Miracle-dz/UniFAR-FTUs huggingface dataset:
Decentralized trustworthiness score management with smart contracts on the trustlend platform

The personal lending marketplace, known as Peer‐to‐Peer (P2P) lending, has increased globally. However, providing unsecured loans to peers without requiring collateral remains a challenge. A platform called TrustLend is proposed to enable trustworthy transactions in the personal lending application. The platform attempts to eliminate or minimize the collateral requirement. The trustworthiness score adds to this platform's variable selection rules and can help lenders decide on reliable candidates as borrowers. The prototype implementing the TrustLend platform based on Ethereum smart contracts that use the trustworthiness score is also described and it is illustrated with a Decentralized Application (DApp) case study and customized smart contracts. The prototype demonstrates fundamental features and supports borrowers, lenders, and recommenders in establishing proposals and approvals. Finally, the prototype shows how end‐users can easily access loans with reduced collateral without hidden costs and swift transactions.
Analisis Optimalisasi Ketahanan Daerah Berbasis GIS, Remote Sensing dan Cloud untuk Manajemen Banjir dan Rob di Kota Semarang

Abstract. Floods and tidal floods are a topic of discussion every year in the city of Semarang because no proper solution has been created for mitigation. From January 2022-May 2023, most of the flood and rob events are located in flat and sloping areas. The disaster had an impact on human life, such as material, physical, economic, and psychological losses. Importance of innovation to create flood resilience is urgently needed to eliminate or minimize the adverse effects of floods and rob. Flood resilience analysis uses a spatial approach supported by GIS optimization, Remote Sensing, and the Cloud platform. Then the model builder is used to speed up and simplify the analysis process. The results of the analysis show that the highest level of flood sensitivity is in flat and gently sloping areas with high population density & buildings. Meanwhile, high capacity levels are in areas close to health facilities, have road accessibility, and have lots of economic activity. In addition, 93.37% of the area's resistance to flooding is still classified as low and medium. This should be of particular concern for areas with dense populations and buildings, such as Pedurungan District, Ngaliyan District, North Semarang District, West Semarang District, and Genuk District. Thus, through regional resilience mapping, it can become a basis for decision-making for local governments with the support of inclusive disaster management.
Application of Anaerobic-Aerobic Combined Bioreactor in Phosphorus Removal

A two-stage anaerobic-aerobic sequencing reactor system was developed in order to enhance the removal of biological phosphorus in the sequencing of combined reactors. Combining both aerobic and anaerobic designs in one reactor improved the efficiency and reduced the construction and operating costs. The combination of an upflow anaerobic fixed bed (UAFB) and a floating activated sludge aerobic bioreactor was designed with respective Kaldnes packing ratios of 90 and 30% for the anaerobic and aerobic sections. The controlled parameters were pH levels within a neutral range, a temperature of 37°C, mixed liquor suspended solids (MLSS) of 1220 and 1030 mg/L for the aerobic and anaerobic sections, respectively, and an attached growth that was equal of 743 and 1190 mg/L for the aerobic and anaerobic sections, respectively. Tests were conducted for three different initial phosphorus concentrations (12.8, 32.0, and 44.8 mg/L), two different volumes for each section, and four chemical oxygen demands (CODs) (500, 1000, 1200, and 1400 mg/L). The results demonstrated that, generally, the phosphorus removal in the anaerobic section fell significantly by increasing the inlet COD, and the maximum removal occurred at COD = 500 mg/L. More than 90% of the phosphorus was removed in the aerobic section at COD = 500 mg/L. In other words, the best performance of the reactor was when the ratio of the COD : N : P = 100 : 5 : 2, composition of phosphorus in industrial wastewater.
Open-Source Finger Vein Acquisition Device for Biometric Applications

Vascular biometrics, including finger-vein recognition, are growing both in terms of academic research and industrial systems deployed in real life. In this article, we present an effective, portable, low-cost, and fully open-source finger-vein data acquisition system: the schematics, printed circuit board (PCB) Gerber and drill files, 3-D models for the 3-D printed case, device software and control, and data collection software for PC are made available. We have collected a test database consisting of 30 people/180 class finger-vein images and obtained preliminary recognition results with half-total error rate (HTER) = 2.8%, using state-of-the-art maximum curvature (MC)/Miura matching (MM) pipeline, which is a promising indicator for the viability of the proposed concept and further research.
A fossil locality predictive model using weighted suitability analysis for the Early Cretaceous Cedar Mountain Formation, Utah, USA

Hard work and chance are nearly always among the deciding factors in finding new, important, and productive paleontological localities. Fossil locality predictive models have the potential to maximize field time and increase chances to find important localities. This study uses remotely sensed data to design and test a fossil locality predictive model for the Early Cretaceous Cedar Mountain Formation. Landsat 8 OLI/TIRS data from known localities were summarized, reclassified, and used in a weighted suitability analysis to categorize fossil locality potential of the study area. Field work was conducted to test model functionality. Field observations were used to refine the weighted suitability analysis. Landsat 8 OLI/TIRS data alone offers a less accurate prescription of fossil locality potential. Additional physical and environmental factors play a role in determining the chance of finding fossils. Slope degree and aspect data from known localities were summarized and analyzed to further refine the model. Forty percent of existing fossil localities within the final model boundaries were located on areas having the highest fossil locality potential as identified by the model. The usefulness of fossil locality predictive models is dependent upon the quality of input data and meth-ods used to determine fossil locality potential. To fully determine the quality of a fossil locality predictive model, field testing is ideal.
New age data on the low-temperature regional metamorphism of Mt. Medvednica (Croatia)

K-Ar age data of illite-K-white mica-rich, <2 µm grain size fraction samples were determined on various lithotypes of the Paleozoic-Mesozoic, very low- to low-grade metamorphic complex, the Jurassic ophiolitic melange and the Cretaceous-Paleocene sedimentary sequence of Mt. Medvednica, Croatia. K-Ar ages of the high-temperature anchizonal-epizonal Paleozoic-Mesozoic complex scatter around ca. 110 Ma for slates, phyllites and marbles with phyllite intercalations, while they are significantly younger (ca. 80 Ma) for metavolcanoclastic rocks that are devoid of detrital K-white mica. Beside the Cretaceous (Alpine) K-Ar radiometric age data, no evidence of a possibly older, Variscan metamorphic event was detected. In the Jurassic ophiolitic melange and the Cretaceous-Paleocene sequence the obtained mixed isotopic age data do not provide reliable estimates for the age of the diagenetic alterations. In the Paleozoic and Mesozoic formations from the Bukk, Szendro and Uppony Mts. (NE Hungary) and in the Paleozoic .
Deep fluid circulation in fault zones

Massive fluid circulation in retrogressive ductile shear zones is a well-established but poorly understood phenomenon. In some cases, surface-derived fluids, which must initially have been at hydrostatic pressures, can be shown to have entered shear zones in which fluids would normally be expected to be at lithostatic pressure. In these circumstances, thermal convection is an unlikely driving force for fluid movement. Underthrusting of a surficial fluid reservoir beneath shear zones is a viable mechanism in some cases, but not for metasomatic shear zones in the Pyrenees where insufficient underthrusting has occurred. Seismic pumping provides an alternative mechanism to explain the paradox of fluid access into ductile shear zones; a kinematic analysis of a hypothetical fault zone shows that stress and dilatancy cycles will be out of phase above and below the frictional/quasiplastic transition. If this effect is sufficiently large, hydraulic gradients may force fluid downward across the transition immediately after earthquake rupture through highly permeable microcrack networks. Between earthquake cycles, plastic creep in mylonites will overprint microcrack networks, increase fluid pressure, and promote slow upward movement of fluid at low permeability. For smaller shear zones, such as those in the Pyrenees, seismic pumping could occur down a shallow decollement with subsequent upward fluid flow through shear zones in the hanging wall of the decollement.
Federated malware detection based on many‐objective optimization in cross‐architectural IoT

With the rising adoption of the Internet of Things (IoT) across a variety of industries, malware is increasingly targeting the large number of IoT devices that lack adequate protection. Malware hunting is challenging in the IoT due to the variety of instruction set architectures of devices, as shown by the differences in the relevant characteristics of malware on different platforms. There are also serious concerns about resource utilization and privacy leaks in the development of conventional detection models. This study suggests a novel federated malware detection framework based on many‐objective optimization (FMDMO) for the IoT to overcome the problems. First, the framework provides a cross‐platform compatible basis with the federated mechanism as the backbone, while avoiding raw data sharing to improve privacy protection. Second, an intelligent optimization‐based client selection method is designed for four objectives: learning performance, architectural selection deviation, time consumption, and training stability, which leads malware detection to retain a high degree of cross‐architectural generalization while enhancing training efficiency. Based on a large IoT malware dataset we constructed, containing 62,515 malware samples across seven typical architectures, the FMDMO is evaluated comprehensively in three scenarios. The experimental results demonstrate the FMDMO substantially enhances the model's cross‐platform detection performance while preserving effective training and flexibility.
Seasonal variability in the phytoplankton community of the North Pacific Subtropical Gyre

Time series measurements of in situ fluorescence, extracted particulate chlorophyll a, primary productivity, extracted adenosine 5′-triphosphate, and fluorescence per cell, as measured by flow cytometry, demonstrate seasonal cycles in fluorescence and chlorophyll concentrations in the North Pacific Subtropical Gyre (22° 45′N, 158° 00′W). Two opposing cycles are evident. In the upper euphotic zone (0–50 m), chlorophyll a concentrations increase in winter, with a maximum in December, and decrease each summer, with a minimum in June or July. In contrast, chlorophyll a concentrations in the lower euphotic zone (100–175 m) increase in spring, with a maximum in May, and decline in fall, with a minimum in October or November. The winter increase in chlorophyll a concentration in the upper 50 m of the water column appears to be a consequence of photoadaptation in response to decreased average mixed-layer light intensity rather than a change in phytoplankton biomass. In the lower euphotic zone, however, the seasonal cycle in pigment concentration does reflect a change in the rate of primary production and in phytoplankton biomass as a consequence of increased light intensity in summer. These observations have important implications for phytoplankton dynamics in the subtropical oceans and for remote sensing of phytoplankton biomass.
Large-Scale Identification of Lysine Crotonylation Reveals Its Potential Role in Oral Squamous Cell Carcinoma

Purpose Lysine crotonylation, an emerging posttranslational modification, has been implicated in the regulation of diverse biological processes. However, its involvement in oral squamous cell carcinoma (OSCC) remains elusive. This study aims to reveal the global crotonylome in OSCC under hypoxic conditions and explore the potential regulatory mechanism of crotonylation in OSCC. Methods Liquid-chromatography fractionation, affinity enrichment of crotonylated peptides, and high-resolution mass spectrometry were employed to detect differential crotonylation in CAL27 cells cultured under hypoxia. The obtained data were further subjected to bioinformatics analysis to uncover the involved biological processes and pathways of the dysregulated crotonylated proteins. A site-mutated plasmid was utilized to investigate the effect of crotonylation on Heat Shock Protein 90 Alpha Family Class B Member 1 (HAP90AB1) function. Results A large-scale crotonylome analysis revealed 1563 crotonylated modification sites on 605 proteins in CAL27 cells under hypoxia. Bioinformatics analysis revealed a significant decrease in histone crotonylation levels, while up-regulated crotonylated proteins were mainly concentrated in non-histone proteins. Notably, glycolysis-related proteins exhibited prominent up-regulation among the identified crotonylated proteins, with HSP90AB1 displaying the most significant changes. Subsequent experimental findings confirmed that mutating lysine 265 of HSP90AB1 into a silent arginine impaired its function in promoting glycolysis. Conclusion Our study provides insights into the crotonylation modification of proteins in OSCC under hypoxic conditions and elucidates the associated biological processes and pathways. Crotonylation of HSP90AB1 in hypoxic conditions may enhance the glycolysis regulation ability in OSCC, offering novel perspectives on the regulatory mechanism of crotonylation in hypoxic OSCC and potential therapeutic targets for OSCC treatment.
Overcoming the challenges of drug development in platinum-resistant ovarian cancer

The definition of “platinum-resistant ovarian cancer” has evolved; it now also reflects cancers for which platinum treatment is no longer an option. Standard of care for platinum-resistant ovarian cancer is single-agent, non-platinum chemotherapy with or without bevacizumab, which produces modest response rates, with the greatest benefits achieved using weekly paclitaxel. Several recent phase 3 trials of pretreated patients with prior bevacizumab exposure failed to meet their primary efficacy endpoints, highlighting the challenge in improving clinical outcomes among these patients. Combination treatment with antiangiogenics has improved outcomes, whereas combination strategies with immune checkpoint inhibitors have yielded modest results. Despite extensive translational research, there has been a lack of reliable and established biomarkers that predict treatment response in platinum-resistant ovarian cancer. Additionally, in the platinum-resistant setting, implications for the time between the penultimate dose of platinum therapy and platinum retreatment remain an area of debate. Addressing the unmet need for an effective treatment in the platinum-resistant setting requires thoughtful clinical trial design based on a growing understanding of the disease. Recent cancer drug approvals highlight the value of incorporating molecular phenotypes to better define patients who are more likely to respond to novel therapies. Clinical trials designed per the Gynecologic Cancer InterGroup recommendations—which advocate against relying solely upon the platinum-free interval—will help advance our understanding of recurrent ovarian cancer response where platinum rechallenge in the platinum-resistant setting may be considered. The inclusion of biomarkers in clinical trials will improve patient stratification and potentially demonstrate correlations with biomarker expression and duration of response. With the efficacy of antibody-drug conjugates shown for the treatment of some solid and hematologic cancers, current trials are evaluating the use of various novel conjugates in the setting of platinum-resistant ovarian cancer. Emerging novel treatments coupled with combination trials and biomarker explorations offer encouraging results for potential strategies to improve response rates and prolong progression-free survival in this population with high unmet need. This review outlines existing data from contemporary clinical trials of patients with platinum-resistant ovarian cancer and suggests historical synthetic benchmarks for non-randomized trials.
The effect of simulation variety on a deep learning-based military vehicle detector

Deep learning has emerged as a powerful tool for image analysis in various fields including the military domain. It has the potential to automate and enhance tasks such as object detection, classification, and tracking. Training images for development of such models are typically scarce, due to the restricted nature of this type of data. Consequently, researchers have focused on using synthetic data for model development, since simulated images are fast to generate and can, in theory, make up a large and diverse data set. When using simulated training data it is important to consider the variety needed to bridge the gap between simulated and real data. So far it is not fully understood what variations are important and how much variation is needed. In this study, we investigate the effect of simulation variety. We do so for the development of a deep learning-based military vehicle detector that is evaluated on real-world images of military vehicles. To construct the synthetic training data, 3D models of the vehicles are placed in front of diverse background scenes. We experiment with the number of images, background scene variations, 3D model variations, model textures, camera-object distance, and various object rotations. The insight that we gain can be used to prioritize future efforts towards creating synthetic data for deep learning-based object detection models.
Exploration of Burnout, Emotional Thriving, and Emotional Recovery in an Academic Medical Center: a Mixed Methods Quality Improvement Project

Thriving (ET), and Emotional Recovery (ER). Qualitative responses were reviewed for themes using mixed induc-tive-deductive analysis. The initial coding was done by small teams with consensus obtained through large group discussions. This study was IRB-approved as non-human subjects research. Results: Of the 671 respondents, 54% met criteria for burnout (Burnout+), 65% for ER (ER+), and 61% for ET (ET+). ER+ and ET+ were present in nearly half of Burnout+ respondents (53% and 43% respectively). Several themes emerged in the qualitative analysis: workload and expectations; tangible resources; work culture; and salary/benefits, with leadership influencing each of the domains. Conclusion: Burnout, ET, and ER can co-exist within the same individual. Employee wellbeing is not adequately reflected by the binary of whether or not an individual is experiencing burnout. All employees at academic medical centers, including staff, researchers, and clinicians, are vulnerable to the same workplace factors driving burnout. Our findings have been used to target areas of intervention during the COVID-19 pandemic at our institution. We propose that other academic medical centers may have similar workplace stressors that they could assess and target for improvement. ABSTRACT
Imaging architecture of the Jakarta Basin, Indonesia with transdimensional inversion of seismic noise

E. Saygin,1 P.R. Cummins,1 A. Cipta,1 R. Hawkins,1 R. Pandhu,2 J. Murjaya,2 Masturyono,2 M. Irsyam,3 S. Widiyantoro4 and B.L. N. Kennett1 1Research School of Earth Sciences, The Australian National University, Canberra ACT 2601, Australia. E-mail: erdinc.saygin@anu.edu. au 2Badan Meteorologi, Klimatologi, dan Geofisika, Jakarta, Indonesia 3Department of Civil Engineering, Bandung Institute of Technology, Bandung, Indonesia 4Global Geophysics Research Group, Faculty of Mining and Petroleum Engineering, Bandung Institute of Technology, Bandung, 40132, Indonesia
Average annual flux of organic carbon to the bottom in the arctic seas of Russia: a comparison of calculated and research instrument data from sediment traps

The studies have shown that the distribution of suspended particulate organic carbon in the seas of the Russian Arctic is characterized by сircumcontinental and vertical zonalities. These zonalities are manifested in decreasing concentrations and therefore fluxes (mgC/m2 /day) of suspended particulate organic carbon in the transition from the near-continental to pelagic areas of the Arctic Ocean, as well as from the surface photic layer to the near-bottom layer by two orders of magnitude, which is confirmed by instrumental data of sedimentation traps and correlates with the zoning of bio-production processes.
Estimation of glacial melt contributions to the Bow River, Alberta, Canada, using a radiation-temperature melt model

Abstract Alberta’s Bow River has its headwaters in the glaciated eastern slopes of the Canadian Rockies and is a major source of water in southern Alberta. Glacial retreat, declining snowpacks and increased water demand are all expected in the coming century, yet there are relatively few studies focusing on quantifying glacial meltwater in the Bow River. We develop a new radiation-temperature melt model for modelling distributed glacier mass balance and runoff in the Bow River basin. The model reflects physical processes through the incorporation of near-surface air temperature and absorbed radiation, while avoiding problems of collinearity through the use of a radiation-decorrelated temperature index. The model is calibrated at Haig Glacier in the southern portion of the basin and validated at Haig and Peyto Glaciers. Application of the model to the entire Bow River basin for 2000-09 shows glacier ice melt is equivalent to 3% of annual discharge in Calgary on average. Modelled ice melt in August is equal to 8-20% of the August Bow River discharge in Calgary. This emphasizes the importance of glacier runoff to late-summer streamflow in the region, particularly in warm, dry years.
Multivariate statistical analyses applied to seismic facies recognition

One of the most important goals of seismic stratigraphy is to recognize and analyze seismic facies with regard to the geologic environment. The first problem is to determine which seismic parameters are discriminant for characterizing the facies, then to take into account all those parameters simultaneously. The second problem is to be sure that there is a link between the seismic parameters and the geologic facies we are investigating. This paper presents a methodology for automatic facies recognition based upon two steps. The first, or learning step, begins with the definition of learning seismic traces for each facies we wish to recognize. The choice of learning traces is based upon either well data or a seismic stratigraphic interpretation. A large number of seismic parameters are then computed from the learning traces; multidimensional analyses are carried out in order to validate the choice of learning traces and to select, among all the available parameters, those that discriminate best. At this st.