Miracle-dz/UniFAR-FTUs huggingface dataset:
Managing People in a Globalized Context

: The main element of innovation is the organizational culture aligned with the organizational model. Furthermore is a factor to attract creative personnel and to provide qualitative services. We will examine the organizational change of an international model but there are potentional problems during the transformation and on the implementation of a new management. Local management be able to cope with changes of internal, external environment and new strategies. Decisions and procedures are taken from different views of Mother Company. Regarding Human Resources Management and following standard procedures we could have quality management.
MIT Open Access Articles Entropic Energy-Time Uncertainty Relation

Energy-time uncertainty plays an important role in quantum foundations and technologies, and it was even discussed by the founders of quantum mechanics. However, standard approaches (e.g., Robertson ’ s uncertainty relation) do not apply to energy-time uncertainty because, in general, there is no Hermitian operator associated with time. Following previous approaches, we quantify time uncertainty by how well one can read off the time from a quantum clock. We then use entropy to quantify the information-theoretic distinguishability of the various time states of the clock. Our main result is an entropic energy-time uncertainty relation for general time-independent Hamiltonians, stated for both the discrete-time and continuous-time cases. Our uncertainty relation is strong, in the sense that it allows for a quantum memory to help reduce the uncertainty, and this formulation leads us to reinterpret it as a bound on the relative entropy of asymmetry. Because of the operational relevance of entropy, we anticipate that our uncertainty relation will have information-processing applications.
APPLICATION OF CRISPR FOR PLANT GENOME EDITING: A REVIEW

: CRISPR-Cas9 technology is a recently created technique for genome editing that offers unparalleled variety, precision, and simplicity. CRISPRs are bacterial DNA segments with brief repeats in their nucleotide sequence. Genome editing usually entails identifying a section of a plant's genome that may be altered to make it more disease and herbicide resistant, improve yields, quality, or other desirable features. In vitro and in vivo model systems; CRISPR/Cas9 genome editing has been utilized to fix DNA mutations ranging from a single base pair to massive deletions. A single guide RNA (typically around 20 nucleotides) that is complementary to a target gene or locus and is anchored by a protospacer-adjacent motif achieves site-specific alteration. DNA double-strand breaks (DSBs) are induced by targetable nucleases such as zinc-finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), and clustered regularly interspaced short palindromic repeats (CRISPR) or CRISPR-associated. The error-prone non-homologous end joining (NHEJ) or homology-directed repair mechanisms, which have evolved over time, are used to repair DSBs (HDR). CRISPR reagents are transported as DNA, RNA, or protein RNA into plant cells, where they assemble into an active site-directed nuclease (SDN) that cleaves specified DNA sequences to produce DSB. Because of its capacity to cut DNA from any genome, the CRISPR/Cas9 system has a wide range of possible applications. The CRISPR gene was discovered in Escherichia coli for the first time. The main objectives of this review shows that the application of CRISPR-Cas9 technology.
A Graph Contrastive Learning with Feature Perturbation for Recommender Systems

—Recommender systems are an effective solution to address the issue of information overload and a thriving research field. This paper focuses on the efficient mining of user-item relationships and the aggregation of neighborhood information in bipartite graph-based recommender systems. Therefore, a Graph Contrastive Learning recommendation model with Feature Perturbation (GCL-FP) is proposed in this work. The proposed model utilizes a graph convolutional encoder with a residual structure, simplifying the feature transformation and the nonlinear activation in graph convolution. This approach helps alleviate the over-smoothing problem of node representation. Furthermore, a data augmentation method, incorporating graph convolution aggregation information into the random noise, is designed. By perturbing the graph node feature matrix in this way when generating the contrastive learning view, we ensure that the noise does not excessively alter the semantic information of node features. Finally, experiments on three benchmark datasets, namely Yelp2018, Douban-Book, and Alibaba-iFashion, were conducted. The experimental results show that our proposed model outperforms the baseline model Simple Graph Contrastive Learning (SimGCL) by improving the Recall evaluation metric by 2.2%, 5.4%, and 1.9%, respectively as well as the Normalized Discounted Cumulative Gain (NDCG) evaluation metric increases by 1.8%, 3.0%, and 1.8%, respectively.
The Internal Seiches in Gullmar Fjord. Part II: Contribution to Basin Water Mixing

The mixing in the basin water (the water below sill level) of Gullmar Fjord has been investigated with the main focus on the contribution from internal seiches. A companion paper reports evidence for dissipation of internal seiche energy in the basin water after near-critical reflection from the bottom. In the present paper the magnitude and variation of basin water mixing is investigated, using the budget method. The results are related to variations of three energy sources, namely (i) the internal seiches, (ii) the internal tides, and (iii) the internal waves generated by the external seiche. The mixing efficiency, defined as the irreversible work against buoyancy forces due to mixing divided by the total mechanical energy loss from the sources mentioned above, is about 7%, similar to results obtained for other fjords. Large variations in mixing are shown to be related to large variations of the energy sources. The internal seiches are found to be important for the mixing, with a contribution that is 144% of the internal tide contribution during the most energetic period and 92% on average over the investigated periods. Including contributions from the external seiche, the wind forcing is responsible for 61% of the basin water mixing, while tidal forcing is responsible for 39%.
Survey Face Detection and Recognition

: In this paper we present a comprehensive and critical survey of face detection algorithms. Face detection is a necessary first-step in face recognition systems, with the purpose of localizing and extracting the face region from the background. It also has several applications in areas such as content-based image retrieval, video coding, video conferencing, crowd surveillance, and intelligent human–computer interfaces. However, it was not until recently that the face detection problem received considerable attention among researchers. The human face is a dynamic object and has a high degree of variability in its apperance, which makes face detection a difficult problem in computer vision. A wide variety of techniques have been proposed, ranging from simple edge-based algorithms to composite high-level approaches utilizing advanced pattern recognition methods. The algorithms presented in this paper are classified as either feature-based or image-based and are discussed in terms of their technical approach and performance. Due to the lack of standardized tests, we do not provide a comprehensive comparative evaluation, but in cases where results are reported on common datasets, comparisons are presented. We also give a presentation of some proposed applications and possible application areas.
The association of bone mineral density measures with incident cardiovascular disease in older men and women: the Health, Aging, and Body Composition Study

Summary The associations of volumetric and areal bone mineral density (BMD) measures with incident cardiovascular disease (CVD) were studied in a biracial cohort of 2,310 older adults. BMD measures were inversely related to CVD in women and white men, independent of age and shared risk factors for osteoporosis and CVD.
Analyses of How Effective and Efficient Logistics Activities Impact Online Retailing: A case of online retailing in Accra

: This research is to analyze and assess how efficient logistics activities can impact online retailing in Ghana and their ability to meet the requirements and expectations of modern Ghanaian consumers. Consumers’ awareness and interest in online retailing were analyzed based on responses from the interviews. A SWOT analysis is conducted three online retailers (Tisu.com, Kaymu.com.gh and Zoobashop.com) to assess the strength of the industry and the opportunity it presents notwithstanding the threats that are likely to hinder its growth and development. A further Cost-Benefit analysis (CBA) is conducted on the use of online retail websites as an alternative to conventional shopping if not a replacement. The study found that majority of the respondents had adequate knowledge of the industry and that challenges such as online security and cybercrime has been a great hindrance to consumers’ patronage of online products. The market for online retailers is varied and has a very bright potential in the foreseeable future of online retailing in Ghana, targets such as career people in commerce, students, education and public servants among others is a great asset to the industry and if efficient and effective measures are taken to address issues hindering consumer patronage, online purchases could more than double in the next five to ten years.
Brand Personality of Tourism Destination: The Case of Nepal

: In an increasingly competitive tourism market, destination personality is considered as an important aspect for building destination brands and crafting a unique identity for tourism places. Destination brand personality can create a unique identity and led to enhance a tourism destination image. Destination brand personality has led to figure out the dimension of Nepal. In this study it shows the brand personality of tourism destination of Nepal. In this study it shows that there exist of five different brand personality dimensions for a tourism destination. “Solidity”, “Attractiveness”, “Excitement”, “Honesty” and “Hospitality” dimension are analyzed. With this dimension it can led marketers to position their destination and better understanding of the scenario and can leads to increase in the number of tourists and make the markets more favorable.
An Integrated Framework of CNN and Cloud for Plant Disease Detection and Classification with Location Tracking

Plant diseases are a major threat to farmers, consumers, environment, and the global economy. In India alone, 35% of field crops are lost to pathogens and pests causing losses to farmers. Indiscriminate use of pesticides is also a serious health concern as many are toxic and biomagnified. These adverse effects can be avoided by early disease detection, crop surveillance and targeted treatments. Most diseases are diagnosed by agricultural experts by examining external symptoms. However, farmers have limited access to experts. Our project is the first integrated and collaborative platform for automated disease diagnosis, tracking and forecasting. Farmers can instantly and accurately identify diseases and get solutions with a mobile app by photographing affected plant parts. Realtime diagnosis is enabled using the latest Artificial Intelligence (AI) algorithms for Cloud-based image processing. The AI model continuously learns from user uploaded images and expert suggestions to enhance its accuracy. Farmers can also interact with local experts through the platform. For preventive measures, disease density maps with spread forecasting are rendered from a Cloud based repository of geo-tagged images and micro-climactic factors. A web interface allows experts to perform disease analytics with geographical visualizations. In our experiments, the AI model (CNN) was trained with large disease datasets, created with plant images self-collected from many farms over 7 months. Test images were diagnosed using the automated CNN model and the results were validated by plant pathologists. Over 95% disease identification accuracy was achieved. Our solution is a novel, scalable and accessible tool for disease management of diverse agricultural crop plants and can be deployed as a Cloud based service for farmers and experts for ecologically sustainable crop production.
ACTA BIOLOGICA

: Impatiens glandulifera Royle (Himalayan balsam) is an annual plant, native to humid parts of the Himalayas. Brought to Europe in the XIX century, it has since successfully naturalized and spread throughout the continent, becoming one of the best-known invasive plants. Even though it has been thoroughly studied by many authors, some aspects of its biology and ecology remain unclear and debatable, such as its spreading dynamics, negative impacts in invaded ecosystems and ecological adaptability regarding moisture, nutrients, and light. This 2019 field study from the Ljubljana Marsh (Slovenia) has proved that Himalayan balsam successfully develops under mesophilic conditions, where it easily compensates moderate deviations from its ecological optimum. The species thrives in riparian zones, out-competing native vegetation, trait shown as potentially the biggest negative impact of chosen species in this context. This study additionally gave us a different insight into - usually highly emphasized - dynamics of species’ spreading. Spreading was not either dependent on hydrochory or as drastic as mentioned in literary sources, giving it a secondary role in species’ invasiveness on the chosen area. Lastly, we proposed rough estimates of eradication expenses for given area, based on our field results of species’ abundance and distribution on chosen area.
Evaluating variables of patient experience and the correlation with Evaluating variables of patient experience and the correlation with design design

The objective of this paper was to understand the variables of patient experience by analyzing recent and relevant evidence and to identify design solutions within the hospital environment that positively impact those variables. A systematic review of literature published from 2008-present was conducted to identify variables that contribute to patient experience benefits. Identified variables were documented and categorized into a design, organizational, and outcome variable matrix. Interviews were conducted with professionals from healthcare institutions, architecture firms and organizations committed to improving the patient experience. Data from healthcare facilities, with high patient experience scores, was also examined to derive effective design solutions. The results showed patient engagement, satisfaction - patient and staff, clinical effectiveness, personalization, patient safety and, admission and discharge process were the variables that demonstrated a strong correlation with patient experience benefits. Analysis of the variables resulted in a range of design solutions, such as private and public interaction spaces, decentralized care models, designated staff areas etc. that could potentially improve the outcomes associated with each variable. This study illustrates that aspects of patient experience adopted before, during, and after the delivery of care have the ability to affect adherence, communication, and ultimately the relationship with the patient. It also shows that design of the physical space has a significant impact on the model of care and the overall experience of the patient and family.
An Eﬀicient Sinogram Domain Fully Convolutional Interpolation Network for Sparse-View Computed Tomography Reconstruction

: Recently, deep learning techniques have been used for low-dose CT (LDCT) reconstruction to reduce the radiation risk for patients. Despite the improvement in performance, the network models used for LDCT reconstruction are becoming increasingly complex and computationally expensive under the mantra of “deeper is better”. However, in clinical settings, lightweight models with a low computational cost and short reconstruction times are more popular. For this reason, this paper proposes a computationally efﬁcient CNN model with a simple structure for sparse-view LDCT reconstruction. Inspired by super-resolution networks for natural images, the proposed model interpolates projection data directly in the sinogram domain with a fully convolutional neural network that consists of only four convolution layers. The proposed model can be used directly for sparse-view CT reconstruction by concatenating the classic ﬁltered back-projection (FBP) module, or it can be incorporated into existing dual-domain reconstruction frameworks as a generic sinogram domain module. The proposed model is validated on both the 2016 NIH-AAPM-Mayo Clinic LDCT Grand Challenge dataset and The Lung Image Database Consortium dataset. It is shown that despite the computational simplicity of the proposed model, its reconstruction performance at lower sparsity levels (1/2 and 1/4 radiation dose) is comparable to that of the sophisticated baseline models and shows some advantages at higher sparsity levels (1/8 and 1/15 radiation dose). Compared to existing sinogram domain baseline models, the proposed model is computationally efﬁcient and easy to train on small training datasets, and is thus well suited for clinical real-time reconstruction tasks.
BIRD: Generalizable Backdoor Detection and Removal for Deep Reinforcement Learning

Backdoor attacks pose a severe threat to the supply chain management of deep reinforcement learning (DRL) policies. Despite initial defenses proposed in recent studies, these methods have very limited generalizability and scalability. To address this issue, we propose BIRD, a technique to detect and remove backdoors from a pretrained DRL policy in a clean environment without requiring any knowledge about the attack specifications and accessing its training process. By analyzing the unique properties and behaviors of backdoor attacks, we formulate trigger restoration as an optimization problem and design a novel metric to detect back-doored policies. We also design a finetuning method to remove the backdoor, while maintaining the agent’s performance in the clean environment. We evaluate BIRD against three backdoor attacks in ten different single-agent or multi-agent environments. Our results verify the effectiveness, efficiency, and generalizability of BIRD, as well as its robustness to different attack variations and adaptions.
ANALYSIS ON COLLECTED AMOUNT OF PER-CAPITA MUNICIPAL SOLID WASTE AND ITS INFLUENCING FACTORS IN TAIHU LAKE BASIN

Annual variations on collected amount of per-capita municipal solid waste(MSW)in Taihu Lake Basin as well as the influencing factors were analyzed in this study.The selected influencing factors were the family size,age distribution of residents,and household consumption expenditure.From 2002～2007,the collected amount of per-capita MSW in shanghai city increased from 3.77million tons to 7.91million tons,which was almost doubled(P0.05). Within the same period,the collected amount of MSW from Jiangsu and Zhejiang provinces increased by 58%and 25%,respectively.The family size with 2～4people dominated in Taihu Lake Basin and their sum accounted for about 76%(P0.01). From 2002to 2007,the age distribution of Chinese resident has showed the signs of aging.The proportion of people between the ages of 0～14decreased yearly.Among those,Shanghai city and Jiangsu province decreased 25%(P 0.01). Statistical analysis showed that the collected per-capita MSW was significant positive correlated with the family size and the amount of household consumption expenditure.There was a significant negative correlation between amount of the collected MSW and the age distribution.Correlation coefficient between the collected MSW and the family size was the minimum.The related prediction equation was built on family size,age distribution,and household consumption expenditure for Taihu Lake Basin.The prediction equation was validated using the data from Zhejiang province,which was consistent between the original and the calculated date with a high degree.
Preoperative Assessment Mapping with Arterial Spin-Labeling for Specificity of Quantitative Functional Brain

BACKGROUND AND PURPOSE: Arterial spin-labeling is a noninvasive MR imaging technique allowing direct and quantitative measurement of brain perfusion. Arterial spin-labeling is well-established in clinics for investigating the overall cerebral perfusion, but it is still occasionally employed during tasks. The typical contrast for functional MR imaging is blood oxygen level – dependent (BOLD) imaging, whose speci ﬁ city could be biased in neurologic patients due to altered neurovascular coupling. This work aimed to validate the use of functional ASL as a noninvasive tool for presurgical functional brain mapping. This is achieved by comparing the spatial accuracy of functional ASL with transcranial magnetic stimulation as the criterion standard. MATERIALS AND METHODS: Twenty-eight healthy participants executed a motor task and received a somatosensory stimulation, while BOLD imaging and arterial spin-labeling were acquired simultaneously. Transcranial magnetic stimulation was subsequently used to de ﬁ ne hand somatotopy. RESULTS: Functional ASL was found more adjacent to transcranial magnetic stimulation than BOLD imaging, with a signi ﬁ cant shift along the inferior-to-superior direction. With respect to BOLD imaging, functional ASL was localized signi ﬁ cantly more laterally, anteriorly, and inferiorly during motor tasks and pneumatic stimulation. CONCLUSIONS: Our results con ﬁ rm the speci ﬁ city of functional ASL in targeting the regional neuronal excitability. Functional ASL could be considered as a valid supplementary technique to BOLD imaging for presurgical mapping when spatial accuracy is crucial for delineating eloquent cortex.
GSM Based Advance Security System by Using Pic Microcontroller

- In this Project PIC Microcontroller is used to interface with PIR Sensor, RFID Module, GSM Module, LCD display, and Keypad. If anybody enters from outside of the border, it will be first detected by the PIR Sensor and once the Person is detected by the PIR Sensor next level of security will be RFID card. It has to be get detected by RFID Reader. Once RFID Card gets detected, person has to enter the secret code by using the Keypad which will be displayed on LCD. If any of the authentications is not correct, the information of unauthorized entry will be sent to headquarter by GSM as SMS/Call
Exploring the Marketing Competencies of Women Entrepreneurs in Micro-Small Enterprises (MSEs) in Khartoum State-Sudan

: This study deals with the marketing competencies and capabilities of women entrepreneurs who own and manage micro-small-and medium (MSMEs)ventures in traditional and non-traditional lines of activities within the urban settings in Khartoum State, Sudan. Entrepreneurial and marketing competencies are part of the dynamic components that contribute to venture survival and success especially for women-owned/ managed MSEs. This study addresses the perceived knowledge gap in the literature regarding the marketing competencies of women entrepreneurs, using the marketing mix approach and the resource-based theory. It is a qualitative study which follows an interpretive approach and applies grounded theory techniques. The study is framed within the Marketing Mix approach and the resource-based theory highlighted within a gendered perspective. It examines how women entrepreneurs perceive and apply the marketing function within their MSEs to maintain success within highly competitive, turbulent and unpredictable markets. In such conditions it is essential to understand what marketing competencies do these women entrepreneurs are endowed with and utilize while practicing the marketing function in their entrepreneurial ventures and thrive for high performance and success.