Miracle-dz/UniFAR-FTUs huggingface dataset:
Factors Influencing Zai Pit Technology Adaptation: The Case of Smallholder Farmers in the Upper East Region of Ghana

Low soil fertility is a major limitation to rain fed agriculture among smallholder farming in Africa [1]. Nutrient depletion and inadequate water in the soil of most African countries for some time now has transformed originally fertile lands that could yielded between 2t ha-1 and 4t ha-1 of cereal grain, into infertile lands where cereal crops yields less than 1t ha-1 [2]. Insufficient water couple with soil infertility is a major drawback to rain fed agriculture among smallholder farming in Africa [1]. To be able to restore soil to sufficient level of fertility, water harvesting techniques and improved soil fertility management technologies should be promoted among the smallholder framers. The soil fertility interventions include use of mineral fertilizer and organics such as animal manure and green manure among others [3]. The use of these technologies enable farmers to deepen their production and thereby increase economic benefits due to increased yields. Water as identified to be one of the important factors that facilitates plant growth needs to be sustained in the soil to improve plant growth. Soil moisture method farmers can adopt includes, macro and micro catchment technologies and rooftop harvesting technologies. Micro-catchment is a method of collecting runoff rains near the growing plant and replenish the soil moisture which are generally used to grow plants like maize, sorghum, groundnuts and millet. The micro-catchment methods includes zai pit, also known in Niger as Tassa and in Mali as Towalen, which has been identified as one of the successful interventions that improve rainfall capturing and lessen runoff and evaporation, and in a long run improves agricultural productivity [4].
An Overview on the Fate of Fast Fashion and Nanoparticle Treated Textiles

Fashion is perceived as a glamor of the world, with numerous fashion shows and photographic services in glossy magazines. Behind, however, there is an evolving industry that is constantly confronted with new problems: from the ecological impact of production and manufacturing chains (1), to the speeding marketing by social media. The textile and fashion industry is the second most polluting sector in the world(2,3). The production of raw materials, including natural and synthetic textile materials, causes deforestation and endangers the safety of farmers and workers in developing countries(2). This industry is also responsible for 20% of water pollution globally (2,4).
Precise Humane Diabetes Management: Synergy Of Physiological And Psychological Data In AI Based Diabetes

: Artificial Intelligence (AI) is revolutionizing the healthcare industry. It has got the eloquence to provide globally accessible, improved diagnosis at affordable price. As, per Forbes report the investment in healthcare AI is expected to reach $6.6 billion by 2021 portraying a compound annual growth rate of 34 percent. The market of AI in diabetes care alone is expected to reach $626.3 million by 2022, as per Infoholic Research LLP. AI is seen as the most germane technology remodeling the diabetes care sector. Diabetes is anticipated to affect an approximate of 642M people by 2040. More funding are being poured into the research of using AI to provide the right diabetes treatment. This paper reviews the appropriateness of AI in providing right and timely diabetes treatment. It also highlights the impediments present in laundering successful accurate diagnosis. Currently, stress is laid upon considering only the physiological data of the patient with diabetes by AI. The human dimension, i.e. the psychological data of the patient is nowhere considered by the AI models. This certainly leads to absurdity. Psychological research has an important role to play in improving the prevention and care of diabetes. Disregardance of this aspect will lead to fallacious treatment being administered to a patient. Putting AI based diabetic care on the wrong track. Hence stressing upon the need to consider both physiological and psychological data of a patient, this review paper highlights the right kind of data to be examined and investigated while modeling the AI algorithms. This aids to administer personalized precise clinical diagnosis and quality diabetes care.
Dynamic Resource Allocation in Cloud Computing Using a Combination of Meta-heuristic Algorithms

Cloud computing can be considered one of the largest IT solutions to industry problems. In fact, this concept can be used for features such as processing power or storage service provider cloud only through the Internet connection costs low without worrying about hardware upgrades or software updates used software. But because of a cloud may not be responsive to user needs, recently introduced a new concept called association clouds. The concept is to meet the needs of users, a few clouds at a time and uses integrated management. On the other hand due to the increasing use of cloud computing technology providers are faced with new challenges that one of the most important scheduling and allocation of resources. The problem is that the NP-hard, there is no capacity to respond to it in a reasonable time using linear methods. So in this study, a metaheuristic method based on a combination of genetic algorithms and colonial competition used. Since these two algorithms have shown a good performance in this area, in this study, the two algorithms are combined in series with the synchronous target to reduce implementation time and costs were considered. Finally, simulations show that the proposed method has better answers to these two algorithms has produced.
An Efficient Video to Video Face Recognition using Neural Networks

The interpretations at face images are difficult owing to its wide variations like appearance, individual, different facial poses and illumination. In biometrics video based face recovery is vital and this paper proposes an efficient algorithmic mode which achieves high recovery rate. The face recognition system proposed in this paper comprises of three stages video partitioning, feature extraction and neural network for recognition. The video partitioning was based on the changes in scene and feature extraction was carried out by local binary pattern and Principal Component Analysis. The algorithm is tested on four publically available datasets and the experimental results substantially prove that the proposed algorithm achieves higher face recognition rate when compared with the recent related work.
Multi –User Secure Data Sharing for Dynamic Groups in the Cloud Mona

— Cloud computing is an emerging computing paradigm in which resources of the computing are provided as services over the Internet and this also come out with many new challenges for data security and access control. Cloud computing is the developing technology, where data owners can remotely store and modify their data on the premise of pay-as-use manner and enjoy on demand high-quality applications. The fundamental service provided by the cloud is data Storage. Data sharing among the cloud user within a group is efficient. While sharing data in a multi-owner manner, preserving data and identity privacy from an untrusted cloud is a challenging issue, because of the frequent change of the membership, sharing data in multi-owner manner become a very difficult task. Therefore we propose secure multi-owner data sharing for dynamic groups by combining group signature and dynamic broadcast encryption techniques. At the same time we compute the number of revoked user and efficiency.
Design of a MP-SOC using on chip interconnections

— This paper presents the silicon-proven design of a novel on-chip network to support guaranteed traffic permutation in multiprocessor system-on-chip applications. The proposed network employs a pipelined circuit-switching approach combined with a dynamic path-setup scheme under a multistage network topology. The dynamic path-setup scheme enables runtime path arrangement for arbitrary traffic permutations. The circuit-switching approach offers a guarantee of permuted data and its compact overhead enables the benefit of stacking multiple networks.Interconnection of various processing elements with guaranteed traffic permutation and optimality of Scheduling over the Network-On-Chip has been reported in this paper. It supports to reduce the complexity of on chip network implementation. The proposed network topology contains a dynamic probing mechanism to interconnect different processing elements to make a path setup dynamically by using the probe routing algorithm, multistage network topology and communication locality on Network-on-Chip were implemented with reduced area and speed constraints on chip.
Input Maping and Simulation Analysis using Adaptive Network Based Fuzzy Inference System

— Fuzzy logic control systems are structured numerical estimators. They combine both the numerical process and human like reasoning. Neural networks are numerical trainable dynamical systems that are able to emulate human brain functions; their connectionist structure can be used to find the proper parameters and structures that resemble human thinking rules for fuzzy logic controllers. Generally fuzzy logic is best applied to non linear, time varying, ill-defined systems, which are too complex for conventional control systems to apply. In this paper a new combinational connectionist structure is proposed which exploits the advantages of both the fuzzy and neural networks avoiding the rule-matching time of the inference engine in the traditional fuzzy logic system. Some examples are presented using MATLAB simulation to illustrate the performance and applicability of the proposed connectionist model.
OPTIC CUP SEGMENTATION USING MANUAL THRESHOLDING LEVEL TECHNIQUE *1Arwa

Optic cup segmentation is a very important step in glaucoma detection, where untreated glaucoma leads to irreversible optic nerve head (ONH) damage and loss of vision. Thus, it is the second cause of blindness around the world. In this paper, manual thresholding level technique in optic cup segmentation is applied, evaluated, and compared with other segmentation techniques. The optic cup segmented by thresholding level 240 from the optic disc partas a region of interest (ROI), after vessel removal by the morphological operation to improve segmentation process, filtering, contrast equalization and finally boundary smoothed by dilating and eroding operation and border clearing. The algorithm was evaluated on DRISHTI-GS database, which has anoptic cup segmentation ground truth. The proposed cup segmentation method evaluate by 3 parameters, which are Dice coefficient (DSC), Jaccard coefficient and structural similarity (SSIM) and achieved best Dice coefficient 73%, Jaccard coefficient 60%, and structural similarity 93%. Because the human visual system is good at perceiving structure, the SSIM quality metric agrees more closely with the subjective quality score, therefore, the obtained results so near to the ophthalmologist segmentation judgment. It was concluded that an optic cup (OC) segmentation algorithm via manual thresholding technique is easy, fast, and inexpensive computationally and obtained good segmentation results and would help in early detection of glaucoma by the ophthalmologist.
Understanding Gamers’ Experience of Engaging in a CyberFusion LAN Party

This paper describes the findings of the research concentrating on gamers’ experience of playing digital games at a LAN (Local Area Network) party. The objective of the study was to understand the gamers’ motives of gaming in a LAN party and to investigate the immersive experience of gamers while playing games. The data were gathered through our observation and survey instrument. The survey among thirty LAN gamers shows that this cohort of exclusively male gamers were motivated mainly by social (M=4.45) and interest (M=4.03) intentions. Furthermore, the gamers tended to have high immersive experiences (M=130.86) during the event .
A Study on Image Retrieval Techniques Based On Visual Features

-The image retrieval is fascinating and quickest developing methodology altogether fields. Recently, the content-based image retrieval has become the recent topic and therefore the techniques of content-based image retrieval (CBIR) are achieved nice development. Content based Image Retrieval (CBIR) could be a one among the image retrieval technique that uses visual features of a picture like color, shape, and texture feature etc. It allows the end user to present the query image so as to retrieve the saved pictures in database in line with their similarity to the query image. This paper includes different approaches used for Image Retrieval such as Relevance feedback, SVM, BTC, Wavelet transform. The present method provides 100% accuracy and computational complexity is less compare to other methods.
MELATONIN RECEPTOR 1A GENE POLYMORPHISM IN MURRAH BUFFALOES WITH POSSIBLE IMPACT ON SUMMER ANESTROUS

In the present study murrah buffaloes were analyzed to investigate polymorphic sites for Mnl I, Rsa I and Hpa11 using three different primers of expected size of 268 bp (1), 824 bp (2) and 856 bp (3). PCR of genomic DNA using primers 1, 2 and 3 resulted in successful amplification of bands of 824 bp, 268 bp and 856 bp corresponding to the main part of the exon II of the MTNR1A melatonin receptor gene. Polymorphism was noticeable at 50 bp and 267 / 110 bp restriction sites upon digestion of 824 bp PCR product with Mnl I and Rsa I, respectively. Genotypic frequency for the presence of 50 bp and 267 / 110 bp restriction sites was 0.62 and 0.95 / 0.33, respectively. Digestion of 856 bp PCR product with Mnl I and Rsa I also showed polymorphism at 236 / 50 bp and 320 / 50 bp restriction sites, respectively. Genotypic frequency for the presence of 236 / 50 bp and 320 / 50 bp restriction sites was 0.04 / 0.76 and 0.10 / 0.05, respectively. Our observations also revealed a difference in polymorphism of MTNR1A gene amid heifers and cows. Polymorphism observed in MTNR1A gene in Murrah buffaloes may have impact on summer anestrous and needs to be investigated.
IMPLEMENTATION OF SNMP(SIMPLE NETWORK MANAGEMENT PROTOCOL) ON SENSOR NETWORK: A REVIEW

— A WSN (Wireless sensor network) interconnected with IP networks could help bridging physical world and also helps in external monitoring and controls Wireless Sensor Network. In this paper implementation of SNMP (Simple Network Management Protocol) on Sensor network in IPv6 based on TCP/IP such as GPRS is shown. By showing packet transmission between the nodes considering them as server and client nodes by using location based IP addressing protocol .
Identifying Nursing Activities to Estimate the Risk of Cross-contamination

Identifying Nursing Activities to Estimate the Risk of Cross-Contamination Kaveh Seyed Momen Doctor of Philosophy Graduate Department of Institute of Biomaterials and Biomedical Engineering University of Toronto 2012 Hospital Acquired Infections (HAI) are a global patient safety challenge, costly to treat, and affect hundreds of millions of patients annually worldwide. It has been shown that the majority of HAI are transferred to patients by caregivers’ hands and therefore, can be prevented by proper hand hygiene (HH). However, many factors including cognitive load, cause caregivers to forget to cleanse their hands. Hand hygiene compliance among caregivers remains low around the world. In this thesis I showed that it is possible to build a wearable accelerometer-based HH reminder system to identify ongoing nursing activities with the patient, indicate the high-risk activities, and prompt the caregivers to clean their hands. Eight subjects participated in this study, each wearing five wireless accelerometer sensors on the wrist, upper arms and the back. A pattern recognition approach was used to classify six nursing activities offline. Time-domain features that included mean, standard deviation, energy, and correlation among accelerometer axes were found to be suitable features. On average, 1-Nearest Neighbour classifier was able to classify the activities with 84% accuracy. A novel algorithm was developed to adaptively segment the accelerometer signals to identify the start and stop time of each nursing activity. The overall accuracy of the algorithm for a total of 96 events performed by 8 subjects was approximately 87%. The accuracy was higher than 91% for 5 out of 8 subjects.
E-Learning Using Cloud Computing: A Study of Coastal Andhra Pradesh

-E-Learning, the use of Electronic Media and Information and Communication Technologies (ICTs) in education, facilitates the forms educational technology in learning and teaching. Under the system of cloud computing, a large number of computers connected through a real-time communication network can be maintained in order to facilitate the scopes of E-Learning. Hence, the situation pertaining to E-Learning has become phenomenal with the massive use of cloud computing. The issues like lack of e-content, infrastructure and knowledge and socio-economic barriers are posing potential threats to the proliferation of cloud computing usages for E-Learning in India. Here, the researcher has attempted to explore the scope and limitation of cloud computing usages in E-Learning in India in general, coastal Andhra Pradesh in particular. Here the researcher has adopted the case study method to examine the use of cloud computing for E-Learning in two colleges of Coastal Andhra Pradesh.
Best practices for the management of vitamin D deficiency

1Zakład Biochemii, Radioimmunologii i Medycyny Doświadczalnej, Instytut „Pomnik – Centrum Zdrowia Dziecka”, Warszawa Kierownik Zakładu: prof. dr hab. med. Roman Janas 2Klinika Endokrynologii, Centrum Medyczne Kształcenia Podyplomowego, Warszawa Kierownik Kliniki: prof. dr hab. med. Wojciech Zgliczyński 3Klinika Pediatrii, Reumatologii, Immunologii i Chorób Metabolicznych Kości, Uniwersytecki Dziecięcy Szpital Kliniczny im. Ludwika Zamenhofa w Białymstoku Kierownik Kliniki: dr hab. med. Jerzy Konstatynowicz 4Zakład Biochemii i Chemii Klinicznej, Warszawski Uniwersytet Medyczny Kierownik Zakładu: prof. dr hab. med. Jacek Łukaszkiewicz 5Klinika Geriatrii, Chorób Wewnętrznych i Chorób Metabolicznych Kości, Centrum Medyczne Kształcenia Podyplomowego, Warszawa Kierownik Kliniki: prof. dr hab. med. Marek Tałałaj
Performance Analysis of Rayleigh Fading Channels in Mobile Communication over AWGN Channel

: In a mobile communication environment the channel is not time invariant and is slowly varying. This characteristic feature of the channel leads to a phenomenon called Fading. Fading channels induce rapid amplitude fluctuations in the received signal. If they are not compensated for then this will lead to serious performance degradation. In this paper simulations have been conducted to study the Bit Error Rate (BER) performance of a Rayleigh fading channel and it is compared to the BER performance of the AWGN channel.
Comparison of the short-term outcomes of robot-assisted minimally invasive, video-assisted minimally invasive, and open esophagectomy

Background The development of minimally invasive surgery has initiated many changes in the surgical treatment of esophageal cancer (EC) patients. The aim of this study was to compare the short-term outcomes of robotic-assisted minimally invasive esophagectomy (RAMIE), video-assisted minimally invasive esophagectomy (VAMIE), and open esophagectomy (OE). Methods Our study included patients who had undergone McKeown esophagectomy at Tianjin Medical University Cancer Institute and Hospital between January 2016 and December 2018. We analyzed clinical baseline data, as well as perioperative and pathological outcomes. Results A total of 312 cases met the inclusion criteria (OE: 77, VAMIE: 144, RAMIE: 91). The OE group had a greater number of late-stage patients as well as those who received the neo-adjuvant therapy, compared with the other two groups (P=0.001). The procedure time in the OE group was also shorter by approximately 20 minutes (P=0.021). Total blood loss was significantly lower in the two MIE groups (P=0.004) than in the OE group. There were no differences in the total number of dissected lymph nodes between the three groups (OE: 24.09±10.77, VAMIE: 23.07±10.18, RAMIE: 22.84±8.37, P=0.680). Both the lymph node number (P=0.155) and achievement rate (P=0.190) in the right recurrent laryngeal nerve (RLN) area were comparable between the three groups. However, in the left RLN area, minimally invasive approaches resulted in a higher number of harvested lymph nodes (P=0.032) and greater achievement rate (P=0.018). Neither MIE procedure increased the incidence of postoperative complications. Conclusions Minimally invasive surgery could guarantee the quality of bilateral RLN lymphadenectomy without increasing postoperative complications, especially in RAMIE patients. The rational choice of different surgical approaches would improve both safety and oncological outcomes for patients.
HYBRID-DOUBLE MULTIPLIER ARCHITECTURE FOR ELLIPTIC CURVE CRYPTOGRAPHY

- High-performance and fast implementation of point multiplication is crucial for elliptic curve cryptographic systems. Recently, considerable research has investigated the implementation of point multiplication on different curves over binary extension fields. In this paper, we propose efficient and high speed architecture implement point multiplication on binary Edwards and generalized Hessian curves. We perform a dataflow analysis and investigate maximum number of parallel multipliers to be employed to reduce the latency of point multiplication on these curves. Then, we modify the addition and doubling formulations and employ a newly proposed digit level hybrid double Gaussian normal basis multiplier to remove the data dependencies and hence reduce the latency of point multiplication. To the best of our knowledge, this is the first time that one employs hybrid double multiplication technique to reduce the computation time of point multiplication. Moreover, we have implemented our proposed architecture for point multiplication on FPGA and obtained the results of timing and area. Our results indicate that the proposed scheme is one step forward to improve the performance of point multiplication on binary Edward and generalized Hessian curves.