Miracle-dz/UniFAR-FTUs huggingface dataset:
A Web‐Based Integrated Modeling and Simulation Method for Forest Growth Research

To facilitate forest research, simulations of the whole forest growth process can be employed to analyze forest dynamics and predict forest yields. Different forest growth models can be integrated for comprehensive process simulation and thus can assist forest growth research. With the development of network technologies, a web environment can provide cross‐platform capability and wide availability for distributed researchers. In order to serve the simulation of complex forest growth processes and help online forest growth research, this article proposes a web‐based integrated modeling and simulation method for forest growth research. The proposed method includes three steps, namely, model preparation, model integration, and forest growth simulation. The corresponding implementation strategies are designed to prepare forest growth models, integrate different models, preprocess model data, and implement forest growth simulations for integrated modeling and simulations via the web. Two applications in the comprehensive prediction of forest growth and comparison of different forest management decisions are introduced to verify the feasibility and capability of the proposed method. The results show that the proposed web‐based integrated modeling and simulation method can be used conveniently for comprehensive simulations of forest growth research.
Synchronization Analysis of Winner-Take-All Neuronal Networks

With the physical limitations of current CMOS technology, it becomes necessary to design and develop new methods to perform simple and complex computations. Nature is efficient, so many in the scientific community attempt to mimic it when optimizing or creating new systems and devices. The human brain is looked to as an efficient computing device, inspiring strong interest in developing powerful computer systems that resemble its architecture and behavior such as neural networks. There is much research focusing on both circuit designs that behave like neurons and arrangement of these electromechanical neurons to compute complex operations. It has been shown previously that the synchronization characteristics of neural oscillators can be used not only for primitive computation functions such as convolution but for complex non-Boolean computations. With strong interest in the research community to develop biologically representative neural networks, this dissertation analyzes and simulates biologically plausible networks, the four-dimensional Hodgkin-Huxley and the simpler two-dimensional Fitzhugh-Nagumo neural models, fashioned in winner-take-all neuronal networks. The synchronization behavior of these neurons coupled together is studied in detail. Different neural network topologies are considered including lateral inhibition and inhibition via a global interneuron. Then, this dissertation analyzes the winner-take-all behaviors, in terms of both firing rates and phases, of neuronal networks with different topologies. A technique based on phase response curve is suggested for the analysis of synchronization phase characteristics of winner-take-all networks. Simulations are performed to validate the analytical results. This study promotes the understanding of winner-take-all operations in biological neuronal networks and provides a fundamental basis for applications of winner-take-all networks in modern computing systems.
A NARX Model to Predict Myocardial Ischemic Beats from ECG Using Features Extracted by ICA and WPD

This paper presents a methodology for predicting Myocardial Ischemic Beats from ECG signal using a Nonlinear Autoregressive Neural Network with Exogenous inputs (NARX) model. This technique utilizes the features extracted by integrating independent component analysis (ICA) and Wavelet packet decomposition (WPD) on ECG for detecting Myocardial Ischemic beats. At first, the denoised ECG beat segments are projected on the bases to create the independent component (IC) vectors. Further, these IC vectors are disintegrated by WPD. The feature set for distinguishing ischemic beats is extracted by calculating entropy, mean and standard deviation from wavelet coefficients. These features are input to NARX model for uncovering ischemic beats from normal beats. Several architectures of NARX models were tested for predicting myocardial ischemic beats. The efficacy of NARX architectures are assessed by comparing MSE and correlation coefficient. The NARX model with 2 hidden neurons and 2 delay lines provided the best results with a MSE of 0.0002 and correlation coefficient 0.99, which implies that the NARX neural network has huge potential in the prognosis of Myocardial Ischemic Beats.
A new technique for subcontractor selection by adopting choosing by advantages

Abstract Proper selection of subcontractor is considered as an important criterion in the successful management of the various construction projects. Previous methods used for subcontractor selection (SS) suffered several drawbacks such as: its complexity and being time consuming. The main aim of this study is to propose a new technique for SS by adopting Choosing By Advantages (CBA). The selection factors for subcontractor are determined. CBA principals, steps, merits and some of its applications in the construction context are demonstrated. Proposed SS technique is then developed, implemented on a case study and compared with recent methods in literature. The main contributions to the body of knowledge are as follows: (1) propose equations which determine exactly how to assess the percentage advantage for each factor for quantitative factors, qualitative factors with different grades/ranks, and qualitative factors on a yes/no basis and (2) subcontractors attain a positive utility when they are able to combine a low price with a superior level of five factors, which are: reputation, acknowledgement certificates, qualification of staff, amount of work in the last five years, and estimated time of the project. Globally, this technique is highly beneficial to industry practitioners because it will facilitate the subcontractor selection process.
Effects of Cigarette Smoking on Auditory Function

Introduction: Few of the studies advocate that cigarette smoking have been associated with development of hearing loss. Both conductive and sensorineural type has been reported. However, comprehensive audiological evaluation on smokers has been scarce and this is especially true with respect to Indian population. Thus there is an extremely important need to assess the hearing status in smokers and to view pathophysiology of auditory system in greater detail. Considering the social relevance of this aspect, particularly in the Indian context, the present study was undertaken. Method: The study consisted of two groups of subjects: the first group (clinical) comprised of 30 males (age range: 15-55 years) who has habit of cigarette smoking and the second group (control) with 30 normal hearing male subjects, without the habit of cigarette smoking, within the same age range. Audiological assessment included: Puretone audiometry including high frequency audiometry, Speech audiometry, Tympanometry, Distortion Product Otoacoustic Emissions (DPOAE) and Brainstem Evoked Response Audiometry (BERA). Results and Discussion: In the present study, pathological auditory involvement was clearly evident in smokers. The puretone audiometric findings indicated reduced hearing sensitivity in smokers. This was particularly evident at high frequency regions as demonstrated by audiogram pattern. Sensorineural hearing loss was more prevalent in smokers. DPOAE findings indicated involvement of cochlear outer hair cells. BERA indicated probable involvement of auditory nerve and brainstem. Conclusions: Periodic audiological evaluation in smokers could demonstrate presence/progress of auditory deficits. Such information may prove effective in helping smokers to quit the habit. Thus audiological evaluation could serve a great social cause.
New scheme to prevent node replication attacks for wireless sensor networks

Wireless sensor networks are a relatively new technology for information gathering and processing. A sensor network usually consists of many, resource constrained sensor nodes. These nodes perform measurements of some physical phenomena, process data, generate reports, and send these reports via multihop communication to a central information processing unit called sink. Depending on the scenario, information gathering and processing is collaboratively performed by multiple sensor nodes, e.g., to determine the average temperature in a certain area. Sensor networks can be used in a plethora of application scenarios. Emerging from military research, e.g., sensor networks for target tracking in a battle field, sensor networks are nowadays used more and more in civil applications such as critical infrastructure monitoring. For ensuring the functionality of a sensor network, especially in malicious environments, security mechanisms are essential for all sensor networks. However, sensor networks differ from classical (wireless) networks and this consequently makes it harder to secure them. Reasons for this are resource constraints of the sensor nodes, the wireless multihop communication, and the possibility of node compromise. Since sensor nodes are often deployed in unattended or even hostile environments and are usually not equipped with tamper-resistant hardware, it is relatively easy to compromise a sensor node. So, the sensor network can suffer from various types of attacks and threats. Among them, a mentionable attack is node clone. In node clone attacks, an attacker seizure a node and gets all the credential like ID, cryptographic key, code, data, etc., of that node. With this information the adversary made more replica node in the network. Various schemes have been offered for identification of node clone attacks. But they have some limitations. This work proposes a centralized system for clone identification. We split the network into segments and select an inspection node for each segment. Inspection node identifies a clone node by checking the node’s ID and cryptographic key. We also proposed honeypot technique which increases the replica detection performance. Finally, we provide solution for two cases of blacklisted replica nodes: The cases are when the cluster head compromised and the when the normal node is compromised. Experimental shows that the solutions improve the network performance.
Multiple Realization in Systems Biology

Thomas Polger and Lawrence Shapiro claim that unlike human-made artifacts cases of multiple realization in naturally occurring systems are uncommon. Drawing on cases from systems biology, I argue that multiple realization in naturally occurring systems is not as uncommon as Polger and Shapiro initially thought. The relevant cases, which I draw from systems biology, involve generalizable design principles called network motifs, which recur in different organisms and species and perform specific functions. I show that network motifs with entirely different underlying causal structures can perform the same function of interest. The article also considers the scope problem of multiple realization.
Survey on Pre-Processing Web Log Files in Web Usage Mining

Web mining is to decide and extricate helpful data. In the web age web applications are expanding at gigantic speed and the web clients are expanding at exponential speed. As number of clients develops, site distributers are having expanding their data for pulling in and fulfilling clients. It is conceivable to follow the clients' quintessence and collaborations with web applications through web server log record and Web log document contains just (.txt) record. The information put away in the web log record comprise of enormous measure of disintegrated, inadequate, and pointless data. In view of enormous measure of superfluous information's accessible in the web log record, a unique log document can't be straightforwardly utilized in the web use mining. So pre-processing method is connected to improve the quality and effectiveness of a web log record. Various procedures are connected in pre-processing that is information cleaning, information combination, information joining. In this paper we will overview distinctive pre-processing system to recognize the issues in web log document and to improve web utilization digging pre-processing for example mining and investigation.
Quality of life in diabetic and non diabetic patients on hemodialysis therapy

Chronic kidney disease is a worldwide public health problem and a chronic disease that deteriorates the quality of life (QOL). Diabetes is recognized as co-morbidity in patients of end stage renal disease (ESRD). This study evaluated QOL in diabetic and non-diabetic patients on maintenance dialysis therapy. A cross-sectional study was conducted on ESRD with or without diabetes on hemodialysis therapy for at least three months at Kidney Center, Sheikh Zayed Medical Complex, Lahore Pakistan. QOL was assessed using WHOQOL-BREF questionnaire by World Health Organization. One hundred and thirty seven (n=137) hemodialysis patients were observed. 59 (41.8%) were with diabetes mellitus (DM) and 78 (55%) were without DM. 81 (54%) were on hemodialysis for more than 2 years. There was no statistically significant (p≥0.066) difference in QOL scores of hemodialysis patients with or without diabetes; however, a significant (p≤0.025) difference was observed in responses of ‘meaningfulness’ and “ability to concentrate” by patients of both groups. The scores were divided in two categories of ‘≤50’ and ‘>50’; a significant (p≤0.047) difference between two groups was observed in physical domain only. The current study on diabetic and non-diabetic hemodialysis patients showed no statistical difference in their QOL except for “meaningfulness of life” and “ability to concentrate”. Key words: End stage renal disease (ESRD), diabetes mellitus (DM), quality of life (QOL), hemodialysis.
Studying the serum ghrelin levels of the elite athletes and sedentary controls fasting at the time of Ramadan

The Ghrelin is produced primarily by enteroendocrine cellsin the gastric mucosa, and increases gastric emptying in normal subjects.The purpose of this study is to determine the differences in the Ghrelin level during different times along the day, and to assess the differences between the athletes and sedentary at the end of Ramadan fasting period of one month. The results indicated that there were no significant difference among the Ghrelin levels of athletes and sedentary during different times through the day (p>0.05). Significant differences were observed between pre- and post-tests mean Ghrelin scores of subjects (p 0.05). A significant and positive relationship was found in the middle level at the percentages of BMI and body fat of both groups. Moreover, there were significant differences among BMI scores (p<0.01) and body fat % (p<0.05) of the athletes and sedentary. Consequently, fasting during Ramadan did not cause any differentiation on the serum Ghrelin level of athletes and sedentary, but without group differentiation there was significant increase from the initial measurement to the last measurement of the serum Ghrelin level. There was a significant and positive relationship at middle level between BMI and body fat % of both groups of athletes and sedentary. There was a significant relationship, on the other hand, between BMI and repeated Ghrelin measurements of subjects.
Consequences of autoimmunity to sperm antigens in vasectomized men.

The development by a large percentage of vasectomized men of sperm autoantibodies is discussed in this monograph chapter. The production of anti-sperm antibodies is attributed to: 1) granuloma formation; 2) increased permeability of epithelial barriers in the rete testis and epididymis; and 3) transport of phagocytic cells to regional lymph nodes. Individual variation in type of antibodies and response to antibody production is documented and is thought to depend on such factors as rate of sperm production the structure of the blood-testis barrier surgical technique and expression of immune response genes. Morphological changes in spermatozoa and testes occurring after vasectomy may be induced by immunological mechanisms. Tests of cell-mediated immunity to sperm antigens are described and more accurate tests are needed. Animal studies provide evidence that chronic immune stimulation can result in formation of circulating immune complexes resulting in deleterious systemic effects including damage to kidneys blood vessels and cells of the immune system. Further studies of the autoimmune orchitis phenomenon may aid in avoidance of immunologically mediated side effects of vasectomy.
Psicogeriatria e medicina generale

Nelle liste dei medici di base le persone con piu di 64 anni sono tra il 17 e il 33%, gli ultra 75enni vanno dall'8 al 22%. I risultati di una ricerca in corso, nell'ambito del progetto finalizzato Invecchiamento del CNR, mostrano che il 23% degli ultra 75enni valutati a domicilio ha un deficit cognitivo mediograve, mentre il 26% delle donne e il 12% degli uomini presenta un quadro depressivo clinicamente rilevante. Da altri studi risulta che gli psicofarmaci e i cerebroattivi sono tra i farmaci piu prescritti: la percentuale di anziani in terapia con queste sostanze e rispettivamente del 25 e del 26% (Spagnoli et al., 1989).
Secure Brain-to-Brain Communication With Edge Computing for Assisting Post-Stroke Paralyzed Patients

Stroke affects 33 million individuals worldwide every year and is one of the prime causes of paralysis. Due to partial or full paralysis, most of the patients affected by stroke depend on caregivers for the rest of their lives. Easy and efficient communication from the patient to the caregiver is a vital parameter determining the quality of life during rehabilitation. Several solutions, such as brain–computer interface (BCI) systems and exoskeletons, are proposed for post-stroke rehabilitation. But, most of these devices are expensive, sophisticated, and put an additional burden on the patient. Also, the communication between the patient and the caregiver is insecure. In this article, the brain-to-brain interface technique is integrated with an efficient encryption algorithm to enable secure transmission of information from the patient’s brain to the caregiver. When a patient thinks of a word or a number, the thought is transmitted with the help of an electroencephalogram (EEG) headset through a wireless medium to the recipient, who correctly interprets the thoughts conveyed by the sender and types the same alphabet on the keyboard at his/her end. The transmitted message at the edge is encrypted with a lightweight novel tiny symmetric algorithm (NTSA), which can only be decrypted at the edge receiver. The Internet of Things integrated system is also flexible to send signals to multiple caregivers at the same time. The proposed method tested on ten users gave an average effective concentration percentage of 78.9% along with the secure transmission, which is a significant result compared with existing solutions.
A highly-verified biometric recognition system using an ultra-speed specifically-developed finger vein sensor

Currently, Biometrics has been utilized the top five modality of face, voice, IRIs, fingerprint, and palm to identify individuals. Comparatively, these Biometrics systems need complex computation to be slow and an easy target to hack. Alternatively, this work proposes a novel biometrics system of highly secured recognition with low computation time using specifically designed biometrics sensor. Consequently, finger vein recognition has been developed. Although, this recognition requires high point of safety measures comes with its individual experiments. The most prominent one being the vein pattern is very difficult to extract because finger vein images are constantly low in quality, seriously hampering the feature extraction and classification stages. Sophisticated algorithms need to be designed with the conventional hardware for capturing finger-vein images is modified by using red Surface Mounted Diode (SMD) leds. For capturing images, Canon 750D camera is used with micro lens. The integrated micro lens gives better quality images, and with some adjustments it can also capture finger print. Results have been comparatively improvement for SDUMLA-HMT database and extensively evaluated with k-nearest neighbors (KNN) algorithm. The (KNN) algorithm is a simple, easy-to-implement supervised machine learning algorithm that can be used to solve both classification and regression problems. KNN calculations are highly accurate in test data. Using stratified 6- fold analysis on all fingers of all hands in collected database, a maximum accuracy of 100% was achieved with an EER of 0% when select right hand and middle finger, based on the analysis of the 106 persons present in the data set. Many approaches have been used to optimize vein image quality. The proposed system has optimum results as compared to existing related works. The work novelty is due to the hardware design of the sensor within the finger-vein recognition system to obtain, simultaneously, finger vein and finger print at low cost, unlimited users for one device and open source.
A 10-mA LDO With 16-nA IQ and Operating From 800-mV Supply

A low-dropout (LDO) regulator with a quiescent current in the tens of nanoampere range and operating from 800-mV supply is proposed. A rail-to-rail buffer with zero input–output (I/O) voltage shift and based on the differential flipped voltage follower is used for combined gate and bulk driving of the output device. Therefore, bulk modulation with forward body bias is implemented without any additional amplifier. The proposed buffer is a crucial block for the sub-1-V supply and for limiting the contribution of the output device to the quiescent current. The error amplifier (EA) is adaptively biased with a bias shaper block, which implements a current limiting at high loads and a linear dependence on the output current at moderate loads. The feedback signal for the bias control is the output of the amplifier instead of the gate voltage of the pass device, thus combining a nanoampere bias at light load with the ability to follow a fast output current transient. Finally, a corner-tracking load is used to set the bias current of the output device to the minimum value at the target stability margin over the temperature and process parameters’ space. The LDO was implemented in a 55-nm CMOS technology. The measured quiescent current is 16 nA with a minimum power supply rejection of 42.7 dB up to 50 kHz and a maximum load current of 10 mA. In order to compare the transient behavior of the state-of-the-art designs, a modified figure of merit is proposed, taking into account the penalty caused by the low supply.
DETECTING AND COUNTING COCONUT TREES IN PLEIADES SATELLITE IMAGERY USING HISTOGRAM OF ORIENTED GRADIENTS AND SUPPORT VECTOR MACHINE

This paper describes the detection of coconut trees using very-high-resolution optical satellite imagery. The satellite imagery used in this study was a panchromatic band of Pleiades imagery with a spatial resolution of 0.5 metres. The authors proposed the use of a histogram of oriented gradients (HOG) algorithm as the feature extractor and a support vector machine (SVM) as the classifier for this detection. The main objective of this study is to find out the parameter combination for the HOG algorithm that could provide the best performance for coconut-tree detection. The study shows that the best parameter combination for the HOG algorithm is a configuration of 3 x 3 blocks, 9 orientation bins, and L2-norm block normalization. These parameters provide overall accuracy, precision and recall of approximately 80%, 73% and 87%, respectively.