Desertification remains a critical global ecological and environmental challenge that threatens sustainable development. Although our understanding of desertification dynamics and their underlying drivers has improved, continued research is needed due to the region-specific nature of these processes. This study focuses on the Western Inner Mongolia Plateau in China as a case study to examine the evolution of desertification and its driving factors using a multifaceted approach, including the Mediterranean Desertification and Land Use (MEDALUS) model. Results show that the desertification sensitivity index (DSI) across the plateau ranged from 1.12 in prairie regions to 1.87 in desert areas, with a spatial gradient decreasing from west to east. Overall, the DSI exhibited a declining trend over the study period, though some areas showed localized degradation. Between 2001 and 2020, the DSI decreased across approximately 64 % of the plateau, with approximately 23 % (primarily desert regions) experiencing a significant reduction. In contrast, 36 % of the area, particularly the southeastern grasslands, saw an increase in DSI. Among the examined factors, seven—precipitation, normalized difference vegetation index (NDVI), leaf area index(LAI), drought resistance, erosion protection, fire risk, and land-use intensity—demonstrated high explanatory power greater than 0.6, highlighting their significant positive or negative impact on desertification. Additional factors such as temperature, sunshine duration, and potential evapotranspiration also influenced desertification, albeit to a lesser extent. Notably, interactions among these variables played a crucial role in shaping desertification trends. Addressing desertification, therefore, requires integrated strategies that account for the complex interplay of soil, climate, vegetation, and land management.
With the advancement of technology, computers have acquired the ability to recognize human facial expressions from images. This technology can play a crucial role in fields such as psychotherapy, autonomous driving, and public safety. The purpose of this paper is to enhance the accuracy of the computer’s classification of facial expressions by applying a neural network to speed up expression-determinant with a high accuracy. Firstly, the project creates a base model uses ResNet50 to pre-train the data on the input 48*48-pixel images. Then, new convolutional blocks with max pooling layer are added to enhance the feature extraction capability. Finally, a global average pooling layer and a fully connected layer with dropout were appended to solve the problem of overfitting. This model performs a classification with 7 different kinds of emotions. This paper employed a Convolution Neural Network (CNN) model and Convolution Neural Network model with residual blocks. And analysis these two models by comparing loss and accuracy. Since the accuracy of both models performs not well, this paper improved the CNN model by appending a ResNet50 network. Eventually, the model based on ResNet50 accomplished the highest efficiency and accuracy which performed best among all the models.
Production is a key component of every nation’s economy, yet the manufacturing sector faces major challenges and opportunities due to rapid digital transformation. Many companies have not fully adapted to these technological shifts, limiting their ability to gain competitive advantages. Research indicates that integrating digital approaches into production processes can enhance efficiency and create significant value, turning digital transformation from a strategic recommendation into a necessity. However, there is still limited guidance on how to systematically assess the digital maturity of manufacturing firms and support their progress toward higher maturity levels. This study aims to develop a comprehensive framework for evaluating digital maturity in the manufacturing sector. Drawing on both literature and empirical data, the framework was designed and validated to help organizations understand their current digital status and identify areas for improvement. It defines evaluation domains, maturity levels, and assessment criteria, along with a structured evaluation method to guide practitioners in achieving higher levels of digital transformation.
Analyzing and measuring the similarity between two signals is a common task in many vibration-based structural health monitoring applications. Coherence between input and response signals serves as a convenient indicator of damage, based on the premise that nonlinearity due to damage in a linear system manifests as a loss of coherence in specific frequency bands. Because input excitations in civil structures are difficult to measure, damage indicators based on the coherence between two response signals have been developed. These indicators have shown promise in detecting nonlinear behavior in structures that were initially linear. This paper proposes a new damage indicator based on Mutual Information, a nonlinear extension of the squared correlation coefficient, to quantify the similarity between two signals without making assumptions about the nature of their interactions or the underlying dynamics of the system. Mutual Information is distinguished from other nonlinear similarity metrics due to its ability to capture all types of nonlinear dependencies, its high computational efficiency, and its invariance to invertible transformations, such as scaling. The proposed approach is demonstrated using a standard dataset containing experimental data from a three-story aluminum frame structure under 17 different damage states. The results show that the proposed metric can detect deviations from the baseline state due to changes in mass, stiffness, or newly induced nonlinear behavior, suggesting its potential for monitoring changes in the structural system.
Jotiar H. Mohammed Kochar, Nizar M. Ali. ALSulaifani
The current research aims to explore the impact of participative leadership represented by (decision-making participation, delegation of authority, human relations, and justice and equality) in human resource sustainability, defined by (human resource well-being, human resource development, human resource retention, and work-life Equilibrium). At the same time, testing the mediating role of the organizational Equilibrium variable represented by (contributions and inducements) in the influential relationship between the two variables. The study community comprised all employees working in private hospitals in the governorate, totaling approximately 650 individuals across 15 hospitals. Data were collected from a random sample of 306 medical staff members working in private hospitals in Duhok Governorate, Kurdistan Region, Iraq, using a questionnaire. The research employs a descriptive-analytical approach, and the results, analyzed using SPSS V.25, indicate a significant direct effect of both participative leadership and organizational Equilibrium on human resource sustainability. Furthermore, the findings confirm the potential of organizational Equilibrium as a partial mediator in the relationship between participative leadership and human resource sustainability. The study recommends that private hospital management enhance human resource sustainability by promoting participative leadership practices and leveraging the dimensions of organizational Equilibrium to achieve this goal..
Management information systems, Economic history and conditions
In the present paper, we precisely conduct a quantum calculus method for the numerical solutions of PDEs. A nonlinear Schrödinger equation is considered. Instead of the known classical discretization methods based on the finite difference scheme, Adomian method, and third modified ver-sions, we consider a discretization scheme leading to subdomains according to q-calculus and provide an approximate solution due to a specific value of the parameter q. Error estimates show that q-calculus may produce effi-cient numerical solutions for PDEs. The q-discretization leads effectively to higher orders of convergence provided with faster algorithms. The numer-ical tests are applied to both propagation and interaction of soliton-type solutions.
Driven by the development of intelligent internet of things (IoT) technology, unmanned aerial vehicle (UAV) swarms have been widely used for sensing and monitoring in emergency and rescue scenarios.The UAVs automatically sense and discover mission targets in the mission area, recruiting neighboring UAVs to form perception and computation task groups to collaboratively complete the perception, acquisition and processing of data.However, repetitive sensory data and imbalance in the supply and demand of computational resources between multiple tasks cause additional computational and communication overheads and increase the end-to-end processing latency.To address this challenge, a multi-task resource allocation approach combining bionics and multi-agent independent reinforcement learning was proposed, making collaborative resource allocation decisions based on local task information.The method represents the resource requirements of individual tasks as situational information concentrations and dynamically updates the heterogeneous resource requirements of each task by spreading the situational information across task groups.At the same time, it combines multi-agent independent reinforcement learning methods for intelligent decision making in order to collaboratively allocate the heterogeneous resources of each task.Simulation results show that this solution can not only effectively reduce the task execution time, but also significantly improve the computational resource utilization.
Information technology, Management information systems
Aurora González-Vidal, Alfonso P. Ramallo-González, Antonio F. Skarmeta
This work assesses the quality of Internet of Things data not only as an intrinsic quality on how well it represents the related phenomenon but also, on how much information it contains to educate an artificial entity. The quality metrics here proposed are tested with real datasets. Also, they are implemented on OpenCPU, so the open data repositories can use them off-the-shelf to rate their datasets without computational cost and minimum human intervention, making them more attractive to potential users and gaining visibility and impact.
Decentralized networks bring us many benefits, but as networks evolve, many nodes either actively or passively become unreachable behind an NAT or a firewall. This has become a hindrance to the development of decentralized networks, where peer-to-peer communication data transfer between unreachable nodes cannot be accomplished, whether in decentralized file systems, decentralized social, or decentralized IoT. The existing scheme requires a series of centralized servers or requires network-wide flooding for consensus data, which can lead to the loss of decentralized nature of the network and cause flooding bottlenecks, contrary to the design concept of decentralization. In this paper, our proposed scheme uses a structured P2P overlay network to store the indexes of unreachable nodes in the whole network, so that the characteristics of a decentralized network are still maintained while ensuring the efficiency of lookup. When nodes communicate, the transmission channel is established so that both nodes continuously transmit data streams peer-to-peer without relying on the central server. Moreover, the scheme guarantees the security and privacy of nodes’ data transmission and the P2P overlay network without relying on centralized trusted institutions. Finally, we deploy a real cluster environment to verify the effectiveness of each module at different network sizes and prove the overall feasibility of the scheme. The scheme has certain advantages over existing solutions in terms of security, privacy, communication efficiency, device democracy, etc.
In this study the author will present how Human Intelligence (HI – nous), in co-operation with Artificial Intelligence (AI) – Internet can communicate his/her knowledge and interdisciplinary research to an international context (i.e., Erasmus exchange programs and/or international conferences). Having over a twenty-year experience of teaching ESP/EAP at a non-English University and over a twenty-five-year experience of editing research papers in English, the writer will present how a nous/student/researcher and an academic can use to his/her advantage IT tools, such as electronic dictionaries and forums. Finally, the author of this study will propose: (a) a couple of methods, which can be applied through AI (i.e., Google or any other search engines) so that the non-English nous/student/researcher/academic (a nous) will be certain that s/he communicates "correctly" and "appropriately" his/her research in an international context whose primary language of communication is English; and (b) a specific bilingual (or multilingual) knowledge management tool (i.e. an electronic TDB: Terminological Data Bank).
Currently parking system that is running in Pt. Sanggraha Daksamitra still uses a conventional parking system. When the driver wants to do parking, driver first presses the analog button on the parking gate to get a parking ticket. Lack of maintenance on the parking gate causes vehicle jams in the parking entrance area. To minimize errors that occur, the fishbone diagram analysis method is used to analyze the problem. After the problem was found, a parking reservation was developed that used qr-code as a scanner identification in the parking system. The system produced in this study is expected to facilitate parking drivers who want to park their vehicles in the parking area of Pt. Sanggraha Daksamitra. The results of this system will be tested using blackbox testing.
The purpose of this study is to determine and analyze the effect of the proportion of independent commissioners, audit committees, and managerial ownership on sustainability report disclosures with firm size as a moderating variable. The research population is mining sector companies listed on the Indonesia Stock Exchange (IDX) in 2015-2017 as many as 41 companies, 8 companies do not meet the criteria. The sampling technique used purposive sampling to produce 99 samples and analysis units. The data analysis tool used is logistic regression analysis and MRA using the IBM SPSS 23.0 software. The results showed that the proportion of independent commissioners had a significant negative effect on sustainability report disclosure, while the audit committee and managerial ownership did not affect the disclosure of sustainability report. The proportion of independent commissioners moderated by firm size showed positive results that strengthened the influence of sustainability report disclosures. In addition, audit committees and managerial ownership that are moderated by firm size did not the effect of disclosure of sustainability reports. The suggestion for future research is to pay attention to the extent of disclosure and quality of the sustainability report content by looking at disclosure indicators in accordance with the Global Reporting Initiative guidelines.
ABSTRACT A production information management system based on industrial Internet of Things (IoT) technology is designed, which makes the manufactory enterprise have the ability of dynamic shop floor scheduling. To solve the rescheduling problem, a hybrid method combining the game theory method and the rolling horizon rescheduling strategy (HGR) is proposed. In a computational experiment, the values of the relative error (RE) and mean ideal distance (MID) are 11.3 and 13.74, respectively, which illustrates the feasibility and efficiency of the proposed HGR method. An engineering experiment shows that the proposed method can handle the practical engineering problems.
Gigih Forda Nama, Ayu Dian Pamungkas, Mardiana Mardiana
et al.
Indonesia merupakan negara yang memiliki keanekaragaman budaya. Salah satu bentuk keragaman budayanya adalah bahasa daerah yang berbeda di setiap wilayah. Bahkan, pada beberapa daerah tertentu memiliki aksara khusus dalam penulisan, salah satunya yaitu aksara Lampung. Ada berbagai macam cara yang biasa dilakukan untuk melestarikan penggunaan aksara dan bahasa daerah, salah satunya dengan memanfaatkan teknologi. Penelitian ini menghasilkan sebuah produk game edukasi belajar bahasa dan aksara Lampung (KOPER APUNG) berbasis android dengan metode scrum dan menggunakan game engine construct 2. Dari 3 kali proses iterasi sprint yang dilakukan, menghasilkan sebuah game edukasi yang sesuai dengan kebutuhan pengguna. Pengujian portability menunjukkan bahwa game dapat berjalan pada perangkat android minimum versi 4.1. Pengujian functionality menunjukkan aset game 100% dapat berjalan baik. User acceptance test dengan melibatkan 25 responden, menunjukkan predikat sangat baik dengan persentase 95,77%. Pengujian efficiency menunjukkan game menggunakan CPU rata-rata sebesar 15% dan RAM 400 MB. Sedangkan pada tahap evaluasi, tingkat kelulusan responden meningkat dari sebelumnya 36 % menjadi 96%. Sehingga, game ini efektif digunakan sebagai salah satu media belajar Aksara Lampung.
Abstract
Indonesia is a country with various cultures. One of them is that every region has its own local language as its special characteristic. Some local languages have their own local letters, for example is the Lampungnese letters. There are a lot of techniques to conserve the use of local language and its letters, one of them is by implementing technologies. This Koper Apung (the Collection of Lampungnese Letters Game) Educational Game was created in the form of game application for android based smartphone by using scrum methodology and assisted by Construct 2 Engine. From the sprint result of the scrum methodology, an educational game which adjusted to the needs of the user was established. The result of portability aspect showed that Koper Apung game could work well on the minimally 4.1 version of android appliances. The functionality test showed that all game assets could satisfactorily 100% work. User Acceptance test indicated, Very good‟ predicate with 95.77% result. The efficiency test denoted that this game used 15% CPU and 400 MB RAM. Meanwhile, on the evaluation phase, the respondents‟ level of success improved from 36% to 96%. These signified that the Koper Apung game is an effective and applicable media to learn Lampungnese Letters.
Tingginya produk cacat di PT. Riau Graindo yang disebabkan oleh efektivitas pekerja yang kurang memberikan kontribusi yang efektif membuat penjualan koran menjadi berkurang yang berdampak kepada profit perusahaan. Langkah-langkah untuk memperbaiki efektivitas pekerja yaitu dengan menggunakan metode Overall Labour Effectiveness (OLE) dan Fault Tree Analysis (FTA). Tujuan penelitian ini yaitu mengetahui nilai OLE perusahaan, mendapatkan kegagalan yang menyebabkan produk cacat, memberikan usulan dan mengiplementasi usulan tersebut. Hasil yang didapatkan dari penelitian ini yaitu usulan yang diberikan yaitu memberikan lembar contoh dengan ukuran A3 yang disesuaikan untuk setiap koran yang berwarna dimana nilai OLE perusahaan bulan Januari-Desember 2016 adalah 63,92%. Hasil implementasi mampu meningkatkan nilai OLE perusahaan sebesar 4,8% menjadi 68,72%. Kenaikan terdapat pada jumlah produksi dimana rata-rata perbulan 117.859 eksemplar naik menjadi 121.456 eksemplar. Selain itu juga jumlah produk cacat turun dari 4360 eksemplar menjadi 3924 eksemplar yang memberikan pengaruh terhadap penambahan income perusahaan sebesar Rp 1.308.000 pada bulan Juli 2017.
In the age of the Internet of Things (IoT), more and more sensors, actuators and smart devices get connected to the network. Application providers often combine this connectivity with novel scenarios involving cloud computing. Before implementing changes in these large-scale systems, an in-depth analysis is often required to identify governance models, bottleneck situations, costs and unexpected behaviours. Distributed systems simulators help in such analysis, but they are often problematic to apply in this newly emerging domain. For example, most simulators are either too detailed (e.g., need extensive knowledge on networking), or not extensible enough to support the new scenarios. To overcome these issues, we discuss our IoT cost analysis oriented extension of DIScrete event baSed Energy Consumption simulaTor for Clouds and Federations (DISSECT-CF). Thus, we present an in-depth analysis of IoT and cloud related pricing models of the most widely used commercial providers. Then, we show how the fundamental properties (e.g., data production frequency) of IoT entities could be linked to the identified pricing models. To allow the adoption of unforeseen scenarios and pricing schemes, we present a declarative modelling language to describe these links. Finally, we validate our extensions by analysing the effects of various identified pricing models through five scenarios coming from the field of weather forecasting.