Miracle-dz/UniFAR-FTUs huggingface dataset:
From Manual to Automatic: The Evolution of Test Case Generation Methods and the Role of GitHub Copilot

The emergence of AI tools, such as GitHub Copilot, introduces innovative approaches to software development tasks, particularly in the realm of test case generation. This research endeavors to evaluate the effectiveness of test cases generated by GitHub Copilot in comparison to manually crafted ones. Through the examination of four programming exercises, This study conducted a comparative analysis based on criteria including test passing rates, uniqueness, and equivalence. Preliminary findings suggest that the quality of test cases produced by Copilot is on par with manually created ones, indicating its potential to diversify the range of test cases when provided with precise prompts. While Copilot has the potential to enhance testing processes, it's important to consider certain limitations, such as constraints related to the range of test cases and potential prompt biases. Further research endeavors should explore a wider range of software development tasks and investigate how developer expertise influences the outcomes of test cases generated by AI tools.
Modelling the determinants of commercial vehicle ownership: An application to Windsor, Ontario, Canada

iv ACKNOWLEDGEMENTS . v LIST OF TABLES . x LIST OF FIGURES . xii LIST OF APPENDICES . xv CHAPTER 1: INTRODUCTION . 1 1.1 Overview . 1 1.2 Statement of the Problem . 3 1.3 Objectives . 5 1.4 Thesis Outline . 5 CHAPTER 2: LITERATURE REVIEW . 7 2.1 Commercial Vehicle Movement . 7 2.1.1 Conventional Approaches . 8 2.1.2 Supply-Chain (Logistics) Approach . 12 2.1.3 Tour-Based Microsimulation Approach . 17 2.2 Vehicle Ownership Modeling . 21 2.3 Population Synthesis . 26 CHAPTER 3: STUDY AREA AND DATA DESCRIPTION . 33
MOBCSA: Multi-Objective Binary Cuckoo Search Algorithm for Features Selection in Bioinformatics

In bioinformatics, medical diagnosis models might be significantly impacted by high-dimensional data generated by high-throughput technologies. This data includes redundant or irrelevant genes, making it challenging to identify the relevant genes from such high-dimensional data. Therefore, an effective feature selection (FS) technique is crucial to mitigate dimensionality, thereby enhancing the performance and accuracy of medical diagnosis. The Cuckoo Search Algorithm (CSA) has proven effective in gene selection, demonstrating prowess in exploitation, exploration, and convergence. However, most of the current CSA-based FS techniques deal with gene selection problems as a single objective rather than adopting a multi-objective mechanism. This article proposes the Multi-Objective Binary Cuckoo Search Algorithm (MOBCSA) for gene selection. MOBCSA extends the standard CSA by incorporating multiple objectives, including accuracy of classification and number of selected genes. MOBCSA utilizes an S-shaped transfer function for transforming the algorithm’s search space from a continuous to a binary search space. MOBCSA integrates two components: an external archive to save the pareto optimal solutions attained during the search process, and an adaptive crowding distance updating mechanism integrated into the archive to maintain diversity and increase the coverage of optimal solutions. To assess MOBCSA’s performance, evaluation experiments were conducted on six benchmark biomedical datasets using three different classifiers. Then, the obtained experimental results were compared against four multi-objective-based state-of-the art FS methods. The findings prove that MOBCSA surpasses the other methods in both accuracy of classification and number of selected genes, where it has obtained an average accuracy ranging from 92.79% to 98.42% and an average number of selected genes ranging from 15.67 to 27.88 for different classifiers and datasets.
Systematic Literature Review Regarding Heart Rate and Respiratory Rate Measurement by Means of Radar Technology

The use of radar technology for non-contact measurement of vital parameters is increasingly being examined in scientific studies. Based on a systematic literature search in the PubMed, German National Library, Austrian Library Network (Union Catalog), Swiss National Library and Common Library Network databases, the accuracy of heart rate and/or respiratory rate measurements by means of radar technology was analyzed. In 37% of the included studies on the measurement of the respiratory rate and in 48% of those on the measurement of the heart rate, the maximum deviation was 5%. For a tolerated deviation of 10%, the corresponding percentages were 85% and 87%, respectively. However, the quantitative comparability of the results available in the current literature is very limited due to a variety of variables. The elimination of the problem of confounding variables and the continuation of the tendency to focus on the algorithm applied will continue to constitute a central topic of radar-based vital parameter measurement. Promising fields of application of research can be found in particular in areas that require non-contact measurements. This includes infection events, emergency medicine, disaster situations and major catastrophic incidents.
High-Dimensional Inference on Dense Graphs with Applications to Coding Theory and Machine Learning

We are living in the era of “Big Data”, an era characterized by a voluminous amount of available data. Such amount is mainly due to the continuing advances in the computational capabilities for capturing, storing, transmitting and processing data. However, it is not always the volume of data that matters, but rather the “relevant” information that resides in it. Exactly 70 years ago, Claude Shannon, the father of information theory, was able to quantify the amount of information in a communication scenario based on a probabilistic model of the data. It turns out that Shannon’s theory can be adapted to various probability-based information processing fields, ranging from coding theory to machine learning. The computation of some information theoretic quantities, such as the mutual information, can help in setting fundamental limits and devising more efficient algorithms for many inference problems. This thesis deals with two different, yet intimately related, inference problems in the fields of coding theory and machine learning. We use Bayesian probabilistic formulations for both problems, and we analyse them in the asymptotic high-dimensional regime. The goal of our analysis is to assess the algorithmic performance on the first hand and to predict the Bayes-optimal performance on the second hand, using an information theoretic approach. To this end, we employ powerful analytical tools from statistical physics. The first problem is a recent forward-error-correction code called sparse superposition code. We consider the extension of such code to a large class of noisy channels by exploiting the similarity with the compressed sensing paradigm. Moreover, we show the amenability of sparse superposition codes to perform joint distribution matching and channel coding. In the second problem, we study symmetric rank-one matrix factorization, a prominent model in machine learning and statistics with many applications ranging from community detection to sparse principal component analysis. We provide an explicit expression for the normalized mutual information and the minimum mean-square error of this model in the asymptotic limit. This allows us to prove the optimality of a certain iterative algorithm on a large set of parameters. A common feature of the two problems stems from the fact that both
Designed Memristor Circuit for Self‐Limited Analog Switching and its Application to a Memristive Neural Network

Analog memristors enable compact neuromorphic computing with low power consumption. One of the issues with the technology is slow precise analog data programming. In this study, a novel analog data programming method utilizing a self‐limited set switching is proposed. The method can transfer any resistance values from reference resistors to the target memristor accurately inside a crossbar array by performing an appropriate voltage clocking. An ideal memristor model based on the method is proposed and a Ti‐doped NbOx charge trap memristor is evaluated as a promising candidate for applications. The characteristic error of the Ti‐doped NbOx memristor device is about 5% on average, compared to the ideal memristor, and configuring optimum parallel resistors in the circuit further improves this to 2.95%. The method is then applied to program a memristive neural network and this error is confirmed negligible; thus the proposed method is viable.
Research on the influence of short video advertising Interactivity on consumers' Purchase Intention - based on S-O-R model

With the increasing popularity and development of short-form video, short-form video has also been used for advertising. However, previous studies have mainly focused on how short-video ads affect consumer engagement behavior.More research needs to be conducted on how the interactivity of short-video ads affects consumers' purchase intention. This paper takes Tiktok short-form video ads as the research object, combines the S-O-R model with scenario-based experiments for analysis, and ultimately shows that high levels of short-form video ad interactivity can stimulate the utilitarian, hedonic, and social values of products and influence consumers' purchase intentions. This study enriches the research in the field of advertising interactivity. It raises new perspectives for the field of short-form video advertising research. It shows how merchants can gradually drive consumers' purchase intention through short-form video advertising interactivity.
Effects of light therapy on sleep and circadian rhythm in older type 2 diabetics living in long-term care facilities: a randomized controlled trial

Background Light influences the secretion of melatonin in the body and regulates circadian rhythms, which play an important role in sleep and mood. The light level of rooms in long-term care facilities is usually far below the threshold required to regulate the body’s circadian rhythm, and insufficient light can easily lead to sleep and mood disturbances among older residents in nursing homes. Therefore, the objective of this study was to investigate the effects of light therapy on sleep and circadian rhythm in older adults with type 2 diabetes residing in long-term care facilities. Methods This study was a prospective, single-blind, randomized controlled trial. Participants were randomly assigned to either the light therapy (LT) group or the control group and received the intervention for four weeks. Primary outcomes included the Pittsburgh Sleep Quality Index (PSQI) and objective sleep parameters recorded by a sleep monitoring bracelet, Morningness-Eveningness Questionnaire (MEQ). The secondary outcome included glycated serum protein (GSP). Data was collected at three time points: at baseline (T0), immediate post-treatment (T1), and 4-week follow-up (T2). A linear mixed model analysis was used to analyzed the data. Results We enrolled 45 long-term care residents. Compared with the control group, significant reductions in PSQI scores were observed at T1 and T2. At T2, the sleep score of objective sleep parameters was significantly higher in the LT group compared to the control group. Additionally, compared to the baseline T0, MEQ scores were significantly lower in the LT group at T1 and T2, with no significant difference in the control group. There was no significant difference between groups in glycated serum protein values at T1 and T2. However, compared to T0, glycated serum protein values decreased in the LT group while increased in the control group at T2. Conclusion Light therapy had a positive effect on subjective sleep quality and circadian rhythm time type in long-term care residents with type 2 diabetes, and had a possible delayed effect on objective sleep. However, no discernible alterations in blood glucose levels were detected in this study.
Hesperidin from Orange Peel as a Promising Skincare Bioactive: An Overview

The pursuit for better skin health, driven by collective and individual perceptions, has led to the demand for sustainable skincare products. Environmental factors and lifestyle choices can accelerate skin aging, causing issues like inflammation, wrinkles, elasticity loss, hyperpigmentation, and dryness. The skincare industry is innovating to meet consumers’ requests for cleaner and natural options. Simultaneously, environmental issues concerning waste generation have been leading to sustainable strategies based on the circular economy. A noteworthy solution consists of citrus by-product valorization, as such by-products can be used as a source of bioactive molecules. Citrus processing, particularly, generates substantial waste amounts (around 50% of the whole fruit), causing unprecedented environmental burdens. Hesperidin, a flavonoid abundant in orange peels, is considered to hold immense potential for clean skin health product applications due to its antioxidant, anti-inflammatory, and anticarcinogenic properties. This review explores hesperidin extraction and purification methodologies as well as key skincare application areas: (i) antiaging and skin barrier enhancement, (ii) UV radiation-induced damage, (iii) hyperpigmentation and depigmentation conditions, (iv) wound healing, and (v) skin cancer and other cutaneous diseases. This work’s novelty lies in the comprehensive coverage of hesperidin’s promising skincare applications while also demonstrating its potential as a sustainable ingredient from a circular economy approach.
Increased Prevalence of Headaches and Migraine in Patients with Psoriatic Arthritis and Axial Spondyloarthritis: Insights from an Italian Cohort Study

Background: Psoriatic arthritis (PsA) and axial spondyloarthritis (axSpA) are inflammatory diseases with shared genetic backgrounds and clinical comorbidities. Headache, a common global health issue, affects over 50% of adults and encompasses various types, including migraine, tension-type, and cluster headaches. Migraine, the most prevalent, recurrent, and disabling type, is often associated with other medical conditions such as depression, epilepsy, and psoriasis, but little is known about the relationship between autoimmune disease and the risk of migraine. Methods: A cross-sectional study was conducted from July to November 2022, enrolling 286 participants, including 216 with PsA, 70 with axSpA, and 87 healthy controls. Results: Headache prevalence was significantly higher in the PsA (39.81%) and axSpA (45.71%) patients compared to the healthy controls. The prevalence of migraine without aura was also significantly higher in both the PsA (18.52%) and axSpA (28.57%) groups compared to the healthy controls. Conclusions: These findings underscore the high burden of headache and migraine in PsA and axSpA participants, highlighting the need for improved management and treatment strategies for these patients.
MoonKV: Optimizing Update-intensive Workloads for NVM-based Key-value Stores

The constantly evolving demands of modern applications pose increasingly higher requirements for key-value (KV) stores, especially when dealing with update-intensive workloads. RocksDB, an LSM-tree-based KV store, has designed a merge operator to optimize update throughput by directly recording the partially modified value fields. However, this operator negatively affects read performance because it will result in the multiple coexistences of partial values for the same key, which thus requires multiple seeks. To address these issues, we propose a KV-store MoonKV designed for update-intensive workload, which implements Logical and Vertical Compaction techniques to efficiently and timely merge partial values, thereby minimizing their coexistence in the LSM-tree. Besides, MoonKV introduces an NVM-based Index and Estimate Search to enhance search efficiency, leveraging high-speed NVM. By incorporating the above techniques, MoonKV significantly reduces the number of partial keys required to search when reading the value of a specific key. Finally, our evaluation shows that MoonKV achieves a 1.6$\times$ −3.2$\times$ throughput improvement compared with other KV stores, such as RocksDB, BlobDB, and MatrixKV, under update-intensive workloads.
The Activation of GABAAR Alleviated Cerebral Ischemic Injury via the Suppression of Oxidative Stress, Autophagy, and Apoptosis Pathways

Ischemic stroke is a devastating disease leading to neurologic impairment. Compounding the issue is the very limited array of available interventions. The activation of a γ-aminobutyric acid (GABA) type A receptor (GABAAR) has been reported to produce neuroprotective properties during cerebral ischemia, but its mechanism of action is not yet fully understood. Here, in a rat model of photochemically induced cerebral ischemia, we found that muscimol, a GABAAR agonist, modulated GABAergic signaling, ameliorated anxiety-like behaviors, and attenuated neuronal damage in rats suffering cerebral ischemia. Moreover, GABAAR activation improved brain antioxidant levels, reducing the accumulation of oxidative products, which was closely associated with the NO/NOS pathway. Notably, the inhibition of autophagy markedly relieved the neuronal insult caused by cerebral ischemia. We further established an oxygen–glucose deprivation (OGD)-induced PC12 cell injury model. Both in vivo and in vitro experiments demonstrated that GABAAR activation obviously suppressed autophagy by regulating the AMPK-mTOR pathway. Additionally, GABAAR activation inhibited apoptosis through inhibiting the Bax/Bcl-2 pathway. These data suggest that GABAAR activation exerts neuroprotective effects during cerebral ischemia through improving oxidative stress and inhibiting autophagy and apoptosis. Our findings indicate that GABAAR serves as a target for treating cerebral ischemia and highlight the GABAAR-mediated autophagy signaling pathway.
A Survey Paper on Analyzing Shortest Path Algorithms on GPU Using OPENCL

— Shortest path algorithms finds applications in large real world domains. All pair shortest path (APSP) and single source shortest path (SSSP) both have their special applications domains. We have discuss various algorithm and identified parallelism and we will divide the algorithm in logical parts and will create different kernel that will be invoked for a number of work items in parallel and our implementation will focus on cache utilization and execution time. Negative cycle detection will also be done using parallel kernel.
PP2A Affects Angiogenesis via Its Interaction with a Novel Phosphorylation Site of TSP1

Alterations in angiogenic properties play a pivotal role in the manifestation and onset of various pathologies, including vascular diseases and cancer. Thrombospondin-1 (TSP1) protein is one of the master regulators of angiogenesis. This study unveils a novel aspect of TSP1 regulation through reversible phosphorylation. The silencing of the B55α regulatory subunit of protein phosphatase 2A (PP2A) in endothelial cells led to a significant decrease in TSP1 expression. Direct interaction between TSP1 and PP2A-B55α was confirmed via various methods. Truncated TSP1 constructs were employed to identify the phosphorylation site and the responsible kinase, ultimately pinpointing PKC as the enzyme phosphorylating TSP1 on Ser93. The biological effects of B55α–TSP1 interaction were also analyzed. B55α silencing not only counteracted the increase in TSP1 expression during wound closure but also prolonged wound closure time. Although B55α silenced cells initiated tube-like structures earlier than control cells, their spheroid formation was disrupted, leading to disintegration. Cells transfected with phosphomimic TSP1 S93D exhibited smaller spheroids and reduced effectiveness in tube formation, revealing insights into the effects of TSP1 phosphorylation on angiogenic properties. In this paper, we introduce a new regulatory mechanism of angiogenesis by reversible phosphorylation on TSP1 S93 by PKC and PP2A B55α.
Preventing Suicide in the North: developing capacity, local knowledge and skills

This report grew out of the recognition that while suicide is an outcome the circumstances and contributing factors to suicide are variable from one part of Australia to another due to regional differences and contexts. Drought, flood, unemployment, financial strain, family relationship breakdown, and commonly depression and other mental health conditions are all circumstances that are known to increase risk, as does being born into an Aboriginal or Torres Strait Islander family. The Northern region centred on Townsville (pop. Est. 200000) includes rural and somewhat isolated communities (e.g., Paluma with 30 residents) ranging from a few hundred residents (e.g., Ravenswood, 349), to Ayr and Home Hill in the Burdekin with about 18 to 20000 residents, and Charters Towers (est. 8300) a large proportion of Aboriginal and Torres Strait Islander residents, Palm island (est. 5000), and a military presence (including families est. >45000). This somewhat unique mix makes for greater complexity in developing a suicide prevention framework suited to this region, planning what to do, and defining the "how to" applications of suicide prevention.
Purine Metabolism and Hexosamine Biosynthetic Pathway Abnormalities in Diarrheal Weaned Piglets Identified Using Metabolomics

Simple Summary Post-weaning diarrhea significantly impacts the survival and growth performance of piglets. This study aimed to obtain further insights into the metabolic changes in post-weaning diarrhea. The results showed that purine metabolism and hexosamine biosynthetic pathway were disturbed in diarrheal weaned piglets. This study suggests further studies could focus on dietary strategies, including energy strategies, ultimately alleviate diarrhea in weaning piglets via purine metabolism and hexosamine biosynthetic pathway. Abstract Post-weaning diarrhea significantly contributes to the high mortality in pig production, but the metabolic changes in weaned piglets with diarrhea remain unclear. This study aimed to identify the differential metabolites in the urine of diarrheal weaned piglets and those of healthy weaned piglets to reveal the metabolic changes associated with diarrhea in weaned piglets. Nine 25-day-old piglets with diarrhea scores above 16 and an average body weight of 5.41 ± 0.18 kg were selected for the diarrhea group. Corresponding to the body weight and sex of the diarrhea group, nine 25-month-old healthy piglets with similar sex and body weights of 5.49 ± 0.21 kg were selected as the control group. Results showed that the serum C-reactive protein and cortisol of piglets in the diarrhea group were higher than those in the control group (p < 0.05). The mRNA expression of TNF-α, IFN-γ in the jejunum and colon, and IL-1β in the jejunum were increased in diarrhea piglets (p < 0.05), accompanied by a reduction in the mRNA expression of ZO-1, ZO-2, and CLDN1 in the jejunum and colon (p < 0.05); mRNA expression of OCLN in the colon also occurred (p < 0.05). Metabolomic analysis of urine revealed increased levels of inosine, hypoxanthine, guanosine, deoxyinosin, glucosamine, glucosamine-1-p, N-Acetylmannosamine, chitobiose, and uric acid, identified as differential metabolites in diarrhea piglets compared to the controls. In summary, elevated weaning stress and inflammatory disease were associated with the abnormalities of purine metabolism and the hexosamine biosynthetic pathway of weaned piglets. This study additionally indicated the presence of energy metabolism-related diseases in diarrheal weaned piglets.