Miracle-dz/UniFAR-FTUs huggingface dataset:
Batch Jobs Load Balancing Scheduling in Cloud Computing Using Distributional Reinforcement Learning

In cloud computing, how to reasonably allocate computing resources for batch jobs to ensure the load balance of dynamic clusters and meet user requests is an important and challenging task. Most existing studies are based on deep Q network, which utilizes neural networks to estimate the expected value of cumulative return in the scheduling process. The value-based DQN algorithms ignore the complete information contained in the value distribution and lack strong adaptability to time-varying batch jobs and dynamic cluster resources. Therefore, to capture the inherent stochasticity of the scheduling process caused by environmental stochasticity, we utilize Distributional Reinforcement Learning to model the value distribution of the cumulative return. Specifically, we formalize the load balancing scheduling as a multi-objective optimization problem and construct a Distributional Reinforcement Learning model. Then we introduce quantile regression to learn the value distribution of the cumulative return during scheduling and propose a dynamic load balancing scheduling algorithm based on Distributional Reinforcement Learning. In addition, we develop a cluster environment for real-time processing of batch jobs to simulate the arrival of batch jobs and train the Distributional Reinforcement Learning-based scheduling agent. We conduct empirical experiments and detailed analysis by using the real Alibaba Cluster cluster traces v2018 and v2020. The results show that compared to the baseline algorithms, the proposed algorithm performs better in terms of cluster load balancing, success rate of instance creation and average completion time of the tasks. The experimental results on different trace datasets also indicate that the propsoed algorithm exhibits excellent scalability.
Studies on heavy metals in the sediments of a storm water drain and the Avon-Heathcote Estuary

The concentrations of chromium, copper, iron, manganese, nickel, lead, and zinc have been estimated, primarily by flame AAS, in the sediments of the City Outfall Drain and Avon-Heathcote Estuary, Christchurch, New Zealand. These, and other metals have also been determined by XRF, and the two sets of results are compared. Sediment samples were obtained by the use of a simple coring device where possible, or by sampling the top two centimetres of sediment where this was not possible. The variation in metal concentration with location, depth, and rainfall are discussed, with reference to metal-metal correlations, organic matter content, particle size distribution, and a theory on the formation of the sediment units in the Avon-Heathcote Estuary. These results are then evaluated with regard to enrichment factors calculated from a set of local background levels defined in this study. The strongest metal-metal correlation is lead with zinc, although lead, zinc, copper, and to a lesser extent chromium, are commonly found in association with one another. Metal concentration generally decreases with depth , except in the case of the estuary core RH2, which exhibits a profile of concentration vs. depth consistent with MacPherson's proposals for the formation of the Estuaries sediment units. Lead and zinc are found to be the metals most highly enriched above background levels in the clay, then sand, then silt fractions. No obvious reason for this was found as for all other metals, including those that correlated well with lead and zinc, the order was clay > silt > sand. The distribution of metal concentrations in the silt-clay size fraction wre investigated using a novel technique not requiring the separation of individual size fractions. The percentage of quartz present in each fraction was found to be the primary factor causing variation in metal concentration down to approximately 4 jlm. Below this size metal speciation becomes dominant. The role of organic matter was not determined but is felt to be of possible significance. The thesis concludes with reccomendations for areas of further research, including sediment dating, application of sequential extraction techniques, and a poosible method of treatment for waste waters. 1
Pick-up point recommendation strategy based on user incentive mechanism

In recent years, with the development of spatial crowdsourcing technology, online car-hailing, as a typical spatiotemporal crowdsourcing task application scenario, has attracted widespread attention. Existing researches on spatial crowdsourcing are mainly based on the coordinate positions of user and worker roles to achieve task allocation with the goal of maximum matching number or lowest cost. However, they ignores the problem of the selection of the pick-up point which needs to be solved in the actual scene of online car booking. This problem needs to take into account the four-dimensional coordinate positions of users, workers, pick-up point and destination. Based on this, this study designs a pick-up point recommendation strategy based on user incentive mechanism. Firstly, a new four-dimensional crowdsourcing model is established, which is closer to the practical application of crowdsourcing problem. Secondly, taking cost optimization as the index, a user incentive mechanism is designed to encourage users to walk to the appropriate pick-up point within a certain distance. Thirdly, a concept of forward rate is proposed to reduce the computation time. Some key factors, such as the maximum walking distance limit of users and task cost, are considered as the recommendation index for measuring the pick-up point. Then, an effective pick-up point recommendation strategy is designed based on this index. Experiments show that the strategy proposed in this article can achieve reasonable recommendation for pick-up points and improve the efficiency of drivers and reduce the total trip cost of orders to the greatest extent.
History and mechanisms for the expansion of the Badain Jaran Desert, northern China, since 20 ka: Geological and luminescence chronological evidence

Desert landscapes are widely distributed in the arid northern China. The Badain Jaran Desert (BJD) is located north-west of the Tengger Desert (TD) and Ulan Buh Desert (UBD), with the Yabrai and Bayan Ulan Mountains separating the BJD from the TD and UBD. Many sand belts with an orientation of NW-SE, which is similar to that of the prevailing sand-transporting winds, connect the BJD with the TD and UBD, forming the sand transportation routes from the BJD to the TD and UBD. Thus, these sand belts are sensitive to the expansion or shrinkage of the BJD, whose chronologies could be critical for understanding the evolution of the BJD. However, age data are still very limited. In this study, field investigation and optically stimulated luminescence (OSL) dating have been conducted in 14 profiles, and 24 OSL ages are obtained. The OSL ages range from approximately 15 ka to modern. Our results and previous chronological data revealed that the BJD’s expansion initiated at 20 ka, and the present-day landscape formed within the latest 2 ka with periodic expansions at 19–15, 11–9, 7.3 and 6–5 ka. We propose that these expansions of the BJD before ~5 ka and during the 0.9–0.8 ka interval were responses to regionally low effective moisture conditions in north-western China, while the expansions at ~2 ka and within the latest 200 years were likely triggered by intensified human activity.
Securing Domain Name Systems with Blockchain

This paper discusses the limitations of the hierarchical and centralized Domain Name System (DNS) and the challenges it poses to net-neutrality and privacy. Despite the use of digital signatures based on public key cryptography, DNS attacks continue to affect data integrity, confidentiality, and service availability. The paper argues for the exploration of emerging technologies, such as blockchain, to promote data encryption and authentication through a decentralized and non-hierarchical approach. The project aims to research and develop novel DNS name-resolution techniques using blockchain technology and to explore their potential benefits and limitations.
Circulating Toll-Like Receptor 4, Nucleotide-Binding Oligomerization Domain-Like Receptor Protein 3, and Cytokines in Patients with Bipolar Depression: A Case–Control Study

Objective: The etiology of bipolar disorder (BD), a complex psychiatric condition, remains uncertain. Previous research has suggested a potential involvement of the host immune system in the development of BD. This study aims to investigate plasma levels of cytokines, circulating toll-like receptor 4 (TLR4), and nucleotide-binding oligomerization domain-like receptor protein 3 (NLRP3) in patients with bipolar depression. Methods: This study recruited patients with a depressive episode of BD and healthy controls (HCs). Inflammatory cytokines were quantified using enzyme linked immunosorbent assay (ELISA) analysis. Results: A total of 26 BD patients with a depressive episode and 14 HCs were enrolled in the study. The findings revealed that individuals with BD with a depressive episode exhibited elevated serum levels of NLRP3 and interleukin-18 compared to HCs. Correlation analyses indicated a favorable association between the frequency of episodes, duration of illness, and TLR4 levels. Conclusion: The results suggest a connection between cytokines associated with the activation of NLRP3 and their potential impact on the pathogenesis of BD.
PREDICTION OF CHLOROPHYLL A CONCENTRATIONS IN FLORIDA LAKES: THE IMPORTANCE OF PHOSPHORUS AND NITROGEN

ABSTRACT: Models for the prediction of chlorophyll a concentrations were developed and tested using data on 223 Florida lakes. A statistical analysis showed that the best model was log (Chl a) =−2.49 + 0.269 log (TP) + 1.06 log (TN) or log (Chl a) =−2.49 + 1.06 log (TN/TP) + 1.33 log (TP) where Chl a is the chlorophyll a concentration (mg m-3), TP is the total phosphorus concentration (mg m-3) and TN is the total nitrogen concentration (mg m-3). The model yields unbiased estimates of chlorophyll a concentrations over a wide range of lake types and has a 95 percent confidence interval of 29–319 percent of the calculated chlorophyll a concentrations. Other models, especially the published Dillon-Rigler and Jones-Bachmann phosphorus-chlorophyll models, are less precise when applied to Florida lakes. The data support the hypothesis that nitrogen is an important limiting nutrient in hypereutrophic lakes.
Physical and chemical aspects of water repellent soils affected by slashburning at Vancouver, British Columbia

The persistence and severity of water repellency i n s o i l s as affected by slashburning was examined i n the municipal watersheds of Vancouver by the water drop penetration time (WDPT) and contact angle method, respectively. Also, hydrophobic materials were extracted from s o i l , separated by column adsorption chromatography, and analysed f o r functional groups by infrared absorption. Slashburning increased s o i l water repellency, as indicated by the WDPT method. Precise responses of s o i l repellency to slashburning were not consistent, as many s i t e s p e c i f i c factors are involved, but increased repellency was evident i n s o i l up to s i x years following burning. The severity of increased s o i l repellency caused by slashburning could not be assessed because contact angle determinations using the c a p i l l a r y r i s e equation and Darcy's Law were not r e l i a b l e . The WDPT method was more consistent, r e l i a b l e and simpler than the contact angle method, hence the WDPT method was considered best to indicate the presence of s o i l water repellency. A combination of polar and non-polar organic solvents, methanol and benzene respectively, extracted hydrophobic compounds from s o i l . The extractant separated into three fr a c t i o n s , using benzene and increasing proportions of acetone as eluting agents. V i r t u a l l y a l l of the extract (90%) was recovered i n Fraction I , indicating that the extraction was predominantly non-polar. Fraction I I and I I I accounted for 4 and 6 per cent, respectively, of the applied extractables. Material i n a l l Fractions
Characterisation of different pneumatic sowing machines.

Summary As a consequence of the acute honey bee poisoning events observed in several European countries during maize drilling, the dust drift topic has received increasingly more attention. We aimed to quantify the dust drift risk, develop reduction measures and innovate sowing techniques using an integrated experimental and modelling approach. To validate and finetune the dust drift CFD-models under development, the designs of three selected sowing machines and associated tractors were digitised. For the two pneumatic drills, the airflows around the machines and at the in- and outlet air ducts were measured. Air velocities at the outlets ranged from only 0.11 m s -1 for the pneumatic bulk drill up to 26.1 m s -1 for the pneumatic precision drill without deflector. The use of a deflector reduced the exit velocities to 13.4 m s -1 as well as the air velocities around the machine. Reference tracer seeds were developed with different qualities regarding dust production in order to measure the dissipation of dust around static operated drills and in field conditions.
A Review of Silver Wire Bonding Techniques

The replacement of gold bonding wire with silver bonding wire can significantly reduce the cost of wire bonding. This paper provides a comprehensive overview of silver wire bonding technology. Firstly, it introduces various types of silver-based bonding wire currently being studied by researchers, including pure silver wire, alloy silver wire, and coated silver wire, and describes their respective characteristics and development statuses. Secondly, the development of silver-based bonding wire in manufacturing and bonding processes is analyzed, including common silver wire manufacturing processes and their impact on silver wire performance, as well as the impact of bonding parameters on silver wire bonding quality and reliability. Subsequently, the reliability of silver wire bonding is discussed, with a focus on analyzing the effects of corrosion, electromigration, and intermetallic compounds on bonding reliability, including the causes and forms of chlorination and sulfurization, the mechanism and path of electromigration, the formation and evolution of intermetallic compounds, and evaluating their impact on bonding strength and reliability. Finally, the development status of silver wire bonding technology is summarized and future research directions for silver wire are proposed.
Effects of forage quality and particle size on feed intake and ruminoreticulum content of goats

Abstract The aim was to evaluate the effect of particle size and hay quality on feed intake, granulometric profile, and composition of the ruminoreticulum content in goats. We used 54 Alpine bucks in a completely randomized design with a factorial arrangement of 3 × 3. Treatments were a combination of Bermuda grass hay (Cynodon dactylon) with three quality levels: high (35 days), medium (50 days), and low (65 d) harvested at regrowth times. Were evaluated three particle sizes: small (16% ≥4.76 mm), medium (48% ≥4.76 mm), and large (75% ≥4.76 mm), which accounted for 66%, 75%, and 94% of physically effective fiber, respectively. Samples of offered diet, intake, and ruminoreticulum content were used to generate the granulometric profile. The offered diet, intake, and ruminoreticulum content presented different granulometric profiles regarding hay quality and particle size. Dry matter intake (DMI) and neutral detergent fiber intake (NDFI) increased (P < 0.05) when low-quality hay and large particles were offered. However, when particle size in low-quality hay was reduced, DMI and NDF decreased (P < 0.05). When analyzing the ruminoreticulum content (DM, NDF, peNDF, and indigestible DM), we did not observe any effect (P > 0.05) of hay quality or particle size on the variables. Thus, reducing hay quality and increasing particle size increase dry matter and fiber intake, presenting an interaction between forage quality and particle size. Forage quality and particle size promote intense selective behavior and chewing, which leads to a homogeneous content of particle profile in ruminoreticulum and a uniform average retention time.
Law and Conservation Conflicts

Conflict is a dominant notion within the legal discipline. In this chapter I review some roles that law and legal research play with respect to conservation conflicts, i.e., conflicts over the conservation of biodiversity. I first introduce briefly law and legal research and then discuss the respective roles of law and legal research with regard to conservation conflicts. Although I focus my discussion on Europe, many if not most of the general principles are applicable elsewhere.
PROTOTYPE APLIKASI RECORDING TERNAK TERINTEGRASI DENGAN SISTEM IDENTIFIKASI DAN SERTIFIKASI TERNAK DI KABUPATEN KEBUMEN

The Government of the Department of Agriculture and Food / Dinas Pertanian dan Pangan (Distapang) of Kebumen Regency has utilized information technology to register livestock and provide identity cards to livestock with the Livestock Card Database System. With this system, Distapang Kebumen can quickly find out the livestock population and household of farmers. Kebumen Regency is one of the best cattle breeding areas at the national level, namely for PO cattle. Therefore, the Agriculture and Food Service Office of Kebumen Regency provides a Breeding Certificate /Surat Keterangan Layak Bibit (SKLB) for livestock. SKLB will increase trust and satisfaction of livestock breed users, and will certainly increase the price of livestock. To make SKLB, Distapang uses the help of the SKLB Recording and Printing System . Unfortunately, between the Livestock Card Database System and the SKLB Recording and Printing System is not integrated, so there is one job carried out by two officers, namely livestock data collection. Data on making livestock cards comes from forms filled out by data officers in the village area from livestock, while data recording comes from recording officers from members of the herd group / designated officers. The output of this research is in the form of an Android-based recording application prototype design, which is integrated with the Livestock Card Database System as an Identification System, and the SKLB Recording and Printing System as a System for Animal Certification
Classification of Biotic And Abiotic Stresses on Grape Berries using Transfer Learning

Grape farming is one of the most lucrative agricultural enterprises in India. There are several biotic and abiotic stress conditions that may adversely affect the yield if not tackled at the right time. It is crucial that the farmer can correctly identify and monitor the type of stress so that steps can be taken to prevent undesirable outcomes. We have gathered a dataset of these stress conditions on grape berries and categorized them into eight classes, necrosis, shriveling, and honeydew by mealybug, mealybug incidence, spray injury, thrips scarring, pink berry, and powdery mildew. Transfer learning was used to test the performance of six major deep learning image classification architectures (namely MobileNet-v2, Inception-v3, Inception-ResNet-v2, ResNet-v2, NASNet, and PNASNet) with variations in training conditions and hyper parameters. The results were compared to determine the most feasible and accurate deep learning architecture and its hyper parameters for the given problem statement. The experiment shows that Inception-ResNet-v2 obtained maximum classification accuracy of 88.75% when learning rate of 0.035 and minibatch size of 10 were applied using 8000 training steps. This result will act as a pre-requisite for the development of an application for mapping vineyard stress conditions on berries and give automated advisory.
STELLAR: Siamese Multiheaded Attention Neural Networks for Overcoming Temporal Variations and Device Heterogeneity With Indoor Localization

Smartphone-based indoor localization has emerged as a cost-effective and accurate solution to localize mobile and IoT devices indoors. However, the challenges of device heterogeneity and temporal variations have hindered its widespread adoption and accuracy. Toward jointly addressing these challenges comprehensively, we propose STELLAR, a novel framework implementing a contrastive learning approach that leverages a Siamese multiheaded attention neural network. STELLAR is the first solution that simultaneously tackles device heterogeneity and temporal variations in indoor localization, without the need for retraining the model (recalibration-free). Our evaluations across diverse indoor environments show 8%–75% improvements in accuracy compared to state-of-the-art techniques, to effectively address the device heterogeneity challenge. Moreover, STELLAR outperforms existing methods by 18%–165% over two years of temporal variations, showcasing its robustness and adaptability.
Anomaly detection method based on temporal spatial information enhancement

The proposed Anomaly Detection Method Based on Temporal Spatial Information Enhancement addresses the limitations of unsupervised techniques in detecting abnormal events beyond boundaries and limited samples. It incorporates a Serial Depth Separable Residual Block (Serial Block) as the backbone for predicting future frame. Additionally, a DenseReserve Subsample Module (DRSM) facilitates feature scale scaling, and a U-shaped Pyramid Attention Module (UPAM) guides feature fusion and enhances spatial details. During the prediction stage, reconstructed optical flow information aids in distinguishing abnormal and normal event features, with the abnormal score determined by a weighted fusion of optical flow reconstruction error and prediction error for future frames. Experimental results demonstrate the method’s outstanding performance, achieving area under the curve metrics of 99.7%, 92.1%, and 78.3% on UCSD Ped2, CUHK Avenue, and ShahanghaiTech datasets, respectively. This method offers significant advancements in detecting anomalous events in complex and multi-scene surveillance videos, with improved operational speed and reduced parameters.
Portable Infrared-Based Glucometer Reinforced with Fuzzy Logic

Diabetes mellitus (DM) is a chronic metabolic condition characterized by high blood glucose levels owing to decreased insulin production or sensitivity. Current diagnostic approaches for gestational diabetes entail intrusive blood tests, which are painful and impractical for regular monitoring. Additionally, typical blood glucose monitoring systems are restricted in their measurement frequency and need finger pricks for blood samples. This research study focuses on the development of a non-invasive, real-time glucose monitoring method based on the detection of glucose in human tears and finger blood using mid-infrared (IR) spectroscopy. The proposed solution combines a fuzzy logic-based calibration mechanism with an IR sensor and Arduino controller. This calibration technique increases the accuracy of non-invasive glucose testing based on MID absorbance in fingertips and human tears. The data demonstrate that our device has high accuracy and reliability, with an error rate of less than 3%, according to the EGA. Out of 360 measurements, 97.5% fell into zone A, 2.2% into zone B, and 0.3% into zone C of the Clarke Error Grid. This suggests that our device can give clinically precise and acceptable estimates of blood glucose levels without inflicting any harm or discomfort on the user.