This research presents a systematic review of the relationship between biophilic architecture and sustainability in the context of building design. In the contemporary era, the challenge of climate change is a crucial aspect that must be responded to in architectural practice, but in reality, the design and construction process still often prioritizes aesthetics and function alone without considering ecological impacts. This imbalance has led to the degradation of urban ecosystems and a decline in the quality of environmental sustainability. In this context, biophilic architecture emerges as a strategic approach that integrates natural elements directly into the built space to enhance psychological well-being and reduce user stress levels. The literature review reveals that while sustainable design tends to emphasize technical solutions, the application of biophilic principles offers a more holistic, adaptive, and human-oriented approach. This study confirms the urgency of developing a more integrative architectural paradigm, where the presence of natural elements is positioned as an essential component in shaping a resilient built environment that supports the health and well-being of its users. Thus, the integration of biophilic strategies in sustainable design not only strengthens environmental performance but also has a positive impact on psychological aspects and human health.

Batteries are intricate electrochemical devices characterized by their non-linearity and stochasticity, which are contingent upon the prevailing circumstances and environmental factors. The complex and challenging issue of accurately estimating the State-of-Charge (SoC) of a battery is attributed to its non-linearity. The accurate estimation of the SoC of an Electric Vehicle (EV) battery is of utmost importance for the purposes of real-time monitoring, safety regulation, optimal energy utilization, and efficient vehicle operation. The utilization of this tool provides the determination of the critical aspects inside the Energy Management System (EMS) of an EV, hence influencing the vehicle’s range and performance. Various methodologies have been employed in the estimation of SoC for EV batteries. This work presents a comparative analysis of different SoC estimating methods for EV batteries. The methods evaluated include Direct Measurement, Coulomb Counting, Model-Based Method, Data-Driven Method, and Hybrid Method. The evaluation of each method’s performance is conducted based on metrics such as accuracy, complexity, and applicability across various driving conditions and environments. This paper constitutes a small part of the author’s critical review for their PhD research. 

Reindeer (Rangifer tarandus) meat is a culturally and nutritionally significant product of Fennoscandian pastoralism. While the meat is available widely in the market in the producing countries, yet it lacks a species-specific quality classification system. The EUROP carcass grading framework, designed for cattle, is used instead, despite not fully suitable for cervids. Technologies, such as image based automated grading, also remain untested on reindeer. This review examines the current state of reindeer meat quality classification and evaluates the potential of hyperspectral imaging (HSI) to address the identified technological gaps. A comprehensive literature search was conducted across Web of Science, Scopus, and PubMed, supplemented by EU regulatory documents and published ethnographic sources on traditional Sámi knowledge systems. The review reveals that applying the EUROP system on reindeer meat requires oversimplification: fat scoring collapses into a single class owing to negligible subcutaneous fat, and conformation scoring reflects bovine muscularity benchmarks irrelevant to cervid anatomy. While this simplification works, some specific, important characters of reindeer meat might not be captured. Neither official veterinary inspection nor traditional Sámi assessment incorporates instrumental quality measurement, leaving ultimate pH, colour, and dark, firm, dry (DFD) status unmeasured. HSI has demonstrated strong predictive performance for these parameters in beef, pork, lamb, and farmed red deer venison, yet a systematic database search confirmed the complete absence of any HSI study on reindeer carcass and meat for grading purposes as of January 2026. This gap is significant because the quality parameters most critical to the reindeer industry are precisely those for which HSI has shown its strongest capability. A phased research roadmap is proposed, encompassing construction of a reindeer-specific spectral reference library, pilot deployment of portable snapshot HSI systems in remote slaughterhouses, and integration of instrumental quality indicators with traditional Sámi knowledge through participatory co-design. This methodological template is transferable to other underserved niche meat species worldwide.

With the advancement in computations and the introduction of renewable energy sources, flexible demands can play the main role in load frequency control. Some industrial and domestic loads can provide synthetic inertia that aids the natural inertia provided by conventional generators. This enhances small signal stability performance, as the total system inertial would be larger. The load frequency control (LFC) system with flexible demand-side control capability, in general, forms a time delay system. In this paper, a new approach to analyse the stability of the LFC system with controllable loads and communication delay is proposed by transforming the transcendental characteristic equation to a nonlinear equation in the complex frequency domain. The marginal frequencies are used to determine the DM. The sweeping test, together with the binary iteration algorithm, is implemented to find these marginal frequencies, yielding accurate DM computation. The method offers advantages in terms of accuracy and easy implementation. The paper also examines the influence of the flexible demand response on frequency stability as the flexible load participation factor is increased beyond 0.2. As the flexible demands participation factor is increased beyond 0.2, the stability of the system becomes delay-dependent. 

Pursuing agricultural intensification to raise productivity has brought challenges such as involvement of high capitals, often in the form of loans, environmental damage, and ecosystem disruption. These challenges increase risks in agricultural practice that require good management and control. This increases the need for real-time, non-destructive monitoring technologies that can improve crop productivity, enhance land use, and facilitate environmentally friendly agriculture. Due to its unique capacity to non-destructively examine plants’ internal biological and structural properties, ultrasound has emerged as a promising non-invasive technique providing insights often unattainable with traditional optical, spectral, or chemical sensors. This review aims to provide an up-to-date state of the art in ultrasound-based monitoring applications within major agricultural areas: soil characterization, seed quality control, plant health, stress monitoring, pests and diseases detection, and fruit ripening assessment. This review explores how contact and non-contact ultrasound measurements are scalable and versatile, bridging the gaps between laboratory and field-deployed systems. Integrating ultrasound monitoring with artificial intelligence and Internet of Things (IOT) frameworks further enhances modality accuracy and can detect stress, diseases, and other physiological changes in crops sooner. Overcoming challenges such as environmental acoustic noise will require further work. Still, recent advances such as improved signal filtering algorithms, new transducer designs, better field sensitivity, and broader collaboration to standardize ultrasound measurement protocols indicate a growing trend toward increased on-field use of ultrasound. Finally, the review also discusses the current limitations and future research directions of how ultrasound-based monitoring can catalyse a new paradigm of sustainable data-driven agriculture that meets food security needs.

Bioclimatic architecture is currently gaining interest among architect and building designers. In response to this trend, a study using bioclimatic design was conducted in Universitas Diponegoro (UNDIP) Mosque. Modeling simulation using Autodesk Ecotect was carried out to simulate the alternative design of joglo roof. Field measurement were done to validate the difference on the thermal performance between the existing and reccomendation design. Result from field measurement showed the highest room temperature was obtained on the second floor area of the mosque by 28°C. Meanwhile according to the simulation, the bioclimatic roof could decrease the indoor temperature of the mosque around 4.3°C - 5.2°C or to the level of 24.5°C. Moreover, compared to the existing design, the bioclimatic design had higher temperature difference with outdoor temperature by 7.5°C. It means, if an air conditioning system is installed inside the mosque with bioclimatic roof, it would require a lower energy consumption. Thus, the application of bioclimatic roof on the UNDIP Mosque can be a prospective solution to improve its indoor thermal comfort and provide an environmentally friendly design. 

The phenomenon of an unstoppable force of urbanization decreases the quantities of natural vegetation, replacing them with concrete buildings and low albedo surfaces. The innovative design of building envelopes has been considered one of the leading solutions for decreasing environmental load, especially in design and construction as a passive design strategy. On the other hand, the use of vegetation on the vertical surface of buildings is rising. Through this study, a numerical analysis using Computational Fluid Dynamic (CFD) model of vertical greening system as a natural shading device was conducted. Several scenarios were conducted with distance as the principal parameter. The aim was to prove the contribution of green technology to improve the cooling performance of the building.

This article presents the design and monitoring of a lab-scale smart farming system through the integration of control and app designs that can be used for teaching embedded control application to electromechanical systems. A combination of sensors and actuators is used to develop an Arduino-based embedded feedback control system that could be implemented in a smart farming environment. Specifically, we look at controlling electromechanical devices to actuate the fan and water pump to provide the optimal temperature and moisture, respectively, to enhance plant growth in a smart farming setting. The effectiveness of the feedback control is tested by conducting a plant growth experiment. Using garden cress (Lepidium sativum) as a case study, the plant grown in the controlled temperature and moisture settings shows substantially healthier growth compared to the one grown in the non-controlled environment. In addition, an app is designed and developed to transform the Arduino data stream from the sensors into valuable insights that could help the users to monitor and improve the overall crop health. The developed system in this paper enables students to learn integral skills from interdisciplinary engineering fields (e.g. systems, control, mechanical and computer) to solve an agricultural problem.

Extensive testing is crucial to ensure exhaust emission compliance for high-horsepower (H-hp) commercial engines. Back pressure sensors, integral to the exhaust system, are prone to producing noisy measurements due to the turbulent nature of the exhaust gas flow and other testing inaccuracies, mandating the use of a noise reduction filter. The time-consuming process of filter tuning, required to remove excessive process/measurement noise, often involves trialand-error. This practice, entailing numerous experiments with live engines, results in high financial costs and emissions due to challenges in extracting the ground truth signal from noisy measurements. Developing a digital twin (DT) of this system is proposed to expedite filter tuning, hence reducing cost and emissions output. Creating such a DT necessitates a method of back pressure sensor noise classification to accurately simulate the signal. This letter introduces a step-by-step procedure for characterising the H-hp engine back pressure sensor through statistical measures, leading to the development of the DT. This letter demonstrates the potential of this approach in an industrial case study, showcasing its viability for application in engine test-bed facilities and across industries. The economic calculation estimates a potential £184,400 reduction in diesel fuel costs and 321,600 kg of CO2 emissions by tuning the filter through the DT compared to current industrial practice

Batteries are intricate electrochemical devices that demonstrate stochastic and nonlinear behavior contingent upon environmental and operational conditions; therefore, battery monitoring is an essential component throughout their application. This paper introduces a web-based battery monitoring and control system that utilizes Long Range (LoRa) communication technology, an element of Internet of Things (IoT), utilizing the ESP32 microcontroller. The system provides comprehensive online data in real-time through the integration of a multitude of sensors. The proposed system aims to overcome the constraints of current communication technology by capitalizing on the advantages of LoRa, a technology known for its efficient long-range and low-energy transmission, which renders it highly suitable for applications involving real-time monitoring. Furthermore, a control operation allows users to manage critical battery functions, including charging and discharging. The study performed a thorough experimental assessment of the proposed system across various operations, and the outcomes effectively corresponded with the primary objective and goals of the research. The system under consideration successfully facilitates remote monitoring and user control in real-time, provides long-term data visualization through data logging, and enables battery condition evaluation. The implementation of data logging was intended to optimize the utilization of future battery evaluation metrics, including State-of-Charge (SOC), State-of-Health (SOH), and Remaining Useful Life (RUL). Consequently, the developed system is well-suited for numerous applications that demand efficient energy storage solutions, including Electric Vehicles (EVs) and renewable energy.