Integrated modeling and optimization for environmental and social sustainability in multi-energy systems

openThe optimization of Multi-Energy Systems (MES) has traditionally been centered around economic objectives and the minimization of operational greenhouse gas emissions (GHG). However, the broader environmental and social impacts of these systems, especially those related to their life cycle and beyond mere GHG emissions, necessitate a more holistic optimization approach. This study introduces a novel and comprehensive objective function, the Inclusive Wealth Index (IWI), for optimizing Multi-Energy Systems. The IWI is defined as the weighted sum of three types of capital—human, natural, and produced—thereby integrating societal and environmental considerations into the optimization process in a comprehensive manner. By conducting a life cycle assessment (LCA) of the technologies and energy carriers within the MES, their implications on human and natural capitals are evaluated, while produced capital is assessed through investments in infrastructure and technology manufacturing, directly influencing economic growth and societal well-being. Utilizing mixed-integer linear programming (MILP) in a Python framework with the Gurobi solver, this research optimizes the design and operation of an MES to both maximize the IWI and reduce overall costs. A reference case is considered, where electricity and heat are supplied through the grid and natural gas boilers, respectively. The optimization of a grid-integrated case study, featuring photovoltaic modules (PV), heat pumps (HP), internal combustion engines (ICE), boilers (BOIL), electrical (EES), and thermal energy storage (TES) demonstrates that focusing solely on cost minimization results in a 46% savings compared to the reference case, yet it adversely impacts the IWI, reducing it to -0.03 points when all capitals are equally weighted. Prioritizing IWI maximization, on the other hand, substantially elevates the index to 0.114 points but incurs costs 42% higher than those associated with the cost-minimization scenario. Through multi-objective optimization that balances cost and IWI objectives, the study reveals that significant enhancements in societal wealth are attainable with low expenses, achieving a notable improvement in IWI of 0.056 points alongside a cost reduction of 41% compared to the reference case, and only 8% higher than the cost-minimization scenario. This research also underscores the critical importance of balanced capital weighting in optimizing MES for sustainable development, paving the way for energy systems that strategically integrate economic, environmental, and social considerations

Analysis of Temperature-Dependent H/D Exchange Mass Spectrometry Experiments.

H/D exchange (HDX) mass spectrometry (MS) is a widely used technique for interrogating protein structure and dynamics. Backbone HDX is mediated by opening/closing (unfolding/refolding) fluctuations. In traditional HDX-MS, proteins are incubated in D2O as a function of time at constant temperature (T). There is an urgent need to complement this traditional approach with experiments that probe proteins in a T-dependent fashion, e.g., for assessing the stability of therapeutic antibodies. A key problem with such studies is the absence of strategies for interpreting HDX-MS data in the context of T-dependent protein dynamics. Specifically, it has not been possible thus far to separate T-induced changes of the chemical labeling step (kch) from thermally enhanced protein fluctuations. Focusing on myoglobin, the current work solves this problem by dissecting T-dependent HDX-MS profiles into contributions from kch(T), as well as local and global protein dynamics. Experimental profiles started off with surprisingly shallow slopes that seemed to defy the quasi-exponential kch(T) dependence. Just below the melting temperature (Tm) the profiles showed a sharp increase. Our analysis revealed that local dynamics dominate at low T, while global events become prevalent closer to Tm. About half of the backbone NH sites exhibited a canonical scenario, where local opening/closing was associated with positive ΔH and ΔS. Many of the remaining sites had negative ΔH and ΔS, thereby accounting for the shallowness of the experimental HDX-MS profiles at low T. In summary, this work provides practitioners with the tools to analyze proteins over a wide temperature range, paving the way toward T-dependent high-throughput screening applications by HDX-MS

Conformational Dynamics and Aggregation of Thermally Stressed Proteins Studied by Hydrogen/Deuterium Exchange Mass Spectrometry

Proteins perform various biological functions, e.g., as enzymes or transporters. In addition to naturally occurring proteins, the use of protein therapeutic drugs for treating cancer and other diseases is a rapidly growing area. A thorough biophysical characterization of proteins and protein therapeutics opens the door to a more comprehensive understanding of their role in health and disease. This dissertation aims to expand the capabilities of an existing technique (Hydrogen Deuterium Exchange Mass Spectrometry, HDX-MS), which is widely used for probing protein structure and dynamics. Conventionally, HDX-MS experiments are performed as a function of labelling time. Here we aim to establish temperature as a complementary variable. Our goal was to unravel the interplay between thermally induced protein dynamic motions, unfolding, and aggregation. Chapter 2 examined the effects of protein heating, using myoglobin (Mb) as model system. MS was used to track deuterium uptake in response to increasing temperature at various labelling time points. The resulting data were captured using a comprehensive temperature- and time-dependent HDX data analysis framework. The HDX trends were dissected into contributions from “chemical” labelling, as well as local and global protein dynamics. Experimental profiles started with shallow slopes and showed a sharp increase close to the melting temperature. Our analysis revealed that local dynamics dominate at low temperatures, while global events become prevalent closer to the melting point. Chapter 3 studied the mechanism of thermally induced Mb aggregation. Upon heating, Mb produced amorphous aggregates. The extent of aggregation was measured by centrifugation and UV-Vis spectroscopy as a function of protein concentration, temperature, and time. From these data, we conclude that aggregation likely proceeds from globally unfolded proteins rather than from semi-unfolded species. The data obtained this way paved the way toward extensive molecular dynamics simulations of protein aggregation. In Chapter 4, we tested the applicability of the thermodynamic framework developed in Chapter 2 to a monoclonal antibody (NISTmAb), representing a model system of a typical protein therapeutic. Differential scanning calorimetry revealed the presence of three successive melting points, reflecting the different stability of the CH2, CH3, and Fab regions. HDX-MS was performed to comprehensively characterize the conformational dynamics of NISTmAb as a function of time and temperature. Global analysis of the entire data set yielded insights into the enthalpic and entropic behavior of different segments. The unfolding of the Fab domain (which has the highest melting temperature) was found to be closely coupled to aggregation. In summary, we developed a method that provides in-depth information on the thermodynamic behavior of thermally stressed proteins based on HDX-MS experiments, and we demonstrated the applicability of this method to proteins of vastly different sizes and complexity

La participación humana con el auge de la inteligencia artificial en el ámbito de la Salud Geoespacial

In the era of Web 3, digital currencies and virtual cities, human decision-making is very special. In It´s crucial to examine human responses alongside artificial intelligence in the healthcare field following an event and to create a collaborative geographic system based on modern technology. The ultimate goal is the restoration of countries´ essential services prioritizing health. In this research, spatial questions were conducted by considering public opinion, stakeholders, and decision makers under varying mental and emotional conditions. additionally, two different statistical and ranking methods were employed to comprehend the intricacies of individuals' mental states in a mathematical environment. All these methods were executed using software designed for location-based decision making under equal conditions. This paper focuses on a specific region, as an example applicable to a country or even to a larger scale. The results showed that time factors are completely related to decision criteria. While emerging technologies are promising, complete reliance on artificial intelligence for decision-making is misguided. Human choices are intrinsic to decision-making, and therefore, all relevant criteria for spatial decision-making have been considered. This pioneering study, conducted for the first time, showed that human choices can be optimal in different situations with similar conditions. Meanwhile, artificial intelligence only makes logical decisions that are not compatible with human thinking. The ongoing research holds potential value for neurologists or mental health specialists emphasizing the significance of geographical sciences in urban development. While it´s conceivable that medical issues and events can be addressed with artificial intelligence it appears that at least for the next few decades, even with the advancement of artificial intelligence, human presence remains essential in healthcare decision-making.En el mundo de la Web 3, las monedas digitales y las ciudades virtuales, las decisiones humanas son muy especiales. De hecho, es muy importante explicar la respuesta humana y la inteligencia artificial en el campo de la salud después de un evento y crear un sistema geográfico colaborativo basado en tecnología moderna para que este proceso conduzca al restablecimiento de servicios esenciales con prioridad de salud pública en un país. En esta investigación, se realizaron preguntas espaciales de acuerdo con la opinión pública, los actores interesados y los tomadores de decisiones en diferentes condiciones mentales y emocionales. Además, se utilizaron dos métodos estadísticos y de clasificación diferentes para comprender la extrañeza de las condiciones mentales de las personas en el entorno matemático. Todos los métodos mencionados fueron realizados mediante el software diseñado para la toma de decisiones de ubicación en igualdad de condiciones. Se investigó una región como ejemplo que puede generalizarse a un país e incluso a una escala mayor. Los resultados muestran que los factores tiempo están completamente relacionados con los criterios de decisión. Usar tecnologías emergentes es genial, pero dejar que la inteligencia artificial tome decisiones es completamente incorrecto. El hombre vive de sus elecciones para tomar decisiones, por lo tanto, utiliza todos los criterios relevantes en la toma de decisiones espaciales. Este estudio pionero, muestra que los humanos pueden tomar decisiones óptimas en diferentes situaciones según las mismas condiciones. Pero la inteligencia artificial sólo toma decisiones lógicas que no son compatibles con la mentalidad humana. La continuación de esta investigación presenta un valor potencial para ser utilizada por neurólogos o especialistas en salud mental enfatizando la importancia de las ciencias geográficas en el desarrollo urbano. Quizás sea posible resolver problemas y eventos médicos con inteligencia artificial, pero pareciera que al menos durante las próximas décadas, incluso con la aparición y continuidad de la inteligencia artificial, no será posible tomar decisiones en el ámbito de la salud sin la presencia de los humanos

Conceptual Explanation of Spatial Mismatch Fields of Job and Residence in urban spaces with emphasis on Iran

Spatial Mismatch theory is one of the theories that examine poverty and inequality in the social-Spatial structure of cities. The aim of the present study was to "conceptually explain the grounds for Spatial Mismatch of work and residence", especially in the cities of developing countries and Iran. This research is theoretical-applied in nature and descriptive-analytical in terms of method. In addition, discourse analysis has been used to explain the fields of formation and the lack of Spatial Mismatch. According to the findings of the present study, Spatial Mismatches in the Great Fields (global and Inclusive changes such as the expansion of Ford-Keynesian and neoliberal , Post Fordism) and its most objective layer (the inequality between access to Appropriate work and access to housing in urban structure and space), are similar.However, geographically, the focus of Spatial Mismatch in developing countries, in contrast to the US, is mostly on the suburb of cities.While Spatial Mismatch in the United States is influenced by unequal racial-ethnic contexts, development of transportation technology, suburbanization, industrialization, and the establishment of industries and factories, in developing countries it is mainly due to its "unequal structure of international economic relations," "implementation." External development strategies include "unequal socio-spatial construction", "getting caught up in the traps of development traps" (political instability, selling natural resources, spreading the consequences of neighborly instability, inefficient governance), and “traps of poverty and corruption" and “unstable pattern of urban development"

HM3alD: Polymorphic Malware Detection Using Program Behavior-Aware Hidden Markov Model

Malware have been tremendously growing in recent years. Most malware use obfuscation techniques for evasion and hiding purposes, but they preserve the functionality and malicious behavior of original code. Although most research work has been mainly focused on program static analysis, some recent contributions have used program behavior analysis to detect malware at run-time. Extracting the behavior of polymorphic malware is one of the major issues that affects the detection result. In this paper, we propose HM3alD, a novel program behavior-aware hidden Markov model for polymorphic malware detection. The main idea is to use an effective clustering scheme to partition the program behavior of malware instances and then apply a novel hidden Markov model (called program behavior-aware HMM) on each cluster to train the corresponding behavior. Low-level program behavior, OS-level system call sequence, is mapped to high-level action sequence and used as transition triggers across states in program behavior-aware HMM topology. Experimental results show that HM3alD outperforms all current dynamic and static malware detection methods, especially in term of FAR, while using a large dataset of 6349 malware

Structural Dynamics of a Thermally Stressed Monoclonal Antibody Characterized by Temperature-Dependent H/D Exchange Mass Spectrometry.

Differential scanning calorimetry (DSC) is a standard tool for probing the resilience of monoclonal antibodies (mAbs) and other protein therapeutics against thermal degradation. Unfortunately, DSC usually only provides insights into global unfolding, although sequential steps are sometimes discernible for multidomain proteins. Temperature-dependent hydrogen/deuterium exchange (HDX) mass spectrometry (MS) has the potential to probe heat-induced events at a much greater level of detail. We recently proposed a strategy to deconvolute temperature-dependent HDX data into contributions from local dynamics, global unfolding/refolding, as well as chemical labeling. However, that strategy was validated only for a small protein (Tajoddin, N. N.; Konermann, L

Mechanism of Thermal Protein Aggregation: Experiments and Molecular Dynamics Simulations on the High-Temperature Behavior of Myoglobin.

Proteins that encounter unfavorable solvent conditions are prone to aggregation, a phenomenon that remains poorly understood. This work focuses on myoglobin (Mb) as a model protein. Upon heating, Mb produces amorphous aggregates. Thermal unfolding experiments at low concentration (where aggregation is negligible), along with centrifugation assays, imply that Mb aggregation proceeds via globally unfolded conformers. This contrasts studies on other proteins that emphasized the role of partially folded structures as aggregate precursors. Molecular dynamics (MD) simulations were performed to gain insights into the mechanism by which heat-unfolded Mb molecules associate with one another. A prerequisite for these simulations was the development of a method for generating monomeric starting structures. Periodic boundary condition artifacts necessitated the implementation of a partially immobilized water layer lining the walls of the simulation box. Aggregation simulations were performed at 370 K to track the assembly of monomeric Mb into pentameric species. Binding events were preceded by multiple unsuccessful encounters. Even after association, protein-protein contacts remained in flux. Binding was mediated by hydrophobic contacts, along with salt bridges that involved hydrophobically embedded Lys residues. Overall, this work illustrates that atomistic MD simulations are well suited for garnering insights into protein aggregation mechanisms

Dealing with Class Imbalance in Android Malware Detection by Cascading Clustering and Classification

The high number of Android devices that are active around the world makes these platforms appealing targets for malware attacks. A malware is shorthand for malicious applications developed by cyber attackers with the intention of gaining access or causing damage to a computer device or network, often while the victim remains oblivious to the fact there’s been a compromise. Android security requires machine learning approaches to quickly and accurately flag malicious applications. This paper describes a supervised learning approach for classifying Android applications as genuine or malicious. It uses reverse engineering to look for dangerous capabilities within the application code and structure before it is executed and applies to an intriguing combination of clustering and classification, in order to deal with the imbalanced data problem and avoid a detection system that skews towards modeling the genuine applications. We use benchmark Android applications to assess that the presented approach is able to correctly detect malware applications. The significance of the computed detection patterns is evaluated using established machine learning metrics