Miracle-dz/UniFAR-FTUs huggingface dataset:
A Wideband Low-Noise Linear LiDAR Analog Front-End Achieving 1.6 GHz Bandwidth, $\boldsymbol{2.7}\ \mathbf{pA}/\mathbf{Hz}^{\boldsymbol{0.5}}$ Input Referred Noise, and 103 $\mathbf{dB}\mathbf{\Omega}$ Transimpedance Gain

Linear LiDAR can obtain distance by measuring time of flight (TOF), which is widely used in unmanned, geomorphic mapping and other fields. In recent years, the demand of long-distance LiDAR is getting increased. The maximum detection range of pulsed LiDAR is mainly dictated by the signal-to-noise ratio (SNR) [1]. Fig. 1 (top) indicates that improving the SNR relies on enhancing the peak emission power of the laser pulse and reducing the noise of the analog front-end (AFE) circuit. Moreover, to comply with safety regulations for eye-safe laser energy [1], narrow high-power laser pulse is required. Since the pulse width of high-power laser has already reached sub-nanosecond range [2], an AFE with a wide bandwidth of over-1 GHz and lower noise is required, thus significantly challenging the circuit design. In the AFE, the input referred noise (IRN) is mainly dominated by the transimpedance amplifier (TIA). Prior shunt-feedback TIA (SF-TIA) [3] performs well with a narrow bandwidth around 100 MHz. But it suffers from strong trade-off between IRN, gain, bandwidth and stability. Thus, the IRN is higher than 4.5 pA/Hz0.5 with over-200-MHz bandwidth [4], [5], indicating unsuitable for the design of low-noise TIA with over-1-GHz bandwidth and high gain. A low IRN of 2.6 pA/Hz0.5 was achieved by using a narrow-band TIA along with an equalizer for bandwidth enhancement (190 MHz) [6]. But this is still not sufficient for the design of over-1-GHz TIA.
On carbon sequestration in desert ecosystems

Recent reports of net ecosysytem production >100gCm � 2 yr � 1 in deserts are incompatible with existing measurements of net primary production and carbon pools in deserts. The comparisions suggest that gas exchange measurements should be used with caution and better validation if they are expected to indicate the magnitude of carbon sink in these ecosysytems.
Mesenchymal stromal cells, metabolism, and mitochondrial transfer in bone marrow normal and malignant hematopoiesis

Hematopoietic stem cell (HSC) transplantation-based treatments are in different phases of clinical development, ranging from current therapies to a promise in the repair and regeneration of diseased tissues and organs. Mesenchymal stromal/stem cells (MSCs), which are fibroblast-like heterogeneous progenitors with multilineage differentiation (osteogenic, chondrogenic, and adipogenic) and self-renewal potential, and exist in the bone marrow (BM), adipose, and synovium, among other tissues, represent one of the most widely used sources of stem cells in regenerative medicine. MSCs derived from bone marrow (BM-MSCs) exhibit a variety of traits, including the potential to drive HSC fate and anti-inflammatory and immunosuppressive capabilities via paracrine activities and interactions with the innate and adaptive immune systems. The role of BM-MSC-derived adipocytes is more controversial and may act as positive or negative regulators of benign or malignant hematopoiesis based on their anatomical location and functional crosstalk with surrounding cells in the BM microenvironment. This review highlights the most recent clinical and pre-clinical findings on how BM-MSCs interact with the surrounding HSCs, progenitors, and immune cells, and address some recent insights on the mechanisms that mediate MSCs and adipocyte metabolic control through a metabolic crosstalk between BM microenvironment cells and intercellular mitochondrial transfer in normal and malignant hematopoiesis.
SISTEMA DE POSICIONAMIENTO LOCAL BASADO EN UNA RED WI-FI Y UNA INTERFAZ GRÁFICA DESARROLLADA CON PYTHON PARA ANALIZAR EL DESEMPEÑO DE JUGADORES DE SOCCER

Resumen Este articulo describe el desarrollo de un sistema de posicionamiento de jugadores de soccer en la cancha para analizar su ubicacion, desplazamiento y velocidad en partidos y entrenamientos, con el fin de utilizar estos datos en la evaluacion cuantitativa de su desempeno. El sistema esta basado en la triangulacion de la posicion, mediante la medida de la intensidad de senal recibida (RSS) de las conexiones de una red Wi-Fi, dicha triangulacion se realiza en un script de Python y los datos se despliegan en una interfaz grafica desarrollada con Tkinter y Matplotlib. Palabras Clave: Python, Sistema de posicionamiento local, Soccer, Wi-Fi. LOCAL POSITIONING SYSTEM BASED ON A WI-FI NETWORK AND A GRAPHICAL INTERFACE DEVELOPED WITH PYTHON TO ANALYZE THE PERFORMANCE OF SOCCER PLAYERS Abstract This article describes the development of a positioning system of soccer players on the field, in order of analysing their location, displacement and velocity in games and training, with the final purpose of using this data for the quantitative evaluation of their performance. The system is based on the position triangulation, by the measurement of strong signal (RSSI) of Wi-Fi web connections, this triangulation works in a Python script and data is shown in a graphic interface developed using Tkinter and Matplotlib. Keywords : Local position system, Python, Soccer, Wi-Fi.
Whole exome sequencing reveals genetic landscape associated with left ventricular outflow tract obstruction in Chinese Han population

Left ventricular outflow tract obstruction (LVOTO), a major form of outflow tract malformation, accounts for a substantial portion of congenital heart defects (CHDs). Unlike its prevalence, the genetic architecture of LVOTO remains largely unknown. To unveil the genetic mutations and risk genes potentially associated with LVOTO, we enrolled a cohort of 106 LVOTO patients and 100 healthy controls and performed a whole-exome sequencing (WES). 71,430 rare deleterious mutations were found in LVOTO patients. By using gene-based burden testing, we further found 32 candidate genes enriched in LVOTO patient including known pathological genes such as GATA5 and GATA6. Most variants of 32 risk genes occur simultaneously rather exclusively suggesting polygenic inherence of LVOTO and 14 genes out of 32 risk genes interact with previously discovered CHD genes. Single cell RNA-seq further revealed dynamic expressions of GATA5, GATA6, FOXD3 and MYO6 in endocardium and neural crest lineage indicating the mutations of these genes lead to LVOTO possibly through different lineages. These findings uncover the genetic architecture of LVOTO which advances the current understanding of LVOTO genetics.
Agreement between Optoelectronic System and Wearable Sensors for the Evaluation of Gait Spatiotemporal Parameters in Progressive Supranuclear Palsy

The use of wearable sensors for calculating gait parameters has become increasingly popular as an alternative to optoelectronic systems, currently recognized as the gold standard. The objective of the study was to evaluate the agreement between the wearable Opal system and the optoelectronic BTS SMART DX system for assessing spatiotemporal gait parameters. Fifteen subjects with progressive supranuclear palsy walked at their self-selected speed on a straight path, and six spatiotemporal parameters were compared between the two measurement systems. The agreement was carried out through paired data test, Passing Bablok regression, and Bland-Altman Analysis. The results showed a perfect agreement for speed, a very close agreement for cadence and cycle duration, while, in the other cases, Opal system either under- or over-estimated the measurement of the BTS system. Some suggestions about these misalignments are proposed in the paper, considering that Opal system is widely used in the clinical context.
Leveraging computer-assisted audit tools for corporate sustainability: Evidence from Ethiopia

The existing ample literature studied the factors for adopting computer-assisted audit techniques (CAATs) by internal and external auditors, frequently ignoring their impact on the quality of audits and companies’ efficiency. This study delivers new evidence on the kinds of CAATs utilized by internal auditors, examines their adoption impact on corporate sustainability, and studies the moderating impact of company characteristics. This study used data from internal auditors in Ethiopia gathered using a survey, and the study hypotheses were tested using the partial least squares-structural equation modeling (PLS-SEM) technique. The study found a moderate utilization of CAATs by internal auditors in executing their activities. The result also revealed a highly positive impact of internal auditors’ CAAT utilization on fraud discovery in the acquisition process. The study found that the intensity of this relationship is impacted by the companies’ characteristics of management commitment. However, the size and type of the company are not impacting it. This study finding complements prior studies and helps practitioners make decisions that can improve CAAT utilization in internal audit functions for a high level of companies’ sustainability.
Addiction-How and Type

Drugs are chemicals. Different drugs, because of their chemical structures, can affect the body in different ways. In fact, some drugs can even change a person's body and brain in ways that last long after the person has stopped taking drugs, maybe even permanently. Most abused drugs directly or indirectly target the brain's reward system by flooding the circuit with dopamine. Dopamine is a neurotransmitter present in regions of the brain that regulate movement, emotion, cognition, motivation, and feelings of pleasure. When drugs enter the brain, they can actually change how the brain performs its jobs. These changes are what lead to compulsive drug use, the hallmark of addiction. Although initial drug use may be voluntary, drugs have been shown to alter brain chemistry, which interferes with an individual's ability to make decisions and can lead to compulsive craving, seeking and use. This then becomes a substance dependency. All drugs of abuse - nicotine, cocaine, marijuana, and others - affect the brains "reward” circuit, which is part of the limbic system. This flood of dopamine is what causes the "high” or euphoria associated with drug abuse.
Degree of Brine Evaporation and Origin of the Mengyejing Potash Deposit: Evidence from Fluid Inclusions in Halite

The Mengyejing potash deposit is located in the southern port of the Simao Basin, Yunnan Province, and is hosted in mid‐Cretaceous strata. The chemical compositions of fluid inclusions in halite crystals, collected from the level‐610 adit in the deposit, were analysed by laser ablation inductively coupled plasma mass spectrometry (LA‐ICP‐MS). The results show that the brine is of the Na‐K‐Mg‐Ca‐Cl type and has K concentrations that are distinctly higher than those of Mg and Ca, unlike normal brines associated with Cretaceous halite. The high K concentrations indicate that the degree of evaporation of the ancient Mengyejing saline lake was very high, reaching the sylvite deposition stage but rarely reaching the carnallite deposition stage. The trajectory of the H and O isotopic compositions of the brines in the halite‐hosted fluid inclusions corresponds to intense evaporation, indicating that the net evaporation exceeded the net inflow of brines. These brine compositions in halite‐hosted fluid inclusions were likely formed by the dissolution of previously deposited K‐bearing minerals by fresh continental and/or seawater, forming a type of modified seawater, with deep hydrothermal fluids potentially supplying additional potassium. The basin likely experienced multiple seawater incursion, dissolution and redeposition events in a high‐temperature environment with high evaporation rates.
Overview of the NTCIR-17 Transfer Task

This paper provides an overview of the NTCIR-17 Transfer task, a pilot task that aims to bring together researchers from Information Retrieval, Machine Learning, and Natural Language Processing to develop a suite of technology for transferring resources generated for one purpose to another in the context of dense retrieval on Japanese texts. Two subtasks were proposed for this round: the Dense First Stage Retrieval subtask and the Dense Reranking sub-task. We received 29 runs for the First Stage Retrieval and 25 runs for the Reranking subtask from three research groups. The evaluation results of these runs are presented and discussed in this paper.
Forecasting the mortality rates of Malaysian population using Lee-Carter method

The population of many countries might undergo dramatic changes in the coming decades due to continuous increases in life expectancy. The sustained reduction in mortality rates and its systematic underestimation has been attracting the significant interest of researchers in recent years because of its potential impact on population size and structure, social security systems, and (from an actuarial perspective) the life insurance and pensions industry worldwide. Among all projection methods, the Lee-Carter method has been widely accepted by the actuarial community. This paper explores the use of the Lee-Carter method to forecast the mortality rates for Malaysian population. The index of the level of mortality for each gender, and the shape and sensitivity coefficients for 18 age groups were obtained through the Lee-Carter method. The Singular Values Decomposition (SVD) is used to forecast the general index for the time period that goes from 2011 to 2030. Since the model involves nonlinear equations that a.
Military Spending and External Debt in the Middle East and North Africa

This study investigates the nexus between military expenditure and exploding external debt in 13 countries of the Middle East and North Africa (MENA) from 2000 to 2019 by employing the second generation of panel econometrics such as cross-sectional independence (CD), CIPS unit root, Pedroni cointegration, Westerlund cointegration, and panel PMG model. The sensitivity of long-run estimates is explored by using fully modified ordinary least square (FMOLS) and dynamic ordinary least square (DOLS) regression analysis. Pedroni and Westerlund’s cointegration findings reveal that the selected variables are cointegrated in the long run. The long-run results of the PMG model revealed that current account balance, fiscal balance, and foreign exchange reserve reveal the significant negative effect of various intensities, whereas increasing military spending shows a significant positive effect of higher intensity. The short-run results of the PMG model reveal an insignificant effect of selected variables. Estimated results of FMOLS and DOLS show the robustness of established long-run relationships. This study urges governments of selected MENA countries to address current account and fiscal imbalances along with the considerable reduction of military expenditures to free capital required for productive economic activities.
Functional identification of PGM1 in the regulating development and depositing of inosine monophosphate specific for myoblasts

Background Inosine monophosphate (IMP) is naturally present in poultry muscle and plays a key role in improving meat flavour. However, IMP deposition is regulated by numerous genes and complex molecular networks. In order to excavate key candidate genes that may regulate IMP synthesis, we performed proteome and metabolome analyses on the leg muscle, compared to the breast muscle control of 180-day-old Jingyuan chickens (hens), which had different IMP content. The key candidate genes identified by a differential analysis were verified to be associated with regulation of IMP-specific deposition. Results The results showed that the differentially expressed (DE) proteins and metabolites jointly involve 14 metabolic pathways, among which the purine metabolic pathway closely related to IMP synthesis and metabolism is enriched with four DE proteins downregulated (with higher expression in breast muscles than in leg muscles), including adenylate kinase 1 (AK1), adenosine monophosphate deaminase 1 (AMPD1), pyruvate kinase muscle isoenzyme 2 (PKM2) and phosphoglucomutase 1 (PGM1), six DE metabolites, Hypoxanthine, Guanosine, L-Glutamine, AICAR, AMP and Adenylsuccinic acid. Analysis of PGM1 gene showed that the high expression of PGM1 promoted the proliferation and differentiation of myoblasts and inhibited the apoptosis of myoblasts. ELISA tests have shown that PGM1 reduced adenosine triphosphate (ATP) and IMP and uric acid (UA), while enhancing the biosynthesis of hypoxanthine (HX). In addition, up-regulation of PGM1 inhibited the expression of purine metabolism pathway related genes, and promoted the IMP de novo and salvage synthesis pathways. Conclusion This study preliminarily explored the mechanism of action of PGM1 in regulating the growth and development of myoblasts and specific IMP deposition in Jingyuan chickens, which provided certain theoretical basis for the development and utilization of excellent traits in Jingyuan chickens.
Thermal Motion Equivalent Method Application for Multicopter Swarm in Exploration Task

The paper investigates the exploration issue by a swarm of quadrocopters. The thermal motion equivalent method is used for agents moving. The solution evaluation criterion is the swarm exploration time of more than 95% of an area square in case of limited inter-agent communication and partial agents' failure (lost). For comparison, a well-known rational-formed solution (called far as trivial) to the exploration issue is given. It consists of each agent disjoint trajectories planning at the swarm launch time. The trajectories' structure takes into account the agent field of view and covers the entire territory. The paper presents a mathematical model of the quadrocopter dynamics and thermal motion equivalent method. The exploration efficiency by a swarm using the thermal motion method depends, among other things, on the initial velocity vector direction. The paper presents the results of swarm simulation. The thermal motion equivalent method generally increases the exploration efficiency in conditions of limited communication and a decrease in the swarm number. However, in some cases, the exploration time is higher than in a trivial solution.
Assessment of 3D MRI Image segmentation and Classification for Brain Tumor Detection Using ConvLSTM

The human brain serves as the principal controller of the humanoid system. Brain tumors are the result of abnormal cell division and proliferation, and the development of these tumors can result in brain cancer. The use of computer vision in diagnostic procedures has the potential to lessen human mistake in judgment. The incorporation of new technology in healthcare is seen as a technique to improve human decision-making in the area of diagnosis. Magnetic Resonance Imaging (MRI) is thought to be comparatively more dependable and secure than other diagnostic imaging techniques. In order to identify brain tumors on (BraTS), we suggested a method using Convolutional Long Short-Term Memory (ConvLSTM) on segmented anomalous portions of 3D MRI brain images in Matlab. Using Matlab, a graphical user interface that is simple to use is created to find brain tumors. This effort sought to identify the precise tumor site by first classifying the findings from various brains imaging into three categories: normal, benign, and malignant.Keywords- Deep Learning, Pytorch, Neural Network, Artificial Intelligence, Natural Language Processing, Tkinter.
The role of roles: Physical cooperation between humans and robots

Since the strict separation of working spaces of humans and robots has experienced a softening due to recent robotics research achievements, close interaction of humans and robots comes rapidly into reach. In this context, physical human–robot interaction raises a number of questions regarding a desired intuitive robot behavior. The continuous bilateral information and energy exchange requires an appropriate continuous robot feedback. Investigating a cooperative manipulation task, the desired behavior is a combination of an urge to fulfill the task, a smooth instant reactive behavior to human force inputs and an assignment of the task effort to the cooperating agents. In this paper, a formal analysis of human–robot cooperative load transport is presented. Three different possibilities for the assignment of task effort are proposed. Two proposed dynamic role exchange mechanisms adjust the robot’s urge to complete the task based on the human feedback. For comparison, a static role allocation strategy not relying on the human agreement feedback is investigated as well. All three role allocation mechanisms are evaluated in a user study that involves large-scale kinesthetic interaction and full-body human motion. Results show tradeoffs between subjective and objective performance measures stating a clear objective advantage of the proposed dynamic role allocation scheme.
Towards Digital Energy Metering Systems by Means of Artificial Intelligence

This article presents a new and innovating method to upgrade the existing energy metering system by utilizing an intelligent optical character recognition (OCR) algorithm. The proposed approach assures small costs of the entire energy system's refurbishment, and it can be implemented on both old and new energy meters. The flow is relatively simple and straightforward: an artificial neural network is used to process and recognize the digits from a photo of the meter's analog display and populate a custom database, from which the data will be later retrieved and used to give different stakeholders consumption insights and patterns, graphs, energy efficiency solutions etc. For the OCR algorithm and transfer learning processes a mature Python library, namely Tesseract, was used. The developed software solution was tested on various gas and electricity meters and the results are presented.
Deployment of Machine Learning Algorithms on Resource-Constrained Hardware Platforms for Prosthetics

Motion intent recognition for controlling prosthetic systems has long relied on machine learning algorithms. Artificial neural networks have shown great promise for solving such nonlinear classification tasks, making them a viable method for this purpose. To bring these advanced methods and algorithms beyond the confines of the laboratory and into the daily lives of prosthetic users, self-contained embedded systems are essential. However, embedded systems face constraints in size, computational power, memory footprint, and power consumption, as they must be non-intrusive and discreetly integrated into commercial prosthetic components. One promising approach to tackle these challenges is to use network quantization, which allows complying with limitations without significant loss in accuracy. Here, we compare network quantization performance for self-contained systems using TensorFlow Lite and the recently developed QKeras platform. Due to internal libraries, the use of TensorFlow Lite led to a 8 times higher flash memory usage than that of the unquantized reference network, disadvantageous for self-contained prosthetic systems. In response, we offer open-source code solutions that leverage the QKeras platform, effectively reducing flash memory requirements by 24 times compared to Tensorflow Lite. Additionally, we conducted a comprehensive comparison of state-of-the-art microcontrollers. Our results reveal that the adoption of new architectures offers substantial reductions in inference time and power consumption. These improvements pave the way for real-time decoding of motor intent using more advanced machine learning algorithms for daily life usage, possibly enabling more reliable and precise control for prosthetic users.