Ray-tracing is a promising alternative for Radio Frequency Planning particularly in urban areas. There are two fundamental techniques used for ray-tracing namely Shooting and Bouncing Rays and Method of Images. In this paper we focus on the former and present simulation results for an urban scenario in the city of Helsinki. We also give an insight into how Shooting and Bouncing Ray method can be implemented using basic linear algebra techniques. We show that ray-tracing can be used to evaluate the performance improvement attained through electromagnetic reflectors. Finally, we close the discussion by outlining the existing challenges and the way forward.

A Primer on Ray-Tracing: Shooting and Bouncing Ray Method

Co-Channel Interference is a classical problem in cellular systems that has been studied extensively and several methods have been proposed to overcome it. These include interference rejection techniques as well as joint detection techniques. We have previously proposed a joint detection technique for MSK type signals that works quite well in certain conditions. In this paper, we formally present what we call Orthogonal MSK and postulate that if two MSK signals have a 90-degree phase offset between them then both can be detected successfully increasing the spectral efficiency twofold. This technique works well even if the two signals are near equal power and have the same carrier frequency.

Orthogonal Minimum Shift Keying: A New Perspective on Interference Rejection

A wireless channel suffers from two fundamental impairments; fading and noise. While fading is multiplicative, noise is additive. It is well-known that higher the noise, lower is the signal to noise ratio and lower the capacity. However, fading can be helpful in increasing the capacity when using multiple transmit and receive antennas. In this paper, we give an intuitive explanation for this. Anybody with a background in linear algebra and matrices can understand this.

Why is MIMO Capacity in a Fading Environment Higher than in an AWGN Environment

Urban air quality remains a critical public health and environmental challenge, exacerbated by rapid urbanization, industrial emissions, and vehicular pollution. Traditional air quality monitoring networks often rely on sparse, static station placements based on historical regulatory guidelines, resulting in spatial and temporal data gaps that hinder accurate exposure assessment and policy formulation. To address this limitation, we propose a novel, machine learning (ML)-driven framework for the dynamic optimization of urban air quality monitoring networks. Our approach integrates high-resolution geospatial data, real-time sensor observations, meteorological variables, traffic patterns, and land-use characteristics into a hybrid ML architecture combining graph neural networks (GNNs) and reinforcement learning (RL). The GNN models capture complex spatial dependencies among monitoring sites, while the RL agent iteratively optimizes sensor placement and data sampling strategies to maximize information gain and minimize redundancy under budgetary and logistical constraints. We validate our methodology using multi-year datasets from major metropolitan areas-including Los Angeles, Beijing, and Delhi-demonstrating up to a 40% improvement in predictive accuracy for key pollutants (PM₂ .₅ , NO₂ , O₃) compared to conventional network designs. Moreover, our system enables adaptive reconfiguration in response to evolving emission sources and extreme events (e.g., wildfires or traffic disruptions), thereby enhancing resilience and responsiveness. The framework also incorporates uncertainty quantification to support decision-making under data scarcity. By transforming static monitoring infrastructures into intelligent, self-optimizing systems, this research bridges the gap between environmental sensing, artificial intelligence, and urban sustainability. The proposed methodology offers a scalable, transferable blueprint for cities worldwide to deploy next-generation, costeffective air quality networks that support evidence-based environmental regulation, public health interventions, and climate adaptation strategies. This work redefines the paradigm of environmental monitoring through the fusion of data science and environmental engineering..

Machine Learning-Driven Optimization of Urban Air Quality Monitoring Networks

Hiring practices are vital for any organization because they have consequences for its success and goals meeting, and it has a significant effect on many other organizational variables as literature shows. On the other hand, project team management practices are very important to any Civil Services Sector (CSS) because they affect projects success that consumes big resources of a nation treasury and can affect citizens' life as literature confirmed. This research aims to examine the influence of hiring practices of Palestinian CSS on project team management practices. This research is designed as quantitative study that uses correlational design. A questionnaire was designed to meet the need for collecting primary data from the community. The tool was conducted through google forms to ensure proper timing of responses and proper collection for data. Moreover, the SPSS program and the Smart PLS Version 4 were used to analyze the data and to confirm research tool reliability and validity. The population was the supervisory-positions employees who work at Ministry Of Education of Palestine (MoE) with total number of 644. A sample of 341 was selected randomly at a confidence level of (0.05). The response rate was 95.9 %. Findings indicated that employment policies implementation and recruitment practices can affect project team monitoring practices. Where no significant effect was found of selection practices on project team monitoring practices. To sum up, improving hiring practices will lead to better project team monitoring practice. This study is very important for CCS leaders, human resources managers, project managers, and policy makers. Increasing project team management efficiency will help Palestinian for better utilization of the scarce resources and will increase the CSS project goals meeting.

Hiring Practices Effect on Project team Monitoring. Bureaucratic Theory at the Public Sector

Finding a roadmap for the relation between Human resources management (HRM) unit and line managers in other units of an organization is a goal that need to be in the center of interest. This will eliminate the overlap between both units roles, which will make any organization more effective and more successful. However, HRM unit becomes crucial in any organization because HRM practices are vital for the success of any organization. Nevertheless, the question remains, can this unit run all human resources in the organization? Do HRM units hear and understand the claim of other units? On the other hand, line managers have crucial role for the success and survival of any organization. Since they have specific roles to conduct in their organization. Then, another question arises, can they do their job alone? Are they delivering their HRM role? Accordingly, an organization must acquire HRM staff, who can hear, understand, and support, and be the guidance idol for every and each line manager. This research is designed as qualitative study. A questionnaire was designed with open question to collect primary data from the community related to hiring practices in the civil sector of Palestine. The tool was conducted through google forms to ensure proper timing of responses and proper collection for data. Moreover, answers were categorized and analyzed, the SPSS program was used to investigate the trends.

The population was the supervisory-positions employees who work at Ministry Of Education of Palestine (MoE). 160 employees send recommendations that are related to hiring practices, where all the recommendations hovered around the need to improve hiring practices, the quality of Employment policies, the strength and weakness of recruitment and selection practices.

HRM and Line Managers, the Recipe of Organizational Success

This research focuses on the development of an intelligent surface system designed to automatically detect and classify different types of waste materials, facilitating sorting into designated garbage containers. The system integrates multiple sensing technologies, including RGB cameras for visual recognition, near-infrared (NIR) spectroscopy for material composition analysis, and capacitive sensors for texture and conductivity data acquisition.

Waste item data collected through these sensors is processed using a hybrid machine learning framework, primarily leveraging a convolutional neural network (CNN) for imagebased classification combined with sensor fusion techniques to improve accuracy.

The proposed model is trained and validated on a comprehensive dataset comprising diverse waste categories, including plastics (PET, HDPE), metals (aluminum, steel), organic waste, paper, and glass. The classification pipeline achieves an accuracy of over 90%, ensuring reliable real-time sorting. The intelligent surface is coupled with an automated mechanical sorting mechanism that directs detected waste into the appropriate bins, thus preventing contamination and enhancing recycling efficiency.

Experimental evaluation includes performance metrics such as classification accuracy, response time, and durability under varying environmental conditions. Results indicate that the system can operate efficiently in real-world scenarios with rapid processing times suitable for public or industrial deployment. This work demonstrates a significant advancement in smart waste management technology by combining sensor fusion, advanced machine learning, and automation, contributing towards sustainable environmental solutions and reducing landfill dependency.

Development of an Intelligent Surface System for Automated Waste Type Detection and Sorting into Appropriate Containers

It is not impetuous to say that management has been practiced since the creation of human being. Individuals works together and has learned how to be more effective, which was very critical for their survival. After that, civilizations emerged and each one of them set management principles (even if it is not written one in some cases).Principles that has been the guidance to their domination and fingerprint making through history. However, Islam emerged in the Middle Ages era, in the time when management science was practices but not schooled yet. Nevertheless, Muslims had built very strong civilizations in very short time. Islamic civilization has something distinguish that gave it the power to raise and overcome in a very short time. In a period of a time that surrounding nations had their own style of management to run their civilizations. As found, Islam has its own philosophy for management, and it has main practices for it. This Philosophy seems to be comprehensive especially when dealing with individuals, work, and learning. The center of philosophy is ethics. It frames all the  principles and practices, which distinguished Islamic philosophy form many other ones. However, this article tackles the Islamic philosophy and how can modern nations get benefits from it. It tries to present this distinguish factor that helps Islamic nation to lead the world in many aspects and it tries to set focus on the main management principles of Islamic theory.

Ethical Management Bases in Islamic Theory

Modern economies optimize for extraction and accumulation, creating systemic incentives that reward noise over signal, transaction volume over genuine value creation, and short-term gains over long-term flourishing. This green paper proposes The Coherence Economy-a new economic paradigm where value generation is measured by alignment rather than extraction, implemented through decentralized infrastructure that makes coherence measurable, verifiable, and compensable. We introduce Proof of Coherence (PoC), a novel consensus mechanism that replaces energy-burning (PoW) and capital-hoarding (PoS) with verified contributions to collective clarity, reduced entropy, knowledge sharing, and sustainable coordination. Through a dual-token architecture combining non-transferable reputation (Soulbound Tokens) with liquid value exchange (Aurum), we create an economic system where your ability to generate prosperity is proportional to your demonstrated capacity to increase coherence. This is not utopian philosophy-it is incentive engineering. The question is no longer "could this work?" but rather "what happens when the first network implements it?"

The Coherence Economy: A Complete Green Paper Redefining Value Through Measurable Alignment Version 0.1 -Draft for Community Review

Modern economies optimize for extraction and accumulation, creating systemic incentives that reward noise over signal, transaction volume over genuine value creation, and short-term gains over long-term flourishing. This green paper proposes The Coherence Economy-a new economic paradigm where value generation is measured by alignment rather than extraction, implemented through decentralized infrastructure that makes coherence measurable, verifiable, and compensable.

We introduce Proof of Coherence (PoC), a novel consensus mechanism that replaces energy-burning (PoW) and capital-hoarding (PoS) with verified contributions to collective clarity, reduced entropy, knowledge sharing, and sustainable coordination. Through a dual-token architecture combining non-transferable reputation (Soulbound Tokens) with liquid value exchange (Aurum), we create an economic system where your ability to generate prosperity is proportional to your demonstrated capacity to increase coherence. This is not utopian philosophy-it is incentive engineering. The question is no longer "could this work?" but rather "what happens when the first network implements it?"