Miracle-dz/UniFAR-FTUs huggingface dataset:
Potential of Snapshot-Type Hyperspectral Imagery Using Support Vector Classifier for the Classification of Tomatoes Maturity

It is necessary to convert to automation in a tomato hydroponic greenhouse because of the aging of farmers, the reduction in agricultural workers as a proportion of the population, COVID-19, and so on. In particular, agricultural robots are attractive as one of the ways for automation conversion in a hydroponic greenhouse. However, to develop agricultural robots, crop monitoring techniques will be necessary. In this study, therefore, we aimed to develop a maturity classification model for tomatoes using both support vector classifier (SVC) and snapshot-type hyperspectral imaging (VIS: 460–600 nm (16 bands) and Red-NIR: 600–860 nm (15 bands)). The spectral data, a total of 258 tomatoes harvested in January and February 2022, was obtained from the tomatoes’ surfaces. Spectral data that has a relationship with the maturity stages of tomatoes was selected by correlation analysis. In addition, the four different spectral data were prepared, such as VIS data (16 bands), Red-NIR data (15 bands), combination data of VIS and Red-NIR (31 bands), and selected spectral data (6 bands). These data were trained by SVC, respectively, and we evaluated the performance of trained classification models. As a result, the SVC based on VIS data achieved a classification accuracy of 79% and an F1-score of 88% to classify the tomato maturity into six stages (Green, Breaker, Turning, Pink, Light-red, and Red). In addition, the developed model was tested in a hydroponic greenhouse and was able to classify the maturity stages with a classification accuracy of 75% and an F1-score of 86%.
Equitable Allocation of Operations and Makespan Minimization for Autonomous Agents

We study the problem of allocating a set of indivisible operations to a set of agents in a fair and efficient manner while also minimizing the makespan. We first present the Operation Trading Algorithm that generates allocations satisfying the DEQx (Duplicated Equitability up to any operation) fairness criterion while also guaranteeing an upper bound of 2 on the makespan for identical agents. The pairwise approach used in this algorithm has the added advantages of being decentralizable and robust. We then define a relaxed version of the DEQ1 (Duplicated Equitability upto some operation) fairness criterion called partial-DEQ1. A market based algorithm is presented to achieve partial-DEQ1 along with Pareto Optimality. Following this, it is shown that the algorithm also guarantees an upper bound of 1.618 on the makespan for 2 non-identical agents. Parametric Pruning further improves the upper bound to 1.5 which is theoretically the best possible upper bound. To the best of our knowledge, these are the first algorithms designed to achieve the mentioned fairness criteria. The algorithms additionally guarantee upper bounds on the makespan. Finally, we show the efficacy of the algorithms in generating allocations with near optimal makespans by numerically evaluating the algorithms on random instances. Note to Practitioners—This paper provides fast algorithms that can be used for task allocation among any number of agents. The algorithms generate allocations that satisfy fairness criteria along with minimizing the makespan, thus achieving two practically important criteria simultaneously. Additionally, some of the algorithms introduced can be implemented in a reactive manner thus making it easy to deal with sudden changes. The algorithms can also be implemented in a decentralized manner in environments where maintaining a centralized situational awareness is difficult due to communication and machine failures.
Changes of Brain Structures and Psychological Characteristics in Predatory, Affective Violent and Nonviolent Offenders

Purpose: Violent subjects were demonstrated to exhibit abnormal brain structures; however, the brain changes may be different between criminals committing affective (VA), predatory violence (VP), and non-violence (NV). Therefore, the purpose of this study was to compare the differences in brain structures and psychological characteristics between VA, VP, and NV offenders. Methods: Twenty male criminal subjects (7 VP; 6 VA; and 7 NV) offenders; and twenty age-matched male healthy non-criminals were enrolled in this study. All subjects received psychological assessments as well as magnetic resonance imaging scans of the brain. Analysis of variance (ANOVA) was performed to understand the differences among four groups with Bonferroni correction. The voxel-based morphometry and voxel-wise diffusion tensor imaging analyses were performed to compare the gray matter (GM) volume and white matter (WM) integrity between the groups. In significant regions, a Spearman correlation analysis was performed to understand the relationship between the brain changes and psychological scores. Results: The ANOVA analysis showed that AUDIT scores were significantly different among four groups, but no significant group difference was noted after Bonferroni correction. The imaging comparisons further demonstrated that the VP and NV offenders exhibited significant alterations of WM and GM tissues in the rectus and superior temporal gyrus, respectively. In addition, the VP offenders exhibited greater GM volumes than VA offenders in the right middle frontal gyrus, and NV offenders had greater GM volumes than VP offenders in the bilateral thalamus. Conclusion: We concluded that the VA, VP, and NV groups exhibited different degrees of alterations in GM and WM tissues in regions involved in emotion and cognition.
Application of Genetic Algorithm Solution Approach to Voltage Drop Issues on 33 kV/11 kV Injection Feeders: A Case Study of Ogbomoso, South West, Nigeria

This work was carried out in collaboration between both authors. Author MOO designed the study, performed the statistical analysis, wrote the protocol, and wrote the first draft of the manuscript. Authors MOO and EOO managed the analyses of the study. Author EOO managed the literature searches. Both authors read and approved the final manuscript. voltage. The parametric modeling of voltage drop revealed several causes of voltage drop in the study area. Different cable sizes were used to mitigate the effect voltage drop, it was discovered that, to attain minimum voltage drop in this station, the 65 mm 2 cable used has to be augmented to 85 mm 2 or reduce to 50 mm 2 while the number of the injection stations should be increase.
Hesperidin: A Review on Extraction Methods, Stability and Biological Activities

Hesperidin is a bioflavonoid occurring in high concentrations in citrus fruits. Its use has been associated with a great number of health benefits, including antioxidant, antibacterial, antimicrobial, anti-inflammatory and anticarcinogenic properties. The food industry uses large quantities of citrus fruit, especially for the production of juice. It results in the accumulation of huge amounts of by-products such as peels, seeds, cell and membrane residues, which are also a good source of hesperidin. Thus, its extraction from these by-products has attracted considerable scientific interest with aim to use as natural antioxidants. In this review, the extraction and determination methods for quantification of hesperidin in fruits and by-products are presented and discussed as well as its stability and biological activities.
Team JU_PAD @ AutoMin 2021: MoM Generation from Multiparty Meeting Transcript

Use of online meeting platforms for long multi-party discussion is gradually increasing and generation of Minutes of Meeting (MoM) is crucial for subsequent events. MOM records all key issues, possible solutions, decisions and actions taken during the meeting. Hence the importance of minuting cannot be overemphasized in a time when a significant number of meetings take place in the virtual space. Automatic generation of MoM can potentially save up to 80% of time while revisiting. In this paper, we present an abstractive approach for automatic generation of meeting minutes. It aims to deal with problems like the nature of spoken text, length of transcripts and lack of document structure and conversation fillers. The system is evaluated on a test dataset. The evaluation score is calculated by both manual and automatic systems. Text summarization metrics ROUGE-1, ROUGE-2, ROUGE-L [1] are used for automated scoring and metrics Adequacy, Grammatical Cor-rectness, Fluency are used for manual scoring. The proposed model achieved 0.221, 0.046, 0,125 for ROUGE-1, ROUGE-2 , ROUGE-L respectively in automated evaluation and 3.5/5, 3/5, 3/5 for Adequacy, Grammatical Correctness, Fluency respectively in manual evaluation.
The Ameliorative Effect of Empagliflozin in Vigabatrin-Induced Cerebellar/Neurobehavioral Deficits: Targeting mTOR/AMPK/SIRT-1 Signaling Pathways

Introduction. Vigabatrin (VGB) is an antiepileptic drug that acts to irreversibly inhibit the γ-aminobutyric acid (GABA) transaminase enzyme, elevating GABA levels. Broad studies have established that long-term treatment and/or high doses of VGB lead to variable visual defects. However, little attention has been paid to its other side effects, especially those demonstrating cerebellar involvement. Sodium glucose-linked co-transporter 2 (SGLT2) inhibitors are antidiabetic agents with protective effects far greater than expected based on their anti-hyperglycemic effect. Method. Our study herein was designed to investigate the possible ameliorative effect of empagliflozin, the SGLT2 inhibitors, in VGB-induced cerebellar toxicity. A total of 40 male Wistar rats were allocated equally into 4 groups: Group I: control group; Group II: VGB group; Group III empagliflozin treated VGB group; and Group IV: empagliflozin treated group. All groups were subjected to the detection of cerebellar messenger RNA gene expression of silent mating type information regulation 2 homolog 1 (SIRT1) and Nucleoporin p62 (P62). Mammalian target of rapamycin (mTOR), adenosine monophosphate-activated protein kinase (AMPK), and beclin1 levels were assessed by the ELISA technique while malondialdehyde (MDA) level and superoxide dismutase (SOD) activity were detected spectrophotometrically. Immuno-histochemical studies, focusing on glial fibrillary acidic protein (GFAP) and S100 were performed, and the optical color density and the mean area percentage of GFAP positive astrocytes and the number of S 100 positive cells were also counted. Results. Following empagliflozin treatment, we documented significant upregulation of both SIRT1 and P62 mRNA gene expression. Additionally, AMPK, Beclin1 levels, and SOD activity were significantly improved, while both mTOR and MDA levels were significantly reduced. Conclusions. We concluded for the first time that empagliflozin efficiently ameliorated the VGB-induced disrupted mTOR/AMPK/SIRT-1 signaling axis with subsequent improvement of the autophagy machinery and mitigation of the oxidative and inflammatory cellular environment, paving the way for an innovative therapeutic potential in managing VGB-induced neurotoxicity.
Omnifood – Exploring the Possibilities of a Consumer System WithUbiquitous Access to Data About the Food We Eat

Data about food, and data about individuals’ purchases and consumption of food are becoming increasingly ubiquitous. Through bonus cards, supermarkets can track exactly which products we buy, through diet apps we can track what we eat, and through blockchains and other technologies producers can track the origin and history of individual products. From a technical point of view, we are not far away from a world where all this information could be combined to one omniscient system - OmniFood. In this paper we explore current possibilities to collect data on what products we buy, how environmental and nutritional data can be mapped to these products and possibilities to track what we actually eat. Next, we present a number of prototype systems where the possibilities to use this data has been explored, and what limitations we have encountered with current implementations and available data. We end with a discussion of some services that could be possible if current technologies would be fully implemented and made available to consumers and system developers. What possibilities could such systems offer for consumers who want to eat both sustainable and healthy food? What limitations would still exist? What ethical aspects would need to be considered? The focus is on using such a system as a decision support system to support consumers in making food purchase choices that are sustainable from both environmental and health perspectives, thereby supporting the global food system to stay within sustainable limits.
JMocker: Refactoring Test-Production Inheritance by Mockito

Mocking frameworks are dedicated to creating, manipulating, and verifying the execution of "faked" objects in unit testing. This helps developers to overcome the challenge of high inter-dependencies among software units. Despite the various benefits offered by existing mocking frameworks, developers often create a subclass of the dependent class and mock its behavior through method overriding. However, this requires tedious implementation and compromises the design quality of unit tests. We contribute a refactoring tool as an Eclipse Plugin, named JMocker, to automatically identify and replace the usage of inheritance by using Mockito—a well received mocking framework for Java projects. We evaluate JMocker on four open source projects and successfully refactored 214 cases in total. The evaluation results show that our framework is efficient, applicable to different projects, and preserves test behaviors. According to the feedback of six real-life developers, JMocker improves the design quality of test cases. JMocker is available at https://github.com/wx930910/JMocker. The tool demo can be found at https://youtu.be/HFoA2ZKCoxM.
Description of two new and six known amphidelphic species of the genus Dorylaimoides Thorne and Swanger, 1936 (Nematoda: Dorylaimida: Tylencholaimoidea) from the Tropical Rainforest, Western Ghats, India

ABSTRACT Two new and six known amphidelphic species belonging to the genus Dorylaimoides from the tropical rainforest, Western Ghats of India, are described and illustrated. Dorylaimoides brevicaudatus sp. nov. is characterised by having a body 0.99–1.1 mm long; lip region offset by slight depression; odontostyle 7.5–8.5 μm long, ventral arm 5.0–5.5 µm long, dorsal arm 6.0–6.5 µm long; odontophore arcuate, 10.5–12.5 μm long; total stylet length 18.5–20 μm; pharynx expanding gradually into a cylindrical basal bulb, 25–30% of total neck length; female genital system didelphic-amphidelphic; tail convex-conoid with rounded terminus; and males with 24.5–30 µm long spicules. Dorylaimoides silvallis sp. nov. is characterised by having a body 1.3–1.4 mm long; lip region continuous; odontostyle 12.5–13.5 μm long, ventral arm 8.5–9.0 µm long, dorsal arm 9.5–10.5 µm long; odontophore arcuate, 14.5–16.5 μm long, total stylet length 27.5–29.5 μm; pharynx expanding abruptly into a cylindrical basal bulb, 31–35% of total neck length; female genital system didelphic-amphidelphic; tail long filiform, 8.6–9.7 times anal body diameter long; and males with spicules 30–32 µm long. Six known species of the genus Dorylaimoides – D. teres, D. micoletzkyi, D. parvus, D. leptura, D. malabaricus and D. mujtabai – are described and illustrated. Dorylaimoides teres is reported here for the first time from India. http://www.zoobank.org/urn:lsid:zoobank.org:pub:0974BA12-57D5-4AFA-8E89-560ABDEF3AB1 http://www.zoobank.org/urn:lsid:zoobank.org:pub:262D02B9-E5CF-429C-B0DF-D1BB42BDB604
Enhancing PM2.5 Prediction Using NARX-Based Combined CNN and LSTM Hybrid Model

In a world where humanity’s interests come first, the environment is flooded with pollutants produced by humans’ urgent need for expansion. Air pollution and climate change are side effects of humans’ inconsiderate intervention. Particulate matter of 2.5 µm diameter (PM2.5) infiltrates lungs and hearts, causing many respiratory system diseases. Innovation in air pollution prediction is a must to protect the environment and its habitants, including those of humans. For that purpose, an enhanced method for PM2.5 prediction within the next hour is introduced in this research work using nonlinear autoregression with exogenous input (NARX) model hosting a convolutional neural network (CNN) followed by long short-term memory (LSTM) neural networks. The proposed enhancement was evaluated by several metrics such as index of agreement (IA) and normalized root mean square error (NRMSE). The results indicated that the CNN–LSTM/NARX hybrid model has the lowest NRMSE and the best IA, surpassing the state-of-the-art proposed hybrid deep-learning algorithms.
Metal-phosphorus network on Pt(111)

Phosphorus (P) has been predicted to possess many two-dimensional (2D) allotropes, which have attracted intensive research attention due to their excellent properties and application promises. While only 2D black P films have been successfully obtained by the exfoliation method, fabrication of other phosphorus structures by epitaxial growth remains challenging. Recently, the metal-phosphorus network (MPhoN), a superstructure consisted of P and metal atoms has been realized, which offers a wider platform to study P and related compounds. In this paper, we report the observation of platinum-phosphorus networks obtained by P adsorption on Pt(111). Our findings not only enrich the diverse structures of the MPhoN family but also provide insights into the formation mechanism of epitaxial P and its interaction with the noble metal substrate.
Green Supply Chain Decisions and Revenue-Sharing Contracts under Manufacturers’ Overconfidence

Overconfidence is a prevalent and potentially catastrophic behaviour in judgment and decision-making. In this paper, we define manufacturers’ overconfidence as a belief bias that they overestimate the impact of product greenness on demand and the accuracy of demand uncertainty. We build a game theory model based on overconfident beliefs, address the decisions of product greenness and price, and discuss the impact of manufacturers’ overconfidence on supply chain decisions and profits. For the adverse effects brought by overconfidence, we further investigate whether revenue-sharing contracts can coordinate green supply chains. We find three new insights. (1) Manufacturers’ overconfidence leads to higher product greenness, a higher wholesale price, and a greater retail price, but resulting in lower profits. (2) Under the cooperation based on revenue-sharing contracts, product greenness is greater, and wholesale price is lower than the case without cooperation. The greenness increases with the manufacturer’s overconfidence, but counter-intuitively, the wholesale price is not affected by overconfidence. (3) Both the overconfident manufacturer and the retailer have an incentive to reach a revenue-sharing contract. Retailers benefit from collaboration, and overconfident manufacturers assume that retailers can make more profit through revenue sharing, but this model does not exist.
A Novel Statistical Preprocessing Approach for Hyperspectral Image Unmixing

Hyperspectral images have wide application in different commercial and military missions. However, these images suffer from the low spatial resolution, and hence, spectral unmixing has been proposed as a suitable technique to solve this deficiency. Recently, some preprocessing modules have been proposed which use the combination of spatial and spectral information of image pixels with the purpose of improving endmember extraction and spectral unmixing in hyperspectral images. In this paper, we propose a statistical preprocessing algorithm using weighted extended cumulative entropy (WECE) as the Edge Detection algorithm to identify spectrally pure pixels and KL distance estimation using the mean and covariance values of pure oversegments and their neighbors to find the spatially homogenous and spectrally pure pixels for the next endmember extraction algorithms. Moreover, the average of KL distances are applied due to the reduction of noise power and local spectral variability. The performance of the novel statistical preprocessing algorithm has been compared with the sate-of-the-art in terms of RMSE reconstruction and average SAD errors on two real hyperspectral images known as HYDICE urban and AVIRIS Indian Pines in combination with several endmember extraction (EE) algorithms and its efficiency is evaluated.
Integrated Teaching Model in Graduate Aerospace Classes: A Trial With Compressible Flow Aerodynamics

Most of the graduate aerospace classes are dominated by complex analysis and a plethora of equations. As such, without proper context, it is very easy for a student to lose sight of the big picture, which hinders them from critically thinking about the knowledge they gained and its applicability in the real world. The recent surge of the modern pedagogical practices at the University of Dayton such as student-centered learning, active learning, co-teaching, problem/project based learning, entrepreneurial mindset learning, flipped classroom, etc., are largely focused on undergraduate classes but not in graduate classes comparatively. This paper documents a teaching model where the homework, projects, activities, lectures and independent studies are all integrated on a single platform (portfolio) in an endeavor to motivate graduate students to practice sustainable learning (long-term learning) and promote critical thinking skills. The author implemented this model for the first time in a graduate compressible flow aerodynamics class with the “portfolio” as a platform of integration. The paper also discusses the application of this model in the compressible flow class with examples, students’ reflections and feedback. Students found this model to be different and more effective than traditional graduate classes and were able to connect, apply, understand and appreciate the relationship between the complex mathematical equations and the real-life applications. It was also found that creating a portfolio takes more time and effort when compared to traditional exam based class and the workload might need to be reduced.
Analytical model for armature reaction of outer rotor brushless permanent magnet DC motor

This study presents an analytical model for calculating the armature reaction magnetic field of outer rotor permanent magnet brushless DC motor (PMBLDCM) in stator stationary coordinate system (SSCS) and rotor rotation coordinate system (RRCS). Firstly, an expression of armature reaction magnetic field of single-phase winding is deduced, without considering slotting effect. Afterwards, analytical expressions of armature reaction magnetic field in both SSCS and RRCS regardless of slotting effect are obtained using vector addition method. Then complex relative permeances of outer rotor BLPMDCM in SSCS and RRCS are analysed, respectively. Finally, analytical solutions of the armature reaction magnetic field in both SSCS and RRCS are derived, respectively, by multiplying the slotless armature reaction magnetic field and the complex relative magnetic permeance, the self- and mutual inductance are calculated by the armature reaction magnetic field. A 46-ploes-51-slots outer rotor BLPMDCM is set as an example, the accuracy of proposed analytical model is verified by the Finite Element Method. Based on this analytical model, the pattern of slotting effect on armature reaction of both outer rotor and internal stator are analysed, respectively, as well as spatial order characteristics and frequency characteristics of armature reaction for outer rotor PMBLDCM in SSCS and in RRCS.