Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiscale Attention-Based Instance Segmentation for Measuring Crystals With Large Size Variation

Quantitative measurement of crystals in high-resolution images allows for important insights into underlying material characteristics. Deep learning has shown great progress in vision-based automatic crystal size measurement, but current instance segmentation methods reach their limits with images that have large variations in crystal size or hard-to-detect crystal boundaries. Even small image segmentation errors, such as incorrectly fused or separated segments, can significantly lower the accuracy of the measured results. Instead of improving the existing pixel-wise boundary segmentation methods, we propose to use an instance-based segmentation method, which gives more robust segmentation results to improve measurement accuracy. Our novel method enhances flow maps with a size-aware multiscale attention (SiMA) module. The attention module adaptively fuses information from multiple scales and focuses on the most relevant scale for each segmented image area. We demonstrate that our proposed attention fusion strategy outperforms state-of-the-art (SOTA) instance and boundary segmentation methods, as well as a simple average fusion of multiscale predictions. We evaluate our method on a refractory raw material dataset of high-resolution images with large variations in crystal size and show that our model can be used to calculate the crystal size more accurately than existing methods.
Organisms Need Mechanisms; Mechanisms Need Organisms

According to new mechanists, mechanisms explain how specific biological phenomena are produced. New mechanists have had little to say about how mechanisms relate to the organism in which they reside. A key feature of organisms, emphasized by the autonomy tradition, is that organisms maintain themselves. To do this, they rely on mechanisms. But mechanisms must be controlled so that they produce the phenomena for which they are responsible when and in the manner needed by the organism. To account for how they are controlled, we characterize mechanisms as sets of constraints on the flow of free energy. Some constraints are flexible and can be acted on by other mechanisms, control mechanisms, that utilize information procured from the organism and its environment to alter the flexible constraints in other mechanisms so that they produce phenomena appropriate to the circumstances. We further show that control mechanisms in living organisms are organized heterarchically—control is carried out primarily by local controllers that integrate information they acquire as well as that which they procure from other control mechanisms. The result is not a hierarchy of control but an integrated network of control mechanisms that has been crafted over the course of evolution.
"There’s No Place like Campus"

This study investigates the relationships between students’ psychosocial needs and their preferences for online vs. offline learning in a post-pandemic environment. A mixed-methods study was conducted at a higher education institution in Norway with 240 Bachelor students. Students in a post-covid learning environment value pedagogical forms of student-centered learning, through exploration and group reflection work. Second, students report a preference for limited amounts of online learning. Third, students express a significant need for social support, in the form of non-digital interaction with peers and teachers, and campus-based activities. Finally, while students prefer receiving feedback from peers in physical form, they are also positive toward receiving feedback in a digital form. These findings indicate that higher education institutions should carefully evaluate the amount of digital teaching offered to students, as such choices influence how and to what degree students are able to socially interact with peers and teachers, and their sense of belonging. More research is needed to understand the challenges associated with psychosocial health and well-being among students, and how this relates to online/offline forms of learning.
THE PROFILE OF STUDENTS’ PSYCHOLOGICAL WELL-BEING AT THE FACULTY OF TARBIYAH AND TEACHER SCIENCES SALATIGA STATE ISLAMIC INSTITUTE

The state of students' psychological well-being has an impact on their mental health. In this study, the psychological well-being of students at the Faculty of Tarbiyah and Teaching Science (FTTS) at Salatiga State Islamic Institute. This study used a cross-sectional survey design and a descriptive quantitative methodology. The study's subjects were 142 students at FTTS, Salatiga State Islamic Institute, who were chosen using a simple random sampling process. The psychological well-being scale used in this study is an adaptation of Riff's Well-Being Scale. Based on the instrument's validity and reliability tests, the instrument's reliability (r) score was 0.867, and the r count validity score ranged from 0.231 to 0.652. These findings show that the instrument is reliable and valid. The IBM SPSS program was used for data analysis. The findings indicated that students at FTTS, Salatiga State Islamic Institute are generally categorized as moderate for their psychological well-being, and according to the t-test (p= 0.090 > 0.05), there is no statistically significant difference between the psychological well-being of male and female students. According to the study's findings, students' psychological well-being can be enhanced by using a variety of services, including counseling and mindfulness therapy provided by counselors.
Transcription Analysis of the THBS2 Gene through Regulation by Potential Noncoding Diagnostic Biomarkers and Oncogenes of Gastric Cancer in the ECM-Receptor Interaction Signaling Pathway: Integrated System Biology and Experimental Investigation

Background Gastric cancer (GC) is the second most frequent cause of cancer-related death worldwide and the fourth most common malignancy. Despite significant improvements in patient survival over the past few decades, the prognosis for patients with GC remains dismal because of the high recurrence rate. In this comprehensive system biology and experimental investigation, we aimed to find new novel diagnostic biomarkers of GC through a regulatory RNA interaction network. Methods Gene expression, coexpression, and survival analyses were performed using microarray and RNAseq datasets (analyzed by RStudio, GEPIA2, and ENCORI). RNA interaction analysis was performed using miRWalk and ENCORI online databases. Gene set enrichment analysis (GSEA) was performed to find related signaling pathways of up- and downregulated genes in the microarray dataset. Gene ontology and pathway enrichment analysis were performed by the enrichr database. Protein interaction analysis was performed by STRING online database. Validation of expression and coexpression analyses was performed using a qRT-PCR experiment. Results Based on bioinformatics analyses, THBS2 (FC: 7.14, FDR < 0.0001) has a significantly high expression in GC samples. lncRNAs BAIAP2-AS1, TSIX, and LINC01215 have RNA interaction with THBS2. BAIAP2-AS1 (FC: 1.44, FDR: 0.018), TSIX (FC: 1.34, FDR: 0.038), and LINC01215 (FC: 1.19, FDR: 0.046) have significant upregulation in GC samples. THBS2 has a significant role in the regulation of the ECM-receptor signaling pathway. miR-4677-5p has a significant RNA interaction with THBS2. The expression level of THBS2, BAIAP2-AS1, TSIX, and LINC01215 has a nonsignificant negative correlation with the survival rate of GC patients (HR: 0.28, logrank p: 0.28). qRT-PCR experiment validates mentioned bioinformatics expression analyses. BAIAP2-AS1 (AUC: 0.7136, p value: 0.0096), TSIX (AUC: 0.7456, p value: 0.0029), and LINC01215 (AUC: 0.7872, p value: 0.0005) could be acceptable diagnostic biomarkers of GC. Conclusion BAIAP2-AS1, lncRNA LINC01215, lncRNA TSIX, and miR-4677-5p might modulate the ECM-receptor signaling pathway via regulation of THBS2 expression level, as the high-expressed noncoding RNAs in GC. Furthermore, mentioned lncRNAs could be considered potential diagnostic biomarkers of GC.
Experimental Study of Fully Passive, Fully Active, and Active–Passive Upper-Limb Exoskeleton Efficiency: An Assessment of Lifting Tasks

Recently, robotic exoskeletons are gaining attention for assisting industrial workers. The exoskeleton power source ranges from fully passive (FP) to fully active (FA), or a mixture of both. The objective of this experimental study was to assess the efficiency of a new active–passive (AP) shoulder exoskeleton using statistical analyses of 11 quantitative measures from surface electromyography (sEMG) and kinematic data and a user survey for weight lifting tasks. Two groups of females and males lifted heavy kettlebells, while a shoulder exoskeleton helped them in modes of fully passive (FP), fully active (FA), and active–passive (AP). The AP exoskeleton outperformed the FP and FA exoskeletons because the participants could hold the weighted object for nearly twice as long before fatigue occurred. Future developments should concentrate on developing sex-specific controllers as well as on better-fitting wearable devices for women.
Geodetic constraints on frictional properties and earthquake hazard in the Imperial Valley, Southern California

We analyze a suite of geodetic observations across the Imperial Fault in southern California that span all parts of the earthquake cycle. Coseismic and postseismic surface slips due to the 1979 M 6.6 Imperial Valley earthquake were recorded with trilateration and alignment surveys by Harsh (1982) and Crook et al. (1982), and interseismic deformation is measured using a combination of multiple interferometric synthetic aperture radar (InSAR)‐viewing geometries and continuous and survey‐mode GPS. In particular, we combine more than 100 survey‐mode GPS velocities with InSAR data from Envisat descending tracks 84 and 356 and ascending tracks 77 and 306 (149 total acquisitions), processed using a persistent scatterers method. The result is a dense map of interseismic velocities across the Imperial Fault and surrounding areas that allows us to evaluate the rate of interseismic loading and along‐strike variations in surface creep. We compare available geodetic data to models of the earthquake cycle with rate‐ and state‐dependent friction and find that a complete record of the earthquake cycle is required to constrain key fault properties including the rate‐dependence parameter (a − b) as a function of depth, the extent of shallow creep, and the recurrence interval of large events. We find that the data are inconsistent with a high (>30 mm/yr) slip rate on the Imperial Fault and investigate the possibility that an extension of the San Jacinto‐Superstition Hills Fault system through the town of El Centro may accommodate a significant portion of the slip previously attributed to the Imperial Fault. Models including this additional fault are in better agreement with the available observations, suggesting that the long‐term slip rate of the Imperial Fault is lower than previously suggested and that there may be a significant unmapped hazard in the western Imperial Valley.
Spectral Analysis of Mare Ingenii Basin (Lunar Farside)

Mare Ingenii is a site of great interest for lunar geology as it is one of the few basaltic plains on the farside of the Moon. It is located within the outer edge of the South Pole‐Aitken basin, the largest and oldest impact basin in our Solar System. Mare Ingenii includes two large craters, Thomson and Thomson M, and a prominent swirl, a high‐albedo sinuous feature whose origin is still debated. We conducted spectral analysis on 28 selected regions of interest within Mare Ingenii, with the aim of inferring its mineralogy. We considered reflectance data acquired in the visible to near‐infrared spectral range by the Moon Mineralogy Mapper (M3) imaging spectrometer onboard the Chandrayaan‐1 mission, to derive a set of spectral parameters. Our results show wide compositional variability, with the dark material of the mare basaltic floor showing the centers of the Fe2+ absorption bands of the pyroxenes shifted toward long wavelengths (0.96–0.99 and 2.03–2.12 μm, respectively), consistent with the spectral characteristics of high‐Ca pyroxenes (as well as swirl material and intermediate albedo regions). In contrast, the bright material of the small surrounding craters shows Fe2+ absorption bands shifted toward short wavelengths (0.91–0.94 and 1.91–2.04 μm, respectively), more consistent with low‐Ca or Ca‐free pyroxenes. The obtained results suggest a mafic signature throughout the surface of Mare Ingenii, probably representative of the composition of the lower lunar crust.
A Survey of Sound Source Localization and Detection Methods and Their Applications

This study is a survey of sound source localization and detection methods. The study provides a detailed classification of the methods used in the fields of science mentioned above. It classifies sound source localization systems based on criteria found in the literature. Moreover, an analysis of classic methods based on the propagation model and methods based on machine learning and deep learning techniques has been carried out. Attention has been paid to providing the most detailed information on the possibility of using physical phenomena, mathematical relationships, and artificial intelligence to determine sound source localization. Additionally, the article underscores the significance of these methods within both military and civil contexts. The study culminates with a discussion of forthcoming trends in the realms of acoustic detection and localization. The primary objective of this research is to serve as a valuable resource for selecting the most suitable approach within this domain.
Distributive Justice, Psychological Empowerment and Job Autonomy’s Influences on the Organizational Commitment of Tertiary School Leaders

This research generally intended to ascertain how organizational commitment was influenced by psychological empowerment, job autonomy, and distributive justice of tertiary school leaders in selected colleges and universities in Southern Philippines. Two hundred fifty-two (252) tertiary school administrators with at least three years of experience in Southern Philippines as program heads, deans, directors, vice presidents, and presidents made up the participants. The "Basic Social Justice Orientations Scale," "Psychological Empowerment Scale," "Job Autonomy Scale," and "Organizational Commitment Scale" were employed to collect data needed for this study. Then, Pearson r product-moment correlation, multiple regression analysis, and path analysis were applied to evaluate the data. Research findings supported the hypotheses that were developed in the theoretical model. The findings demonstrated that job autonomy acted as a mediator between distributive justice and psychological empowerment, which has direct and indirect consequences on the organizational commitment of school leaders. Additionally, it was discovered that both psychological empowerment and job autonomy greatly affected organizational commitment. Therefore, it can be said that the distributive justice, psychological empowerment, and job autonomy of selected tertiary school leaders in Southern Philippines had influences on their organizational commitment. This means that distributive justice, psychological empowerment, and job autonomy are good predictors of the organizational commitment of tertiary school leaders.
A Survey of Computing-in-Memory Processor: From Circuit to Application

The computing-in-memory (CIM) technique is emerging with the evolvement of big data and artificial intelligence (AI) application. The manuscript presents a systematic review of existing CIM works in a bottom-up view from circuit to application. Various types of CIM circuits based on different volatile/nonvolatile devices are introduced. The micro CIM architectures are illustrated to support multibit precision computation. After that, several types of processor-level CIM chips are analyzed to reveal the system architecture design considerations. The corresponding CIM tool chains and applications beyond AI applications are also introduced. From circuit to application levels, this manuscript analyzes the design tradeoffs, remained challenges, and possible future design trends at different design hierarchies of CIM processors.
Semi-automated workflow for natural disaster assessment : a case study of Banda Aceh, Indonesia

The past decade has witnessed many natural disasters hitting highly populated areas causing billions of dollars in damage as well as many human casualties. During natural disasters, when attaining ground measurements are limited, remote sensing and geographical information systems (GIS) are useful tools for in-depth analysis of the affected area. This report will introduce a new semi-automatic workflow in which the road network will be used to break up the area into “blocks” and then zonal statistics will be applied to detect change based on the created blocks rather than the conventional methods of change detection; pixel by pixel and object oriented. This hybrid approach will take advantage of the simplicity and ease of applying pixel change detection methods on fixed objects or “blocks” to assess for damage. The change detection analysis results can then be used to map and quantify damage caused by natural disasters using pre and post Landsat imagery of the affected area. Multi-Criteria Analysis is performed on the damage map, proximity to roads, proximity to waterbodies and building size to find the most suitable locations for temporary housing sites. The image differencing of NDWI mean produced the highest overall accuracy of 71.70% among eleven bands/indices and the multi-criteria analysis successfully selected fourteen temporary housing center sites from a possible 114. When time is of essence with limited resources and GIS expertise on the field, local authorities can greatly benefit from a rapid generalized analysis that will provide a “bird-eye view” of the affected area to efficiently and effectively allocate emergency efforts within a short
GCN-Based Heterogeneous Complex Feature Learning to Enhance Predictability for LncRNA–Disease Associations

Using computational models to predict potential lncRNA-disease associations (LDAs) has emerged as an effective supplement to bioexperiments for exploring the pathogenesis of diseases. However, current computational models still face limitations in their ability to learn the complex features of bionetworks. In this study, HGCNLDA, a model which combines graph convolutional network (GCN)-based aggregation, heterogeneous information fusion, and a bilinear-decoder to infer LDAs was proposed. Recognizing the need to extract essential features during data processing, our HGCNLDA explored four key steps for uncovering interaction patterns within the bionetwork: (1) a novel type of tripartite heterogeneous network, known as the lncRNA-disease-miRNA network (LDMN), was constructed using computed similarities and known associations. (2) Homogeneous and heterogeneous features of nodes were extracted from domains within the LDMN by a GCN-based encoder. (3) Feature fusions, including bipolymerization operations and attention mechanism, were employed to capture a more accurate and comprehensive representation of nodes. (4) Bilinear-decoder was used to rebuild the edge type (or rating type) for a specific node pair, resulting in the predicted association score. Through a 5-fold cross-validation on two data sets, namely, data set1 and data set2, our HGCNLDA consistently demonstrated superior performance compared to five related models. It almost achieved the highest AUROC and AUPR values on both data sets, especially on data set2 where the results obtained were more challenging and objective. Case studies involving three real cancer scenarios further validated the practicality of HGCNLDA in identifying potential LDAs in real-world contexts. The source code and data for this study are available at https://github.com/zywait/HGCNLDA.
Floatability of coal and pyrite

An experimental investigation was carried out to improve the froth flotation method of separating coal pyrite. The effect of several surface active agents on the floatability of an Iowa coal and its associated pyrite was studied in both the presence and absence of methyl isobutyl carbinol (MIBC), a commonly used frother. The effectiveness of a special method of chemical pretreatment for depressing pyrite was also investigated. The zeta potential of the same coal and pyrite was measured under the same conditions used in the flotation tests (in the absence of frother). In the absence of MIBC, the floatability of coal and pyrite was low, and appeared to depend on pH. The maximum recovery of pyrite was obtained at a pH of 4.5 with recovery being less at higher or lower pH. The addition of a small amount of MIBC significantly increased the recovery of coal and pyrite with the increase in the recovery of coal being much greater than the increase in the recovery of pyrite. Also in the presence of MIBC, the floatability of coal and pyrite seemed to be affected more by pH than in the absence of MIBC. On the other hand, several potential pyrite depressants weremore » found to be ineffective in the presence of MIBC. The special chemical pretreatment reduced the floatability of pyrite markedly while not greatly affecting the floatability of coal. Measurement of the zeta potential of coal and pyrite suspensions in dilute aqueous solutions of various reagents provided an indication of adsorption phenomena. However, there appeared to be no consistent direct correlation between zeta potential and floatability. « less
LargeRSDet: A Large Mini-Batch Object Detector for Remote Sensing Images

Deep neural network models based on vision transformer (ViT) have shown unprecedented performance in the field of remote sensing image object detection. However, those models often require massive training data, which cost a lot of time to train and greatly prevent the research progress. Distributed training is a common way to accelerate the training period. In this letter, we propose a large batch object detector named LargeRSDet for remote sensing image object detection task, which can train with a batch size up to 1024 with only a little acceptable performance loss. Using the LargeRSDet, we can effectively utilize at most 1024 GPUs and greatly improve the training speed, which enables several benefits that not only help our model converge in a faster way but also provide the ability to reach a higher accuracy. Experimental results demonstrate that our method can finish training DIOR remote sensing image dataset in less than 5 min, and finally, the model achieves 75% mAP at 0.5.
Case studies and comparative analysis of energy efficiency in wheat production in different climatic conditions of Eu- rope

The paper presents results concerning energy efficiency of wheat production considered in the context of specific energy input variation in different climatic conditions of Europe as well as case studies on implementation of selected energy saving measures in practice. It was shown that the highest wheat yield (6.7-8.7 t·ha -1 ) at the lowest specific energy input (2.082.56 GJ·t -1 ) is unique for temperate climate conditions. The yield in continental and Mediterranean climatic conditions is on average lower by 1.3 t·ha -1 and 2.7 t·ha -1 and energy efficiency lower by 14% and 38%, respectively. The case studies have shown that the energy saving activities in wheat production may be universal for the climatic zones or specific for a given geographical location. It was stated that trade-offs between energy, economic and environmental effects, which are associated with implementation of a given energy saving measure or a set of measures to a great extent depend on the current energy efficiency status of the farm and opportunity for investment, which varies substantially across Europe.