plan under constraints of execution time, cardinality, cost, and mean-squared error (MSE). The experimental results on real datasets demonstrated the superiority of the proposed approach over state-of-the-art baselines. Compared with PostgreSQL, we observed a reduction of 29.73% in total latency and 28.36% in tail latency.

The American Headache Society First Contact-Headache in Primary Care program: Current metrics, knowledge assessments, and direction for future initiatives.

This study examines the American Headache Society First Contact-Headache in Primary Care program metrics to date in order to assess the program's reach and provide direction for future initiatives. Approximately 4 million primary care office visits annually are headache-specific encounters. Therefore, it is important that primary care providers are knowledgeable about headache management. Recognizing the need, the American Headache Society First Contact designed the comprehensive First Contact-Headache in Primary Care program with input from an advisory board comprised of a diverse group of physicians and advanced practice providers with backgrounds in family and internal medicine, pediatrics, obstetrics and gynecology, and neurology. This is the first study to assess the reach of the program and critically examine how to best meet the needs of clinicians and patients going forward. We report descriptive statistics for the First Contact website metrics from October 2020 to June 2023 and grand rounds program data from May 2020 to December 2023. We also conducted a cross-sectional analysis of survey data from presentations conducted at two large national family medicine symposia, as well as a thematic analysis of the question: "Please indicate what areas of your practice could be enhanced or improved with additional education?" The First Contact program homepage was the second most visited page on the American Headache Society website (>100,000 views). A total of 20 podcast episodes were created for the program (>3500 plays). The First Contact program held 99 events (72 institutional grand rounds, 22 State-level meetings, and five national meetings), reaching >7000 clinicians. The institutional grand rounds and state-level meetings were held across 27 States and Washington D.C. Only 31.9% (30/94) of First Contact program events (excluding national meetings) occurred in the West census region, which has the fewest headache subspecialists and lowest headache subspecialist density in the United States. When examining survey data of participants who attended the two virtual national family medicine symposia (39.3% response rate, N = 636/1620), 85.7% (544/635) reported being "completely confident" or "veryconfident" in their ability to recognize and accurately diagnose patients presenting with a primary complaint of headache and 81.5% (517/634) reported being "completely confident" or "very confident" in their ability to develop evidence-based treatment plans that are tailored to the needs of individual patients. The use of diagnostic tools to recognize patients with migraine (60.4%, 384/636) and translating standards of care to the practice setting (42.5%, 270/636) were the most reported intended changes by participants. Most participants reported that program content was of clinical relevance and would improve their patients' outcomes (90.5% [571/631] and 90.6% [572/631], respectively). Over three-quarters (77.8%, 495/636) of participants reported areas of their practice that can be improved by additional education specifically regarding workflow, diagnosis, and management. This study evaluates one of the first national initiatives for primary care education. Data from the two First Contact Family Medicine national symposia indicate the program is generally well received with most participants reporting improved confidence and intention to implement key changes in practice to improve care for patients with headache; however, there remain areas of exploration for education that could further enhance participant experience and expand the reach of the initiatives. Areas for future programming include continued education on multifactorial approaches to headache treatment and suggestions for addressing cost, insurance, and time constraints. Also, future work may examine where the First Contact program might focus initiatives based on specific areas of need in headache care, such as geographic "desert" areas, racial and ethnic disparities, and uninsured/underinsured populations.

A Multi-Sized Unit Cell Method for the Design of LPBF Lattice Support Structures Concerning Complex Geometries

Abstract Composed of individual unit cells strategically arranged to achieve a desired function, lattices are a promising solution for laser powder bed fusion support structure design in additive manufacturing. Despite their many advantages (e.g., multifunctionality and reduced material cost), prior work in lattice support structure design primarily focuses on horizontal support domains that are not translatable to support domains for complex geometries, thereby limiting their application. This work introduces a multi-sized unit cell design optimization (MSO) method to create lattice support structures (LSS) for parts with complex geometries. The proposed method utilizes voxelization to generate LSS using box-like unit cells of different sizes. It also allows for efficient, high-dimensional design optimization for the types and locations of user-specified unit cells through a modified simulated annealing-based optimization algorithm. The effectiveness and efficiency of the MSO method are demonstrated through the case study of an adapter pipe for a high-temperature heat exchanger. For this demonstration, LSS using multi-sized unit cells is designed to increase heat transfer rate while satisfying structural integrity and material cost constraints. The case study results indicate that the design of the LSS derived from the MSO method fulfills all constraints, including the design constraint of 50% material cost reduction, compared to the solid support structure. In contrast, the lattice support structure designs derived from equal-sized unit cell methods either cannot satisfy all design constraints or have a lower heat transfer rate than the design of the MSO method.

Robust Workforce Management with Crowdsourced Delivery

Revolutionize Workforce Management of Crowdsourced Delivery Platforms The surge in online shopping is driving e-retailers to revamp their logistics for efficient, cost-effective deliveries. Many are embracing crowdsourced delivery, where independent couriers use personal vehicles for swift shipments. Major players like Amazon and Walmart have pioneered this shift in delivery methods. To tackle uncertainties in this model, platforms are blending ad hoc couriers with prehired couriers. This hybrid approach ensures reliability in customer service while managing costs effectively. However, balancing this mix poses challenges, as future demands are still being determined. This study proposes a robust satisficing framework to optimize workforce management in delivery platforms. This innovative method aims to enhance cost-effectiveness and service quality by addressing uncertainties in ad hoc couriers’ availability and behavior. It offers a strategic tool for platforms to navigate workforce resources efficiently amidst fluctuating demands and cost constraints.

Energy-efficient time and cost constraint scheduling algorithm using improved multi-objective differential evolution in fog computing

The recent surge in Internet of Things (IoT) applications and smart devices has led to a substantial rise in the data generation. One of the major issues involved is to meet strict quality of service (QoS) requirements for computing these applications in terms of execution time, cost and in an energy-efficient manner. To extract useful information, fast processing and analysis of data is needed. Consequently, moving all the data to centralized cloud data centers would lead to high processing times, increased cost and energy consumption and more bandwidth usage; thus, processing of applications with strict latency requirements becomes challenging. The addition of fog layer between cloud and IoT devices has provided promising solutions to such issues. However, efficient employment of computing resources in the hybrid infrastructure of fog and cloud nodes is of great significance and demands an optimal scheduling strategy. Toward this direction, a novel Pareto-based algorithm in fog computing, namely energy-efficient time and cost (ETC) constraint scheduling algorithm, is introduced in this paper for scheduling workflow applications. ETC attempts to optimize monetary cost along with time and energy objectives. Improved multi-objective differential evolution (I-MODE) meta-heuristic is introduced and incorporated with deadline-aware stepwise frequency scaling approach that is based on our previously proposed energy makespan multi-objective optimization (EM-MOO) algorithm. Synthetic and real-world application workflows are used to conduct evaluation of the proposed work with existing well-known algorithms from the literature. The experimental results for synthetic workflows reveal that the proposed algorithm lessens energy utilization by 14–21%, execution time by almost 25% and cost consumption by 22–27%, while for real-world application workflows, energy consumption is reduced by 12–24%, execution time by 14–16% and cost consumption by 23–29%.

Zebrafish Experimental Animal Models for AD: A Comprehensive Review.

AD disease (AD) is a multifaceted and intricate neurodegenerative disorder characterized by intracellular neurofibrillary tangle (NFT) formation and the excessive production and deposition of Aβ senile plaques. While transgenic AD models have been found instrumental in unravelling AD pathogenesis, they involve cost and time constraints during the preclinical phase. Zebrafish, owing to their simplicity, well-defined behavioural patterns, and relevance to neurodegenerative research, have emerged as a promising complementary model. Zebrafish possess glutaminergic and cholinergic pathways implicated in learning and memory, actively contributing to our understanding of neural transmission processes. This review sheds light on the molecular mechanisms by which various neurotoxic agents, including okadaic acid (OKA), cigarette smoke extract, metals, and transgenic zebrafish models with genetic similarities to AD patients, induce cognitive impairments and neuronal degeneration in mammalian systems. These insights may facilitate the identification of effective neurotoxic agents for replicating AD pathogenesis in the zebrafish brain. In this comprehensive review, the pivotal role of zebrafish models in advancing our comprehension of AD is emphasized. These models hold immense potential for shaping future research directions and clinical interventions, ultimately contributing to the development of novel AD therapies.

THE EFFECT OF AI-DRIVEN INVENTORY MANAGEMENT SYSTEMS ON HEALTHCARE OUTCOMES AND SUPPLY CHAIN PERFORMANCE: A DATA-DRIVEN ANALYSIS

There has been much focus toward increasing integration of AI in healthcare generally, with a specific focus on inventory management systems in particular. As more hospitals in the United States public and private side face increasing costs and concerns over cost and length of operation all systems supporting inventory must be made most efficient and AI system offers the US hospitals the opportunity to maintain accurate inventory and hence improve the quality of patient care. But the literature review presents a small number of studies that focus on the effects of these systems as the means to improve healthcare results and organization. In this study, it is proposed that improving inventory management by actioning artificial intelligence in healthcare organizations will result in enhanced patient care and improved inventory accuracy, cost reduction and supply chain efficiency in the United States of America’s healthcare institutions. A cross-sectional, quantitative survey was administrated to 200 healthcare personnel and supply chain managers from different healthcare organizations based on hospitals, clinics and specialty medical centers of the USA. Sample design: Participants were purposefully chosen, who had prior exposure to the AI-induced systems or are aware about workflow of such systems. Respondents’ data was gathered via an online questionnaire and analyzed using SPSS: descriptive analysis, chi-square test, one-way ANOVA, correlation analysis, multiple regression analysis was used to establish the relationships of the study variables: AI systems, healthcare outcomes and supply chain performance. The results showed that the use of AI in inventory management systems enhances its accuracy in ordering order itself and overall patient care. Separate studies showed that where the management of a facility has deeper insight of the AI system then there will be more operations worked on to improve efficiency or cut costs. Adoption of these pathways among smaller providers including clinics and long-term care facilities was difficult, especially due to cost constraints. An analysis of the multiple regression equation showed that knowledge of AI improved the projection of potential healthcare outcome, creating greater worth and also increases the performance of supply chain. The outcomes of using AI based inventory management systems in the United States seem to have a lot of advantages that may help improve the efficacy of operations in the healthcare system. Issues such as funding and skills needed for implementation, especially in small healthcare organization, continue to slow the uptake of the technology. These existing problems may be solved if more focused investments in the development of comprehensive AI training or systematic solutions are given to enable the healthcare industry to reach the full potential of the application of artificial intelligence.

Functional Outcomes of Screw Fixation of the Fracture Head of the Femur: A Descriptive Study

Introduction: Femoral head fractures are uncommon injuries where patient outcomes depend on immediate surgical workup and postoperative rehabilitation. This study aimed to evaluate the functional outcomes of patients treated by open reduction and internal fixation (ORIF) using the screw fixation technique for the fracture head of the femur. Materials and Methods: Fourteen patients with femoral head fractures were treated by ORIF using the screw fixation technique in the Department of Orthopedics at a tertiary care hospital in Coimbatore, India for a period of 2 years. They were followed up for a minimum of 1 year to assess postoperative fracture union, postoperative complications, and functional and radiological outcomes by using relevant scoring systems such as Harris Hip Score, Oxford Hip Score, and Thompson-Epstein Hip Score. Results: Femoral head fracture patients in the age group of 36 ± 12.5 years (M: F = 1.8:1), classified as per Pipkins and Thompson-Epstein system were operated using the ORIF technique and followed up for 3.3 ± 1.8 years; the mean range of motion at the operated hip joints achieved were flexion 115.5°, extension 24.4°, abduction 35.5°, adduction 26.5°, internal rotation 34.4°, and external rotation 36.6°. Harris’s hip score was excellent in 57%, good in 14%, fair in 7%, and poor in 21% of the subjects. Oxford’s hip scores were satisfactory in 93% of the subjects. Osteoarthritis (28.9%) and avascular necrosis (7.14%) were seen as complications. Conclusion: According to our Indian population and cost constraints prevailing, we extrapolate these results obtained to our community population and thereby recommend ORIF of fracture femoral head by the screw fixation technique and postoperative non-weight-bearing protocol as a safe treatment schedule.

Development of a Machine-learning-based Framework to Assess the Performance of Water Distribution Systems

Recent statistics indicate that the national water supply coverage is approximately 99%, reflecting a high level of service. However, the aging infrastructure continues to cause issues such as pipe failures and water quality concerns, creating operational difficulties. To solve these issues, performance assessments are used to quantitatively evaluate the system conditions and ensure effective maintenance. In Korea, these assessments are based on a scoring system that combines direct and indirect assessments. However, direct assessment of all pipes is limited by cost and time constraints. To address this issue, a deep neural network is used to assess the aging of water distribution systems. This study develops a framework to estimate direct results from indirect data by analyzing the correlation between indirect and direct assessments. Additionally, data augmentation is considered to compensate for the lack of training data in direct assessments, improve the reliability of performance assessments, and support better prioritization in system maintenance.

Optimizing Road Pavement Projects Through Earned Value Concepts

Every construction project faces constraints of cost, schedule, and quality that must be managed to achieve the desired objectives. This research aims to control the aspects of time and cost in construction projects by applying the Earned Value Concept and Crash Program methods. The subject of the study is the Preservation of Roads and Bridges project in Bireuen City - BTS, Lhokseumawe/Aceh Utara, Krueng Mane Buket Rata, SP. Krueng Geukeueh - PEL. Krueng Geukeueh (PEL. Lhokseumawe). The data analyzed includes the Budget Plan (RAB), project schedule, and reports on planned and actual work. This data is processed into three main indicators: BCWS, BCWP, and ACWP, which are then analyzed through Schedule Variance (SV), Cost Variance (CV), Schedule Performance Index (SPI), and Cost Performance Index (CPI). The analysis results show that during weeks 22 to 23, the project had a positive Schedule Variance (SV) (0), which then turned negative during weeks 24 to 28, with an average Schedule Performance Index (SPI) of 1.0096, indicating that project execution was ahead of schedule. Meanwhile, the Cost Variance (CV) was positive throughout the project, and the average Cost Performance Index (CPI) was 1.2118, indicating that actual costs did not exceed the budget. The application of the Crash Program method resulted in changes in heavy equipment rental costs and worker wages, from IDR 1,440,469,946.99 over 50 days to IDR 1,853,072,988.06 over 25 days, with a cost increase of IDR 412,603,041.07 and an acceleration ratio of 1.29. This research demonstrates that the Earned Value Concept and Crash Program methods are effective in controlling time and cost in construction projects, thereby helping to achieve better efficiency in project management.

Robust Control of Electrical Machines in Renewable Energy Systems: Challenges and Solutions

The increasing global demand for energy driven by population growth necessitates a shift towards renewable energy sources to address sustainability and environmental concerns. Renewable energy sources and how they function in tandem with electrical equipment to generate power are covered extensively in this study. Renewable sources like solar-thermal, hydro, wind, wave, tidal, geothermal, and biomass-thermal utilize electric machines to convert various forms of stored energy into electricity. The paper classifies these machines based on their design and application in renewable systems, highlighting the most common types, including DC, induction, and synchronous machines. It also evaluates their performance according to efficiency, power density, and cost-effectiveness. A primary focus is on the challenges associated with controlling electrical machines in renewable energy systems, including issues related to intermittency, grid integration, nonlinear dynamics, fault tolerance, harmonics, efficiency optimization, thermal management, scalability, real-time control, and cost constraints. The paper concludes by discussing the need for advanced control strategies and solutions to address these challenges, aiming to enhance a reliability, efficiency, and performance of renewable energy systems.

Improving Workplace Well-being in Modern Organizations: A Review of Large Language Model-based Mental Health Chatbots

The global rise in mental disorders, particularly in workplaces, necessitated innovative and scalable solutions for delivering therapy. Large Language Model (LLM)-based mental health chatbots have rapidly emerged as a promising tool for overcoming the time, cost, and accessibility constraints often associated with traditional mental health therapy. However, LLM-based mental health chatbots are in their nascency, with significant opportunities to enhance their capabilities to operate within organizational contexts. To this end, this research seeks to examine the role and development of LLMs in mental health chatbots over the past half-decade. Through our review, we identified over 50 mental health-related chatbots, including 22 LLM-based models targeting general mental health, depression, anxiety, stress, and suicide ideation. These chatbots are primarily used for emotional support and guidance but often lack capabilities specifically designed for workplace mental health, where such issues are increasingly prevalent. The review covers their development, applications, evaluation, ethical concerns, integration with traditional services, LLM-as-a-service, and various other business implications in organizational settings. We provide a research illustration of how LLM-based approaches could overcome the identified limitations and also offer a system that could help facilitate systematic evaluation of LLM-based mental health chatbots. We offer suggestions for future research tailored to workplace mental health needs.

Demand management and pricing in renewable integrated plug-in electric vehicle charging station using reinforcement learning

ABSTRACT The increasing adaption of Plug-in Electric Vehicles (PEVs) has increased the power demand for charging. Renewable generation can significantly help to meet this demand. The uncertainty of renewable generation and randomness in PEV charging makes the demand management of charging stations challenging. To address these challenges, this study proposes a novel demand management pricing strategy for renewable integrated charging stations. The proposed strategy uses reinforcement learning for charging coordination of PEVs. The renewable generation is estimated using weather data and accordingly PEVs are scheduled for demand management. The strategy decides the PEV power price based on real-time price, renewable tariff and PEV load. The strategy uses the bidirectional power flow with battery degradation cost. The distribution transformer operating cost and loading constraints are included to resemble the real-time environment. The PEV randomness and uncertainty of renewable generations are incorporated in the study. The results of the numerical case study show that the proposed strategy can manage the charging station load efficiently. The proposed strategy has optimized the cost of charging, discharging and charging station profit and increased the service capability of the charging station.

Modified ICP based Framework for 3D Localization and Mapping in GNSS-denied Environments using LiDAR

Abstract. Lidar odometry and mapping has emerged as fundamental technology in various fields, including accurate positioning and mapping, autonomous vehicles, robotics, and environmental monitoring. The ability to create accurate maps and calculate trajectories is essential for localization and navigation in complex environments, particularly in scenarios where GNSS signal are unavailable. In this context, we propose a novel method for simultaneous localization and mapping (SLAM) specifically designed for GNSS-denied environments and without the need for high-accuracy ranging or inertial measurement units (IMUs). Our approach aims to address the challenges posed by GNSS-denied environments and the cost constraints associated with using multiple sensors. By using only lidar sensors, specifically the Velodyne VLP 16 sensor, we offer a cost-effective solution. Our method utilizes a modified version of the Iterative Closest Point (ICP) algorithm to handle consecutive lidar scans seamlessly. In addition to our method, we conducted thorough accuracy assessments on the created map. The reason behind extensive accuracy evaluation is to ensure the quality of registration. By comparing alignment between the features of different frames within the map and determining the error between the registered features and the original, we gain a more comprehensive understanding of registration quality.