Abstract In the modern era, organizations are facing several challenges due to the dynamic nature of the environment. One of the many challenges for a business is to satisfy its employees in order to cope up with the ever changing and evolving environment and to achieve success and remain in competition. In order to increase efficiency, effectiveness, productivity and job commitment of employees, the business must satisfy the needs of its employees by providing good working conditions.The objective of this paper is to analyse the impact of working environment on employee job satisfaction.The study employed a quantitative methodology. Data was collected through a self-administered survey questionnaire. The questionnaire is adopted from a previous validated survey. The target population consists of educational institutes, banking sector and telecommunication industry operating in the city of Quetta, Pakistan. Simple random sampling is used for collection of data from 210 employees.Theresults indicates a positive relationship between working environment and employee job satisfaction.The study concludes with some brief prospects that the businesses need to realize the importance of good working environment for maximizing the level of job satisfaction. This paper may benefit society by encouraging people to contribute more to their jobs and may help them in their personal growth and development. Hence, it is essential for an organization to motivate their employees to work hard for achieving the organizational goals and objectives.
The optical conductance of monolayer graphene is defined solely by the fine structure constant, α = $e^2 /\hbar c$ (where e is the electron charge, $\hbar $ is Dirac's constant and c is the speed of light). The absorbance has been predicted to be independent of frequency. In principle, the interband optical absorption in zero‐gap graphene could be saturated readily under strong excitation due to Pauli blocking. Here, use of atomic layer graphene as saturable absorber in a mode‐locked fiber laser for the generation of ultrashort soliton pulses (756 fs) at the telecommunication band is demonstrated. The modulation depth can be tuned in a wide range from 66.5% to 6.2% by varying the graphene thickness. These results suggest that ultrathin graphene films are potentially useful as optical elements in fiber lasers. Graphene as a laser mode locker can have many merits such as lower saturation intensity, ultrafast recovery time, tunable modulation depth, and wideband tunability.
Ali Zaenal Abidin, I Ketut Agung Enriko, Aloysius Adya Pramudita
Energy audits play a pivotal role in improving energy efficiency and reducing carbon emissions in office buildings. However, conventional audits often suffer from fragmented insights, lack of system-level monitoring, establishing energy baseline, and insufficient incorporation of occupant behavior. To address these challenges, this study conducts a systematic literature review of recent applications of Internet of Things (IoT), machine learning (ML), and digital twin (DT) technologies in the energy audit domain. The review, guided by PRISMA methodology, analyzes eleven selected studies published between 2022 and 2024, revealing that while ML dominates in predictive modeling, IoT and DT remain underutilized in delivering integrated, efficiency recommendations. The analysis identifies three key engineering gaps: limited use of occupant behavior data, absence of continuous energy baseline modeling, and lack of systems capable of generating real-time efficiency recommendations. In response, this paper proposes a novel AIoT-based energy audit framework that combines real-time monitoring via IoT with ML-driven analytics and optimization, supported optionally by DT-based simulation. The proposed framework aims to enable continuous, system-level audits aligned with ISO 50000 standards, offering practical pathways for building managers to diagnose inefficiencies and implement energy-saving actions. Validating the model in real-world office environments, expanding input variables, and integration strategy with building automation systems are further important study to realize intelligent and scalable energy audit solutions.
Yolanda Blanco-Fernández, Javier Otero-Vizoso, Alberto Gil-Solla
et al.
This paper presents an approach to identifying political fake news in Spanish using Transformer architectures. Current methodologies often overlook political news due to the lack of quality datasets, especially in Spanish. To address this, we created a synthetic dataset of 57,231 Spanish political news articles, gathered via automated web scraping and enhanced with generative large language models. This dataset is used for fine-tuning and benchmarking Transformer models like BERT and RoBERTa for fake news detection. Our fine-tuned models showed outstanding performance on this dataset, with accuracy ranging from 97.4% to 98.6%. However, testing with a smaller, independent hand-curated dataset, including statements from political leaders during Spain’s July 2023 electoral debates, revealed a performance drop to 71%. Although this suggests that the model needs additional refinements to handle the complexity and variability of real-world political discourse, achieving over 70% accuracy seems a promising result in the under-explored domain of Spanish political fake news detection.
To solve the problem of resource shortage of ternary content addressable memory (TCAM) in the data plane of software defined network (SDN), a deep flow table aggregation method was proposed based on content entry trees, and a storage architecture of large-scale SDN flow tables named ADAFT was established. The architecture relaxed the Hamming distance requirement between ag-gregated flow entries, and a content entry tree was constructed to aggregate flow entries with different action sets, for significantly en-hancing the aggregation degree of flow tables. Then a dynamic limitation mechanism was designed for the height of content entry trees based on the awareness of TCAM load ratio, to minimize the lookup overhead of aggregated flow tables. Meanwhile, an adaptive selec-tion strategy of flow entry aggregation was presented in the light of TCAM load ratio, to strike a balance between the aggregation degree and lookup overhead of flow tables. Experimental results indicate that the ADAFT architecture achieves much higher flow table com-pression ratios up to 65.74% than existing methods.
Ibrahime Hassan Nejdi, Seddik Bri, Mohamed Marzouk
et al.
The present study proposes a new, highly efficient fractal antenna with ultra-wideband (UWB) characteristics. The proposed patch offers a wide simulated operating band that reaches 8.3 GHz, a simulated gain that varies between 2.47 and 7.73 dB throughout the operating range, and a high simulated efficiency that comes to 98% due to the modifications made to the antenna geometry. The modifications carried out on the antenna are composed of several stages, a circular ring extracted from a circular antenna in which four rings are integrated and, in each ring, four other rings are integrated with a reduction factor of 3/8. To further improve the adaptation of the antenna, a modification of the shape of the ground plane is carried out. In order to test the simulation results, the prototype of the suggested patch was built and tested. The measurement results validate the suggested dual ultra-wideband antenna design approach, which demonstrates good compliance with the simulation. From the measured results, the suggested antenna with a compact volume of 40 × 24.5 × 1.6 mm<sup>3</sup> asserts ultra-wideband operation with a measured impedance bandwidth of 7.33 GHz. A high measured efficiency of 92% and a measured gain of 6.52 dB is also achieved. The suggested UWB can effectively cover several wireless applications such as WLAN, WiMAX, and C and X bands.
Massive multiple-input-multiple-output (MIMO) is a key enabler for obtaining higher data rates in the next generation wireless technology. While it has the power to transform cellular communication, with potential for spatial diversity and multiplexing, a bottleneck that often gets overlooked is the fronthaul capacity. The fronthaul link that connects a massive MIMO Remote Radio Head (RRH) and carries in-phase and quadrature (IQ) samples to the Baseband Unit (BBU) of the base station can throttle the network capacity/speed if appropriate data compression techniques are not applied, particularly in the uplink. This paper proposes an iterative technique for fronthaul load reduction in the uplink for massive MIMO systems utilizing the convolution structure of the received signals. The proposed algorithm provides compression ratios of about 30-<inline-formula> <tex-math notation="LaTeX">$50\times $ </tex-math></inline-formula>. This work provides extensive analysis of the performance of the proposed method for a plethora of practical scenarios and constraints, such as different channel parameters and models, receive antenna correlation, and under imperfect channel information. It also discusses the numerical convergence and complexity of the proposed algorithm and compares the performance against other existing compression techniques.
Telecommunication, Transportation and communications
M. O. Shpotak, N. H. Ivanushkina, K. O. Ivanko
et al.
Modern biomedical technologies use a combination of microelectrode array (MEA) systems and artificially grown cells to study disease mechanisms and test drug effects. MEA systems measure extracellular field potentials (FPs) of cell cultures or tissues, but they cannot record intracellular action potentials (APs) without some modifications or additional devices, limiting the depth of electrophysiological analysis. One of the possible solutions to the inability of MEA systems to measure APs is to mathematically reconstruct them using recorded FPs. However, accurately reconstructing APs of multiple cells is challenging task, which is complicated by many factors such as the number of cells, synchronicity of their APs, identification of their electrophysiological parameters, and noise. This paper aims to address the mathematical problem of AP synchronicity, asynchronicity and partial synchronicity between multiple cells. In this study, mathematical techniques were employed to derive a system of equations capable of reconstructing the APs of N cells simultaneously, using the FPs recorded with N+1 electrodes. The equations take into account the number of cells, synchronicity and variation of their APs and specific electrical properties of the cells and the medium. In numerical experiments the equations were applied to reconstruct APs from FPs for cases with different types of synchronicity in noise-free and noisy conditions. The reconstructed APs, when combined with recorded FPs, expand the number of electrophysiological characteristics available for cardiotoxicity assessment in MEA systems.
Insect pests have always been one of the main hazards affecting crop yield and quality in traditional agriculture. An accurate and timely pest detection algorithm is essential for effective pest control; however, the existing approach suffers from a sharp performance drop when it comes to the pest detection task due to the lack of learning samples and models for small pest detection. In this paper, we explore and study the improvement methods of convolutional neural network (CNN) models on the Teddy Cup pest dataset and further propose a lightweight and effective agricultural pest detection method for small target pests, named Yolo-Pest, for the pest detection task in agriculture. Specifically, we tackle the problem of feature extraction in small sample learning with the proposed CAC3 module, which is built in a stacking residual structure based on the standard BottleNeck module. By applying a ConvNext module based on the vision transformer (ViT), the proposed method achieves effective feature extraction while keeping a lightweight network. Comparative experiments prove the effectiveness of our approach. Our proposal achieves 91.9% mAP0.5 on the Teddy Cup pest dataset, which outperforms the Yolov5s model by nearly 8% in mAP0.5. It also achieves great performance on public datasets, such as IP102, with a great reduction in the number of parameters.
Human capital is a key factor in achieving high performance by a company. However, employees who are dissatisfied with their work have low productivity, which has a negative impact on the company's performance. Therefore, it is important for company management to identify and evaluate the factors that have a significant impact on the employee performance in modern conditions. The purpose of this study is to examine the effect of job satisfaction and work-life balance on employee performance and organizational commitment as mediating variable. The object of this study is group customer operations in telecommunication companies. Data was collected using the online questionnaire on Microsoft 365. This sampling technique uses a non-probability sampling technique with purposive sampling method where six divisions of customer operations groups in a telecommunications company used as respondents can return 195 questionnaires. The sample consisted of males 116 (59.5%) and 79 females (40.5%). In this study the authors use the data analysis method using SEM (Structural Equation Model) with calculation tools. The study results show that job satisfaction and organizational commitment are the main factors that can improve employee performance and employee resilience. At the same time, work-life balance does not affect the employee performance in the studied telecommunication companies. Job satisfaction and organizational commitment in group customer operation in telecommunication must be further improved so that management gets more productive and effective human resources in increasing the company's target to become a superior company in the telecommunications industry.
Sameer Alani, Zahriladha Zakaria, Tale Saeidi
et al.
Near-field microwave investigation and tomography has many practical applications, especially where the trend of fields and signals in different environments is vital. This article shows an elliptical patch ultra-wideband antenna fed by a transmission line for the near-field characterization of cancerous cells in the skin. The antenna comprises an elliptical patch, stub loading to shift the band to lower bands, and an electronic bandgap structure on the ground side. Even though the antenna has a low profile of 15 × 15 mm2, the proposed antenna has more promising results than recent studies. Furthermore, both simulated near-field and far-field results show a broad bandwidth of 3.9–30 GHz and a resonance at 2.4 GHz applicable for industrial, scientific, and medical band applications. The proposed antenna also illustrates a peak gain of 6.48 dBi and a peak directivity of 7.09 dBi. Free space and skin (on a layer of breast fat and a tumor with a diameter of 4 mm at the boundary of skin and breast) are used as test environments during the simulation and measurement of near-field and far-field investigations while considering a phantom breast shape. Both far-field and near-field microwave investigations are performed in Computer Simulation Technology studio, and results are then compared with the measured data. The simulated and measured results are in good agreement, and the focused energy around the tumor is completely reconstructed. Therefore, the proposed antenna can be an adequate candidate for the differentiation of breast skin and tumor to reconstruct the tumor’s image.
A variety of algorithms allows gesture recognition in video sequences. Alleviating the need for interpreters is of interest to hearing impaired people, since it allows a great degree of self-sufficiency in communicating their intent to the non-sign language speakers without the need for interpreters. State-of-theart in currently used algorithms in this domain is capable of either real-time recognition of sign language in low resolution videos or non-real-time recognition in high-resolution videos. This paper proposes a novel approach to real-time recognition of fingerspelling alphabet letters of American Sign Language (ASL) in ultra-high-resolution (UHD) video sequences. The proposed approach is based on adaptive Laplacian of Gaussian (LoG) filtering with local extrema detection using Features from Accelerated Segment Test (FAST) algorithm classified by a Convolutional Neural Network (CNN). The recognition rate of our algorithm was verified on real-life data.
In this paper, a new 11T SRAM cell using FinFET technology has been proposed, the basic component of the cell is the 6T SRAM cell with 4 NMOS access transistors to improve the stability and also makes it a dual port memory cell. The proposed cell uses a header scheme in which one extra PMOS transistor is used which is biased at different voltages to improve the read and write stability thus, helps in reducing the leakage power and active power. The cell shows improvement in RSNM (Read Static Noise Margin) with LP8T by 2.39x at sub-threshold voltage 2.68x with D6T SRAM cell, 5.5x with TG8T. The WSNM (Write Static Noise Margin) and HM (Hold Margin) of the SRAM cell at 0.9V is 306mV and 384mV. At sub-threshold operation also it shows improvement. The Leakage power reduced by 0.125x with LP8T, 0.022x with D6T SRAM cell, TG8T and SE8T. Also, impact of process variation on cell stability is discussed.
Goran Petkovic, Snezana Konjikusic, Lidija Barjaktarović
et al.
Abstract: Research Question: This paper focuses on the basic financial decision making practices of domestic companies and their knowledge of the operational risk management concept. Motivation: Chief Financial Officers (CFOs) play a significant role in the company’s development and achieving the company’s objectives (Van Horne & Wachowicz, 2007). Different authors carry out research based on one of the fields of finances-related decision making, like Graham & Harvey, 2001. The goal of this research is to show that difference in decision making process on companies’ finances and risk management depends on the company’s ownership structure. Idea: The paper is aimed at the recognition of differences found in corporate decision making, relating both to capital budgeting and capital structure, dividend payout policy and risk management concept, depending on the ownership structure. Data: During the first half of 2016, a research consisting of the 392-companies sample operating in the Republic of Serbia was conducted, including all four listed fields concerning their economic operations. Nevertheless, only a total of 54 CFOs completed and handed back the distributed questionnaires. Tools: Some basic descriptive statistics techniques, one-way analysis of variance (ANOVA) and Kruskal-Wallis test were applied throughout the complete data analysis, while data processing involved the use of standard statistical package IBM SPSS 20.0. Findings: As shown by research findings, there is a notable difference in decision making, both on dividend payout and on risk management concept, depending on the types of ownership, with companies operating in our domestic market. The future research will concentrate on practice of companies merging both domestic and foreign entities, in order to determine whether a similar pattern might be found. Contribution: Furthermore, the research is intended to be complemented by the data contained in the official financial reports of those companies, with a view to ascertaining if the statements made by their CFOs are actually applied in the corporate practice.