Miracle-dz/UniFAR-FTUs huggingface dataset:
Factors that precipitated human plague in Zambia from 1914 to 2014-An overview for a century (100 years)

Plague is a zoonotic and re-emerging disease caused by Yersinia pestis. The disease has caused a devastating effect on the entire world since the time of Justinian plague in the sixth century. Data were searched, screened and compiled from various sources on time, place and magnitude of human plague occurrences in Zambia. It was revealed that plague disease outbreak was first reported in Zambia in early 20th century and human infections had been reported in three zones since 1914. During the first half of the century, the number of human deaths steadily decreased from 93 in 1917 to 1 in 1947. Another outbreak was reported in 1956 after which it remained quiescent until 1987. More human infections were reported between 1990 and 2008. Factors which contributed to plague outbreaks and spread in Zambia included heavy rains, which was usually followed by a large increase in rodent and flea populations, socio-cultural human behaviour and life-style practiced by communities. Such practices were optimal for occurrence and rapid spread of plague including polygamy, hunting, overcrowding in houses, and belief in witchcraft, consequently seeking treatment from traditional healers, and man-rodent contact which was intensified by hunting practice. Despite smaller numbers of cases reported, there is much public health concern about the current plague situation in the country and hence, the disease shouldn’t be neglected. Improving surveillance strategies for the disease and intensifying public health education are therefore desirable in order to sensitise the communities and consequently minimise transmission of the disease.
Predictive Factors in Metastatic Melanoma Treated with Immune Checkpoint Inhibitors: From Clinical Practice to Future Perspective

Simple Summary Metastatic melanoma treatment has greatly changed in the last decade due to the introduction of target therapies and immune checkpoint inhibitors. The combination of immune checkpoint inhibitors led to an unprecedented median overall survival of 72 months, but still, an important portion of patients did not significantly benefit from this approach. In this scenario, the identification of predictive factors is mandatory to improve treatment choices in daily practice. In this review, we summarize the most updated data on trials evaluating predictive factors in metastatic melanoma patients treated with immune checkpoint inhibitors, providing information to support daily practice decisions, and at the same time, highlighting the most promising future perspectives. Abstract The introduction of immunotherapy revolutionized the treatment landscape in metastatic melanoma. Despite the impressive results associated with immune checkpoint inhibitors (ICIs), only a portion of patients obtain a response to this treatment. In this scenario, the research of predictive factors is fundamental to identify patients who may have a response and to exclude patients with a low possibility to respond. These factors can be host-associated, immune system activation-related, and tumor-related. Patient-related factors can vary from data obtained by medical history (performance status, age, sex, body mass index, concomitant medications, and comorbidities) to analysis of the gut microbiome from fecal samples. Tumor-related factors can reflect tumor burden (metastatic sites, lactate dehydrogenase, C-reactive protein, and circulating tumor DNA) or can derive from the analysis of tumor samples (driver mutations, tumor-infiltrating lymphocytes, and myeloid cells). Biomarkers evaluating the immune system activation, such as IFN-gamma gene expression profile and analysis of circulating immune cell subsets, have emerged in recent years as significantly correlated with response to ICIs. In this manuscript, we critically reviewed the most updated literature data on the landscape of predictive factors in metastatic melanoma treated with ICIs. We focus on the principal limits and potentiality of different methods, shedding light on the more promising biomarkers.
Determinants of Net Interest Margin in Indonesian Banking Industry: The Moderating Role of Central Bank Interest Rate

This article is to examine the relationship between net interest margin (NIM) and its determinants: risk averse level, liquidity risk, and credit risk. In addition, the article aims to investigate the moderating role of central bank interest rate on the relationship. We observe 17 Indonesian banks, which are listed on Indonesia Stock Exchange in 2017 – 2021 period. We run panel data regression model with 380 firm-year total observations. The Hausman test suggests that we use Random Effect Model (REM) for the parameter estimation. Our study confirms that NIM is determined by credit risk, liquidity risk, and risk averse level. Other than that, we found that central bank interest rate moderates well the relationship of NIM and its determinants by which the dynamic of the relationship of net interest margin and its determinants can be explained. The presence of the central bank interest rate consistently explains the dynamics of the relationship between NIM, risk averse level, liquidity risk, and credit risk. Our model accommodates the positive/negative relationship of NIM and its determinants: credit risk, liquidity risk, and risk averse level. These findings are expected to shed light on the mixed and inconsistent findings of previous research. The ongoing impact of the central bank interest rate consistently elucidates the dynamics between NIM, risk aversion level, liquidity risk, and credit risk. These findings are expected to bring clarity to the mixed and inconclusive results observed in prior research.
A Compact Dual-Band Millimeter Wave Antenna for Smartwatch and IoT Applications with Link Budget Estimation

Advancement in smartwatch sensors and connectivity features demands low latency communication with a wide bandwidth. ISM bands below 6 GHz are reaching a threshold. The millimeter-wave (mmWave) spectrum is the solution for future smartwatch applications. Therefore, a compact dual-band antenna operating at 25.5 and 38 GHz is presented here. The characteristics mode theory (CMT) aids the antenna design process by exciting Mode 1 and 2 as well as Mode 1–3 at their respective bands. In addition, the antenna structure generates two traverse modes, TM10 and TM02, at the lower and higher frequency bands. The antenna measured a bandwidth (BW) of 1.5 (25–26.5 GHz) and 2.5 GHz (37–39.5 GHz) with a maximum gain of 7.4 and 7.3 dBi, respectively. The antenna performance within the watch case (stainless steel) showed a stable |S11| with a gain improvement of 9.9 and 10.9 dBi and a specific absorption rate (SAR) of 0.063 and 0.0206 W/kg, respectively, at the lower and higher bands. The link budget analysis for various rotation angles of the watch indicated that, for a link margin of 20 dB, the antenna can transmit/receive 1 Gbps of data. However, significant fading was noticed at certain angles due to the shadowing effect caused by the watch case itself. Nonetheless, the antenna has a workable bandwidth, a high gain, and a low SAR, making it suitable for smartwatch and IoT applications.
Synthesis of solketal over zirconia based acid catalysts

Microwave assisted synthesis of ‘solketal’ over zirconia (ZrO2) and its modified forms (Mo(VI)/ZrO2, W(VI)/ZrO2 and V(V)/ZrO2) coated on honeycomb monoliths is reported. The solid acids were coated on honeycomb monoliths by ‘dip and dry’ method and characterized for their surface acidity, crystalinity, functionality, morphology and elemental analysis by using techniques such as NH3-Temperature programmed desorption (NH3-TPD), powder X-ray diffraction (PXRD), Fourier transform-infrared spectroscopy (FTIR), Scanning electron microscopy (SEM) and Inductively coupled plasma-optical emission spectroscopy (ICP-OES). The honeycomb monoliths coated with solid acids were employed as catalysts in the condensation of glycerol with acetone to synthesize solketal under microwave irradiation conditions. The reaction conditions such as molar ratio of the reactants (glycerol: acetone), reaction temperature, reaction time were studied to obtain highest possible yield of solketal with greater selectivity. The catalytic activity of honeycomb catalytic material was compared with the powder forms of the catalysts. 98% yield of solketal with 98% selectivity was achieved over Mo(VI)/ZrO2 coated on a honeycomb monolith in a very shorter reaction time of 8 min at 60 oC under microwave heating. These honeycomb catalytic material were found to be easily reactivable and reusable atleast for 6 reaction cycles. Hence, honeycomb catalysts were found to be efficient, economical and effective in the synthesis of value added chemical such as solketal. A possible mechanism for the formation of solketal is proposed. Bangladesh J. Sci. Ind. Res.54(1), 31-38, 2019
Ten-Year Analysis of Bacterial Colonisation and Outcomes of Major Burn Patients with a Focus on Pseudomonas aeruginosa

A retrospective descriptive study included patients admitted with severe burns over the course of 10 years (2008–2018). Across all patients, there were 39 different species of bacteria, with 23 species being Gram-negative and 16 being Gram-positive bacteria, with also five different species of fungi cultured. Pseudomonas aeruginosa was the most commonly isolated organism, with 57.45% of patients having a positive culture. There was a significant difference in the number of P. aeruginosa isolated from patients that acquired their burns at work, in a garden, inside a vehicle, in a garage or in a public place. In patients that were positive for P. aeruginosa, the number of operations was higher (2.4) and the length of stay was significantly increased (80.1 days). Patients that suffered from substance abuse demonstrated significantly higher numbers of isolated P. aeruginosa (14.8%). Patients that suffered from both mental health illness and substance abuse demonstrated significantly higher numbers of P. aeruginosa isolated (18.5%). In the P. aeruginosa-negative group, there were significantly fewer patients that had been involved in a clothing fire. Furthermore, in the P. aeruginosa-negative patient cohort, the mortality rate was significantly higher (p = 0.002). Since the incidence of P. aeruginosa was also associated with a decreased mortality rate, it may be that patients admitted to hospital for shorter periods of time were less likely to be colonised with P. aeruginosa. This study demonstrates novel factors that may increase the incidence of P. aeruginosa isolated from burn patients.
Effects of Peroxide and Sulfur Curing Systems on Physical and Mechanical Properties of Nitrile Rubber Composites: A Comparative Study

This study compares the effect of sulfur and dicumyl peroxide (DCP) vulcanizing systems on the physical and mechanical properties of rubber compounds based on acrylonitrile butadiene rubber (NBR). NBR compounds cured by different amounts of DCP and NBR vulcanizates filled with various concentrations of carbon black (CB) and a constant amount of sulfur or DCP were prepared. The vulcanizates were characterized by tensile testing, dynamic mechanical thermal analysis (DMTA), and cross-link density determination. The tensile strength and Young’s modulus were found to increase with the rising amount of DCP and CB, while elongation at break decreased. The samples vulcanized by the sulfur system and filled with CB show a substantial increase in tensile strength from 13.1 to 21.2 MPa. Higher storage modulus and glass transition temperature were observed with the increase in the amount of peroxide and filler, and consequently, the increase in cross-link density, indicating rigidity increase and lower molecular mobility. The changes in the physical and mechanical properties of the NBR vulcanizates were in correlation with the changes in solvent uptake and cross-link density.
Relationship of the humus components and the calcium form with the development of limestone soil

Selecting the limestone soil samples at different developing stages(black limestone soil,brown limestone soil and yellow limestone soil),the form and contents of the humus and calcium have been measured,the results show that:(1) at different developing stages of the limestone soil,the total carbon in the humus of black limestone soil,brown limestone soil and yellow limestone soil is 230.15 mg/g,37.49 mg/g and 17.94 mg/g respectively,the proportion of humic acid is 31.94 %、9.44 % and 7.25 % respectively,fulvic acid accounts for 1.04 %,36.14 % and 66.16 %,and humin is 67.02 %,54.41 % and 26.59 % respectively;(2) the total calcium of the black limestone soil,brown limestone soil and yellow limestone soil is 21 486.66 mg/kg,6 913.33 mg/kg and 5 540.17 mg/kg respectively,the acid-soluble calcium has the highest proportion,with the proportion of 57.58 %,74.69 % and 80.83 %,followed by the reduced calcium,oxidized calcium and residual calcium;(3)the correlation analysis shows that the soil calcium and the different forms are positively related with SOC,humic acid and humin,negatively related with fluvic acid.It means that the forms and components of the humus can influence the form and components of the soil calcium to a large extent.
The Adsorption of P2X2 Receptors Interacting with IgG Antibodies Revealed by Combined AFM Imaging and Mechanical Simulation

The adsorption of proteins onto surfaces significantly impacts biomaterials, medical devices, and biological processes. This study aims to provide insights into the irreversible adsorption process of multiprotein complexes, particularly focusing on the interaction between anti-His6 IgG antibodies and the His6-tagged P2X2 receptor. Traditional approaches to understanding protein adsorption have centered around kinetic and thermodynamic models, often examining individual proteins and surface coverage, typically through Molecular Dynamics (MD) simulations. In this research, we introduce a computational approach employing Autodesk Maya 3D software for the investigation of multiprotein complexes’ adsorption behavior. Utilizing Atomic Force Microscopy (AFM) imaging and Maya 3D-based mechanical simulations, our study yields real-time structural and kinetic observations. Our combined experimental and computational findings reveal that the P2X2 receptor–IgG antibody complex likely undergoes absorption in an ‘extended’ configuration. Whereas the P2X2 receptor is less adsorbed once is complexed to the IgG antibody compared to its individual state, the opposite is observed for the antibody. This insight enhances our understanding of the role of protein–protein interactions in the process of protein adsorption.
Pianissimo: A Sub-mW Class DNN Accelerator With Progressively Adjustable Bit-Precision

With the widespread adoption of edge AI, the diversity of application requirements and fluctuating computational demands present significant challenges. Conventional accelerators suffer from increased memory footprints due to the need for multiple models to adapt to these varied requirements over time. In such dynamic edge conditions, it is crucial to accommodate these changing computational needs within strict memory and power constraints while maintaining the flexibility to support a wide range of applications. In response to these challenges, this article proposes a sub-mW class inference accelerator called Pianissimo that achieves competitive power efficiency while flexibly adapting to changing edge environment conditions at the architecture level. The heart of the design concept is a novel datapath architecture with a progressive bit-by-bit datapath. This unique datapath is augmented by software-hardware (SW-HW) cooperative control with a reduced instruction set computer processor and HW counters. The integrated SW-HW control enables adaptive inference schemes of adaptive/mixed precision and Block Skip, optimizing the balance between computational efficiency and accuracy. The 40 nm chip, with 1104 KB memory, dissipates 793- $1032~\mu \text{W}$ at 0.7 V on MobileNetV1, achieving 0.49-1.25 TOPS/W at this ultra-low power range.
Consideration of SHP-1 as a Molecular Target for Tumor Therapy

Abnormal activation of receptor tyrosine kinases (RTKs) contributes to tumorigenesis, while protein tyrosine phosphatases (PTPs) contribute to tumor control. One of the most representative PTPs is Src homology region 2 (SH2) domain-containing phosphatase 1 (SHP-1), which is associated with either an increased or decreased survival rate depending on the cancer type. Hypermethylation in the promoter region of PTPN6, the gene for the SHP-1 protein, is a representative epigenetic regulation mechanism that suppresses the expression of SHP-1 in tumor cells. SHP-1 comprises two SH2 domains (N-SH2 and C-SH2) and a catalytic PTP domain. Intramolecular interactions between the N-SH2 and PTP domains inhibit SHP-1 activity. Opening of the PTP domain by a conformational change in SHP-1 increases enzymatic activity and contributes to a tumor control phenotype by inhibiting the activation of the Janus kinase/signal transducer and activator of transcription (JAK/STAT3) pathway. Although various compounds that increase SHP-1 activation or expression have been proposed as tumor therapeutics, except sorafenib and its derivatives, few candidates have demonstrated clinical significance. In some cancers, SHP-1 expression and activation contribute to a tumorigenic phenotype by inducing a tumor-friendly microenvironment. Therefore, developing anticancer drugs targeting SHP-1 must consider the effect of SHP-1 on both cell biological mechanisms of SHP-1 in tumor cells and the tumor microenvironment according to the target cancer type. Furthermore, the use of combination therapies should be considered.
Analyzing and Providing Comprehensive Feedback for French CVS with Readerbench

In their everyday activities, recruiters are faced with the difficult task of analyzing and judging the quality of a wide range of CVs. Both the content quality and the visual hues, such as colors and their overall structure, need to be considered. This article enhances previous researches with a larger dataset, refined indices, and a more advanced technique of parsing the input documents. After applying various processing techniques from ReaderBench, an advanced Natural Language Processing framework, on a manually annotated dataset of 96 positive and negative French CVs, several writing indices were determined and filtered by leveraging statistical analyses. In addition, our experiment introduces a web application in which users can submit, gather an evaluation, and acquire valuable feedback on their CV.
Machine Learning-Based Fire Detection: A Comprehensive Review and Evaluation of Classification Models

Fires, regardless of their origin being natural events or human-induced, provide substantial economic and environmental hazards. Therefore, the development of efficient fire detection systems is of utmost importance. This study provides a comprehensive examination of the extant body of literature about studies on fire detection utilizing machine learning techniques. Significantly, the studies employed three distinct categories of datasets: pictures, data derived from Wireless Sensor Networks (WSNs), or a hybrid amalgamation of both. Our work mainly aims to categorize fire-related data utilizing four distinct classification models: Support Vector Machines (SVMs), Decision Trees, Logistic Regression, and Multi-Layer Perceptron (MLP). The model with the highest accuracy and ROC curve performance was identified through experimental analysis. The results of our study indicate that the MLP model exhibits the highest overall accuracy, achieving a score of 0.997. In this study, we analyze the learning curves to showcase the positive training dynamics of our model. Additionally, we explore the scalability of our model to ensure its suitability in real-world situations. In general, our research underscores the possibility of employing machine learning methodologies for fire detection, specifically emphasizing the effectiveness of the Multilayer Perceptron (MLP) model. This study contributes to the existing literature by offering valuable insights into the performance of several categorization models and conducting a comprehensive investigation of the Multilayer Perceptron (MLP) architecture. The results of our study have the potential to contribute to the advancement of fire detection systems, leading to enhanced accuracy and efficiency. This, in turn, may mitigate the adverse impacts of fires on both society and the environment.
Impact of Dynamic Tip-Surface Interactions on Microdroplet Formation via Fluid Force Microscopy

The emergence of FluidFM technology has presented a novel opportunity for selective aspiration and distribution of liquids at the sub-micron scale. However, precise control over droplet generation volume remains a challenging issue. This study investigates the impact of dynamic parameters between the tip and surface on droplet formation. Initially, we establish a finite element model to simulate the process from the formation of a liquid bridge between the hollow cantilever probe and the substrate to the rupture resulting in droplet formation. Additionally, we propose an image-based method for quantifying droplet volume. Subsequently, we construct a comprehensive experimental framework to delve into the influence of these parameters on microdroplet formation by monitoring the dynamic interaction parameters between the probe tip and the liquid surface. Finally, we optimize the parameters during the droplet generation process, enabling the formation of uniform arrays of microdroplets under controlled experimental conditions. Our study provides a reference for automation parameters of serial experiments and improves experimental throughput.
Palm print recognition

This paper presents a complete study for palm print identification that aims to see how high recognition accuracy can we reach by comparing some results of the previously used line based methods such as Gabor, Canny filters and Modified Finite Radon Transform to represent palm lines and our proposed method that uses basically Radon Transform to describe a person's palm lines. Radon coefficients are used as input features vector, two techniques of classification are used: Matching by correlation and Support Vector Machines. The whole process is applied on two palm print data bases CASIA and PolyU.
Cross-Band Decoupling Method for Dual-Band Aperture-Shared Antenna Array Using Metasurfaces

One method of using a metasurface to reduce the cross-band mutual interference of the dual-band aperture-shared antenna array is proposed. A base-station dual-band antenna array is designed to validate the method and is composed of a lower-band (LB) antenna element, $2\times2$ higher-band (HB) antenna elements, and a metasurface. The metasurface exhibits the bandstop characteristic in the HB and cancels the HB radiation from the LB antenna. Meanwhile, the metasurface absorbs part of the HB electromagnetic wave from the LB antenna. As a result, the mutual coupling between the LB and HB antennas is suppressed substantially. The metasurface causes little loss for the LB antenna. The dual-band aperture-shared antenna array covers the bands of 1.7–2.7 GHz (45.5%) and 3.3–3.8 GHz (14.1%). The simulated and measured results show that the proposed array has high port-to-port isolation, wide bandwidth, and stable radiation patterns in both the LB and HB.