bluuebunny/crossref_metadata_2025 huggingface dataset:
Multimodal Hybrid Deep Learning Approach to Detect Tomato Leaf Disease Using Attention Based Dilated Convolution Feature Extractor with Logistic Regression Classification
Authors: Islam Md Shofiqul , Sultana Sunjida , Farid Fahmid Al , Islam Md Nahidul , Rashid Mamunur , Bari Bifta Sama , Hashim Noramiza , Husen Mohd Nizam
Published: August 2022

Automatic leaf disease detection techniques are effective for reducing the time-consuming effort of monitoring large crop farms and early identification of disease symptoms of plant leaves. Although crop tomatoes are seen to be susceptible to a variety of diseases that can reduce the production of the crop. In recent years, advanced deep learning methods show successful applications for plant disease detection based on observed symptoms on leaves. However, these methods have some limitations. This study proposed a high-performance tomato leaf disease detection approach, namely attention-based dilated CNN logistic regression (ADCLR). Firstly, we develop a new feature extraction method using attention-based dilated CNN to extract most relevant features in a faster time. In our preprocessing, we use Bilateral filtering to handle larger features to make the image smoother and the Ostu image segmentation process to remove noise in a fast and simple way. In this proposed method, we preprocess the image with bilateral filtering and Otsu segmentation. Then, we use the Conditional Generative Adversarial Network (CGAN) model to generate a synthetic image from the image which is preprocessed in the previous stage. The synthetic image is generated to handle imbalance and noisy or wrongly labeled data to obtain good prediction results. Then, the extracted features are normalized to lower the dimensionality. Finally, extracted features from preprocessed data are combined and then classified using fast and simple logistic regression (LR) classifier. The experimental outcomes show the state-of-the-art performance on the Plant Village database of tomato leaf disease by achieving 100%, 100%, 96.6% training, testing, and validation accuracy, respectively, for multiclass. From the experimental analysis, it is clearly demonstrated that the proposed multimodal approach can be utilized to detect tomato leaf disease precisely, simply and quickly. We have a potential plan to improve the model to make it cloud-based automated leaf disease classification for different plants.
Taxonomy of Anomaly Detection Techniques in Crowd Scenes
Authors: Aldayri Amnah , Albattah Waleed
Published: August 2022

With the widespread use of closed-circuit television (CCTV) surveillance systems in public areas, crowd anomaly detection has become an increasingly critical aspect of the intelligent video surveillance system. It requires workforce and continuous attention to decide on the captured event, which is hard to perform by individuals. The available literature on human action detection includes various approaches to detect abnormal crowd behavior, which is articulated as an outlier detection problem. This paper presents a detailed review of the recent development of anomaly detection methods from the perspectives of computer vision on different available datasets. A new taxonomic organization of existing works in crowd analysis and anomaly detection has been introduced. A summarization of existing reviews and datasets related to anomaly detection has been listed. It covers an overview of different crowd concepts, including mass gathering events analysis and challenges, types of anomalies, and surveillance systems. Additionally, research trends and future work prospects have been analyzed.
Research on Data Security Communication Scheme of Heterogeneous Swarm Robotics System in Emergency Scenarios
Authors: Sun Yi , Shao Ying
Published: August 2022

In emergency scenarios where the on-site information is completely lacking or the original environmental state has been completely changed, autonomous and mobile swarm robotics are used to quickly build a rescue support system to ensure the safety of follow-up rescuers and improve rescue efficiency. To address the data security problem caused by the complex and changeable topology of the heterogeneous swarm robotics network in the process of building the rescue support system, this paper introduced a decentralized data security communication scheme for heterogeneous swarm robotics. First, we built a decentralized network topology model by using base robot, communication robotics, and business robotics, and it can ensure the stability of the system. Moreover, based on the decentralized network topology model, we designed a storage model using the master–slave blockchain method. The master chain is composed of base robot and communication robotics, which mainly store the digests of robot data in multiple slave chains to reach the global data consensus of the system. The slave chains are composed of business robotics and communication robotics, which mainly store all data on the slave chains to reach the local data consensus of the system. The whole data storage system adopts the Delegated Proof of Stake consensus mechanism to elect proxy nodes to participate in the data consensus tasks in the system and to ensure the data consistency of each robot node in the decentralized network. Additionally, a prototype of the heterogeneous swarm robotics system based on the master–slave chains is constructed to verify the effectiveness of the proposed model. The experimental results show that the scheme effectively solves the data security problem caused by the unstable communication link of the heterogeneous swarm robotics system.
Carbon Microfiber-Doped Smart Concrete Sensors for Strain Monitoring in Reinforced Concrete Structures: An Experimental Study at Various Scales
Authors: D’Alessandro Antonella , Birgin Hasan Borke , Ubertini Filippo
Published: August 2022

Concrete constructions need widespread monitoring for the control of their state of integrity during their service life. In particular, after critical events such as earthquakes, this type of structure may experience the formation and development of cracks and damage. A quick and affordable assessment of structural behavior is indicated to identify conditions of danger for users and the incipient collapse of structural elements. This work presents investigations on multifunctional concretes with self-sensing capabilities to carry out static and dynamic monitoring. The materials were produced by the addition of conductive carbon microfibers to the concrete matrix. Electrical and sensing tests were carried out on samples with small-, medium-, and full-scale dimensions. The tests demonstrated the good electrical and electromechanical properties of the proposed smart concrete sensors, which appear promising for their use in civil elements or structures. In particular, tests on real-scale beams demonstrated the capability of the material to monitor the dynamic behavior of full-scale structural elements.
Detection of Electronic Devices Using FMCW Nonlinear Radar
Authors: Cha Kyuho , Oh Sooyoung , Hong Hayoung , Park Hongsoo , Hong Sun K.
Published: August 2022

Nonlinear radars can be utilized to detect electronic devices, which are difficult to detect with conventional radars due to their small radar cross sections (RCS). Since the receiver in a nonlinear radar is designed to only receive harmonic or intermodulated echoes from electronic devices, it is able to separate electronic devices from non-electronic scatters (clutter) by rejecting their echoes at fundamental frequencies. This paper presents a harmonic-based nonlinear radar scheme utilizing frequency-modulated continuous-wave (FMCW) signals for the detection of various electronic devices at short range. Using a laboratory experiment setup for FMCW radar at S-band for Tx (C-band for Rx), measurements are carried out to detect electronic devices of various sizes. The results show that the detection of small electronic devices is possible with nonlinear FMCW radar when appropriate system parameters are selected. Furthermore, we also discuss the maximum detectable range estimation for electronic targets using the radar range equation for FMCW nonlinear radar.
EVtracker: An Event-Driven Spatiotemporal Method for Dynamic Object Tracking
Authors: Zhang Shixiong , Wang Wenmin , Li Honglei , Zhang Shenyong
Published: August 2022

An event camera is a novel bio-inspired sensor that effectively compensates for the shortcomings of current frame cameras, which include high latency, low dynamic range, motion blur, etc. Rather than capturing images at a fixed frame rate, an event camera produces an asynchronous signal by measuring the brightness change of each pixel. Consequently, an appropriate algorithm framework that can handle the unique data types of event-based vision is required. In this paper, we propose a dynamic object tracking framework using an event camera to achieve long-term stable tracking of event objects. One of the key novel features of our approach is to adopt an adaptive strategy that adjusts the spatiotemporal domain of event data. To achieve this, we reconstruct event images from high-speed asynchronous streaming data via online learning. Additionally, we apply the Siamese network to extract features from event data. In contrast to earlier models that only extract hand-crafted features, our method provides powerful feature description and a more flexible reconstruction strategy for event data. We assess our algorithm in three challenging scenarios: 6-DoF (six degrees of freedom), translation, and rotation. Unlike fixed cameras in traditional object tracking tasks, all three tracking scenarios involve the simultaneous violent rotation and shaking of both the camera and objects. Results from extensive experiments suggest that our proposed approach achieves superior accuracy and robustness compared to other state-of-the-art methods. Without reducing time efficiency, our novel method exhibits a 30% increase in accuracy over other recent models. Furthermore, results indicate that event cameras are capable of robust object tracking, which is a task that conventional cameras cannot adequately perform, especially for super-fast motion tracking and challenging lighting situations.
Testing for a Random Walk Structure in the Frequency Evolution of a Tone in Noise
Authors: Abramson Scarlett , Moran William , Evans Robin , Melatos Andrew
Published: August 2022

Inference and hypothesis testing are typically constructed on the basis that a specific model holds for the data. To determine the veracity of conclusions drawn from such data analyses, one must be able to identify the presence of the assumed structure within the data. In this paper, a model verification test is developed for the presence of a random walk-like structure in the variations in the frequency of complex-valued sinusoidal signals measured in additive Gaussian noise. This test evaluates the joint inference of the random walk hypothesis tests found in economics literature that seek random walk behaviours in time series data, with an additional test to account for how the random walk behaves in frequency space.
Monitoring and Identification of Agricultural Crops through Multitemporal Analysis of Optical Images and Machine Learning Algorithms
Authors: Espinosa-Herrera José M. , Macedo-Cruz Antonia , Fernández-Reynoso Demetrio S. , Flores-Magdaleno Héctor , Fernández-Ordoñez Yolanda M. , Soria-Ruíz Jesús
Published: August 2022

The information about where crops are distributed is useful for agri-environmental assessments, but is chiefly important for food security and agricultural policy managers. The quickness with which this information becomes available, especially over large areas, is important for decision makers. Methodologies have been proposed for the study of crops. Most of them require field survey for ground truth data and a single crop map is generated for the whole season at the end of the crop cycle and for the next crop cycle a new field survey is necessary. Here, we present models for recognizing maize (Zea mays L.), beans (Phaseolus vulgaris L.), and alfalfa (Medicago sativa L.) before the crop cycle ends without current-year field survey for ground truth data. The models were trained with an exhaustive field survey at plot level in a previous crop cycle. The field surveys begin since days before the emergence of crops to maturity. The algorithms used for classification were support vector machine (SVM) and bagged tree (BT), and the spectral information captured in the visible, red-edge, near infrared, and shortwave infrared regions bands of Sentinel 2 images was used. The models were validated within the next crop cycle each fifteen days before the mid-season. The overall accuracies range from 71.9% (38 days after the begin of cycle) to 87.5% (81 days after the begin cycle) and a kappa coefficient ranging from 0.53 at the beginning to 0.74 at mid-season
Fall-from-Height Detection Using Deep Learning Based on IMU Sensor Data for Accident Prevention at Construction Sites
Authors: Lee Seunghee , Koo Bummo , Yang Sumin , Kim Jongman , Nam Yejin , Kim Youngho
Published: August 2022

Workers at construction sites are prone to fall-from-height (FFH) accidents. The severity of injury can be represented by the acceleration peak value. In the study, a risk prediction against FFH was made using IMU sensor data for accident prevention at construction sites. Fifteen general working movements (NF: non-fall), five low-hazard-fall movements, (LF), and five high-hazard-FFH movements (HF) were performed by twenty male subjects and a dummy. An IMU sensor was attached to the T7 position of the subject to measure the three-axis acceleration and angular velocity. The peak acceleration value, calculated from the IMU data, was 4 g or less in general work movements and 9 g or more in FFHs. Regression analysis was performed by applying various deep learning models, including 1D-CNN, 2D-CNN, LSTM, and Conv-LSTM, to the risk prediction, and then comparing them in terms of their mean absolute error (MAE) and mean squared error (MSE). The FFH risk level was estimated based on the predicted peak acceleration. The Conv-LSTM model trained by MAE showed the smallest error (MAE: 1.36 g), and the classification with the predicted peak acceleration showed the best accuracy (97.6%). This study successfully predicted the FFH risk levels and could be helpful to reduce fatal injuries at construction sites.
ESEERP: Enhanced Smart Energy Efficient Routing Protocol for Internet of Things in Wireless Sensor Nodes
Authors: Dogra Roopali , Rani Shalli , Kavita  , Shafi Jana , Kim SeongKi , Ijaz Muhammad Fazal
Published: August 2022

Wireless sensor networks (WSNs) have recently been viewed as the basic architecture that prepared the way for the Internet of Things (IoT) to arise. Nevertheless, when WSNs are linked with the IoT, a difficult issue arises due to excessive energy utilization in their nodes and short network longevity. As a result, energy constraints in sensor nodes, sensor data sharing and routing protocols are the fundamental topics in WSN. This research presents an enhanced smart-energy-efficient routing protocol (ESEERP) technique that extends the lifetime of the network and improves its connection to meet the aforementioned deficiencies. It selects the Cluster Head (CH) depending on an efficient optimization method derived from several purposes. It aids in the reduction of sleepy sensor nodes and decreases energy utilization. A Sail Fish Optimizer (SFO) is used to find an appropriate route to the sink node for data transfer following CH selection. Regarding energy utilization, bandwidth, packet delivery ratio and network longevity, the proposed methodology is mathematically studied, and the results have been compared to identical current approaches such as a Genetic algorithm (GA), Ant Lion optimization (ALO) and Particle Swarm Optimization (PSO). The simulation shows that in the proposed approach for the longevity of the network, there are 3500 rounds; energy utilization achieves a maximum of 0.5 Joules; bandwidth transmits the data at the rate of 0.52 MBPS; the packet delivery ratio (PDR) is at the rate of 96% for 500 nodes, respectively.
The Psychology of Financial Giving: Values Congruence and Normative Organizational Commitment as Predictors of Alumni Monetary Donations to Higher Education
Authors: Gardner Donald G. , Pierce Jon L.
Published: August 2022

Many universities around the world depend on financial donations to maintain and enhance their operations. We propose that donating money to an organization is a specific form of organizational citizenship behavior. We theorize that perceived values congruence between alumni and their colleges/universities and normative organizational commitment each provide motivation for people to donate money. We tested our hypotheses using alumni from a private college in the USA, measuring both the amount as well as the frequency of their donations to the college. We also measured alumni self-reports of values congruence and normative organizational commitment. We found empirical support for a positive relationship between values congruence and commitment and between commitment and financial giving. Values congruence, however, was not related to giving behavior; normative organizational commitment fully mediated the relationship between values congruence, and the alumnus’ financial giving behaviors. Higher education organizations that depend on donations from members to sustain their operations might focus advancement efforts on developing a perception of values congruence in potential donors, and/or by stimulating a sense of obligation to give back to the colleges from which they have previously derived benefits.
A New Time of Reckoning, a Time for New Reckoning: Views on Health and Society, Tensions between Medicine and the Social Sciences, and the Process of Medicalization
Authors: da Cunha Diogo Silva , Raposo Hélder
Published: August 2022

This article seeks to capture variations and tensions in the relationships between the health–illness–medicine complex and society. It presents several theoretical reconstructions, established theses and arguments are reassessed and criticized, known perspectives are realigned according to a new theorizing narrative, and some new notions are proposed. In the first part, we argue that relations between the medical complex and society are neither formal–abstract nor historically necessary. In the second part, we take the concept of medicalization and the development of medicalization critique as an important example of the difficult coalescence between health and society, but also as an alternative to guide the treatment of these relationships. Returning to the medicalization studies, we suggest a new synthesis, reconceptualizing it as a set of modalities, including medical imperialism. In the third part, we endorse replacing a profession-based approach to medicalization with a knowledge-based approach. However, we argue that such an approach should include varieties of sociological knowledge. In this context, we propose an enlarged knowledge-based orientation for standardizing the relationships between the health–illness–medicine complex and society.
Rapid Evidence Assessment Protocol for the Meta-Analysis of Initiatives, Interventions and Programmes That Target Rural NEETs
Authors: Flynn Paul , McCauley Veronica , Mujčinović Alen , Radović Vesela , Bojnec Stefan , Simões Francisco
Published: August 2022

The acronym NEET refers to youths aged between 15 and 34 years old who are excluded from employment, education or training. However, historically, the NEET demographic has been depicted as a largely homogenous group. Against this backdrop and given the dependency of rural economies on agricultural practices for survival, such practices have been in decline for a number of years, seriously threatening rural communities’ sustainability. While these rural NEETs can present as registered unemployed and also within the reporting statistics of various different state-funded initiatives, interventions and programmes, in the case of Rural NEETs, there is a dearth of reporting categories that highlight the specificity of this group resulting in their presence being largely overlooked within official dissemination. In order to advance this emergent field of research, presented here is a Rapid Evidence Assessment protocol that will aid future work of the authors and for others to adapt and/or adopt.
Exploring the Impacts of COVID-19 on Coastal Tourism to Inform Recovery Strategies in Nelson Mandela Bay, South Africa
Authors: Vermeulen-Miltz Estee , Clifford-Holmes Jai Kumar , Snow Bernadette , Lombard Amanda Talita
Published: August 2022

Globally, the COVID-19 pandemic bought devastating impacts to multiple economic sectors, with a major downfall observed in the tourism sector owing to explicit travel bans on foreign and domestic tourism. In Nelson Mandela Bay (NMB), South Africa, tourism plays an important role; however, negative effects from the pandemic and resulting restrictions has left the sector dwindling and in need of a path to recovery. Working together with local government and stakeholders, this study applied system dynamics modelling to investigate the impacts of COVID-19 on coastal tourism in NMB to provide decision-support and inform tourism recovery strategies. Through model analysis, a suite of management interventions was tested under two ‘what-if’ scenarios, with reference to the business-as-usual governance response scenario. Scenario one specifically aimed to investigate a desirable tourism recovery strategy assuming governance control, whereas scenario two investigated a scenario where the effects of governance responses were impeded on by the exogenous effects from the virus. Results suggest that uncertainty remained prevalent in the trajectory of the infection rate as well as in associated trends in tourism; however, through the lifting of travel restrictions and the continual administration of vaccines, a path to recovery was shown to be evident.
Myocarditis or Pericarditis Following the COVID-19 Vaccination in Adolescents: A Systematic Review
Authors: Li Minglong , Wang Xingxing , Feng Junjie , Feng Ziqi , Li Wenwen , Ya Bailiu
Published: August 2022

Background: By 16 May 2022, 12,186,798,032 people had been vaccinated with COVID-19 vaccines. Our study found that myocarditis/pericarditis may occur in adolescents after COVID-19 vaccination. Methods: In this regard, we conducted a meta-analysis of seven groups of adolescents aged 12–19 years to compare the incidence of myocarditis/pericarditis after vaccination and compare the relative risk incidence after the first and second doses of a COVID-19 vaccine, and between males and females for risk incidence. Results: We analyzed 22,020,997 subjects from seven studies, including 130 cases of confirmed myocarditis/pericarditis. The overall mean incidence rate was 1.69 cases per 100,000 person-years. Of these, 19 of the 12,122,244 people who received a first dose of a COVID-19 vaccine had myocarditis/pericarditis, an incidence rate of 0.0022% (95% CI 0.0001–0.0034), and 111 of the 1,008,753 people who received a second dose had myocarditis/pericarditis, an incidence rate of 0.0107% (95% CI 0.0059–0.0155). The prevalence relative ratio (RR) after the first and second doses was RR = 5.53 (95% CI: 3.01–10.16), with a higher prevalence after the second dose than after the first dose of a COVID-19 vaccine. After a second dose of a COVID-19 vaccine, the RR for males relative to females was RR = 13.91 (95% CI: 4.30–44.95), with a more pronounced risk of disease in males than in females. Conclusions: Our study showed that myocarditis/pericarditis occurred after vaccination with the BNT162b2 or Comirnaty vaccine, especially after the second vaccination in male adolescents, but the incidence of myocarditis/pericarditis after vaccination with the above vaccines was very rare (0.0022%). Therefore, it is recommended that adolescents should be vaccinated with the COVID-19 universal vaccine as soon as possible and closely monitored for subsequent adverse reactions, which can be treated promptly.
Venous Thromboembolism following Two Doses of COVID-19 mRNA Vaccines in the US Population, 2020–2022
Authors: Lai Daoyuan , Zhang Yan Dora , Lu Junfeng
Published: August 2022

The COVID-19 mRNA vaccine is one of the most effective strategies used to fight against COVID-19. Recently, venous thromboembolism (VTE) events after COVID-19 mRNA vaccination have been reported in various research. Such a concern may hamper the ongoing COVID-19 vaccination campaign. Based on the US Vaccine Adverse Event Reporting System data, this modified self-controlled case series study investigated the association of COVID-19 mRNA vaccination with VTE events among US adults. We found the VTE incidence rate in the recommended dose interval does not change significantly after receiving COVID-19 mRNA vaccines. This conclusion still holds if the analysis is stratified by age and gender. The VTE onset may not be significantly associated with COVID-19 mRNA vaccination.
On Some Optimization Problems on Permutations
Authors: Donets Georgy , Biletskyi Vasyl
Published: August 2022

Numerous studies consider combinatorial optimization problems and their solution methods, since a large number of practical problems are described by means of combinatorial optimization models. Among these problems, the most prominent ones are function optimization problems on combinatorial configurations. Many of the studies mentioned above propose approaches and describe methods to solve combinatorial optimization problems for linear and fractionally linear functions on combinatorial sets such as permutations and arrangements.
This work describes new approaches and methods to solve some maximization problems for linear, fractionally linear and quadratic functions on permutation set. Algorithms for solving these problems are given.
For linear function, we provide a considerably easy method to find the permutation on which the function attains its maximum value.
We describe the general algorithm to find fractionally linear function maximum. We consider the cases in which variables of numerator and denominator do not intersect, and when the numerator function and the denominator function have one common variable. We also describe the case in which the numerator function and the denominator function in total have incomplete variable list, i.e. when the total quantity of variables is less than the quantity of numbers in the permutation set.
As for solving the problem of finding quadratic function maximum on permutation set, it turned out that there is no universal algorithm to solve this problem for any quadratic function. In this paper, we describe a method of finding maximum on permutation set for quadratic function consisting of two items.
We provide examples of solving the considered optimization problems.
Keywords: function, permutation, set, transposition, coefficients.
On Investigation of Natural Algorithms and Their Complex Application for Optimization of Logistics Tasks
Authors: Andriichuk Victor , Tretynyk Violeta
Published: August 2022

Introduction. At present, with the increase in the number of data and the complexity of various processes in many branches of the national economy, the classical methods of answering have ceased to meet the needs. So, for example, if you consider the logistics industry (this and transportation, and various cartographic services for consumers), we see how dynamically developing these areas. To construct optimal routes, if we continued to use old algorithms, we would have to spend a huge amount of computer resources and time to get results. Instead, the study finds new methods that make it possible to speed up and simplify the solution of problems. So, for example, for logistics problems, instead of classical combinatorial problems, algorithms are being replaced by environmental scientists. They were called natural algorithms. For example, scientists have noticed that ants find the shortest route to food using the iteration and intensity of a pheromone trace that leaves previous ants who are moving along the path. This idea formed the basis of the algorithm of the ant colony. Even if in reality ants find the optimal way more difficult than the simplified idea of researchers, it turned out that this simplification was enough to find an optimal route between some points of the type like ants looking for an optimal route food.
To develop a complex meta-algorithm that would solve the problem of logistics network, the following methods are taken into account: natural algorithms, salesman problem, dynamic programming methods, combinatorial approaches, algorithms for complex data analysis.
This paper considers the application of methods of natural algorithms to solve the problem of coordinated logistics.
The purpose of the paper. The aim of the work is to increase the efficiency of using natural algorithms in logistics problems. In general, the main tasks of logistics are to forecast the amount of costs, products and resources under certain circumstances. However, the required amount of material, information, financial, service and other data flows is not always available in logistics tasks, many variables are unknown. Then heuristic methods of algorithms come to the rescue to solve such problems of an applied nature.
Results. The software implementation of the model in the Python programming language was performed. Some methods of dynamic programming, fuzzy logic were used and the hyperopt library was used to implement the script. Keywords: natural algorithms, logistics, ant algorithm, meta-algorithm.
دور نظم المعلومات التسويقية في تحقيق الميزة التنافسية للمنشآت الغذائية الأردنية
Authors: Altarawneh Mohammad S.
Published: August 2022

استهدفت الدراسة تحديد دور نظم المعلومات التسويقية في تحقيق الميزة التنافسية للمنشآت الغذائية الأردنية، ولتحقيق أهداف الدراسة تم تطوير استبانة لغرض جمع البيانات، حيث تكون مجتمع الدراسة من50 منشأة غذائية في مدينة الملك عبدالله الصناعية، واستخدمت برمجية (20SPSS) لتحليل بيانات الاستبانة، بينت نتائج الدراسة أن مستوى معرفة المبحوثين نحو المعرفة بنظم المعلومات التسويقية والمعرفة بالميزة التنافسية كان مرتفعاً، وأوضحت النتائج أن أهم الأهداف من استخدام نظم المعلومات التسويقية كانت في زيادة المنافسة وزيادة الإنتاجية وزيادة الأرباح. بينت النتائج وجود تأثير وعلاقة ارتباطية إيجابية ذات دلالة إحصائية عند مستوى أقل 0.05 بين نظم المعلومات التسويقية والميزة التنافسية. أوصت الدراسة بتوجيه اهتمام الإدارة نحو الاهتمام بنظم المعلومات التسويقية لما لها من أثر في تحقيق الميزة التنافسية، والاهتمام بالمنافسين على المستويين المحلي والعالمي.
Olive Cake in Livestock Nutrition
Authors: Obeidat Belal S.
Published: August 2022

Depending on species and the utilized production system, feed represents the biggest variable cost to livestock producers. For example, feed cost represents 40-50% of the production cost of intensively managed cows and up to 60-70% of the cost of extensively managed sheep and goats. In either case, its proportion is substantial to the profitability of any animal operation. Livestock producers in Jordan face great hardships and challenges related to the environmental impact, animal health, market variability, and, most importantly, feed prices due to the limited feed production stemming from low rainfall. Most of the main feed ingredients in Jordan’s livestock sector are imported making them quite expensive. This review discusses the use of alternative feed ingredients (namely olive cake (OC)) in animal nutrition as a means of reducing the cost of feeding and improving returns to producers. Olive oil is one of the healthiest oils available and represents an important part of the Mediterranean diet. The increased demand for olive oil and olive fruits accelerated the increase in planting olive trees to meet these needs. This, in turn, has led to a significant increase in olive residues, such as olive cake (OC). These residues (byproducts) can be a source of pollution if not disposed of properly. The main objective of this review is to focus on the importance of OC in animal nutrition and its impact on the economics of livestock production. Most of the studies we dealt with in this review have shown that incorporating OC (in small substitutions) into the rations of livestock has no adverse effects on animal health and productivity. Thus, utilizing low-cost, agro-industrial by-products, such as OC, can be beneficial from environmental and economic points of view.
Genetic Diversity among Single, Double, And Triple Cross Hybrids in Beit Alpha Cucumber (Cucumis sativus)
Authors: Altamari Adel M. , Akash Muhanad W. , Kasrawi Mahmoud A.
Published: August 2022

Genetic variation was investigated in fifteen single, eighteen three-way, and nine double hybrid crosses and their parents along with nine elite commercial cucumber hybrids. Six lines (NS5, NS9, NS17, NS29, NS31, and NS33) were used to develop these cross hybrids. Based on AFLP and SSR analysis, the studied cucumber genotypes have a narrow genetic base. The results also showed genetic differentiation compared to commercial varieties. Out of 1352 bands produced from 12 AFLP primer pairs, only 47 were polymorphic. Similarly, out of 26 SSR primers, only one primer was polymorphic. The highest heterozygosity index (0.6044), polymorphic information content value (0.5392), and marker Index (0.6044) were observed with M-CTT_E-AAG AFLP primer combination, while the highest effective multiplex ratio (2.8246) and resolving power (4.8421) were observed with M-CTC_E-AGC AFLP primer combination.  Genetic distance estimation revealed clear distinctiveness of the studied genotypes with the control varieties. The cluster analysis by UPGMA showed two main clusters covering 84% of the studied genotypes grouped. This research shows that double and three-way hybrid crosses can be used in cucumber hybrid breeding.
Sustainable Control Tactics of the Cereal Leafminer Syringopais Temperatella Led. (Lep., Scythrididae) in Jordan: Resistant Cultivars and Combined Use of Tillage and Insecticides
Authors: Shawawreh M , Al-Zyoud Firas , Shakhatreh Y.
Published: August 2022

The cereal leafminer, Syringopais temperatella Led. (Lep., Scythrididae) is considered one of the most serious insect pests that infests wheat in the field, and causes economic damages to the crop in Jordan. However, little attention has been paid to the susceptibility of common wheat cultivars and no attention has been paid to the combined use of tillage regime and insecticides against S. temperatella. Concomitan  tly, this study aimed at investigating the susceptibility of the most commonly grown wheat cultivars in Jordan to S. temperatella under field conditions, and the effect of the combined use of tillage regime and insecticide applications in controlling the pest on wheat. The 1st experiment was set up to determine the susceptibility of four common wheat cultivars to the pest in the 2016/2017 cropping season, while the 2nd one was set up to investigate the effect of the combined use of tillage regime and insecticides against the pest in 2017/2018 cropping season in Al-Qasr, Karak. Four common wheat cultivars (Deer Alla, Em-Qees, Sham, and Horani 27), were used in the 1st experiment, while in the 2nd experiment, 6 different treatments were done using the wheat cultivar, Horani 27, namely, T1: no-tillage, T2: deep plowing in summer, T3: deep plowing in summer and treating seeds with diazinon, T4: chisel plow at seed sowing time with treating seeds with diazinon, T5: no-tillage and treating seeds with diazinon, and T6: chisel plow at seed sowing time. In the two experiments, data on larvae number, infestation, grain and straw weights, and plant height were recorded. Results showed that infestation and larvae numbers were the lowest, and grain and straw weights were the highest in Horani 27 compared to other cultivars. Data showed that the lowest infestation and larvae number were obtained in the no-tillage, and grain and straw weights were the highest in deep plowing in summer and treating seeds with diazinon. While the tallest plant was recorded in deep plowing in summer and deep plowing in summer by treating seeds with diazinon. In conclusion, Horani 27 is the least susceptible cultivar to the pest, and no-tillage reduces larvae number and infestation while plowing in summer by treating seeds with diazinon increased yield and strawweight, and plant height.
Economic and Environmental Indicators of Sustainable Production in Smart Farms in Jordan
Authors: Abu Siny Yahia , AlTarawneh Mohammad , ALKiyyam Muad
Published: August 2022

The study aimed to identify the most important economic and environmental indicators for sustainable production in smart farms in Jordan. A comprehensive survey was conducted of all 22 smart farms in Jordan, of which 16 farms responded. The results of the study showed that the studied characteristics of farmers, in addition to the knowledge, importance, and opinions of farmers, all have an impact on the adoption of the application of smart agriculture. Natural resources, reduce costs in the long run, as well as increase the amount of production per unit area. In light of the results, the study recommended a set of recommendations, the most important of which is the dissemination of knowledge among farmers about smart agriculture, and support for the gradual transformation of the use of its systems.
Medical Students’ Knowledge about Research Ethics
Authors: Masri Amira , Alfryjat Areej , Alfryjat Dyala , Abu saleem Ro’ya , Elhaj Shaima’
Published: August 2022

Medical students’ concerns about research publishing increase the need for evaluate their knowledge about research ethics and misconduct issues. This study carried out through Facebook group contains all sixth year medical students at the University of Jordan to evaluate medical students’ knowledge about research ethics using an adopted questionnaire of 3 parts. 158 (39.5%) out of 400 students answered the questionnaire 107 (67.3%) were females and 52 (32.7) were males, only 18 (11.4%) of them had attended extra courses, and 23 (14.6%) had co-authored published manuscript, 129 (81.6%) of them had heard about research ethics, while most of them knew that publication ethics in research are an essential elements of paper writing, their knowledge about other aspects vary from about 4.4% for the time researcher need to wait before resubmit the manuscript again and 42.8% for plagiarism definition. A score of ten had been calculated for each student with an average of 2.4 and median of 3. In conclusion, the students need more training on research ethics and misconduct as their knowledge about it was inadequate.
Rancang Bangun Sistem Informasi Pada CV. Prayitno Rusni Creative Bagian Percetakan
Authors: Paisal Paisal
Published: August 2022

Prayitno Rusni Creative Printing Section is a business entity that is engaged in printing that is trying to develop its business that its printing ordering service is still manual to become computerized. The problem faced by this business entity is the process of providing printing information that has not been real time, both between sections in the CV. Prayitno Rusni Creative, CV. Prayitno Rusni Creative with suppliers and CV. Prayitno Rusni Creative with distributors. It is expected that the information system for ordering at. CV Prayitno Rusni Creative can help manage information needed by consumers and in real time delivery, on time and strengthen the relationship between CV. Prayitno Rusni Creative with consumers and suppliers. For ordering information on the information system CV. Prayitno Rusni Creative includes: supplier data collection, suppliers, ordering. Hopefully with this information system CV. Prayitno Rusni Creative Printing Section is getting more and more consumers
Penggunaan Metode Analytic Hierarchy Process (AHP) Dalam Sistem Pendukung Keputusan Kelulusan Penerimaan Mahasiswa Baru Di Sekolah Tinggi Teknologi Pagar Alam
Authors: Aminah Siti
Published: August 2022

Metode Analitycal Hierarchy Process (AHP) merupakan teori umum penerapan cerdas yang digunakan untuk pendekatan matematis yang kompleks, namun berdasarkan pendekatan kualitatif yang dapat diterima oleh semua stakeholder dan pengelola program. Sistem Pendukung Keputusan dapat digunakan dengan menanamkan metode AHP pada sistem pendukung keputusan pada penerimaan mahasiswa baru. Pada proses penerimaan mahasiswa baru Sekolah Tinggi Teknologi Pagar Alam, sistem penyeleksian digunakan dengan beberapa kriteria untuk mengetahui kompetensi calon mahasiswa. Seleksi calon mahasiswa selama ini masih menggunakan perhitungan manual, yaitu dengan mengakumulasikan hasil tes sesuai dengan kriteria yang telah ditetapkan, proses yang lama dan rumit karena banyaknya peserta pendaftaran dapat menimbulkan terjadinya draw nilai antar calon mahasiswa, maka dari itu, untuk meminimalisir kesalahan perhitungan hasil seleksi mahasiswa baru dan mempermudah perankingan dibuat sistem pendukung keputusan kelulusan tes mahasiswa baru. Metode yang digunakan untuk menunjang penelitian ini adalah waterfall, dengan 5 tahapan yang sesuai dengan penelitian yang dilakukan. Dari metode yang dilakukan, penelitian ini menghasilkan sistem pendukung keputusan untuk mengetahui kelulusan calon mahasiswa baru pada proses penerimaan mahasiswa baru.
Pengembangan Teknologi ERP Untuk Modul Human Resource Management Studi Kasus PT. Imperium Happy Puppy Sampit
Authors: Muhaimin Muhaimin , Bachtiar Lukman
Published: August 2022

PT. Imperium Happy Puppy merupakan sebuah perusahaan yang bergerak dibidang hiburan karaoke pertama yang mengambil konsep karaoke keluarga, dimana perusahaan ini memiliki banyak cabang yang tersebar hampir diseluruh Indonesia salah satunya berada diwilayah Sampit, Kalimantan Tengah. Namun pada perusahaan tersebut terdapat masalah dalam memanajemen karyawan yaitu : perekrutan, pengelolaan biodata karyawan, penjadwalan, mengelola kehadiran, pengajuan cuti, promosi, penilaian, penggajian yang masih belum terkomputerisasi secara penuh dalam hal ini masih menggunakan Ms. Office untuk mengelola data-data tersebut. Sehingga sangat sulit bagi pimpinan HRD melihat data-data laporan yang menyangkut karyawan. Oleh karena itu, maka Teknologi ERP (Enterprise Resources Planning) dapat menjadi sarana yang tepat dalam memecahakan masalah tersebut. Salah satunya dengan menggunakan modul HRM (Human Resources Management), dengan harapan dapat menghasilkan informasi yang cepat dan tepat serta membantu perusahaan dalam mengambil keputusan.
PENERAPAN METODE SMART PADA SELEKSI PENERIMAAN BANTUAN RUMAH TIDAK LAYAK HUNI STUDI KASUS KECAMATAN LUBUKLINGGAU TIMUR II
Authors: Cindi Cindi Wulandari , Elmayati  , Dela Olympia Pratiwi
Published: August 2022

Rumah Tidak Layak Huni (RTLH) merupakan rumah yang mana persyaratan keselamatan bangunan dan kecukupan minimal dalam unsur ruangan dan luas ruangan yang belum terpenuhi. Selain itu juga, memperhatikan kualitas material pembangunan seperti dinding, sehingga aspek pendidikan bagi siapapun menempatinya dapat terpenuhi. Dalam proses penentuan bantuan kepada penerima bantuan masih bersifat subjektif sehingga mengakibatkan masalah pada pemeriksaan oleh inspektorat, ditemukan rumah-rumah yang harusnya tidak berhak mendapatkan bantuan selain itu manajemen pendataan masih dilakukan secara manual sehingga saat pimpinan daerah meminta data kepada dinas sosial membutuhkan waktu yang lama sehingga dianggap tidak efesien dan ekfetif. Sistem pendukung keputusan ini dibantu dengan menggunakan metode Simple Multi Attribut Ratting Technique (SMART) merupakan suatu metode pengambilan keputusan multi kriteria yang setiap alternatif terdiri dari sejumlah kriteria yang memiliki nilai daqn setiap kriteria memiliki bobot untuk menjelaskan seberapa pentingnya nilai dibandingkan dengan kriteria yang lain [4]. Metode SMART lebih sering digunakan karena kesederhanaanya dalam merespon kebutuhan pembuatan keputusan dan menganalisis respon, kriteria yang berpengaruh seperti penghasilan, dinding rumah, lantai, jenis rumah,luas bangunan, jumlah keluarga, tagihan listrik, lokasi rumah dan atap rumah. Pada imlementasi dan hitungan manual pada contoh perhitungan menggunakan metode SMART di dapatkan bahwa alternative bernama Suryono mendapatkan nilai tertinggi dengan nilai 2,769230
APLIKASI SISTEM INFORMASI KURSUS MENGEMUDI BERBASIS WEB ( STUDI KASUS : KURSUS SETIR MOBIL SANTA )
Authors: Ryandhika Mahdy Naufal , Gilland Kasyrafurhman  , Bagas Ramadhan  , Dwi Ade Handayani Capah
Published: August 2022

The course is one of the education provided outside the official school (non-formal) to develop self-ability and skills. Santa Car Driving Course is a skills education institution that specializes its students to be proficient in driving a car. However, the institution still registers courses by coming to the institution's location. This study aims to provide an application that helps institutions in providing course schedule information and online course registration. The method used in making this application is the PIECES method for running system analysis and the waterfall method for system requirements analysis. The result of this research is an application that is able to provide information on course schedules, registration, attendance and report notes.
Anesthesia Strategies for Patients with Ankylosing Spondylitis Undergoing Total Hip Replacement Surgery
Authors: SAMALI Mehdi , Meskine Amine , Andaloussi Mohammed Rabi , koundi A. , Bensghir M. , Doghmi B. , Balkhi H. , Baite A.
Published: August 2022

Ankylosing spondylitis (AS) is a chronic inflammatory disease of the axial skeleton and peripheral joints. Its main characteristic is the fusion of vertebral bones, which leads to loss of flexibility of the back and neck. Who present the most challenging regional anesthesia and airway management for the anesthesiologists. We report a case of patient admitted for total hip replacement under général anesthésia by fiberoptic bronchoscope. The objective of this study is to discuss the various general and locoregionale anesthetic techniques.
Opportunities and Challenges in the Implementation of Social Studies Curriculum towards Fostering National Cohesion and Integration in Kenya: A Case of Uasin Gishu County
Authors: Ruto Zipporah Jerotich
Published: August 2022

Social Studies Curriculum (SSC) foster national cohesion and integration in terms of knowledge, intellectual capabilities, and democratic disposition, which are expected of pupils to actively participate and coexist in society. SSC is based on societal needs, challenges, and aspirations, which enable pupils to acquire appropriate and acceptable values. Therefore, in the teaching of SSC in primary schools, teachers' and pupils' perception is paramount. The study sought to find out the challenges encountered by teachers in the implementation of the Social Studies curriculum in fostering national cohesion and integration. Data was sourced from a sample of 12 curriculum support officers, 47 head teachers, 188 Social Studies teachers, and 5,640 pupils from the primary schools in Uasin Gishu County who were selected using stratified and simple random sampling techniques. Instruments for data collection included a questionnaire, observation, interview schedules, and focus group discussions. Data analysis generated frequencies and percentages for interpretation and discussions. The challenges in the implementation of the SSC were found to be the gradual subordination of teachers, demoralisation, poor teaching practices and the contradictions between what learners are taught in school with what they see in the community identified in the study. The study recommended that primary teachers should effectively implement values inherent in Social Studies Curriculum for learners to emerge as responsible, respectful, and cooperative citizens for societal harmony. In conclusion, shows that there was a need for teacher training, workshops, and seminars with more emphasis on the acquisition of new knowledge and current trends on citizenship and the utilisation of appropriate pedagogical practices in the social studies curriculum.