Miracle-dz/UniFAR-FTUs huggingface dataset:
Pneumatic Humanoid Robot with Adsorption Mechanisms on the Sole to Realize a Stepping Motion in Badminton

Using robots as badminton training partners, the robots preferred to be humanoid in shape. However, existing humanoid robots lack the tipping stability required to perform badminton footwork. In this research, to improve the tipping stability of humanoid robots, we proposed to install adsorption mechanisms on the sole. The robot with three degrees of freedom (DOFs) on one leg was developed as a preliminary step for the evaluation of the mechanisms. First, the basic characteristics of the adsorption mechanisms were evaluated. The evaluation showed that the tipping stability of the robot was improved. In addition, three motions were performed with the developed robot: maintaining a standing posture, stepping forward, and stepping backward. These motions were realized by using a combination of feedforward control and proportional-integral-differential (PID) control. In the forward stepping motion, the robot achieved a quick approach speed of 1.56 m/s. Also, the estimation of the zero moment point (ZMP) during the motion showed that the robot could be prevented from tipping by the adsorption mechanisms.
Hukum Waris Adat pada Masyarakat Muslim Suku Kaili Perspektif Hukum Waris Islam

This article aims to determine the system used in applying inheritance distribution according to the Muslim community of the Kaili Tribe in West Palu District, as well as to analyze the Kaili Tribe's customary inheritance law in inheritance distribution application and to look at the review from the perspective of Islamic inheritance law. This study employs a qualitative approach and data collection through field research, specifically using primary data resulting from interviews or observations and secondary data in the form of citations, journals, papers, theses, articles, and literature relating to related issues, with the goal of underpinning the theoretical foundation of writing this article. According to the findings of this study, the majority of inheritance distribution to Muslim communities in West Palu District is done by deliberation and consensus using customary inheritance law. Meanwhile, asset distribution is mandated and must occur before or after the testator's death. This is directly related to Islamic inheritance law, which requires inheritance to be distributed to heirs. The determination of heirs in Kaili customary law is still being reviewed from a kinship standpoint. Children, parents, mawali (posambei), widows or widowers who have lived the longest are the primary heirs. This is antithetical to Islamic law's determination of heir distribution.
The Phenomenon of Fear of Missing Out (FoMO) in West Sumatra PTKIN Students

The digital era that makes it easier for humans to do activities and communicate makes every day use smartphones. Students use the internet as a way to find information, besides that it also provides an opportunity for each student to see other people's activities which are considered more valuable than their own experiences, causing anxiety and fear for themselves when not involved in social media called FoMO (Fear of Missing Out). This study aims to determine the condition of FoMO in students living in Ma'had Al-Jami'ah PTKIN West Sumatra, using a mix method by combining quantitative and qualitative methods to understand research problems The sampling technique in this study was total sampling with a total sample of 80 people living in Ma'had. The measuring instrument used in this study is the scale of the adaptation of the FoMO scala by Przybyski, et al (2013). The results of this study indicate that in general they are in the high category, which means that students are less able to control feelings of anxiety, fear, worry about losing precious moments without their involvement in the media.
Genetic Differentiation of the Bloodsucking Midge Forcipomyia taiwana (Diptera: Ceratopogonidae): Implication of the Geographic Isolation by the Central Mountain Ranges in Taiwan

Simple Summary Our study elucidates a genetic differentiation of F. taiwana populations across the central mountain ranges of Taiwan, which stands out as a significant geographical feature shaping these populations’ genetic landscape. The mountain range acts as a significant topographic barrier, influencing gene flow and contributing to distinct genetic clusters in eastern and western regions. Despite potential mechanisms for passive dispersal, such as human-mediated transportation and monsoon winds, the genetic divergence remains pronounced. These findings provide crucial insights for understanding the species’ molecular ecology and can inform targeted pest management strategies. Future research, involving more detailed genomic analyses, will be pivotal in exploring specific genetic adaptations and further elucidating gene flow across these geographical divisions. Abstract Forcipomyia (Lasiohelea) taiwana, a small bloodsucking midge, thrives in moderately moist habitats and is commonly found in grassy and bushy areas at an elevation below 250 m. This species exhibits a diurnal biting pattern and shows a marked preference for human blood. Although not known to transmit arthropod-borne diseases, the bites of F. taiwana can induce severe allergic reactions in some individuals. As a significant nuisance in Taiwan, affecting both daily life and the tourism industry, comprehensive studies on its population genetics across different geographical regions remain scarce. The central mountain ranges in Taiwan, comprising more than two hundred peaks above 3000 m in elevation, extend from the north to the south of the island, creating distinct eastern and western geographical divisions. This study utilizes microsatellite markers to explore the genetic differentiation of F. taiwana populations located in the eastern and western regions of the mountain ranges. Our findings reveal substantial genetic differentiation among populations inhabiting Taiwan’s western region compared to those in the eastern region. This indicates that the topographical barriers presented by the mountain ranges significantly restrict gene flow, particularly given the species’ limited active flight ability and habitat preferences. Although passive dispersal mechanisms, like wind or human activity, could contribute, this study concludes that the gene flow of F. taiwana between the western and eastern regions is primarily influenced by topographical constraints.
Peptide receptor radionuclide therapy combinations for neuroendocrine tumours in ongoing clinical trials: status 2023

A growing body of literature reports on the combined use of peptide receptor radionuclide therapy (PRRT) with other anti-tumuor therapies in order to anticipate synergistic effects with perhaps increased safety issues. Combination treatments to enhance PRRT outcome are based on improved tumour perfusion, upregulation of somatostatin receptors (SSTR), radiosensitization with DNA damaging agents or targeted therapies. Several Phase 1 or 2 trials are currently recruiting patients in combined regimens. The combination of PRRT with cytotoxic chemotherapy, capecitabine and temozolomide (CAPTEM), seems to become clinically useful especially in pancreatic neuroendocrine tumours (pNETs) with acceptable safety profile. Neoadjuvant PRRT prior to surgery, PRRT combinations of intravenous and intraarterial routes of application, combinations of PRRT with differently radiolabelled (alpha, beta, Auger) SSTR-targeting agonists and antagonists, inhibitors of immune checkpoints (ICIs), poly (ADP-ribose) polymerase-1 (PARP1i), tyrosine kinase (TKI), DNA-dependent protein kinase, ribonucleotide reductase or DNA methyltransferase (DMNT) are tested in currently ongoing clinical trials. The combination with [131I]I-MIBG in rare NETs (such as paraganglioma, pheochromocytoma) and new non-SSTR-targeting radioligands are used in the personalization process of treatment. The present review will provide an overview of the current status of ongoing PRRT combination treatments.
Legway: Design and Development of a Transformable Wheel-Leg Hybrid robot

This study aims to develop a wheel-legged hybrid robot that combines the advantages of a humanoid robot and wheeled mobile robots. In this study, a transformation mechanism for the wheel-leg hybrid robot that does not require additional actuators, enabling efficient switching between wheel mode and leg mode, was developed. The developed mechanism provides leg mode for rough terrain walking, wheel mode for high-speed locomotion, and station mode for stable stationary situations. Considering the limited range of motion of the five-bar linkage based mechanism, the link lengths were optimized. Also, three states along with state transition algorithms were defined by a Finite State Machine. Finally, the performance of the developed wheel-legged hybrid robot and mode transition algorithms were verified through simulations, and the feasibility of the proposed system was confirmed through experiments using the real robot.
Bipedal Running: Bioinspired Fundamentals for versatile Humanoid Robot Locomotion

The field of humanoid robots has grown in recent years with several companies and research laboratories developing new humanoid systems. However, the number of running robots did not noticeably rise. Despite the need for fast locomotion to quickly serve given tasks, which require traversing complex terrain by running and jumping over obstacles. To provide an overview of the design of humanoid robots with bioinspired mechanisms, this paper introduces the fundamental functions of the human running gait. The paper surveys multiple concepts, i.e. to protect the system against impacts, store kinetic energy at touchdown, use natural dynamics, and transfer energy between joints through couplings. The understanding of the fundamental functions can support engineers to design versatile humanoid robots, without sacrificing versatility against capability.
Algorithm Package of AI-Driven SDN Controller-Switch Load Balancing Strategies

Recently, Software-Defined Networking (SDN) is receiving much research attention due to its ability to decouple the data plane from the control architecture by associating the network switches to one (centralized) or more (distributed) controller(s). Traditionally, switches are assigned to the controllers in a static manner which results in under-utilization of the resources of the controllers and increased response delays to user requests. In this paper, we consider a practical load-balancing and agile scenario by formulating the dynamic associations of switches and controllers as an NP-hard optimization problem to minimize the maximum resource utilization of the controllers. Therefore, we propose an Ant Colony Optimization (ACO)-based algorithm to deal with the aforementioned request satisfiability issue in large SDN systems in polynomial-time. Furthermore, we envision a hybrid deep learning model consisting of Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) structures to achieve near-optimal resource utilization for real-time SDN applications. Experimental results demonstrate that our customized CNN-GRU model outperforms the other techniques in terms of resource utilization (15% - 45% optimality gap) within a sianlficantly reduced computational running time (≤ 0.1s).
A Deep Mixture of Experts Network for Drone Trajectory Intent Classification and Prediction using Non-Cooperative Radar Data

The intent prediction of unmanned aerial vehicles (UAVs) also known as drones is a challenging task due to the different mission profiles and tasks that the drone can perform. To alleviate this issue, this paper proposes a deep mixture of experts network to classify and predict drones trajectories measured from non-cooperative radars. Telemetry data of open-access datasets are converted to simulated radar tracks to generate a pool of heterogeneous trajectories and construct three independent datasets to train, validate, and test the proposed architecture. The network is composed of two main components: i) a deep network that predicts the class associated to the input trajectories and ii) a set of deep experts models that learns the extreme bounds of the trajectories in different future time steps. The proposed approach is tested and compared with different deep models to verify its effectiveness under different flight profiles and time-windows.
Parasitic Contamination of Street Food Samples From School-Based Food Vendors of Dhaka City, Bangladesh

Street food is an old notion that arose from the need of quick access of light food to satisfy hunger. With the mention of street food in Bangladesh, a picture is visualized which includes open stalls with uncovered food, unawareness of vendors about hygienic practices, lack of safe source of water for cleaning and preparing food, increasing pollution in surrounding environment leading to infestation with various parasites and causing diarrhoeal diseases in people. The aim of the present study was to determine the prevalence of parasites in school-based street foods in Dhaka city. A total of 200 food samples were examined containing three representative types of street foods: tamarind water of snacks (79 samples), sliced fruits (71 samples) and fruit juices (50 samples) using Formal-ether concentration technique. Among them, 164 (82%) samples were contaminated with Entamoeba histolytica, Blastocystis hominis, Cystoisospora belli, Diphyllobothrium latum, Hymenolepis diminuta, Hymenolepis nana, Ascaris lumbricoides, Enterobius vermicularis, Trichuris trichiura and Ancylostoma duodenale. C. belli was the most prevalent (38%), followed by A. lumbricoides (33%) and B. hominis (33%). While comparing the parasite prevalence in three types of street foods, the highest prevalence was observed for C. belli (21.5% in tamarind water of snacks, 6.5% in sliced fruits and 10% in fruit juices) followed by A. lumbricoides, B. hominis and D. latum. There were significant associations (p<0.05) among the different food types with B. hominis, C. belli, D. latum and A. lumbricoides positive samples. The study implicates that parasitic contamination of school-based street foods is alarming and it is urgent to take necessary steps to ensure the food safety. Bangladesh J. Zool. 51(2): 217-229, 2023
Credit Risk Evaluation of Real Estate Industry Based on GA-GARCH-KMV Model

Credit risk assessment in the real estate industry has garnered significant attention from government regulators, investors, and business scholars. However, the evaluation of credit risk in this sector poses numerous challenges, primarily due to the intricate interplay of economic cycles and political landscapes. In this study, we propose a novel method that leverages the GARCH(1,1) model in conjunction with the Genetic Algorithm (GA) to enhance the KMV model's performance. By refining the default point and equity value volatility in the KMV model, our approach offers more accurate credit risk evaluations in the real estate industry. Empirical results demonstrate the superior accuracy of our improved KMV model, providing valuable insights for early credit risk warning in the real estate sector.
White Matter Bundles Linked to Cognitive Impairment in Alzheimer’s Patients and Intermediate Stages

This paper analyzes the relationship between DTI measures obtained in white matter bundles and the degree of cognitive impairment in subjects with different levels of impairment, from healthy subjects to subjects with Alzheimer’s disease, including intermediate stages such as subjective cognitive impairment and mild cognitive impairment. Subjects’ fibers were segmented using deep and superficial white matter bundle atlases to extract diffusion measures for each bundle. These measures were correlated with subjects’ cognitive scores obtained from the Montreal Cognitive Assessment (MoCA). The bundles presenting significant correlations were then used to determine the significant differences in diffusion-based measures across the progression stages of cognitive impairment. The results showed a total of 16 long bundles and 8 short bundles presenting correlation of DTI measures with cognitive impairment, and significant differences between groups.
Drone-to-drone interception path planning by Deep Q-network with Graph Neural Network based (DQN-GNN) model

With the increasing ubiquity of drones, criminals may also start using drones more frequently. This may necessitate catching these hostile drones with friendly drones. This paper proposes using reinforcement learning to train an agent involving a deep Q-network (DQN) with an underlying GNN-based model to move and intercept a moving target drone. The results from this approach seem to suggest that the use of GNNs with deep reinforcement learning is a feasible way to tackle this problem. With the method proposed in this work, the chaser drone can be trained to intercept the target drone. Furthermore, the chaser drone can catch the target in a reasonable amount of time.
Masking (Not Masking) Up: An STS Visual-Intersectional Approach to Understanding Publics and Science in Times of Rapid Change

In this paper we argue that a visual-intersectional approach can advance the field of science and technology studies (STS). Although there is a small but important body of work using visual methods in STS, it has rarely incorporated intersectional approaches. We use visual imagery about mask wearing during the COVID-19 pandemic as a case study to illustrate the potential power of a visual-intersectional approach in STS. We chose masks for three reasons. First, debates about mask-wearing were rich with visual imagery, from home-made signs posted in homes and businesses to professionally designed and printed imagery circulated by public health agencies. Second, recommendations about face coverings outside of healthcare settings changed dramatically in the first 18 months of the COVID-19 pandemic, providing an opportunity to analyze how rapidly changing scientific recommendations were debated in the public sphere. Finally, as masks entered the public sphere, they quickly became evocative technologies, deeply imbued with cultural and political meanings. By focusing on signs about masks in the United States, we demonstrate how integrating visual methods of data collection and an intersectional, visual analytical lens can strengthen STS by developing deeper understandings of (a) publics and science in times of uncertainty, (b) public health as a distinct form of scientific expertise, and (c) the role of humor and place-specific messaging in science. We close with analytic priorities for future research on mask use that could advance STS theories of the collective construction of scientific knowledge and productively inform the development of public health interventions.
Modularity in Humanoid Robot Design for Flexibility in System Structure and Application

In this work we present the modularity aspect in the development of neoDavid, a robust humanoid robot with dexterous manipulation skills. We highlight the benefits of modularity in humanoid robot design for flexible service robotic applications. Our modular approach to system development begins with the system architecture and extends through the mechatronics, connectivity, and control components to the higher-level software and applications that match the growing system architecture. We show how the modularity scales and how it was used to gradually expand the system from an arm and hand to a full humanoid upper body on wheels. The modularity allowed us to adapt our system to changing needs as our focus shifted from technology basics to skills and finally to applications in the human environment with human tools.
MOBILE GAS SENSING FOR LABORATORY INFRASTRUCTURE

Indoor air quality has become a growing concern in modern society due to prolonged indoor working hours that lead to the frequent exposure to numerous toxic gases from various sources. These pollutants, including volatile organic compounds (VOCs), pose severe health risks such as asthma and lung cancer. To address this critical issue, this project focuses on developing and evaluating an advanced gas detection system that explicitly targets VOCs by integrating two novel metal oxide semiconductor (MOX)-based gas sensors, ENS 160 and TED110. Different sensor parameters, such as the air quality index (AQI) and volatile organic compounds (VOCs), were evaluated using 12 volatile organic chemicals. The findings revealed that the ENS 160 sensor performs excellently, detecting 60 gas samples out of 72, with an average detection rate of approximately 83%. In contrast, the TED110 sensor demonstrated considerably lower performance and response in 24 out of 72 gas samples, with a detection rate of about 33%. The results contribute insights into the gas sensor's characteristics, providing essential information to enhance indoor air quality monitoring technology, particularly in laboratory environments. ABSTRAK: Setiap hari, banyak gas toksik, letupan dan beracun berlaku di dalam dan di luar rumah daripada pelbagai sumber. Dalam masyarakat moden, kebanyakan orang menghabiskan 90% masa bekerja mereka di dalam rumah; oleh itu, kualiti udara dalaman secara beransur-ansur bertambah buruk daripada suasana luar. Projek ini sedang membangunkan sistem pengesanan dan pemantauan moden yang canggih untuk mengesan pelbagai gas berbahaya, seperti sebatian organik meruap (VOC). Dua penderia gas berasaskan semikonduktor oksida logam (MOX) novel telah diperkenalkan dalam projek ini dengan mikropengawal yang dikemas kini untuk pemerolehan data dan pemprosesan data. Tambahan pula, parameter sensor yang berbeza (AQI, TVOC) telah dinilai dengan 12 bahan kimia organik yang tidak menentu. Semua ujian telah dijalankan dalam tudung kimia tradisional dengan tiga kuantiti sampel yang berbeza (5? L, 10? L, 50? L) pada jarak 40 cm dan 100 cm. Akhir sekali, volum minimum yang boleh dikesan berdasarkan jarak antara nod sensor dan sumber bocor telah dianalisis selepas eksperimen yang meluas dengan kedua-dua sensor. Sensor ENS 160 sedang mengesan 60 sampel gas daripada 72, dalam ketiga-tiga parameter seperti AQI, TVOC dan kadar pengesanan CO2 sekitar 83%. TED110 menunjukkan prestasi yang sangat rendah; ia telah bertindak balas kepada 24 daripada 72 sampel gas, dan kadar pengesanan ialah 33%.
Video-Based Skeleton Data Analysis for ADHD Detection

Attention Deficit Hyperactivity Disorder (ADHD) is a common neurobehavioral disorder in humans worldwide. While extensive research has focused on machine learning methods for ADHD detection and diagnosis. Most methods rely on high-cost equipment and trained staff for data collection, e.g., Magnetic Resonance Image (MRI) machine and Electroencephalography (EEG) patch. Therefore, low-cost sensors-based easy-to-process methods for ADHD detection by exploiting action and behavior symptoms are required. We present that skeleton-based action recognition has the potential to address the application due to the action-focused nature of ADHD. Hence, this work proposes a novel ADHD detection system with a privacy-mitigating skeleton-based action recognition framework by utilizing our new real multi-modal ADHD dataset. Compared to the conventional methods, the proposed method shows cost efficiency and significant performance improvement. This method also outperforms the conventional methods in accuracy and AUC on the real multi-modal dataset. Furthermore, our proposed method based on simple non-wearable sensors is widely applicable for ADHD screening.