Miracle-dz/UniFAR-FTUs huggingface dataset:
Primary Growth Increments in Otoliths of Six Tropical Myctophid Species

AbstractThe otoliths of six species of myctophids belonging to the genera Benthosema, Diaphus, Myctophum, Lampanyctus, and Symbolophorus were collected in the western Arabian Sea in 1979 and revealed primary growth increments; the readability of the otoliths, however, varied considerably among the six species. The hypothesis of a daily deposition rate of the increments could not be verified, but strong circumstantial evidence for this was put forward. Under this hypothesis, power curves were fitted to the length at age data, yielding length = 0.59 Age0.77 and length = 0.69 Age0.79 for B. pterotum in the Gulf of Oman and the Gulf of Aden respectively, and length = 2.65 Age0.53 for B. fibulatum. A prominent check in the otoliths is suggested to be caused by metamorphosis.
RELIGIOSITY REDUCE CORRUPTION INTENTION IN CIVIL SERVANT?

Aceh Province is an area that has special autonomy and authority in regulating regional policies. One of them is regional financial management based on facts that are vulnerable to corruption. Corruption is a deviant behavior that is manifested from misuse of tasks for personal gain, other people and institutions. Corruption is an act that is prohibited by religion, but corruption behavior still occurs. Religiosity should be an individual defense to not commit corruption. The purpose of this study was to determine the relationship between religiosity and the intention of corruption among civil servants in Aceh provincial government agencies. This research uses quantitative research method with incidental sampling technique. The sample in this study were 350 respondents (n = 350) from eight Aceh provincial government agencies. The results of this study indicate that there is a negative relationship between religiosity and corrupt intentions of civil servants, with the meaning that the higher the religiosity, the lower the intention of corruption. This is an open-access article under the CC BY-SA license.
A New Era of Morphological Investigations: Reviewing Methods for Comparative Anatomical Studies

Synopsis The increased use of imaging technology in biological research has drastically altered morphological studies in recent decades and allowed for the preservation of important collection specimens alongside detailed visualization of bony and soft-tissue structures. Despite the benefits associated with these newer imaging techniques, there remains a need for more “traditional” methods of morphological examination in many comparative studies. In this paper, we describe the costs and benefits of the various methods of visualizing, examining, and comparing morphological structures. There are significant differences not only in the costs associated with these different methods (monetary, time, equipment, and software), but also in the degree to which specimens are destroyed. We argue not for any one particular method over another in morphological studies, but instead suggest a combination of methods is useful not only for breadth of visualization, but also for the financial and time constraints often imposed on early-career research scientists.
The Mouseion at the JAXlibrary The Mouseion at the JAXlibrary

Background Colorectal cancers are the fourth most diagnosed cancer and the second leading cancer in number of deaths. Many clinical variables, pathological features, and genomic signatures are associated with patient risk, but reliable patient strati ﬁ cation in the clinic remains a challenging task. Here we assess how image, clinical, and genomic features can be combined to predict risk. Methods We developed and evaluated integrative deep learning models combining formalin-ﬁ xed, paraf ﬁ n-embedded (FFPE) whole slide images (WSIs), clinical variables, and mutation signatures to stratify colon adenocarcinoma (COAD) patients based on their risk of mortality. Our models were trained using a dataset of 108 patients from The Cancer Genome Atlas (TCGA), and were externally validated on newly generated dataset from Wayne State University (WSU) of 123 COAD patients and rectal adenocarcinoma (READ) patients in TCGA (N = 52). Findings We ﬁ rst observe that deep learning models trained on FFPE WSIs of TCGA-COAD separate high-risk (OS < 3 years, N = 38) and low-risk (OS > 5 years, N = 25) patients (AUC = 0.81 ± 0.08, 5 year survival p < 0.0001, 5 year relative risk = 1.83 ± 0.04) though such models are less effective at predicting overall survival (OS) for moderate-risk (3 years < OS < 5 years, N = 45) patients (5 year survival p-value = 0.5, 5 year relative risk = 1.05 ± 0.09). We ﬁ nd that our integrative models combining WSIs, clinical variables, and mutation signatures can improve patient strati ﬁ cation for moderate-risk patients (5 year survival p < 0.0001, 5 year relative risk = 1.87 ± 0.07). Our integrative model combining image and clinical variables is also effective on an independent pathology dataset (WSU-COAD, N = 123) generated by our team (5 year survival p < 0.0001, 5 year relative risk = 1.52 ± 0.08), and the TCGA-READ data (5 year survival p < 0.0001, 5 year relative risk = 1.18 ± 0.17). Our multicenter integrative image and clinical model trained on combined TCGA-COAD and WSU-COAD is effective in predicting risk on TCGA-READ (5 year survival p < 0.0001, 5 year relative risk = 1.82 ± 0.13) data. Pathologist review of image-based heatmaps suggests that nuclear size pleomorphism, intense cellularity, and abnormal structures are associated with high-risk, while low-risk regions have more regular and small cells. Quantitative analysis shows high cellularity, high ratios of tumor cells, large tumor nuclei, and low immune in ﬁ ltration are indicators of high-risk tiles.
Robust Adaptive Consensus Control of Multi - Agent Systems with Input Saturation

This paper focuses on consensus control of multi-agent systems with input saturation. Based on the input satu-ration affine model, a robust adaptive consensus controller is proposed for multi-agent systems by combining the Nussbaum gain function, the dynamic surface control, and the leader-state observer. In addition, the designed adaptive control scheme is characterized by fewer adaptive laws, which reduces the controller computation and saves computation time. The stability of the closed-loop system is proved theoretically. The effectiveness of the proposed controller is verified by a simulation experiment result.
Design and Evaluation of Sentiment Analysis as a Decision Tool Regarding Market Assets

With the most recent advances in Natural Language Processing (NLP), sentiment analysis - computationally identifying and categorizing opinions expressed in text - reached unprecedented accuracy and is being considered a tool in quantitative financial analysis. In order to evaluate the effect of market agents sentiment with price movements of publicly traded assets, different techniques were considered for the construction of the NLP model: vectorization of words (GLOVE and TF-IDF), classification models (Naive-Bayes and SVC) and neural networks models (GRU and BERT). A database containing publications from the social network Twitter related to the open market assets was used, in order to evaluate models accuracy. With the best-obtained accuracy of around 95%, it was possible to relate the sentiment of the posts with the market movements with a Pearson correlation of 43% for the average of the evaluated companies.
Serum Testosterone Levels and Coronary Artery Disease

: Introduction: Coronary Artery Disease (CAD) is the leading cause of death worldwide. Testosterone has protective effect on cardiovascular system (CVS). Men are more prone to develop CAD than women and the mechanism of this different susceptibility is not well understood. Objectives: To estimate the concentration of serum testosterone levels in male subjects with and without CAD and to determine the relationship between serum Testosterone levels and angiographic severity of CAD. Methodology: A cross - sectional study, consisting of 110 subjects out of which 55 subjects diagnosed with CAD and 55 healthy subjects, was carried out in the department of Biochemistry in collaboration with the department of Cardiology of RIMS, Imphal from January 2021 to October 2022. Serum free and total testosterone levels were measured and the severity of CAD was assessed using angiographic Gensini score. Result: The mean ± SD of serum total and free testosterone levels were significantly lower in case group (2.05 ± 1.18 ng/ml, 3.79 ± 2.71 pg/ml) when compared to control group (4.97 ± 0.65 ng/ml, 16.22 ± 4.60 pg/ml). Cases with Gensini score >30 had significantly lower total testosterone levels and free testosterone levels than cases whose Gensini score were ≤30. A multiple linear regression analysis showed that low testosterone was an independent risk factor the severity of CAD (β= - .238, p=.014). Conclusion: Men with coronary artery disease have significantly lower testosterone than normal controls. This study also suggests low serum testosterone as an independent risk factor for CAD.
Vehicle Trajectory Prediction Based on Triplet Refinement LSTM

Vehicle trajectory prediction is a vital component for autonomous vehicles environment perception system, which remains a challenging problem due to the high density, frequent occlusions with interaction, and the randomicity of surrounding objects’ short-term path. In this paper, we propose a triplet refinement LSTM network for vehicle trajectory prediction. A novel LSTM module is proposed to capture historical information interaction between current object and surrounding objects. Furthermore, we derive a Graph Convolutional Attention (GAT) module to model the spatial interactions between object groups. Then, we present a temporal attention module to estimate the importance of objects’ behavior fragment. Finally, an end-to-end framework is composed by above three components. The quantitative and qualitative experiments on D2-City dataset proved that our algorithm can achieve competitive performance than state-of-the-art methods.
The stratigraphic record of submarine channel-lobe transition zones

Channel-lobe transition zones (CLTZs) represent critical areas between the submarine slope and basin floor systems, where sediment gravity flows transition from confined to unconfined. In modern systems, this area is characterised by a distinctive assemblage of erosional and depositional features. However, the transfer of the CLTZ into the stratigraphic record is not well constrained. By the detailed study of various well-constrained exhumed base-of-slope systems within the Tanqua and Laingsburg depocentres of the Karoo Basin, South Africa, the sedimentary and stratigraphic record of CLTZs is investigated. The first detailed study of key depositional and erosional bedforms that characterise ancient CLTZs, including sediment waves and giant scour-fills, are presented. Their process record suggests complicated flow-bedform interactions, where both the preservation and sedimentary characteristics are dependent on the evolution of the feeder channel and the lateral position to the channel-mouth. Within the base-of-slope environments, juxtaposition of lobes and channel-fills is common. The lobes in these proximal fan environments are sandstone-prone and differ in facies and architectural characteristics to lobes downdip on the basin floor. The sand-rich nature and juxtaposition of elements in ancient CLTZs means that they are considered attractive hydrocarbon reservoir targets in the subsurface. However, fine-scale reservoir modelling and streamline simulations suggests that the relatively limited sand-volume of channel-fills in comparison to lobe deposits in these environments can have a negative impact on reservoir performance due to its effect on reservoir connectivity. Overall, the stratigraphic record of CLTZs shows high diversity in architecture, facies characteristics and volume, which can be related to three primary controls: a) spatial variability, b) flow efficiency and c) channel evolution. These three controls infer that CTLZs are highly dynamic, migrating and changing in dimensions over time; this needs to be considered when assessing how CLTZs are recorded in the rock record.
Exploring the Factors and Moderators Influencing the Use of Radar Visualisation of Student Performance from Parents’ Perspective

The purpose of this study is to investigate the factors that influence parents’ acceptance and usage of radar visualisation to understand children’s learning performance in a secondary school in Macao. Guided by the Unified Theory of Acceptance and Use of Technology (UTAUT), a 29-item survey was designed, piloted and administered with 706 valid responses from 773 parents (valid response rate was 91.33%). The Cronbach’s alpha was found to be 0.964. Data was analysed using structural equation modelling (SEM), and the finding showed that three of the four core factors from UTAUT (performance expectancy, effort expectancy, social influence and facilitating conditions) significantly influenced continued intention of using radar visualisation to understand student performance, and some moderators also fully or partially supported the hypotheses. To the best of our knowledge, this study is the first to investigate the acceptance and use of radar visualisation feedback from parents’ perspective at secondary school level. It can serve as a basis for further discussing and exploring the ways to enhance the effectiveness of different formats of displaying student performance through acceptance models.
Deep Learning-based Early Lung Cancer Detection using Multi-Model Image Fusion Technique

Lung cancer, a significant global health concern, continues to take many lives, especially as a result of late-stage diagnoses. Early detection continues to have the most significant impact on patient outcomes and mortality rates. This research examines how modern image-processing methods might transform the early diagnosis of lung cancer. To investigate the application of deep learning algorithms, computer-aided diagnosis systems, and the synergistic integration of multimodal imaging techniques to increase the accuracy and efficiency of lung cancer diagnosis. The article thoroughly examines current approaches, presents a novel framework designed for early detection, and rigorously assesses the efficacy of the suggested system using actual medical imaging data. The research has the potential to radically alter lung cancer diagnosis, encourage a new era in early intervention, and eventually help to significantly lower lung cancer mortality rates by bridging the gap between cutting-edge image processing and clinical practice.
Artificial Intelligence's Effects on Corporate Decision-Making Processes

With the introduction of artificial intellectual ability life choice machine learning, how has decision-changed? The predictability of the judgement state space, communication resources of the life choice procedure and result, size of the unconventional set, life choice rate, and representativeness are five key plans in place factors in this blog entry as the peculiarities of human and Machine learning strategic planning. The paper develops a novel paradigm describing why both modalities of judgement may be merged to best benefit the level of company judgement call upon an examination of individual and Digital taking decisions or something along those parameters. Three major categories-full human to AI outsourcing, mixed mortal and Cognitive technologies sequence strategic planning, and merged decision-making between humans and AI- are presented by the structure as ways in which party members' choices can be coupled with AI-based judgments.
Experimental Estimation of Gamma and Electron Detection Ratios for Training and Evaluating Signal Discriminators for Intraoperative Probes

Radioguided surgery (RGS) for cancer resection is a widely performed practice for accurate localisation of cancerous tissue. The medical radioisotope 99mTc can be detected during RGS with the use of an intraoperative probe to detect cancerous tissue. For accurate localization, the internal conversion (IC) electrons from 99mTc are set as the target emission due to their shorter range in tissue. However, the inability to isolate the IC electrons and gamma emissions mean that a labelled dataset is not available in practice, yet this is required for training a discriminator. In this study, an experimental method relying on evaporation is proposed to obtain ground truth information relating to emissions present within the dataset using physics-informed modelling of the IC electron and gamma signals. By experimental design, both signals vary differently over time thus allowing the ratio between the two signal sources to be estimated. This ground truth ratio information has been measured and hence used i) to assess the intrinsic response of an intraoperative probe for IC electron detection, and ii) to evaluate experimentally different discriminator algorithms. Furthermore, the data can also be used to partially label a measured dataset, allowing both training and testing of discriminators with known ratios. In summary, an experimental method was proposed to allow the evaluation of detector sensitivities and the development of discriminators for unlabelled datasets.
AI-Driven Mustard Disease Identification: A Multiclass and Binary Classification Approach for Advanced Crop Health Monitoring

Important for both food and oil production, mustard plants are frequently susceptible to maladies that can result in significant yield losses. Early detection and diagnosis of these diseases are essential for effective management and control, thereby contributing to the maintenance of the production chain and crop yield. In this study, we present a deep learning-based method for detecting mustard disease using leaf images. We have trained and evaluated a convolutional neural network (CNN) model dedicated to classifying mustard leaves as healthy or diseased. This article focuses on the binary classification of healthy and infected mustard crop samples. In addition, we have conducted multi-classification concentrating on the classification of five main mustard diseases, including stem rot, white rust, bacterial blight, powdery mildew, and downy mildew. This article is the first of its kind to perform classification concerning this particular mustard disease strain. The model provides adequate recall, precision, and f1 score performance. In precision agriculture, the proposed method can be used to automatically detect disease and manage crops to increase yield and crop quality as the predicted precision for healthy and unhealthy disease classification is 95.17% and 93.8% respectively.
A quater-century of deep-sea malacological exploration in the South and West Pacific: Where do we stand? How far to go?

L'Institut de Recherche pour le Developpement (IRD, anterieurement ORSTOM) et le Museum national d'Histoire naturelle (MNHN) ont lance au debut des annees 1980 une serie de campagnes oceanographiques pour echantillonner le benthos profond des regions tropicales du Pacifique Sud et Ouest, en particulier dans la tranche bathymetrique 100-1500 m. Ce chapitre met en perspective les developpements du programme; il decrit la chaine de traitement des echantillons recoltes, et sa mise en valeur par un reseau international de taxonomistes. A ce jour, 1028 especes de mollusques, dont 601 especes nouvelles (58,4 %), ont ete recensees a plus de 100 metres de profondeur dans la Zone Economique Exclusive de la Nouvelle-Caledonie, et 142 especes nouvelles additionnelles ont ete decrites des autres archipels (Iles Salomon, Vanuatu, Fidji, Wallis et Futuna, Tonga, Marquises, et Iles Australes). Les familles hyper-diverses n'ont pas encore ete touchees. Les differences entre les archipels sont elevees, et il est possible de reconnaitre plusieurs aires de micro-endemisme au sein meme de la region neo-caledonienne, la mieux echantillonnee. Nous speculons que la faune malacologique de profondeur de la Nouvelle-Caledonie atteint peut-etre 15 a 20.000 especes, le nombre correspondant pour l'ensemble du Pacifique Sud se situant autour de 20 a 30.000 especes.
eSilo: Making Silo Secure with SGX

It is not easy to prove that an adversary with administrator privileges, such as a cloud provider, is not attacking. To deal with this problem, EnclaveDB is proposed, which applies TEE to the database. However, the logging mechanism runs sequentially, not utilizing modern storage devices with parallel I/O. In this paper, we propose eSilo, which performs Silo processing and logging within the enclave. eSilo ensures the confidentiality of sensitive records and procedures by storing, processing, encrypting, and exporting logs inside the enclave generated by Intel SGX. In the implementation of Silo, it is not possible to use the standard C/C++ library due to the specifications of the SGX. We rebuilt the Silo system based on CCBench by replacing the alternative library included in the SGX SDK provided by Intel and re-implementing unsupported functions. In YCSB-A workload experiments, eSilo peaked at 6.28 Mtps with 112 worker threads and 16 logger threads. We also implemented the Silo system without the enclave. We observed the maximum of 7.57 Mtps on 112 worker threads and 16 logger threads. Our eSilo demonstrated 17.0% lower performance compared to vanilla Silo.
COMPARATIVE GROWTH PERFORMANCE OF CAMEL CALVES KEPT UNDER FARM/FARMER'S CONDITIONS

A 6-month study was undertaken on 14 camel calves aged 7 days to observe their comparative growth performance. Five calves belonged to the Barani Livestock.Production Research Institute (BLPRI), District Attock, Pakistan, while the rest were owned by the farmers living in the vicinity of BLPRI. All calves sucked milk from their respective dams ad libitum. They started nibbling almost at 4 weeks of age. Vegetation for browsing/grazing mainly comprised Pholai (Acacia modesta), Kandair (A/ha}i came/orum) and Kao (O/ea cuspidata). Monthly growth rate of the Institute calves was determined by actual weighing the animals coupled with their weight. assessed by a standard formula based on body measurements. Because of non-availability of weigh bridge at farmers premises, growth rate of farmer's calves was assessed by using the formula method only. Institute calves on an average attained a net weight gain of 135.451 ± 6.35 kg, while farmer's calves gained 149.20 ± 3.06 kg. Daily growth rate in respect of Institute and fanner's camel calves worked out to be 0.75 and 0.82 kg respectively. On overall basis, daily growth rate of camel calves' could be due to personal attention given to them by their owners. The present findings are indicative of the great potential possessed by camel calves which can further be exploited under optimum feeding and management conditions for alleviating the animal protein deficiency prevailing in overpopulated and underdeveloped countries like Pakistan.
Runoff and soil losses for conventional, reduced, and no-till corn

ABSTRACT: Runoff and soil losses from natural rainfall during a 6-year period were compared among conventional, reduced, and no-till corn grown for grain and no-till corn grown for silage both with and without a winter cover crop. A winter cover crop significantly reduced both soil and water losses for no-till corn harvested for silage, compared to the equivalent treatment without winter cover. Average annual runoff was about 20% less for reduced tillage than for conventional or no-till methods for grain. Average annual soil losses differed significantly among tillage methods for corn grown for grain; highest losses were with conventional tillage followed by reduced tillage, then no-till. Average annual C values derived from soil losses and rainfall data ranged from 11 to 57% of currently accepted values. Crop yields were similar among treatments.