Miracle-dz/UniFAR-FTUs huggingface dataset:
Early detection of the low dose of Lead exposure prevents mental disorders using non-invasive fluorescence and multi-omics

Lead poisoning is an epidemic that can affect anyone. Lead toxicity most commonly affects children and causes neurodevelopmental problems chronically. Lead is a heavy metal that alters calcium metabolism and deactivates enzymes essential in activating heme in the blood. Heme is an integral part of homeostasis and body defense in the shuttling of oxygen throughout the tissues, most notably the brain metabolism. The lack of oxygen throughout the brain slows down brain development (e.g., Neuronal cell migration, polarization, neuronal cell differentiation) and leads to lower IQs, learning disabilities, and lifelong ailments. The most common form of lead exposure comes from lead-based paint, toys, cosmetic materials, other household materials, various environments, and occupational pollutants. The current biomarkers used for lead toxicity successfully detect current lead levels in the blood but are not excellent at determining duration. Bone-Pb allows for the detection of lead toxicity duration, but it is not widely accepted throughout the United States. Significant limitations exist because all biomarkers test for lead poisoning after it has already happened which makes it challenging to prevent lead exposure in a clinical setting. Prevention needs to happen at the governmental and, more importantly at the individual levels. The focus of this study is on lead toxicity and prevention followed by reducing risk and improvement of detection utilized tools-driven molecular-based assessment along with visual imaging technique. It examines how lead finds its way into the human body for storage or metabolomic by-products. Identifying the risks of exposure would inform the mechanisms that can be employed to prevent further exposure and toxicity in other organs or intervene with metabolism or biotransformation. It predicts the potential counteract mechanisms through which lead affects the health of pregnant mothers, or interaction between prenatal, or the unborn, and post-natal status, different stages of developmental, and post-menopausal women. The study outlines the various ways of detecting the presence of low levels of lead in the blood and bones using non-invasive fluorescence combined with multidimensional omics such as metabolomic and proteomic by which early detection of organ dysfunction can be used to monitor Ca2+ metabolism in the cell or tissue/organ level before the onset of disease
SensorTalk3: 使用機器學習校正土壤導電度感測器 SensorTalk3: A Machine Learning Approach for Calibration of Soil Electrical Conductivity Sensors

Smart agriculture utilizes Internet of Things (IoT) technologies to enable low-cost Electrical Conductivity (EC) sensors to support farming intelligence. Because of aging and changing of weather and soil conditions, the EC sensors are prone to long-term drift over years of operation. Therefore, they must be recalibrated on a regular basis to guarantee data accuracy. In most existing solutions, an EC sensor is calibrated by the standard sensor to build the calibration table. This paper proposes SensorTalk3, an ensemble approach of machine learning models including XGBOOST and Random Forest, which can be executed at an edge device (e.g., Raspberry Pi) without GPU acceleration. Our study indicates that the soil information (both temperature and moisture sensor data) plays an important role in SensorTalk3 that significantly outperforms the existing lookup table calibration approaches. The MAPE of Sen-sorTalk3 can be as low as 1.738%. Our study indicates that the accuracies of moisture/temperature are not significant on EC calibration. That is, even if uncalibrated moisture/temperature sensors are used, SensorTalk3 can still calibrate EC well. SensorTalk3 can perform model training during data collection at the edge node. When the training date are completely collected, AI training is also finished at the edge node. Such AI training approach has not been found in most edge AI approaches.
A REVIEW BASED ON SENTIMENT ANALYSIS TECHNIQUES

Sentiment analysis, is known as opinion mining approaches, it is the way to analyse customer reviews on various platforms of social media sites or shopping sites, for example audit, blogs, twitter, facebook, amazon, flipcart etc, in order to predict the positive or negative sentiment arise by customer or blogger related to the particular person, article or any product. Opinion mining term is generally used by the customers to decide whether to purchase a particular product or not, also by market analyser to forecast whether & by how much sale of a product must be increased or decreased as per the real time condition. These research can further be implemented to classify live tweets on social media sites on any topic. The basic idea is to create human decision making and the polarity of the text and classify it into positive, negative or neutral. This research paper discusses related issues of sentiment analysis techniques and challenges involved in it
Mappings Text to Occupational Characteristics; Mapping Job Titles to SOC Codes; Mapping Job Titles to OCC Codes

In this document, we outline and justify the procedures we use ﬁrst, to convert the text from job ads to diﬀerent occupational characteristics, and second to map the job title to either an SOC code or an OCC code. For the ﬁrst task of constructing mappings from job ad text to occupational characteristics, we rely on three complementary approaches: i) mappings based on our own judgment of the meaning of words and phrases, ii) mappings based on a continuous bag of words model which we have constructed
2.4 Automated Anomaly Detection Through Assembly and Packaging Process

In the semiconductor industry the desired quality and effectiveness in the process of assembling integrated circuits is nowadays at the limit and without safety margin. To achieve important competitive advantages, this process must be continuously optimized and adjusted. Such process is indeed strongly dependent on parameters that are distributed among various control technology assemblies, materials, and the environment. However, the current inspection tools deployed for defect detection through assembly and packaging process are mainly based on rigid and simple rules. The latter are handcrafted by engineers, which can only extract shallow features. Therefore, the accuracy of classiﬁcation by tools is quite low, which provides incomplete information for root cause investigation and can cause yield-loss costs due to over reject. Hence, automatic inspection tools for visual defect detection, acting as ﬁnal quality gate before shipping to end customers is very demanding. Therefore, a deviation detection model based on machine learning is developed. On the other side, due to the lack of existing labelled images, an anomaly detection is proposed, in some cases as an assistant tool for collecting defect images with less effort. Results show that artiﬁcial intelligent (AI) solutions can achieve a better performance than the classical tools and overcome the human ability in detecting the deviation in the data.
DIAGNOSTIC ACCURACY OF DOPPLER ULTRASOUND IN DETECTION OF PORTAL VEIN THROMBOSIS IN HEPATOCELLULAR CARCINOMA

Objective: To determine diagnostic accuracy of Doppler ultrasound in detection of portal vein thrombosis in hepatocellular carcinoma keeping findings on multi-detector Computed tomography as the gold standard. Methods: A cross-sectional study was conducted at Radiology Department, Hayatabad Medical Complex, and Peshawar from November 2019 to April 2020. 129 known cases of hepatocellular carcinoma were subjected to Doppler ultrasound to detect portal vein thrombus. Thrombus detected was carefully examined for internal color signals. After that all patients were subjected to multi-detector computed tomography to detect the presence or absence of portal vein thrombus. Results: The mean age of patients in our study was 56 ±8 years. The frequency of portal vein thrombosis in patients with hepatocellular carcinoma was 28%. Color Doppler sonography had 89.3% sensitivity and 95.8% specificity in detection of portal vein thrombosis in comparison with multidetector computed tomography. For the detection of arterial flow in the thrombus (malignant thrombus) its sensitivity and specificity were 80.7% and 100% respectively. Conclusion: Color Doppler sonography is an effective, non-invasive and radiation free method in detecting and evaluating the nature of portal vein thrombosis in HCC.
Learning Explicit Infeasibility from Implicit Configuration Space Connectivity

—Sampling-based algorithms generate plans using samples from implicit representations of high dimensional configuration spaces. These samples contain unexploited information, which can be use to build a better understanding towards the configuration space’s connectivity. We aim to use these samples as data to learn explicit infeasibility proofs. Previous work has shown success in up-to 4 DoF manipulation scenes. In this work, we focus on improving the learning step of the previous algorithm and show experimental results on a 5-DoF manipulator
Incentives to di⁄erentiate under environmental liability laws : Product customization and precautionary e⁄ort

We endogenize location/product speci(cid:133)cation choices in a spatial Cournot duopoly on the linear market, when (cid:133)rms(cid:146) output entails an accidental harm to the environment. Under a strict liability regime, the equilibrium involves no di⁄erentiation when the expected harm is low enough. This outcome is suboptimal, and identical to the spatial pattern obtained under a no-liability regime. With larger harm, the equilibrium displays some dispersion/product di⁄erentiation, the degree of which is increasing with the level of harm towards the (cid:133)rst best locations/product choices. Our results are robusts when allowing for (cid:133)rms(cid:146)investment in environmental measures. Moreover, we show that vertical/care di⁄erentiation occurs whenever horizontal product di⁄erentiation arises. Finally, we show that under a negligence rule, (cid:133)rms always comply with the due care level, but the equilibrium involves no di⁄erentiation, either horizontal/product or vertical/care.
PARAMETER ESTIMATION FOR UNIFORM MAXIMUM PROCESS

Lewis and McKenzie have described the maximum process with marginal distribution U(0;1). In this paper, we discuss some pro- perties of this process. We also apply some estimation methods for es- timating the parameter of the process. It is shown that the conditional least squares estimator is strongly consistent and asymptotically normal.
Common Causes and Prevention of Post Harvest Losses in Fruits and Vegetables

The quality of fruits and vegetables is generally impacted by the period of time and storage conditions they are subjected to between harvest and consumption. Careful handling of fresh produce is greatly important as they are living tissue and therefore continue to be physiologically active even after harvest. The respiration rate of fruits and vegetables, together with the various processes applied to them eventually impact the final quality. Nonetheless, a decrease in the quality of fresh produce is harmful as it leads to postharvest losses (consumer unacceptability or waste). Postharvest losses usually stem from bruising, decay, water loss, chilling injury and excessive softening due to improper maturity at harvest. These losses are majorly the resultant effect of improper handling, damaging fruit tissue, insufficient cooling, along with failure to maintain ideal storage temperature, as well as failure to sort and remove low quality items along with inappropriate packaging materials and methods. However, there have been proven actions/ processes performed on fresh items to maintain quality and safety of the harvested fruit in order to extend their shelf-life; these are generally referred to as postharvest treatments.
Rotational angular momenta of close binary system components. II. Analytic models.

The ratio between rotational angular momentum, J rot , and orbital angular momentum, J orb , in close binary systems and its variation with mass ratio is studied. The tables and the graphs give this variation for detached systems, contact systems, semidetached systems and for systems containing a supernova-remnant component and a contact component. For this study some statistical relations of close binary stars were used. The ratio J rot / J orb is sensitive to the variation of the mass ratio q. If q differs much from unity and if the concentration of the stellar matter is moderate (polytropic index n ~ 3), the neglect of rotational angular momentum, J rot , is not justified.
Intrusion Detection Techniques Used For Internet of Things

Internet of Things (IoT) is a collection of embedded devices as a network in daily life. It comprises of electronics, connectivity and coding. Users are communicating with these devices but enable to control or access information. Providing security and privacy to the data in these networks is a crucial task and there is a need of intrusion detection systems (IDSs) developed for the IoT related security attacks. This article provides a study of new IDSs techniques developed for the IoT network model and to the layers. Key considerations for the development of such IDSs square measure introduced as a future outlook at the tip of this survey.
Comparative Study of the Machine Learning Techniques for Predicting the Employee Attrition

: Employee Attrition is one of the biggest business problems in HR Analytics . Companies invest a lot in the training of the employees keeping in mind the returns they would provide to the company in the future . If an employee leaves the company, it is the loss of opportunity cost to the company . Attrition is particularly prevailing in the mass recruiting companies . In this paper, we have taken a sample of the employee database of IBM USA . Using the information value concept, we found out that the characteristics of employees like Job Role, overtime, job level affect the attrition largely . We implemented various classification algorithms like logistic regression, LDA, ridge classification, lasso classification, decision trees, random forests to predict the probability of attrition of any new employee and simultaneously tested them. Using various model evaluation metrics we made a comparative analysis of the models & found out that LDA gave the highest accuracy, logistic gave the highest precision and ridge gave the highest recall. The comparative study enables the organization to select a model depending on its business requirements. It will help an organization find out the probability of attrition of any new candidate which they might be hiring . Also, it would help companies decide the salary hike & changes in perks for the existing employees and tweak it in such a way that the employee is retained somehow .
ELF-based Code Storage Support for the Eclipse OMR Ahead-of-Time Compiler: A WebAssembly Use Case

Over the years, Ahead-of-Time (AOT) compilation has drawn significant attention in the research community due to its ability to accelerate the startup of a runtime system. AOT compilation involves compiling, persisting and re-using existing compiled code in later runs, thereby avoiding costly re-compilation. Typically, a program is compiled and translated into machine language or native code, which persists in a binary container format. At present, the most commonly used code container options are either cache-based or object-based. Eclipse OMR is a collection of components, which are used to build robust language runtimes. The WebAssembly AOT compiler (wabtaot) is constructed using the Eclipse OMR JitBuilder library. Currently, the wabtaot compiled code is stored in a prototype of the Eclipse OMR shared cache. The goal of this research is to find a better lightweight AOT code storage option for resource-constrained systems. To achieve this goal, the Eclipse OMR ELF generation module is enhanced by implementing an ELF shared object generator that can store AOT data and use it as part of the Eclipse OMR AOT component. The design and implementation of the ELF shared object for Eclipse OMR is discussed and demonstrated using wabtaot. Evaluation is carried out by comparing the ELF shared-object approach with the existing shared cache in the wabtaot environment on metrics like execution speed, memory footprint, file size and sharing. By doing so, the execution time trade-offs, lower memory consumption and compact-size ELF objects are witnessed, thereby
Trichoderma :A Plant Growth Promoting Fungi

Plant growth promoting fungi improve plant growth, development and productivity in many crops. Trichoderma spp. , a major cultivable PGPF in soils, extensively promotes plant growth biomass and yield by improving the creation of plant stimulatory compounds, such as growth hormones (Indole Acetic Acids, Cytokinin, Gibberellins and Zeatin) and by the secretion of antifungal substances i.e., iron chelators (Siderophore), hydrogen cyanide and Ammonia, which can protect plants from infection by phytopathogens and promotes plant growth and improves fruit and seed production. The objective of this study to evaluate the efficacy of nineteen Trichoderma isolates to produce Indole Acetic Acid (IAA), Hydrogen Cyanide (HCN) and Ammonia (NH 3 ). In present study, nineteen isolates of Trichoderma spp. were tested for their ability to produce IAA, HCN and NH 3 on Czapek-Dox medium, Malt Extract Agar medium and 1% peptone water. Among the 19 isolates used, isolates 101 and 102 showed significantly higher percentage of IAA production whereas isolate Number- 22 and 36 were weakest IAA producer after 17 th days of incubation. 3 isolates were moderate producers of HCN i.e., isolate number-13, 59 and 73. And 6 isolates (isolate number-22, 62, 72, 93, 95 and 102) were detected as weak producers of HCN. NH3 production was detected in 100% isolates, out of them isolate number-4, 13, 22, 23, 37, 59, 62, 70, 101 and 102 were strong producers while isolate number- 24, 36, 72, 73, 89, 93, 95, 97 and 99 were weak producers. The study confined that all nineteen isolates that were used, showed the good production of IAA, HCN, NH 3 . Hence these can be applied in the field as Plant Growth Promoting Fungi (PGPF).
Is income inequality good or bad for growth? Further empirical evidence using data of all Brazil cities

This paper estimates the impacts of personal income inequality on subsequent growth in a massive spatial dataset for 5564 Brazilian cities observed in 1991, 2000, and 2010 using instrumental variables methods. Instead of assuming exogenous income inequality, we relax this assumption and let the data speak for themselves. Applying Wu-Hausman test data reject inequality exogeneity regarding subsequent growth. Our methods allow identifying causal effects between inequality and subsequent growth in conditional quantiles beyond the average. Income inequality signiﬁcantly predicts adverse effects on subsequent growth using Brazilian city’s data in the conditional mean, and in all considered conditional quantiles below and above average, being larger in more extreme quantiles. In the conditional mean, the magnitude of the estimate implies that lowering personal income inequality by 1 Gini point would translate to an increase in a cumulative economic growth of 2 . 51 percentage points in the following ten years. Our ﬁndings call attention to the risk of following Litschig and Lombardi [ J. Economic Growth , 2019, vol. 24, pp. 155-187] when studying the inﬂuence of inequality on growth neglecting the endogeneity of the former to the latter. Desigualdade pessoal de renda afeta negativamente o crescimento econ ˆ omico subsequente na m ´ edia, acima e abaixo da m ´ edia, e nos extremos da distribuic¸ ˜ ao condicional na regress ˜ ao de crescimento de Forbes. Para a m ´ edia condicional, estima-se que a reduc¸ ˜ ao de uma unidade no ´ ındice de Gini corresponda a um aumento acumulado de 2,51% em termos de crescimento econ ˆ omico na d´ecada seguinte. As conclus˜oes n˜ao mudam quando os efeitos positivos e negativos de aglomerac¸˜ao em cidades s˜ao inclu´ıdos no modelo de regress˜ao de Forbes. Endogeneidade negligenciada pode levar a conclus˜oes equivocadas.
Microbes and Infectious Diseases Microbiological profiles of neonatal sepsis in Northern Egypt

study aimed at analyzing the microbiological profile of neonatal sepsis in Egypt and to determine the antibiotic susceptibility patterns of the isolated microorganisms. Methods: Two thousand and four hundred blood samples were collected from neonates showing symptoms suggestive of septicemia, inoculated into BACT/ALERT culture bottles. Positive blood culture samples were identified and tested for antimicrobial susceptibility. Results: Among the neonates included in our study, 457 (19%) neonates were positively diagnosed with sepsis. early onset neonatal sepsis (EOS) was detected in 181 (39.6%), while late-onset neonatal sepsis (LOS) in 276 (60.4%) cases. Klebsiella pneumoniae was the most commonly isolated microorganism. Staphylococcus aureus and Enterococcus spp. were the most common isolated Gram-positive bacteria. Candida spp. was more encountered in LOS. An alarming feature of the present study is the high incidence of multidrug resistant microorganisms (65%). Among Gram negative isolates (56%) of were extended spectrum beta-lactamase ( ESBL) producers and (70.5%) were carbapenem resistant. In Gram positive bacteria, resistance to methicillin in S. aureus and coagulase negative staphylococci were detected in (50%) and (41%) of isolates respectively. Additionally, 17% of Enterococcus isolates were vancomycin resistant. Coclusion: Our bacteriological profile of neonatal sepsis showed that Gram negative bacteria represented the majority of isolates. Klebsiella pneumoniae was the predominant isolate. In our study, both EOS & LOS share a nosocomial infection profile, as high antimicrobial resistance was observed among our isolates. The susceptibility profiles of the isolates may urge for the change of the current used empirical therapies. the isolated resistant strains patterns and resistance in
Comparison of percutaneous cross screw xation versus open reduction and internal xation for Day type II crescent fracture-dislocation: Case-control study

Guangheng Xiang Wenzhou Medical University Second A liated Hospital Xiaoyu Dong Wenzhou Medical University Second A liated Hospital Xingan Jiang Wenzhou Medical University Second A liated Hospital Leyi Cai Wenzhou Medical University Second A liated Hospital Jianshun Wang Wenzhou Medical University Second A liated Hospital Xiaoshan Guo Wenzhou Medical University Second A liated Hospital Jian Xiao Wenzhou Medical University Second A liated Hospital Yongzeng Feng (  wenzhouykdx@163.com ) Wenzhou Medical University Second A liated Hospital