bluuebunny/crossref_metadata_2025 huggingface dataset:
Neuro-distributed cognitive adaptive optimization for training neural networks in a parallel and asynchronous manner
Authors: Michailidis Panagiotis , Michailidis Iakovos T. , Gkelios Sokratis , Karatzinis Georgios , Kosmatopoulos Elias B.
Published: August 2023

Distributed Machine learning has delivered considerable advances in training neural networks by leveraging parallel processing, scalability, and fault tolerance to accelerate the process and improve model performance. However, training of large-size models has exhibited numerous challenges, due to the gradient dependence that conventional approaches integrate. To improve the training efficiency of such models, gradient-free distributed methodologies have emerged fostering the gradient-independent parallel processing and efficient utilization of resources across multiple devices or nodes. However, such approaches, are usually restricted to specific applications, due to their conceptual limitations: computational and communicational requirements between partitions, limited partitioning solely into layers, limited sequential learning between the different layers, as well as training a potential model in solely synchronous mode. In this paper, we propose and evaluate, the Neuro-Distributed Cognitive Adaptive Optimization (ND-CAO) methodology, a novel gradient-free algorithm that enables the efficient distributed training of arbitrary types of neural networks, in both synchronous and asynchronous manner. Contrary to the majority of existing methodologies, ND-CAO is applicable to any possible splitting of a potential neural network, into blocks (partitions), with each of the blocks allowed to update its parameters fully asynchronously and independently of the rest of the blocks. Most importantly, no data exchange is required between the different blocks during training with the only information each block requires is the global performance of the model. Convergence of ND-CAO is mathematically established for generic neural network architectures, independently of the particular choices made, while four comprehensive experimental cases, considering different model architectures and image classification tasks, validate the algorithms’ robustness and effectiveness in both synchronous and asynchronous training modes. Moreover, by conducting a thorough comparison between synchronous and asynchronous ND-CAO training, the algorithm is identified as an efficient scheme to train neural networks in a novel gradient-independent, distributed, and asynchronous manner, delivering similar – or even improved results in Loss and Accuracy measures.
A hybrid quantum-behaved particle swarm optimization solution to non-convex economic load dispatch with multiple fuel types and valve-point effects
Authors: Chen Qidong , Jun Sun , Palade Vasile
Published: August 2023

Economic dispatch problems (EDPs) can be reduced to non-convex constrained optimization problems, and most of the population-based algorithms are prone to have problems of premature and falling into local optimum when solving EDPs. Therefore, this paper proposes a hybrid quantum-behaved particle swarm optimization (HQPSO) algorithm to alleviate the above problems. In the HQPSO, the Solis and Wets local search method is used to enhance the local search ability of the QPSO so that the algorithm can find solutions that is close to optimal when the constraints are met, and two evolution operators are proposed and incorporated for the purpose of making a better balance between local search and global search abilities at the later search stage. The performance comparison is made among the HQPSO and the other ten population-based random search methods under two different experimental configurations and four different power systems in terms of solution quality, robustness, and convergence property. The experimental results show that the HQPSO improves the convergence properties of the QPSO and finally obtains the best total generation cost without violating any constraints. In addition, the HQPSO outperforms all the other algorithms on 7 cases of all 8 experimental cases in terms of global best position and mean position, which verifies the effectiveness of the algorithm.
A fast and distributed C4.5 algorithm for urban big data
Authors: Cheng Wan-Shu , Huang Peng-Yu , Huang Jheng-Yu , Chen Ju-Chin , Lin Kawuu W.
Published: August 2023

The amount of information nowadays is rapidly growing. Aside from valuable information, information that is unrelated to a target or is meaningless is also growing. Big data and broader digital technologies are considered the primary components of smart city governance and planning. Big data analysis is considered to define a new era in urban planning, research, and policy. Effective data mining and pattern detection techniques are becoming very important these days. Processing such a large amount of data entails the use of data mining, a technique that clarifies the association between valid information and excludes irrelevant data to implement a practical decision tree. A large amount of data affects processing time and I/O costs during data mining. This study proposes to distribute data among multiple clients and distribute a large amount of data computation equally to improve the resource cost problem of exploration. Following that, the main server consolidates the computation results and generates the survey results. Experiment results show that the proposed algorithm is superior, thus allowing a larger amount of data to be processed while producing high-quality results.
Mining skyline frequent-utility patterns from big data environment based on MapReduce framework
Authors: Wu Jimmy Ming-Tai , Li Ranran , Wu Mu-En , Lin Jerry Chun-Wei
Published: August 2023

When the concentration focuses on data mining, frequent itemset mining (FIM) and high-utility itemset mining (HUIM) are commonly addressed and researched. Many related algorithms are proposed to reveal the general relationship between utility, frequency, and items in transaction databases. Although these algorithms can mine FIMs or HUIMs quickly, these algorithms merely take into account frequency or utility as a unilateral criterion for itemsets but the other factors (e.g., distance, price) could be also valuable for decision-making. A new skyline framework has been presented to mine frequent high utility patterns (SFUPs) to better support user decision-making. Several new algorithms have been proposed one after another. However, the Internet of Things (IoT), mobile Internet, and traditional Internet are generating massive amounts of data every day, and these cutting-edge standalone algorithms can not satisfy the new challenge of finding interesting patterns from this data. Big Data uses a distributed architecture in the form of cloud computing to filter and process this data to extract useful information. This paper proposes a novel parallel algorithm on Hadoop as a three-stage iterative algorithm based on MapReduce. MapReduce is used to divide the mining tasks of the whole large data set into multiple independent sub-tasks to find frequent and high utility patterns in parallel. Numerous experiments were done in this paper, and from the results, the algorithm can handle large datasets and show good performance on Hadoop clusters.
Data management scheme for building internet of things based on blockchain sharding
Authors: Wang Xu , Zheng Wenhu , Wang Jinlong , Xiong Xiaoyun , Shen Yumin , Mu Wei , Fan Zengliang
Published: August 2023

As an important part of digital building, building internet of things (BIoT) plays a positive role in promoting the construction of smart cities. Existing schemes utilize blockchain to achieve trusted data storage in BIoT. However, the full-copy storage mechanism of blockchain and the management requirements of massive data have brought computing and storage challenges to edge nodes with limited resources. Therefore, a data management scheme for BIoT based on blockchain sharding is proposed. The scheme proposes a hybrid storage mechanism, which uses inter-planetary file system (IPFS) to ensure the integrity and availability of data outside the chain, and reduces the storage overhead of edge nodes. Based on the hybrid storage mechanism, the sharding algorithm is designed to divide the blockchain into multiple shards, and the storage overhead and computing overhead are offloaded to each shard, which effectively balances the computing and storage overhead of edge nodes. Finally, comparative analysis was made with existing schemes, and effectiveness of proposed scheme was verified from the perspectives of storage overhead, computation overhead, access delay and throughput. Results show that proposed scheme can effectively reduce storage overhead and computing overhead of edge nodes in BIoT scenario.
I2R: Intra and inter-modal representation learning for code search
Authors: Zhang Xu , Xiang Yanzheng , Liu Zejie , Hu Xiaoyu , Zhou Deyu
Published: August 2023

Code search, which locates code snippets in large code repositories based on natural language queries entered by developers, has become increasingly popular in the software development process. It has the potential to improve the efficiency of software developers. Recent studies have demonstrated the effectiveness of using deep learning techniques to represent queries and codes accurately for code search. In specific, pre-trained models of programming languages have recently achieved significant progress in code searching. However, we argue that aligning programming and natural languages are crucial as there are two different modalities. Existing pre-train models based approaches for code search do not effectively consider implicit alignments of representations across modalities (inter-modal representation). Moreover, the existing methods do not take into account the consistency constraint of intra-modal representations, making the model ineffective. As a result, we propose a novel code search method that optimizes both intra-modal and inter-modal representation learning. The alignment of the representation between the two modalities is achieved by introducing contrastive learning. Furthermore, the consistency of intra-modal feature representation is constrained by KL-divergence. Our experimental results confirm the model’s effectiveness on seven different test datasets. This paper proposes a code search method that significantly improves existing methods. Our source code is publicly available on GitHub.1
Temporal attention-aware evidential recurrent network for trustworthy prediction of Alzheimer’s disease progression
Authors: Zhang Chenran , Bao Qingsen , Zhang Feng , Li Ping , Chen Lei
Published: August 2023

Accurate and reliable prediction of Alzheimer’s disease (AD) progression is crucial for effective interventions and treatment to delay its onset. Recently, deep learning models for AD progression achieve excellent predictive accuracy. However, their predictions lack reliability due to the non-calibration defects, that affects their recognition and acceptance. To address this issue, this paper proposes a temporal attention-aware evidential recurrent network for trustworthy prediction of AD progression. Specifically, evidential recurrent network explicitly models uncertainty of the output and converts it into a reliability measure for trustworthy AD progression prediction. Furthermore, considering that the actual scenario of AD progression prediction frequently relies on historical longitudinal data, we introduce temporal attention into evidential recurrent network, which improves predictive performance. We demonstrate the proposed model on the TADPOLE dataset. For predictive performance, the proposed model achieves mAUC of 0.943 and BCA of 0.881, which is comparable to the SOTA model MinimalRNN. More importantly, the proposed model provides reliability measures of the predicted results through uncertainty estimation and the ECE of the method on the TADPOLE dataset is 0.101, which is much lower than the SOTA model at 0.147, indicating that the proposed model can provide important decision-making support for risk-sensitive prediction of AD progression.
Energy saving management technology for electrical automation and power distribution network dispatching
Authors: Zhang Zhenyuan
Published: August 2023

With the rapid development of the economy, the demand for electric power is increasing, and the operation quality of the power system directly affects the quality of people’s production and life. The electric energy provided by the electric power system is the foundation of social operation. Through continuous optimization of the functions of the electric power system, the efficiency of social operation can be improved, and economic benefits can be continuously created, thereby promoting social progress and people’s quality of life. In the power system, the responsibility of the power distribution network (PDN) is to transmit electricity to all parts of the country, and its transmission efficiency would directly affect the operational efficiency of the power system. PDN scheduling plays an important role in improving power supply reliability, optimizing resource allocation, reducing energy waste, and reducing environmental pollution. It is of great significance for promoting social and economic development and environmental protection. However, in the PDN scheduling, due to the inflexibility of the power system scheduling, it leads to the loss and waste of electric energy. Therefore, it is necessary to upgrade the operation of the PDN automatically and use automation technology to improve the operational efficiency and energy utilization rate of the power system. This article optimized the energy-saving management of PDN dispatching through electrical automation technology. The algorithm proposed in this paper was a distribution scheduling algorithm based on electrical automation technology. Through this algorithm, real-time monitoring, analysis, and scheduling of PDNs can be achieved, thereby improving the efficiency and reliability of distribution systems and reducing energy consumption. The experimental results showed that before using the distribution scheduling algorithm based on electrical automation technology, the high loss distribution to transformation ratios of power distribution stations in the first to fourth quarters were 21.93%, 22.95%, 23.61%, and 22.47%, respectively. After using the distribution scheduling algorithm, the high loss distribution to transformation ratios for the four quarters were 15.75%, 13.81%, 14.77%, and 13.12%, respectively. This showed that the algorithm can reduce the high loss distribution to transformation ratio of power distribution stations and reduce their distribution losses, which saved electric energy. The research results of this article indicated that electrical automation technology can play an excellent role in the field of PDN scheduling, which optimized the energy-saving management technology of PDN scheduling, indicating an advanced development direction for intelligent management of PDN scheduling.
System construction of deep learning AI cloud service mode
Authors: Lin Chunhua
Published: August 2023

Deep learning (DL) is the basis of many applications of artificial intelligence (AI), and cloud service is the main way of modern computer capabilities. DL functions provided by cloud services have attracted great attention. At present, the application of AI in various fields of life is gradually playing an important role, and the demand and enthusiasm of governments at all levels for building AI computing capacity are also growing. The AI logic evaluation process is often based on complex algorithms that use or generate large amounts of data. Due to the higher requirements for the data processing and storage capacity of the device itself, which are often not fully realized by humans because the current data processing technology and information storage technology are relatively backward, this has become an obstacle to the further development of AI cloud services. Therefore, this paper has studied the requirements and objectives of the cloud service system under AI by analyzing the operation characteristics, service mode and current situation of DL, constructed design principles according to its requirements, and finally designed and implemented a cloud service system, thereby improving the algorithm scheduling quality of the cloud service system. The data processing capacity, resource allocation capacity and security management capacity of the AI cloud service system were superior to the original cloud service system. Among them, the data processing capacity of AI cloud service system was 7.3% higher than the original cloud service system; the resource allocation capacity of AI cloud service system was 6.7% higher than the original cloud service system; the security management capacity of AI cloud service system was 8.9% higher than the original cloud service system. In conclusion, DL plays an important role in the construction of AI cloud service system.
Hybrid deep learning models based emotion recognition with speech signals
Authors: Chowdary M. Kalpana , Priya E. Anu , Danciulescu Daniela , Anitha J. , Hemanth D. Jude
Published: August 2023

Emotion recognition is one of the most important components of human-computer interaction, and it is something that can be performed with the use of voice signals. It is not possible to optimise the process of feature extraction as well as the classification process at the same time while utilising conventional approaches. Research is increasingly focusing on many different types of “deep learning” in an effort to discover a solution to these difficulties. In today’s modern world, the practise of applying deep learning algorithms to categorization problems is becoming increasingly important. However, the advantages available in one model is not available in another model. This limits the practical feasibility of such approaches. The main objective of this work is to explore the possibility of hybrid deep learning models for speech signal-based emotion identification. Two methods are explored in this work: CNN and CNN-LSTM. The first model is the conventional one and the second is the hybrid model. TESS database is used for the experiments and the results are analysed in terms of various accuracy measures. An average accuracy of 97% for CNN and 98% for CNN-LSTM is achieved with these models.
Impact of differing eccentric-concentric phase durations on muscle damage and anabolic hormones
Authors: Kizilay Fatma , Emin Kafkas Muhammed , Çağatay Taşkapan Mehmet , Haydar Demirel Ali , Radak Zsolt
Published: August 2023

BACKGROUND:

Resistance exercises are widely used to enhance muscle hypertrophy. Hypertrophy occurs with effect of mechanical loading, metabolic stress, and muscle damage. The time under tension in eccentric, isometric, and concentric phases of resistance exercises can alter muscle damage and anabolic hormone responses.

OBJECTIVES:

The aim of the study was to reveal the effect of the time spent in the concentric and eccentric phases of the movement on muscle damage and anabolic hormone response during resistance exercise.

METHODS:

Ten male subjects participated in the study. A, B, C tempo protocols were created by changing the time under tension to be 1 or 2 seconds in the eccentric and concentric phases of bench press and squat movements. In all protocols, isometric phases were 0 sec. The metronome was used to apply tempos. Blood samples were taken before each protocol, after administration, and in the 24[Formula: see text], 48[Formula: see text] and 72[Formula: see text] hours after exercise; and serum insulin, testosterone, Insuline-like Growth Factor-1 (IGF-1), Creatine Kinase (CK) and Lactate Dehydrogenase (LDH) levels were evaluated.

RESULTS:

Time-wise changes in CK were statistically different for all protocols ([Formula: see text] 0.05). CK levels in the 24[Formula: see text] hour of the protocol C and the 48[Formula: see text] hour of the protocol B were significantly higher than those in the other protocols ([Formula: see text] 0.05). IGF-1 and testosterone levels were significantly higher in the protocol B in the post-test, compared to the other protocols ([Formula: see text] 0.05). Insulin levels in the 24[Formula: see text] hour were significantly higher in the protocol B compared to those in others ([Formula: see text] 0.05). Time-wise changes in LDH parameters were not statistically significant in any hours ([Formula: see text] 0.05).

CONCLUSIONS:

Extending time under tension in the eccentric phase of a bout of resistance exercise appears to affect the anabolic hormone response and muscle damage. In this way, increased metabolic response and mechanical stress can induce muscle mass gain.
Comparison of trunk muscle activity while lifting objects of expected and unexpected weight with and without low back pain
Authors: Sekine Chie , Hayashi Haruna , Hirabayashi Ryo , Yokota Hirotake , Saisu Kazusa , Takabayashi Tomoya , Edama Mutsuaki
Published: August 2023

BACKGROUND:

Lifting heavy objects can induce postural stress and low back pain.

OBJECTIVE:

This study aimed to examine the effect of object weight expectations on trunk muscle activity and assess trunk muscle activity in people with chronic low back pain.

METHODS:

Twenty-two male college students (11 healthy and 11 participants with chronic low back pain) were recruited. The procedure was performed in three settings: lifting an expected 5-kg object, lifting an unexpected 10-kg object, and lifting an expected 10-kg object. Lifting was divided into five phases, and the muscle activity in each phase was compared between groups (chronic low back pain/control), object weight predictions, and phases.

RESULTS:

Compared to the control group, the chronic low back pain group had higher erector spinae muscle activity, regardless of weight or prediction, and significantly higher rectus femoris muscle activity in the early lifting phase of the expected 10-kg object ([Formula: see text] 0.043). Compared to when lifting the expected 10-kg object, erector spinae muscle activity was higher in the early lifting phase of the control group when lifting the unexpected 10-kg object ([Formula: see text] 0.016).

CONCLUSIONS:

Healthy individuals and individuals with chronic low back pain had different recruitment strategies for lifting objects heavier than predicted.
Association Between Retinal Nerve Fiber Layer Thickness and Incident Dementia in the European Prospective Investigation into Cancer in Norfolk Cohort
Authors: Yin Grace S. , van der Heide Frank , Littlejohns Thomas J. , Kuźma Elżbieta , Hayat Shabina , Brayne Carol , Foster Paul J. , Luben Robert , Khawaja Anthony P.
Published: August 2023

Background: Retinal nerve fiber layer (RNFL) thickness may reflect cerebral status. Objective: This study assessed the relationship between RNFL thickness and incident all-cause dementia in the European Prospective Investigation into Cancer in Norfolk (EPIC-Norfolk) Eye Study. Methods: Glaucoma detection with variable corneal compensation (GDx-VCC) and Heidelberg Retinal Tomograph II (HRT II) derived global mean RNFL thickness from dementia-free participants at baseline within the EPIC-Norfolk Eye Study were analyzed. Incident dementia was identified through linkage to electronic medical records. Cox proportional hazard mixed-effects regression models adjusted for key confounders were used to examine the associations between RNFL thickness and incident dementia in four separate models. Results: 6,239 participants were included with 322 cases of incident dementia and mean age of 67.5-years old, with 49.7% women (median follow-up 13.2-years, interquartile range (11.7 to 14.6 years). Greater RNFL thickness (GDx-VCC) was not significantly associated with a lower risk of incident dementia in the full adjusted model [HR per quartile increase 0.95; 95% CI 0.82–1.10]. Similarly, RNFL thickness assessed with HRT II was also not associated with incident dementia in any model (full adjusted model; HR per quartile increase: 1.06; [95% CI 0.93–1.19]. Gender did not modify any associations under study. Conclusion: GDx-VCC and HRT II derived RNFL thickness are unlikely to be useful predictors of incident dementia. Higher resolution optical imaging technologies may clarify whether there are useful relationships between neuro-retinal morphology and brain measures.
“I Want to Know Everything ...  ”: The Return of Research Results and the Importance of Transparency in the Acceptability of Lumbar Punctures for African American Older Adults
Authors: Passmore Susan Racine , Longhurst Colin , Gerbitz Abigail , Green-Harris Gina , Norris Nia , Edwards Dorothy Farrar
Published: August 2023

Background: Although African Americans experience the highest risk of Alzheimer’s disease (AD), they are dramatically underrepresented in preclinical biomarker research. This is especially true for studies involving lumbar puncture as it may involve more perceived risk even for those participants who are otherwise supportive of research. Objective: To understand the unique concerns of African American participants regarding biomarker studies involving lumbar puncture who demonstrate support for AD research. Methods: Study participants were African American adults contacted through an AD research registry. We employed a novel method used to create hypothetical research studies varying on a set number of factors. The method is designed to collect potential patterns in decision making regarding research participation but differs from experimental vignette design in that the survey is administered with an accompanying qualitive interview to determine the meaning participants ascribe to factors independently and in conjunction with one another. Results: Sixty-one participants each reviewed three randomly selected research scenarios and created their “ideal” study involving lumbar puncture. Scenario variables included: disclosure of research results, racial and ethnic identity of the researcher, recruitment method, and amount of incentive. Conclusion: Findings indicate that transparency in the return of AD research results to be the strongest driver of participation, followed by race of the researcher and amount of incentive. Recruitment method had limited impact on hypothetical decision making.
Intracellular Amyloid-β in the Normal Rat Brain and Human Subjects and Its relevance for Alzheimer’s Disease
Authors: Kobro-Flatmoen Asgeir , Hormann Thea Meier , Gouras Gunnar
Published: August 2023

Background: Amyloid-β (Aβ) is a normal product of neuronal activity, including that of the aggregation-prone Aβ42 variant that is thought to cause Alzheimer’s disease (AD). Much knowledge about AD comes from studies of transgenic rodents expressing mutated human amyloid-β protein precursor (AβPP) to increase Aβ production or the Aβ42/40 ratio. Yet, little is known about the normal expression of Aβ42 in rodent brains. Objective: To characterize the brain-wide expression of Aβ42 throughout the life span of outbred Wistar rats, and to relate these findings to brains of human subjects without neurological disease. Methods: Aβ42 immunolabeling of 12 Wistar rat brains (3–18 months of age) and brain sections from six human subjects aged 20–88 years. Results: In healthy Wistar rats, we find intracellular Aβ42 (iAβ42) in neurons throughout the brain at all ages, but levels vary greatly between brain regions. The highest levels are in neurons of entorhinal cortex layer II, alongside hippocampal neurons at the CA1/subiculum border. Concerning entorhinal cortex layer II, we find similarly high levels of iAβ42 in the human subjects. Conclusion: Expression of iAβ42 in healthy Wistar rats predominates in the same structures where iAβ accumulates and Aβ plaques initially form in the much used, Wistar based McGill-R-Thy1-APP rat model for AD. The difference between wild-type Wistar rats and these AD model rats, with respect to Aβ42, is therefore quantitative rather that qualitative. This, taken together with our human results, indicate that the McGill rat model in fact models the underlying wild-type neuronal population-specific vulnerability to Aβ42 accumulation.
Psychopharmacological Medication Use in Frontotemporal Dementia at the Time of Diagnosis: Comparison with Alzheimer’s Disease
Authors: Katisko Kasper , Krüger Johanna , Soppela Helmi , Hartikainen Päivi , Haapasalo Annakaisa , Remes Anne M. , Solje Eino
Published: August 2023

Background: Due to the significant presence of neuropsychiatric symptoms in patients with frontotemporal dementia (FTD) spectrum disorders, psychiatric misdiagnoses, diagnostic delay, and use of psychiatric treatments are common prior to the FTD diagnosis. Furthermore, treatment of diagnosed FTD patients mainly relies on off-label psychopharmacological approaches. Currently, limited real-world data are available regarding the actual use of psychopharmacological medications in FTD. Objective: To evaluate psychopharmacological medication use at the time of FTD diagnosis. Methods: Psychopharmacological medication use was evaluated in a Finnish FTD cohort containing 222 FTD patients, including the major clinical disease phenotypes (behavioral, language, and motor variants) and genetic patients carrying the C9orf72 repeat expansion. A cohort of 214 Alzheimer’s disease (AD) patients was used as a neurodegenerative disease reference group. Results: Active use of psychopharmacological medications at the time of diagnosis was significantly more common in FTD compared to AD, especially in the case of antidepressants (26.1% versus 15.0%, OR = 2.01, p = 0.008), antipsychotics (23.9% versus 9.3%, OR = 3.15, p < 0.001), and mood-stabilizers (6.3% versus 1.9%, OR = 2.93, p = 0.085; not statistically significant), whereas the use of cholinesterase inhibitors or memantine was nearly nonexistent in FTD patients. Female gender and behavioral variant of FTD phenotype alongside with depressive and psychotic symptoms were the most prominent factors associating with the use of these medications among the FTD spectrum patients. Conclusion: Use of off-label psychopharmacological medication and polypharmacy is substantially common at the time of FTD diagnosis. This likely reflects the challenges in using symptom-driven treatment approaches, especially prior to the eventual diagnosis.
A hybrid multi-criteria decision-making framework of EWM-BWM-TODIM based on Linguistic Pythagorean fuzzy environment
Authors: Fan Jianping , Wang Min , Wu Meiqin
Published: August 2023

Linguistic Pythagorean fuzzy set (LPFS) combines Pythagorean fuzzy sets and linguistic term sets, which can effectively deal with fuzzy information in multi-criteria decision-making (MCDM). The entropy weight method (EWM) can reflect the objectivity of decision information, while the best-worst method (BWM) can reflect the subjectivity of decision-makers. The interactive multi-criteria decision-making (TODIM) method can describe the different preferences of decision-makers for gains and losses. In this paper, EWM, BWM, and TODIM are combined and applied to LPFS for the first time. First, we calculate the objective weight and subjective weight of each criterion through EWM and BWM and combine them to get the final weight to balance subjectivity and objectivity. Then, this paper selects the best scheme through TODIM sorting. In conclusion, the LPFS-EWM-BWM-TODIM model is established in this paper. Finally, the paper applies this model to the selection of corporate investment strategy and green mine, verifies the effectiveness of the method, and carries out comparative analysis and sensitivity analysis, proving the rationality and robustness of the model.
Congestion cost estimation using adaptive red fox algorithm in restructured electricity markets
Authors: Chellam S. , Kuruseelan S. , Pravin Rose T. , Jasmine Gnana Malar A.
Published: August 2023

Congestion of the power system is the most common challenge an Independent System Operator (ISO) faces in restructured electricity markets. It affects the efficiency of the market when transmission lines are congested causing transmission costs to rise. To prevent transmission line congestion, ISO needs to take the necessary steps. To solve these issues, this paper introduces a new method namely the Adaptive Red Fox Optimization algorithm (ARFOA) to compute the congestion cost considering the power losses in the transmission line system. Initially, all the generators in the system are selected to reschedule real power outputs. Second, by establishing a proposed optimization issue, ARFOA is employed to control transmission line congestion. The implementation of the proposed method is evaluated on the IEEE 30 bus system. The algorithm’s adaptability is tested using several case studies involving the base case and line outages, also compared with the other existing techniques such as PSO, ASO, and GSO approaches. The simulation outcomes indicate that the proposed strategy outperforms existing techniques in terms of congestion cost, power loss, generation rescheduled power, and computational time.
Dual-feature and multi-scale fusion using U2-net deep learning model for ECG biometric recognition
Authors: Hu Zunmei , Huang Yuwen , Yang Yuzhen
Published: August 2023

Aiming at the challenges that the traditional photoplethysmography (PPG) biometrics is not robust and precision of recognition, this paper proposes a dual-feature and multi-scale fusion using U2-net deep learning model (DMFUDM). First, to obtain complementary information of different features, we extract the local and global features of one-dimensional multi-resolution local binary patterns (1DMRLBP) and multi-scale differential feature (MSDF). Then, to extract robust discriminant feature information from the 1DMRLBP and MSDF features, a novel two-branch U2-net framework is constructed. In addition, a multi-scale extraction module is designed to capture the transition information. It consists of multiple convolution layers with different receptive fields for capturing multi-scale transition information. At last, a two-level attention module is used to adaptively capture valuable information for ECG biometrics. DMFUDM can obtain the average subject recognition rates of 99.76%, 98.31%, 98.97% and 98.87% on four databases, respectively, and experiment results show that it performs competitively with state-of-the-art methods on all four databases.
Region-Attention Prompt Learning for CLIP
Authors: Pan Yiming , Cheng Hua , Fang Yiquan , Liu Yufei
Published: August 2023

Pre-trained Visual Language Models (VLMs) like CLIP have shown great potential in the multimodal domain. Among this, using different modal contexts and interaction features to construct prompt can stimulate the model’s prior knowledge circuit more accurately, thus generating better outputs. However, in CLIP, the formal mismatch of textual descriptions between the pre-training and inference phases results in a suboptimal representation ability of prompt, which is detrimental to model alignment learning. Therefore, Region-Attention Prompt (RAP) is proposed, which introduces region features to enrich the semantic representation of prompt. RAP is acquired by the Cross-Attention mechanism between images and texts, and it is essentially a region-level prompt with category-sensitive properties. For each category, RAP adaptively assigns greater attention weight to image regions that are more semantically relevant to the category. Besides, CLIP is equipped with RAP (called RA-CLIP) to improve image classification performance in generalization scenarios. Extensive experiments demonstrate that RA-CLIP outperforms the current SOTA CoCoOp 0.4% - 4.16% on base classes and 0.25% - 11.34% on new classes, across 7 datasets. In addition, we show that focusing on category-related regions to construct prompt can further improve the model’s alignment ability.
RETRACTED: Enhanced grey relational analysis method for neutrosophic cubic number madm and applications to decorative wall hanging design effect evaluation
Authors: Liu Pengyu
Published: August 2023

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.
American knock-out options based on floating interest rate in uncertain financial market
Authors: Jia Lifen , Jiang Jiarui , Li Dongao , Guo Fengjia
Published: August 2023

The knock-out options are considered as path-dependent barrier options that only expire worthless once the value of the underlying asset reaches a specific threshold. The uncertain differential equations are typically used to describe stock fluctuations in uncertain financial markets. In this study, we build a stock model considering floating interest rate based on uncertainty theory. On this basis, we mainly study the pricing scheme of American call and put options. Based on this model, we mainly research the pricing schemes for call and put options with the American barrier option. Moreover, we develope the parameter estimation for the uncertain stock model and analyze the results of the uncertain hypothesis test. Finally, we design numerical algorithms for the corresponding option pricing formulas. As an application, we verify the validity of the formulas through numerical experiments.
Enhancing QoE predictions with complex neural networks and diverse datasets
Authors: Mahaboob Basha S.K. , Kalaiselvan S.A.
Published: August 2023

Quality of Experience (QoE) is a critical aspect of multimedia applications, which directly impacts user satisfaction and adoption. QoE predictions are used to optimize various parameters such as video quality, bitrate, and network bandwidth to enhance the user experience. However, accurate QoE prediction is a challenging task, as it involves various factors such as network conditions, video content, and user preferences. Therefore, there is a need for enhancing QoE predictions with advanced techniques to improve user satisfaction and adoption. This paper proposes incorporating more complex neural network architectures and using more diverse datasets to improve the accuracy and generalization of Quality of Experience (QoE) predictions. The paper suggests experimenting with more advanced architectures such as convolutional neural networks and recurrent neural networks, which have been shown to be effective in various applications. Additionally, the paper highlights the limitation of using a single dataset and proposes using more diverse datasets that capture different types of video content and network conditions. Enhancing QoE predictions with complex neural networks and diverse datasets include improved accuracy, better generalization, more sophisticated models, enhanced user satisfaction and increased adoption. These enhancements are expected to lead to more accurate and reliable QoE predictions, which are crucial for improving user experience in multimedia applications.
Humanization of the mdx Mouse Phenotype for Duchenne Muscular Dystrophy Modeling: A Metabolic Perspective
Authors: Donen Graham , Milad Nadia , Bernatchez Pascal
Published: August 2023

Duchenne muscular dystrophy (DMD) is a severe form of muscular dystrophy (MD) that is characterized by early muscle wasting and lethal cardiorespiratory failure. While the mdx mouse is the most common model of DMD, it fails to replicate the severe loss of muscle mass and other complications observed in patients, in part due to the multiple rescue pathways found in mice. This led to several attempts at improving DMD animal models by interfering with these rescue pathways through double transgenic approaches, resulting in more severe phenotypes with mixed relevance to the human pathology. As a growing body of literature depicts DMD as a multi-system metabolic disease, improvements in mdx-based modeling of DMD may be achieved by modulating whole-body metabolism instead of muscle homeostasis. This review provides an overview of the established dual-transgenic approaches that exacerbate the mild mdx phenotype by primarily interfering with muscle homeostasis and highlights how advances in DMD modeling coincide with inducing whole-body metabolic changes. We focus on the DBA2/J strain-based D2. mdx mouse with heightened transforming growth factor (TGF)-β signaling and the dyslipidemic mdx/apolipoprotein E ( mdx/ApoE) knock-out (KO) mouse, and summarize how these novel models emulate the metabolic changes observed in DMD.
Exploring the Complex Phenotypes of Impaired Finger Dexterity in Mild-to-moderate Stage Parkinson’s Disease: A Time-Series Analysis
Authors: Panyakaew Pattamon , Duangjino Kotchakorn , Kerddonfag Apiwoot , Ploensin Teerit , Piromsopa Krerk , Kongkamol Chanon , Bhidayasiri Roongroj
Published: August 2023

Background:

Impaired dexterity is an early motor symptom in Parkinson’s disease (PD) that significantly impacts the daily activity of patients; however, what constitutes complex dexterous movements remains controversial.

Objective:

To explore the characteristics of finger dexterity in mild-to-moderate stage PD.

Methods:

We quantitatively assessed finger dexterity in 48 mild-to-moderate stage PD patients and 49 age-matched controls using a simple alternating two-finger typing test for 15 seconds. Time-series analyses of various kinematic parameters with machine learning were compared between sides and groups.

Results:

Both the more and less affected hands of patients with PD had significantly lower typing frequency and slower typing velocity than the non-dominant and the dominant hands of controls ( p = 0.019, p = 0.016, p < 0.001, p < 0.001). The slope of the typing velocity decreased with time, indicating a sequence effect in the PD group. A typing duration of 6 seconds was determined sufficient to discriminate PD patients from controls. Typing error, repetition, and repetition rate were significantly higher in the more affected hands of patients with PD than in the non-dominant hand of controls ( p < 0.001, p = 0.03, p < 0.001). The error rate was constant, whereas the repetition rate was steep during the initiation of typing. A predictive model of the more affected hand demonstrated an accuracy of 70% in differentiating PD patients from controls.

Conclusion:

Our study demonstrated complex components of impaired finger dexterity in mild-to-moderate stage PD, namely bradykinesia with sequence effects, error, and repetition at the initiation of movement, suggesting that multiple neural networks may be involved in dexterity deficits in PD.
Assessment of the frequency of fruit and vegetable consumption and adolescents’ knowledge of their benefits in Ain Temouchent (Western Algeria)
Authors: Khalfa Ali , Bensalah Fatima , Bouazza Sofiane , Brikhou Slimane , Chihab Mounir , Khitri Walid , Bennabi Farid
Published: August 2023

BACKGROUND: Adolescence is a critical and sensitive period of life, a fundamental stage of development, and special attention must be paid to the quality of food. Healthy eating habits at adolescence can be an important factor for healthy eating habits later in life. OBJECTIVE: To assess the frequency and knowledge of fruit and vegetable consumption among adolescents in Ain Temouchent (Western Algeria). METHODS: A study was conducted from February 1 to April 30, 2022. 103 adolescents responded to the questionnaire. The participants were boys (n = 48) and girls (n = 55) aged 12 to 18 years from two educational levels (middle school and high school). RESULTS: Adolescents surveyed had a mean BMI equal to 23.2 (kg/m2), considered overweight. The frequency of fruit and vegetable consumption it does not differ between girls and boys (p > 0.05). The consumption of fruits and vegetables was below recommended levels. The evaluation showed that most adolescents do not have a good knowledge of food composition and are not interested in fruits and vegetables in their daily diet (p > 0.05). CONCLUSION: This study showed that adolescents consume few fruits and vegetables, and they have little knowledge of the value of fruits and vegetables in their daily diet.
The post-pandemic new normal for central bank statistics
Authors: Jahangir-Abdoelrahman Saira , Tissot Bruno
Published: August 2023

Official statisticians managed to quickly adapt to the consequences of the COVID-19 pandemic. Looking forward, an important issue is whether, and how, one should be fundamentally rethinking the way to produce and consume data in a “new normal” state of the world. The pandemic underlined that data producers have to provide more and more varied types of information to their users. It was also a reminder that the statistical landscape has to permanently evolve. As regards central banks’ statisticians, this calls for relying more heavily on data science, making a better use of the large amount of micro-level information available in today’s modern societies, adapting statistical frameworks to meet evolving policy objectives and user needs, and continuing to closely cooperate with other relevant stakeholders
Head and vestibular kinematics during vertical semicircular canal impulses
Authors: Karabin Michelle J. , Harrell Regan G. , Sparto Patrick J. , Furman Joseph M. , Redfern Mark S.
Published: August 2023

BACKGROUND: The video head impulse test (vHIT) is a common assessment of semicircular canal function during high-speed impulses. Reliability of the vHIT for assessing vertical semicircular canals is uncertain. Vertical head impulses require a complex head movement, making it difficult to isolate a single semicircular canal and interpret resulting eye rotations. OBJECTIVE: The purpose of this study was to provide descriptive head kinematics and vestibular stimuli during vertical plane impulses to ultimately improve impulse delivery and interpretation of vHIT results for vertical semicircular canals. METHODS: Six participants received right anterior (RA) and left posterior (LP) semicircular canal impulses. Linear displacements, rotational displacements, and rotational velocities of the head were measured. Peak velocities in semicircular canal planes and peak-to-peak gravitoinertial accelerations at the otolith organs were derived from head kinematics. RESULTS: The largest rotational velocities occurred in the target semicircular canal plane, with non-negligible velocities occurring in non-target planes. Larger vertical displacements and accelerations occurred on the right side of the head compared to the left for RA and LP impulses. CONCLUSIONS: These results provide a foundation for designing protocols to optimize stimulation applied to a singular vertical semicircular canal and for interpreting results from the vHIT for vertical semicircular canals.
A Unique Approach for Performance Analysis of a Blockchain and Cryptocurrency based Carbon Footprint Reduction System
Authors: Panch Ankit , Sharma Dr. Om Prakash
Published: August 2023

Blockchain technology is commonly used as a replicated and distributed database in different areas. In this paper, a smart home blockchain network connects smart homes through smart devices for reducing carbon footprint and thereby earning bitcoin value in the network. The network is composed of different smart homes interconnected with smart devices. The user makes a transaction request through the network layer and matches the user’s activity with the reward table located at the incentive layer to estimate the bitcoin value. Furthermore, the miner verifies the transaction and sends the bitcoin value to the user, and adds the respective block to the network structure. The optimal parameter used to estimate the bitcoin value is computed using the proposed Improved Invasive Weed Mayfly Optimization (IIWMO) algorithm. The developed method attained higher performance with the metrics, like coins earned, Annual Carbon Reduction (ACR), and fitness as 0.00357BTC, 23.891, and 0.6618 for 200 users. For 200 users the fitness obtained by the proposed method is 14.41%, 16.68%, and 11.68% higher when compared to existing approaches namely, Without optimization, IIWO, and MA, respectively.