bluuebunny/crossref_metadata_2025 huggingface dataset:
IMATSA – an improved and adaptive intelligent optimization algorithm based on tunicate swarm algorithm
Authors: Chen Yan , Dong Weizhen , Hu Xiaochun
Published: February 2024

Swarm intelligence optimization algorithm has been proved to perform well in the field of parameter optimization. In order to further improve the performance of intelligent optimization algorithm, this paper proposes an improved and adaptive tunicate swarm algorithm (IMATSA) based on tunicate swarm algorithm (TSA). IMATSA improves TSA in the following four aspects: population diversity, local search convergence speed, jumping out of local optimal position, and balancing global and local search. Firstly, IMATSA adopts Tent map and quadratic interpolation to initialize population and enhance the diversity. Secondly, IMATSA uses Golden-Sine algorithm to accelerate the convergence of local search. Thirdly, in the process of global development, IMATSA adopts Levy flight and the improved Gauss disturbance method to adaptively improves and coordinates the ability of global development and local search. Then, this paper verifies the performance of IMATSA based on 14 benchmark functions experiment, ablation experiment, parameter optimization experiments of Support Vector Machine (SVM) and Gradient Boosting Decision Tree (GBDT), Wilcoxon signed rank test and image multi-threshold segmentation experiment with the performance metrics are convergence speed, convergence value, significance level P-value, Peak Signal-to-Noise Ratio (PSNR) and Standard Deviation (STD). Experimental results show that IMATSA performs better in three kinds of benchmark functions; each component of IMATSA has a positive effect on the performance; IMATSA performs better in parameter optimization experiments of SVM experiment and GBDT; there is significant difference between IMATSA and other algorithms by Wilcoxon signed rank test; in image segmentation, the performance is directly proportional to the number of thresholds, and compared with other algorithms, IMATSA has better comprehensive performance.
The Impact of Data-Based Boosting on User Click-Through Intention Under AI Personalized Recommendations
Authors: Li Li , Ali Dhakir Abbas , Guo Yuanting
Published: February 2024

As the main means of product information recommendation, data boost will have an important impact on consumers’ purchasing behavior. Although there are a lot of research on data boost at present, they mainly focus on system design and algorithm research, and pay less attention to data aid under AI personalized recommendation. In view of this, this paper studies how the data boost pair under AI personalized recommendation has an impact on consumers’ willingness to click. Through scenario simulation method, three experiments were carried out with college students as experimental objects to carry out empirical research. The results show that the data boost of AI personalized recommendation positively affects click intention; Under AI personalized recommendation, perceived benefits play an important intermediary role in consumer behavior; Privacy concerns play a moderating role in the influence mechanism of AI personalized recommendation on consumers’ click intention.
Investigation into the Long-Term Dynamics of Microbiomes in Hong Kong Outdoor Bioaerosols
Authors: Xu Lingxiao , Tong Xinzhao
Published: February 2024

The composition of outdoor microorganisms is dynamic in nature, often affected by factors such as geography, seasonality, environmental conditions. In this study, bioinformatic platform QIIME2 (v2022.8) was used to process the 93 outdoor air samples after 16S rRNA gene sequencing, aiming to understand the dynamics of the outdoor bioaerosol microbial communities in Hong Kong and identify the significant factor underlying such variations. The results suggested the diversity and composition of outdoor microorganisms in Hong Kong significantly varied by seasonal changes, with the outdoor air bacterial community being highest in summer and autumn and lowest in winter. The study also identified indicator species for the three seasons, spring, autumn and winter, to investigate the changes in bacterial habits, living conditions and environment during the different seasons. The results indicate that bacteria in the aquatic environment are more abundant during these seasons, and that spring has seen the emergence of bacteria often associated with human epidemics. However, within one season, geographic area had minimal influence on the bacterial community diversity and composition, suggesting outdoor airborne bacterial communities tended to be homogeneous across different locations on a city-wide scale within a season.
Multiprocessing as a Way to Optimize Queries
Authors: Khrystynets Nataliia , Melnyk Kateryna , Lavrenchuk Svitlana , Miskevych Oksana , Kostiuchko Serhii
Published: February 2024

Developing an effective web application involves the use of various methods and techniques to ensure fast and efficient processing of requests. Sometimes it is not possible to solve the problem of multiprocessing with a single tool, such as a programming language or framework. This work investigates the use of asynchronous methods of processing requests using queues. Job operation in background and non-background modes relative to the main web process is studied. Analytics are provided to analyze a web application with 13,000 requests to process daily. It is proposed to optimize the processing by using the Laravel framework and the Python server dual-tasking using the Supervisor tool on Linux, as well as using a task scheduler for each task. The paper presents positive findings about this algorithm, which contributes to the efficiency of web development and provides a great user experience on the website. Fast processing of web application requests can be a valuable competitive advantage for a business or organization. Research in this field helps to maintain their high competitiveness. In addition, the study of query processing speed is important in scientific research, as it contributes to the development of new algorithms, optimization methods and technologies.
Quality Evaluation of Innovation and Entrepreneurship Education in Colleges and Universities Based on CIPP Model and AHP
Authors: Chen Xiaoshi , Zhu Jixu
Published: February 2024

Assessing the quality of education related to innovation and entrepreneurship in higher education institutions facilitates the identification and analysis of issues that arise during the process of developing these skills, as well as the advancement of the effectiveness of this process. Based on the CIPP model introduction, this paper attempts to build an index system from four perspectives: context evaluation, input evaluation, process evaluation and product evaluation. The analytic hierarchy process (AHP) is then used to determine the index weights, producing three primary and six subsidiary indicators that have a major influence on how well innovation and entrepreneurship education are taught at colleges and universities, among other things. Lastly, based on these viewpoints, enhancement strategies for the teaching of innovation and entrepreneurship in colleges and universities are suggested. To serve as a guide for enhancing the standard of entrepreneurship and innovation education in colleges and institutions.
Six-Link Teaching Methods for Improving the Creative Practical Ability – Taking “Mechanical Principle” as an Example
Authors: Li Wenhui , Meng Chaoying , Lian Xun , He Qin , Kong Min , Gao Yang
Published: February 2024

In the construction of “Emerging Engineering Education” and “First-Class Course”, “Mechanical Principle” is oriented to the requirements of talent training, the application of innovative thinking and creative practical ability in teaching. Firstly, the teaching system of “Mechanical Principle” is reconstructed. Secondly, based on TRIZ Theory in teaching methods is improved in the “Teaching, Learning, Practice, Creation, Research, Competition” six links. Thirdly, closed-loop process evaluation is adopted in teaching evaluation to reflect the situation of teachers and students. The effect of teaching reform shows that teacher evaluation, student achievement, student work and the achievement of curriculum objectives have been improved after the curriculum improvement. Especially in the second classroom, such as subject competition, innovation, entrepreneurship. The students’ innovative practical ability is effectively improved.
The prognostic and antitumor roles of key genes of ferroptosis in liver hepatocellular cancer and stomach adenocarcinoma
Authors: Pei Wenceng , Jiang Minren , Liu Haiyan , Song Jiahong , Hu Jian
Published: February 2024

BACKGROUND:

Liver hepatocellular cancer (LIHC) and stomach adenocarcinoma (STAD) are common malignancies with high lethal ratios worldwide. Great progress has been achieved by using diverse therapeutic strategies; however, these diseases still have an unfavourable prognosis. Ferroptosis inducer drugs, unlike apoptosis-related drugs, can overcome the resistance to cancer therapy caused by traditional chemicals. However, the relationship between overall survival (OS) and ferroptosis-related genes, as well as the mechanisms involved, are largely unclear.

METHODS:

The expression levels of AIFM2, GPX4, ACSL4, FTH1, NOS1, and PTGS2 in LIHC and STAD were obtained from UALCAN. The correlations of OS with these gene expression levels were obtained using the Kaplan-Meier Plotter database. The OS associated with genetic mutations of those genes compared to that of unchanged genes was analysed using the TIMER website. GO and KEGG enrichment analyses of ferroptosis-related genes and their coexpressed genes in LIHC and STAD were conducted using the STRING and DAVID databases. The relationship of PTGS2 and ACSL4 to immune cell infiltration was analysed using the TIMER website. The viability and GPX5 expression levels in LIHC cells treated with RSL3 and As2O3 were detected by MTT methods and western blotting, respectively.

RESULTS:

Our results showed that GPX4, FTH1 and AIFM2 were overexpressed in LIHC and STAD. High levels of GPX4, FTH1 and AIFM2 were prominently correlated with better prognosis in LIHC. However, GPX and FTH1 in STAD did not show significant correlations with OS. AIFM2 in STAD had the opposite trend with OS compared with that in LIHC. Moreover, a high mutation rate of these genes (35.74%) was also observed in LIHC patients, and genetic mutation of these genes was correlated with shorter OS. In contrast, the genetic mutation of these genes did not change OS in STAD. Enrichment analysis showed that the respiratory electron transport chain, cell chemotaxis and T-cell migration were related to ferroptosis. ASCL4 and PTGS2 coexpressed with cytokines associated with immune cell infiltration. Compared to RSL3 or As2O3 alone, As2O3 plus RSL3 significantly inhibited the growth of Huh7 cells. GPX4 was downregulated to an undetectable level when in combination with RSL3.

CONCLUSIONS:

Our results indicated that ferroptosis-related genes might play an important role in LIHC and STAD and might be risk factors for overall survival in LIHC and STAD.
The Development of a Multi-Dimensional Coding System to Categorise Negative Online Experiences Including Cyberbullying Behaviors Among Adolescents with Lower Socioeconomic Status
Authors: Purdy Noel , Scheithauer Herbert , Harris Jonathan , Willems Roy A. , Mameli Consuelo , Guarini Annalisa , Brighi Antonella , Menin Damiano , Culbert Catherine , Hamilton Jayne , Völlink Trijntje , Ballentine Mark , Fiedler Nora , Smith Peter K.
Published: February 2024

This original paper, based on data from the Erasmus+Blurred Lives Project, presents a new multi-dimensional categorisation model to describe negative online experiences, including forms of cyberbullying, based on a study of internet usage among over N = 2,500 adolescents with lower socio-economic status (SES) backgrounds across five European countries. The paper first sets out the rationale for the development of a new coding system, before describing the current study and nature of the survey data collected. There follows a description of the development of the new system and the series of reliability checks undertaken by the research team (N= 11, from 5 countries) and of the refinements made to the categories and codes. The resulting coding system is presented with consideration of the strengths and limitations, and description of two early pilot studies which have successfully adopted the new system.
Trajectory outlier detection method based on group division
Authors: Chen Chuanming , Xu Dongsheng , Jin Qi , Wang Wenkai , Sun Liping , Zheng Xiaoyao , Yu Qingying
Published: February 2024

Trajectory-outlier detection can be used to discover the fraudulent behaviour of taxi drivers during operations. Existing detection methods typically consider each trajectory as a whole, resulting in low accuracy and slow speed. In this study, a trajectory outlier detection method based on group division is proposed. First, the urban vector region is divided into a series of grids of fixed size, and the grid density is calculated based on the urban road network. Second, according to the grid density, the grids were divided into high- and low-density grids, and the code sequence for each trajectory was obtained using grid coding and density. Third, the trajectory dataset is divided into several groups based on the number of low-density grids through which each trajectory passes. Finally, based on the high-density grid sequences, a regular subtrajectory dataset was obtained within each trajectory group, which was used to calculate the trajectory deviation to detect outlying trajectories. Based on experimental results using real trajectory datasets, it has been found that the proposed method performs better at detecting abnormal trajectories than other similar methods.
Evaluation on high-performance image compaction algorithms in spatio-temporal data processing
Authors: Li Guozhang , Xing Kongduo , Alfred Rayner , Wang Yetong
Published: February 2024

With the passage of time, the importance of spatio-temporal data (STD) is increasing day by day, but the spatiotemporal characteristics of STD bring huge challenges to data processing. Aiming at the problems of image information loss, limited compression ratio, slow compression speed and low compression efficiency, this method based on image compression. This article intended to focus on aircraft trajectory data, meteorological data, and remote sensing image data as the main research objects. The research results would provide more accurate and effective data support for research in related fields. The image compaction algorithm based on deep learning in this article consisted of two parts: encoder and decoder, and this method was compared with the JPEG (Joint Photographic Experts Group) method. When compressing meteorological data, the algorithm proposed in this paper can achieve a maximum compaction rate of 0.400, while the maximum compaction rate of the JPEG compaction algorithm was only 0.322. If a set of aircraft trajectory data containing 100 data points is compressed to 2:1, the storage space required for the algorithm in this paper is 4.2 MB, while the storage space required for the lossless compression algorithm is 5.6 MB, which increases the compression space by 33.33%. This article adopted an image compaction algorithm based on deep learning and data preprocessing, which can significantly improve the speed and quality of image compaction while maintaining the same compaction rate, and effectively compress spatial and temporal dimensional data.
Resource allocation for content distribution in IoT edge cloud computing environments using deep reinforcement learning
Authors: Neelakantan Puligundla , Gangappa Malige , Rajasekar Mummalaneni , Sunil Kumar Talluri , Suresh Reddy Gali
Published: February 2024

This study presents a novel approach to optimize resource allocation, aiming to boost the efficiency of content distribution in Internet of Things (IoT) edge cloud computing environments. The proposed method termed the Caching-based Deep Q-Network (CbDQN) framework, dynamically allocates computational and storage resources across edge devices and cloud servers. Despite its need for increased storage capacity, the high cost of edge computing, and the inherent limitations of wireless networks connecting edge devices, the CbDQN strategy addresses these challenges. By considering constraints such as limited bandwidth and potential latency issues, it ensures efficient data transfer without compromising performance. The method focuses on mitigating inefficient resource usage, particularly crucial in cloud-based edge computing environments where resource costs are usage-based. To overcome these issues, the CbDQN method efficiently distributes limited resources, optimizing efficiency, minimizing costs, and enhancing overall performance. The approach improves content delivery, reduces latency, and minimizes network congestion. The simulation results substantiate the efficacy of the suggested method in optimizing resource utilization and enhancing system performance, showcasing its potential to address challenges associated with content spreading in IoT edge cloud calculating situations. Our proposed approach evaluated metrics achieves high values of Accuracy is 99.85%, Precision at 99.85%, specificity is 99.82%, sensitivity is 99.82%, F-score is 99.82% and AUC is 99.82%.
Constructing a MOEA approach for product form Kansei design based on text mining and BPNN
Authors: Wang Tianxiong , Xu Mengmeng , Yang Liu , Zhou Meiyu , Sun Xin
Published: February 2024

Kansei Engineering (KE) is a product design method that aims to develop products to meet users’ emotional preferences. However, traditional KE faces the problem that the acquisition of Kansei factors does not represent the real consumers demands based on manual and reports, and using traditional methods to calculate relationship between Kansei factors and specific design elements, which can lead to the omission of key information. To address these problems, this study adopts text mining and backward propagation neural networks (BPNN) to propose a product form design method from a multi-objective optimization perspective. Firstly, Term Frequency-Inverse Document Frequency (TF-IDF) and WordNet are used to extract key user Kansei requirements from online review texts to obtain more accurate Kansei knowledge. Secondly, the BPNN is used to establish the non-linear relationship between product Kansei factors and specific design elements, and a preference mapping prediction model is constructed. Finally, BPNN is transformed into an iterative prediction value of non-dominated sorting genetic algorithm-II (NSGA-II), and the model is solved through multi-objective evolutionary algorithm (MOEA) to obtain the Pareto optimal solution set that satisfies the user’s multiple emotional needs, and the fuzzy Delphi method is used to obtain the best product form design scheme that meets the user’s multiple emotional images. Using the example of electric bicycle form design could show that this proposed method can effectively complete multi-objective product solutions innovation design.
RETRACTED: Implementation of a dynamic planning algorithm in accounting information technology administration
Authors: Gao Yuan
Published: February 2024

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.
A vision imaging compensation algorithm for quadruped robot based on walking jitter
Authors: Chen Zhipeng , Liu Xiao , Qin Jianhua
Published: February 2024

To solve the problem that the walking jitter of quadruped robots leads to the degradation of clarity of visual imaging, a quadruped robot visual imaging jitter compensation algorithm based on the theory of walking jitter is proposed. The D-H coordinate transformation method is used to establish the coordinate system of each joint of the leg. The kinetic equations of the leg are derived from the relationship between the rotational velocity and the moment of the leg joint, and the kinetic equilibrium equations of the quadruped robot body are established based on the spatial moment equilibrium theorem; the spring-mass model of the leg of the quadruped robot is used to construct the kinetic equations of the leg jittering, and the kinetic equations of the body jittering are derived using the moment equilibrium condition of the body center of gravity position and under the effect of the leg and body jitter to obtain the visual imaging device jitter quantity; finally, the tremor quantity is combined with the jitter quantity and rotation matrix to derive the walking jitter mathematical model of the quadruped robot visual imager, and the jitter compensation algorithm of quadruped robot visual imager is verified. The experimental results show that compared with the traditional Wiener filter algorithm for jitter compensation and the BP neural network jitter compensation algorithm, this algorithm improves the visual imaging by 10.8% and 3.3% in the two evaluation indexes of peak signal-to-noise ratio and structural similarity, respectively, and the de-jittering effect is better.
A novel method for indian vehicle registration number plate detection and recognition using CNN
Authors: Pandey Vibha , Choubey Siddhartha , Patra Jyotiprakash , Mall Shachi , Choubey Abha
Published: February 2024

Automated reading of license plate and its detection is a crucial component of the competent transportation system. Toll payment and parking management e-payment systems may benefit from this software’s features. License plate detection and identification algorithms abound, and each has its own set of strengths and weaknesses. Computer vision has advanced rapidly in terms of new breakthroughs and techniques thanks to the emergence and proliferation of deep learning principles across several branches of AI. The practice of automating the monitoring process in traffic management, parking management, and police surveillance has become much more effective thanks to the development of Automatic License Plate Recognition (ALPR). Even though license plate recognition (LPR) is a technology that is extensively utilized and has been developed, there is still a significant amount of work to be done before it can achieve its full potential. In the last several years, there have been substantial advancements in both the scientific community’s methodology and its level of efficiency. In this era of deep learning, there have been numerous developments and techniques established for LPR, and the purpose of this research is to review and examine those developments and approaches. In light of this, the authors of this study suggest a four-stage technique to automated license plate detection and identification (ALPDR), which includes, image pre-processing, license plate extraction, character segmentation, and character recognition. And the first three phases are known as “extraction,” “pre-processing,” and “segmentation,” and each of these processes has been shown to benefit from its own unique technique. In light of the fact that character recognition is an essential component of license plate identification and detection, the Convolution Neural Network (CNN), MobileNet, Inception V3, and ResNet 50 have all been put through their paces in this regard.
Rough set model of incomplete interval rough number decision systems
Authors: Zhou Yinwei , Hu Jun
Published: February 2024

The rough set model has been extended to interval rough number decision systems, but the existing studies do not consider interval rough number decision systems with missing values. To this end, a rough set model of incomplete interval rough number decision systems (IIRNDSs) is proposed, and its uncertainty measures are investigated. Firstly, the similarity of two incomplete interval rough numbers (IIRNs) are defined by calculating their optimistic and pessimistic distances of the lower and upper approximation intervals of IIRNs. Then, the rough sets in IIRNDSs are constructed by the induced similarity relation. Next, four uncertainty measures, including approximation accuracy, approximation roughness, conditional entropy, and decision rough entropy are given, which exhibit a monotonic variation with changes in the size of attribute sets, α, and θ. Finally, the experimental results demonstrate the proposed rough set model of IIRNDSs is feasible and effective.
Assessment of drug utilization and potentially inappropriate medications in hemodialysis patients with end-stage renal dysfunction: A study in a tertiary care hospital in Bahrain
Authors: Sridharan Kannan
Published: February 2024

BACKGROUND: Patients undergoing dialysis pose therapeutic challenges in terms of polypharmacy, administration of potentially inappropriate drugs, and drugs with the potential risk of toxicity. OBJECTIVE: This study evaluated the use of drugs, potentially inappropriate medicines (PIM), drugs with risk of Torsades de Pointes (TdP), and the complexity of the prescribed regimen using the medication regimen complexity index scale in patients undergoing hemodialysis. METHODS: A retrospective cohort study was carried out amongst patients receiving hemodialysis. Drugs were classified into one of four classes: (i) drugs used in managing renal complications, (ii) cardiovascular drugs, (iii) anti-diabetic drugs, (iv) drugs for symptomatic management, and (v) others. Drugs were considered as PIM according to the Can-SOLVE CKD working group from a network of Canadian nephrology health professionals. The study adhered to the CredibleMeds classification of drugs with known, possible, and conditional risk of TdP and the complexity of prescribed medicines was evaluated based on the pre-validated medication regimen complexity index scale based on form/route, frequency of dosing, and requirement of special instructions. RESULTS: Sixty-three participants were included in the study (49 males and 14 females) with the median (range) age of 45 (21–66) years. Cardiovascular drugs followed by drugs used for managing renal complications were the most common classes administered. Notably, 12 (19.1%) patients received one of the non-steroidal anti-inflammatory drugs, 21 (33.3%) received a proton pump inhibitor, three (4.8%) received pregabalin, two (3.2%) received opioid drugs, and one (1.6%) was administered celecoxib. Atorvastatin, furosemide, omeprazole, and allopurinol were the most common PIM drugs administered to the study participants followed by others. Drugs used for symptomatic management had significantly more PIM compared to other classes (p < 0.0001). Six (9.5%) patients received drugs with known TdP risk, one with possible TdP risk, and 61 with conditional risk. Median (range) medical regimen complexity index score was 26.5 (2–62.5). CONCLUSION: A huge burden of drug therapy was observed in the hemodialysis patients in terms of higher proportions of PIM, complex medical regimen, and prescription of drugs with risk of TdP. Implementation of clinical decision support tools enhancing rational prescription and identification of drugs with TdP risk, introducing antimicrobial stewardship, and stepwise deprescription of the drugs with the least benefit-risk ratio are warranted.
The promise and the challenge of pre-employment transition services: The Workforce Innovation and Opportunity Act after ten years
Authors: Taylor Joshua P. , Whittenburg Holly N.
Published: February 2024

As we mark the 10th anniversary of the passage of the Workforce Innovation and Opportunity Act of 2014 (WIOA), one of its marquee policies— pre-employment transition services (Pre-ETS)— remains a work in progress. WIOA focused on achieving competitive integrated employment (CIE) for people with disabilities by establishing it as the primary goal for services and by reallocating resources and emphasizing coordinated service delivery to bend the trajectories of youth with disabilities toward CIE and minimize segregated and subminimum wage alternatives. However, along with the promise of opportunity and innovation, WIOA has introduced challenges and complexity in implementing Pre-ETS and other requirements. In this paper, we describe the promise of WIOA and Pre-ETS as well as many of these ongoing challenges related to its implementation. Finally, we provide recommendations for how federal and state leaders can build on the innovation occurring within Pre-ETS implementation to improve outcomes and achieve the promise WIOA set forth.
Transition self-assessment tool: The development and field testing of a statewide assessment of pre-employment transition services
Authors: Poppen Marcus , Sheftel Anya , Lindstrom Lauren E. , Rowe Dawn A. , Unruh Deanne , Mazzotti Valerie L.
Published: February 2024

BACKGROUND: Under the Workforce Innovation and Opportunity Act of 2014 (WIOA), vocational rehabilitation (VR) agencies are required to evaluate their students’ need for pre-employment transition services and use this information to inform their goals, priorities and strategies. OBJECTIVE: This manuscript presents the development and field testing of the Transition Self-Assessment Tool (TSAT), a web-based instrument designed to support VR agencies’ evaluation and strategic planning initiatives by measuring the availability, accessibility, and coordination of school-based transition services. METHOD: Our methods describe the TSAT development procedures and the methods of conducting a TSAT field test. RESULTS: Findings suggest high schools offer a broad array of career-related services for their students. However, not all potentially eligible students with disabilities have access to those services and when these services are provided, they are frequently delivered without coordination with VR. Additionally, data show more services are available, accessed, and coordinated in high schools participating in a statewide transition program. CONCLUSION: Results from the TSAT field test suggest it is feasible to conduct a statewide needs assessment of school-based transition services using this instrument. Furthermore, the findings from the TSAT align with prior knowledge about school-based transition services, and can be used to inform a state’s goals, priorities and strategies.
Enhancing transition outcomes: A toolkit to facilitate data-driven pre-employment transition services
Authors: Fleming Allison R. , Phillips Brian N. , Riesen Tim , Langone Aimee
Published: February 2024

BACKGROUND: The Rehabilitation Act, as amended in the Workforce Innovation and Opportunity Act of 2014 (WIOA), highlights the need to improve outcomes for transition-age students and youth with disabilities. The amendments require state vocational rehabilitation agencies to allocate 15% of their federal funding to provide pre-employment transition services (Pre-ETS) to an expanded population of transition-age students and youth with disabilities. Rapid implementation of this mandate required state vocational rehabilitation agencies to begin offering the five required Pre-ETS before mechanisms for monitoring and improvement could be created and field-tested. Nearly a decade since the passage of WIOA, many states are still without practical mechanisms for evaluating Pre-ETS despite increasing emphasis on using evidence-based practices in public programs. OBJECTIVE: The objective of this article is to present insights from the systematic implementation of The Transition Readiness Toolkit (TRT) within a state vocational rehabilitation agency. METHODS: We detail the creation of the TRT, our approach for implementing it across multiple states, and how it supports data-driven decision making for Pre-ETS providers and agencies. RESULTS: Initial results of implementation are promising. State VR agencies and Pre-ETS providers are using the data to increase accountability and understand the impact of their services. CONCLUSION: We present reflections on use of the TRT in a state agency and implications from an evidenced-based policy framework.
Leveraging the stages of learning to optimize pre-employment transition service delivery
Authors: Rowe Dawn A. , Diehl Melissa , Fowler Catherine H.
Published: February 2024

Pre-employment transition services (Pre-ETS) is a specific set of services outlined in Section 113 of the Rehabilitation Act as amended by WIOA, with the purpose of expanding career exploration, postsecondary training options, and the development of career readiness skills for students with disabilities through work-based learning experiences. Pre-ETS aims to facilitate the transition of students with disabilities from school to adulthood by equipping them with the essential skills, knowledge, and experiences needed to thrive in the workforce. However, there is great variability across states on how vocational rehabilitation agencies work with State and local education agencies to coordinate and sequence services to facilitate the development of a more comprehensive career pathway for students with disabilities. This paper provides insights and applicable guidance for practitioners seeking to optimize Pre-ETS by aligning interventions with the phases of learning. Grounded in experiential knowledge and effective practices for secondary transition and Pre-ETS, this paper offers a strategic framework designed to enhance the effectiveness of transition services for diverse individuals preparing to enter the workforce. It aims to equip practitioners with a toolbox of effective strategies and approaches, fostering a more dynamic and personalized delivery of Pre-ETS. By offering practical guidance on navigating the learning phases, this paper seeks to empower practitioners to create more impactful and tailored programs, ultimately enhancing the workforce readiness of individuals with diverse abilities.
Dry needling combined with exercise therapy: Effects on wrist flexors spasticity in post-stroke patients – A randomized controlled trial
Authors: Babazadeh-Zavieh Seyedeh Saeideh , Ansari Noureddin Nakhostin , Ghotbi Nastaran , Naghdi Soofia , Jafar Haeri Seyed Mohammad
Published: February 2024

BACKGROUND: The positive contribution of dry needling (DN) in conjunction with exercise therapy for patients with stroke and spasticity remains uncertain. OBJECTIVE: To examine the effects of DN combined with exercise therapy on wrist flexor spasticity and motor function in patients with stroke. METHODS: Twenty-four participants with stroke were randomly assigned to either the DN and exercise therapy group or the DN alone group. Assessments were conducted at baseline, after the 4th treatment session, and 3 weeks post-treatment. RESULTS: A significant Group×Time interaction was observed for wrist active range of motion (ROM) (P = 0.046), favoring the DN with exercise therapy group (∼10° at baseline, ∼15° immediately after the 4th session, and 15.4° at follow-up). The improvements in spasticity, passive ROM, and H-reflex latency were sustained during follow-up. However, there were no significant between-group differences in any outcome at any measurement time point. CONCLUSION: The combined DN and exercise therapy did not exhibit superiority over DN alone concerning spasticity severity and motor function. However, it demonstrated additional advantages, particularly in improving motor neuron excitability and wrist passive extension.
REhabilitation Approaches in CHildren with cerebellar mutism syndrome (REACH): An international cross-disciplinary survey study
Authors: Samargia-Grivette Sharyl , Hartley Helen , Walsh Karin , Lemiere Jurgen , Payne Allison D. , Litke Emma , Knight Ashley
Published: February 2024

OBJECTIVE: Pediatric cerebellar mutism syndrome (pCMS) can occur following resection of a posterior fossa tumor and, although some symptoms are transient, many result in long-lasting neurological deficits. A multi-disciplinary rehabilitation approach is often used in cases of pCMS; however, there have been no clinical trials to determine gold standards in rehabilitation practice in this population, which remains a research priority. The purpose of this study was to identify and compare intervention practices used in pCMS throughout the disciplines of occupational and physical therapy, speech-language pathology, and neuropsychology across geographic regions. METHODS: A 55-question e-survey was created by an international multidisciplinary research group made up of members of the Posterior Fossa Society and sent to rehabilitation professionals in pediatric neuro-oncology centers in the US, Canada, and Europe. RESULTS: Although some differences in the type of intervention used in pCMS were identified within each discipline, many of the targeted interventions including dose, frequency, and intensity were similar within disciplines across geographic regions. In addition, there were common themes identified across disciplines regarding challenges in the rehabilitation of this population. CONCLUSION: These results provide a foundation of current practices on which to build future intervention-based clinical trials.
Using IoT and AI to replenish household food supplies: A systematic review
Authors: Almassar Khaled Mosaed , Khasawneh Mohammad T.
Published: February 2024

Food wastage because of the lack or incompletion of a household replenishment system is an essential topic to be addressed. An appropriate utilization of Internet of Things (IoT) and Artificial Intelligence (AI) technologies with particular components is needed to design a smart household replenishment system to reduce food wastage. Therefore, this systematic review is dedicated to survey papers utilizing IoT and AI tools for perishable items storage compartments, as they are always full of items that need to be monitored. This study was conducted by following the PRISMA search strategy. It examined 70 papers in chronological order starting from 2000 when LG Electronics invented the first smart refrigerator, and research on technology involvement in food storage compartments increased. This comprehensive research aims to point out the approaches, contributions, used components and limitations of the reviewed papers to develop a unified framework for a household replenishment system. The analysis resulted in 43 approaches using IoT technology, 27 using AI, and recently the use of AIoT has been trending in the past two years. This systematic review provides future directions for researchers acquired from the limitations of the reviewed papers to enhance the household replenishment system by developing and adding required features in smart food storage compartments. Further investigation into smart home appliances would lead to extensive approaches like smart shops, industries, and eventually smart cities.
Upregulation of vesicle-associated membrane protein 7 in breast cancer tissues
Authors: Huang Yu , Wu Mei , Li Jian-Di , Qin Zhen , Huang Ke-Qiang , Cui Jin-Zhu , Ou Hai-Ling
Published: February 2024

BACKGROUND: Vesicle-associated membrane protein 7 (VAMP7) plays oncogenic roles in cancers. However, its clinical significance in breast cancer (BC) tissues remains unknown. OBJECTIVE: To elucidate the clinical implications of VAMP7, as well as its involvement in the tumor microenvironment and molecular pathways of breast cancer. METHODS: BC (n=100) and non-cancerous breast tissues (n= 100) were collected for an immunohistochemical experiment (1:200). The protein expression level of VAMP7 was determined by using a semi-quantitative scoring method. High-throughput RNA-sequencing data of BC tissues were analyzed to confirm the mRNA expression trend of VAMP7. Additionally, the largest BC prognosis cohort data were collected to mine the potential impact VAMP7 has on BC progression. The association between VAMP7 and the microenvironment of BC was evaluated by using a CIBERSORT algorithm. Moreover, we explored the co-expressed molecular mechanisms of VAMP7 in BC by calculating Pearson correlation coefficients and overexpressed genes. Finally, the biological mechanism underlying the relationship between VAMP7 and the key pathways was also explored using gene set enrichment analysis (GSEA). Potential therapeutic strategies were predicted targeting VAMP7. RESULTS: VAMP7 protein was significantly over-expressed in BC tissue than that in controls (p< 0.001). Compared with 459 normal breast tissues and 113 non-cancerous breast tissues, the expression level of VAMP7 mRNA was significantly increased in 1111 BC tissues. CD4+T cells, macrophages, and naïve B cells had a higher infiltration rate in BC tissues with high VAMP7 expression, while regulatory T cells and CD8+T cells had a lower infiltration rate. Over-expressed VAMP7 was associated with macrophages activation and transition from M1 to M2 polarization. Upregulated VAMP7 could predicted poorer OS, DMFS, PPS, and RFS outcomes. Upregulated VAMP7 co-expressed genes were significantly enriched in the cell cycle checkpoints. GSEA confirmed that over-expressed VAMP7 are markedly associated with functional enrichment in cell cycle related categories, including mitotic spindle, G2M checkpoint, and E2F targets. KU-55933 was predicted as a putative therapeutic drug for BC targeting VAMP7. CONCLUSIONS: VAMP7 was upregulated in BC tissue and correlated with poor prognosis of BC patients. VAMP7 may promote BC progression by targeting the cell cycle pathway.
Dendritic cell-cytokine killer combined with microwave ablation reduced recurrence for hepatocellular carcinoma compared to ablation alone
Authors: Wang Luo , Li Xin , Dong Xue-Juan , Yu Xiao-Ling , Zhang Jing , Cheng Zhi-Gang , Han Zhi-Yu , Liu Fang-Yi , Yu Jie , Liang Ping
Published: February 2024

BACKGROUND: Several international practice guidelines have recommended local ablation as the first-line treatment for early-stage hepatocellular carcinoma (HCC). OBJECTIVE: This study aims to investigate the synergetic anti-tumor impact of dendritic cell-cytokine killer (DC-CIK) combined with microwave ablation (MWA) for HCC. METHODS: This retrospective study included 1,141 patients from the American Joint Committee on Cancer stage I-II HCC, who were treated with therapeutic MWA. The immunotherapy group encompassing 40 patients received additional immunotherapy with DC-CIK, whereas the control group consisting of 1,101 patients was treated with MWA alone. Propensity score matching (PSM) with ratio of 1:3 was employed to balance selection bias. The oncological outcome and immune status were measured after combination therapy. RESULTS: The immunotherapy group patients exhibited significant longer disease-free survival (DFS, primary HCC: p= 0.036; recurrent HCC: p= 0.026). For patients with primary HCC, the recurrence frequency was reduced (p= 0.002), and recurrence interval (19 months vs. 9 months, p< 0.001) was prolonged in the immunotherapy group. Subgroup analysis revealed that patients ⩽ 60 years old, moderately-differentiated HCC, or co-infected with Hepatitis B Virus (HBV) had a significant benefit over DFS in the immunotherapy group. After combination therapy, the serum CD3+ (p= 0.049), CD8/CD28+ (p= 0.045) were elevated. CONCLUSION: Combination therapy with DC-CIK and MWA can significantly reduce the recurrence and prolong DFS, especially for patients ⩽ 60 years old or with moderately-differentiated HCC or co-infected with HBV.
Software that combines deep learning, 3D reconstruction and CFD to analyze the state of carotid arteries from ultrasound imaging
Authors: Tomasevic Smiljana , Anic Milos , Arsic Branko , Gakovic Branko , Filipovic Nenad , Djukic Tijana
Published: February 2024

BACKGROUND: Ultrasound is one of the non-invasive techniques that are used in clinical diagnostics of carotid artery disease. OBJECTIVE: This paper presents software methodology that can be used in combination with this imaging technique to provide additional information about the state of patient-specific artery. METHODS: Overall three modules are combined within the proposed methodology. A clinical dataset is used within the deep learning module to extract the contours of the carotid artery. This data is then used within the second module to perform the three-dimensional reconstruction of the geometry of the carotid bifurcation and ultimately this geometry is used within the third module, where the hemodynamic analysis is performed. The obtained distributions of hemodynamic quantities enable a more detailed analysis of the blood flow and state of the arterial wall and could be useful to predict further progress of present abnormalities in the carotid bifurcation. RESULTS: The performance of the deep learning module was demonstrated through the high values of relevant common classification metric parameters. Also, the accuracy of the proposed methodology was shown through the validation of results for the reconstructed parameters against the clinically measured values. CONCLUSION: The presented methodology could be used in combination with standard clinical ultrasound examination to quickly provide additional quantitative and qualitative information about the state of the patient’s carotid bifurcation and thus ensure a treatment that is more adapted to the specific patient.
Implemented classification techniques for osteoporosis using deep learning from the perspective of healthcare analytics
Authors: Liu Lili
Published: February 2024

BACKGROUND: Osteoporosis is a medical disorder that causes bone tissue to deteriorate and lose density, increasing the risk of fractures. Applying Neural Networks (NN) to analyze medical imaging data and detect the presence or severity of osteoporosis in patients is known as osteoporosis classification using Deep Learning (DL) algorithms. DL algorithms can extract relevant information from bone images and discover intricate patterns that could indicate osteoporosis. OBJECTIVE: DCNN biases must be initialized carefully, much like their weights. Biases that are initialized incorrectly might affect the network’s learning dynamics and hinder the model’s ability to converge to an ideal solution. In this research, Deep Convolutional Neural Networks (DCNNs) are used, which have several benefits over conventional ML techniques for image processing. METHOD: One of the key benefits of DCNNs is the ability to automatically Feature Extraction (FE) from raw data. Feature learning is a time-consuming procedure in conventional ML algorithms. During the training phase of DCNNs, the network learns to recognize relevant characteristics straight from the data. The Squirrel Search Algorithm (SSA) makes use of a combination of Local Search (LS) and Random Search (RS) techniques that are inspired by the foraging habits of squirrels. RESULTS: The method made it possible to efficiently explore the search space to find prospective values while using promising areas to refine and improve the solutions. Effectively recognizing optimum or nearly optimal solutions depends on balancing exploration and exploitation. The weight in the DCNN is optimized with the help of SSA, which enhances the performance of the classification. CONCLUSION: The comparative analysis with state-of-the-art techniques shows that the proposed SSA-based DCNN is highly accurate, with 96.57% accuracy.