Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Snow Avalanche Disturbance on Regeneration of Subalpine Abies mariesii Forest, Northern Japan

Abstract We examined the effects of snow avalanche on stand structure and the subsequent regeneration of subalpine Abies mariesii Mast forest in northern Japan, and discussed whether gap formation after avalanche disturbance leads to immediate seedling establishment or not. Tree size and age, tree-ring chronology, and seedling density were compared between, on, and around a relatively large avalanche path, which was created in the mid-1980s within Hachimantai National Park. On the avalanche path, only smaller and younger trees (height <5 m, 50–100 yr old) escaped the mechanical damage of avalanche by leaning into the remaining snowpack, and old canopy trees were mostly killed by stem breakage. Tree size structure of the avalanche path, including both living and dead individuals, was relatively similar to that of nearby undisturbed forest, indicating that a mature stand (>ca. 200 yr old) had been previously developed on the avalanche slope without large-scale disturbances in the past. Most of the surviving, younger trees showed abrupt growth release during the few years (1987–1989) after the avalanche event. However, densities of both post-avalanche (<15 yr old) and pre-avalanche (≥15 yr old) seedlings were much lower on the avalanche path than in the nearby forest, especially at microsites covered with dense dwarf bamboo (Sasa kurilensis) shrub. The pre-avalanche seedlings on the avalanche path sharply increased annual height growth rates after the mid-1980s avalanche, but such positive growth response did not continue longer than 6 to 7 yr. These findings suggested that gap formation following the infrequent, large-scale avalanche disturbance did not necessarily lead to immediate seedling recruitment and/or further growth release of pre-avalanche seedlings. The constraint of seedling establishment was primarily explained by the lack of potential seed supply, and shading effect of the dwarf bamboo bush. Consequently, post-avalanche regeneration of the subalpine fir forest was likely to depend on the smaller individuals (2–5 m in height) that were able to avoid both the mechanical damage of avalanche and mortality from shading by the dwarf bamboo.
The Design, Simulation, and Measurement of a 30 GHz Microstrip Patch Antenna for Millimeter-Wave Applications

This study addresses the challenges of miniaturization and impedance matching for mmWave antennas to achieve efficient radiation in the desired frequency range. The problem statement focuses on the limitations of traditional antenna designs at mm Wave frequencies, which often result in bulky structures and poor impedance matching, leading to suboptimal radiation characteristics. To address these challenges, this study describes the design, simulation, and measurement of a 30 GHz microstrip patch antenna. With a measured reflection coefficient of −19.25 dB and a gain of 7.65 dB, the aerial structure strikes a chord at 30 GHz. It measures 14 × 18 × 0.586 mm3. The results of the measurements show that the proposed design is appropriate for millimeter wave band 5G wireless applications. The key innovation lies in enabling the antenna to be significantly smaller compared to conventional designs. The proposed impedance matching techniques further enhance the antenna's efficiency and radiation characteristics. The results demonstrate a significant improvement in the antenna's miniaturization, gain, and directivity when compared to traditional mmWave antenna designs. Extensive simulations and measurements validate the proposed design's performance in controlled environments.
Locating Microseismic Events With S-Wave Data Only

Microseismic monitoring is a practical technique for mapping hydraulic fractures. Accurate imaging of the event location is critical for fracture interpretation. Microseismic data, however, often present weak P-waves or no P-waves at all because of low signal-to-noise ratio. S-waves, on the other hand, are relatively large, and reliably observed. Therefore, it will be significantly meaningful if we can use only S-phase to determine the source location. We design two synthetic experiments to understand the possibility of using only S-phase to locate the microseismic events. In the first experiment we use only S-wave traveltime information to locate events, while in the second experiment we utilize S-wave traveltime along with incident angle to enhance accuracy. The experiment results suggest that using only S-wave traveltime information is not feasible to locate the microseismic events. But using S-wave traveltime and incident angle can produce an acceptable solution if the source-receiver distance is comparable with the acquisition aperture.
Towards a theoretical framework for diaspora-ased tourism marketing (DBTM) in Zimbabwe: A review of the general systems theory, social exchange theory, stakeholder theory and expectancy theory in relation to DBTM

Abstract In most developing nations, Zimbabwe in particular, the value of diaspora-based tourist marketing (DBTM) in igniting socio-economic aspects toward a destination remains underutilized. The theoretical framework for diaspora-based tourist marketing (DBTM), which serves as the foundation upon which DBTM study is formed, is highlighted in this paper. In this paper, a theoretical framework consisting of four theoretical perspectives, namely General Systems Theory (GST), Social Exchange Theory (SET), Stakeholder Theory (ST), and Expectancy Theory (VIE), has been adopted in order to develop a DBTM framework that harnesses the diaspora to improve tourism traffic. Using content analysis, the theoretical analysis paper explores related theories that enrich the appreciation of DBTM’s potential and in the process identify an overarching theory or theories in relation to DBTM. Theoretical framework contributions will aid in guiding the study that will shed light on the advantages of using the diaspora as destination marketers and the improvement of tourism traffic to Zimbabwe. The theoretical framework results will also be incorporated into the development of a DBTM framework that will harness the diaspora to improve tourism traffic to Zimbabwe.
Control and evaluation of a humanoid robot with rolling contact joints on its lower body

In this paper, we introduce a new teen-sized humanoid platform dubbed DRACO 3, custom-built by Apptronik and altered for practical use by the Human Centered Robotics Laboratory at The University of Texas at Austin. The form factor of DRACO 3 is such that it can operate safely in human environments while reaching objects at human heights. To approximate the range of motion of humans, this robot features proximal actuation and mechanical artifacts to provide a high range of hip, knee, and ankle motions. In particular, rolling contact mechanisms on the lower body are incorporated using a proximal actuation principle to provide an extensive vertical pose workspace. To enable DRACO 3 to perform dexterous tasks while dealing with these complex transmissions, we introduce a novel whole-body controller (WBC) incorporating internal constraints to model the rolling motion behavior. In addition, details of our WBC for DRACO 3 are presented with an emphasis on practical points for hardware implementation. We perform a design analysis of DRACO 3, as well as empirical evaluations under the lens of the Centroidal Inertia Isotropy (CII) design metric. Lastly, we experimentally validate our design and controller by testing center of mass (CoM) balancing, one-leg balancing, and stepping-in-place behaviors.
Rational Design, Synthesis and Binding Affinity Studies of Anthraquinone Derivatives Conjugated to Gonadotropin-Releasing Hormone (GnRH) Analogues towards Selective Immunosuppression of Hormone-Dependent Cancer

Gonadotropin-releasing hormone (GnRH) is pivotal in regulating human reproduction and fertility through its specific receptors. Among these, gonadotropin-releasing hormone receptor type I (GnRHR I), which is a member of the G-protein-coupled receptor family, is expressed on the surface of both healthy and malignant cells. Its presence in cancer cells has positioned this receptor as a primary target for the development of novel anti-cancer agents. Moreover, the extensive regulatory functions of GnRH have underscored decapeptide as a prominent vehicle for targeted drug delivery, which is accomplished through the design of appropriate conjugates. On this basis, a rationally designed series of anthraquinone/mitoxantrone–GnRH conjugates (con1–con8) has been synthesized herein. Their in vitro binding affinities range from 0.06 to 3.42 nM, with six of them (con2–con7) demonstrating higher affinities for GnRH than the established drug leuprolide (0.64 nM). Among the mitoxantrone based GnRH conjugates, con3 and con7 show the highest affinities at 0.07 and 0.06 nM, respectively, while the disulfide bond present in the conjugates is found to be readily reduced by the thioredoxin (Trx) system. These findings are promising for further pharmacological evaluation of the synthesized conjugates with the prospect of performing future clinical studies.
Decentralized raw sewage utilization for irrigation of green areas in arid cities

To reduce the use of potable water for irrigation of public green areas in urban arid regions, a specific decentralized approach has been developed. As part of a multi-barrier concept, a compact and simplified treatment system was designed for efficient utilization of wastewater from existing sewage systems. The treatment system consists of only two purification steps: a mechanical pre-treatment and a subsequent disinfection by UV irradiation. Departed from the mainstream opinion, preparatory laboratory scale tests indicate that the disinfection of only mechanically purified wastewater by UV irradiation is a feasible solution with acceptable levels of UV doses and resulting power consumption. Especially according to the requirements for irrigation water, disinfection by UV light is an appropriate option for point-of-use systems, which makes it preferable for decentralized wastewater treatment systems. Keywords compact wastewater treatment system; disinfection; sewage reuse; UV irradiation
The Impact of ESG on Sales Revenue: From Customers’ Perspective

In today's increasingly competitive business market, the significance of environmental, social, and governance (ESG) factors has emerged as a crucial non-financial aspect influencing corporate performance. This study aims to investigate the correlation between ESG indices and sales growth, with a particular focus on customers' perspectives. To achieve this, the research leverages data from the reputable WIND and CSMAR databases and employs statistical analysis and regression techniques. The findings of this study reveal a noteworthy positive association between ESG indices and sales revenue, indicating that a strong ESG performance positively influences customer attitudes towards products. This outcome underscores the value of environmental, social, and governance considerations for businesses. It encourages corporations to place more emphasis on these factors, thereby fostering social and environmental sustainability in the corporate world. Ultimately, this study emphasizes the importance of integrating ESG principles into corporate strategies to drive growth and foster a positive impact on both business performance and society at large.
Perspectives of the generation and measurement of small electric currents

This paper provides a comprehensive overview of the historical background, current status, and future prospects related to the generation and measurement of small electric currents. It specifically caters to nonprofessional readers, with the aim of making the information comprehensible. A range of technologies are introduced, applicable in both basic research and industrial context. Quantum-mechanical approaches have been the focus of extensive efforts in this field, encompassing various types of single-electron pumps and combinations of two other quantum standards: the Josephson voltage standard and the quantized Hall resistance standard. These methods offer a reliable and precise means of generating and measuring small electric currents, minimizing uncertainties. However, operating complex cryogenic systems requires specialized expertise. Alternatively, conventional room-temperature systems are comparatively easier to handle. They employ low-noise amplifiers in conjunction with stable high-value resistors or capacitors charged with voltage ramps. This paper not only examines the characteristics of the both quantum and classical approaches from multiple perspectives but also outlines current and future applications for the generation and measurement of small electric currents.
Pemetaan Risiko Relatif Kasus Demam Berdarah Dengue di Kota Makassar Menggunakan Model Bayesian Spasial

ABSTRAK ⎯ Demam Berdarah Dengue (DBD) adalah salah satu penyakit yang masih merupakan masalah pada kesehatan masyarakat di Indonesia. Penelitian ini bertujuan untuk memetakan risiko relatif (RR) kasus DBD di Kota Makassar dengan menggunakan dua model Spasial Conditional Autoregressive (CAR) dengan menggunakan pendekatan Bayesian yaitu model Besag- York-Molliѐ (BYM) dan Leroux. Data yang digunakan merupakan data kasus DBD tahun 2016-2018 untuk 15 wilayah Kecamatan di Kota Makassar. Model terbaik dipilih berdasarkan pada kriteria kecocokan model, yaitu Watanabe Akaike Information Criterion (WAIC) dan Deviance Information Criterion (DIC). Hasil yang diperoleh menunjukkan bahwa model terbaik yang digunakan untuk memetakan RR kasus DBD tahun 2016 dan 2017 adalah model CAR BYM, sedangkan model terbaik untuk tahun 2018 adalah model CAR Leroux. Berdasarkan hasil analisis disimpulkan bahwa pada tahun 2016 wilayah dengan RR tertinggi
Reducing Range Sidelobes and Ghost Targets in PMCW Radars With π/K-Zadoff Code Sequences

This paper proposes a solution to reduce the range sidelobes and ghost targets in PMCW radars by improving the Zadoff sequences with the help of a progressive phase rotation. First, we show that the proposed code sequences, named <inline-formula> <tex-math notation="LaTeX">$\pi /K$ </tex-math></inline-formula>-Zadoff, are robust to IQ imbalance, DC offset and baseband third order non-linear distortions. We provide the criteria to select the values of <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> which minimize the range sidelobes produced by those non-idealities. Then, we show how to attenuate the remaining range sidelobes due to other transceiver analog front-end non-idealities by changing the transmitter code sequence every slow time repetition. This technique will be adapted to the specific case of Zadoff sequences to preserve two of their properties: the robustness to Doppler frequency shift and power amplifier non-linearity. The resulting PMCW radar is able to achieve low sidelobes even in presence of front-end non-idealities and moving targets. We demonstrate the effectiveness of our methods by simulation with a realistic automotive scenario.
Infill Density in Additive Manufacturing and Application to the DFMA of an Iron Man Helmet

Additive manufacturing has become increasingly popular for rapid prototyping and industrial applications, particularly in the context of Industry 4.0. However, given the vast design parameter space, there remains a lack of characterisation of the mechanical properties for varying design parameters. Consequently, flexural tests are undertaken following the ISO 178:2019 for fused deposition modelling (FDM) samples, with three infill patterns (lines, gyroid and triangles), four thermoplastic materials, namely acrylonitrile butadiene styrene (ABS), polylactic acid (PLA and PLA+), and polyethylene terephthalate glycol (PETG), and infill densities (print material to part volume ratio) ranging from 0.10 to 1.00. Here we show that (i) the modulus and strength are independent of the tested infill types; (ii) the mechanical properties increase linearly with infill density; and (iii) considering mechanical properties, mass and cost, PLA+ appears as the most suitable overall material choice, with PETG appropriate for strength-driven, low-cost applications. Ultimately, PLA+ is applied to the design for manufacturing and assembly (DFMA) case study of an Iron Man helmet. These findings provide novel insights into the variations of mechanical properties with infill type, density and material for 3D printing applications and may contribute to future development in lightweight and cost-effective additive manufacturing.
Depression Episodes Detection in Unipolar and Bipolar Patients: A Methodology with Feature Extraction and Feature Selection with Genetic Algorithms Using Activity Motion Signal as Information Source

Depression is a mental disorder which typically includes recurrent sadness and loss of interest in the enjoyment of the positive aspects of life, and in severe cases fatigue, causing inability to perform daily activities, leading to a progressive loss of quality of life. Monitoring depression (unipolar and bipolar patients) stats relays on traditional method reports from patients; however, bias is commonly present, given the patients’ interpretation of the experiences. Nevertheless, to overcome this problem, Ecological Momentary Assessment (EMA) reports have been proposed and widely used. These reports includes data of the behaviour, feelings, and other type of activities recorded almost in real time using different types of portable devices, which nowadays include smartphones and other wearables such as smartwatches. In this study is proposed a methodology to detect depressive patients with the motion data generated by patient activity, recorded with a smartband, obtained from the “Depresjon” database. Using this signal as information source, a feature extraction approach of statistical features, in time and spectral evolution of the signal, is done. Subsequently, a clever feature selection with a genetic algorithm approach is done to reduce the amount of information required to give a fast noninvasive diagnostic. Results show that the feature extraction approach can achieve a value of 0.734 of area under the curve (AUC), and after applying feature selection approach, a model comprised by two features from the motion signal can achieve a 0.647 AUC. These results allow us to conclude that using the activity signal from a smartband, it is possible to distinguish between depressive states, providing a preliminary and automated tool to specialists for the diagnosis of depression almost in real time.
Towards Industry 5.0: Augmented Reality Assistance Systems for People-Centred Digitalisation and Smart Manufacturing

Although Industry 4.0 has brought multiple positive impacts, the lack of consideration for the human operator in the loop has been a key driver for an ‘Industry 5.0’ (r)evolution. Human-centricity is a core pillar of Industry 5.0, however, the breadth of emerging Industrial Technologies and methods for worker assistance and their integration in factories' systems are not well understood yet. This paper carries out a systematic review of human-centric manufacturing and discusses the emerging research topics, methods and approaches that are contributing to the next industrial revolution. The key factors on human operator wellbeing, methods and techniques for human-centric manufacturing, and augmented reality assistance systems are analysed. Moreover, the research challenges and gaps are identified, and recommendations on future research directions are provided for the further development of people-centred digitalisation and smart manufacturing research.
High Gain Dual Wide-Band Annular Ring Two-Port Antenna for Millimeter Wave Applications

In today's wireless communication networks, the primary objective is to attain extremely high data transfer rates to meet the ever-increasing demands. The robust 5G standards will enable data transfer rates comparable to the speed of light, seamless connections, large data capacities, and extremely low latency. The proposed antenna is a circular ring powered by a 50-ohm feeding strip printed on the underside of a Rogers RT Duroid substrate with a flawed ground plane. The proposed radiating structure resonates in two frequency bands: one from 20 to 24.5 GHz and the other from 42 to 46.5 GHz. The respective center frequencies of each band are 22,25 GHz and 44,25 GHz. Due to the bandwidth of 4.5 GHz, can experience almost unimaginable speeds on the dual bands. It is possible to attain maximum gains of 6 dBi and 11 dBi while maintaining an efficiency of greater than 95% across dual bands. Future 5G millimeter-wave wireless communication systems may face formidable competition from the antenna in the future.
A Short-term Demand of Bike-sharing Forecasting Model Based on Spatio-temporal Graph Data

The research aims to use deep learning to develop a site-level bike-sharing demand prediction model to address the uneven distribution of free-flowing vehicles due to the growth of bike-sharing into the market. In recent years, cycling has become an important form of supportive public transportation, especially for “last mile” commuting. However, with the increase of bike-sharing activities in the market, some free-flowing vehicles are facing different spatial and temporal distribution problems. To overcome these challenges, we use a Graph Convolutional Neural Networks (GCN) to capture the spatial relationships between bike-sharing sites, a Gate Recurrent Unit (GRU) to capture the temporal proximity and periodicity of each site's historical data, and an Attention mechanism to dynamically capture the temporal dependencies and improve the model's performance. It is shown that the proposed approach has better performance compared to other models, as demonstrated by MAE and RMSE measurements, which have signals of 1.09 and 2.21 on this dataset, respectively. the error is reduced by at least 21.4% compared to other comparative models, showing strong predictive performance. Thus, this paper implements a deep learning model that can accurately predict the demand of bike-sharing stations, which provides a decision basis for solving the scheduling of unbalanced spatial and temporal distribution of bike-sharing.
Bridge Crack Detection Using Horse Herd Optimization Algorithm

Detection of Bridge Cracks is an important task for ensuring the safety and structural integrity of bridges. Cracks in bridges can lead to serious safety hazards and require frequent inspections to prevent accidents. However, manual inspections can be time-consuming and expensive. Automated detection systems can help in identifying cracks and reducing the cost and time associated with manual inspections. In previous years, several methods have been proposed for automating the crack-detecting procedure using Machine Learning and optimization techniques. In this study, a novel approach to detect bridge cracks is described using the Horse Herd Optimization Algorithm (HHOA). The HHOA is a population-based meta-heuristic algorithm motivated by the behaviour of horses in a herd. It is a relatively new algorithm that has shown promising results in several optimization problems. The Convolution Neural Network (CNN) is a popular deep learning technique used for image classification, object detection, and segmentation tasks. The basic idea behind CNN is to extract important features from an image using convolutional layers and pooling layers, followed by fully connected layers to classify the image. This study proposes an algorithm that can detect cracks in bridge structures efficiently and accurately using HHOA with CNN is giving an accuracy of 97%.