Miracle-dz/UniFAR-FTUs huggingface dataset:
Stage-Wise and Hierarchical Training of Linked Deep Neural Networks for Large-Scale Multi-Building and Multi-Floor Indoor Localization Based on Wi-Fi Fingerprinting

This paper present a new solution to the problem of large-scale multi-building and multi-floor indoor localization based on linked deep neural networks (DNNs)—each of which is dedicated to a sub-estimation problem (i.e., building/floor and floor-level location)—trained under the stage-wise and hierarchical training framework. The proposed hierarchical stage-wise training framework extends the original stage-wise training framework to the case of multiple networks by training the DNN for the estimation of floor-level location based on the prior knowledge gained from the training of the DNN for the estimation of building and floor identifiers. The experimental results, with the publicly-available UJIIndoorLoc multi-building and multi-floor Wi-Fi fingerprint database, demonstrate that the linked DNNs trained under the newly-proposed stage-wise and hierarchical training framework can achieve a three-dimensional localization error of 8.19 m, which, to the best of the authors’ knowledge, is the most accurate results obtained for the whole of the UJIIndoorLoc database based on DNN-based models.
A Deep Reinforcement Learning Approach to Configuration Sampling Problem

Configurable software systems have become increasingly popular as they enable customized software variants. The main challenge in dealing with configuration problems is that the number of possible configurations grows exponentially as the number of features increases. Therefore, algorithms for testing customized software have to deal with the challenge of tractably finding potentially faulty configurations given exponentially large configurations. To overcome this problem, prior works focused on sampling strategies to significantly reduce the number of generated configurations, guaranteeing a high t-wise coverage. In this work, we address the configuration sampling problem by proposing a deep reinforcement learning (DRL) based sampler that efficiently finds the trade-off between exploration and exploitation, allowing for the efficient identification of a minimal subset of configurations that covers all t-wise feature interactions while minimizing redundancy. We also present the CS-Gym, an environment for the configuration sampling. We benchmark our results against heuristic-based sampling methods on eight different feature models of software product lines and show that our method outperforms all sampling methods in terms of sample size. Our findings indicate that the achieved improvement has major implications for cost reduction, as the reduction in sample size results in fewer configurations that need to be tested.
Techniques for evaluating nutrient status in farm animals

It is the aim of the study to present a literature review on methods and techniques for determining nutrient status of different farm animal species (ruminants, pigs and poultry). The study focusses especially on the options to determine nutrient status in farm animals from a research perspective rather than on the possibilities for their practical application and related technical issues.
A Mixed-State Streaming Edge Partitioning based on Combinatorial Design

Graph partitioning is crucial in distributed graph computing systems, while impacting load balancing and communication between machines. To cope with the soaring scale of graphs, the streaming model has shown promising performance in graph partitioning. Although streaming model can deal with the bottleneck of memory usage for large-scale graphs, existing streaming partitioning algorithms not only lack sufficient quality but also cannot provide theoretical boundaries for graph partitioning. In addition, most streaming partitioning algorithms are sensitive to the order of edge streaming. In this paper, we model the edge partitioning problem as a combinatorial design problem, and provide a tight theoretical boundary. Based on the balanced edge partitioning design, we proposed a mixed-state streaming edge partitioning algorithm, which can generate high-quality graph partitions by mapping matrix and use the historical partition information to further optimize the partition quality and load balance. The experiments show that our proposed algorithm reduces partitioning time by more than half compared to the mainstream HDRF algorithm while maintaining load balance, and improves partitioning quality by about three times.
Stripped Bed Roughness Effect on Flow Measurement in Trapezoidal Channels with Free Overfalls

An experiment was conducted to study the effects of three drought stress levels, mild (70%) and severe (30%) with a control of 100% soil field capacity on six bread wheat cultivars(Sabeer-beg, Aras, Panda, Adena, Rabeaa and Azady) grown in Kurdistan region and their interaction on morphological and growth parameters including; plant height, number of tillers, total dry weight and its partitioning, flag leaf area, relative growth rate at three growth stages tillering, stem elongation and heading. Assuming the decrease percent in yield and its components including; spike length (cm), number of spikes plant-1, number of grains spike-1, 1000-grain weight (g) and grain yield (g plant-1). Analyzing the variance of data shows a great reduction at sever 30% and less reduction at mild 70% field capacity as compared with control 100% in all studied characteristics except spike length, which did not affected due to applying drought stress levels. The cultivars also show significant variation among them in their response to drought through the morphological and growth parameters except in number of tillers at tillering stage, which finally reflected on the final yield, Azady recorded the higher yield and then Rabeaa, Adene, Aras, Panda and finally Sabeer-beg respectively under sever water stress. The combination between bread wheat cultivars and drought stress level also led to a significant variation in all studied characteristics except in plant height at stem elongation, tiller number at the three stages, flag leaf area, stem dry weight at stem elongation, number of spikes per plant and weight of 1000 grains. Finally, an overall sequence water stress tolerance was Adene> Rabeaa> Aras> Sabeer-beg> Azady>Panda.
Accelerating Key-Value Store with Layer-2 Transparent Proxy Cache Using DPDK

In-memory Key-Value Stores (KVSs) such as Redis and Memcached can be used as caches for relational databases to reduce the database load while decreasing the response time. However, in-memory KVSs perform poorly due to bottlenecks in the Linux network stack. Although the Linux network stack can be bypassed using DPDK, this approach requires redesigning the network stack and the in-memory KVS, which incurs high costs. By contrast, using eBPF to process requests in the kernel space before network stack execution incurs a large overhead inherent in the Linux kernel. In this paper, we propose a method of caching using a DPDK-based layer-2 transparent proxy without modifying the cache server. This proxy uses DPDK to reduce the overhead of the Linux kernel and process requests in layer 2. This eliminates the need for a network stack and minimizes implementation costs while eliminating traditional bottlenecks. In addition, because the proxy caches transparently, it does not require any modification to the client or server applications and can be widely deployed in existing systems. We apply the proposed proxy to Redis and evaluate its performance. The throughput of the proposed method is up to 13.6x better than that without the proxy, and up to 1.5x better than that of an in-kernel cache using eBPF. The throughput degradation due to the proxy overhead is confirmed to be approximately 3%.
Modeling the Effects of a Protective Film for Spontaneously-Polarized Electrets on the Power Harvesting Performance of a Wind Energy Harvester

One bottleneck for developing electret-based energy harvesters is the electret charging process. This increases production time, and raises fabrication cost and safety concerns. Spontaneously-polarized electrets, as alternatives, do not need any charging process. However, the main drawback is their susceptibility to UV-Vis light. This is a big concern for harvesters in open environments that cannot be kept in perfect darkness. Focusing on a leaf-like wind energy harvester, this paper analytically examines the effects of covering SP electret with a protective layer on performance characteristics of the harvester. This layer is not only an optical barrier, but can also act as a mechanical stopper to determine the minimum gap distance and prevent direct contact between the electret and the counter-electrode in a gap-closing motion.
Quantitative Upper Limb Impairment Assessment for Stroke Rehabilitation: A Review

With the number of people surviving a stroke soaring, automated upper limb impairment assessment has been extensively investigated in the past decades since it lays the foundation for personalized precision rehabilitation. The recent advancement of sensor systems, such as high-precision and real-time data transmission, have made it possible to quantify the kinematic and physiological parameters of stroke patients. In this article, we review the development of sensor-based upper limb quantitative impairment assessment, concentrating on the capable of comprehensively and accurately detecting motion parameters and measuring physiological indicators to achieve the objective and rapid quantification of the stroke severity. The article discusses various features used by different sensors, detectable actions, their utilization techniques, and effects of sensor placement on system accuracy and stability. In addition, both the advantages and disadvantages of the model-based and model-free methods are also reviewed. Furthermore, challenges encompassing comprehensive assessment of medical scales, neurological deficits assessment, random movement detection, effects of the sensor placement, and effects of the number of sensors are also discussed.
Efficient and Effective Entity Alignment for Evolving Temporal Knowledge Graphs

Temporal Knowledge Graphs (TKGs), which record the evolution of relationships among entities over time, have been increasingly used in a myriad of applications. Despite their growing importance, the challenge of aligning entities in these evolving structures has yet to be satisfactorily addressed. Most existing techniques struggle to keep pace with the continual stream of new entities and relations, which is a defining characteristic of TKGs. In response to this challenge, we propose a novel teacher-student approach for incremental entity alignment in evolving TKGs. Our solution leverages a Graph Attention Network (GAT) as the teacher model and a sampling Graph Convolutional Network (GCN) as a lightweight, adaptable student model. This approach efficiently navigates the evolving complexities inherent in TKGs, leading to remarkable improvements in the efficiency and effectiveness of entity alignment. The experimental results substantiate the superior performance of our approach in achieving effective entity alignment promptly, outstripping existing state-of-the-art models. As such, our study contributes a crucial step towards efficiently handling evolving entity alignment tasks in TKGs.
PERCEIVED TOURISM IMPACTS IN PINDUL CAVE, YOGYAKARTA, INDONESIA

Gunungkidul Regency, Yogyakarta Province , Indonesia is experiencing significant increase in its visitors due to the massive development of its tourism objects, especially natural ones such as caves and beaches. Pindul Cave is one of the most prominent tourism object in Gunungkidul which is also visited by a high number of tourists although it was just opened in the late 2011s. Therefore, by conducting interviews with locals and the management as well as observation to collect data, this study investigated the perceived impacts of tourism in Pindul Cave. The analysis results show that the residents have positive attitudes towards socio-cultural, economic, and environmental impact although they also perceive negative impacts such as the way of employee’s recruitment by the management and the degradation of environment in the cave. However, these negative impacts are perceived minor by the residents so that, overall, they perceive that the development of tourism in Pindul Cave is positive.
A Nonlinear Energy Harvester Using Bi-Stability for Self-Powered Health Monitoring of High-Speed Trains

A bistable nonlinear energy harvester for rail transport is proposed, designed to effectively harvest vibration energy during train operation and facilitate health monitoring. Under the influence of two limit springs and a stationary magnet, the movable magnet exhibits bistable dynamics. The outer coil of the sleeve features a tapered shape, resulting in a notable increase in the system's output power. A prototype was fabricated, and experimental testing was conducted under conditions similar to freight trains. The harvester demonstrates a wide operating bandwidth from 9 to 44 Hz. A measured peak output power of 60 mW was obtained with a resistive load of 2600 ohms, under the acceleration of 3.2g and 25 Hz. This work provides a promising solution to power wireless monitoring of rail trains in a self-sustained and battery-free manner.
Stock Management Implication of Caspian kutum (Rutilus frisii kutum Kamensky, 1901) in Iranian Waters of the Caspian Sea

Recently the previous Caspian ecosystem is in a catastrophic condition which caused changes in the absolute and relative abundance of the commercially important Caspian kutum (Rutilus frisii kutum) in Iranian waters. The paper presents estimates of yield-per-recruit and spawning biomass-per-recruit under various harvest strategies of Fmax, F0.1, F30%. It proposes a method for estimating acceptable biological catch (ABC) that accounts for large differences in the quality and quantity of information and available data. The current average Y/R (with F=0.61/y, and tc=3.2 y) was 218.3 g per recruit, which indicates that the fishery is operating below the maximum Y/R at 236.9 g when tc = 3.5 y. The Y/R was the highest at Fmax and F0.1, when tc = 4 y (244.8 g and 214.2 g), respectively. The F30% value was 0.85/y at tc of 4 y with the SB/R of 338.3 g. The Fcurrent (0.61/y at current tc is 3.2 y) is higher than the corresponding reference points, F0.1 (0.47/y) and F30% (0.46/y). The ABC of kutum was estimated at 7,850 mt in 2009-2010.
Graph Collaborative Optimization for Sequential Recommendation

Sequential recommendation is to predict the next item by capturing the item transformation in the user historical sequences. Although existing methods have achieved convincing results in recommendation tasks, they ignore the relation between discontinuous items within sequences or those across different sequences. In this paper, we focus on how to fully capture the potential item relation within and across sequences. Specifically, we propose a novel graph collaborative optimization-based method named GOSR, which not only comprehensively explores the real item relation from many perspectives by constructing and optimizing the item relation graph but also collaborates user-item interaction graph to model the interactive behaviors of users and items. Our model converts the loose item sequences to a tight item relation graph and continuously optimize the graph by comprehensively considering the item relation within and across sequences. That is beneficial to dynamically capture the actual item relation. Furthermore, we employ the attention module to extract the long short-term preferences and characters of users and items by collaborating the user-item interaction graph. Finally, we use the refined representations of users and items to predict the next item that a user is most likely to interact with. To the best of our knowledge, this is the first attempt to explore the item relation by constructing and optimizing the item relation graph for better recommendation. Extensive experiments on three popular benchmark datasets demonstrate that GOSR outperforms several state-of-the-art methods.
Physical Description of the Barnegat Bay Little Egg Harbor Estuarine System

KENNISH, M.J., 2001. Physical Description of the Barnegat Bay-Little Egg Harbor Estuarine System. Journal of Coastal Research, Sl(32), 13-27. West Palm Beach (Florida), ISSN 0749-0208. Barnegat Bay, Manahawkin Bay, and Little Egg Harbor comprise a shallow, lagoon-type estuary along the central New Jersey coastline. With a surface area of 279 km2 and an average depth of less than 2 m, the estuary forms an irregular tidal basin that extends north-south for nearly 70 km. The elongated embayment is separated from the Atlantic Ocean by a narrow barrier island complex (i.e., Island Beach and Long Beach Island) which is breached at Barnegat Inlet ? 35 km south of Point Pleasant and at Little Egg Inlet near the southern margin. Saline waters enter through these two inlets as well as through the Point Pleasant Canal at the northern perimeter. Winds, tides, salinity gradients, inlet size and configuration, and the geomorphology of the estuary strongly control water circulation in the system. The shallowness of the open bay waters, the extensive shoals and marsh islands near the inlets, and the configuration of the perimeter areas restrict tidal flow and attenuate tidal energy in the embayment. The Barnegat Bay-Little Egg Harbor estuary and watershed are characterized by a wide range of habi tats, including vegetated and unvegetated subtidal bay bottoms, intertidal flats and bay islands, dunes and beaches, tidal and freshwater marshes, upland and wetland forests, and other natural and altered areas. Bottom sediments in the estuary, consisting of a mosaic of sand, silt, clay, shells, and organic matter, support rich and diverse biotic communities. The watershed, encompassing an area of? 1,730 km2, likewise harbors numerous assemblages of organisms. More than 100 endangered, threatened, or rare species of plants and animals have been chronicled in the watershed. Many of these species continue to be affected by water quality degradation, the loss and alteration of habitat areas, and overuse or misuse of natural resources due to a burgeoning coastal human population. tiiiittlu.
Perceived Parenting Stress Is Related to Cardiac Flexibility in Mothers: Data from the NorBaby Study

Heart rate variability (HRV) is an indicator of autonomic nervous system activity, and high levels of stress and/or depressive symptoms may reduce HRV. Here, we assessed whether (a) parental stress affected HRV in mothers during the perinatal period and whether this is mediated by bonding and (b) whether antenatal maternal mental states, specifically repetitive negative thinking, depressive symptoms, and pregnancy-related anxiety, have an impact on infant HRV, and lastly, we investigated (c) the relationship between maternal HRV and infant HRV. Data are from the Northern Babies Longitudinal Study (NorBaby). In 111 parent–infant pairs, cardiac data were collected 6 months after birth. In the antenatal period, we used the Pregnancy-Related Anxiety Questionnaire—Revised, the Edinburgh Postnatal Depression Scale, and the Perseverative Thinking Questionnaire; in the postnatal period, we used the Parenting Stress Index and the Maternal Postnatal Attachment Scale. Higher levels of perceived parenting stress but not depressive symptoms were associated with lower HRV in mothers (τ = −0.146), and this relationship was not mediated by maternal bonding. Antenatal maternal mental states were not associated with infant HRV. There was no significant correlation between maternal HRV and infant HRV. Our observational data suggest that perceived stress reduces cardiac flexibility. Future studies should measure HRV and parenting stress repeatedly during the perinatal period.
Pneumonia Classification and Localization in Enhanced X-ray Images with Transfer Learning and CNN Architectures

Pneumonia is a pulmonary inflammation that can be caused by a variety of germs: bacteria, viruses, or fungi. Worldwide, pneumonia is a common disease responsible for an estimated $15 \%$ of deaths in kids under 5 years old. In general, the risk of pulmonary infection increases steadily after the age of 40 and is particularly high among individuals over 65. Despite the progress made in treating pulmonary infections, pneumonia remains a major cause of hospitalization and requires particular attention concerning efficient diagnosis and treatment. This study is built on a dataset made up of 5863 JPEG X-ray chest images. Through different convolutional neural network architectures based on transfer learning associated with data augmentation and various image enhancements such as Noise 2 void denoising and histogram equalization. We have implemented several models for pneumonia detection and localization; most of these networks are extremely promising in terms of performance, and most of the time they accurately exceed $98 \%$ in all metrics, which can simplify and speed up medical diagnosis, reduce pressure on hospitals, and save a lot of lives.
Mendelian randomization analysis demonstrates the causal effects of IGF family members in diabetes

Background Observational studies have consistently shown significant associations between the IGF family and metabolic diseases, including diabetes. However, these associations can be influenced by confounding factors and reverse causation. This study aimed to assess the causal relationship between the IGF family and diabetes using Mendelian randomization (MR). Methods We conducted a two-sample MR analysis to investigate the causal effects of the IGF family on diabetes. Instrumental variables for the IGF family and diabetes were derived from summary-level statistics obtained from genome-wide association studies. Horizontal pleiotropy was assessed using MR-Egger regression and the weighted median method. We applied the inverse-variance weighted method as part of the conventional MR analysis to evaluate the causal impact of the IGF family on diabetes risk. To test the robustness of the results, we also employed MR-Egger regression, the weighted median method, and a leave-one-out analysis. Results Our study revealed that IGF-1 causally increases the risk of Type 2 Diabetes (T2D), while IGFBP-6, adiponectin and INSR decreases the risk (IGF-1, OR 1.02 [95% CI 1–1.03], p = 0.01; IGFBP-6, OR 0.92 [95% CI 0.87–0.98], p = 0.01; Adiponectin, OR 0.837 [95% CI 0.721–0.970], p = 0.018; INSR, OR 0.910 [95% CI 0.872–0.950], p = 1.52 × 10–5). Additionally, genetically lower levels of IGF-1 and IGFBP-5, along with higher levels of IGFBP-7, were associated with an increased risk of Type 1 Diabetes (T1D) (IGF-1, OR 0.981 [95% CI 0.963–0.999], p = 0.037; IGFBP-5, OR 0.882 [95% CI 0.778–0.999], p = 0.049; IGFBP-7, OR 1.103 [95% CI 1.008–1.206], p = 0.033). Conclusion In summary, our investigation has unveiled causal relationships between specific IGF family members and T1D and T2D through MR analysis. Generally, the IGF family appears to reduce the risk of T1D, but it presents a more complex and controversial role in the context of T2D. These findings provide compelling evidence that T2D is intricately linked with developmental impairment. Our study results offer fresh insights into the pathogenesis and the significance of serum IGF family member concentrations in assessing diabetes risk.